API Reference¶

Advanced Options¶

class datarobot.helpers.AdvancedOptions(weights=None, response_cap=None, blueprint_threshold=None, seed=None, smart_downsampled=False, majority_downsampling_rate=None, offset=None, exposure=None, accuracy_optimized_mb=None, scaleout_modeling_mode=None, events_count=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, only_include_monotonic_blueprints=None, allowed_pairwise_interaction_groups=None, blend_best_models=None, scoring_code_only=None, prepare_model_for_deployment=None, consider_blenders_in_recommendation=None, min_secondary_validation_model_count=None, shap_only_mode=None, autopilot_data_sampling_method=None, run_leakage_removed_feature_list=None, autopilot_with_feature_discovery=False, feature_discovery_supervised_feature_reduction=True, exponentially_weighted_moving_alpha=None, external_time_series_baseline_dataset_id=None, use_supervised_feature_reduction=True, primary_location_column=None, protected_features=None, preferable_target_value=None, fairness_metrics_set=None, fairness_threshold=None)¶

Used when setting the target of a project to set advanced options of modeling process.

Parameters:

weights : string, optional: The name of a column indicating the weight of each row
response_cap : float in [0.5, 1), optional: Quantile of the response distribution to use for response capping.
blueprint_threshold : int, optional: Number of hours models are permitted to run before being excluded from later autopilot stages Minimum 1
seed : int: a seed to use for randomization
smart_downsampled : bool: whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects.
majority_downsampling_rate : float: the percentage between 0 and 100 of the majority rows that should be kept. Specify only if using smart downsampling. May not cause the majority class to become smaller than the minority class.
offset : list of str, optional: (New in version v2.6) the list of the names of the columns containing the offset of each row
exposure : string, optional: (New in version v2.6) the name of a column containing the exposure of each row
accuracy_optimized_mb : bool, optional: (New in version v2.6) Include additional, longer-running models that will be run by the autopilot and available to run manually.
scaleout_modeling_mode : string, optional: (New in version v2.8) Specifies the behavior of Scaleout models for the project. This is one of datarobot.enums.SCALEOUT_MODELING_MODE. If datarobot.enums.SCALEOUT_MODELING_MODE.DISABLED, no models will run during autopilot or show in the list of available blueprints. Scaleout models must be disabled for some partitioning settings including projects using datetime partitioning or projects using offset or exposure columns. If datarobot.enums.SCALEOUT_MODELING_MODE.REPOSITORY_ONLY, scaleout models will be in the list of available blueprints but not run during autopilot. If datarobot.enums.SCALEOUT_MODELING_MODE.AUTOPILOT, scaleout models will run during autopilot and be in the list of available blueprints. Scaleout models are only supported in the Hadoop enviroment with the corresponding user permission set.
events_count : string, optional: (New in version v2.8) the name of a column specifying events count.
monotonic_increasing_featurelist_id : string, optional: (new in version 2.11) the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.
monotonic_decreasing_featurelist_id : string, optional: (new in version 2.11) the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.
only_include_monotonic_blueprints : bool, optional: (new in version 2.11) when true, only blueprints that support enforcing monotonic constraints will be available in the project or selected for the autopilot.
allowed_pairwise_interaction_groups : list of tuple, optional: (New in version v2.19) For GA2M models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [(A, B, C), (C, D)] then GA2M models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered.
blend_best_models: bool, optional: (New in version v2.19) blend best models during Autopilot run
scoring_code_only: bool, optional: (New in version v2.19) Keep only models that can be converted to scorable java code during Autopilot run
shap_only_mode: bool, optional: (New in version v2.21) Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. Defaults to False.
prepare_model_for_deployment: bool, optional: (New in version v2.19) Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
consider_blenders_in_recommendation: bool, optional: (New in version 2.22.0) Include blenders when selecting a model to prepare for deployment in an Autopilot Run. Defaults to False.
min_secondary_validation_model_count: int, optional: (New in version v2.19) Compute “All backtest” scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.
autopilot_data_sampling_method: str, optional: (New in version v2.23) one of datarobot.enums.DATETIME_AUTOPILOT_DATA_SAMPLING_METHOD. Applicable for OTV projects only, defines if autopilot uses “random” or “latest” sampling when iteratively building models on various training samples. Defaults to “random” for duration-based projects and to “latest” for row-based projects.
run_leakage_removed_feature_list: bool, optional: (New in version v2.23) Run Autopilot on Leakage Removed feature list (if exists).
autopilot_with_feature_discovery: bool, optional.: (New in version v2.23) If true, autopilot will run on a feature list that includes features found via search for interactions.
feature_discovery_supervised_feature_reduction: bool, default ``True` optional: (New in version v2.23) Run supervised feature reduction for feature discovery projects.
exponentially_weighted_moving_alpha: float, optional: (New in version v2.26) defaults to None, value between 0 and 1 (inclusive), indicates alpha parameter used in exponentially weighted moving average within feature derivation window.
external_time_series_baseline_dataset_id: str, optional.: (New in version v2.26) If provided, will generate metrics scaled by external model predictions metric for time series projects. The external predictions catalog must be validated before autopilot starts, see Project.validate_external_time_series_baseline and external baseline predictions documentation for further explanation.
use_supervised_feature_reduction: bool, default ``True` optional: Time Series only. When true, during feature generation DataRobot runs a supervised algorithm to retain only qualifying features. Setting to false can severely impact autopilot duration, especially for datasets with many features.
primary_location_column: str, optional.: The name of primary location column.
protected_features: str, optional.: (New in version v2.24) A list of project features to mark as protected for Bias and Fairness testing calculations. Max number of protected features allowed is 10.
preferable_target_value: str, optional.: (New in version v2.24) A target value that should be treated as a favorable outcome for the prediction. For example, if we want to check gender discrimination for giving a loan and our target is named is_bad, then the positive outcome for the prediction would be No, which means that the loan is good and that’s what we treat as a favorable result for the loaner.
fairness_metrics_set: str, optional.: (New in version v2.24) Metric to use for calculating fairness. Can be one of proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity or favorableAndUnfavorablePredictiveValueParity. Used and required only if Bias & Fairness in AutoML feature is enabled.
fairness_threshold: str, optional.: (New in version v2.24) Threshold value for the fairness metric. Can be in a range of [0.0, 1.0]. If the relative (i.e. normalized) fairness score is below the threshold, then the user will see a visual indication on the

Examples

import datarobot as dr
advanced_options = dr.AdvancedOptions(
    weights='weights_column',
    offset=['offset_column'],
    exposure='exposure_column',
    response_cap=0.7,
    blueprint_threshold=2,
    smart_downsampled=True, majority_downsampling_rate=75.0)

Anomaly Assessment¶

class datarobot.models.anomaly_assessment.AnomalyAssessmentRecord(status, status_details, start_date, end_date, prediction_threshold, preview_location, delete_location, latest_explanations_location, **record_kwargs)¶

Object which keeps metadata about anomaly assessment insight for the particular subset, backtest and series and the links to proceed to get the anomaly assessment data.

New in version v2.25.

Notes

Record contains:

record_id : the ID of the record.
project_id : the project ID of the record.
model_id : the model ID of the record.
backtest : the backtest of the record.
source : the source of the record.
series_id : the series id of the record for the multiseries projects.
status : the status of the insight.
status_details : the explanation of the status.
start_date : the ISO-formatted timestamp of the first prediction in the subset. Will be None if status is not AnomalyAssessmentStatus.COMPLETED.
end_date : the ISO-formatted timestamp of the last prediction in the subset. Will be None if status is not AnomalyAssessmentStatus.COMPLETED.
prediction_threshold : the threshold, all rows with anomaly scores greater or equal to it have shap explanations computed. Will be None if status is not AnomalyAssessmentStatus.COMPLETED.
preview_location : URL to retrieve predictions preview for the subset. Will be None if status is not AnomalyAssessmentStatus.COMPLETED.
latest_explanations_location : the URL to retrieve the latest predictions with the shap explanations. Will be None if status is not AnomalyAssessmentStatus.COMPLETED.
delete_location : the URL to delete anomaly assessment record and relevant insight data.

Attributes:

record_id: str: The ID of the record.
project_id: str: The ID of the project record belongs to.
model_id: str: The ID of the model record belongs to.
backtest: int or “holdout”: The backtest of the record.
source: “training” or “validation”: The source of the record
series_id: str or None: The series id of the record for the multiseries projects. Defined only for the multiseries projects.
status: str: The status of the insight. One of datarobot.enums.AnomalyAssessmentStatus
status_details: str: The explanation of the status.
start_date: str or None: See start_date info in Notes for more details.
end_date: str or None: See end_date info in Notes for more details.
prediction_threshold: float or None: See prediction_threshold info in Notes for more details.
preview_location: str or None: See preview_location info in Notes for more details.
latest_explanations_location: str or None: See latest_explanations_location info in Notes for more details.
delete_location: str: The URL to delete anomaly assessment record and relevant insight data.

classmethod list(project_id, model_id, backtest=None, source=None, series_id=None, limit=100, offset=0, with_data_only=False)¶

Retrieve the list of the anomaly assessment records for the project and model. Output can be filtered and limited.

Parameters:

project_id: str: The ID of the project record belongs to.
model_id: str: The ID of the model record belongs to.
backtest: int or “holdout”: The backtest to filter records by.
source: “training” or “validation”: The source to filter records by.
series_id: str, optional: The series id to filter records by. Can be specified for multiseries projects.
limit: int, optional: 100 by default. At most this many results are returned.
offset: int, optional: This many results will be skipped.
with_data_only: bool, False by default: Filter by status == AnomalyAssessmentStatus.COMPLETED. If True, records with no data or not supported will be omitted.

Returns:

AnomalyAssessmentRecord: The anomaly assessment record.

classmethod compute(project_id, model_id, backtest, source, series_id=None)¶

Request anomaly assessment insight computation on the specified subset.

Parameters:	project_id: str The ID of the project to compute insight for. model_id: str The ID of the model to compute insight for. backtest: int or “holdout” The backtest to compute insight for. source: “training” or “validation” The source to compute insight for. series_id: str, optional The series id to compute insight for. Required for multiseries projects.
Returns:	AnomalyAssessmentRecord The anomaly assessment record.

delete()¶: Delete anomaly assessment record with preview and explanations.

get_predictions_preview()¶

Retrieve aggregated predictions statistics for the anomaly assessment record.

Returns:	AnomalyAssessmentPredictionsPreview

get_latest_explanations()¶

Retrieve latest predictions along with shap explanations for the most anomalous records.

Returns:	AnomalyAssessmentExplanations

get_explanations(start_date=None, end_date=None, points_count=None)¶

Retrieve predictions along with shap explanations for the most anomalous records in the specified date range/for defined number of points. Two out of three parameters: start_date, end_date or points_count must be specified.

Parameters:	start_date: str, optional The start of the date range to get explanations in. Example: `2020-01-01T00:00:00.000000Z` end_date: str, optional The end of the date range to get explanations in. Example: `2020-10-01T00:00:00.000000Z` points_count: int, optional The number of the rows to return.
Returns:	AnomalyAssessmentExplanations

get_explanations_data_in_regions(regions, prediction_threshold=0.0)¶

Get predictions along with explanations for the specified regions, sorted by predictions in descending order.

Parameters:	regions: list of preview_bins For each region explanations will be retrieved and merged. prediction_threshold: float, optional If specified, only points with score greater or equal to the threshold will be returned.
Returns:	dict in a form of {‘explanations’: explanations, ‘shap_base_value’: shap_base_value}

class datarobot.models.anomaly_assessment.AnomalyAssessmentExplanations(shap_base_value, data, start_date, end_date, count, **record_kwargs)¶

Object which keeps predictions along with shap explanations for the most anomalous records in the specified date range/for defined number of points.

New in version v2.25.

Notes

AnomalyAssessmentExplanations contains:

record_id : the id of the corresponding anomaly assessment record.
project_id : the project ID of the corresponding anomaly assessment record.
model_id : the model ID of the corresponding anomaly assessment record.
backtest : the backtest of the corresponding anomaly assessment record.
source : the source of the corresponding anomaly assessment record.
series_id : the series id of the corresponding anomaly assessment record for the multiseries projects.
start_date : the ISO-formatted first timestamp in the response. Will be None of there is no data in the specified range.
end_date : the ISO-formatted last timestamp in the response. Will be None of there is no data in the specified range.
count : The number of points in the response.
shap_base_value : the shap base value.
data : list of DataPoint objects in the specified date range.

DataPoint contains:

shap_explanation : None or an array of up to 10 ShapleyFeatureContribution objects. Only rows with the highest anomaly scores have Shapley explanations calculated. Value is None if prediction is lower than prediction_threshold.

timestamp (str) : ISO-formatted timestamp for the row.

prediction (float) : The output of the model for this row.

ShapleyFeatureContribution contains:

feature_value (str) : the feature value for this row. First 50 characters are returned.

strength (float) : the shap value for this feature and row.

feature (str) : the feature name.

Attributes:

record_id: str: The ID of the record.
project_id: str: The ID of the project record belongs to.
model_id: str: The ID of the model record belongs to.
backtest: int or “holdout”: The backtest of the record.
source: “training” or “validation”: The source of the record.
series_id: str or None: The series id of the record for the multiseries projects. Defined only for the multiseries projects.
start_date: str or None: The ISO-formatted datetime of the first row in the data.
end_date: str or None: The ISO-formatted datetime of the last row in the data.
data: array of `data_point` objects or None: See data info in Notes for more details.
shap_base_value: float: Shap base value.
count: int: The number of points in the data.

classmethod get(project_id, record_id, start_date=None, end_date=None, points_count=None)¶

Retrieve predictions along with shap explanations for the most anomalous records in the specified date range/for defined number of points. Two out of three parameters: start_date, end_date or points_count must be specified.

Parameters:

project_id: str: The ID of the project.
record_id: str: The ID of the anomaly assessment record.
start_date: str, optional: The start of the date range to get explanations in. Example: 2020-01-01T00:00:00.000000Z
end_date: str, optional: The end of the date range to get explanations in. Example: 2020-10-01T00:00:00.000000Z
points_count: int, optional: The number of the rows to return.

Returns:

AnomalyAssessmentExplanations

class datarobot.models.anomaly_assessment.AnomalyAssessmentPredictionsPreview(start_date, end_date, preview_bins, **record_kwargs)¶

Aggregated predictions over time for the corresponding anomaly assessment record. Intended to find the bins with highest anomaly scores.

New in version v2.25.

Notes

AnomalyAssessmentPredictionsPreview contains:

record_id : the id of the corresponding anomaly assessment record.
project_id : the project ID of the corresponding anomaly assessment record.
model_id : the model ID of the corresponding anomaly assessment record.
backtest : the backtest of the corresponding anomaly assessment record.
source : the source of the corresponding anomaly assessment record.
series_id : the series id of the corresponding anomaly assessment record for the multiseries projects.
start_date : the ISO-formatted timestamp of the first prediction in the subset.
end_date : the ISO-formatted timestamp of the last prediction in the subset.
preview_bins : list of PreviewBin objects. The aggregated predictions for the subset. Bins boundaries may differ from actual start/end dates because this is an aggregation.

PreviewBin contains:

start_date (str) : the ISO-formatted datetime of the start of the bin.
end_date (str) : the ISO-formatted datetime of the end of the bin.
avg_predicted (float or None) : the average prediction of the model in the bin. None if there are no entries in the bin.
max_predicted (float or None) : the maximum prediction of the model in the bin. None if there are no entries in the bin.
frequency (int) : the number of the rows in the bin.

Attributes:

record_id: str: The ID of the record.
project_id: str: The ID of the project record belongs to.
model_id: str: The ID of the model record belongs to.
backtest: int or “holdout”: The backtest of the record.
source: “training” or “validation”: The source of the record
series_id: str or None: The series id of the record for the multiseries projects. Defined only for the multiseries projects.
start_date: str: the ISO-formatted timestamp of the first prediction in the subset.
end_date: str: the ISO-formatted timestamp of the last prediction in the subset.
preview_bins: list of preview_bin objects.: The aggregated predictions for the subset. See more info in Notes.

classmethod get(project_id, record_id)¶

Retrieve aggregated predictions over time.

Parameters:	project_id: str The ID of the project. record_id: str The ID of the anomaly assessment record.
Returns:	AnomalyAssessmentPredictionsPreview

find_anomalous_regions(max_prediction_threshold=0.0)¶

Sort preview bins by max_predicted value and select those with max predicted value: greater or equal to max prediction threshold. Sort the result by max predicted value in descending order.

Parameters:	max_prediction_threshold: float, optional Return bins with maximum anomaly score greater or equal to max_prediction_threshold.
Returns:	preview_bins: list of preview_bin Filtered and sorted preview bins

Batch Predictions¶

class datarobot.models.BatchPredictionJob(data, completed_resource_url=None)¶

A Batch Prediction Job is used to score large data sets on prediction servers using the Batch Prediction API.

Attributes:	id : str the id of the job

classmethod score(deployment, intake_settings=None, output_settings=None, csv_settings=None, timeseries_settings=None, num_concurrent=None, chunk_size=None, passthrough_columns=None, passthrough_columns_set=None, max_explanations=None, threshold_high=None, threshold_low=None, prediction_warning_enabled=None, include_prediction_status=False, skip_drift_tracking=False, prediction_instance=None, abort_on_error=True, column_names_remapping=None, include_probabilities=True, include_probabilities_classes=None, download_timeout=120, download_read_timeout=660, upload_read_timeout=600)¶

Create new batch prediction job, upload the scoring dataset and return a batch prediction job.

The default intake and output options are both localFile which requires the caller to pass the file parameter and either download the results using the download() method afterwards or pass a path to a file where the scored data will be downloaded to afterwards.

Returns:

BatchPredictionJob: Instance of BatchPredictionJob

Attributes:

deployment : Deployment or string ID

Deployment which will be used for scoring.

intake_settings : dict (optional)

A dict configuring how data is coming from. Supported options:

type : string, either localFile, s3, azure, gcp, dataset, jdbc snowflake, synapse or bigquery

To score from a local file, add the this parameter to the settings:

file : file-like object, string path to file or a pandas.DataFrame of scoring data

To score from S3, add the next parameters to the settings:

url : string, the URL to score (e.g.: s3://bucket/key)

credential_id : string (optional)

To score from JDBC, add the next parameters to the settings:

data_store_id : string, the ID of the external data store connected to the JDBC data source (see Database Connectivity).

query : string (optional if table, schema and/or catalog is specified), a self-supplied SELECT statement of the data set you wish to predict.

table : string (optional if query is specified), the name of specified database table.

schema : string (optional if query is specified), the name of specified database schema.

catalog : string (optional if query is specified), (new in v2.22) the name of specified database catalog.

fetch_size : int (optional), Changing the fetchSize can be used to balance throughput and memory usage.

credential_id : string (optional) the ID of the credentials holding information about a user with read-access to the JDBC data source (see Credentials).

output_settings : dict (optional)

A dict configuring how scored data is to be saved. Supported options:

type : string, either localFile, s3, azure, gcp, jdbc, snowflake, synapse or bigquery

To save scored data to a local file, add this parameters to the settings:

path : string (optional), path to save the scored data as CSV. If a path is not specified, you must download the scored data yourself with job.download(). If a path is specified, the call will block until the job is done. if there are no other jobs currently processing for the targeted prediction instance, uploading, scoring, downloading will happen in parallel without waiting for a full job to complete. Otherwise, it will still block, but start downloading the scored data as soon as it starts generating data. This is the fastest method to get predictions.

To save scored data to S3, add the next parameters to the settings:

url : string, the URL for storing the results (e.g.: s3://bucket/key)

credential_id : string (optional)

To save scored data to JDBC, add the next parameters to the settings:

data_store_id : string, the ID of the external data store connected to the JDBC data source (see Database Connectivity).

table : string, the name of specified database table.

schema : string (optional), the name of specified database schema.

catalog : string (optional), (new in v2.22) the name of specified database catalog.

statement_type : string, the type of insertion statement to create, one of datarobot.enums.AVAILABLE_STATEMENT_TYPES.

update_columns : list(string) (optional), a list of strings containing those column names to be updated in case statement_type is set to a value related to update or upsert.

where_columns : list(string) (optional), a list of strings containing those column names to be selected in case statement_type is set to a value related to insert or update.

credential_id : string, the ID of the credentials holding information about a user with write-access to the JDBC data source (see Credentials).

create_table_if_not_exists : bool (optional), If no existing table is detected, attempt to create it before writing data with the strategy defined in the statementType parameter.

csv_settings : dict (optional)

CSV intake and output settings. Supported options:

delimiter : string (optional, default ,), fields are delimited by this character. Use the string tab to denote TSV (TAB separated values). Must be either a one-character string or the string tab.
quotechar : string (optional, default “), fields containing the delimiter must be quoted using this character.
encoding : string (optional, default utf-8), encoding for the CSV files. For example (but not limited to): shift_jis, latin_1 or mskanji.

timeseries_settings : dict (optional)

Configuration for time-series scoring. Supported options:

type : string, must be forecast or historical (default if not passed is forecast). forecast mode makes predictions using forecast_point or rows in the dataset without target. historical enables bulk prediction mode which calculates predictions for all possible forecast points and forecast distances in the dataset within predictions_start_date/predictions_end_date range.
forecast_point : datetime (optional), forecast point for the dataset, used for the forecast predictions, by default value will be inferred from the dataset. May be passed if timeseries_settings.type=forecast.
predictions_start_date : datetime (optional), used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset. May be passed if timeseries_settings.type=historical.
predictions_end_date : datetime (optional), used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset. May be passed if timeseries_settings.type=historical.
relax_known_in_advance_features_check : bool, (default False). If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed.

num_concurrent : int (optional)

Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.

chunk_size : string or int (optional)

Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. - auto: use fixed or dynamic based on flipper - fixed: use 1MB for explanations, 5MB for regular requests - dynamic: use dynamic chunk sizes - int: use this many bytes per chunk

passthrough_columns : list[string] (optional)

Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.

passthrough_columns_set : string (optional)

To pass through every column from the scoring dataset, set this to all. Takes precedence over passthrough_columns if set.

max_explanations : int (optional)

Compute prediction explanations for this amount of features.

threshold_high : float (optional)

Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.

threshold_low : float (optional)

Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.

prediction_warning_enabled : boolean (optional)

Add prediction warnings to the scored data. Currently only supported for regression models.

include_prediction_status : boolean (optional)

Include the prediction_status column in the output, defaults to False.

skip_drift_tracking : boolean (optional)

Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to False.

prediction_instance : dict (optional)

Defaults to instance specified by deployment or system configuration. Supported options:

hostName : string

sslEnabled : boolean (optional, default true). Set to false to run prediction requests from the batch prediction job without SSL.

datarobotKey : string (optional), if running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key

apiKey : string (optional), by default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.

abort_on_error : boolean (optional)

Default behaviour is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.

column_names_remapping : dict (optional)

Mapping with column renaming for output table. Defaults to {}.

include_probabilities : boolean (optional)

Flag that enables returning of all probability columns. Defaults to True.

include_probabilities_classes : list (optional)

List the subset of classes if a user doesn’t want all the classes. Defaults to [].

download_timeout : int (optional)

New in version 2.22.

If using localFile output, wait this many seconds for the download to become available. See download().

download_read_timeout : int (optional, default 660)

New in version 2.22.

If using localFile output, wait this many seconds for the server to respond between chunks.

upload_read_timeout: int (optional, default 600)

New in version 2.28.

If using localFile intake, wait this many seconds for the server to respond after whole dataset upload.

classmethod score_to_file(deployment, intake_path, output_path, **kwargs)¶

Create new batch prediction job, upload the scoring dataset and download the scored CSV file concurrently.

Will block until the entire file is scored.

Refer to the score method for details on the other kwargs parameters.

Returns:	BatchPredictionJob Instance of BatchPredictionJob
Attributes:	deployment : Deployment or string ID Deployment which will be used for scoring. intake_path : file-like object/string path to file/pandas.DataFrame Scoring data output_path : str Filename to save the result under

classmethod score_s3(deployment, source_url, destination_url, credential=None, **kwargs)¶

Create new batch prediction job, with a scoring dataset from S3 and writing the result back to S3.

This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job)

Refer to the score method for details on the other kwargs parameters.

Returns:	BatchPredictionJob Instance of BatchPredictionJob
Attributes:	deployment : Deployment or string ID Deployment which will be used for scoring. source_url : string The URL for the prediction dataset (e.g.: s3://bucket/key) destination_url : string The URL for the scored dataset (e.g.: s3://bucket/key) credential : string or Credential (optional) The AWS Credential object or credential id

classmethod score_azure(deployment, source_url, destination_url, credential=None, **kwargs)¶

Create new batch prediction job, with a scoring dataset from Azure blob storage and writing the result back to Azure blob storage.

This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job).

Refer to the score method for details on the other kwargs parameters.

Returns:

BatchPredictionJob: Instance of BatchPredictionJob

Attributes:

deployment : Deployment or string ID: Deployment which will be used for scoring.
source_url : string: The URL for the prediction dataset (e.g.: https://storage_account.blob.endpoint/container/blob_name)
destination_url : string: The URL for the scored dataset (e.g.: https://storage_account.blob.endpoint/container/blob_name)
credential : string or Credential (optional): The Azure Credential object or credential id

classmethod score_gcp(deployment, source_url, destination_url, credential=None, **kwargs)¶

Create new batch prediction job, with a scoring dataset from Google Cloud Storage and writing the result back to one.

This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job).

Refer to the score method for details on the other kwargs parameters.

Returns:

BatchPredictionJob: Instance of BatchPredictionJob

Attributes:

deployment : Deployment or string ID: Deployment which will be used for scoring.
source_url : string: The URL for the prediction dataset (e.g.: http(s)://storage.googleapis.com/[bucket]/[object])
destination_url : string: The URL for the scored dataset (e.g.: http(s)://storage.googleapis.com/[bucket]/[object])
credential : string or Credential (optional): The GCP Credential object or credential id

classmethod score_from_existing(batch_prediction_job_id)¶

Create a new batch prediction job based on the settings from a previously created one

Returns:	BatchPredictionJob Instance of BatchPredictionJob
Attributes:	batch_prediction_job_id: str ID of the previous batch prediction job

classmethod score_pandas(deployment, df, read_timeout=660, **kwargs)¶

Run a batch prediction job, with a scoring dataset from a pandas dataframe. The output from the prediction will be joined to the passed DataFrame and returned.

Use columnNamesRemapping to drop or rename columns in the output

This method blocks until the job has completed or raises an exception on errors.

Refer to the score method for details on the other kwargs parameters.

Returns:	BatchPredictionJob Instance of BatchPredictonJob pandas.DataFrame The original dataframe merged with the predictions
Attributes:	deployment : Deployment or string ID Deployment which will be used for scoring. df : pandas.DataFrame The dataframe to score

classmethod get(batch_prediction_job_id)¶

Get batch prediction job

Returns:	BatchPredictionJob Instance of BatchPredictionJob
Attributes:	batch_prediction_job_id: str ID of batch prediction job

download(fileobj, timeout=120, read_timeout=660)¶

Downloads the CSV result of a prediction job

Attributes:

fileobj: file-like object

Write CSV data to this file-like object

timeout : int (optional, default 120)

New in version 2.22.

Seconds to wait for the download to become available.

The download will not be available before the job has started processing. In case other jobs are occupying the queue, processing may not start immediately.

If the timeout is reached, the job will be aborted and RuntimeError is raised.

Set to -1 to wait infinitely.

read_timeout : int (optional, default 660)

New in version 2.22.

Seconds to wait for the server to respond between chunks.

delete(ignore_404_errors=False)¶: Cancel this job. If this job has not finished running, it will be removed and canceled.

get_status()¶

Get status of batch prediction job

Returns:	BatchPredictionJob status data Dict with job status

classmethod list_by_status(statuses=None)¶

Get jobs collection for specific set of statuses

Returns:	BatchPredictionJob statuses List of job statuses dicts with specific statuses
Attributes:	statuses List of statuses to filter jobs ([ABORTED\|COMPLETED…]) if statuses is not provided, returns all jobs for user

class datarobot.models.BatchPredictionJobDefinition(id=None, name=None, enabled=None, schedule=None, batch_prediction_job=None, created=None, updated=None, created_by=None, updated_by=None, last_failed_run_time=None, last_successful_run_time=None, last_started_job_status=None, last_scheduled_run_time=None)¶

classmethod get(batch_prediction_job_definition_id)¶

Get batch prediction job definition

Returns:	BatchPredictionJobDefinition Instance of BatchPredictionJobDefinition

Examples

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.get('5a8ac9ab07a57a0001be501f')
>>> definition
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

Attributes:	batch_prediction_job_definition_id: str ID of batch prediction job definition

classmethod list()¶

Get job all definitions

Returns:	BatchPredictionJobDefinitions List of job definitions the user has access to see

Examples

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.list()
>>> definition
[
    BatchPredictionJobDefinition(60912e09fd1f04e832a575c1),
    BatchPredictionJobDefinition(6086ba053f3ef731e81af3ca)
]

classmethod create(enabled, batch_prediction_job, name=None, schedule=None)¶

Creates a new batch prediction job definition to be run either at scheduled interval or as a manual run.

Returns:	BatchPredictionJobDefinition Instance of BatchPredictionJobDefinition

Examples

>>> import datarobot as dr
>>> job_spec = {
...    "num_concurrent": 4,
...    "deployment_id": "foobar",
...    "intake_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...    "output_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...}
>>> schedule = {
...    "day_of_week": [
...        1
...    ],
...    "month": [
...        "*"
...    ],
...    "hour": [
...        16
...    ],
...    "minute": [
...        0
...    ],
...    "day_of_month": [
...        1
...    ]
...}
>>> definition = BatchPredictionJobDefinition.create(
...    enabled=False,
...    batch_prediction_job=job_spec,
...    name="some_definition_name",
...    schedule=schedule
... )
>>> definition
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

Attributes:

enabled : bool (default False)

Whether or not the definition should be active on a scheduled basis. If True, schedule is required.

batch_prediction_job: dict

The job specifications for your batch prediction job. It requires the same job input parameters as used with score(), only it will not initialize a job scoring, only store it as a definition for later use.

name : string (optional)

The name you want your job to be identified with. Must be unique across the organization’s existing jobs. If you don’t supply a name, a random one will be generated for you.

schedule : dict (optional)

The schedule payload defines at what intervals the job should run, which can be combined in various ways to construct complex scheduling terms if needed. In all of the elements in the objects, you can supply either an asterix ["*"] denoting “every” time denomination or an array of integers (e.g. [1, 2, 3]) to define a specific interval.

The schedule payload is split up in the following items:

Minute:

The minute(s) of the day that the job will run. Allowed values are either ["*"] meaning every minute of the day or [0 ... 59]

Hour: The hour(s) of the day that the job will run. Allowed values are either ["*"] meaning every hour of the day or [0 ... 23].

Day of Month: The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.

Month: The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., “jan” or “october”). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}

Day of Week: The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., “sunday”, “Sunday”, “sun”, or “Sun”, all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field.

update(enabled, batch_prediction_job=None, name=None, schedule=None)¶

Updates a job definition with the changed specs.

Takes the same input as create()

Returns:	BatchPredictionJobDefinition Instance of the updated BatchPredictionJobDefinition

Examples

>>> import datarobot as dr
>>> job_spec = {
...    "num_concurrent": 5,
...    "deployment_id": "foobar_new",
...    "intake_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...    "output_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...}
>>> schedule = {
...    "day_of_week": [
...        1
...    ],
...    "month": [
...        "*"
...    ],
...    "hour": [
...        "*"
...    ],
...    "minute": [
...        30, 59
...    ],
...    "day_of_month": [
...        1, 2, 6
...    ]
...}
>>> definition = BatchPredictionJobDefinition.create(
...    enabled=False,
...    batch_prediction_job=job_spec,
...    name="updated_definition_name",
...    schedule=schedule
... )
>>> definition
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

Attributes:	enabled : bool (default False) Same as `enabled` in `create()`. batch_prediction_job: dict Same as `batch_prediction_job` in `create()`. name : string (optional) Same as `name` in `create()`. schedule : dict Same as `schedule` in `create()`.

run_on_schedule(schedule)¶

Sets the run schedule of an already created job definition.

If the job was previously not enabled, this will also set the job to enabled.

Returns:	BatchPredictionJobDefinition Instance of the updated BatchPredictionJobDefinition with the new / updated schedule.

Examples

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.create('...')
>>> schedule = {
...    "day_of_week": [
...        1
...    ],
...    "month": [
...        "*"
...    ],
...    "hour": [
...        "*"
...    ],
...    "minute": [
...        30, 59
...    ],
...    "day_of_month": [
...        1, 2, 6
...    ]
...}
>>> definition.run_on_schedule(schedule)
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

Attributes:	schedule : dict Same as `schedule` in `create()`.

run_once()¶

Manually submits a batch prediction job to the queue, based off of an already created job definition.

Returns:	BatchPredictionJob Instance of BatchPredictionJob

Examples

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.create('...')
>>> job = definition.run_once()
>>> job.wait_for_completion()

delete()¶

Deletes the job definition and disables any future schedules of this job if any. If a scheduled job is currently running, this will not be cancelled.

Examples

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.get('5a8ac9ab07a57a0001be501f')
>>> definition.delete()

Blueprint¶

class datarobot.models.Blueprint(id=None, processes=None, model_type=None, project_id=None, blueprint_category=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, supports_monotonic_constraints=None, recommended_featurelist_id=None, supports_composable_ml=None)¶

A Blueprint which can be used to fit models

Attributes:

id : str: the id of the blueprint
processes : list of str: the processes used by the blueprint
model_type : str: the model produced by the blueprint
project_id : str: the project the blueprint belongs to
blueprint_category : str: (New in version v2.6) Describes the category of the blueprint and the kind of model it produces.
recommended_featurelist_id: str or null: (New in v2.18) The ID of the feature list recommended for this blueprint. If this field is not present, then there is no recommended feature list.
supports_composable_ml : bool or None: (New in version v2.26) whether this blueprint is supported in the Composable ML.

classmethod get(project_id, blueprint_id)¶

Retrieve a blueprint.

Parameters:	project_id : str The project’s id. blueprint_id : str Id of blueprint to retrieve.
Returns:	blueprint : Blueprint The queried blueprint.

get_chart()¶

Retrieve a chart.

Returns:	BlueprintChart The current blueprint chart.

get_documents()¶

Get documentation for tasks used in the blueprint.

Returns:	list of BlueprintTaskDocument All documents available for blueprint.

class datarobot.models.BlueprintTaskDocument(title=None, task=None, description=None, parameters=None, links=None, references=None)¶

Document describing a task from a blueprint.

Attributes:

title : str: Title of document.
task : str: Name of the task described in document.
description : str: Task description.
parameters : list of dict(name, type, description): Parameters that task can receive in human-readable format.
links : list of dict(name, url): External links used in document
references : list of dict(name, url): References used in document. When no link available url equals None.

class datarobot.models.BlueprintChart(nodes, edges)¶

A Blueprint chart that can be used to understand data flow in blueprint.

Attributes:	nodes : list of dict (id, label) Chart nodes, id unique in chart. edges : list of tuple (id1, id2) Directions of data flow between blueprint chart nodes.

classmethod get(project_id, blueprint_id)¶

Retrieve a blueprint chart.

Parameters:	project_id : str The project’s id. blueprint_id : str Id of blueprint to retrieve chart.
Returns:	BlueprintChart The queried blueprint chart.

to_graphviz()¶

Get blueprint chart in graphviz DOT format.

Returns:	unicode String representation of chart in graphviz DOT language.

class datarobot.models.ModelBlueprintChart(nodes, edges)¶

A Blueprint chart that can be used to understand data flow in model. Model blueprint chart represents reduced repository blueprint chart with only elements that used to build this particular model.

Attributes:	nodes : list of dict (id, label) Chart nodes, id unique in chart. edges : list of tuple (id1, id2) Directions of data flow between blueprint chart nodes.

classmethod get(project_id, model_id)¶

Retrieve a model blueprint chart.

Parameters:	project_id : str The project’s id. model_id : str Id of model to retrieve model blueprint chart.
Returns:	ModelBlueprintChart The queried model blueprint chart.

to_graphviz()¶

Get blueprint chart in graphviz DOT format.

Returns:	unicode String representation of chart in graphviz DOT language.

Calendar File¶

class datarobot.CalendarFile(calendar_end_date=None, calendar_start_date=None, created=None, id=None, name=None, num_event_types=None, num_events=None, project_ids=None, role=None, multiseries_id_columns=None)¶

Represents the data for a calendar file.

For more information about calendar files, see the calendar documentation.

Attributes:

id : str: The id of the calendar file.
calendar_start_date : str: The earliest date in the calendar.
calendar_end_date : str: The last date in the calendar.
created : str: The date this calendar was created, i.e. uploaded to DR.
name : str: The name of the calendar.
num_event_types : int: The number of different event types.
num_events : int: The number of events this calendar has.
project_ids : list of strings: A list containing the projectIds of the projects using this calendar.
multiseries_id_columns: list of str or None: A list of columns in calendar which uniquely identify events for different series. Currently, only one column is supported. If multiseries id columns are not provided, calendar is considered to be single series.
role : str: The access role the user has for this calendar.

classmethod create(file_path, calendar_name=None, multiseries_id_columns=None)¶

Creates a calendar using the given file. For information about calendar files, see the calendar documentation

The provided file must be a CSV in the format:

Date,   Event,          Series ID
<date>, <event_type>,   <series id>
<date>, <event_type>,

A header row is required, and the “Series ID” column is optional.

Once the CalendarFile has been created, pass its ID with the DatetimePartitioningSpecification when setting the target for a time series project in order to use it.

Parameters:	file_path : string A string representing a path to a local csv file. calendar_name : string, optional A name to assign to the calendar. Defaults to the name of the file if not provided. multiseries_id_columns : list of str or None a list of the names of multiseries id columns to define which series an event belongs to. Currently only one multiseries id column is supported.
Returns:	calendar_file : CalendarFile Instance with initialized data.
Raises:	AsyncProcessUnsuccessfulError Raised if there was an error processing the provided calendar file.

Examples

# Creating a calendar with a specified name
cal = dr.CalendarFile.create('/home/calendars/somecalendar.csv',
                                         calendar_name='Some Calendar Name')
cal.id
>>> 5c1d4904211c0a061bc93013
cal.name
>>> Some Calendar Name

# Creating a calendar without specifying a name
cal = dr.CalendarFile.create('/home/calendars/somecalendar.csv')
cal.id
>>> 5c1d4904211c0a061bc93012
cal.name
>>> somecalendar.csv

# Creating a calendar with multiseries id columns
cal = dr.CalendarFile.create('/home/calendars/somemultiseriescalendar.csv',
                             calendar_name='Some Multiseries Calendar Name',
                             multiseries_id_columns=['series_id'])
cal.id
>>> 5da9bb21962d746f97e4daee
cal.name
>>> Some Multiseries Calendar Name
cal.multiseries_id_columns
>>> ['series_id']

classmethod create_calendar_from_country_code(country_code, start_date, end_date)¶

Generates a calendar based on the provided country code and dataset start date and end dates. The provided country code should be uppercase and 2-3 characters long. See CalendarFile.get_allowed_country_codes for a list of allowed country codes.

Parameters:	country_code : string The country code for the country to use for generating the calendar. start_date : datetime.datetime The earliest date to include in the generated calendar. end_date : datetime.datetime The latest date to include in the generated calendar.
Returns:	calendar_file : CalendarFile Instance with initialized data.

classmethod get_allowed_country_codes(offset=None, limit=None)¶

Retrieves the list of allowed country codes that can be used for generating the preloaded calendars.

Parameters:	offset : int Optional, defaults to 0. This many results will be skipped. limit : int Optional, defaults to 100, maximum 1000. At most this many results are returned.
Returns:	list A list dicts, each of which represents an allowed country codes. Each item has the following structure: `name` : (str) The name of the country. `code` : (str) The code for this country. This is the value that should be supplied to `CalendarFile.create_calendar_from_country_code`.

classmethod get(calendar_id)¶

Gets the details of a calendar, given the id.

Parameters:	calendar_id : str The identifier of the calendar.
Returns:	calendar_file : CalendarFile The requested calendar.
Raises:	DataError Raised if the calendar_id is invalid, i.e. the specified CalendarFile does not exist.

Examples

cal = dr.CalendarFile.get(some_calendar_id)
cal.id
>>> some_calendar_id

classmethod list(project_id=None, batch_size=None)¶

Gets the details of all calendars this user has view access for.

Parameters:	project_id : str, optional If provided, will filter for calendars associated only with the specified project. batch_size : int, optional The number of calendars to retrieve in a single API call. If specified, the client may make multiple calls to retrieve the full list of calendars. If not specified, an appropriate default will be chosen by the server.
Returns:	calendar_list : list of `CalendarFile` A list of CalendarFile objects.

Examples

calendars = dr.CalendarFile.list()
len(calendars)
>>> 10

classmethod delete(calendar_id)¶

Deletes the calendar specified by calendar_id.

Parameters:	calendar_id : str The id of the calendar to delete. The requester must have OWNER access for this calendar.
Raises:	ClientError Raised if an invalid calendar_id is provided.

Examples

# Deleting with a valid calendar_id
status_code = dr.CalendarFile.delete(some_calendar_id)
status_code
>>> 204
dr.CalendarFile.get(some_calendar_id)
>>> ClientError: Item not found

classmethod update_name(calendar_id, new_calendar_name)¶

Changes the name of the specified calendar to the specified name. The requester must have at least READ_WRITE permissions on the calendar.

Parameters:	calendar_id : str The id of the calendar to update. new_calendar_name : str The new name to set for the specified calendar.
Returns:	status_code : int 200 for success
Raises:	ClientError Raised if an invalid calendar_id is provided.

Examples

response = dr.CalendarFile.update_name(some_calendar_id, some_new_name)
response
>>> 200
cal = dr.CalendarFile.get(some_calendar_id)
cal.name
>>> some_new_name

classmethod share(calendar_id, access_list)¶

Shares the calendar with the specified users, assigning the specified roles.

Parameters:	calendar_id : str The id of the calendar to update access_list: A list of dr.SharingAccess objects. Specify None for the role to delete a user’s access from the specified CalendarFile. For more information on specific access levels, see the sharing documentation.
Returns:	status_code : int 200 for success
Raises:	ClientError Raised if unable to update permissions for a user. AssertionError Raised if access_list is invalid.

Examples

# assuming some_user is a valid user, share this calendar with some_user
sharing_list = [dr.SharingAccess(some_user_username,
                                 dr.enums.SHARING_ROLE.READ_WRITE)]
response = dr.CalendarFile.share(some_calendar_id, sharing_list)
response.status_code
>>> 200

# delete some_user from this calendar, assuming they have access of some kind already
delete_sharing_list = [dr.SharingAccess(some_user_username,
                                        None)]
response = dr.CalendarFile.share(some_calendar_id, delete_sharing_list)
response.status_code
>>> 200

# Attempt to add an invalid user to a calendar
invalid_sharing_list = [dr.SharingAccess(invalid_username,
                                         dr.enums.SHARING_ROLE.READ_WRITE)]
dr.CalendarFile.share(some_calendar_id, invalid_sharing_list)
>>> ClientError: Unable to update access for this calendar

classmethod get_access_list(calendar_id, batch_size=None)¶

Retrieve a list of users that have access to this calendar.

Parameters:	calendar_id : str The id of the calendar to retrieve the access list for. batch_size : int, optional The number of access records to retrieve in a single API call. If specified, the client may make multiple calls to retrieve the full list of calendars. If not specified, an appropriate default will be chosen by the server.
Returns:	access_control_list : list of `SharingAccess` A list of `SharingAccess` objects.
Raises:	ClientError Raised if user does not have access to calendar or calendar does not exist.

Automated Documentation¶

class datarobot.models.automated_documentation.AutomatedDocument(entity_id=None, document_type=None, output_format=None, locale=None, template_id=None, id=None, filepath=None, created_at=None)¶

An automated documentation object.

New in version v2.24.

Attributes:

document_type : str or None: Type of automated document. You can specify: MODEL_COMPLIANCE, AUTOPILOT_SUMMARY depending on your account settings. Required for document generation.
entity_id : str or None: ID of the entity to generate the document for. It can be model ID or project ID. Required for document generation.
output_format : str or None: Format of the generate document, either docx or html. Required for document generation.
locale : str or None: Localization of the document, dependent on your account settings. Default setting is EN_US.
template_id : str or None: Template ID to use for the document outline. Defaults to standard DataRobot template. See the documentation for ComplianceDocTemplate for more information.
id : str or None: ID of the document. Required to download or delete a document.
filepath : str or None: Path to save a downloaded document to. Either include a file path and name or the file will be saved to the directory from which the script is launched.
created_at : datetime or None: Document creation timestamp.

classmethod list_available_document_types()¶

Get a list of all available document types and locales.

Returns:	List of dicts

Examples

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)
doc_types = dr.AutomatedDocument.list_available_document_types()

generate()¶

Request generation of an automated document.

Required attributes to request document generation: document_type, entity_id, and output_format.

Returns:	requests.models.Response

Examples

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)

doc = dr.AutomatedDocument(
        document_type="MODEL_COMPLIANCE",
        entity_id="6f50cdb77cc4f8d1560c3ed5",
        output_format="docx",
        locale="EN_US",
        template_id="50efc9db8aff6c81a374aeec",
        filepath="/Users/username/Documents/example.docx"
        )

doc.generate()
doc.download()

download()¶

Download a generated Automated Document. Document ID is required to download a file.

Returns:	requests.models.Response

Examples

Generating and downloading the generated document:

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)

doc = dr.AutomatedDocument(
        document_type="AUTOPILOT_SUMMARY",
        entity_id="6050d07d9da9053ebb002ef7",
        output_format="docx",
        filepath="/Users/username/Documents/Project_Report_1.docx"
        )

doc.generate()
doc.download()

Downloading an earlier generated document when you know the document ID:

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)
doc = dr.AutomatedDocument(id='5e8b6a34d2426053ab9a39ed')
doc.download()

Notice that filepath was not set for this document. In this case, the file is saved to the directory from which the script was launched.

Downloading a document chosen from a list of earlier generated documents:

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)

model_id = "6f5ed3de855962e0a72a96fe"
docs = dr.AutomatedDocument.list_generated_documents(entity_ids=[model_id])
doc = docs[0]
doc.filepath = "/Users/me/Desktop/Recommended_model_doc.docx"
doc.download()

delete()¶

Delete a document using its ID.

Returns:	requests.models.Response

Examples

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)
doc = dr.AutomatedDocument(id="5e8b6a34d2426053ab9a39ed")
doc.delete()

If you don’t know the document ID, you can follow the same workflow to get the ID as in the examples for the AutomatedDocument.download method.

classmethod list_generated_documents(document_types=None, entity_ids=None, output_formats=None, locales=None, offset=None, limit=None)¶

Get information about all previously generated documents available for your account. The information includes document ID and type, ID of the entity it was generated for, time of creation, and other information.

Parameters:

document_types : List of str or None: Query for one or more document types.
entity_ids : List of str or None: Query generated documents by one or more entity IDs.
output_formats : List of str or None: Query for one or more output formats.
locales : List of str or None: Query generated documents by one or more locales.
offset: int or None: Number of items to skip. Defaults to 0 if not provided.
limit: int or None: Number of items to return, maximum number of items is 1000.

Returns:

List of AutomatedDocument objects, where each object contains attributes described in
AutomatedDocument

Examples

To get a list of all generated documents:

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)
docs = AutomatedDocument.list_generated_documents()

To get a list of all AUTOPILOT_SUMMARY documents:

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)
docs = AutomatedDocument.list_generated_documents(document_types=["AUTOPILOT_SUMMARY"])

To get a list of 5 recently created automated documents in html format:

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)
docs = AutomatedDocument.list_generated_documents(output_formats=["html"], limit=5)

To get a list of automated documents created for specific entities (projects or models):

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)
docs = AutomatedDocument.list_generated_documents(
    entity_ids=["6051d3dbef875eb3be1be036",
                "6051d3e1fbe65cd7a5f6fde6",
                "6051d3e7f86c04486c2f9584"]
    )

Note, that the list of results contains AutomatedDocument objects, which means that you can execute class-related methods on them. Here’s how you can list, download, and then delete from the server all automated documents related to a certain entity:

import datarobot as dr

dr.Client(token=my_token, endpoint=endpoint)

ids = ["6051d3dbef875eb3be1be036", "5fe1d3d55cd810ebdb60c517f"]
docs = AutomatedDocument.list_generated_documents(entity_ids=ids)
for doc in docs:
    doc.download()
    doc.delete()

Class Mapping Aggregation Settings¶

For multiclass projects with a lot of unique values in target column you can specify the parameters for aggregation of rare values to improve the modeling performance and decrease the runtime and resource usage of resulting models.

class datarobot.helpers.ClassMappingAggregationSettings(max_unaggregated_class_values=None, min_class_support=None, excluded_from_aggregation=None, aggregation_class_name=None)¶

Class mapping aggregation settings. For multiclass projects allows fine control over which target values will be preserved as classes. Classes which aren’t preserved will be - aggregated into a single “catch everything else” class in case of multiclass - or will be ignored in case of multilabel. All attributes are optional, if not specified - server side defaults will be used.

Attributes:

max_unaggregated_class_values : int, optional: Maximum amount of unique values allowed before aggregation kicks in.
min_class_support : int, optional: Minimum number of instances necessary for each target value in the dataset. All values with less instances will be aggregated.
excluded_from_aggregation : list, optional: List of target values that should be guaranteed to kept as is, regardless of other settings.
aggregation_class_name : str, optional: If some of the values will be aggregated - this is the name of the aggregation class that will replace them.

Clustering¶

class datarobot.models.ClusteringModel(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, model_type=None, model_category=None, is_frozen=None, is_n_clusters_dynamically_determined=None, blueprint_id=None, metrics=None, project=None, data=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, n_clusters=None, has_empty_clusters=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, model_number=None, parent_model_id=None, use_project_settings=None, supports_composable_ml=None)¶

ClusteringModel extends Model class. It provides provides properties and methods specific to clustering projects.

compute_insights(max_wait=600)¶

Compute and retrieve cluster insights for model. This method awaits completion of job computing cluster insights and returns results after it is finished. If computation takes longer than specified max_wait exception will be raised.

Parameters:	project_id: str Project to start creation in. model_id: str Project’s model to start creation in. max_wait: int Maximum number of seconds to wait before giving up
Returns:	List of ClusterInsight
Raises:	ClientError Server rejected creation due to client error. Most likely cause is bad `project_id` or `model_id`. AsyncFailureError If any of the responses from the server are unexpected AsyncProcessUnsuccessfulError If the cluster insights computation has failed or was cancelled. AsyncTimeoutError If the cluster insights computation did not resolve in time

insights¶

Return actual list of cluster insights if already computed.

Returns:	List of ClusterInsight

clusters¶

Return actual list of Clusters.

Returns:	List of Cluster

update_cluster_names(cluster_name_mappings)¶

Change many cluster names at once based on list of name mappings.

Parameters:	cluster_name_mappings: List of tuples Cluster names mapping consisting of current cluster name and old cluster name. Example: cluster_name_mappings = [ ("current cluster name 1", "new cluster name 1"), ("current cluster name 2", "new cluster name 2")]
Returns:	List of Cluster
Raises:	datarobot.errors.ClientError Server rejected update of cluster names. Possible reasons include: incorrect format of mapping, mapping introduces duplicates.

update_cluster_name(current_name, new_name)¶

Change cluster name from current_name to new_name.

Parameters:	current_name: str Current cluster name. new_name: str New cluster name.
Returns:	List of Cluster
Raises:	datarobot.errors.ClientError Server rejected update of cluster names.

class datarobot.models.cluster.Cluster(**kwargs)¶

Representation of a single cluster.

Attributes:	name: str Current cluster name percent: float Percent of data contained in the cluster. This value is reported after cluster insights are computed for the model.

classmethod list(project_id, model_id)¶

Retrieve a list of clusters in the model.

Parameters:	project_id: str ID of the project that the model is part of. model_id: str ID of the model.
Returns:	List of clusters

classmethod update_multiple_names(project_id, model_id, cluster_name_mappings)¶

Update many clusters at once based on list of name mappings.

Parameters:	project_id: str ID of the project that the model is part of. model_id: str ID of the model. cluster_name_mappings: List of tuples Cluster name mappings, consisting of current and previous names for each cluster. Example: cluster_name_mappings = [ ("current cluster name 1", "new cluster name 1"), ("current cluster name 2", "new cluster name 2")]
Returns:	List of clusters
Raises:	datarobot.errors.ClientError Server rejected update of cluster names. ValueError Invalid cluster name mapping provided.

classmethod update_name(project_id, model_id, current_name, new_name)¶

Change cluster name from current_name to new_name

Parameters:	project_id: str ID of the project that the model is part of. model_id: str ID of the model. current_name: str Current cluster name new_name: str New cluster name
Returns:	List of Cluster

class datarobot.models.cluster_insight.ClusterInsight(**kwargs)¶

Holds data on all insights related to feature as well as breakdown per cluster.

Parameters:

feature_name: str: Name of a feature from the dataset.
feature_type: str: Type of feature.
insights : List of classes (ClusterInsight): List provides information regarding the importance of a specific feature in relation to each cluster. Results help understand how the model is grouping data and what each cluster represents.
feature_impact: float: Impact of a feature ranging from 0 to 1.

classmethod compute(project_id, model_id, max_wait=600)¶

Starts creation of cluster insights for the model and if successful, returns computed ClusterInsights. This method allows calculation to continue for a specified time and if not complete, cancels the request.

Parameters:	project_id: str ID of the project to begin creation of cluster insights for. model_id: str ID of the project model to begin creation of cluster insights for. max_wait: int Maximum number of seconds to wait canceling the request.
Returns:	ClusterInsights
Raises:	ClientError Server rejected creation due to client error. Most likely cause is bad `project_id` or `model_id`. AsyncFailureError Indicates whether any of the responses from the server are unexpected. AsyncProcessUnsuccessfulError Indicates whether the cluster insights computation failed or was cancelled. AsyncTimeoutError Indicates whether the cluster insights computation did not resolve within the specified time limit (max_wait).

Compliance Documentation Templates¶

class datarobot.models.compliance_doc_template.ComplianceDocTemplate(id, creator_id, creator_username, name, org_id=None, sections=None)¶

A compliance documentation template. Templates are used to customize contents of ComplianceDocumentation.

New in version v2.14.

Notes

Each section dictionary has the following schema:

title : title of the section
type : type of section. Must be one of “datarobot”, “user” or “table_of_contents”.

Each type of section has a different set of attributes described bellow.

Section of type "datarobot" represent a section owned by DataRobot. DataRobot sections have the following additional attributes:

content_id : The identifier of the content in this section. You can get the default template with get_default for a complete list of possible DataRobot section content ids.
sections : list of sub-section dicts nested under the parent section.

Section of type "user" represent a section with user-defined content. Those sections may contain text generated by user and have the following additional fields:

regularText : regular text of the section, optionally separated by \n to split paragraphs.
highlightedText : highlighted text of the section, optionally separated by \n to split paragraphs.
sections : list of sub-section dicts nested under the parent section.

Section of type "table_of_contents" represent a table of contents and has no additional attributes.

Attributes:

id : str: the id of the template
name : str: the name of the template.
creator_id : str: the id of the user who created the template
creator_username : str: username of the user who created the template
org_id : str: the id of the organization the template belongs to
sections : list of dicts: the sections of the template describing the structure of the document. Section schema is described in Notes section above.

classmethod get_default(template_type=None)¶

Get a default DataRobot template. This template is used for generating compliance documentation when no template is specified.

Parameters:	template_type : str or None Type of the template. Currently supported values are “normal” and “time_series”
Returns:	template : ComplianceDocTemplate the default template object with `sections` attribute populated with default sections.

classmethod create_from_json_file(name, path)¶

Create a template with the specified name and sections in a JSON file.

This is useful when working with sections in a JSON file. Example:

default_template = ComplianceDocTemplate.get_default()
default_template.sections_to_json_file('path/to/example.json')
# ... edit example.json in your editor
my_template = ComplianceDocTemplate.create_from_json_file(
    name='my template',
    path='path/to/example.json'
)

Parameters:	name : str the name of the template. Must be unique for your user. path : str the path to find the JSON file at
Returns:	template : ComplianceDocTemplate the created template

classmethod create(name, sections)¶

Create a template with the specified name and sections.

Parameters:	name : str the name of the template. Must be unique for your user. sections : list list of section objects
Returns:	template : ComplianceDocTemplate the created template

classmethod get(template_id)¶

Retrieve a specific template.

Parameters:	template_id : str the id of the template to retrieve
Returns:	template : ComplianceDocTemplate the retrieved template

classmethod list(name_part=None, limit=None, offset=None)¶

Get a paginated list of compliance documentation template objects.

Parameters:	name_part : str or None Return only the templates with names matching specified string. The matching is case-insensitive. limit : int The number of records to return. The server will use a (possibly finite) default if not specified. offset : int The number of records to skip.
Returns:	templates : list of ComplianceDocTemplate the list of template objects

sections_to_json_file(path, indent=2)¶

Save sections of the template to a json file at the specified path

Parameters:	path : str the path to save the file to indent : int indentation to use in the json file.

update(name=None, sections=None)¶

Update the name or sections of an existing doc template.

Note that default or non-existent templates can not be updated.

Parameters:	name : str, optional the new name for the template sections : list of dicts list of sections

delete()¶: Delete the compliance documentation template.

Compliance Documentation¶

class datarobot.models.compliance_documentation.ComplianceDocumentation(project_id, model_id, template_id=None)¶

A compliance documentation object.

New in version v2.14.

Examples

doc = ComplianceDocumentation('project-id', 'model-id')
job = doc.generate()
job.wait_for_completion()
doc.download('example.docx')

Attributes:	project_id : str the id of the project model_id : str the id of the model template_id : str or None optional id of the template for the generated doc. See documentation for `ComplianceDocTemplate` for more info.

generate()¶

Start a job generating model compliance documentation.

Returns:	Job an instance of an async job

download(filepath)¶

Download the generated compliance documentation file and save it to the specified path. The generated file has a DOCX format.

Parameters:	filepath : str A file path, e.g. “/path/to/save/compliance_documentation.docx”

Confusion Chart¶

class datarobot.models.confusion_chart.ConfusionChart(source, data, source_model_id)¶

Confusion Chart data for model.

Notes

ClassMetrics is a dict containing the following:

class_name (string) name of the class

actual_count (int) number of times this class is seen in the validation data

predicted_count (int) number of times this class has been predicted for the validation data

f1 (float) F1 score

recall (float) recall score

precision (float) precision score

was_actual_percentages (list of dict) one vs all actual percentages in format specified below.

other_class_name (string) the name of the other class

percentage (float) the percentage of the times this class was predicted when is was actually class (from 0 to 1)

was_predicted_percentages (list of dict) one vs all predicted percentages in format specified below.

other_class_name (string) the name of the other class

percentage (float) the percentage of the times this class was actual predicted (from 0 to 1)

confusion_matrix_one_vs_all (list of list) 2d list representing 2x2 one vs all matrix.

This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like:

[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]

Attributes:

source : str: Confusion Chart data source. Can be ‘validation’, ‘crossValidation’ or ‘holdout’.
raw_data : dict: All of the raw data for the Confusion Chart
confusion_matrix : list of list: The NxN confusion matrix
classes : list: The names of each of the classes
class_metrics : list of dicts: List of dicts with schema described as ClassMetrics above.
source_model_id : str: ID of the model this Confusion chart represents; in some cases, insights from the parent of a frozen model may be used

Credentials¶

class datarobot.models.Credential(credential_id=None, name=None, credential_type=None, creation_date=None, description=None)¶

classmethod list()¶

Returns list of available credentials.

Returns:	credentials : list of Credential instances contains a list of available credentials.

Examples

>>> import datarobot as dr
>>> data_sources = dr.Credential.list()
>>> data_sources
[
    Credential('5e429d6ecf8a5f36c5693e03', 'my_s3_cred', 's3'),
    Credential('5e42cc4dcf8a5f3256865840', 'my_jdbc_cred', 'jdbc'),
]

classmethod get(credential_id)¶

Gets the Credential.

Parameters:	credential_id : str the identifier of the credential.
Returns:	credential : Credential the requested credential.

Examples

>>> import datarobot as dr
>>> cred = dr.Credential.get('5a8ac9ab07a57a0001be501f')
>>> cred
Credential('5e429d6ecf8a5f36c5693e03', 'my_s3_cred', 's3'),

delete()¶

Deletes the Credential the store.

Parameters:	credential_id : str the identifier of the credential.
Returns:	credential : Credential the requested credential.

Examples

>>> import datarobot as dr
>>> cred = dr.Credential.get('5a8ac9ab07a57a0001be501f')
>>> cred.delete()

classmethod create_basic(name, user, password, description=None)¶

Creates the credentials.

Parameters:	name : str the name to use for this set of credentials. user : str the username to store for this set of credentials. password : str the password to store for this set of credentials. description : str, optional the description to use for this set of credentials.
Returns:	credential : Credential the created credential.

Examples

>>> import datarobot as dr
>>> cred = dr.Credential.create_basic(
...     name='my_basic_cred',
...     user='username',
...     password='password',
... )
>>> cred
Credential('5e429d6ecf8a5f36c5693e03', 'my_basic_cred', 'basic'),

classmethod create_oauth(name, token, refresh_token, description=None)¶

Creates the OAUTH credentials.

Parameters:	name : str the name to use for this set of credentials. token: str the OAUTH token refresh_token: str The OAUTH token description : str, optional the description to use for this set of credentials.
Returns:	credential : Credential the created credential.

Examples

>>> import datarobot as dr
>>> cred = dr.Credential.create_oauth(
...     name='my_oauth_cred',
...     token='XXX',
...     refresh_token='YYY',
... )
>>> cred
Credential('5e429d6ecf8a5f36c5693e03', 'my_oauth_cred', 'oauth'),

classmethod create_s3(name, aws_access_key_id=None, aws_secret_access_key=None, aws_session_token=None, description=None)¶

Creates the S3 credentials.

Parameters:	name : str the name to use for this set of credentials. aws_access_key_id : str, optional the AWS access key id. aws_secret_access_key : str, optional the AWS secret access key. aws_session_token : str, optional the AWS session token. description : str, optional the description to use for this set of credentials.
Returns:	credential : Credential the created credential.

Examples

>>> import datarobot as dr
>>> cred = dr.Credential.create_s3(
...     name='my_s3_cred',
...     aws_access_key_id='XXX',
...     aws_secret_access_key='YYY',
...     aws_session_token='ZZZ',
... )
>>> cred
Credential('5e429d6ecf8a5f36c5693e03', 'my_s3_cred', 's3'),

classmethod create_azure(name, azure_connection_string, description=None)¶

Creates the Azure storage credentials.

Parameters:	name : str the name to use for this set of credentials. azure_connection_string : str the Azure connection string. description : str, optional the description to use for this set of credentials.
Returns:	credential : Credential the created credential.

Examples

>>> import datarobot as dr
>>> cred = dr.Credential.create_azure(
...     name='my_azure_cred',
...     azure_connection_string='XXX',
... )
>>> cred
Credential('5e429d6ecf8a5f36c5693e03', 'my_azure_cred', 'azure'),

classmethod create_gcp(name, gcp_key=None, description=None)¶

Creates the GCP credentials.

Parameters:	name : str the name to use for this set of credentials. gcp_key : str \| dict the GCP key in json format or parsed as dict. description : str, optional the description to use for this set of credentials.
Returns:	credential : Credential the created credential.

Examples

>>> import datarobot as dr
>>> cred = dr.Credential.create_gcp(
...     name='my_gcp_cred',
...     gcp_key='XXX',
... )
>>> cred
Credential('5e429d6ecf8a5f36c5693e03', 'my_gcp_cred', 'gcp'),

Custom Models¶

class datarobot.models.custom_model_version.CustomModelFileItem(id, file_name, file_path, file_source, created_at=None)¶

A file item attached to a DataRobot custom model version.

New in version v2.21.

Attributes:	id: str id of the file item file_name: str name of the file item file_path: str path of the file item file_source: str source of the file item created_at: str, optional ISO-8601 formatted timestamp of when the version was created

class datarobot.CustomInferenceImage(**kwargs)¶

An image of a custom model.

New in version v2.21.

Deprecated since version v2.23.

Attributes:

id: str: image id
custom_model: dict: dict with 2 keys: id and name, where id is the ID of the custom model and name is the model name
custom_model_version: dict: dict with 2 keys: id and label, where id is the ID of the custom model version and label is the version label
execution_environment: dict: dict with 2 keys: id and name, where id is the ID of the execution environment and name is the environment name
execution_environment_version: dict: dict with 2 keys: id and label, where id is the ID of the execution environment version and label is the version label
latest_test: dict, optional: dict with 3 keys: id, status and completedAt, where id is the ID of the latest test, status is the testing status and completedAt is ISO-8601 formatted timestamp of when the testing was completed

classmethod create(custom_model_id, custom_model_version_id, environment_id, environment_version_id=None)¶

Create a custom model image.

New in version v2.21.

Parameters:	custom_model_id: str the id of the custom model custom_model_version_id: str the id of the custom model version environment_id: str the id of the execution environment environment_version_id: str, optional the id of the execution environment version
Returns:	CustomInferenceImage created custom model image
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod list(testing_status=None, custom_model_id=None, custom_model_version_id=None, environment_id=None, environment_version_id=None)¶

List custom model images.

New in version v2.21.

Parameters:	testing_status: str, optional the testing status to filter results by custom_model_id: str, optional the id of the custom model custom_model_version_id: str, optional the id of the custom model version environment_id: str, optional the id of the execution environment environment_version_id: str, optional the id of the execution environment version
Returns:	List[CustomModelImage] a list of custom model images
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(custom_model_image_id)¶

Get custom model image by id.

New in version v2.21.

Parameters:	custom_model_image_id: str the id of the custom model image
Returns:	CustomInferenceImage retrieved custom model image
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

refresh()¶

Update custom inference image with the latest data from server.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

get_feature_impact(with_metadata=False)¶

Get custom model feature impact.

New in version v2.21.

Parameters:	with_metadata : bool The flag indicating if the result should include the metadata as well.
Returns:	feature_impacts : list of dict The feature impact data. Each item is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

calculate_feature_impact(max_wait=600)¶

Calculate custom model feature impact.

New in version v2.22.

Parameters:	max_wait: int, optional max time to wait for feature impact calculation. If set to None - method will return without waiting. Defaults to 10 min
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

class datarobot.CustomInferenceModel(*args, **kwargs)¶

A custom inference model.

New in version v2.21.

Attributes:

id: str: id of the custom model
name: str: name of the custom model
language: str: programming language of the custom model. Can be “python”, “r”, “java” or “other”
description: str: description of the custom model
target_type: datarobot.TARGET_TYPE: target type of the custom inference model. Values: [datarobot.TARGET_TYPE.BINARY, datarobot.TARGET_TYPE.REGRESSION, datarobot.TARGET_TYPE.MULTICLASS, datarobot.TARGET_TYPE.UNSTRUCTURED, datarobot.TARGET_TYPE.ANOMALY]
target_name: str, optional: Target feature name; it is optional(ignored if provided) for datarobot.TARGET_TYPE.UNSTRUCTURED or datarobot.TARGET_TYPE.ANOMALY target type
latest_version: datarobot.CustomModelVersion or None: latest version of the custom model if the model has a latest version
deployments_count: int: number of a deployments of the custom models
target_name: str: custom model target name
positive_class_label: str: for binary classification projects, a label of a positive class
negative_class_label: str: for binary classification projects, a label of a negative class
prediction_threshold: float: for binary classification projects, a threshold used for predictions
training_data_assignment_in_progress: bool: flag describing if training data assignment is in progress
training_dataset_id: str, optional: id of a dataset assigned to the custom model
training_dataset_version_id: str, optional: id of a dataset version assigned to the custom model
training_data_file_name: str, optional: name of assigned training data file
training_data_partition_column: str, optional: name of a partition column in a training dataset assigned to the custom model
created_by: str: username of a user who user who created the custom model
updated_at: str: ISO-8601 formatted timestamp of when the custom model was updated
created_at: str: ISO-8601 formatted timestamp of when the custom model was created
network_egress_policy: datarobot.NETWORK_EGRESS_POLICY, optional: Determines whether the given custom model is isolated, or can access the public network. Can be either ‘datarobot.NONE’ or ‘datarobot.PUBLIC’
maximum_memory: int, optional: The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed by k8s
replicas: int, optional: A fixed number of replicas that will be deployed in the cluster

classmethod list(is_deployed=None, search_for=None, order_by=None)¶

List custom inference models available to the user.

New in version v2.21.

Parameters:	is_deployed: bool, optional flag for filtering custom inference models. If set to True, only deployed custom inference models are returned. If set to False, only not deployed custom inference models are returned search_for: str, optional string for filtering custom inference models - only custom inference models that contain the string in name or description will be returned. If not specified, all custom models will be returned order_by: str, optional property to sort custom inference models by. Supported properties are “created” and “updated”. Prefix the attribute name with a dash to sort in descending order, e.g. order_by=’-created’. By default, the order_by parameter is None which will result in custom models being returned in order of creation time descending
Returns:	List[CustomInferenceModel] a list of custom inference models.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(custom_model_id)¶

Get custom inference model by id.

New in version v2.21.

Parameters:	custom_model_id: str id of the custom inference model
Returns:	CustomInferenceModel retrieved custom inference model
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

download_latest_version(file_path)¶

Download the latest custom inference model version.

New in version v2.21.

Parameters:	file_path: str path to create a file with custom model version content
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

classmethod create(name, target_type, target_name=None, language=None, description=None, positive_class_label=None, negative_class_label=None, prediction_threshold=None, class_labels=None, class_labels_file=None, network_egress_policy=None, maximum_memory=None, replicas=None)¶

Create a custom inference model.

New in version v2.21.

Parameters:	name: str name of the custom inference model target_type: datarobot.TARGET_TYPE target type of the custom inference model. Values: [datarobot.TARGET_TYPE.BINARY, datarobot.TARGET_TYPE.REGRESSION, datarobot.TARGET_TYPE.MULTICLASS, datarobot.TARGET_TYPE.UNSTRUCTURED] target_name: str, optional Target feature name; it is optional(ignored if provided) for datarobot.TARGET_TYPE.UNSTRUCTURED target type language: str, optional programming language of the custom learning model description: str, optional description of the custom learning model positive_class_label: str, optional custom inference model positive class label for binary classification negative_class_label: str, optional custom inference model negative class label for binary classification prediction_threshold: float, optional custom inference model prediction threshold class_labels: List[str], optional custom inference model class labels for multiclass classification Cannot be used with class_labels_file class_labels_file: str, optional path to file containing newline separated class labels for multiclass classification. Cannot be used with class_labels network_egress_policy: datarobot.NETWORK_EGRESS_POLICY, optional Determines whether the given custom model is isolated, or can access the public network. Can be either ‘datarobot.NONE’ or ‘datarobot.PUBLIC’ maximum_memory: int, optional The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed by k8s replicas: int, optional A fixed number of replicas that will be deployed in the cluster
Returns:	CustomInferenceModel created a custom inference model
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod copy_custom_model(custom_model_id)¶

Create a custom inference model by copying existing one.

New in version v2.21.

Parameters:	custom_model_id: str id of the custom inference model to copy
Returns:	CustomInferenceModel created a custom inference model
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

update(name=None, language=None, description=None, target_name=None, positive_class_label=None, negative_class_label=None, prediction_threshold=None, class_labels=None, class_labels_file=None)¶

Update custom inference model properties.

New in version v2.21.

Parameters:

name: str, optional: new custom inference model name
language: str, optional: new custom inference model programming language
description: str, optional: new custom inference model description
target_name: str, optional: new custom inference model target name
positive_class_label: str, optional: new custom inference model positive class label
negative_class_label: str, optional: new custom inference model negative class label
prediction_threshold: float, optional: new custom inference model prediction threshold
class_labels: List[str], optional: custom inference model class labels for multiclass classification Cannot be used with class_labels_file
class_labels_file: str, optional: path to file containing newline separated class labels for multiclass classification. Cannot be used with class_labels

Raises:

datarobot.errors.ClientError: if the server responded with 4xx status.
datarobot.errors.ServerError: if the server responded with 5xx status.

refresh()¶

Update custom inference model with the latest data from server.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

delete()¶

Delete custom inference model.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

assign_training_data(dataset_id, partition_column=None, max_wait=600)¶

Assign training data to the custom inference model.

New in version v2.21.

Parameters:	dataset_id: str the id of the training dataset to be assigned partition_column: str, optional name of a partition column in the training dataset max_wait: int, optional max time to wait for a training data assignment. If set to None - method will return without waiting. Defaults to 10 min
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

class datarobot.CustomModelTest(**kwargs)¶

An custom model test.

New in version v2.21.

Attributes:

id: str: test id
custom_model_image_id: str: id of a custom model image
image_type: str: the type of the image, either CUSTOM_MODEL_IMAGE_TYPE.CUSTOM_MODEL_IMAGE if the testing attempt is using a CustomModelImage as its model or CUSTOM_MODEL_IMAGE_TYPE.CUSTOM_MODEL_VERSION if the testing attempt is using a CustomModelVersion with dependency management
overall_status: str: a string representing testing status. Status can be - ‘not_tested’: the check not run - ‘failed’: the check failed - ‘succeeded’: the check succeeded - ‘warning’: the check resulted in a warning, or in non-critical failure - ‘in_progress’: the check is in progress
detailed_status: dict: detailed testing status - maps the testing types to their status and message. The keys of the dict are one of ‘errorCheck’, ‘nullValueImputation’, ‘longRunningService’, ‘sideEffects’. The values are dict with ‘message’ and ‘status’ keys.
created_by: str: a user who created a test
dataset_id: str, optional: id of a dataset used for testing
dataset_version_id: str, optional: id of a dataset version used for testing
completed_at: str, optional: ISO-8601 formatted timestamp of when the test has completed
created_at: str, optional: ISO-8601 formatted timestamp of when the version was created
network_egress_policy: datarobot.NETWORK_EGRESS_POLICY, optional: Determines whether the given custom model is isolated, or can access the public network. Can be either ‘datarobot.NONE’ or ‘datarobot.PUBLIC’
maximum_memory: int, optional: The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed by k8s
replicas: int, optional: A fixed number of replicas that will be deployed in the cluster

classmethod create(custom_model_id, custom_model_version_id, dataset_id=None, environment_id=None, environment_version_id=None, max_wait=600, network_egress_policy=None, maximum_memory=None, replicas=None)¶

Create and start a custom model test.

New in version v2.21.

Parameters:	custom_model_id: str the id of the custom model custom_model_version_id: str the id of the custom model version dataset_id: str, optional The id of the testing dataset for non-unstructured custom models. Ignored and not required for unstructured models. environment_id: str, optional the id of the execution environment. If specified, the environment will be used as is; if the custom model version has dependencies, they will not be installed at runtime. This has been deprecated in favor of using the base environment of the custom model version itself environment_version_id: str, optional the id of the execution environment version. This has been deprecated in favor of using the base environment of the custom model version itself max_wait: int, optional max time to wait for a test completion. If set to None - method will return without waiting. network_egress_policy: datarobot.NETWORK_EGRESS_POLICY, optional Determines whether the given custom model is isolated, or can access the public network. Can be either ‘datarobot.NONE’ or ‘datarobot.PUBLIC’ maximum_memory: int, optional The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed by k8s replicas: int, optional A fixed number of replicas that will be deployed in the cluster
Returns:	CustomModelTest created custom model test
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod list(custom_model_id)¶

List custom model tests.

New in version v2.21.

Parameters:	custom_model_id: str the id of the custom model
Returns:	List[CustomModelTest] a list of custom model tests
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(custom_model_test_id)¶

Get custom model test by id.

New in version v2.21.

Parameters:	custom_model_test_id: str the id of the custom model test
Returns:	CustomModelTest retrieved custom model test
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

get_log()¶

Get log of a custom model test.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

get_log_tail()¶

Get log tail of a custom model test.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

cancel()¶

Cancel custom model test that is in progress.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

refresh()¶

Update custom model test with the latest data from server.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

class datarobot.CustomModelVersion(**kwargs)¶

A version of a DataRobot custom model.

New in version v2.21.

Attributes:

id: str

id of the custom model version

custom_model_id: str

id of the custom model

version_minor: int

a minor version number of custom model version

version_major: int

a major version number of custom model version

is_frozen: bool

a flag if the custom model version is frozen

items: List[CustomModelFileItem]

a list of file items attached to the custom model version

base_environment_id: str

id of the environment to use with the model

base_environment_version_id: str

id of the environment version to use with the model

label: str, optional

short human readable string to label the version

description: str, optional

custom model version description

created_at: str, optional

ISO-8601 formatted timestamp of when the version was created

dependencies: List[CustomDependency]

the parsed dependencies of the custom model version if the version has a valid requirements.txt file

network_egress_policy: datarobot.NETWORK_EGRESS_POLICY, optional

Determines whether the given custom model is isolated, or can access the public network. Can be either ‘datarobot.NONE’ or ‘datarobot.PUBLIC’

maximum_memory: int, optional

The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed by k8s

replicas: int, optional

A fixed number of replicas that will be deployed in the cluster

required_metadata_values: List[RequiredMetadataValue]

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys.

required_metadata: Dict[str, str]

Deprecated in v2.26. Use require_metadata_values instead.

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.

classmethod from_server_data(data, keep_attrs=None)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place keep_attrs : list List of the dotted namespace notations for attributes to keep within the object structure even if their values are None

classmethod create_clean(custom_model_id, base_environment_id, is_major_update=True, folder_path=None, files=None, network_egress_policy=None, maximum_memory=None, replicas=None, required_metadata=None, required_metadata_values=None)¶

Create a custom model version without files from previous versions.

New in version v2.21.

Parameters:	custom_model_id: str the id of the custom model base_environment_id: str the id of the base environment to use with the custom model version is_major_update: bool the flag defining if a custom model version will be a minor or a major version. Default to True folder_path: str, optional the path to a folder containing files to be uploaded. Each file in the folder is uploaded under path relative to a folder path files: list, optional the list of tuples, where values in each tuple are the local filesystem path and the path the file should be placed in the model. if list is of strings, then basenames will be used for tuples Example: [(“/home/user/Documents/myModel/file1.txt”, “file1.txt”), (“/home/user/Documents/myModel/folder/file2.txt”, “folder/file2.txt”)] or [“/home/user/Documents/myModel/file1.txt”, “/home/user/Documents/myModel/folder/file2.txt”] network_egress_policy: datarobot.NETWORK_EGRESS_POLICY, optional Determines whether the given custom model is isolated, or can access the public network. Can be either ‘datarobot.NONE’ or ‘datarobot.PUBLIC’ maximum_memory: int, optional The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed by k8s replicas: int, optional A fixed number of replicas that will be deployed in the cluster required_metadata: Dict[str, str] Deprecated in v2.26. Use require_metadata_values instead. Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version. required_metadata_values: List[RequiredMetadataValue] Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys.
Returns:	CustomModelVersion created custom model version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod create_from_previous(custom_model_id, base_environment_id, is_major_update=True, folder_path=None, files=None, files_to_delete=None, network_egress_policy=None, maximum_memory=None, replicas=None, required_metadata=None, required_metadata_values=None)¶

Create a custom model version containing files from a previous version.

New in version v2.21.

Parameters:	custom_model_id: str the id of the custom model base_environment_id: str the id of the base environment to use with the custom model version is_major_update: bool, optional the flag defining if a custom model version will be a minor or a major version. Default to True folder_path: str, optional the path to a folder containing files to be uploaded. Each file in the folder is uploaded under path relative to a folder path files: list, optional the list of tuples, where values in each tuple are the local filesystem path and the path the file should be placed in the model. if list is of strings, then basenames will be used for tuples Example: [(“/home/user/Documents/myModel/file1.txt”, “file1.txt”), (“/home/user/Documents/myModel/folder/file2.txt”, “folder/file2.txt”)] or [“/home/user/Documents/myModel/file1.txt”, “/home/user/Documents/myModel/folder/file2.txt”] files_to_delete: list, optional the list of a file items ids to be deleted Example: [“5ea95f7a4024030aba48e4f9”, “5ea6b5da402403181895cc51”] network_egress_policy: datarobot.NETWORK_EGRESS_POLICY, optional Determines whether the given custom model is isolated, or can access the public network. Can be either ‘datarobot.NONE’ or ‘datarobot.PUBLIC’ maximum_memory: int, optional The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed by k8s replicas: int, optional A fixed number of replicas that will be deployed in the cluster required_metadata: Dict[str, str] Deprecated in v2.26. Use require_metadata_values instead. Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version. required_metadata_values: List[RequiredMetadataValue] Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys.
Returns:	CustomModelVersion created custom model version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod list(custom_model_id)¶

List custom model versions.

New in version v2.21.

Parameters:	custom_model_id: str the id of the custom model
Returns:	List[CustomModelVersion] a list of custom model versions
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(custom_model_id, custom_model_version_id)¶

Get custom model version by id.

New in version v2.21.

Parameters:	custom_model_id: str the id of the custom model custom_model_version_id: str the id of the custom model version to retrieve
Returns:	CustomModelVersion retrieved custom model version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

download(file_path)¶

Download custom model version.

New in version v2.21.

Parameters:	file_path: str path to create a file with custom model version content
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

update(description=None, required_metadata=None, required_metadata_values=None)¶

Update custom model version properties.

New in version v2.21.

Parameters:

description: str

new custom model version description

required_metadata: Dict[str, str]

Deprecated in v2.26. Use require_metadata_values instead.

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.

required_metadata_values: List[RequiredMetadataValue]

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys.

Raises:

datarobot.errors.ClientError: if the server responded with 4xx status.
datarobot.errors.ServerError: if the server responded with 5xx status.

refresh()¶

Update custom model version with the latest data from server.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

get_feature_impact(with_metadata=False)¶

Get custom model feature impact.

New in version v2.23.

Parameters:	with_metadata : bool The flag indicating if the result should include the metadata as well.
Returns:	feature_impacts : list of dict The feature impact data. Each item is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

calculate_feature_impact(max_wait=600)¶

Calculate custom model feature impact.

New in version v2.23.

Parameters:	max_wait: int, optional max time to wait for feature impact calculation. If set to None - method will return without waiting. Defaults to 10 min
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

class datarobot.models.execution_environment.RequiredMetadataKey(**kwargs)¶

Definition of a metadata key that custom models using this environment must define

New in version v2.25.

Attributes:	field_name: str The required field key. This value will be added as an environment variable when running custom models. display_name: str A human readable name for the required field.

class datarobot.CustomModelVersionDependencyBuild(**kwargs)¶

Metadata about a DataRobot custom model version’s dependency build

New in version v2.22.

Attributes:	custom_model_id: str id of the custom model custom_model_version_id: str id of the custom model version build_status: str the status of the custom model version’s dependency build started_at: str ISO-8601 formatted timestamp of when the build was started completed_at: str, optional ISO-8601 formatted timestamp of when the build has completed

classmethod get_build_info(custom_model_id, custom_model_version_id)¶

Retrieve information about a custom model version’s dependency build

New in version v2.22.

Parameters:	custom_model_id: str the id of the custom model custom_model_version_id: str the id of the custom model version
Returns:	CustomModelVersionDependencyBuild the dependency build information

classmethod start_build(custom_model_id, custom_model_version_id, max_wait=600)¶

Start the dependency build for a custom model version dependency build

New in version v2.22.

Parameters:	custom_model_id: str the id of the custom model custom_model_version_id: str the id of the custom model version max_wait: int, optional max time to wait for a build completion. If set to None - method will return without waiting.

get_log()¶

Get log of a custom model version dependency build.

New in version v2.22.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

cancel()¶

Cancel custom model version dependency build that is in progress.

New in version v2.22.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

refresh()¶

Update custom model version dependency build with the latest data from server.

New in version v2.22.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

class datarobot.ExecutionEnvironment(**kwargs)¶

An execution environment entity.

New in version v2.21.

Attributes:

id: str: the id of the execution environment
name: str: the name of the execution environment
description: str, optional: the description of the execution environment
programming_language: str, optional: the programming language of the execution environment. Can be “python”, “r”, “java” or “other”
is_public: bool, optional: public accessibility of environment, visible only for admin user
created_at: str, optional: ISO-8601 formatted timestamp of when the execution environment version was created
latest_version: ExecutionEnvironmentVersion, optional: the latest version of the execution environment

classmethod create(name, description=None, programming_language=None, required_metadata_keys=None)¶

Create an execution environment.

New in version v2.21.

Parameters:	name: str execution environment name description: str, optional execution environment description programming_language: str, optional programming language of the environment to be created. Can be “python”, “r”, “java” or “other”. Default value - “other” required_metadata_keys: List[RequiredMetadataKey] Definition of a metadata keys that custom models using this environment must define
Returns:	ExecutionEnvironment created execution environment
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod list(search_for=None)¶

List execution environments available to the user.

New in version v2.21.

Parameters:	search_for: str, optional the string for filtering execution environment - only execution environments that contain the string in name or description will be returned.
Returns:	List[ExecutionEnvironment] a list of execution environments.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(execution_environment_id)¶

Get execution environment by it’s id.

New in version v2.21.

Parameters:	execution_environment_id: str ID of the execution environment to retrieve
Returns:	ExecutionEnvironment retrieved execution environment
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

delete()¶

Delete execution environment.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

update(name=None, description=None, required_metadata_keys=None)¶

Update execution environment properties.

New in version v2.21.

Parameters:	name: str, optional new execution environment name description: str, optional new execution environment description required_metadata_keys: List[RequiredMetadataKey] Definition of a metadata keys that custom models using this environment must define
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

refresh()¶

Update execution environment with the latest data from server.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

class datarobot.ExecutionEnvironmentVersion(**kwargs)¶

A version of a DataRobot execution environment.

New in version v2.21.

Attributes:

id: str: the id of the execution environment version
environment_id: str: the id of the execution environment the version belongs to
build_status: str: the status of the execution environment version build
label: str, optional: the label of the execution environment version
description: str, optional: the description of the execution environment version
created_at: str, optional: ISO-8601 formatted timestamp of when the execution environment version was created
docker_context_size: int, optional: The size of the uploaded Docker context in bytes if available or None if not
docker_image_size: int, optional: The size of the built Docker image in bytes if available or None if not

classmethod create(execution_environment_id, docker_context_path, label=None, description=None, max_wait=600)¶

Create an execution environment version.

New in version v2.21.

Parameters:	execution_environment_id: str the id of the execution environment docker_context_path: str the path to a docker context archive or folder label: str, optional short human readable string to label the version description: str, optional execution environment version description max_wait: int, optional max time to wait for a final build status (“success” or “failed”). If set to None - method will return without waiting.
Returns:	ExecutionEnvironmentVersion created execution environment version
Raises:	datarobot.errors.AsyncTimeoutError if version did not reach final state during timeout seconds datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod list(execution_environment_id, build_status=None)¶

List execution environment versions available to the user.

New in version v2.21.

Parameters:	execution_environment_id: str the id of the execution environment build_status: str, optional build status of the execution environment version to filter by. See datarobot.enums.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS for valid options
Returns:	List[ExecutionEnvironmentVersion] a list of execution environment versions.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(execution_environment_id, version_id)¶

Get execution environment version by id.

New in version v2.21.

Parameters:	execution_environment_id: str the id of the execution environment version_id: str the id of the execution environment version to retrieve
Returns:	ExecutionEnvironmentVersion retrieved execution environment version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

download(file_path)¶

Download execution environment version.

New in version v2.21.

Parameters:	file_path: str path to create a file with execution environment version content
Returns:	ExecutionEnvironmentVersion retrieved execution environment version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

get_build_log()¶

Get execution environment version build log and error.

New in version v2.21.

Returns:	Tuple[str, str] retrieved execution environment version build log and error. If there is no build error - None is returned.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

refresh()¶

Update execution environment version with the latest data from server.

New in version v2.21.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

Custom Tasks¶

class datarobot.CustomTask(id, target_type, latest_version, created_at, updated_at, name, description, language, created_by, calibrate_predictions=None)¶

A custom task. This can be in a partial state or a complete state. When the latest_version is None, the empty task has been initialized with some metadata. It is not yet use-able for actual training. Once the first CustomTaskVersion has been created, you can put the CustomTask in UserBlueprints to train Models in Projects

New in version v2.26.

Attributes:

id: str

id of the custom task

name: str

name of the custom task

language: str

programming language of the custom task. Can be “python”, “r”, “java” or “other”

description: str

description of the custom task

target_type: datarobot.enums.CUSTOM_TASK_TARGET_TYPE

the target type of the custom task. One of:

datarobot.enums.CUSTOM_TASK_TARGET_TYPE.BINARY
datarobot.enums.CUSTOM_TASK_TARGET_TYPE.REGRESSION
datarobot.enums.CUSTOM_TASK_TARGET_TYPE.MULTICLASS
datarobot.enums.CUSTOM_TASK_TARGET_TYPE.ANOMALY
datarobot.enums.CUSTOM_TASK_TARGET_TYPE.TRANSFORM

latest_version: datarobot.CustomTaskVersion or None

latest version of the custom task if the task has a latest version. If the latest version is None, the custom task is not ready for use in user blueprints. You must create its first CustomTaskVersion before you can use the CustomTask

created_by: str

username of a user who user who created the custom task

updated_at: str

ISO-8601 formatted timestamp of when the custom task was updated

created_at: str

ISO-8601 formatted timestamp of when the custom task was created

calibrate_predictions: bool

whether anomaly predictions should be calibrated to be between 0 and 1 by DR. only applies to custom estimators with target type datarobot.enums.CUSTOM_TASK_TARGET_TYPE.ANOMALY

classmethod from_server_data(data, keep_attrs=None)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place keep_attrs : list List of the dotted namespace notations for attributes to keep within the object structure even if their values are None

classmethod list(order_by=None, search_for=None)¶

List custom tasks available to the user.

New in version v2.26.

Parameters:	search_for: str, optional string for filtering custom tasks - only tasks that contain the string in name or description will be returned. If not specified, all custom task will be returned order_by: str, optional property to sort custom tasks by. Supported properties are “created” and “updated”. Prefix the attribute name with a dash to sort in descending order, e.g. order_by=’-created’. By default, the order_by parameter is None which will result in custom tasks being returned in order of creation time descending
Returns:	List[CustomTask] a list of custom tasks.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(custom_task_id)¶

Get custom task by id.

New in version v2.26.

Parameters:	custom_task_id: str id of the custom task
Returns:	CustomTask retrieved custom task
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

classmethod copy(custom_task_id)¶

Create a custom task by copying existing one.

New in version v2.26.

Parameters:	custom_task_id: str id of the custom task to copy
Returns:	CustomTask
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod create(name, target_type, language=None, description=None, calibrate_predictions=None, **kwargs)¶

Creates only the metadata for a custom task. This task will not be use-able until you have created a CustomTaskVersion attached to this task.

New in version v2.26.

Parameters:	name: str name of the custom task target_type: datarobot.enums.CUSTOM_TASK_TARGET_TYPE the target typed based on the following values. Anything else will raise an error datarobot.enums.CUSTOM_TASK_TARGET_TYPE.BINARY datarobot.enums.CUSTOM_TASK_TARGET_TYPE.REGRESSION datarobot.enums.CUSTOM_TASK_TARGET_TYPE.MULTICLASS datarobot.enums.CUSTOM_TASK_TARGET_TYPE.ANOMALY datarobot.enums.CUSTOM_TASK_TARGET_TYPE.TRANSFORM language: str, optional programming language of the custom task. Can be “python”, “r”, “java” or “other” description: str, optional description of the custom task calibrate_predictions: bool, optional whether anomaly predictions should be calibrated to be between 0 and 1 by DR. if None, uses default value from DR app (True). only applies to custom estimators with target type datarobot.enums.CUSTOM_TASK_TARGET_TYPE.ANOMALY
Returns:	CustomTask
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

update(name=None, language=None, description=None, **kwargs)¶

Update custom task properties.

New in version v2.26.

Parameters:	name: str, optional new custom task name language: str, optional new custom task programming language description: str, optional new custom task description
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

refresh()¶

Update custom task with the latest data from server.

New in version v2.26.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

delete()¶

Delete custom task.

New in version v2.26.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

download_latest_version(file_path)¶

Download the latest custom task version.

New in version v2.26.

Parameters:	file_path: str the full path of the target zip file
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

get_access_list()¶

Retrieve access control settings of this custom task.

New in version v2.27.

Returns:	list of : class:SharingAccess <datarobot.SharingAccess>

share(access_list)¶

Update the access control settings of this custom task.

New in version v2.27.

Parameters:	access_list : list of `SharingAccess` A list of SharingAccess to update.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

Examples

Transfer access to the custom task from old_user@datarobot.com to new_user@datarobot.com

import datarobot as dr

new_access = dr.SharingAccess(new_user@datarobot.com,
                              dr.enums.SHARING_ROLE.OWNER, can_share=True)
access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access]

dr.CustomTask.get('custom-task-id').share(access_list)

class datarobot.models.custom_task_version.CustomTaskFileItem(id, file_name, file_path, file_source, created_at=None)¶

A file item attached to a DataRobot custom task version.

New in version v2.26.

Attributes:	id: str id of the file item file_name: str name of the file item file_path: str path of the file item file_source: str source of the file item created_at: str ISO-8601 formatted timestamp of when the version was created

class datarobot.CustomTaskVersion(id, custom_task_id, version_major, version_minor, label, created_at, is_frozen, items, description=None, required_metadata=None, base_environment_id=None, maximum_memory=None, base_environment_version_id=None, dependencies=None, required_metadata_values=None, arguments=None)¶

A version of a DataRobot custom task.

New in version v2.26.

Attributes:

id: str

id of the custom task version

custom_task_id: str

id of the custom task

version_minor: int

a minor version number of custom task version

version_major: int

a major version number of custom task version

label: str

short human readable string to label the version

created_at: str

ISO-8601 formatted timestamp of when the version was created

is_frozen: bool

a flag if the custom task version is frozen

items: List[CustomTaskFileItem]

a list of file items attached to the custom task version

description: str, optional

custom task version description

required_metadata: Dict[str, str]

Deprecated in v2.26. Use require_metadata_values instead.

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.

base_environment_id: str, optional

id of the environment to use with the task

base_environment_version_id: str, optional

id of the environment version to use with the task

dependencies: List[CustomDependency]

the parsed dependencies of the custom task version if the version has a valid requirements.txt file

required_metadata_values: List[RequiredMetadataValue]

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys.

arguments: List[UserBlueprintTaskArgument]

A list of custom task version arguments.

classmethod from_server_data(data, keep_attrs=None)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place keep_attrs : list List of the dotted namespace notations for attributes to keep within the object structure even if their values are None

classmethod create_clean(custom_task_id, base_environment_id, maximum_memory=None, is_major_update=True, folder_path=None, required_metadata=None, required_metadata_values=None)¶

Create a custom task version without files from previous versions.

New in version v2.26.

Parameters:	custom_task_id: str the id of the custom task base_environment_id: str the id of the base environment to use with the custom task version is_major_update: bool, optional if the current version is 2.3, True would set the new version at 3.0. False would set the new version at 2.4. Default to True folder_path: str, optional the path to a folder containing files to be uploaded. Each file in the folder is uploaded under path relative to a folder path required_metadata: Dict[str, str] Deprecated in v2.26. Use require_metadata_values instead. Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version. required_metadata_values: List[RequiredMetadataValue] Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. maximum_memory: int A number in bytes about how much memory custom tasks’ inference containers can run with.
Returns:	CustomTaskVersion created custom task version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod create_from_previous(custom_task_id, base_environment_id, is_major_update=True, folder_path=None, files_to_delete=None, required_metadata=None, required_metadata_values=None, maximum_memory=None)¶

Create a custom task version containing files from a previous version.

New in version v2.26.

Parameters:	custom_task_id: str the id of the custom task base_environment_id: str the id of the base environment to use with the custom task version is_major_update: bool, optional if the current version is 2.3, True would set the new version at 3.0. False would set the new version at 2.4. Default to True folder_path: str, optional the path to a folder containing files to be uploaded. Each file in the folder is uploaded under path relative to a folder path files_to_delete: list, optional the list of a file items ids to be deleted Example: [“5ea95f7a4024030aba48e4f9”, “5ea6b5da402403181895cc51”] required_metadata: Dict[str, str] Deprecated in v2.26. Use require_metadata_values instead. Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version. required_metadata_values: List[RequiredMetadataValue] Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. maximum_memory: int A number in bytes about how much memory custom tasks’ inference containers can run with.
Returns:	CustomTaskVersion created custom task version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod list(custom_task_id)¶

List custom task versions.

New in version v2.26.

Parameters:	custom_task_id: str the id of the custom task
Returns:	List[CustomTaskVersion] a list of custom task versions
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(custom_task_id, custom_task_version_id)¶

Get custom task version by id.

New in version v2.26.

Parameters:	custom_task_id: str the id of the custom task custom_task_version_id: str the id of the custom task version to retrieve
Returns:	CustomTaskVersion retrieved custom task version
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

download(file_path)¶

Download custom task version.

New in version v2.26.

Parameters:	file_path: str path to create a file with custom task version content
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

update(description=None, required_metadata=None, required_metadata_values=None)¶

Update custom task version properties.

New in version v2.26.

Parameters:

description: str

new custom task version description

required_metadata: Dict[str, str]

Deprecated in v2.26. Use require_metadata_values instead.

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.

required_metadata_values: List[RequiredMetadataValue]

Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment’s requiredMetadataKeys.

Raises:

datarobot.errors.ClientError: if the server responded with 4xx status.
datarobot.errors.ServerError: if the server responded with 5xx status.

refresh()¶

Update custom task version with the latest data from server.

New in version v2.26.

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

start_dependency_build()¶

Start the dependency build for a custom task version and return build status. .. versionadded:: v2.27

Returns:	CustomTaskVersionDependencyBuild DTO of custom task version dependency build.

start_dependency_build_and_wait(max_wait)¶

Start the dependency build for a custom task version and wait while pulling status. .. versionadded:: v2.27

Parameters:	max_wait: int max time to wait for a build completion
Returns:	CustomTaskVersionDependencyBuild DTO of custom task version dependency build.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status datarobot.errors.AsyncTimeoutError Raised if the dependency build is not finished after max_wait.

cancel_dependency_build()¶

Cancel custom task version dependency build that is in progress. .. versionadded:: v2.27

Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

get_dependency_build()¶

Retrieve information about a custom task version’s dependency build. .. versionadded:: v2.27

Returns:	CustomTaskVersionDependencyBuild DTO of custom task version dependency build.

download_dependency_build_log(file_directory='.')¶

Get log of a custom task version dependency build. .. versionadded:: v2.27

Parameters:	file_directory: str (optional, default is “.”) Directory path where downloaded file is to save.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

Database Connectivity¶

class datarobot.DataDriver(id=None, creator=None, base_names=None, class_name=None, canonical_name=None)¶

A data driver

Attributes:	id : str the id of the driver. class_name : str the Java class name for the driver. canonical_name : str the user-friendly name of the driver. creator : str the id of the user who created the driver. base_names : list of str a list of the file name(s) of the jar files.

classmethod list()¶

Returns list of available drivers.

Returns:	drivers : list of DataDriver instances contains a list of available drivers.

Examples

>>> import datarobot as dr
>>> drivers = dr.DataDriver.list()
>>> drivers
[DataDriver('mysql'), DataDriver('RedShift'), DataDriver('PostgreSQL')]

classmethod get(driver_id)¶

Gets the driver.

Parameters:	driver_id : str the identifier of the driver.
Returns:	driver : DataDriver the required driver.

Examples

>>> import datarobot as dr
>>> driver = dr.DataDriver.get('5ad08a1889453d0001ea7c5c')
>>> driver
DataDriver('PostgreSQL')

classmethod create(class_name, canonical_name, files)¶

Creates the driver. Only available to admin users.

Parameters:	class_name : str the Java class name for the driver. canonical_name : str the user-friendly name of the driver. files : list of str a list of the file paths on file system file_path(s) for the driver.
Returns:	driver : DataDriver the created driver.
Raises:	ClientError raised if user is not granted for Can manage JDBC database drivers feature

Examples

>>> import datarobot as dr
>>> driver = dr.DataDriver.create(
...     class_name='org.postgresql.Driver',
...     canonical_name='PostgreSQL',
...     files=['/tmp/postgresql-42.2.2.jar']
... )
>>> driver
DataDriver('PostgreSQL')

update(class_name=None, canonical_name=None)¶

Updates the driver. Only available to admin users.

Parameters:	class_name : str the Java class name for the driver. canonical_name : str the user-friendly name of the driver.
Raises:	ClientError raised if user is not granted for Can manage JDBC database drivers feature

Examples

>>> import datarobot as dr
>>> driver = dr.DataDriver.get('5ad08a1889453d0001ea7c5c')
>>> driver.canonical_name
'PostgreSQL'
>>> driver.update(canonical_name='postgres')
>>> driver.canonical_name
'postgres'

delete()¶

Removes the driver. Only available to admin users.

Raises:	ClientError raised if user is not granted for Can manage JDBC database drivers feature

class datarobot.Connector(id=None, creator_id=None, configuration_id=None, base_name=None, canonical_name=None)¶

A connector

Attributes:	id : str the id of the connector. creator_id : str the id of the user who created the connector. base_name : str the file name of the jar file. canonical_name : str the user-friendly name of the connector. configuration_id : str the id of the configuration of the connector.

classmethod list()¶

Returns list of available connectors.

Returns:	connectors : list of Connector instances contains a list of available connectors.

Examples

>>> import datarobot as dr
>>> connectors = dr.Connector.list()
>>> connectors
[Connector('ADLS Gen2 Connector'), Connector('S3 Connector')]

classmethod get(connector_id)¶

Gets the connector.

Parameters:	connector_id : str the identifier of the connector.
Returns:	connector : Connector the required connector.

Examples

>>> import datarobot as dr
>>> connector = dr.Connector.get('5fe1063e1c075e0245071446')
>>> connector
Connector('ADLS Gen2 Connector')

classmethod create(file_path)¶

Creates the connector from a jar file. Only available to admin users.

Parameters:	file_path : str the file path on file system file_path(s) for the connector.
Returns:	connector : Connector the created connector.
Raises:	ClientError raised if user is not granted for Can manage connectors feature

Examples

>>> import datarobot as dr
>>> connector = dr.Connector.create('/tmp/connector-adls-gen2.jar')
>>> connector
Connector('ADLS Gen2 Connector')

update(file_path)¶

Updates the connector with new jar file. Only available to admin users.

Parameters:	file_path : str the file path on file system file_path(s) for the connector.
Returns:	connector : Connector the updated connector.
Raises:	ClientError raised if user is not granted for Can manage connectors feature

Examples

>>> import datarobot as dr
>>> connector = dr.Connector.get('5fe1063e1c075e0245071446')
>>> connector.base_name
'connector-adls-gen2.jar'
>>> connector.update('/tmp/connector-s3.jar')
>>> connector.base_name
'connector-s3.jar'

delete()¶

Removes the connector. Only available to admin users.

Raises:	ClientError raised if user is not granted for Can manage connectors feature

class datarobot.DataStore(data_store_id=None, data_store_type=None, canonical_name=None, creator=None, updated=None, params=None, role=None)¶

A data store. Represents database

Attributes:	id : str the id of the data store. data_store_type : str the type of data store. canonical_name : str the user-friendly name of the data store. creator : str the id of the user who created the data store. updated : datetime.datetime the time of the last update params : DataStoreParameters a list specifying data store parameters.

classmethod list()¶

Returns list of available data stores.

Returns:	data_stores : list of DataStore instances contains a list of available data stores.

Examples

>>> import datarobot as dr
>>> data_stores = dr.DataStore.list()
>>> data_stores
[DataStore('Demo'), DataStore('Airlines')]

classmethod get(data_store_id)¶

Gets the data store.

Parameters:	data_store_id : str the identifier of the data store.
Returns:	data_store : DataStore the required data store.

Examples

>>> import datarobot as dr
>>> data_store = dr.DataStore.get('5a8ac90b07a57a0001be501e')
>>> data_store
DataStore('Demo')

classmethod create(data_store_type, canonical_name, driver_id, jdbc_url)¶

Creates the data store.

Parameters:	data_store_type : str the type of data store. canonical_name : str the user-friendly name of the data store. driver_id : str the identifier of the DataDriver. jdbc_url : str the full JDBC url, for example jdbc:postgresql://my.dbaddress.org:5432/my_db.
Returns:	data_store : DataStore the created data store.

Examples

>>> import datarobot as dr
>>> data_store = dr.DataStore.create(
...     data_store_type='jdbc',
...     canonical_name='Demo DB',
...     driver_id='5a6af02eb15372000117c040',
...     jdbc_url='jdbc:postgresql://my.db.address.org:5432/perftest'
... )
>>> data_store
DataStore('Demo DB')

update(canonical_name=None, driver_id=None, jdbc_url=None)¶

Updates the data store.

Parameters:	canonical_name : str optional, the user-friendly name of the data store. driver_id : str optional, the identifier of the DataDriver. jdbc_url : str optional, the full JDBC url, for example jdbc:postgresql://my.dbaddress.org:5432/my_db.

Examples

>>> import datarobot as dr
>>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae')
>>> data_store
DataStore('Demo DB')
>>> data_store.update(canonical_name='Demo DB updated')
>>> data_store
DataStore('Demo DB updated')

delete()¶: Removes the DataStore

test(username, password)¶

Tests database connection.

Parameters:	username : str the username for database authentication. password : str the password for database authentication. The password is encrypted at server side and never saved / stored
Returns:	message : dict message with status.

Examples

>>> import datarobot as dr
>>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae')
>>> data_store.test(username='db_username', password='db_password')
{'message': 'Connection successful'}

schemas(username, password)¶

Returns list of available schemas.

Parameters:	username : str the username for database authentication. password : str the password for database authentication. The password is encrypted at server side and never saved / stored
Returns:	response : dict dict with database name and list of str - available schemas

Examples

>>> import datarobot as dr
>>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae')
>>> data_store.schemas(username='db_username', password='db_password')
{'catalog': 'perftest', 'schemas': ['demo', 'information_schema', 'public']}

tables(username, password, schema=None)¶

Returns list of available tables in schema.

Parameters:	username : str optional, the username for database authentication. password : str optional, the password for database authentication. The password is encrypted at server side and never saved / stored schema : str optional, the schema name.
Returns:	response : dict dict with catalog name and tables info

Examples

>>> import datarobot as dr
>>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae')
>>> data_store.tables(username='db_username', password='db_password', schema='demo')
{'tables': [{'type': 'TABLE', 'name': 'diagnosis', 'schema': 'demo'}, {'type': 'TABLE',
'name': 'kickcars', 'schema': 'demo'}, {'type': 'TABLE', 'name': 'patient',
'schema': 'demo'}, {'type': 'TABLE', 'name': 'transcript', 'schema': 'demo'}],
'catalog': 'perftest'}

classmethod from_server_data(data, keep_attrs=None)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place keep_attrs : list List of the dotted namespace notations for attributes to keep within the object structure even if their values are None

get_access_list()¶

Retrieve what users have access to this data store

New in version v2.14.

Returns:	list of : class:SharingAccess <datarobot.SharingAccess>

share(access_list)¶

Modify the ability of users to access this data store

New in version v2.14.

Parameters:	access_list : list of `SharingAccess` the modifications to make.
Raises:	datarobot.ClientError : if you do not have permission to share this data store, if the user you’re sharing with doesn’t exist, if the same user appears multiple times in the access_list, or if these changes would leave the data store without an owner.

Examples

Transfer access to the data store from old_user@datarobot.com to new_user@datarobot.com

import datarobot as dr

new_access = dr.SharingAccess(new_user@datarobot.com,
                              dr.enums.SHARING_ROLE.OWNER, can_share=True)
access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access]

dr.DataStore.get('my-data-store-id').share(access_list)

class datarobot.DataSource(data_source_id=None, data_source_type=None, canonical_name=None, creator=None, updated=None, params=None, role=None)¶

A data source. Represents data request

Attributes:	id : str the id of the data source. type : str the type of data source. canonical_name : str the user-friendly name of the data source. creator : str the id of the user who created the data source. updated : datetime.datetime the time of the last update. params : DataSourceParameters a list specifying data source parameters.

classmethod list()¶

Returns list of available data sources.

Returns:	data_sources : list of DataSource instances contains a list of available data sources.

Examples

>>> import datarobot as dr
>>> data_sources = dr.DataSource.list()
>>> data_sources
[DataSource('Diagnostics'), DataSource('Airlines 100mb'), DataSource('Airlines 10mb')]

classmethod get(data_source_id)¶

Gets the data source.

Parameters:	data_source_id : str the identifier of the data source.
Returns:	data_source : DataSource the requested data source.

Examples

>>> import datarobot as dr
>>> data_source = dr.DataSource.get('5a8ac9ab07a57a0001be501f')
>>> data_source
DataSource('Diagnostics')

classmethod create(data_source_type, canonical_name, params)¶

Creates the data source.

Parameters:	data_source_type : str the type of data source. canonical_name : str the user-friendly name of the data source. params : DataSourceParameters a list specifying data source parameters.
Returns:	data_source : DataSource the created data source.

Examples

>>> import datarobot as dr
>>> params = dr.DataSourceParameters(
...     data_store_id='5a8ac90b07a57a0001be501e',
...     query='SELECT * FROM airlines10mb WHERE "Year" >= 1995;'
... )
>>> data_source = dr.DataSource.create(
...     data_source_type='jdbc',
...     canonical_name='airlines stats after 1995',
...     params=params
... )
>>> data_source
DataSource('airlines stats after 1995')

update(canonical_name=None, params=None)¶

Creates the data source.

Parameters:	canonical_name : str optional, the user-friendly name of the data source. params : DataSourceParameters optional, the identifier of the DataDriver.

Examples

>>> import datarobot as dr
>>> data_source = dr.DataSource.get('5ad840cc613b480001570953')
>>> data_source
DataSource('airlines stats after 1995')
>>> params = dr.DataSourceParameters(
...     query='SELECT * FROM airlines10mb WHERE "Year" >= 1990;'
... )
>>> data_source.update(
...     canonical_name='airlines stats after 1990',
...     params=params
... )
>>> data_source
DataSource('airlines stats after 1990')

delete()¶: Removes the DataSource

classmethod from_server_data(data, keep_attrs=None)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place keep_attrs : list List of the dotted namespace notations for attributes to keep within the object structure even if their values are None

get_access_list()¶

Retrieve what users have access to this data source

New in version v2.14.

Returns:	list of : class:SharingAccess <datarobot.SharingAccess>

share(access_list)¶

Modify the ability of users to access this data source

New in version v2.14.

Parameters:	access_list : list of `SharingAccess` the modifications to make.
Raises:	datarobot.ClientError : if you do not have permission to share this data source, if the user you’re sharing with doesn’t exist, if the same user appears multiple times in the access_list, or if these changes would leave the data source without an owner

Examples

Transfer access to the data source from old_user@datarobot.com to new_user@datarobot.com

import datarobot as dr

new_access = dr.SharingAccess(new_user@datarobot.com,
                              dr.enums.SHARING_ROLE.OWNER, can_share=True)
access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access]

dr.DataSource.get('my-data-source-id').share(access_list)

create_dataset(username=None, password=None, do_snapshot=None, persist_data_after_ingestion=None, categories=None, credential_id=None, use_kerberos=None)¶

Create a Dataset from this data source.

New in version v2.22.

Parameters:

username: string, optional: The username for database authentication.
password: string, optional: The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.
do_snapshot: bool, optional: If unset, uses the server default: True. If true, creates a snapshot dataset; if false, creates a remote dataset. Creating snapshots from non-file sources requires an additional permission, Enable Create Snapshot Data Source.
persist_data_after_ingestion: bool, optional: If unset, uses the server default: True. If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and doSnapshot to true will result in an error.
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
credential_id: string, optional: The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.
use_kerberos: bool, optional: If unset, uses the server default: False. If true, use kerberos authentication for database authentication.

Returns:

response: Dataset: The Dataset created from the uploaded data

class datarobot.DataSourceParameters(data_store_id=None, table=None, schema=None, partition_column=None, query=None, fetch_size=None)¶

Data request configuration

Attributes:

data_store_id : str: the id of the DataStore.
table : str: optional, the name of specified database table.
schema : str: optional, the name of the schema associated with the table.
partition_column : str: optional, the name of the partition column.
query : str: optional, the user specified SQL query.
fetch_size : int: optional, a user specified fetch size in the range [1, 20000]. By default a fetchSize will be assigned to balance throughput and memory usage

Datasets¶

class datarobot.Dataset(dataset_id, version_id, name, categories, created_at, created_by, is_data_engine_eligible, is_latest_version, is_snapshot, processing_state, data_persisted=None, size=None, row_count=None)¶

Represents a Dataset returned from the api/v2/datasets/ endpoints.

Attributes:

id: string: The ID of this dataset
name: string: The name of this dataset in the catalog
is_latest_version: bool: Whether this dataset version is the latest version of this dataset
version_id: string: The object ID of the catalog_version the dataset belongs to
categories: list(string): An array of strings describing the intended use of the dataset. The supported options are “TRAINING” and “PREDICTION”.
created_at: string: The date when the dataset was created
created_by: string: Username of the user who created the dataset
is_snapshot: bool: Whether the dataset version is an immutable snapshot of data which has previously been retrieved and saved to Data_robot
data_persisted: bool, optional: If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.
is_data_engine_eligible: bool: Whether this dataset can be a data source of a data engine query.
processing_state: string: Current ingestion process state of the dataset
row_count: int, optional: The number of rows in the dataset.
size: int, optional: The size of the dataset as a CSV in bytes.

classmethod create_from_file(file_path=None, filelike=None, categories=None, read_timeout=600, max_wait=600)¶

A blocking call that creates a new Dataset from a file. Returns when the dataset has been successfully uploaded and processed.

Warning: This function does not clean up it’s open files. If you pass a filelike, you are responsible for closing it. If you pass a file_path, this will create a file object from the file_path but will not close it.

Parameters:

file_path: string, optional: The path to the file. This will create a file object pointing to that file but will not close it.
filelike: file, optional: An open and readable file object.
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
read_timeout: int, optional: The maximum number of seconds to wait for the server to respond indicating that the initial upload is complete
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: A fully armed and operational Dataset

classmethod create_from_in_memory_data(data_frame=None, records=None, categories=None, read_timeout=600, max_wait=600)¶

A blocking call that creates a new Dataset from in-memory data. Returns when the dataset has been successfully uploaded and processed.

The data can be either a pandas DataFrame or a list of dictionaries with identical keys.

Parameters:

data_frame: DataFrame, optional: The data frame to upload
records: list[dict], optional: A list of dictionaries with identical keys to upload
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
read_timeout: int, optional: The maximum number of seconds to wait for the server to respond indicating that the initial upload is complete
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: The Dataset created from the uploaded data

classmethod create_from_url(url, do_snapshot=None, persist_data_after_ingestion=None, categories=None, max_wait=600)¶

A blocking call that creates a new Dataset from data stored at a url. Returns when the dataset has been successfully uploaded and processed.

Parameters:

url: string: The URL to use as the source of data for the dataset being created.
do_snapshot: bool, optional: If unset, uses the server default: True. If true, creates a snapshot dataset; if false, creates a remote dataset. Creating snapshots from non-file sources may be disabled by the permission, Disable AI Catalog Snapshots.
persist_data_after_ingestion: bool, optional: If unset, uses the server default: True. If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and doSnapshot to true will result in an error.
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: The Dataset created from the uploaded data

classmethod create_from_data_source(data_source_id, username=None, password=None, do_snapshot=None, persist_data_after_ingestion=None, categories=None, credential_id=None, use_kerberos=None, credential_data=None, max_wait=600)¶

A blocking call that creates a new Dataset from data stored at a DataSource. Returns when the dataset has been successfully uploaded and processed.

New in version v2.22.

Parameters:

data_source_id: string: The ID of the DataSource to use as the source of data.
username: string, optional: The username for database authentication.
password: string, optional: The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.
do_snapshot: bool, optional: If unset, uses the server default: True. If true, creates a snapshot dataset; if false, creates a remote dataset. Creating snapshots from non-file sources requires may be disabled by the permission, Disable AI Catalog Snapshots.
persist_data_after_ingestion: bool, optional: If unset, uses the server default: True. If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and doSnapshot to true will result in an error.
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
credential_id: string, optional: The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.
use_kerberos: bool, optional: If unset, uses the server default: False. If true, use kerberos authentication for database authentication.
credential_data: dict, optional: The credentials to authenticate with the database, to use instead of user/password or credential ID.
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: The Dataset created from the uploaded data

classmethod create_from_query_generator(generator_id, dataset_id=None, dataset_version_id=None, max_wait=600)¶

A blocking call that creates a new Dataset from the query generator. Returns when the dataset has been successfully processed. If optional parameters are not specified the query is applied to the dataset_id and dataset_version_id stored in the query generator. If specified they will override the stored dataset_id/dataset_version_id, e.g. to prep a prediction dataset.

Parameters:

generator_id: str: The id of the query generator to use.
dataset_id: str, optional: The id of the dataset to apply the query to.
dataset_version_id: str, optional: The id of the dataset version to apply the query to. If not specified the latest version associated with dataset_id (if specified) is used.
max_wait : int: optional, the maximum number of seconds to wait before giving up.

Returns:

response: Dataset: The Dataset created from the query generator

classmethod get(dataset_id)¶

Get information about a dataset.

Parameters:	dataset_id : string the id of the dataset
Returns:	dataset : Dataset the queried dataset

classmethod delete(dataset_id)¶

Soft deletes a dataset. You cannot get it or list it or do actions with it, except for un-deleting it.

Parameters:	dataset_id: string The id of the dataset to mark for deletion
Returns:	None

classmethod un_delete(dataset_id)¶

Un-deletes a previously deleted dataset. If the dataset was not deleted, nothing happens.

Parameters:	dataset_id: string The id of the dataset to un-delete
Returns:	None

classmethod list(category=None, filter_failed=None, order_by=None)¶

List all datasets a user can view.

Parameters:

category: string, optional: Optional. If specified, only dataset versions that have the specified category will be included in the results. Categories identify the intended use of the dataset; supported categories are “TRAINING” and “PREDICTION”.
filter_failed: bool, optional: If unset, uses the server default: False. Whether datasets that failed during import should be excluded from the results. If True invalid datasets will be excluded.
order_by: string, optional: If unset, uses the server default: “-created”. Sorting order which will be applied to catalog list, valid options are: - “created” – ascending order by creation datetime; - “-created” – descending order by creation datetime.

Returns:

list[Dataset]: a list of datasets the user can view

classmethod iterate(offset=None, limit=None, category=None, order_by=None, filter_failed=None)¶

Get an iterator for the requested datasets a user can view. This lazily retrieves results. It does not get the next page from the server until the current page is exhausted.

Parameters:

offset: int, optional: If set, this many results will be skipped
limit: int, optional: Specifies the size of each page retrieved from the server. If unset, uses the server default.
category: string, optional: Optional. If specified, only dataset versions that have the specified category will be included in the results. Categories identify the intended use of the dataset; supported categories are “TRAINING” and “PREDICTION”.
filter_failed: bool, optional: If unset, uses the server default: False. Whether datasets that failed during import should be excluded from the results. If True invalid datasets will be excluded.
order_by: string, optional: If unset, uses the server default: “-created”. Sorting order which will be applied to catalog list, valid options are: - “created” – ascending order by creation datetime; - “-created” – descending order by creation datetime.

Yields:

Dataset: An iterator of the datasets the user can view

update()¶

Updates the Dataset attributes in place with the latest information from the server.

Returns:	None

modify(name=None, categories=None)¶

Modifies the Dataset name and/or categories. Updates the object in place.

Parameters:	name: string, optional The new name of the dataset categories: list[string], optional A list of strings describing the intended use of the dataset. The supported options are “TRAINING” and “PREDICTION”. If any categories were previously specified for the dataset, they will be overwritten.
Returns:	None

get_details()¶

Gets the details for this Dataset

Returns:	DatasetDetails

get_all_features(order_by=None)¶

Get a list of all the features for this dataset.

Parameters:	order_by: string, optional If unset, uses the server default: ‘name’. How the features should be ordered. Can be ‘name’ or ‘featureType’.
Returns:	list[DatasetFeature]

iterate_all_features(offset=None, limit=None, order_by=None)¶

Get an iterator for the requested features of a dataset. This lazily retrieves results. It does not get the next page from the server until the current page is exhausted.

Parameters:	offset: int, optional If set, this many results will be skipped. limit: int, optional Specifies the size of each page retrieved from the server. If unset, uses the server default. order_by: string, optional If unset, uses the server default: ‘name’. How the features should be ordered. Can be ‘name’ or ‘featureType’.
Yields:	DatasetFeature

get_featurelists()¶

Get DatasetFeaturelists created on this Dataset

Returns:	feature_lists: list[DatasetFeaturelist]

create_featurelist(name, features)¶

Create a new dataset featurelist

Parameters:	name : str the name of the modeling featurelist to create. Names must be unique within the dataset, or the server will return an error. features : list of str the names of the features to include in the dataset featurelist. Each feature must be a dataset feature.
Returns:	featurelist : DatasetFeaturelist the newly created featurelist

Examples

dataset = Dataset.get('1234deadbeeffeeddead4321')
dataset_features = dataset.get_all_features()
selected_features = [feat.name for feat in dataset_features][:5]  # select first five
new_flist = dataset.create_featurelist('Simple Features', selected_features)

get_file(file_path=None, filelike=None)¶

Retrieves all the originally uploaded data in CSV form. Writes it to either the file or a filelike object that can write bytes.

Only one of file_path or filelike can be provided and it must be provided as a keyword argument (i.e. file_path=’path-to-write-to’). If a file-like object is provided, the user is responsible for closing it when they are done.

The user must also have permission to download data.

Parameters:	file_path: string, optional The destination to write the file to. filelike: file, optional A file-like object to write to. The object must be able to write bytes. The user is responsible for closing the object
Returns:	None

get_projects()¶

Retrieves the Dataset’s projects as ProjectLocation named tuples.

Returns:	locations: list[ProjectLocation]

create_project(project_name=None, user=None, password=None, credential_id=None, use_kerberos=None, credential_data=None)¶

Create a datarobot.models.Project from this dataset

Parameters:

project_name: string, optional: The name of the project to be created. If not specified, will be “Untitled Project” for database connections, otherwise the project name will be based on the file used.
user: string, optional: The username for database authentication.
password: string, optional: The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored
credential_id: string, optional: The ID of the set of credentials to use instead of user and password.
use_kerberos: bool, optional: Server default is False. If true, use kerberos authentication for database authentication.
credential_data: dict, optional: The credentials to authenticate with the database, to use instead of user/password or credential ID.

Returns:

Project

classmethod create_version_from_file(dataset_id, file_path=None, filelike=None, categories=None, read_timeout=600, max_wait=600)¶

A blocking call that creates a new Dataset version from a file. Returns when the new dataset version has been successfully uploaded and processed.

Warning: This function does not clean up it’s open files. If you pass a filelike, you are responsible for closing it. If you pass a file_path, this will create a file object from the file_path but will not close it.

New in version v2.23.

Parameters:

dataset_id: string: The ID of the dataset for which new version to be created
file_path: string, optional: The path to the file. This will create a file object pointing to that file but will not close it.
filelike: file, optional: An open and readable file object.
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
read_timeout: int, optional: The maximum number of seconds to wait for the server to respond indicating that the initial upload is complete
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: A fully armed and operational Dataset version

classmethod create_version_from_in_memory_data(dataset_id, data_frame=None, records=None, categories=None, read_timeout=600, max_wait=600)¶

A blocking call that creates a new Dataset version for a dataset from in-memory data. Returns when the dataset has been successfully uploaded and processed.

The data can be either a pandas DataFrame or a list of dictionaries with identical keys.

New in version v2.23.

Parameters:

dataset_id: string: The ID of the dataset for which new version to be created
data_frame: DataFrame, optional: The data frame to upload
records: list[dict], optional: A list of dictionaries with identical keys to upload
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
read_timeout: int, optional: The maximum number of seconds to wait for the server to respond indicating that the initial upload is complete
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: The Dataset version created from the uploaded data

classmethod create_version_from_url(dataset_id, url, categories=None, max_wait=600)¶

A blocking call that creates a new Dataset from data stored at a url for a given dataset. Returns when the dataset has been successfully uploaded and processed.

New in version v2.23.

Parameters:

dataset_id: string: The ID of the dataset for which new version to be created
url: string: The URL to use as the source of data for the dataset being created.
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: The Dataset version created from the uploaded data

classmethod create_version_from_data_source(dataset_id, data_source_id, username=None, password=None, categories=None, credential_id=None, use_kerberos=None, credential_data=None, max_wait=600)¶

A blocking call that creates a new Dataset from data stored at a DataSource. Returns when the dataset has been successfully uploaded and processed.

New in version v2.23.

Parameters:

dataset_id: string: The ID of the dataset for which new version to be created
data_source_id: string: The ID of the DataSource to use as the source of data.
username: string, optional: The username for database authentication.
password: string, optional: The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.
categories: list[string], optional: An array of strings describing the intended use of the dataset. The current supported options are “TRAINING” and “PREDICTION”.
credential_id: string, optional: The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.
use_kerberos: bool, optional: If unset, uses the server default: False. If true, use kerberos authentication for database authentication.
credential_data: dict, optional: The credentials to authenticate with the database, to use instead of user/password or credential ID.
max_wait: int, optional: Time in seconds after which project creation is considered unsuccessful

Returns:

response: Dataset: The Dataset version created from the uploaded data

class datarobot.DatasetDetails(dataset_id, version_id, categories, created_by, created_at, data_source_type, error, is_latest_version, is_snapshot, is_data_engine_eligible, last_modification_date, last_modifier_full_name, name, uri, data_persisted=None, data_engine_query_id=None, data_source_id=None, description=None, eda1_modification_date=None, eda1_modifier_full_name=None, feature_count=None, feature_count_by_type=None, processing_state=None, row_count=None, size=None, tags=None)¶

Represents a detailed view of a Dataset. The to_dataset method creates a Dataset from this details view.

Attributes:

dataset_id: string: The ID of this dataset
name: string: The name of this dataset in the catalog
is_latest_version: bool: Whether this dataset version is the latest version of this dataset
version_id: string: The object ID of the catalog_version the dataset belongs to
categories: list(string): An array of strings describing the intended use of the dataset. The supported options are “TRAINING” and “PREDICTION”.
created_at: string: The date when the dataset was created
created_by: string: Username of the user who created the dataset
is_snapshot: bool: Whether the dataset version is an immutable snapshot of data which has previously been retrieved and saved to Data_robot
data_persisted: bool, optional: If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.
is_data_engine_eligible: bool: Whether this dataset can be a data source of a data engine query.
processing_state: string: Current ingestion process state of the dataset
row_count: int, optional: The number of rows in the dataset.
size: int, optional: The size of the dataset as a CSV in bytes.
data_engine_query_id: string, optional: ID of the source data engine query
data_source_id: string, optional: ID of the datasource used as the source of the dataset
data_source_type: string: the type of the datasource that was used as the source of the dataset
description: string, optional: the description of the dataset
eda1_modification_date: string, optional: the ISO 8601 formatted date and time when the EDA1 for the dataset was updated
eda1_modifier_full_name: string, optional: the user who was the last to update EDA1 for the dataset
error: string: details of exception raised during ingestion process, if any
feature_count: int, optional: total number of features in the dataset
feature_count_by_type: list[FeatureTypeCount]: number of features in the dataset grouped by feature type
last_modification_date: string: the ISO 8601 formatted date and time when the dataset was last modified
last_modifier_full_name: string: full name of user who was the last to modify the dataset
tags: list[string]: list of tags attached to the item
uri: string: the uri to datasource like: - ‘file_name.csv’ - ‘jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME’ - ‘jdbc:DATA_SOURCE_GIVEN_NAME/<query>’ - for query based datasources - ‘https://s3.amazonaws.com/datarobot_test/kickcars-sample-200.csv’ - etc.

classmethod get(dataset_id)¶

Get details for a Dataset from the server

Parameters:	dataset_id: str The id for the Dataset from which to get details
Returns:	DatasetDetails

to_dataset()¶

Build a Dataset object from the information in this object

Returns:	Dataset

Data Engine Query Generator¶

class datarobot.DataEngineQueryGenerator(**generator_kwargs)¶

DataEngineQueryGenerator is used to set up time series data prep.

New in version v2.27.

Attributes:	id: str id of the query generator query: str text of the generated Spark SQL query datasets: list(QueryGeneratorDataset) datasets associated with the query generator generator_settings: QueryGeneratorSettings the settings used to define the query generator_type: str “TimeSeries” is the only supported type

classmethod create(generator_type, datasets, generator_settings)¶

Creates a query generator entity.

New in version v2.27.

Parameters:	generator_type : str Type of data engine query generator datasets : List[QueryGeneratorDataset] Source datasets in the Data Engine workspace. generator_settings : dict Data engine generator settings of the given generator_type.
Returns:	query_generator : DataEngineQueryGenerator The created generator

Examples

import datarobot as dr
from datarobot.models.data_engine_query_generator import (
   QueryGeneratorDataset,
   QueryGeneratorSettings,
)
dataset = QueryGeneratorDataset(
   alias='My_Awesome_Dataset_csv',
   dataset_id='61093144cabd630828bca321',
   dataset_version_id=1,
)
settings = QueryGeneratorSettings(
   datetime_partition_column='date',
   time_unit='DAY',
   time_step=1,
   default_numeric_aggregation_method='sum',
   default_categorical_aggregation_method='mostFrequent',
)
g = dr.DataEngineQueryGenerator.create(
   generator_type='TimeSeries',
   datasets=[dataset],
   generator_settings=settings,
)
g.id
>>>'54e639a18bd88f08078ca831'
g.generator_type
>>>'TimeSeries'

classmethod get(generator_id)¶

Gets information about a query generator.

Parameters:	generator_id : str The identifier of the query generator you want to load.
Returns:	query_generator : DataEngineQueryGenerator The queried generator

Examples

import datarobot as dr
g = dr.DataEngineQueryGenerator.get(generator_id='54e639a18bd88f08078ca831')
g.id
>>>'54e639a18bd88f08078ca831'
g.generator_type
>>>'TimeSeries'

create_dataset(dataset_id=None, dataset_version_id=None, max_wait=600)¶

A blocking call that creates a new Dataset from the query generator. Returns when the dataset has been successfully processed. If optional parameters are not specified the query is applied to the dataset_id and dataset_version_id stored in the query generator. If specified they will override the stored dataset_id/dataset_version_id, i.e. to prep a prediction dataset.

Parameters:	dataset_id: str, optional The id of the unprepped dataset to apply the query to dataset_version_id: str, optional The version_id of the unprepped dataset to apply the query to
Returns:	response: Dataset The Dataset created from the query generator

Datetime Trend Plots¶

class datarobot.models.datetime_trend_plots.AccuracyOverTimePlotsMetadata(project_id, model_id, forecast_distance, resolutions, backtest_metadata, holdout_metadata, backtest_statuses, holdout_statuses)¶

Accuracy over Time metadata for datetime model.

New in version v2.25.

Notes

Backtest/holdout status is a dict containing the following:

training: string

Status backtest/holdout training. One of datarobot.enums.DATETIME_TREND_PLOTS_STATUS
validation: string

Status backtest/holdout validation. One of datarobot.enums.DATETIME_TREND_PLOTS_STATUS

Backtest/holdout metadata is a dict containing the following:

training: dict

Start and end dates for the backtest/holdout training.
validation: dict

Start and end dates for the backtest/holdout validation.

Each dict in the training and validation in backtest/holdout metadata is structured like:

start_date: datetime.datetime or None

The datetime of the start of the chart data (inclusive). None if chart data is not computed.
end_date: datetime.datetime or None

The datetime of the end of the chart data (exclusive). None if chart data is not computed.

Attributes:

project_id: string: The project ID.
model_id: string: The model ID.
forecast_distance: int or None: The forecast distance for which the metadata was retrieved. None for OTV projects.
resolutions: list of string: A list of datarobot.enums.DATETIME_TREND_PLOTS_RESOLUTION, which represents available time resolutions for which plots can be retrieved.
backtest_metadata: list of dict: List of backtest metadata dicts. The list index of metadata dict is the backtest index. See backtest/holdout metadata info in Notes for more details.
holdout_metadata: dict: Holdout metadata dict. See backtest/holdout metadata info in Notes for more details.
backtest_statuses: list of dict: List of backtest statuses dict. The list index of status dict is the backtest index. See backtest/holdout status info in Notes for more details.
holdout_statuses: dict: Holdout status dict. See backtest/holdout status info in Notes for more details.

class datarobot.models.datetime_trend_plots.AccuracyOverTimePlot(project_id, model_id, start_date, end_date, resolution, bins, statistics, calendar_events)¶

Accuracy over Time plot for datetime model.

New in version v2.25.

Notes

Bin is a dict containing the following:

start_date: datetime.datetime

The datetime of the start of the bin (inclusive).
end_date: datetime.datetime

The datetime of the end of the bin (exclusive).
actual: float or None

Average actual value of the target in the bin. None if there are no entries in the bin.
predicted: float or None

Average prediction of the model in the bin. None if there are no entries in the bin.
frequency: int or None

Indicates number of values averaged in bin.

Statistics is a dict containing the following:

durbin_watson: float or None

The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. More info https://wikipedia.org/wiki/Durbin%E2%80%93Watson_statistic

Calendar event is a dict containing the following:

name: string

Name of the calendar event.
date: datetime

Date of the calendar event.
series_id: string or None

The series ID for the event. If this event does not specify a series ID, then this will be None, indicating that the event applies to all series.

Attributes:

project_id: string: The project ID.
model_id: string: The model ID.
resolution: string: The resolution that is used for binning. One of datarobot.enums.DATETIME_TREND_PLOTS_RESOLUTION
start_date: datetime.datetime: The datetime of the start of the chart data (inclusive).
end_date: datetime.datetime: The datetime of the end of the chart data (exclusive).
bins: list of dict: List of plot bins. See bin info in Notes for more details.
statistics: dict: Statistics for plot. See statistics info in Notes for more details.
calendar_events: list of dict: List of calendar events for the plot. See calendar events info in Notes for more details.

class datarobot.models.datetime_trend_plots.AccuracyOverTimePlotPreview(project_id, model_id, start_date, end_date, bins)¶

Accuracy over Time plot preview for datetime model.

New in version v2.25.

Notes

Bin is a dict containing the following:

start_date: datetime.datetime

The datetime of the start of the bin (inclusive).
end_date: datetime.datetime

The datetime of the end of the bin (exclusive).
actual: float or None

Average actual value of the target in the bin. None if there are no entries in the bin.
predicted: float or None

Average prediction of the model in the bin. None if there are no entries in the bin.

Attributes:	project_id: string The project ID. model_id: string The model ID. start_date: datetime.datetime The datetime of the start of the chart data (inclusive). end_date: datetime.datetime The datetime of the end of the chart data (exclusive). bins: list of dict List of plot bins. See bin info in Notes for more details.

class datarobot.models.datetime_trend_plots.ForecastVsActualPlotsMetadata(project_id, model_id, resolutions, backtest_metadata, holdout_metadata, backtest_statuses, holdout_statuses)¶

Forecast vs Actual plots metadata for datetime model.

New in version v2.25.

Notes

Backtest/holdout status is a dict containing the following:

training: dict

Dict containing each of datarobot.enums.DATETIME_TREND_PLOTS_STATUS as dict key, and list of forecast distances for particular status as dict value.
validation: dict

Dict containing each of datarobot.enums.DATETIME_TREND_PLOTS_STATUS as dict key, and list of forecast distances for particular status as dict value.

Backtest/holdout metadata is a dict containing the following:

training: dict

Start and end dates for the backtest/holdout training.
validation: dict

Start and end dates for the backtest/holdout validation.

Each dict in the training and validation in backtest/holdout metadata is structured like:

start_date: datetime.datetime or None

The datetime of the start of the chart data (inclusive). None if chart data is not computed.
end_date: datetime.datetime or None

The datetime of the end of the chart data (exclusive). None if chart data is not computed.

Attributes:

project_id: string: The project ID.
model_id: string: The model ID.
resolutions: list of string: A list of datarobot.enums.DATETIME_TREND_PLOTS_RESOLUTION, which represents available time resolutions for which plots can be retrieved.
backtest_metadata: list of dict: List of backtest metadata dicts. The list index of metadata dict is the backtest index. See backtest/holdout metadata info in Notes for more details.
holdout_metadata: dict: Holdout metadata dict. See backtest/holdout metadata info in Notes for more details.
backtest_statuses: list of dict: List of backtest statuses dict. The list index of status dict is the backtest index. See backtest/holdout status info in Notes for more details.
holdout_statuses: dict: Holdout status dict. See backtest/holdout status info in Notes for more details.

class datarobot.models.datetime_trend_plots.ForecastVsActualPlot(project_id, model_id, forecast_distances, start_date, end_date, resolution, bins, calendar_events)¶

Forecast vs Actual plot for datetime model.

New in version v2.25.

Notes

Bin is a dict containing the following:

start_date: datetime.datetime

The datetime of the start of the bin (inclusive).
end_date: datetime.datetime

The datetime of the end of the bin (exclusive).
actual: float or None

Average actual value of the target in the bin. None if there are no entries in the bin.
forecasts: list of float

A list of average forecasts for the model for each forecast distance. Empty if there are no forecasts in the bin. Each index in the forecasts list maps to forecastDistances list index.
error: float or None

Average absolute residual value of the bin. None if there are no entries in the bin.
normalized_error: float or None

Normalized average absolute residual value of the bin. None if there are no entries in the bin.
frequency: int or None

Indicates number of values averaged in bin.

Calendar event is a dict containing the following:

name: string

Name of the calendar event.
date: datetime

Date of the calendar event.
series_id: string or None

The series ID for the event. If this event does not specify a series ID, then this will be None, indicating that the event applies to all series.

Attributes:

project_id: string: The project ID.
model_id: string: The model ID.
forecast_distances: list of int: A list of forecast distances that were retrieved.
resolution: string: The resolution that is used for binning. One of datarobot.enums.DATETIME_TREND_PLOTS_RESOLUTION
start_date: datetime.datetime: The datetime of the start of the chart data (inclusive).
end_date: datetime.datetime: The datetime of the end of the chart data (exclusive).
bins: list of dict: List of plot bins. See bin info in Notes for more details.
calendar_events: list of dict: List of calendar events for the plot. See calendar events info in Notes for more details.

class datarobot.models.datetime_trend_plots.ForecastVsActualPlotPreview(project_id, model_id, start_date, end_date, bins)¶

Forecast vs Actual plot preview for datetime model.

New in version v2.25.

Notes

Bin is a dict containing the following:

start_date: datetime.datetime

The datetime of the start of the bin (inclusive).
end_date: datetime.datetime

The datetime of the end of the bin (exclusive).
actual: float or None

Average actual value of the target in the bin. None if there are no entries in the bin.
predicted: float or None

Average prediction of the model in the bin. None if there are no entries in the bin.

Attributes:	project_id: string The project ID. model_id: string The model ID. start_date: datetime.datetime The datetime of the start of the chart data (inclusive). end_date: datetime.datetime The datetime of the end of the chart data (exclusive). bins: list of dict List of plot bins. See bin info in Notes for more details.

class datarobot.models.datetime_trend_plots.AnomalyOverTimePlotsMetadata(project_id, model_id, resolutions, backtest_metadata, holdout_metadata, backtest_statuses, holdout_statuses)¶

Anomaly over Time metadata for datetime model.

New in version v2.25.

Notes

Backtest/holdout status is a dict containing the following:

training: string

Status backtest/holdout training. One of datarobot.enums.DATETIME_TREND_PLOTS_STATUS
validation: string

Status backtest/holdout validation. One of datarobot.enums.DATETIME_TREND_PLOTS_STATUS

Backtest/holdout metadata is a dict containing the following:

training: dict

Start and end dates for the backtest/holdout training.
validation: dict

Start and end dates for the backtest/holdout validation.

Each dict in the training and validation in backtest/holdout metadata is structured like:

start_date: datetime.datetime or None

The datetime of the start of the chart data (inclusive). None if chart data is not computed.
end_date: datetime.datetime or None

The datetime of the end of the chart data (exclusive). None if chart data is not computed.

Attributes:

project_id: string: The project ID.
model_id: string: The model ID.
resolutions: list of string: A list of datarobot.enums.DATETIME_TREND_PLOTS_RESOLUTION, which represents available time resolutions for which plots can be retrieved.
backtest_metadata: list of dict: List of backtest metadata dicts. The list index of metadata dict is the backtest index. See backtest/holdout metadata info in Notes for more details.
holdout_metadata: dict: Holdout metadata dict. See backtest/holdout metadata info in Notes for more details.
backtest_statuses: list of dict: List of backtest statuses dict. The list index of status dict is the backtest index. See backtest/holdout status info in Notes for more details.
holdout_statuses: dict: Holdout status dict. See backtest/holdout status info in Notes for more details.

class datarobot.models.datetime_trend_plots.AnomalyOverTimePlot(project_id, model_id, start_date, end_date, resolution, bins, calendar_events)¶

Anomaly over Time plot for datetime model.

New in version v2.25.

Notes

Bin is a dict containing the following:

start_date: datetime.datetime

The datetime of the start of the bin (inclusive).
end_date: datetime.datetime

The datetime of the end of the bin (exclusive).
predicted: float or None

Average prediction of the model in the bin. None if there are no entries in the bin.
frequency: int or None

Indicates number of values averaged in bin.

Calendar event is a dict containing the following:

name: string

Name of the calendar event.
date: datetime

Date of the calendar event.
series_id: string or None

The series ID for the event. If this event does not specify a series ID, then this will be None, indicating that the event applies to all series.

Attributes:

project_id: string: The project ID.
model_id: string: The model ID.
resolution: string: The resolution that is used for binning. One of datarobot.enums.DATETIME_TREND_PLOTS_RESOLUTION
start_date: datetime.datetime: The datetime of the start of the chart data (inclusive).
end_date: datetime.datetime: The datetime of the end of the chart data (exclusive).
bins: list of dict: List of plot bins. See bin info in Notes for more details.
calendar_events: list of dict: List of calendar events for the plot. See calendar events info in Notes for more details.

class datarobot.models.datetime_trend_plots.AnomalyOverTimePlotPreview(project_id, model_id, prediction_threshold, start_date, end_date, bins)¶

Anomaly over Time plot preview for datetime model.

New in version v2.25.

Notes

Bin is a dict containing the following:

start_date: datetime.datetime

The datetime of the start of the bin (inclusive).
end_date: datetime.datetime

The datetime of the end of the bin (exclusive).

Attributes:

project_id: string: The project ID.
model_id: string: The model ID.
prediction_threshold: float: Only bins with predictions exceeding this threshold are returned in the response.
start_date: datetime.datetime: The datetime of the start of the chart data (inclusive).
end_date: datetime.datetime: The datetime of the end of the chart data (exclusive).
bins: list of dict: List of plot bins. See bin info in Notes for more details.

Deployment¶

class datarobot.Deployment(id=None, label=None, description=None, default_prediction_server=None, model=None, capabilities=None, prediction_usage=None, permissions=None, service_health=None, model_health=None, accuracy_health=None, importance=None, fairness_health=None, governance=None, owners=None, prediction_environment=None)¶

A deployment created from a DataRobot model.

Attributes:

id : str: the id of the deployment
label : str: the label of the deployment
description : str: the description of the deployment
default_prediction_server : dict: information on the default prediction server of the deployment
importance : str, optional: deployment importance
model : dict: information on the model of the deployment
capabilities : dict: information on the capabilities of the deployment
prediction_usage : dict: information on the prediction usage of the deployment
permissions : list: (New in version v2.18) user’s permissions on the deployment
service_health : dict: information on the service health of the deployment
model_health : dict: information on the model health of the deployment
accuracy_health : dict: information on the accuracy health of the deployment
fairness_health : dict: information on the fairness health of a deployment
governance : dict: information on approval and change requests of a deployment
owners : dict: information on the owners of a deployment
prediction_environment : dict: information on the prediction environment of a deployment

classmethod create_from_learning_model(model_id, label, description=None, default_prediction_server_id=None, importance=None, prediction_threshold=None)¶

Create a deployment from a DataRobot model.

New in version v2.17.

Parameters:

model_id : str: id of the DataRobot model to deploy
label : str: a human readable label of the deployment
description : str, optional: a human readable description of the deployment
default_prediction_server_id : str, optional: an identifier of a prediction server to be used as the default prediction server
importance : str, optional: deployment importance
prediction_threshold : float, optional: threshold used for binary classification in predictions

Returns:

deployment : Deployment: The created deployment

Examples

from datarobot import Project, Deployment
project = Project.get('5506fcd38bd88f5953219da0')
model = project.get_models()[0]
deployment = Deployment.create_from_learning_model(model.id, 'New Deployment')
deployment
>>> Deployment('New Deployment')

classmethod create_from_custom_model_image(custom_model_image_id, label, description=None, default_prediction_server_id=None, max_wait=600)¶

Create a deployment from a DataRobot custom model image.

Parameters:

custom_model_image_id : str: id of the DataRobot custom model image to deploy
label : str: a human readable label of the deployment
description : str, optional: a human readable description of the deployment
default_prediction_server_id : str, optional: an identifier of a prediction server to be used as the default prediction server
max_wait : int, optional: seconds to wait for successful resolution of a deployment creation job. Deployment supports making predictions only after a deployment creating job has successfully finished

Returns:

deployment : Deployment: The created deployment

classmethod create_from_custom_model_version(custom_model_version_id, label, description=None, default_prediction_server_id=None, max_wait=600, importance=None)¶

Create a deployment from a DataRobot custom model image.

Parameters:

custom_model_version_id : str: id of the DataRobot custom model version to deploy The version must have a base_environment_id.
label : str: a human readable label of the deployment
description : str, optional: a human readable description of the deployment
default_prediction_server_id : str, optional: an identifier of a prediction server to be used as the default prediction server
max_wait : int, optional: seconds to wait for successful resolution of a deployment creation job. Deployment supports making predictions only after a deployment creating job has successfully finished
importance : str, optional: deployment importance

Returns:

deployment : Deployment: The created deployment

classmethod list(order_by=None, search=None, filters=None)¶

List all deployments a user can view.

New in version v2.17.

Parameters:

order_by : str, optional

(New in version v2.18) the order to sort the deployment list by, defaults to label

Allowed attributes to sort by are:

label
serviceHealth
modelHealth
accuracyHealth
recentPredictions
lastPredictionTimestamp

If the sort attribute is preceded by a hyphen, deployments will be sorted in descending order, otherwise in ascending order.

For health related sorting, ascending means failing, warning, passing, unknown.

search : str, optional

(New in version v2.18) case insensitive search against deployment’s label and description.

filters : datarobot.models.deployment.DeploymentListFilters, optional

(New in version v2.20) an object containing all filters that you’d like to apply to the resulting list of deployments. See DeploymentListFilters for details on usage.

Returns:

deployments : list: a list of deployments the user can view

Examples

from datarobot import Deployment
deployments = Deployment.list()
deployments
>>> [Deployment('New Deployment'), Deployment('Previous Deployment')]

from datarobot import Deployment
from datarobot.enums import DEPLOYMENT_SERVICE_HEALTH_STATUS
filters = DeploymentListFilters(
    role='OWNER',
    service_health=[DEPLOYMENT_SERVICE_HEALTH.FAILING]
)
filtered_deployments = Deployment.list(filters=filters)
filtered_deployments
>>> [Deployment('Deployment I Own w/ Failing Service Health')]

classmethod get(deployment_id)¶

Get information about a deployment.

New in version v2.17.

Parameters:	deployment_id : str the id of the deployment
Returns:	deployment : Deployment the queried deployment

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.id
>>>'5c939e08962d741e34f609f0'
deployment.label
>>>'New Deployment'

update(label=None, description=None, importance=None)¶: Update the label and description of this deployment.

New in version v2.19.

delete()¶: Delete this deployment.

New in version v2.17.

replace_model(new_model_id, reason, max_wait=600)¶

Replace the model used in this deployment. To confirm model replacement eligibility, use: validate_replacement_model() beforehand.

New in version v2.17.

Model replacement is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Predictions made against this deployment will start using the new model as soon as the initial request is completed. There will be no interruption for predictions throughout the process.

Parameters:

new_model_id : str: The id of the new model to use. If replacing the deployment’s model with a CustomInferenceModel, a specific CustomModelVersion ID must be used.
reason : MODEL_REPLACEMENT_REASON: The reason for the model replacement. Must be one of ‘ACCURACY’, ‘DATA_DRIFT’, ‘ERRORS’, ‘SCHEDULED_REFRESH’, ‘SCORING_SPEED’, or ‘OTHER’. This value will be stored in the model history to keep track of why a model was replaced
max_wait : int, optional: (new in version 2.22) The maximum time to wait for model replacement job to complete before erroring

Examples

from datarobot import Deployment
from datarobot.enums import MODEL_REPLACEMENT_REASON
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.model['id'], deployment.model['type']
>>>('5c0a979859b00004ba52e431', 'Decision Tree Classifier (Gini)')

deployment.replace_model('5c0a969859b00004ba52e41b', MODEL_REPLACEMENT_REASON.ACCURACY)
deployment.model['id'], deployment.model['type']
>>>('5c0a969859b00004ba52e41b', 'Support Vector Classifier (Linear Kernel)')

validate_replacement_model(new_model_id)¶

Validate a model can be used as the replacement model of the deployment.

New in version v2.17.

Parameters:

new_model_id : str: the id of the new model to validate

Returns:

status : str: status of the validation, will be one of ‘passing’, ‘warning’ or ‘failing’. If the status is passing or warning, use replace_model() to perform a model replacement. If the status is failing, refer to checks for more detail on why the new model cannot be used as a replacement.
message : str: message for the validation result
checks : dict: explain why the new model can or cannot replace the deployment’s current model

get_features()¶

Retrieve the list of features needed to make predictions on this deployment.

Returns:	features: list a list of feature dict

Notes

Each feature dict contains the following structure:

name : str, feature name
feature_type : str, feature type
importance : float, numeric measure of the relationship strength between the feature and target (independent of model or other features)
date_format : str or None, the date format string for how this feature was interpreted, null if not a date feature, compatible with https://docs.python.org/2/library/time.html#time.strftime.
known_in_advance : bool, whether the feature was selected as known in advance in a time series model, false for non-time series models.

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
features = deployment.get_features()
features[0]['feature_type']
>>>'Categorical'
features[0]['importance']
>>>0.133

submit_actuals(data, batch_size=10000)¶

Submit actuals for processing. The actuals submitted will be used to calculate accuracy metrics.

Parameters:

data: list or pandas.DataFrame
batch_size: the max number of actuals in each request
If `data` is a list, each item should be a dict-like object with the following keys and
values; if `data` is a pandas.DataFrame, it should contain the following columns:
- association_id: str, a unique identifier used with a prediction,: max length 128 characters
- actual_value: str or int or float, the actual value of a prediction;: should be numeric for deployments with regression models or string for deployments with classification model
- was_acted_on: bool, optional, indicates if the prediction was acted on in a way that: could have affected the actual outcome
- timestamp: datetime or string in RFC3339 format, optional. If the datetime provided: does not have a timezone, we assume it is UTC.

Raises:

ValueError: if input data is not a list of dict-like objects or a pandas.DataFrame if input data is empty

Examples

from datarobot import Deployment, AccuracyOverTime
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
data = [{
    'association_id': '439917',
    'actual_value': 'True',
    'was_acted_on': True
}]
deployment.submit_actuals(data)

get_predictions_by_forecast_date_settings()¶

Retrieve predictions by forecast date settings of this deployment.

New in version v2.27.

Returns:

settings : dict

Predictions by forecast date settings of the deployment is a dict with the following format:

enabled : bool: Is ‘’True’’ if predictions by forecast date is enabled for this deployment. To update this setting, see update_predictions_by_forecast_date_settings()
column_name : string: The column name in prediction datasets to be used as forecast date.
datetime_format : string: The datetime format of the forecast date column in prediction datasets.

update_predictions_by_forecast_date_settings(enable_predictions_by_forecast_date, forecast_date_column_name=None, forecast_date_format=None, max_wait=600)¶

Update predictions by forecast date settings of this deployment.

New in version v2.27.

Updating predictions by forecast date setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters:

enable_predictions_by_forecast_date : bool: set to ‘’True’’ if predictions by forecast date is to be turned on or set to ‘’False’’ if predictions by forecast date is to be turned off.
forecast_date_column_name: string, optional: The column name in prediction datasets to be used as forecast date. If ‘’enable_predictions_by_forecast_date’’ is set to ‘’False’’, then the parameter will be ignored.
forecast_date_format: string, optional: The datetime format of the forecast date column in prediction datasets. If ‘’enable_predictions_by_forecast_date’’ is set to ‘’False’’, then the parameter will be ignored.
max_wait : int, optional: seconds to wait for successful

get_challenger_models_settings()¶

Retrieve challenger models settings of this deployment.

New in version v2.27.

Returns:	settings : dict Challenger models settings of the deployment is a dict with the following format: enabled : bool Is ‘’True’’ if challenger models is enabled for this deployment. To update existing ‘’challenger_models’’ settings, see `update_challenger_models_settings()`

update_challenger_models_settings(challenger_models_enabled, max_wait=600)¶

Update challenger models settings of this deployment.

New in version v2.27.

Updating challenger models setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters:	challenger_models_enabled : bool set to ‘’True’’ if challenger models is to be turned on or set to ‘’False’’ if challenger models is to be turned off max_wait : int, optional seconds to wait for successful resolution

get_segment_analysis_settings()¶

Retrieve segment analysis settings of this deployment.

New in version v2.27.

Returns:

settings : dict

Segment analysis settings of the deployment containing two items with keys enabled and attributes, which are further described below.

enabled : bool: Set to ‘’True’’ if segment analysis is enabled for this deployment. To update existing setting, see update_segment_analysis_settings()
attributes : list: To create or update existing segment analysis attributes, see update_segment_analysis_settings()

update_segment_analysis_settings(segment_analysis_enabled, segment_analysis_attributes=None, max_wait=600)¶

Update segment analysis settings of this deployment.

New in version v2.27.

Updating segment analysis setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters:	segment_analysis_enabled : bool set to ‘’True’’ if segment analysis is to be turned on or set to ‘’False’’ if segment analysis is to be turned off segment_analysis_attributes: list, optional A list of strings that gives the segment attributes selected for tracking. max_wait : int, optional seconds to wait for successful resolution

get_drift_tracking_settings()¶

Retrieve drift tracking settings of this deployment.

New in version v2.17.

Returns:

settings : dict

Drift tracking settings of the deployment containing two nested dicts with key target_drift and feature_drift, which are further described below.

Target drift setting contains:

enabled : bool: If target drift tracking is enabled for this deployment. To create or update existing ‘’target_drift’’ settings, see update_drift_tracking_settings()

Feature drift setting contains:

enabled : bool: If feature drift tracking is enabled for this deployment. To create or update existing ‘’feature_drift’’ settings, see update_drift_tracking_settings()

update_drift_tracking_settings(target_drift_enabled=None, feature_drift_enabled=None, max_wait=600)¶

Update drift tracking settings of this deployment.

New in version v2.17.

Updating drift tracking setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters:	target_drift_enabled : bool, optional if target drift tracking is to be turned on feature_drift_enabled : bool, optional if feature drift tracking is to be turned on max_wait : int, optional seconds to wait for successful resolution

get_association_id_settings()¶

Retrieve association ID setting for this deployment.

New in version v2.19.

Returns:	association_id_settings : dict in the following format: column_names : list[string], optional name of the columns to be used as association ID, required_in_prediction_requests : bool, optional whether the association ID column is required in prediction requests

update_association_id_settings(column_names=None, required_in_prediction_requests=None, max_wait=600)¶

Update association ID setting for this deployment.

New in version v2.19.

Parameters:	column_names : list[string], optional name of the columns to be used as association ID, currently only support a list of one string required_in_prediction_requests : bool, optional whether the association ID column is required in prediction requests max_wait : int, optional seconds to wait for successful resolution

get_predictions_data_collection_settings()¶

Retrieve predictions data collection settings of this deployment.

New in version v2.21.

Returns:	predictions_data_collection_settings : dict in the following format: enabled : bool If predictions data collection is enabled for this deployment. To update existing ‘’predictions_data_collection’’ settings, see `update_predictions_data_collection_settings()`

update_predictions_data_collection_settings(enabled, max_wait=600)¶

Update predictions data collection settings of this deployment.

New in version v2.21.

Updating predictions data collection setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters:	enabled: bool if predictions data collection is to be turned on max_wait : int, optional seconds to wait for successful resolution

get_prediction_warning_settings()¶

Retrieve prediction warning settings of this deployment.

New in version v2.19.

Returns:

settings : dict in the following format:

enabled : bool

If target prediction_warning is enabled for this deployment. To create or update existing ‘’prediction_warning’’ settings, see update_prediction_warning_settings()

custom_boundaries : dict or None

If None default boundaries for a model are used. Otherwise has following keys:

upper : float: All predictions greater than provided value are considered anomalous
lower : float: All predictions less than provided value are considered anomalous

update_prediction_warning_settings(prediction_warning_enabled, use_default_boundaries=None, lower_boundary=None, upper_boundary=None, max_wait=600)¶

Update prediction warning settings of this deployment.

New in version v2.19.

Parameters:

prediction_warning_enabled : bool: If prediction warnings should be turned on.
use_default_boundaries : bool, optional: If default boundaries of the model should be used for the deployment.
upper_boundary : float, optional: All predictions greater than provided value will be considered anomalous
lower_boundary : float, optional: All predictions less than provided value will be considered anomalous
max_wait : int, optional: seconds to wait for successful resolution

get_prediction_intervals_settings()¶

Retrieve prediction intervals settings for this deployment.

New in version v2.19.

Returns:	dict in the following format: enabled : bool Whether prediction intervals are enabled for this deployment percentiles : list[int] List of enabled prediction intervals’ sizes for this deployment. Currently we only support one percentile at a time.

Notes

Note that prediction intervals are only supported for time series deployments.

update_prediction_intervals_settings(percentiles, enabled=True, max_wait=600)¶

Update prediction intervals settings for this deployment.

New in version v2.19.

Parameters:	percentiles : list[int] The prediction intervals percentiles to enable for this deployment. Currently we only support setting one percentile at a time. enabled : bool, optional (defaults to True) Whether to enable showing prediction intervals in the results of predictions requested using this deployment. max_wait : int, optional seconds to wait for successful resolution
Raises:	AssertionError If `percentiles` is in an invalid format AsyncFailureError If any of the responses from the server are unexpected AsyncProcessUnsuccessfulError If the prediction intervals calculation job has failed or has been cancelled. AsyncTimeoutError If the prediction intervals calculation job did not resolve in time

Notes

Updating prediction intervals settings is an asynchronous process, which means some preparatory work may be performed before the settings request is completed. This function will not return until all work is fully finished.

Note that prediction intervals are only supported for time series deployments.

get_service_stats(model_id=None, start_time=None, end_time=None, execution_time_quantile=None, response_time_quantile=None, slow_requests_threshold=None)¶

Retrieve value of service stat metrics over a certain time period.

New in version v2.18.

Parameters:

model_id : str, optional: the id of the model
start_time : datetime, optional: start of the time period
end_time : datetime, optional: end of the time period
execution_time_quantile : float, optional: quantile for executionTime, defaults to 0.5
response_time_quantile : float, optional: quantile for responseTime, defaults to 0.5
slow_requests_threshold : float, optional: threshold for slowRequests, defaults to 1000

Returns:

service_stats : ServiceStats: the queried service stats metrics information

get_service_stats_over_time(metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None, quantile=None, threshold=None)¶

Retrieve information about how a service stat metric changes over a certain time period.

New in version v2.18.

Parameters:

metric : SERVICE_STAT_METRIC, optional: the service stat metric to retrieve
model_id : str, optional: the id of the model
start_time : datetime, optional: start of the time period
end_time : datetime, optional: end of the time period
bucket_size : str, optional: time duration of a bucket, in ISO 8601 time duration format
quantile : float, optional: quantile for ‘executionTime’ or ‘responseTime’, ignored when querying other metrics
threshold : int, optional: threshold for ‘slowQueries’, ignored when querying other metrics

Returns:

service_stats_over_time : ServiceStatsOverTime: the queried service stats metric over time information

get_target_drift(model_id=None, start_time=None, end_time=None, metric=None)¶

Retrieve target drift information over a certain time period.

New in version v2.21.

Parameters:	model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period metric : str (New in version v2.22) metric used to calculate the drift score
Returns:	target_drift : TargetDrift the queried target drift information

get_feature_drift(model_id=None, start_time=None, end_time=None, metric=None)¶

Retrieve drift information for deployment’s features over a certain time period.

New in version v2.21.

Parameters:	model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period metric : str (New in version v2.22) The metric used to calculate the drift score. Allowed values include psi, kl_divergence, dissimilarity, hellinger, and js_divergence.
Returns:	feature_drift_data : [FeatureDrift] the queried feature drift information

get_accuracy(model_id=None, start_time=None, end_time=None, start=None, end=None)¶

Retrieve values of accuracy metrics over a certain time period.

New in version v2.18.

Parameters:	model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period
Returns:	accuracy : Accuracy the queried accuracy metrics information

get_accuracy_over_time(metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None)¶

Retrieve information about how an accuracy metric changes over a certain time period.

New in version v2.18.

Parameters:	metric : ACCURACY_METRIC the accuracy metric to retrieve model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period bucket_size : str time duration of a bucket, in ISO 8601 time duration format
Returns:	accuracy_over_time : AccuracyOverTime the queried accuracy metric over time information

update_secondary_dataset_config(secondary_dataset_config_id, credential_ids=None)¶

Update the secondary dataset config used by Feature discovery model for a given deployment.

New in version v2.23.

Parameters:	secondary_dataset_config_id: str Id of the secondary dataset config credential_ids: list or None List of DatasetsCredentials used by the secondary datasets

Examples

from datarobot import Deployment
deployment = Deployment(deployment_id='5c939e08962d741e34f609f0')
config = deployment.update_secondary_dataset_config('5df109112ca582033ff44084')
config
>>> '5df109112ca582033ff44084'

get_secondary_dataset_config()¶

Get the secondary dataset config used by Feature discovery model for a given deployment.

New in version v2.23.

Returns:	secondary_dataset_config : SecondaryDatasetConfigurations Id of the secondary dataset config

Examples

from datarobot import Deployment
deployment = Deployment(deployment_id='5c939e08962d741e34f609f0')
deployment.update_secondary_dataset_config('5df109112ca582033ff44084')
config = deployment.get_secondary_dataset_config()
config
>>> '5df109112ca582033ff44084'

get_prediction_results(model_id=None, start_time=None, end_time=None, actuals_present=None, offset=None, limit=None)¶

Retrieve a list of prediction results of the deployment.

New in version v2.24.

Parameters:	model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period actuals_present : bool filters predictions results to only those who have actuals present or with missing actuals offset : int this many results will be skipped limit : int at most this many results are returned
Returns:	prediction_results: list[dict] a list of prediction results

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
results = deployment.get_prediction_results()

download_prediction_results(filepath, model_id=None, start_time=None, end_time=None, actuals_present=None, offset=None, limit=None)¶

Download prediction results of the deployment as a CSV file.

New in version v2.24.

Parameters:

filepath : str: path of the csv file
model_id : str: the id of the model
start_time : datetime: start of the time period
end_time : datetime: end of the time period
actuals_present : bool: filters predictions results to only those who have actuals present or with missing actuals
offset : int: this many results will be skipped
limit : int: at most this many results are returned

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
results = deployment.download_prediction_results('path_to_prediction_results.csv')

download_scoring_code(filepath, source_code=False, include_agent=False, include_prediction_explanations=False, include_prediction_intervals=False)¶

Retrieve scoring code of the current deployed model.

New in version v2.24.

Parameters:

filepath : str: path of the scoring code file
source_code : bool: whether source code or binary of the scoring code will be retrieved
include_agent : bool: whether the scoring code retrieved will include tracking agent
include_prediction_explanations : bool: whether the scoring code retrieved will include prediction explanations
include_prediction_intervals : bool: whether the scoring code retrieved will support prediction intervals

Notes

When setting include_agent or include_predictions_explanations or include_prediction_intervals to True, it can take a considerably longer time to download the scoring code.

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
results = deployment.download_scoring_code('path_to_scoring_code.jar')

class datarobot.models.deployment.DeploymentListFilters(role=None, service_health=None, model_health=None, accuracy_health=None, execution_environment_type=None, importance=None)¶

class datarobot.models.ServiceStats(period=None, metrics=None, model_id=None)¶

Deployment service stats information.

Attributes:	model_id : str the model used to retrieve service stats metrics period : dict the time period used to retrieve service stats metrics metrics : dict the service stats metrics

classmethod get(deployment_id, model_id=None, start_time=None, end_time=None, execution_time_quantile=None, response_time_quantile=None, slow_requests_threshold=None)¶

Retrieve value of service stat metrics over a certain time period.

New in version v2.18.

Parameters:

deployment_id : str: the id of the deployment
model_id : str, optional: the id of the model
start_time : datetime, optional: start of the time period
end_time : datetime, optional: end of the time period
execution_time_quantile : float, optional: quantile for executionTime, defaults to 0.5
response_time_quantile : float, optional: quantile for responseTime, defaults to 0.5
slow_requests_threshold : float, optional: threshold for slowRequests, defaults to 1000

Returns:

service_stats : ServiceStats: the queried service stats metrics

class datarobot.models.ServiceStatsOverTime(buckets=None, summary=None, metric=None, model_id=None)¶

Deployment service stats over time information.

Attributes:	model_id : str the model used to retrieve accuracy metric metric : str the service stat metric being retrieved buckets : dict how the service stat metric changes over time summary : dict summary for the service stat metric

classmethod get(deployment_id, metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None, quantile=None, threshold=None)¶

Retrieve information about how a service stat metric changes over a certain time period.

New in version v2.18.

Parameters:

deployment_id : str: the id of the deployment
metric : SERVICE_STAT_METRIC, optional: the service stat metric to retrieve
model_id : str, optional: the id of the model
start_time : datetime, optional: start of the time period
end_time : datetime, optional: end of the time period
bucket_size : str, optional: time duration of a bucket, in ISO 8601 time duration format
quantile : float, optional: quantile for ‘executionTime’ or ‘responseTime’, ignored when querying other metrics
threshold : int, optional: threshold for ‘slowQueries’, ignored when querying other metrics

Returns:

service_stats_over_time : ServiceStatsOverTime: the queried service stat over time information

bucket_values¶

The metric value for all time buckets, keyed by start time of the bucket.

Returns:	bucket_values: OrderedDict

class datarobot.models.TargetDrift(period=None, metric=None, model_id=None, target_name=None, drift_score=None, sample_size=None, baseline_sample_size=None)¶

Deployment target drift information.

Attributes:	model_id : str the model used to retrieve target drift metric period : dict the time period used to retrieve target drift metric metric : str the data drift metric target_name : str name of the target drift_score : float target drift score sample_size : int count of data points for comparison baseline_sample_size : int count of data points for baseline

classmethod get(deployment_id, model_id=None, start_time=None, end_time=None, metric=None)¶

Retrieve target drift information over a certain time period.

New in version v2.21.

Parameters:	deployment_id : str the id of the deployment model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period metric : str (New in version v2.22) metric used to calculate the drift score
Returns:	target_drift : TargetDrift the queried target drift information

Examples

from datarobot import Deployment, TargetDrift
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
target_drift = TargetDrift.get(deployment.id)
target_drift.period['end']
>>>'2019-08-01 00:00:00+00:00'
target_drift.drift_score
>>>0.03423
accuracy.target_name
>>>'readmitted'

class datarobot.models.FeatureDrift(period=None, metric=None, model_id=None, name=None, drift_score=None, feature_impact=None, sample_size=None, baseline_sample_size=None)¶

Deployment feature drift information.

Attributes:	model_id : str the model used to retrieve feature drift metric period : dict the time period used to retrieve feature drift metric metric : str the data drift metric name : str name of the feature drift_score : float feature drift score sample_size : int count of data points for comparison baseline_sample_size : int count of data points for baseline

classmethod list(deployment_id, model_id=None, start_time=None, end_time=None, metric=None)¶

Retrieve drift information for deployment’s features over a certain time period.

New in version v2.21.

Parameters:	deployment_id : str the id of the deployment model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period metric : str (New in version v2.22) metric used to calculate the drift score
Returns:	feature_drift_data : [FeatureDrift] the queried feature drift information

Examples

from datarobot import Deployment, TargetDrift
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
feature_drift = FeatureDrift.list(deployment.id)[0]
feature_drift.period
>>>'2019-08-01 00:00:00+00:00'
feature_drift.drift_score
>>>0.252
feature_drift.name
>>>'age'

class datarobot.models.Accuracy(period=None, metrics=None, model_id=None)¶

Deployment accuracy information.

Attributes:	model_id : str the model used to retrieve accuracy metrics period : dict the time period used to retrieve accuracy metrics metrics : dict the accuracy metrics

classmethod get(deployment_id, model_id=None, start_time=None, end_time=None)¶

Retrieve values of accuracy metrics over a certain time period.

New in version v2.18.

Parameters:	deployment_id : str the id of the deployment model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period
Returns:	accuracy : Accuracy the queried accuracy metrics information

Examples

from datarobot import Deployment, Accuracy
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
accuracy = Accuracy.get(deployment.id)
accuracy.period['end']
>>>'2019-08-01 00:00:00+00:00'
accuracy.metric['LogLoss']['value']
>>>0.7533
accuracy.metric_values['LogLoss']
>>>0.7533

metric_values¶

The value for all metrics, keyed by metric name.

Returns:	metric_values: OrderedDict

metric_baselines¶

The baseline value for all metrics, keyed by metric name.

Returns:	metric_baselines: OrderedDict

percent_changes¶

The percent change of value over baseline for all metrics, keyed by metric name.

Returns:	percent_changes: OrderedDict

class datarobot.models.AccuracyOverTime(buckets=None, summary=None, baseline=None, metric=None, model_id=None)¶

Deployment accuracy over time information.

Attributes:	model_id : str the model used to retrieve accuracy metric metric : str the accuracy metric being retrieved buckets : dict how the accuracy metric changes over time summary : dict summary for the accuracy metric baseline : dict baseline for the accuracy metric

classmethod get(deployment_id, metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None)¶

Retrieve information about how an accuracy metric changes over a certain time period.

New in version v2.18.

Parameters:	deployment_id : str the id of the deployment metric : ACCURACY_METRIC the accuracy metric to retrieve model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period bucket_size : str time duration of a bucket, in ISO 8601 time duration format
Returns:	accuracy_over_time : AccuracyOverTime the queried accuracy metric over time information

Examples

from datarobot import Deployment, AccuracyOverTime
from datarobot.enums import ACCURACY_METRICS
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
accuracy_over_time = AccuracyOverTime.get(deployment.id, metric=ACCURACY_METRIC.LOGLOSS)
accuracy_over_time.metric
>>>'LogLoss'
accuracy_over_time.metric_values
>>>{datetime.datetime(2019, 8, 1): 0.73, datetime.datetime(2019, 8, 2): 0.55}

classmethod get_as_dataframe(deployment_id, metrics, model_id=None, start_time=None, end_time=None, bucket_size=None)¶

Retrieve information about how a list of accuracy metrics change over a certain time period as pandas DataFrame.

In the returned DataFrame, the columns corresponds to the metrics being retrieved; the rows are labeled with the start time of each bucket.

Parameters:	deployment_id : str the id of the deployment metrics : [ACCURACY_METRIC] the accuracy metrics to retrieve model_id : str the id of the model start_time : datetime start of the time period end_time : datetime end of the time period bucket_size : str time duration of a bucket, in ISO 8601 time duration format
Returns:	accuracy_over_time: pd.DataFrame

bucket_values¶

The metric value for all time buckets, keyed by start time of the bucket.

Returns:	bucket_values: OrderedDict

bucket_sample_sizes¶

The sample size for all time buckets, keyed by start time of the bucket.

Returns:	bucket_sample_sizes: OrderedDict

External Scores and Insights¶

class datarobot.ExternalScores(project_id, scores, model_id=None, dataset_id=None, actual_value_column=None)¶

Metric scores on prediction dataset with target or actual value column in unsupervised case. Contains project metrics for supervised and special classification metrics set for unsupervised projects.

New in version v2.21.

Examples

List all scores for a dataset

import datarobot as dr
scores = dr.Scores.list(project_id, dataset_id=dataset_id)

Attributes:

project_id: str: id of the project the model belongs to
model_id: str: id of the model
dataset_id: str: id of the prediction dataset with target or actual value column for unsupervised case
actual_value_column: str, optional: For unsupervised projects only. Actual value column which was used to calculate the classification metrics and insights on the prediction dataset.
scores: list of dicts in a form of {‘label’: metric_name, ‘value’: score}: Scores on the dataset.

classmethod create(project_id, model_id, dataset_id, actual_value_column=None)¶

Compute an external dataset insights for the specified model.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model for which insights is requested dataset_id : str id of the dataset for which insights is requested actual_value_column : str, optional actual values column label, for unsupervised projects only
Returns:	job : Job an instance of created async job

classmethod list(project_id, model_id=None, dataset_id=None, offset=0, limit=100)¶

Fetch external scores list for the project and optionally for model and dataset.

Parameters:

project_id: str: id of the project
model_id: str, optional: if specified, only scores for this model will be retrieved
dataset_id: str, optional: if specified, only scores for this dataset will be retrieved
offset: int, optional: this many results will be skipped, default: 0
limit: int, optional: at most this many results are returned, default: 100, max 1000. To return all results, specify 0

Returns:

A list of : py:class:External Scores <datarobot.ExternalScores> objects

classmethod get(project_id, model_id, dataset_id)¶

Retrieve external scores for the project, model and dataset.

Parameters:	project_id: str id of the project model_id: str if specified, only scores for this model will be retrieved dataset_id: str if specified, only scores for this dataset will be retrieved
Returns:	`External Scores` object

class datarobot.ExternalLiftChart(dataset_id, bins)¶

Lift chart for the model and prediction dataset with target or actual value column in unsupervised case.

New in version v2.21.

LiftChartBin is a dict containing the following:

actual (float) Sum of actual target values in bin

predicted (float) Sum of predicted target values in bin

bin_weight (float) The weight of the bin. For weighted projects, it is the sum of the weights of the rows in the bin. For unweighted projects, it is the number of rows in the bin.

Attributes:	dataset_id: str id of the prediction dataset with target or actual value column for unsupervised case bins: list of dict List of dicts with schema described as `LiftChartBin` above.

classmethod list(project_id, model_id, dataset_id=None, offset=0, limit=100)¶

Retrieve list of the lift charts for the model.

Parameters:

project_id: str: id of the project
model_id: str: if specified, only lift chart for this model will be retrieved
dataset_id: str, optional: if specified, only lift chart for this dataset will be retrieved
offset: int, optional: this many results will be skipped, default: 0
limit: int, optional: at most this many results are returned, default: 100, max 1000. To return all results, specify 0

Returns:

A list of : py:class:ExternalLiftChart <datarobot.ExternalLiftChart> objects

classmethod get(project_id, model_id, dataset_id)¶

Retrieve lift chart for the model and prediction dataset.

Parameters:	project_id: str project id model_id: str model id dataset_id: str prediction dataset id with target or actual value column for unsupervised case
Returns:	`ExternalLiftChart` object

class datarobot.ExternalRocCurve(dataset_id, roc_points, negative_class_predictions, positive_class_predictions)¶

ROC curve data for the model and prediction dataset with target or actual value column in unsupervised case.

New in version v2.21.

Attributes:

dataset_id: str: id of the prediction dataset with target or actual value column for unsupervised case
roc_points: list of dict: List of precalculated metrics associated with thresholds for ROC curve.
negative_class_predictions: list of float: List of predictions from example for negative class
positive_class_predictions: list of float: List of predictions from example for positive class

classmethod list(project_id, model_id, dataset_id=None, offset=0, limit=100)¶

Retrieve list of the roc curves for the model.

Parameters:

project_id: str: id of the project
model_id: str: if specified, only lift chart for this model will be retrieved
dataset_id: str, optional: if specified, only lift chart for this dataset will be retrieved
offset: int, optional: this many results will be skipped, default: 0
limit: int, optional: at most this many results are returned, default: 100, max 1000. To return all results, specify 0

Returns:

A list of : py:class:ExternalRocCurve <datarobot.ExternalRocCurve> objects

classmethod get(project_id, model_id, dataset_id)¶

Retrieve ROC curve chart for the model and prediction dataset.

Parameters:	project_id: str project id model_id: str model id dataset_id: str prediction dataset id with target or actual value column for unsupervised case
Returns:	`ExternalRocCurve` object

Feature¶

class datarobot.models.Feature(id, project_id=None, name=None, feature_type=None, importance=None, low_information=None, unique_count=None, na_count=None, date_format=None, min=None, max=None, mean=None, median=None, std_dev=None, time_series_eligible=None, time_series_eligibility_reason=None, time_step=None, time_unit=None, target_leakage=None, feature_lineage_id=None, key_summary=None, multilabel_insights=None)¶

A feature from a project’s dataset

These are features either included in the originally uploaded dataset or added to it via feature transformations. In time series projects, these will be distinct from the ModelingFeature s created during partitioning; otherwise, they will correspond to the same features. For more information about input and modeling features, see the time series documentation.

The min, max, mean, median, and std_dev attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features or features created prior to these summary statistics becoming available, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e. date type features will have their summary statistics expressed as ISO-8601 formatted date strings.

Attributes:

id : int

the id for the feature - note that name is used to reference the feature instead of id

project_id : str

the id of the project the feature belongs to

name : str

the name of the feature

feature_type : str

the type of the feature, e.g. ‘Categorical’, ‘Text’

importance : float or None

numeric measure of the strength of relationship between the feature and target (independent of any model or other features); may be None for non-modeling features such as partition columns

low_information : bool

whether a feature is considered too uninformative for modeling (e.g. because it has too few values)

unique_count : int

number of unique values

na_count : int or None

number of missing values

date_format : str or None

For Date features, the date format string for how this feature was interpreted, compatible with https://docs.python.org/2/library/time.html#time.strftime . For other feature types, None.

min : str, int, float, or None

The minimum value of the source data in the EDA sample

max : str, int, float, or None

The maximum value of the source data in the EDA sample

mean : str, int, or, float

The arithmetic mean of the source data in the EDA sample

median : str, int, float, or None

The median of the source data in the EDA sample

std_dev : str, int, float, or None

The standard deviation of the source data in the EDA sample

time_series_eligible : bool

Whether this feature can be used as the datetime partition column in a time series project.

time_series_eligibility_reason : str

Why the feature is ineligible for the datetime partition column in a time series project, or ‘suitable’ when it is eligible.

time_step : int or None

For time series eligible features, a positive integer determining the interval at which windows can be specified. If used as the datetime partition column on a time series project, the feature derivation and forecast windows must start and end at an integer multiple of this value. None for features that are not time series eligible.

time_unit : str or None

For time series eligible features, the time unit covered by a single time step, e.g. ‘HOUR’, or None for features that are not time series eligible.

target_leakage : str

Whether a feature is considered to have target leakage or not. A value of ‘SKIPPED_DETECTION’ indicates that target leakage detection was not run on the feature. ‘FALSE’ indicates no leakage, ‘MODERATE’ indicates a moderate risk of target leakage, and ‘HIGH_RISK’ indicates a high risk of target leakage

feature_lineage_id : str

id of a lineage for automatically discovered features or derived time series features.

key_summary: list of dict

Statistics for top 50 keys (truncated to 103 characters) of Summarized Categorical column example:

{‘key’:’DataRobot’, ‘summary’:{‘min’:0, ‘max’:29815.0, ‘stdDev’:6498.029, ‘mean’:1490.75, ‘median’:0.0, ‘pctRows’:5.0}}

where,

key: string or None

name of the key

summary: dict

statistics of the key

max: maximum value of the key. min: minimum value of the key. mean: mean value of the key. median: median value of the key. stdDev: standard deviation of the key. pctRows: percentage occurrence of key in the EDA sample of the feature.

multilabel_insights_key : str or None

For multicategorical columns this will contain a key for multilabel insights. The key is unique for a project, feature and EDA stage combination. This will be the key for the most recent, finished EDA stage.

classmethod get(project_id, feature_name)¶

Retrieve a single feature

Parameters:	project_id : str The ID of the project the feature is associated with. feature_name : str The name of the feature to retrieve
Returns:	feature : Feature The queried instance

get_multiseries_properties(multiseries_id_columns, max_wait=600)¶

Retrieve time series properties for a potential multiseries datetime partition column

Multiseries time series projects use multiseries id columns to model multiple distinct series within a single project. This function returns the time series properties (time step and time unit) of this column if it were used as a datetime partition column with the specified multiseries id columns, running multiseries detection automatically if it had not previously been successfully ran.

Parameters:	multiseries_id_columns : list of str the name(s) of the multiseries id columns to use with this datetime partition column. Currently only one multiseries id column is supported. max_wait : int, optional if a multiseries detection task is run, the maximum amount of time to wait for it to complete before giving up
Returns:	properties : dict A dict with three keys: time_series_eligible : bool, whether the column can be used as a partition column time_unit : str or null, the inferred time unit if used as a partition column time_step : int or null, the inferred time step if used as a partition column

get_cross_series_properties(datetime_partition_column, cross_series_group_by_columns, max_wait=600)¶

Retrieve cross-series properties for multiseries ID column.

This function returns the cross-series properties (eligibility as group-by column) of this column if it were used with specified datetime partition column and with current multiseries id column, running cross-series group-by validation automatically if it had not previously been successfully ran.

Parameters:	datetime_partition_column : datetime partition column cross_series_group_by_columns : list of str the name(s) of the columns to use with this multiseries ID column. Currently only one cross-series group-by column is supported. max_wait : int, optional if a multiseries detection task is run, the maximum amount of time to wait for it to complete before giving up
Returns:	properties : dict A dict with three keys: name : str, column name eligibility : str, reason for column eligibility isEligible : bool, is column eligible as cross-series group-by

get_multicategorical_histogram()¶

Retrieve multicategorical histogram for this feature

New in version v2.24.

Returns:	`datarobot.models.MulticategoricalHistogram`
Raises:	datarobot.errors.InvalidUsageError if this method is called on a unsuited feature ValueError if no multilabel_insights_key is present for this feature

get_pairwise_correlations()¶

Retrieve pairwise label correlation for multicategorical features

New in version v2.24.

Returns:	`datarobot.models.PairwiseCorrelations`
Raises:	datarobot.errors.InvalidUsageError if this method is called on a unsuited feature ValueError if no multilabel_insights_key is present for this feature

get_pairwise_joint_probabilities()¶

Retrieve pairwise label joint probabilities for multicategorical features

New in version v2.24.

Returns:	`datarobot.models.PairwiseJointProbabilities`
Raises:	datarobot.errors.InvalidUsageError if this method is called on a unsuited feature ValueError if no multilabel_insights_key is present for this feature

get_pairwise_conditional_probabilities()¶

Retrieve pairwise label conditional probabilities for multicategorical features

New in version v2.24.

Returns:	`datarobot.models.PairwiseConditionalProbabilities`
Raises:	datarobot.errors.InvalidUsageError if this method is called on a unsuited feature ValueError if no multilabel_insights_key is present for this feature

class datarobot.models.ModelingFeature(project_id=None, name=None, feature_type=None, importance=None, low_information=None, unique_count=None, na_count=None, date_format=None, min=None, max=None, mean=None, median=None, std_dev=None, parent_feature_names=None, key_summary=None, is_restored_after_reduction=None)¶

A feature used for modeling

In time series projects, a new set of modeling features is created after setting the partitioning options. These features are automatically derived from those in the project’s dataset and are the features used for modeling. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, ModelingFeatures and Features will behave the same.

For more information about input and modeling features, see the time series documentation.

As with the Feature object, the min, max, `mean, median, and std_dev attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e. date type features will have their summary statistics expressed as ISO-8601 formatted date strings.

Attributes:

project_id : str

the id of the project the feature belongs to

name : str

the name of the feature

feature_type : str

the type of the feature, e.g. ‘Categorical’, ‘Text’

importance : float or None

numeric measure of the strength of relationship between the feature and target (independent of any model or other features); may be None for non-modeling features such as partition columns

low_information : bool

whether a feature is considered too uninformative for modeling (e.g. because it has too few values)

unique_count : int

number of unique values

na_count : int or None

number of missing values

date_format : str or None

For Date features, the date format string for how this feature was interpreted, compatible with https://docs.python.org/2/library/time.html#time.strftime . For other feature types, None.

min : str, int, float, or None

The minimum value of the source data in the EDA sample

max : str, int, float, or None

The maximum value of the source data in the EDA sample

mean : str, int, or, float

The arithmetic mean of the source data in the EDA sample

median : str, int, float, or None

The median of the source data in the EDA sample

std_dev : str, int, float, or None

The standard deviation of the source data in the EDA sample

parent_feature_names : list of str

A list of the names of input features used to derive this modeling feature. In cases where the input features and modeling features are the same, this will simply contain the feature’s name. Note that if a derived feature was used to create this modeling feature, the values here will not necessarily correspond to the features that must be supplied at prediction time.

key_summary: list of dict

Statistics for top 50 keys (truncated to 103 characters) of Summarized Categorical column example:

{‘key’:’DataRobot’, ‘summary’:{‘min’:0, ‘max’:29815.0, ‘stdDev’:6498.029, ‘mean’:1490.75, ‘median’:0.0, ‘pctRows’:5.0}}

where,

key: string or None

name of the key

summary: dict

statistics of the key

max: maximum value of the key. min: minimum value of the key. mean: mean value of the key. median: median value of the key. stdDev: standard deviation of the key. pctRows: percentage occurrence of key in the EDA sample of the feature.

classmethod get(project_id, feature_name)¶

Retrieve a single modeling feature

Parameters:	project_id : str The ID of the project the feature is associated with. feature_name : str The name of the feature to retrieve
Returns:	feature : ModelingFeature The requested feature

class datarobot.models.DatasetFeature(id_, dataset_id=None, dataset_version_id=None, name=None, feature_type=None, low_information=None, unique_count=None, na_count=None, date_format=None, min_=None, max_=None, mean=None, median=None, std_dev=None, time_series_eligible=None, time_series_eligibility_reason=None, time_step=None, time_unit=None, target_leakage=None, target_leakage_reason=None)¶

A feature from a project’s dataset

These are features either included in the originally uploaded dataset or added to it via feature transformations.

The min, max, mean, median, and std_dev attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features or features created prior to these summary statistics becoming available, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e. date type features will have their summary statistics expressed as ISO-8601 formatted date strings.

Attributes:

id : int: the id for the feature - note that name is used to reference the feature instead of id
dataset_id : str: the id of the dataset the feature belongs to
dataset_version_id : str: the id of the dataset version the feature belongs to
name : str: the name of the feature
feature_type : str, optional: the type of the feature, e.g. ‘Categorical’, ‘Text’
low_information : bool, optional: whether a feature is considered too uninformative for modeling (e.g. because it has too few values)
unique_count : int, optional: number of unique values
na_count : int, optional: number of missing values
date_format : str, optional: For Date features, the date format string for how this feature was interpreted, compatible with https://docs.python.org/2/library/time.html#time.strftime . For other feature types, None.
min : str, int, float, optional: The minimum value of the source data in the EDA sample
max : str, int, float, optional: The maximum value of the source data in the EDA sample
mean : str, int, float, optional: The arithmetic mean of the source data in the EDA sample
median : str, int, float, optional: The median of the source data in the EDA sample
std_dev : str, int, float, optional: The standard deviation of the source data in the EDA sample
time_series_eligible : bool, optional: Whether this feature can be used as the datetime partition column in a time series project.
time_series_eligibility_reason : str, optional: Why the feature is ineligible for the datetime partition column in a time series project, or ‘suitable’ when it is eligible.
time_step : int, optional: For time series eligible features, a positive integer determining the interval at which windows can be specified. If used as the datetime partition column on a time series project, the feature derivation and forecast windows must start and end at an integer multiple of this value. None for features that are not time series eligible.
time_unit : str, optional: For time series eligible features, the time unit covered by a single time step, e.g. ‘HOUR’, or None for features that are not time series eligible.
target_leakage : str, optional: Whether a feature is considered to have target leakage or not. A value of ‘SKIPPED_DETECTION’ indicates that target leakage detection was not run on the feature. ‘FALSE’ indicates no leakage, ‘MODERATE’ indicates a moderate risk of target leakage, and ‘HIGH_RISK’ indicates a high risk of target leakage
target_leakage_reason: string, optional: The descriptive text explaining the reason for target leakage, if any.

get_histogram(bin_limit=None)¶

Retrieve a feature histogram

Parameters:	bin_limit : int or None Desired max number of histogram bins. If omitted, by default endpoint will use 60.
Returns:	featureHistogram : DatasetFeatureHistogram The requested histogram with desired number or bins

class datarobot.models.DatasetFeatureHistogram(plot)¶

classmethod get(dataset_id, feature_name, bin_limit=None, key_name=None)¶

Retrieve a single feature histogram

Parameters:	dataset_id : str The ID of the Dataset the feature is associated with. feature_name : str The name of the feature to retrieve bin_limit : int or None Desired max number of histogram bins. If omitted, by default the endpoint will use 60. key_name: string or None (Only required for summarized categorical feature) Name of the top 50 keys for which plot to be retrieved
Returns:	featureHistogram : FeatureHistogram The queried instance with plot attribute in it.

class datarobot.models.FeatureHistogram(plot)¶

classmethod get(project_id, feature_name, bin_limit=None, key_name=None)¶

Retrieve a single feature histogram

Parameters:	project_id : str The ID of the project the feature is associated with. feature_name : str The name of the feature to retrieve bin_limit : int or None Desired max number of histogram bins. If omitted, by default endpoint will use 60. key_name: string or None (Only required for summarized categorical feature) Name of the top 50 keys for which plot to be retrieved
Returns:	featureHistogram : FeatureHistogram The queried instance with plot attribute in it.

class datarobot.models.InteractionFeature(rows, source_columns, bars, bubbles)¶

Interaction feature data

New in version v2.21.

Attributes:	rows: int Total number of rows source_columns: list(str) names of two categorical features which were combined into this one bars: list(dict) dictionaries representing frequencies of each independent value from the source columns bubbles: list(dict) dictionaries representing frequencies of each combined value in the interaction feature.

classmethod get(project_id, feature_name)¶

Retrieve a single Interaction feature

Parameters:	project_id : str The id of the project the feature belongs to feature_name : str The name of the Interaction feature to retrieve
Returns:	feature : InteractionFeature The queried instance

class datarobot.models.MulticategoricalHistogram(feature_name, histogram)¶

Histogram for Multicategorical feature.

New in version v2.24.

Notes

HistogramValues contains:

values.[].label : string - Label name
values.[].plot : list - Histogram for label
values.[].plot.[].label_relevance : int - Label relevance value
values.[].plot.[].row_count : int - Row count where label has given relevance
values.[].plot.[].row_pct : float - Percentage of rows where label has given relevance

Attributes:	feature_name : str Name of the feature values : list(dict) List of Histogram values with a schema described as `HistogramValues`

classmethod get(multilabel_insights_key)¶

Retrieves multicategorical histogram

You might find it more convenient to use Feature.get_multicategorical_histogram instead.

Parameters:	multilabel_insights_key: string Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via `Feature.multilabel_insights_key`.
Returns:	MulticategoricalHistogram The multicategorical histogram for multilabel_insights_key

to_dataframe()¶

Convenience method to get all the information from this multicategorical_histogram instance in form of a pandas.DataFrame.

Returns:	pandas.DataFrame Histogram information as a multicategorical_histogram. The dataframe will contain these columns: feature_name, label, label_relevance, row_count and row_pct

class datarobot.models.PairwiseCorrelations(*args, **kwargs)¶

Correlation of label pairs for multicategorical feature.

New in version v2.24.

Notes

CorrelationValues contain:

values.[].label_configuration : list of length 2 - Configuration of the label pair
values.[].label_configuration.[].label : str – Label name
values.[].statistic_value : float – Statistic value

Attributes:	feature_name : str Name of the feature values : list(dict) List of correlation values with a schema described as `CorrelationValues` statistic_dataframe : pandas.DataFrame Correlation values for all label pairs as a DataFrame

classmethod get(multilabel_insights_key)¶

Retrieves pairwise correlations

You might find it more convenient to use Feature.get_pairwise_correlations instead.

Parameters:	multilabel_insights_key: string Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via `Feature.multilabel_insights_key`.
Returns:	PairwiseCorrelations The pairwise label correlations

as_dataframe()¶

The pairwise label correlations as a (num_labels x num_labels) DataFrame.

Returns:	pandas.DataFrame The pairwise label correlations. Index and column names allow the interpretation of the values.

class datarobot.models.PairwiseJointProbabilities(*args, **kwargs)¶

Joint probabilities of label pairs for multicategorical feature.

New in version v2.24.

Notes

ProbabilityValues contain:

values.[].label_configuration : list of length 2 - Configuration of the label pair
values.[].label_configuration.[].relevance : int – 0 for absence of the labels, 1 for the presence of labels
values.[].label_configuration.[].label : str – Label name
values.[].statistic_value : float – Statistic value

Attributes:

feature_name : str

Name of the feature

values : list(dict)

List of joint probability values with a schema described as ProbabilityValues

statistic_dataframes : dict(pandas.DataFrame)

Joint Probability values as DataFrames for different relevance combinations.

E.g. The probability P(A=0,B=1) can be retrieved via: pairwise_joint_probabilities.statistic_dataframes[(0,1)].loc['A', 'B']

classmethod get(multilabel_insights_key)¶

Retrieves pairwise joint probabilities

You might find it more convenient to use Feature.get_pairwise_joint_probabilities instead.

Parameters:	multilabel_insights_key: string Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via `Feature.multilabel_insights_key`.
Returns:	PairwiseJointProbabilities The pairwise joint probabilities

as_dataframe(relevance_configuration)¶

Joint probabilities of label pairs as a (num_labels x num_labels) DataFrame.

Parameters:

relevance_configuration: tuple of length 2

Valid options are (0, 0), (0, 1), (1, 0) and (1, 1). Values of 0 indicate absence of labels and 1 indicates presence of labels. The first value describes the presence for the labels in axis=0 and the second value describes the presence for the labels in axis=1.

For example the matrix values for a relevance configuration of (0, 1) describe the probabilities of absent labels in the index axis and present labels in the column axis.

E.g. The probability P(A=0,B=1) can be retrieved via: pairwise_joint_probabilities.as_dataframe((0,1)).loc['A', 'B']

Returns:

pandas.DataFrame: The joint probabilities for the requested relevance_configuration. Index and column names allow the interpretation of the values.

class datarobot.models.PairwiseConditionalProbabilities(*args, **kwargs)¶

Conditional probabilities of label pairs for multicategorical feature.

New in version v2.24.

Notes

ProbabilityValues contain:

values.[].label_configuration : list of length 2 - Configuration of the label pair
values.[].label_configuration.[].relevance : int – 0 for absence of the labels, 1 for the presence of labels
values.[].label_configuration.[].label : str – Label name
values.[].statistic_value : float – Statistic value

Attributes:

feature_name : str

Name of the feature

values : list(dict)

List of conditional probability values with a schema described as ProbabilityValues

statistic_dataframes : dict(pandas.DataFrame)

Conditional Probability values as DataFrames for different relevance combinations. The label names in the columns are the events, on which we condition. The label names in the index are the events whose conditional probability given the indexes is in the dataframe.

E.g. The probability P(A=0|B=1) can be retrieved via: pairwise_conditional_probabilities.statistic_dataframes[(0,1)].loc['A', 'B']

classmethod get(multilabel_insights_key)¶

Retrieves pairwise conditional probabilities

You might find it more convenient to use Feature.get_pairwise_conditional_probabilities instead.

Parameters:	multilabel_insights_key: string Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via `Feature.multilabel_insights_key`.
Returns:	PairwiseConditionalProbabilities The pairwise conditional probabilities

as_dataframe(relevance_configuration)¶

Conditional probabilities of label pairs as a (num_labels x num_labels) DataFrame. The label names in the columns are the events, on which we condition. The label names in the index are the events whose conditional probability given the indexes is in the dataframe.

E.g. The probability P(A=0|B=1) can be retrieved via: pairwise_conditional_probabilities.as_dataframe((0, 1)).loc['A', 'B']

Parameters:

relevance_configuration: tuple of length 2

Valid options are (0, 0), (0, 1), (1, 0) and (1, 1). Values of 0 indicate absence of labels and 1 indicates presence of labels. The first value describes the presence for the labels in axis=0 and the second value describes the presence for the labels in axis=1.

For example the matrix values for a relevance configuration of (0, 1) describe the probabilities of absent labels in the index axis given the presence of labels in the column axis.

Returns:

pandas.DataFrame: The conditional probabilities for the requested relevance_configuration. Index and column names allow the interpretation of the values.

Feature Association¶

class datarobot.models.FeatureAssociationMatrix(strengths=None, features=None, project_id=None)¶

Feature association statistics for a project.

Note

Projects created prior to v2.17 are not supported by this feature.

Examples

import datarobot as dr

# retrieve feature association matrix
feature_association_matrix = dr.FeatureAssociationMatrix.get(project_id)
feature_association_matrix.strengths
feature_association_matrix.features

# retrieve feature association matrix for a metric, association type or a feature list
feature_association_matrix = dr.FeatureAssociationMatrix.get(
    project_id,
    metric=enums.FEATURE_ASSOCIATION_METRIC.SPEARMAN,
    association_type=enums.FEATURE_ASSOCIATION_TYPE.CORRELATION,
    featurelist_id=featurelist_id,
)

Attributes:	project_id : str Id of the associated project. strengths : list of dict Pairwise statistics for the available features as structured below. features : list of dict Metadata for each feature and where it goes in the matrix.

classmethod get(project_id, metric=None, association_type=None, featurelist_id=None)¶

Get feature association statistics.

Parameters:

project_id : str: Id of the project that contains the requested associations.
metric : enums.FEATURE_ASSOCIATION_METRIC: The name of a metric to get pairwise data for. Since ‘v2.19’ this is optional and defaults to enums.FEATURE_ASSOCIATION_METRIC.MUTUAL_INFO.
association_type : enums.FEATURE_ASSOCIATION_TYPE: The type of dependence for the data. Since ‘v2.19’ this is optional and defaults to enums.FEATURE_ASSOCIATION_TYPE.ASSOCIATION.
featurelist_id : str or None: Optional, the feature list to lookup FAM data for. By default, depending on the type of the project “Informative Features” or “Timeseries Informative Features” list will be used. (New in version v2.19)

Returns:

FeatureAssociationMatrix: Feature association pairwise metric strength data, feature clustering data, and ordering data for Feature Association Matrix visualization.

Feature Association Matrix Details¶

class datarobot.models.FeatureAssociationMatrixDetails(project_id=None, chart_type=None, values=None, features=None, types=None, featurelist_id=None)¶

Plotting details for a pair of passed features present in the feature association matrix.

Note

Projects created prior to v2.17 are not supported by this feature.

Attributes:

project_id : str: Id of the project that contains the requested associations.
chart_type : str: Which type of plotting the pair of features gets in the UI. e.g. ‘HORIZONTAL_BOX’, ‘VERTICAL_BOX’, ‘SCATTER’ or ‘CONTINGENCY’
values : list: The data triplets for pairwise plotting e.g. {“values”: [[460.0, 428.5, 0.001], [1679.3, 259.0, 0.001], …] The first entry of each list is a value of feature1, the second entry of each list is a value of feature2, and the third is the relative frequency of the pair of datapoints in the sample.
features : list: A list of the requested features, [feature1, feature2]
types : list: The type of feature1 and feature2. Possible values: “CATEGORICAL”, “NUMERIC”
featurelist_id : str: Id of the feature list to lookup FAM details for.

classmethod get(project_id, feature1, feature2, featurelist_id=None)¶

Get a sample of the actual values used to measure the association between a pair of features

New in version v2.17.

Parameters:

project_id : str: Id of the project of interest.
feature1 : str: Feature name for the first feature of interest.
feature2 : str: Feature name for the second feature of interest.
featurelist_id : str: Optional, the feature list to lookup FAM data for. By default, depending on the type of the project “Informative Features” or “Timeseries Informative Features” list will be used.

Returns:

FeatureAssociationMatrixDetails: The feature association plotting for provided pair of features.

Feature Association Featurelists¶

class datarobot.models.FeatureAssociationFeaturelists(project_id=None, featurelists=None)¶

Featurelists with feature association matrix availability flags for a project.

Attributes:	project_id : str Id of the project that contains the requested associations. featurelists : list fo dict The featurelists with the featurelist_id, title and the has_fam flag.

classmethod get(project_id)¶

Get featurelists with feature association status for each.

Parameters:	project_id : str Id of the project of interest.
Returns:	FeatureAssociationFeaturelists Featurelist with feature association status for each.

Feature Discovery¶

Relationships Configuration¶

class datarobot.models.RelationshipsConfiguration(id, dataset_definitions=None, relationships=None, feature_discovery_mode=None, feature_discovery_settings=None)¶

A Relationships configuration specifies a set of secondary datasets as well as the relationships among them. It is used to configure Feature Discovery for a project to generate features automatically from these datasets.

Attributes:

id : string

Id of the created relationships configuration

dataset_definitions: list

Each element is a dataset_definitions for a dataset.

relationships: list

Each element is a relationship between two datasets

feature_discovery_mode: str

Mode of feature discovery. Supported values are ‘default’ and ‘manual’

feature_discovery_settings: list

List of feature discovery settings used to customize the feature discovery process

The `dataset_definitions` structure is

identifier: string

Alias of the dataset (used directly as part of the generated feature names)

catalog_id: str, or None

Identifier of the catalog item

catalog_version_id: str

Identifier of the catalog item version

primary_temporal_key: string, optional

Name of the column indicating time of record creation

feature_list_id: string, optional

Identifier of the feature list. This decides which columns in the dataset are used for feature generation

snapshot_policy: str

Policy to use when creating a project or making predictions. Must be one of the following values: ‘specified’: Use specific snapshot specified by catalogVersionId ‘latest’: Use latest snapshot from the same catalog item ‘dynamic’: Get data from the source (only applicable for JDBC datasets)

feature_lists: list

List of feature list info

data_source: dict

Data source info if the dataset is from data source

data_sources: list

List of Data source details for a JDBC datasets

is_deleted: bool, optional

Whether the dataset is deleted or not

The `data source info` structured is

data_store_id: str

Id of the data store.

data_store_name : str

User-friendly name of the data store.

url : str

Url used to connect to the data store.

dbtable : str

Name of table from the data store.

schema: str

Schema definition of the table from the data store

catalog: str

Catalog name of the data source.

The `feature list info` structure is

id : str

Id of the featurelist

name : str

Name of the featurelist

features : list of str

Names of all the Features in the featurelist

dataset_id : str

Project the featurelist belongs to

creation_date : datetime.datetime

When the featurelist was created

user_created : bool

Whether the featurelist was created by a user or by DataRobot automation

created_by: str

Name of user who created it

description : str

Description of the featurelist. Can be updated by the user and may be supplied by default for DataRobot-created featurelists.

dataset_id: str

Dataset which is associated with the feature list

dataset_version_id: str or None

Version of the dataset which is associated with feature list. Only relevant for Informative features

The `relationships` schema is

dataset1_identifier: str or None

Identifier of the first dataset in this relationship. This is specified in the indentifier field of dataset_definition structure. If None, then the relationship is with the primary dataset.

dataset2_identifier: str

Identifier of the second dataset in this relationship. This is specified in the identifier field of dataset_definition schema.

dataset1_keys: list of str (max length: 10 min length: 1)

Column(s) from the first dataset which are used to join to the second dataset

dataset2_keys: list of str (max length: 10 min length: 1)

Column(s) from the second dataset that are used to join to the first dataset

time_unit: str, or None

Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, the feature engineering Graph will perform time-aware joins.

feature_derivation_window_start: int, or None

How many time_units of each dataset’s primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, If present, the feature engineering Graph will perform time-aware joins.

feature_derivation_window_end: int, or None

How many timeUnits of each dataset’s record primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, the feature engineering Graph will perform time-aware joins.

feature_derivation_window_time_unit: int or None

Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR If present, time-aware joins will be used. Only applicable when dataset1Identifier is not provided.

feature_derivation_windows: list of dict, or None

List of feature derivation windows settings. If present, time-aware joins will be used. Only allowed when feature_derivation_window_start, feature_derivation_window_end and feature_derivation_window_time_unit are not provided.

prediction_point_rounding: int, or None

Closest value of prediction_point_rounding_time_unit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present.Only applicable when dataset1_identifier is not provided.

prediction_point_rounding_time_unit: str, or None

time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR Only applicable when dataset1_identifier is not provided.

The `feature_derivation_windows` is a list of dictionary with schema:

start: int: How many time_units of each dataset’s primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin.
end: int: How many timeUnits of each dataset’s record primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end.
unit: string: Time unit of the feature derivation window. One of datarobot.enums.AllowedTimeUnitsSAFER.

The `feature_discovery_settings` structure is:

name: str

Name of the feature discovery setting

value: bool

Value of the feature discovery setting

To see the list of possible settings, create a RelationshipConfiguration without specifying

settings and check its `feature_discovery_settings` attribute, which is a list of possible

settings with their default values.

classmethod create(dataset_definitions, relationships, feature_discovery_settings=None)¶

Create a Relationships Configuration

Parameters:

dataset_definitions: list of dataset definitions: Each element is a datarobot.helpers.feature_discovery.DatasetDefinition
relationships: list of relationships: Each element is a datarobot.helpers.feature_discovery.Relationship
feature_discovery_settings : list of feature discovery settings, optional: Each element is a dictionary or a datarobot.helpers.feature_discovery.FeatureDiscoverySetting. If not provided, default settings will be used.

Returns:

relationships_configuration: RelationshipsConfiguration: Created relationships configuration

Examples

dataset_definition = dr.DatasetDefinition(
    identifier='profile',
    catalog_id='5fd06b4af24c641b68e4d88f',
    catalog_version_id='5fd06b4af24c641b68e4d88f'
)
relationship = dr.Relationship(
    dataset2_identifier='profile',
    dataset1_keys=['CustomerID'],
    dataset2_keys=['CustomerID'],
    feature_derivation_window_start=-14,
    feature_derivation_window_end=-1,
    feature_derivation_window_time_unit='DAY',
    prediction_point_rounding=1,
    prediction_point_rounding_time_unit='DAY'
)
dataset_definitions = [dataset_definition]
relationships = [relationship]
relationship_config = dr.RelationshipsConfiguration.create(
    dataset_definitions=dataset_definitions,
    relationships=relationships
)
>>> relationship_config.id
'5c88a37770fc42a2fcc62759'

get()¶

Retrieve the Relationships configuration for a given id

Returns:	relationships_configuration: RelationshipsConfiguration The requested relationships configuration
Raises:	ClientError Raised if an invalid relationships config id is provided.

Examples

relationships_config = dr.RelationshipsConfiguration(valid_config_id)
result = relationships_config.get()
>>> result.id
'5c88a37770fc42a2fcc62759'

replace(dataset_definitions, relationships, feature_discovery_settings=None)¶

Update the Relationships Configuration which is not used in the feature discovery Project

Parameters:

dataset_definitions: list of dataset definition: Each element is a datarobot.helpers.feature_discovery.DatasetDefinition
relationships: list of relationships: Each element is a datarobot.helpers.feature_discovery.Relationship
feature_discovery_settings : list of feature discovery settings, optional: Each element is a dictionary or a datarobot.helpers.feature_discovery.FeatureDiscoverySetting. If not provided, default settings will be used.

Returns:

relationships_configuration: RelationshipsConfiguration: the updated relationships configuration

delete()¶

Delete the Relationships configuration

Raises:	ClientError Raised if an invalid relationships config id is provided.

Examples

# Deleting with a valid id
relationships_config = dr.RelationshipsConfiguration(valid_config_id)
status_code = relationships_config.delete()
status_code
>>> 204
relationships_config.get()
>>> ClientError: Relationships Configuration not found

Dataset Definition¶

class datarobot.helpers.feature_discovery.DatasetDefinition(identifier, catalog_id, catalog_version_id, feature_list_id=None, snapshot_policy='latest', primary_temporal_key=None)¶

Dataset definition for the Feature Discovery

New in version v2.25.

Examples

import datarobot as dr
dataset_definition = dr.DatasetDefinition(
    identifier='profile',
    catalog_id='5ec4aec1f072bc028e3471ae',
    catalog_version_id='5ec4aec2f072bc028e3471b1',
)

dataset_definition = dr.DatasetDefinition(
    identifier='transaction',
    catalog_id='5ec4aec1f072bc028e3471ae',
    catalog_version_id='5ec4aec2f072bc028e3471b1',
    primary_temporal_key='Date'
)

Attributes:

identifier: string: Alias of the dataset (used directly as part of the generated feature names)
catalog_id: string, optional: Identifier of the catalog item
catalog_version_id: string: Identifier of the catalog item version
primary_temporal_key: string, optional: Name of the column indicating time of record creation
feature_list_id: string, optional: Identifier of the feature list. This decides which columns in the dataset are used for feature generation
snapshot_policy: string, optional: Policy to use when creating a project or making predictions. If omitted, by default endpoint will use ‘latest’. Must be one of the following values: ‘specified’: Use specific snapshot specified by catalogVersionId ‘latest’: Use latest snapshot from the same catalog item ‘dynamic’: Get data from the source (only applicable for JDBC datasets)

Relationship¶

class datarobot.helpers.feature_discovery.Relationship(dataset2_identifier, dataset1_keys, dataset2_keys, dataset1_identifier=None, feature_derivation_window_start=None, feature_derivation_window_end=None, feature_derivation_window_time_unit=None, feature_derivation_windows=None, prediction_point_rounding=None, prediction_point_rounding_time_unit=None)¶

Relationship between dataset defined in DatasetDefinition

New in version v2.25.

Examples

import datarobot as dr
relationship = dr.Relationship(
    dataset1_identifier='profile',
    dataset2_identifier='transaction',
    dataset1_keys=['CustomerID'],
    dataset2_keys=['CustomerID']
)

relationship = dr.Relationship(
    dataset2_identifier='profile',
    dataset1_keys=['CustomerID'],
    dataset2_keys=['CustomerID'],
    feature_derivation_window_start=-14,
    feature_derivation_window_end=-1,
    feature_derivation_window_time_unit='DAY',
    prediction_point_rounding=1,
    prediction_point_rounding_time_unit='DAY'
)

Attributes:

dataset1_identifier: string, optional

Identifier of the first dataset in this relationship. This is specified in the indentifier field of dataset_definition structure. If None, then the relationship is with the primary dataset.

dataset2_identifier: string

Identifier of the second dataset in this relationship. This is specified in the identifier field of dataset_definition schema.

dataset1_keys: list of string (max length: 10 min length: 1)

Column(s) from the first dataset which are used to join to the second dataset

dataset2_keys: list of string (max length: 10 min length: 1)

Column(s) from the second dataset that are used to join to the first dataset

feature_derivation_window_start: int, or None

How many time_units of each dataset’s primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, If present, the feature engineering Graph will perform time-aware joins.

feature_derivation_window_end: int, optional

How many timeUnits of each dataset’s record primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, the feature engineering Graph will perform time-aware joins.

feature_derivation_window_time_unit: int, optional

Time unit of the feature derivation window. One of datarobot.enums.AllowedTimeUnitsSAFER If present, time-aware joins will be used. Only applicable when dataset1_identifier is not provided.

feature_derivation_windows: list of dict, or None

List of feature derivation windows settings. If present, time-aware joins will be used. Only allowed when feature_derivation_window_start, feature_derivation_window_end and feature_derivation_window_time_unit are not provided.

prediction_point_rounding: int, optional

Closest value of prediction_point_rounding_time_unit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present.Only applicable when dataset1_identifier is not provided.

prediction_point_rounding_time_unit: string, optional

Time unit of the prediction point rounding. One of datarobot.enums.AllowedTimeUnitsSAFER Only applicable when dataset1_identifier is not provided.

The `feature_derivation_windows` is a list of dictionary with schema:

start: int: How many time_units of each dataset’s primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin.
end: int: How many timeUnits of each dataset’s record primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end.
unit: string: Time unit of the feature derivation window. One of datarobot.enums.AllowedTimeUnitsSAFER.

Feature Lineage¶

class datarobot.models.FeatureLineage(steps=None)¶

Lineage of an automatically engineered feature.

Attributes:

steps: list: list of steps which were applied to build the feature.
`steps` structure is:
id : int: step id starting with 0.
step_type: str: one of the data/action/json/generatedData.
name: str: name of the step.
description: str: description of the step.
parents: list[int]: references to other steps id.
is_time_aware: bool: indicator of step being time aware. Mandatory only for action and join steps. action step provides additional information about feature derivation window in the timeInfo field.
catalog_id: str: id of the catalog for a data step.
catalog_version_id: str: id of the catalog version for a data step.
group_by: list[str]: list of columns which this action step aggregated by.
columns: list: names of columns involved into the feature generation. Available only for data steps.
time_info: dict: description of the feature derivation window which was applied to this action step.
join_info: list[dict]: join step details.
`columns` structure is
data_type: str: the type of the feature, e.g. ‘Categorical’, ‘Text’
is_input: bool: indicates features which provided data to transform in this lineage.
name: str: feature name.
is_cutoff: bool: indicates a cutoff column.
`time_info` structure is:
latest: dict: end of the feature derivation window applied.
duration: dict: size of the feature derivation window applied.
`latest` and `duration` structure is:
time_unit: str: time unit name like ‘MINUTE’, ‘DAY’, ‘MONTH’ etc.
duration: int: value/size of this duration object.
`join_info` structure is:
join_type: str: kind of join, left/right.
left_table: dict: information about a dataset which was considered as left.
right_table: str: information about a dataset which was considered as right.
`left_table` and `right_table` structure is:
columns: list[str]: list of columns which datasets were joined by.
datasteps: list[int]: list of data steps id which brought the columns into the current step dataset.

classmethod get(project_id, id)¶

Retrieve a single FeatureLineage.

Parameters:	project_id : str The id of the project the feature belongs to id : str id of a feature lineage to retrieve
Returns:	lineage : FeatureLineage The queried instance

Secondary Dataset Configurations¶

class datarobot.models.SecondaryDatasetConfigurations(id=None, project_id=None, config=None, secondary_datasets=None, name=None, creator_full_name=None, creator_user_id=None, created=None, featurelist_id=None, credential_ids=None, is_default=None, project_version=None)¶

Create secondary dataset configurations for a given project

New in version v2.20.

Attributes:

id : str: Id of this secondary dataset configuration
project_id : str: Id of the associated project.
config: list of DatasetConfiguration (Deprecated in version v2.23): List of secondary dataset configurations
secondary_datasets: list of SecondaryDataset (new in v2.23): List of secondary datasets (secondaryDataset)
name: str: Verbose name of the SecondaryDatasetConfig. null if it wasn’t specified.
created: datetime.datetime: DR-formatted datetime. null for legacy (before DR 6.0) db records.
creator_user_id: str: Id of the user created this config.
creator_full_name: str: fullname or email of the user created this config.
featurelist_id: str, optional: Id of the feature list. null if it wasn’t specified.
credential_ids: list of DatasetsCredentials, optional: credentials used by the secondary datasets if the datasets used in the configuration are from datasource
is_default: bool, optional: Boolean flag if default config created during feature disovery aim
project_version: str, optional: Version of project when its created (Release version)

classmethod create(project_id, secondary_datasets, name, featurelist_id=None)¶

create secondary dataset configurations

New in version v2.20.

Parameters:	project_id : str id of the associated project. secondary_datasets: list of SecondaryDataset (New in version v2.23) list of secondary datasets used by the configuration each element is a `datarobot.helpers.feature_discovery.SecondaryDataset` name: str (New in version v2.23) Name of the secondary datasets configuration featurelist_id: str, or None (New in version v2.23) Id of the featurelist
Returns:	an instance of SecondaryDatasetConfigurations
Raises:	ClientError raised if incorrect configuration parameters are provided

Examples

profile_secondary_dataset = dr.SecondaryDataset(
    identifier='profile',
    catalog_id='5ec4aec1f072bc028e3471ae',
    catalog_version_id='5ec4aec2f072bc028e3471b1',
    snapshot_policy='latest'
)

transaction_secondary_dataset = dr.SecondaryDataset(
    identifier='transaction',
    catalog_id='5ec4aec268f0f30289a03901',
    catalog_version_id='5ec4aec268f0f30289a03900',
    snapshot_policy='latest'
)

secondary_datasets = [profile_secondary_dataset, transaction_secondary_dataset]
new_secondary_dataset_config = dr.SecondaryDatasetConfigurations.create(
    project_id=project.id,
    name='My config',
    secondary_datasets=secondary_datasets
)

>>> new_secondary_dataset_config.id
'5fd1e86c589238a4e635e93d'

delete()¶

Removes the Secondary datasets configuration

New in version v2.21.

Raises:	ClientError Raised if an invalid or already deleted secondary dataset config id is provided

Examples

# Deleting with a valid secondary_dataset_config id
status_code = dr.SecondaryDatasetConfigurations.delete(some_config_id)
status_code
>>> 204

get()¶

Retrieve a single secondary dataset configuration for a given id

New in version v2.21.

Returns:	secondary_dataset_configurations : SecondaryDatasetConfigurations The requested secondary dataset configurations

Examples

config_id = '5fd1e86c589238a4e635e93d'
secondary_dataset_config = dr.SecondaryDatasetConfigurations(id=config_id).get()
>>> secondary_dataset_config
{
     'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()),
     'creator_full_name': u'[email protected]',
     'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a',
     'credential_ids': None,
     'featurelist_id': None,
     'id': u'5fd1e86c589238a4e635e93d',
     'is_default': True,
     'name': u'My config',
     'project_id': u'5fd06afce2456ec1e9d20457',
     'project_version': None,
     'secondary_datasets': [
            {
                'snapshot_policy': u'latest',
                'identifier': u'profile',
                'catalog_version_id': u'5fd06b4af24c641b68e4d88f',
                'catalog_id': u'5fd06b4af24c641b68e4d88e'
            },
            {
                'snapshot_policy': u'dynamic',
                'identifier': u'transaction',
                'catalog_version_id': u'5fd1e86c589238a4e635e98e',
                'catalog_id': u'5fd1e86c589238a4e635e98d'
            }
     ]
}

classmethod list(project_id, featurelist_id=None, limit=None, offset=None)¶

Returns list of secondary dataset configurations.

New in version v2.23.

Parameters:	project_id: str The Id of project featurelist_id: str, optional Id of the feature list to filter the secondary datasets configurations
Returns:	secondary_dataset_configurations : list of SecondaryDatasetConfigurations The requested list of secondary dataset configurations for a given project

Examples

pid = '5fd06afce2456ec1e9d20457'
secondary_dataset_configs = dr.SecondaryDatasetConfigurations.list(pid)
>>> secondary_dataset_configs[0]
    {
         'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()),
         'creator_full_name': u'[email protected]',
         'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a',
         'credential_ids': None,
         'featurelist_id': None,
         'id': u'5fd1e86c589238a4e635e93d',
         'is_default': True,
         'name': u'My config',
         'project_id': u'5fd06afce2456ec1e9d20457',
         'project_version': None,
         'secondary_datasets': [
                {
                    'snapshot_policy': u'latest',
                    'identifier': u'profile',
                    'catalog_version_id': u'5fd06b4af24c641b68e4d88f',
                    'catalog_id': u'5fd06b4af24c641b68e4d88e'
                },
                {
                    'snapshot_policy': u'dynamic',
                    'identifier': u'transaction',
                    'catalog_version_id': u'5fd1e86c589238a4e635e98e',
                    'catalog_id': u'5fd1e86c589238a4e635e98d'
                }
         ]
    }

Secondary Dataset¶

class datarobot.helpers.feature_discovery.SecondaryDataset(identifier, catalog_id, catalog_version_id, snapshot_policy='latest')¶

A secondary dataset to be used for feature discovery

New in version v2.25.

Examples

import datarobot as dr
dataset_definition = dr.SecondaryDataset(
    identifier='profile',
    catalog_id='5ec4aec1f072bc028e3471ae',
    catalog_version_id='5ec4aec2f072bc028e3471b1',
)

Attributes:

identifier: string: Alias of the dataset (used directly as part of the generated feature names)
catalog_id: string: Identifier of the catalog item
catalog_version_id: string: Identifier of the catalog item version
snapshot_policy: string, optional: Policy to use while creating a project or making predictions. If omitted, by default endpoint will use ‘latest’. Must be one of the following values: ‘specified’: Use specific snapshot specified by catalogVersionId ‘latest’: Use latest snapshot from the same catalog item ‘dynamic’: Get data from the source (only applicable for JDBC datasets)

Feature Effects¶

class datarobot.models.FeatureEffects(project_id, model_id, source, feature_effects, backtest_index=None)¶

Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Notes

featureEffects is a dict containing the following:

feature_name (string) Name of the feature

feature_type (string) dr.enums.FEATURE_TYPE, Feature type either numeric, categorical or datetime

feature_impact_score (float) Feature impact score

weight_label (string) optional, Weight label if configured for the project else null

partial_dependence (List) Partial dependence results

predicted_vs_actual (List) optional, Predicted versus actual results, may be omitted if there are insufficient qualified samples

partial_dependence is a dict containing the following:

is_capped (bool) Indicates whether the data for computation is capped
data (List) partial dependence results in the following format

data is a list of dict containing the following:

label (string) Contains label for categorical and numeric features as string
dependence (float) Value of partial dependence

predicted_vs_actual is a dict containing the following:

is_capped (bool) Indicates whether the data for computation is capped
data (List) pred vs actual results in the following format

data is a list of dict containing the following:

label (string) Contains label for categorical features for numeric features contains range or numeric value.
bin (List) optional, For numeric features contains labels for left and right bin limits
predicted (float) Predicted value
actual (float) Actual value. Actual value is null for unsupervised timeseries models
row_count (int or float) Number of rows for the label and bin. Type is float if weight or exposure is set for the project.

Attributes:	project_id: string The project that contains requested model model_id: string The model to retrieve Feature Effects for source: string The source to retrieve Feature Effects for feature_effects: list Feature Effects for every feature backtest_index: string, required only for DatetimeModels, The backtest index to retrieve Feature Effects for.

classmethod from_server_data(data)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing.

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place

class datarobot.models.FeatureEffectMetadata(status, sources)¶

Feature Effect Metadata for model, contains status and available model sources.

Notes

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

class datarobot.models.FeatureEffectMetadataDatetime(data)¶

Feature Effect Metadata for datetime model, contains list of feature effect metadata per backtest.

Notes

feature effect metadata per backtest contains:

status : string.
backtest_index : string.
sources : list(string).

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

backtest_index is expected parameter to submit compute request and retrieve Feature Effect. One of provided backtest indexes shall be used.

Attributes:	data : list[FeatureEffectMetadataDatetimePerBacktest] List feature effect metadata per backtest

class datarobot.models.FeatureEffectMetadataDatetimePerBacktest(ff_metadata_datetime_per_backtest)¶: Convert dictionary into feature effect metadata per backtest which contains backtest_index, status and sources.

Feature Fit¶

class datarobot.models.FeatureFit(project_id, model_id, source, feature_fit, backtest_index=None)¶

Feature Fit provides partial dependence and predicted vs actual values for top-500 features ordered by feature importance score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Notes

featureFit is a dict containing the following:

feature_name (string) Name of the feature

feature_type (string) dr.enums.FEATURE_TYPE, Feature type either numeric, categorical or datetime

feature_importance_score (float) Feature importance score

weight_label (string) optional, Weight label if configured for the project else null

partial_dependence (List) Partial dependence results

predicted_vs_actual (List) optional, Predicted versus actual results, may be omitted if there are insufficient qualified samples

partial_dependence is a dict containing the following:

is_capped (bool) Indicates whether the data for computation is capped
data (List) partial dependence results in the following format

data is a list of dict containing the following:

label (string) Contains label for categorical and numeric features as string
dependence (float) Value of partial dependence

predicted_vs_actual is a dict containing the following:

is_capped (bool) Indicates whether the data for computation is capped
data (List) pred vs actual results in the following format

data is a list of dict containing the following:

label (string) Contains label for categorical features for numeric features contains range or numeric value.
bin (List) optional, For numeric features contains labels for left and right bin limits
predicted (float) Predicted value
actual (float) Actual value. Actual value is null for unsupervised timeseries models
row_count (int or float) Number of rows for the label and bin. Type is float if weight or exposure is set for the project.

Attributes:	project_id: string The project that contains requested model model_id: string The model to retrieve Feature Fit for source: string The source to retrieve Feature Fit for feature_fit: list Feature Fit data for every feature backtest_index: string, required only for DatetimeModels, The backtest index to retrieve Feature Fit for.

classmethod from_server_data(data)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing.

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place

class datarobot.models.FeatureFitMetadata(status, sources)¶

Feature Fit Metadata for model, contains status and available model sources.

Notes

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

class datarobot.models.FeatureFitMetadataDatetime(data)¶

Feature Fit Metadata for datetime model, contains list of feature fit metadata per backtest.

Notes

feature fit metadata per backtest contains:

status : string.
backtest_index : string.
sources : list(string).

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

backtest_index is expected parameter to submit compute request and retrieve Feature Fit. One of provided backtest indexes shall be used.

Attributes:	data : list[FeatureFitMetadataDatetimePerBacktest] list feature fit metadata per backtest

class datarobot.models.FeatureFitMetadataDatetimePerBacktest(ff_metadata_datetime_per_backtest)¶: Convert dictionary into feature fit metadata per backtest which contains backtest_index, status and sources.

Feature List¶

class datarobot.DatasetFeaturelist(id=None, name=None, features=None, dataset_id=None, dataset_version_id=None, creation_date=None, created_by=None, user_created=None, description=None)¶

A set of features attached to a dataset in the AI Catalog

Attributes:

id : str: the id of the dataset featurelist
dataset_id : str: the id of the dataset the featurelist belongs to
dataset_version_id: str, optional: the version id of the dataset this featurelist belongs to
name : str: the name of the dataset featurelist
features : list of str: a list of the names of features included in this dataset featurelist
creation_date : datetime.datetime: when the featurelist was created
created_by : str: the user name of the user who created this featurelist
user_created : bool: whether the featurelist was created by a user or by DataRobot automation
description : basestring, optional: the description of the featurelist. Only present on DataRobot-created featurelists.

classmethod get(dataset_id, featurelist_id)¶

Retrieve a dataset featurelist

Parameters:	dataset_id : str the id of the dataset the featurelist belongs to featurelist_id : str the id of the dataset featurelist to retrieve
Returns:	featurelist : DatasetFeatureList the specified featurelist

delete()¶

Delete a dataset featurelist

Featurelists configured into the dataset as a default featurelist cannot be deleted.

update(name=None)¶

Update the name of an existing featurelist

Note that only user-created featurelists can be renamed, and that names must not conflict with names used by other featurelists.

Parameters:	name : str, optional the new name for the featurelist

class datarobot.models.Featurelist(id=None, name=None, features=None, project_id=None, created=None, is_user_created=None, num_models=None, description=None)¶

A set of features used in modeling

Attributes:

id : str: the id of the featurelist
name : str: the name of the featurelist
features : list of str: the names of all the Features in the featurelist
project_id : str: the project the featurelist belongs to
created : datetime.datetime: (New in version v2.13) when the featurelist was created
is_user_created : bool: (New in version v2.13) whether the featurelist was created by a user or by DataRobot automation
num_models : int: (New in version v2.13) the number of models currently using this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.
description : basestring: (New in version v2.13) the description of the featurelist. Can be updated by the user and may be supplied by default for DataRobot-created featurelists.

classmethod get(project_id, featurelist_id)¶

Retrieve a known feature list

Parameters:	project_id : str The id of the project the featurelist is associated with featurelist_id : str The ID of the featurelist to retrieve
Returns:	featurelist : Featurelist The queried instance

delete(dry_run=False, delete_dependencies=False)¶

Delete a featurelist, and any models and jobs using it

All models using a featurelist, whether as the training featurelist or as a monotonic constraint featurelist, will also be deleted when the deletion is executed and any queued or running jobs using it will be cancelled. Similarly, predictions made on these models will also be deleted. All the entities that are to be deleted with a featurelist are described as “dependencies” of it. To preview the results of deleting a featurelist, call delete with dry_run=True

When deleting a featurelist with dependencies, users must specify delete_dependencies=True to confirm they want to delete the featurelist and all its dependencies. Without that option, only featurelists with no dependencies may be successfully deleted and others will error.

Featurelists configured into the project as a default featurelist or as a default monotonic constraint featurelist cannot be deleted.

Featurelists used in a model deployment cannot be deleted until the model deployment is deleted.

Parameters:

dry_run : bool, optional: specify True to preview the result of deleting the featurelist, instead of actually deleting it.
delete_dependencies : bool, optional: specify True to successfully delete featurelists with dependencies; if left False by default, featurelists without dependencies can be successfully deleted and those with dependencies will error upon attempting to delete them.

Returns:

result : dict

A dictionary describing the result of deleting the featurelist, with the following keys

dry_run : bool, whether the deletion was a dry run or an actual deletion
can_delete : bool, whether the featurelist can actually be deleted
deletion_blocked_reason : str, why the featurelist can’t be deleted (if it can’t)
num_affected_models : int, the number of models using this featurelist
num_affected_jobs : int, the number of jobs using this featurelist

update(name=None, description=None)¶

Update the name or description of an existing featurelist

Note that only user-created featurelists can be renamed, and that names must not conflict with names used by other featurelists.

Parameters:	name : str, optional the new name for the featurelist description : str, optional the new description for the featurelist

class datarobot.models.ModelingFeaturelist(id=None, name=None, features=None, project_id=None, created=None, is_user_created=None, num_models=None, description=None)¶

A set of features that can be used to build a model

In time series projects, a new set of modeling features is created after setting the partitioning options. These features are automatically derived from those in the project’s dataset and are the features used for modeling. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, ModelingFeaturelists and Featurelists will behave the same.

For more information about input and modeling features, see the time series documentation.

Attributes:

id : str: the id of the modeling featurelist
project_id : str: the id of the project the modeling featurelist belongs to
name : str: the name of the modeling featurelist
features : list of str: a list of the names of features included in this modeling featurelist
created : datetime.datetime: (New in version v2.13) when the featurelist was created
is_user_created : bool: (New in version v2.13) whether the featurelist was created by a user or by DataRobot automation
num_models : int: (New in version v2.13) the number of models currently using this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.
description : basestring: (New in version v2.13) the description of the featurelist. Can be updated by the user and may be supplied by default for DataRobot-created featurelists.

classmethod get(project_id, featurelist_id)¶

Retrieve a modeling featurelist

Modeling featurelists can only be retrieved once the target and partitioning options have been set.

Parameters:	project_id : str the id of the project the modeling featurelist belongs to featurelist_id : str the id of the modeling featurelist to retrieve
Returns:	featurelist : ModelingFeaturelist the specified featurelist

delete(dry_run=False, delete_dependencies=False)¶

Delete a featurelist, and any models and jobs using it

All models using a featurelist, whether as the training featurelist or as a monotonic constraint featurelist, will also be deleted when the deletion is executed and any queued or running jobs using it will be cancelled. Similarly, predictions made on these models will also be deleted. All the entities that are to be deleted with a featurelist are described as “dependencies” of it. To preview the results of deleting a featurelist, call delete with dry_run=True

When deleting a featurelist with dependencies, users must specify delete_dependencies=True to confirm they want to delete the featurelist and all its dependencies. Without that option, only featurelists with no dependencies may be successfully deleted and others will error.

Featurelists configured into the project as a default featurelist or as a default monotonic constraint featurelist cannot be deleted.

Featurelists used in a model deployment cannot be deleted until the model deployment is deleted.

Parameters:

dry_run : bool, optional: specify True to preview the result of deleting the featurelist, instead of actually deleting it.
delete_dependencies : bool, optional: specify True to successfully delete featurelists with dependencies; if left False by default, featurelists without dependencies can be successfully deleted and those with dependencies will error upon attempting to delete them.

Returns:

result : dict

A dictionary describing the result of deleting the featurelist, with the following keys

dry_run : bool, whether the deletion was a dry run or an actual deletion
can_delete : bool, whether the featurelist can actually be deleted
deletion_blocked_reason : str, why the featurelist can’t be deleted (if it can’t)
num_affected_models : int, the number of models using this featurelist
num_affected_jobs : int, the number of jobs using this featurelist

update(name=None, description=None)¶

Update the name or description of an existing featurelist

Note that only user-created featurelists can be renamed, and that names must not conflict with names used by other featurelists.

Parameters:	name : str, optional the new name for the featurelist description : str, optional the new description for the featurelist

Restoring Discarded Features¶

class datarobot.models.restore_discarded_features.DiscardedFeaturesInfo(total_restore_limit, remaining_restore_limit, count, features)¶

An object containing information about time series features which were reduced during time series feature generation process. These features can be restored back to the project. They will be included into All Time Series Features and can be used to create new feature lists.

New in version v2.27.

Attributes:	total_restore_limit : int The total limit indicating how many features can be restored in this project. remaining_restore_limit : int The remaining available number of the features which can be restored in this project. features : list of strings Discarded features which can be restored. count : int Discarded features count.

classmethod restore(project_id, features_to_restore, max_wait=600)¶

Restore discarded during time series feature generation process features back to the project. After restoration features will be included into All Time Series Features.

New in version v2.27.

Parameters:	project_id: string features_to_restore: list of strings List of the feature names to restore max_wait: int, optional max time to wait for features to be restored. Defaults to 10 min
Returns:	status: FeatureRestorationStatus information about features which were restored and which were not.

classmethod retrieve(project_id)¶

Retrieve the discarded features information for a given project.

New in version v2.27.

Parameters:	project_id: string
Returns:	info: DiscardedFeaturesInfo information about features which were discarded during feature generation process and limits how many features can be restored.

Job¶

class datarobot.models.Job(data, completed_resource_url=None)¶

Tracks asynchronous work being done within a project

Attributes:	id : int the id of the job project_id : str the id of the project the job belongs to status : str the status of the job - will be one of `datarobot.enums.QUEUE_STATUS` job_type : str what kind of work the job is doing - will be one of `datarobot.enums.JOB_TYPE` is_blocked : bool if true, the job is blocked (cannot be executed) until its dependencies are resolved

classmethod get(project_id, job_id)¶

Fetches one job.

Parameters:	project_id : str The identifier of the project in which the job resides job_id : str The job id
Returns:	job : Job The job
Raises:	AsyncFailureError Querying this resource gave a status code other than 200 or 303

cancel()¶: Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)¶

Parameters:	params : dict or None Query parameters to be added to request to get results. For featureEffects and featureFit, source param is required to define source, otherwise the default is `training`
Returns:	result : object Return type depends on the job type: for model jobs, a Model is returned for predict jobs, a pandas.DataFrame (with predictions) is returned for featureImpact jobs, a list of dicts by default (see `with_metadata` parameter of the `FeatureImpactJob` class and its `get()` method). for primeRulesets jobs, a list of Rulesets for primeModel jobs, a PrimeModel for primeDownloadValidation jobs, a PrimeFile for reasonCodesInitialization jobs, a ReasonCodesInitialization for reasonCodes jobs, a ReasonCodes for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization for predictionExplanations jobs, a PredictionExplanations for featureEffects, a FeatureEffects for featureFit, a FeatureFit
Raises:	JobNotFinished If the job is not finished, the result is not available. AsyncProcessUnsuccessfulError If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)¶

Parameters:	max_wait : int, optional How long to wait for the job to finish. params : dict, optional Query parameters to be added to request.
Returns:	result: object Return type is the same as would be returned by Job.get_result.
Raises:	AsyncTimeoutError If the job does not finish in time AsyncProcessUnsuccessfulError If the job errored or was aborted

refresh()¶: Update this object with the latest job data from the server.

wait_for_completion(max_wait=600)¶

Waits for job to complete.

Parameters:	max_wait : int, optional How long to wait for the job to finish.

class datarobot.models.TrainingPredictionsJob(data, model_id, data_subset, **kwargs)¶

classmethod get(project_id, job_id, model_id=None, data_subset=None)¶

Fetches one training predictions job.

The resulting TrainingPredictions object will be annotated with model_id and data_subset.

Parameters:	project_id : str The identifier of the project in which the job resides job_id : str The job id model_id : str The identifier of the model used for computing training predictions data_subset : dr.enums.DATA_SUBSET, optional Data subset used for computing training predictions
Returns:	job : TrainingPredictionsJob The job

refresh()¶: Update this object with the latest job data from the server.

cancel()¶: Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)¶

Parameters:	params : dict or None Query parameters to be added to request to get results. For featureEffects and featureFit, source param is required to define source, otherwise the default is `training`
Returns:	result : object Return type depends on the job type: for model jobs, a Model is returned for predict jobs, a pandas.DataFrame (with predictions) is returned for featureImpact jobs, a list of dicts by default (see `with_metadata` parameter of the `FeatureImpactJob` class and its `get()` method). for primeRulesets jobs, a list of Rulesets for primeModel jobs, a PrimeModel for primeDownloadValidation jobs, a PrimeFile for reasonCodesInitialization jobs, a ReasonCodesInitialization for reasonCodes jobs, a ReasonCodes for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization for predictionExplanations jobs, a PredictionExplanations for featureEffects, a FeatureEffects for featureFit, a FeatureFit
Raises:	JobNotFinished If the job is not finished, the result is not available. AsyncProcessUnsuccessfulError If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)¶

Parameters:	max_wait : int, optional How long to wait for the job to finish. params : dict, optional Query parameters to be added to request.
Returns:	result: object Return type is the same as would be returned by Job.get_result.
Raises:	AsyncTimeoutError If the job does not finish in time AsyncProcessUnsuccessfulError If the job errored or was aborted

wait_for_completion(max_wait=600)¶

Waits for job to complete.

Parameters:	max_wait : int, optional How long to wait for the job to finish.

class datarobot.models.ShapMatrixJob(data, model_id, dataset_id, **kwargs)¶

classmethod get(project_id, job_id, model_id=None, dataset_id=None)¶

Fetches one SHAP matrix job.

Parameters:	project_id : str The identifier of the project in which the job resides job_id : str The job identifier model_id : str The identifier of the model used for computing prediction explanations dataset_id : str The identifier of the dataset against which prediction explanations should be computed
Returns:	job : ShapMatrixJob The job
Raises:	AsyncFailureError Querying this resource gave a status code other than 200 or 303

refresh()¶: Update this object with the latest job data from the server.

cancel()¶: Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)¶

Parameters:	params : dict or None Query parameters to be added to request to get results. For featureEffects and featureFit, source param is required to define source, otherwise the default is `training`
Returns:	result : object Return type depends on the job type: for model jobs, a Model is returned for predict jobs, a pandas.DataFrame (with predictions) is returned for featureImpact jobs, a list of dicts by default (see `with_metadata` parameter of the `FeatureImpactJob` class and its `get()` method). for primeRulesets jobs, a list of Rulesets for primeModel jobs, a PrimeModel for primeDownloadValidation jobs, a PrimeFile for reasonCodesInitialization jobs, a ReasonCodesInitialization for reasonCodes jobs, a ReasonCodes for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization for predictionExplanations jobs, a PredictionExplanations for featureEffects, a FeatureEffects for featureFit, a FeatureFit
Raises:	JobNotFinished If the job is not finished, the result is not available. AsyncProcessUnsuccessfulError If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)¶

Parameters:	max_wait : int, optional How long to wait for the job to finish. params : dict, optional Query parameters to be added to request.
Returns:	result: object Return type is the same as would be returned by Job.get_result.
Raises:	AsyncTimeoutError If the job does not finish in time AsyncProcessUnsuccessfulError If the job errored or was aborted

wait_for_completion(max_wait=600)¶

Waits for job to complete.

Parameters:	max_wait : int, optional How long to wait for the job to finish.

class datarobot.models.FeatureImpactJob(data, completed_resource_url=None, with_metadata=False)¶

Custom Feature Impact job to handle different return value structures.

The original implementation had just the the data and the new one also includes some metadata.

In general, we aim to keep the number of Job classes low by just utilizing the job_type attribute to control any specific formatting; however in this case when we needed to support a new representation with the _same_ job_type, customzing the behavior of _make_result_from_location allowed us to achieve our ends without complicating the _make_result_from_json method.

classmethod get(project_id, job_id, with_metadata=False)¶

Fetches one job.

Parameters:	project_id : str The identifier of the project in which the job resides job_id : str The job id with_metadata : bool To make this job return the metadata (i.e. the full object of the completed resource) set the with_metadata flag to True.
Returns:	job : Job The job
Raises:	AsyncFailureError Querying this resource gave a status code other than 200 or 303

cancel()¶: Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)¶

Parameters:	params : dict or None Query parameters to be added to request to get results. For featureEffects and featureFit, source param is required to define source, otherwise the default is `training`
Returns:	result : object Return type depends on the job type: for model jobs, a Model is returned for predict jobs, a pandas.DataFrame (with predictions) is returned for featureImpact jobs, a list of dicts by default (see `with_metadata` parameter of the `FeatureImpactJob` class and its `get()` method). for primeRulesets jobs, a list of Rulesets for primeModel jobs, a PrimeModel for primeDownloadValidation jobs, a PrimeFile for reasonCodesInitialization jobs, a ReasonCodesInitialization for reasonCodes jobs, a ReasonCodes for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization for predictionExplanations jobs, a PredictionExplanations for featureEffects, a FeatureEffects for featureFit, a FeatureFit
Raises:	JobNotFinished If the job is not finished, the result is not available. AsyncProcessUnsuccessfulError If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)¶

Parameters:	max_wait : int, optional How long to wait for the job to finish. params : dict, optional Query parameters to be added to request.
Returns:	result: object Return type is the same as would be returned by Job.get_result.
Raises:	AsyncTimeoutError If the job does not finish in time AsyncProcessUnsuccessfulError If the job errored or was aborted

refresh()¶: Update this object with the latest job data from the server.

wait_for_completion(max_wait=600)¶

Waits for job to complete.

Parameters:	max_wait : int, optional How long to wait for the job to finish.

Lift Chart¶

class datarobot.models.lift_chart.LiftChart(source, bins, source_model_id, target_class)¶

Lift chart data for model.

Notes

LiftChartBin is a dict containing the following:

actual (float) Sum of actual target values in bin

predicted (float) Sum of predicted target values in bin

bin_weight (float) The weight of the bin. For weighted projects, it is the sum of the weights of the rows in the bin. For unweighted projects, it is the number of rows in the bin.

Attributes:

source : str: Lift chart data source. Can be ‘validation’, ‘crossValidation’ or ‘holdout’.
bins : list of dict: List of dicts with schema described as LiftChartBin above.
source_model_id : str: ID of the model this lift chart represents; in some cases, insights from the parent of a frozen model may be used
target_class : str, optional: For multiclass lift - target class for this lift chart data.

Missing Values Report¶

class datarobot.models.missing_report.MissingValuesReport(missing_values_report)¶

Missing values report for model, contains list of reports per feature sorted by missing count in descending order.

Notes

Report per feature contains:

feature : feature name.
type : feature type – ‘Numeric’ or ‘Categorical’.
missing_count : missing values count in training data.
missing_percentage : missing values percentage in training data.
tasks : list of information per each task, which was applied to feature.

task information contains:

id : a number of task in the blueprint diagram.
name : task name.
descriptions : human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.

classmethod get(project_id, model_id)¶

Retrieve a missing report.

Parameters:	project_id : str The project’s id. model_id : str The model’s id.
Returns:	MissingValuesReport The queried missing report.

Models¶

Model¶

class datarobot.models.Model(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, model_type=None, model_category=None, is_frozen=None, is_n_clusters_dynamically_determined=None, blueprint_id=None, metrics=None, project=None, data=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, n_clusters=None, has_empty_clusters=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, model_number=None, parent_model_id=None, use_project_settings=None, supports_composable_ml=None)¶

A model trained on a project’s dataset capable of making predictions

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

id : str: the id of the model
project_id : str: the id of the project the model belongs to
processes : list of str: the processes used by the model
featurelist_name : str: the name of the featurelist used by the model
featurelist_id : str: the id of the featurelist used by the model
sample_pct : float or None: the percentage of the project dataset used in training the model. If the project uses datetime partitioning, the sample_pct will be None. See training_row_count, training_duration, and training_start_date and training_end_date instead.
training_row_count : int or None: the number of rows of the project dataset used in training the model. In a datetime partitioned project, if specified, defines the number of rows used to train the model and evaluate backtest scores; if unspecified, either training_duration or training_start_date and training_end_date was used to determine that instead.
training_duration : str or None: only present for models in datetime partitioned projects. If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
training_start_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
training_end_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
model_type : str: what model this is, e.g. ‘Nystroem Kernel SVM Regressor’
model_category : str: what kind of model this is - ‘prime’ for DataRobot Prime models, ‘blend’ for blender models, and ‘model’ for other models
is_frozen : bool: whether this model is a frozen model
is_n_clusters_dynamically_determined : bool: (New in version v2.27) optional, if this model determines number of clusters dynamically
blueprint_id : str: the id of the blueprint used in this model
metrics : dict: a mapping from each metric to the model’s scores for that metric
monotonic_increasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced.
monotonic_decreasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced.
n_clusters : int: (New in version v2.27) optional, number of data clusters discovered by model
has_empty_clusters: bool: (New in version v2.27) optional, whether clustering models produces empty clusters.
supports_monotonic_constraints : bool: optinonal, whether this model supports enforcing monotonic constraints
is_starred : bool: whether this model marked as starred
prediction_threshold : float: for binary classification projects, the threshold used for predictions
prediction_threshold_read_only : bool: indicated whether modification of the prediction threshold is forbidden. Threshold modification is forbidden once a model has had a deployment created or predictions made via the dedicated prediction API.
model_number : integer: model number assigned to a model
parent_model_id : str or None: (New in version v2.20) the id of the model that tuning parameters are derived from
use_project_settings : bool or None: (New in version v2.20) Only present for models in datetime-partitioned projects. If True, indicates that the custom backtest partitioning settings specified by the user were used to train the model and evaluate backtest scores.
supports_composable_ml : bool or None: (New in version v2.26) whether this model is supported in the Composable ML.

classmethod get(project, model_id)¶

Retrieve a specific model.

Parameters:	project : str The project’s id. model_id : str The `model_id` of the leaderboard item to retrieve.
Returns:	model : Model The queried instance.
Raises:	ValueError passed `project` parameter value is of not supported type

classmethod fetch_resource_data(url, join_endpoint=True)¶

(Deprecated.) Used to acquire model data directly from its url.

Consider using get instead, as this is a convenience function used for development of datarobot

Parameters:	url : str The resource we are acquiring join_endpoint : boolean, optional Whether the client’s endpoint should be joined to the URL before sending the request. Location headers are returned as absolute locations, so will _not_ need the endpoint
Returns:	model_data : dict The queried model’s data

get_features_used()¶

Query the server to determine which features were used.

Note that the data returned by this method is possibly different than the names of the features in the featurelist used by this model. This method will return the raw features that must be supplied in order for predictions to be generated on a new set of data. The featurelist, in contrast, would also include the names of derived features.

Returns:	features : list of str The names of the features used in the model.

get_supported_capabilities()¶

Retrieves a summary of the capabilities supported by a model.

New in version v2.14.

Returns:

supportsBlending: bool

whether the model supports blending

supportsMonotonicConstraints: bool

whether the model supports monotonic constraints

hasWordCloud: bool

whether the model has word cloud data available

eligibleForPrime: bool

whether the model is eligible for Prime

hasParameters: bool

whether the model has parameters that can be retrieved

supportsCodeGeneration: bool

(New in version v2.18) whether the model supports code generation

supportsShap: bool

(New in version v2.18) True if the model supports Shapley package. i.e. Shapley based: feature Importance

supportsEarlyStopping: bool

(New in version v2.22) True if this is an early stopping tree-based model and number of trained iterations can be retrieved.

get_num_iterations_trained()¶

Retrieves the number of estimators trained by early-stopping tree-based models.

– versionadded:: v2.22

Returns:	projectId: str id of project containing the model modelId: str id of the model data: array list of numEstimatorsItem objects, one for each modeling stage. numEstimatorsItem will be of the form: stage: str indicates the modeling stage (for multi-stage models); None of single-stage models numIterations: int the number of estimators or iterations trained by the model

delete()¶: Delete a model from the project’s leaderboard.

get_leaderboard_ui_permalink()¶

Returns:	url : str Permanent static hyperlink to this model at leaderboard.

open_model_browser()¶

Opens model at project leaderboard in web browser.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>)¶

Train the blueprint used in model on a particular featurelist or amount of data.

This method creates a new training job for worker and appends it to the end of the queue for this project. After the job has finished you can get the newly trained model by retrieving it from the project leaderboard, or by retrieving the result of the job.

Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither are specified, a default of the maximum amount of data that can safely be used to train any blueprint without going into the validation data will be selected.

In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class.

Note

For datetime partitioned projects, see train_datetime instead.

Parameters:

sample_pct : float, optional: The amount of data to use for training, as a percentage of the project dataset from 0 to 100.
featurelist_id : str, optional: The identifier of the featurelist to use. If not defined, the featurelist of this model is used.
scoring_type : str, optional: Either SCORING_TYPE.validation or SCORING_TYPE.cross_validation. SCORING_TYPE.validation is available for every partitioning type, and indicates that the default model validation should be used for the project. If the project uses a form of cross-validation partitioning, SCORING_TYPE.cross_validation can also be used to indicate that all of the available training/validation combinations should be used to evaluate the model.
training_row_count : int, optional: The number of rows to use to train the requested model.
monotonic_increasing_featurelist_id : str: (new in version 2.11) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id : str: (new in version 2.11) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.

Returns:

model_job_id : str: id of created job, can be used as parameter to ModelJob.get method or wait_for_async_model_creation function

Examples

project = Project.get('project-id')
model = Model.get('project-id', 'model-id')
model_job_id = model.train(training_row_count=project.max_train_rows)

train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>, use_project_settings=False, sampling_method=None)¶

Trains this model on a different featurelist or sample size.

Requires that this model is part of a datetime partitioned project; otherwise, an error will occur.

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

featurelist_id : str, optional: the featurelist to use to train the model. If not specified, the featurelist of this model is used.
training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, neither training_duration nor use_project_settings may be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, neither training_row_count nor use_project_settings may be specified.
use_project_settings : bool, optional: (New in version v2.20) defaults to False. If True, indicates that the custom backtest partitioning settings specified by the user will be used to train the model and evaluate backtest scores. If specified, neither training_row_count nor training_duration may be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.
monotonic_increasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.

Returns:

job : ModelJob: the created job to build the model

retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None)¶

Submit a job to the queue to train a blender model.

Parameters:

sample_pct: str, optional: The sample size in percents (1 to 100) to use in training. If this parameter is used then training_row_count should not be given.
featurelist_id : str, optional: The featurelist id
training_row_count : str, optional: The number of rows used to train the model. If this parameter is used, then sample_pct should not be given.
n_clusters: int, optional: (new in version 2.27) number of clusters to use in an unsupervised clustering model. This parameter is used only for unsupervised clustering models that do not determine the number of clusters automatically.

Returns:

job : ModelJob: The created job that is retraining the model

request_predictions(dataset_id, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None)¶

Requests predictions against a previously uploaded dataset.

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
include_prediction_intervals : bool, optional: (New in v2.16) For time series projects only. Specifies whether prediction intervals should be calculated for this request. Defaults to True if prediction_intervals_size is specified, otherwise defaults to False.
prediction_intervals_size : int, optional: (New in v2.16) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if include_prediction_intervals is True. Prediction intervals size must be between 1 and 100 (inclusive).
forecast_point : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
predictions_start_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
explanation_algorithm: (New in version v2.21) optional; If set to ‘shap’, the: response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).
max_explanations: (New in version v2.21) optional; specifies the maximum number of: explanation values that should be returned for each row, ordered by absolute value, greatest to least. If null, no limit. In the case of ‘shap’: if the number of features is greater than the limit, the sum of remaining values will also be returned as shapRemainingTotal. Defaults to null. Cannot be set if explanation_algorithm is omitted.

Returns:

job : PredictJob: The job computing the predictions

get_feature_impact(with_metadata=False)¶

Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model.

Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features.

If a feature is a redundant feature, i.e. once other features are considered it doesn’t contribute much in addition, the ‘redundantWith’ value is the name of feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used.

Elsewhere this technique is sometimes called ‘Permutation Importance’.

Requires that Feature Impact has already been computed with request_feature_impact.

Parameters:	with_metadata : bool The flag indicating if the result should include the metadata as well.
Returns:	list or dict The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with actual data or just a list with that data. Each List item is a dict with the keys `featureName`, `impactNormalized`, and `impactUnnormalized`, `redundantWith` and `count`. For dict response available keys are: `featureImpacts` - Feature Impact data as a dictionary. Each item is a dict with keys: `featureName`, `impactNormalized`, and `impactUnnormalized`, and `redundantWith`. `shapBased` - A boolean that indicates whether Feature Impact was calculated using Shapley values. `ranRedundancyDetection` - A boolean that indicates whether redundant feature identification was run while calculating this Feature Impact. `rowCount` - An integer or None that indicates the number of rows that was used to calculate Feature Impact. For the Feature Impact calculated with the default logic, without specifying the rowCount, we return None here. `count` - An integer with the number of features under the `featureImpacts`.
Raises:	ClientError (404) If the feature impacts have not been computed.

get_multiclass_feature_impact()¶

For multiclass it’s possible to calculate feature impact separately for each target class. The method for calculation is exactly the same, calculated in one-vs-all style for each target class.

Requires that Feature Impact has already been computed with request_feature_impact.

Returns:	feature_impacts : list of dict The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’.
Raises:	ClientError (404) If the multiclass feature impacts have not been computed.

request_feature_impact(row_count=None, with_metadata=False)¶

Request feature impacts to be computed for the model.

See get_feature_impact for more information on the result of the job.

Parameters:	row_count : int The sample size (specified in rows) to use for Feature Impact computation. This is not supported for unsupervised, multi-class (that has a separate method) and time series projects.
Returns:	job : Job A Job representing the feature impact computation. To get the completed feature impact data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature impacts have already been requested.

request_external_test(dataset_id, actual_value_column=None)¶

Request external test to compute scores and insights on an external test dataset

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
Returns
——-
job : Job: a Job representing external dataset insights computation

get_or_request_feature_impact(max_wait=600, **kwargs)¶

Retrieve feature impact for the model, requesting a job if it hasn’t been run previously

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature impact job to complete before erroring **kwargs Arbitrary keyword arguments passed to `request_feature_impact`.
Returns:	feature_impacts : list or dict The feature impact data. See `get_feature_impact` for the exact schema.

get_feature_effect_metadata()¶

Retrieve Feature Effects metadata. Response contains status and available model sources.

Feature Fit for the training partition is always available, with the exception of older projects that only supported Feature Fit for validation.
When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout.
Feature Effects for holdout is not available when holdout was not unlocked for the project.

Use source to retrieve Feature Effects, selecting one of the provided sources.

Returns:	feature_effect_metadata: FeatureEffectMetadata

get_feature_fit_metadata()¶

Retrieve Feature Fit metadata. Response contains status and available model sources.

Feature Fit of training is always available (except for the old project which supports only Feature Fit for validation).
When a model is trained into validation or holdout without stacked prediction (e.g. no out-of-sample prediction in validation or holdout), Feature Fit is not available for validation or holdout.
Feature Fit for holdout is not available when there is no holdout configured for the project.

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

Returns:	feature_effect_metadata: FeatureFitMetadata

request_feature_effect(row_count=None)¶

Request feature effects to be computed for the model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less.
Returns:	job : Job A Job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None)¶

Request Feature Effects computation for the multiclass model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int The number of rows from dataset to use for Feature Impact calculation. top_n_features : int or None Number of top features (ranked by feature impact) used to calculate Feature Effects. features : list or None The list of features used to calculate Feature Effects.
Returns:	job : Job A Job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete.

get_feature_effect(source, include_ice_plots=False)¶

Retrieve Feature Effects for the model.

Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Effects are retrieved for. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_effects : FeatureEffects The feature effects data.
Raises:	ClientError (404) If the feature effects have not been computed or source is not valid value.

get_feature_effects_multiclass(source='training', class_=None)¶

Retrieve Feature Effects for the multiclass model.

Feature Effects provide partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : str The source Feature Effects are retrieved for. class_ : str or None The class name Feature Effects are retrieved for.
Returns:	list The list of multiclass feature effects.
Raises:	ClientError (404) If Feature Effects have not been computed or source is not valid value.

get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600)¶

Retrieve Feature Effects for the multiclass model, requesting a job if it hasn’t been run previously.

Parameters:

source : string: The source Feature Effects retrieve for.
class_ : str or None: The class name Feature Effects retrieve for.
row_count : int: The number of rows from dataset to use for Feature Impact calculation.
top_n_features : int or None: Number of top features (ranked by Feature Impact) used to calculate Feature Effects.
features : list or None: The list of features used to calculate Feature Effects.
max_wait : int, optional: The maximum time to wait for a requested Feature Effects job to complete before erroring.

Returns:

feature_effects : list of FeatureEffectsMulticlass: The list of multiclass feature effects data.

get_or_request_feature_effect(source, max_wait=600, row_count=None)¶

Retrieve feature effect for the model, requesting a job if it hasn’t been run previously

See get_feature_effect_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature effect job to complete before erroring row_count : int, optional (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less. source : string The source Feature Effects are retrieved for.
Returns:	feature_effects : FeatureEffects The feature effects data.

request_feature_fit()¶

Request feature fit to be computed for the model.

See get_feature_effect for more information on the result of the job.

Returns:	job : Job A Job representing the feature fit computation. To get the completed feature fit data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

get_feature_fit(source, include_ice_plots=False)¶

Retrieve Feature Fit for the model.

Feature Fit provides partial dependence and predicted vs actual values for top-500 features ordered by feature importance score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Fit has already been computed with request_feature_effect.

See get_feature_fit_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources]. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_fit : FeatureFit The feature fit data.
Raises:	ClientError (404) If the feature fit have not been computed or source is not valid value.

get_or_request_feature_fit(source, max_wait=600)¶

Retrieve feature fit for the model, requesting a job if it hasn’t been run previously

See get_feature_fit_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature fit job to complete before erroring source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources].
Returns:	feature_effects : FeatureFit The feature fit data.

get_prime_eligibility()¶

Check if this model can be approximated with DataRobot Prime

Returns:	prime_eligibility : dict a dict indicating whether a model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message)

request_approximation()¶

Request an approximation of this model using DataRobot Prime

This will create several rulesets that could be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally.

Returns:	job : Job the job generating the rulesets

get_rulesets()¶

List the rulesets approximating this model generated by DataRobot Prime

If this model hasn’t been approximated yet, will return an empty list. Note that these are rulesets approximating this model, not rulesets used to construct this model.

Returns:	rulesets : list of Ruleset

download_export(filepath)¶

Download an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

Parameters:	filepath : str The path at which to save the exported model file.

request_transferable_export(prediction_intervals_size=None)¶

Request generation of an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

This function does not download the exported file. Use download_export for that.

Parameters:	prediction_intervals_size : int, optional (New in v2.19) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Prediction intervals size must be between 1 and 100 (inclusive).

Examples

model = datarobot.Model.get('project-id', 'model-id')
job = model.request_transferable_export()
job.wait_for_completion()
model.download_export('my_exported_model.drmodel')

# Client must be configured to use standalone prediction server for import:
datarobot.Client(token='my-token-at-standalone-server',
                 endpoint='standalone-server-url/api/v2')

imported_model = datarobot.ImportedModel.create('my_exported_model.drmodel')

request_frozen_model(sample_pct=None, training_row_count=None)¶

Train a new frozen model with parameters from this model

Note

This method only works if project the model belongs to is not datetime partitioned. If it is, use request_frozen_datetime_model instead.

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

Parameters:	sample_pct : float optional, the percentage of the dataset to use with the model. If not provided, will use the value from this model. training_row_count : int (New in version v2.9) optional, the integer number of rows of the dataset to use with the model. Only one of sample_pct and training_row_count should be specified.
Returns:	model_job : ModelJob the modeling job training a frozen model

request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None)¶

Train a new frozen model with parameters from this model.

Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job.

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

In addition of training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified.

Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked).

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, training_duration may not be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, training_row_count may not be specified.
training_start_date : datetime.datetime, optional: the start date of the data to train to model on. Only rows occurring at or after this datetime will be used. If training_start_date is specified, training_end_date must also be specified.
training_end_date : datetime.datetime, optional: the end date of the data to train the model on. Only rows occurring strictly before this datetime will be used. If training_end_date is specified, training_start_date must also be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.

Returns:

model_job : ModelJob: the modeling job training a frozen model

get_parameters()¶

Retrieve model parameters.

Returns:	ModelParameters Model parameters for this model.

get_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve the model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	LiftChart Model lift chart data
Raises:	ClientError If the insight is not available for this model

get_all_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_multiclass_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_all_multiclass_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_multilabel_lift_charts(source, fallback_to_parent_insights=False)¶

Retrieve model Lift charts for the specified source.

New in version v2.24.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_residuals_chart(source, fallback_to_parent_insights=False)¶

Retrieve model residuals chart for the specified source.

Parameters:	source : str Residuals chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent if the residuals chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return residuals data from this model’s parent.
Returns:	ResidualsChart Model residuals chart data
Raises:	ClientError If the insight is not available for this model

get_all_residuals_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ResidualsChart Data for all available model residuals charts.

get_pareto_front()¶

Retrieve the Pareto Front for a Eureqa model.

This method is only supported for Eureqa models.

Returns:	ParetoFront Model ParetoFront data

get_confusion_chart(source, fallback_to_parent_insights=False)¶

Retrieve them model’s confusion matrix for the specified source.

Parameters:	source : str Confusion chart source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent if the confusion chart is not available for this model and the defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	ConfusionChart Model ConfusionChart data
Raises:	ClientError If the insight is not available for this model

get_all_confusion_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all confusion matrices available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ConfusionChart Data for all available confusion charts for model.

get_roc_curve(source, fallback_to_parent_insights=False)¶

Retrieve the ROC curve for the specified source.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	RocCurve Model ROC curve data
Raises:	ClientError If the insight is not available for this model

get_all_roc_curves(fallback_to_parent_insights=False)¶

Retrieve a list of all ROC curves available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of RocCurve Data for all available model ROC curves.

get_labelwise_roc_curves(source, fallback_to_parent_insights=False)¶

Retrieve a list of LabelwiseRocCurve instances for the given source and all labels.

New in version v2.24.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	list of : class:LabelwiseRocCurve <datarobot.models.roc_curve.LabelwiseRocCurve> Labelwise ROC Curve instances for `source` and all labels
Raises:	ClientError If the insight is not available for this model

get_word_cloud(exclude_stop_words=False)¶

Retrieve word cloud data for the model.

Parameters:	exclude_stop_words : bool, optional Set to True if you want stopwords filtered out of response.
Returns:	WordCloud Word cloud data for the model.

download_scoring_code(file_name, source_code=False)¶

Download the Scoring Code JAR.

Parameters:	file_name : str File path where scoring code will be saved. source_code : bool, optional Set to True to download source code archive. It will not be executable.

get_model_blueprint_documents()¶

Get documentation for tasks used in this model.

Returns:	list of BlueprintTaskDocument All documents available for the model.

get_model_blueprint_chart()¶

Retrieve a diagram that can be used to understand data flow in the blueprint.

Returns:	ModelBlueprintChart The queried model blueprint chart.

get_missing_report_info()¶

Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model.

Returns:	An iterable of MissingReportPerFeature The queried model missing report, sorted by missing count (DESCENDING order).

get_frozen_child_models()¶

Retrieve the IDs for all models that are frozen from this model.

Returns:	A list of Models

request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None)¶

Start a job to build training predictions

Parameters:

data_subset : str

data set definition to build predictions on. Choices are:

dr.enums.DATA_SUBSET.ALL or string all for all data available. Not valid for

models in datetime partitioned projects

dr.enums.DATA_SUBSET.VALIDATION_AND_HOLDOUT or string validationAndHoldout for

all data except training set. Not valid for models in datetime partitioned projects

dr.enums.DATA_SUBSET.HOLDOUT or string holdout for holdout data set only

dr.enums.DATA_SUBSET.ALL_BACKTESTS or string allBacktests for downloading

the predictions for all backtest validation folds. Requires the model to have successfully scored all backtests. Datetime partitioned projects only.

explanation_algorithm : dr.enums.EXPLANATIONS_ALGORITHM

(New in v2.21) Optional. If set to dr.enums.EXPLANATIONS_ALGORITHM.SHAP, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to None (no prediction explanations).

max_explanations : int

(New in v2.21) Optional. Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of dr.enums.EXPLANATIONS_ALGORITHM.SHAP: If not set, explanations are returned for all features. If the number of features is greater than the max_explanations, the sum of remaining values will also be returned as shap_remaining_total. Max 100. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Is ignored if explanation_algorithm is not set.

Returns:

Job: an instance of created async job

cross_validate()¶

Run cross validation on the model.

Note

To perform Cross Validation on a new model with new parameters, use train instead.

Returns:	ModelJob The created job to build the model

get_cross_validation_scores(partition=None, metric=None)¶

Return a dictionary, keyed by metric, showing cross validation scores per partition.

Cross Validation should already have been performed using cross_validate or train.

Note

Models that computed cross validation before this feature was added will need to be deleted and retrained before this method can be used.

Parameters:	partition : float optional, the id of the partition (1,2,3.0,4.0,etc…) to filter results by can be a whole number positive integer or float value. 0 corresponds to the validation partition. metric: unicode optional name of the metric to filter to resulting cross validation scores by
Returns:	cross_validation_scores: dict A dictionary keyed by metric showing cross validation scores per partition.

advanced_tune(params, description=None)¶

Generate a new model with the specified advanced-tuning parameters

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Parameters:	params : dict Mapping of parameter ID to parameter value. The list of valid parameter IDs for a model can be found by calling get_advanced_tuning_parameters(). This endpoint does not need to include values for all parameters. If a parameter is omitted, its current_value will be used. description : unicode Human-readable string describing the newly advanced-tuned model
Returns:	ModelJob The created job to build the model

get_advanced_tuning_parameters()¶

Get the advanced-tuning parameters available for this model.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:

dict

A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuningDescription and tuningParameters.

tuningDescription an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter.

tuningParameters is a list of a dicts, each has the following keys

parameterName : (unicode) name of the parameter (unique per task, see below)
parameterId : (unicode) opaque ID string uniquely identifying parameter
defaultValue : (*) default value of the parameter for the blueprint
currentValue : (*) value of the parameter that was used for this model
taskName : (unicode) name of the task that this parameter belongs to
constraints: (dict) see the notes below

Notes

The type of defaultValue and currentValue is defined by the constraints structure. It will be a string or numeric Python type.

constraints is a dict with at least one, possibly more, of the following keys. The presence of a key indicates that the parameter may take on the specified type. (If a key is absent, this means that the parameter may not take on the specified type.) If a key on constraints is present, its value will be a dict containing all of the fields described below for that key.

"constraints": {
    "select": {
        "values": [<list(basestring or number) : possible values>]
    },
    "ascii": {},
    "unicode": {},
    "int": {
        "min": <int : minimum valid value>,
        "max": <int : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "float": {
        "min": <float : minimum valid value>,
        "max": <float : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "intList": {
        "length": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <int : minimum valid value>,
        "max_val": <int : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "floatList": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <float : minimum valid value>,
        "max_val": <float : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    }
}

The keys have meaning as follows:

select: Rather than specifying a specific data type, if present, it indicates that the parameter is permitted to take on any of the specified values. Listed values may be of any string or real (non-complex) numeric type.
ascii: The parameter may be a unicode object that encodes simple ASCII characters. (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed constraints, ASCII keys currently may not contain either newlines or semicolons.
unicode: The parameter may be any Python unicode object.
int: The value may be an object of type int within the specified range (inclusive). Please note that the value will be passed around using the JSON format, and some JSON parsers have undefined behavior with integers outside of the range [-(2**53)+1, (2**53)-1].
float: The value may be an object of type float within the specified range (inclusive).
intList, floatList: The value may be a list of int or float objects, respectively, following constraints as specified respectively by the int and float types (above).

Many parameters only specify one key under constraints. If a parameter specifies multiple keys, the parameter may take on any value permitted by any key.

start_advanced_tuning_session()¶

Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:	AdvancedTuningSession Session for setting up and running Advanced Tuning on a model

star_model()¶

Mark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

unstar_model()¶

Unmark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

set_prediction_threshold(threshold)¶

Set a custom prediction threshold for the model.

May not be used once prediction_threshold_read_only is True for this model.

Parameters:	threshold : float only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive).

download_training_artifact(file_name)¶

Retrieve trained artifact(s) from a model containing one or more custom tasks.

Artifact(s) will be downloaded to the specified local filepath.

Parameters:	file_name : str File path where trained model artifact(s) will be saved.

request_fairness_insights(fairness_metrics_set=None)¶

Request fairness insights to be computed for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores.
Returns:	status_id : str A statusId of computation request.

get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100)¶

Retrieve a list of Per Class Bias insights for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores. offset : int, optional Number of items to skip. limit : int, optional Number of items to return.
Returns:	json

request_data_disparity_insights(feature, compared_class_names)¶

Request data disparity insights to be computed for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. compared_class_names : list(str) List of two classes to compare
Returns:	status_id : str A statusId of computation request.

get_data_disparity_insights(feature, class_name1, class_name2)¶

Retrieve a list of Cross Class Data Disparity insights for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. class_name1 : str One of the compared classes class_name2 : str Another compared class
Returns:	json

request_cross_class_accuracy_scores()¶

Request data disparity insights to be computed for the model.

Returns:	status_id : str A statusId of computation request.

get_cross_class_accuracy_scores()¶

Retrieves a list of Cross Class Accuracy scores for the model.

Returns:	json

PrimeModel¶

class datarobot.models.PrimeModel(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, model_type=None, model_category=None, is_frozen=None, blueprint_id=None, metrics=None, parent_model_id=None, ruleset_id=None, rule_count=None, score=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, model_number=None, supports_composable_ml=None)¶

Represents a DataRobot Prime model approximating a parent model with downloadable code.

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

id : str: the id of the model
project_id : str: the id of the project the model belongs to
processes : list of str: the processes used by the model
featurelist_name : str: the name of the featurelist used by the model
featurelist_id : str: the id of the featurelist used by the model
sample_pct : float: the percentage of the project dataset used in training the model
training_row_count : int or None: the number of rows of the project dataset used in training the model. In a datetime partitioned project, if specified, defines the number of rows used to train the model and evaluate backtest scores; if unspecified, either training_duration or training_start_date and training_end_date was used to determine that instead.
training_duration : str or None: only present for models in datetime partitioned projects. If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
training_start_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
training_end_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
model_type : str: what model this is, e.g. ‘DataRobot Prime’
model_category : str: what kind of model this is - always ‘prime’ for DataRobot Prime models
is_frozen : bool: whether this model is a frozen model
blueprint_id : str: the id of the blueprint used in this model
metrics : dict: a mapping from each metric to the model’s scores for that metric
ruleset : Ruleset: the ruleset used in the Prime model
parent_model_id : str: the id of the model that this Prime model approximates
monotonic_increasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced.
monotonic_decreasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced.
supports_monotonic_constraints : bool: optional, whether this model supports enforcing monotonic constraints
is_starred : bool: whether this model is marked as starred
prediction_threshold : float: for binary classification projects, the threshold used for predictions
prediction_threshold_read_only : bool: indicated whether modification of the prediction threshold is forbidden. Threshold modification is forbidden once a model has had a deployment created or predictions made via the dedicated prediction API.
supports_composable_ml : bool or None: (New in version v2.26) whether this model is supported in the Composable ML.

classmethod get(project_id, model_id)¶

Retrieve a specific prime model.

Parameters:	project_id : str The id of the project the prime model belongs to model_id : str The `model_id` of the prime model to retrieve.
Returns:	model : PrimeModel The queried instance.

request_download_validation(language)¶

Prep and validate the downloadable code for the ruleset associated with this model.

Parameters:	language : str the language the code should be downloaded in - see `datarobot.enums.PRIME_LANGUAGE` for available languages
Returns:	job : Job A job tracking the code preparation and validation

advanced_tune(params, description=None)¶

Generate a new model with the specified advanced-tuning parameters

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Parameters:	params : dict Mapping of parameter ID to parameter value. The list of valid parameter IDs for a model can be found by calling get_advanced_tuning_parameters(). This endpoint does not need to include values for all parameters. If a parameter is omitted, its current_value will be used. description : unicode Human-readable string describing the newly advanced-tuned model
Returns:	ModelJob The created job to build the model

cross_validate()¶

Run cross validation on the model.

Note

To perform Cross Validation on a new model with new parameters, use train instead.

Returns:	ModelJob The created job to build the model

delete()¶: Delete a model from the project’s leaderboard.

download_export(filepath)¶

Download an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

Parameters:	filepath : str The path at which to save the exported model file.

download_scoring_code(file_name, source_code=False)¶

Download the Scoring Code JAR.

Parameters:	file_name : str File path where scoring code will be saved. source_code : bool, optional Set to True to download source code archive. It will not be executable.

download_training_artifact(file_name)¶

Retrieve trained artifact(s) from a model containing one or more custom tasks.

Artifact(s) will be downloaded to the specified local filepath.

Parameters:	file_name : str File path where trained model artifact(s) will be saved.

classmethod fetch_resource_data(url, join_endpoint=True)¶

(Deprecated.) Used to acquire model data directly from its url.

Consider using get instead, as this is a convenience function used for development of datarobot

Parameters:	url : str The resource we are acquiring join_endpoint : boolean, optional Whether the client’s endpoint should be joined to the URL before sending the request. Location headers are returned as absolute locations, so will _not_ need the endpoint
Returns:	model_data : dict The queried model’s data

get_advanced_tuning_parameters()¶

Get the advanced-tuning parameters available for this model.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:

dict

A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuningDescription and tuningParameters.

tuningDescription an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter.

tuningParameters is a list of a dicts, each has the following keys

parameterName : (unicode) name of the parameter (unique per task, see below)
parameterId : (unicode) opaque ID string uniquely identifying parameter
defaultValue : (*) default value of the parameter for the blueprint
currentValue : (*) value of the parameter that was used for this model
taskName : (unicode) name of the task that this parameter belongs to
constraints: (dict) see the notes below

Notes

The type of defaultValue and currentValue is defined by the constraints structure. It will be a string or numeric Python type.

constraints is a dict with at least one, possibly more, of the following keys. The presence of a key indicates that the parameter may take on the specified type. (If a key is absent, this means that the parameter may not take on the specified type.) If a key on constraints is present, its value will be a dict containing all of the fields described below for that key.

"constraints": {
    "select": {
        "values": [<list(basestring or number) : possible values>]
    },
    "ascii": {},
    "unicode": {},
    "int": {
        "min": <int : minimum valid value>,
        "max": <int : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "float": {
        "min": <float : minimum valid value>,
        "max": <float : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "intList": {
        "length": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <int : minimum valid value>,
        "max_val": <int : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "floatList": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <float : minimum valid value>,
        "max_val": <float : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    }
}

The keys have meaning as follows:

select: Rather than specifying a specific data type, if present, it indicates that the parameter is permitted to take on any of the specified values. Listed values may be of any string or real (non-complex) numeric type.
ascii: The parameter may be a unicode object that encodes simple ASCII characters. (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed constraints, ASCII keys currently may not contain either newlines or semicolons.
unicode: The parameter may be any Python unicode object.
int: The value may be an object of type int within the specified range (inclusive). Please note that the value will be passed around using the JSON format, and some JSON parsers have undefined behavior with integers outside of the range [-(2**53)+1, (2**53)-1].
float: The value may be an object of type float within the specified range (inclusive).
intList, floatList: The value may be a list of int or float objects, respectively, following constraints as specified respectively by the int and float types (above).

Many parameters only specify one key under constraints. If a parameter specifies multiple keys, the parameter may take on any value permitted by any key.

get_all_confusion_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all confusion matrices available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ConfusionChart Data for all available confusion charts for model.

get_all_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_multiclass_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_residuals_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ResidualsChart Data for all available model residuals charts.

get_all_roc_curves(fallback_to_parent_insights=False)¶

Retrieve a list of all ROC curves available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of RocCurve Data for all available model ROC curves.

get_confusion_chart(source, fallback_to_parent_insights=False)¶

Retrieve them model’s confusion matrix for the specified source.

Parameters:	source : str Confusion chart source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent if the confusion chart is not available for this model and the defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	ConfusionChart Model ConfusionChart data
Raises:	ClientError If the insight is not available for this model

get_cross_class_accuracy_scores()¶

Retrieves a list of Cross Class Accuracy scores for the model.

Returns:	json

get_cross_validation_scores(partition=None, metric=None)¶

Return a dictionary, keyed by metric, showing cross validation scores per partition.

Cross Validation should already have been performed using cross_validate or train.

Note

Models that computed cross validation before this feature was added will need to be deleted and retrained before this method can be used.

Parameters:	partition : float optional, the id of the partition (1,2,3.0,4.0,etc…) to filter results by can be a whole number positive integer or float value. 0 corresponds to the validation partition. metric: unicode optional name of the metric to filter to resulting cross validation scores by
Returns:	cross_validation_scores: dict A dictionary keyed by metric showing cross validation scores per partition.

get_data_disparity_insights(feature, class_name1, class_name2)¶

Retrieve a list of Cross Class Data Disparity insights for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. class_name1 : str One of the compared classes class_name2 : str Another compared class
Returns:	json

get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100)¶

Retrieve a list of Per Class Bias insights for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores. offset : int, optional Number of items to skip. limit : int, optional Number of items to return.
Returns:	json

get_feature_effect(source, include_ice_plots=False)¶

Retrieve Feature Effects for the model.

Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Effects are retrieved for. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_effects : FeatureEffects The feature effects data.
Raises:	ClientError (404) If the feature effects have not been computed or source is not valid value.

get_feature_effect_metadata()¶

Retrieve Feature Effects metadata. Response contains status and available model sources.

Feature Fit for the training partition is always available, with the exception of older projects that only supported Feature Fit for validation.
When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout.
Feature Effects for holdout is not available when holdout was not unlocked for the project.

Use source to retrieve Feature Effects, selecting one of the provided sources.

Returns:	feature_effect_metadata: FeatureEffectMetadata

get_feature_effects_multiclass(source='training', class_=None)¶

Retrieve Feature Effects for the multiclass model.

Feature Effects provide partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : str The source Feature Effects are retrieved for. class_ : str or None The class name Feature Effects are retrieved for.
Returns:	list The list of multiclass feature effects.
Raises:	ClientError (404) If Feature Effects have not been computed or source is not valid value.

get_feature_fit(source, include_ice_plots=False)¶

Retrieve Feature Fit for the model.

Feature Fit provides partial dependence and predicted vs actual values for top-500 features ordered by feature importance score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Fit has already been computed with request_feature_effect.

See get_feature_fit_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources]. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_fit : FeatureFit The feature fit data.
Raises:	ClientError (404) If the feature fit have not been computed or source is not valid value.

get_feature_fit_metadata()¶

Retrieve Feature Fit metadata. Response contains status and available model sources.

Feature Fit of training is always available (except for the old project which supports only Feature Fit for validation).
When a model is trained into validation or holdout without stacked prediction (e.g. no out-of-sample prediction in validation or holdout), Feature Fit is not available for validation or holdout.
Feature Fit for holdout is not available when there is no holdout configured for the project.

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

Returns:	feature_effect_metadata: FeatureFitMetadata

get_feature_impact(with_metadata=False)¶

Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model.

Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features.

If a feature is a redundant feature, i.e. once other features are considered it doesn’t contribute much in addition, the ‘redundantWith’ value is the name of feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used.

Elsewhere this technique is sometimes called ‘Permutation Importance’.

Requires that Feature Impact has already been computed with request_feature_impact.

Parameters:	with_metadata : bool The flag indicating if the result should include the metadata as well.
Returns:	list or dict The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with actual data or just a list with that data. Each List item is a dict with the keys `featureName`, `impactNormalized`, and `impactUnnormalized`, `redundantWith` and `count`. For dict response available keys are: `featureImpacts` - Feature Impact data as a dictionary. Each item is a dict with keys: `featureName`, `impactNormalized`, and `impactUnnormalized`, and `redundantWith`. `shapBased` - A boolean that indicates whether Feature Impact was calculated using Shapley values. `ranRedundancyDetection` - A boolean that indicates whether redundant feature identification was run while calculating this Feature Impact. `rowCount` - An integer or None that indicates the number of rows that was used to calculate Feature Impact. For the Feature Impact calculated with the default logic, without specifying the rowCount, we return None here. `count` - An integer with the number of features under the `featureImpacts`.
Raises:	ClientError (404) If the feature impacts have not been computed.

get_features_used()¶

Query the server to determine which features were used.

Note that the data returned by this method is possibly different than the names of the features in the featurelist used by this model. This method will return the raw features that must be supplied in order for predictions to be generated on a new set of data. The featurelist, in contrast, would also include the names of derived features.

Returns:	features : list of str The names of the features used in the model.

get_frozen_child_models()¶

Retrieve the IDs for all models that are frozen from this model.

Returns:	A list of Models

get_labelwise_roc_curves(source, fallback_to_parent_insights=False)¶

Retrieve a list of LabelwiseRocCurve instances for the given source and all labels.

New in version v2.24.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	list of : class:LabelwiseRocCurve <datarobot.models.roc_curve.LabelwiseRocCurve> Labelwise ROC Curve instances for `source` and all labels
Raises:	ClientError If the insight is not available for this model

get_leaderboard_ui_permalink()¶

Returns:	url : str Permanent static hyperlink to this model at leaderboard.

get_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve the model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	LiftChart Model lift chart data
Raises:	ClientError If the insight is not available for this model

get_missing_report_info()¶

Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model.

Returns:	An iterable of MissingReportPerFeature The queried model missing report, sorted by missing count (DESCENDING order).

get_model_blueprint_chart()¶

Retrieve a diagram that can be used to understand data flow in the blueprint.

Returns:	ModelBlueprintChart The queried model blueprint chart.

get_model_blueprint_documents()¶

Get documentation for tasks used in this model.

Returns:	list of BlueprintTaskDocument All documents available for the model.

get_multiclass_feature_impact()¶

For multiclass it’s possible to calculate feature impact separately for each target class. The method for calculation is exactly the same, calculated in one-vs-all style for each target class.

Requires that Feature Impact has already been computed with request_feature_impact.

Returns:	feature_impacts : list of dict The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’.
Raises:	ClientError (404) If the multiclass feature impacts have not been computed.

get_multiclass_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_multilabel_lift_charts(source, fallback_to_parent_insights=False)¶

Retrieve model Lift charts for the specified source.

New in version v2.24.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_num_iterations_trained()¶

Retrieves the number of estimators trained by early-stopping tree-based models.

– versionadded:: v2.22

Returns:	projectId: str id of project containing the model modelId: str id of the model data: array list of numEstimatorsItem objects, one for each modeling stage. numEstimatorsItem will be of the form: stage: str indicates the modeling stage (for multi-stage models); None of single-stage models numIterations: int the number of estimators or iterations trained by the model

get_or_request_feature_effect(source, max_wait=600, row_count=None)¶

Retrieve feature effect for the model, requesting a job if it hasn’t been run previously

See get_feature_effect_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature effect job to complete before erroring row_count : int, optional (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less. source : string The source Feature Effects are retrieved for.
Returns:	feature_effects : FeatureEffects The feature effects data.

get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600)¶

Retrieve Feature Effects for the multiclass model, requesting a job if it hasn’t been run previously.

Parameters:

source : string: The source Feature Effects retrieve for.
class_ : str or None: The class name Feature Effects retrieve for.
row_count : int: The number of rows from dataset to use for Feature Impact calculation.
top_n_features : int or None: Number of top features (ranked by Feature Impact) used to calculate Feature Effects.
features : list or None: The list of features used to calculate Feature Effects.
max_wait : int, optional: The maximum time to wait for a requested Feature Effects job to complete before erroring.

Returns:

feature_effects : list of FeatureEffectsMulticlass: The list of multiclass feature effects data.

get_or_request_feature_fit(source, max_wait=600)¶

Retrieve feature fit for the model, requesting a job if it hasn’t been run previously

See get_feature_fit_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature fit job to complete before erroring source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources].
Returns:	feature_effects : FeatureFit The feature fit data.

get_or_request_feature_impact(max_wait=600, **kwargs)¶

Retrieve feature impact for the model, requesting a job if it hasn’t been run previously

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature impact job to complete before erroring **kwargs Arbitrary keyword arguments passed to `request_feature_impact`.
Returns:	feature_impacts : list or dict The feature impact data. See `get_feature_impact` for the exact schema.

get_parameters()¶

Retrieve model parameters.

Returns:	ModelParameters Model parameters for this model.

get_pareto_front()¶

Retrieve the Pareto Front for a Eureqa model.

This method is only supported for Eureqa models.

Returns:	ParetoFront Model ParetoFront data

get_prime_eligibility()¶

Check if this model can be approximated with DataRobot Prime

Returns:	prime_eligibility : dict a dict indicating whether a model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message)

get_residuals_chart(source, fallback_to_parent_insights=False)¶

Retrieve model residuals chart for the specified source.

Parameters:	source : str Residuals chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent if the residuals chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return residuals data from this model’s parent.
Returns:	ResidualsChart Model residuals chart data
Raises:	ClientError If the insight is not available for this model

get_roc_curve(source, fallback_to_parent_insights=False)¶

Retrieve the ROC curve for the specified source.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	RocCurve Model ROC curve data
Raises:	ClientError If the insight is not available for this model

get_rulesets()¶

List the rulesets approximating this model generated by DataRobot Prime

If this model hasn’t been approximated yet, will return an empty list. Note that these are rulesets approximating this model, not rulesets used to construct this model.

Returns:	rulesets : list of Ruleset

get_supported_capabilities()¶

Retrieves a summary of the capabilities supported by a model.

New in version v2.14.

Returns:

supportsBlending: bool

whether the model supports blending

supportsMonotonicConstraints: bool

whether the model supports monotonic constraints

hasWordCloud: bool

whether the model has word cloud data available

eligibleForPrime: bool

whether the model is eligible for Prime

hasParameters: bool

whether the model has parameters that can be retrieved

supportsCodeGeneration: bool

(New in version v2.18) whether the model supports code generation

supportsShap: bool

(New in version v2.18) True if the model supports Shapley package. i.e. Shapley based: feature Importance

supportsEarlyStopping: bool

(New in version v2.22) True if this is an early stopping tree-based model and number of trained iterations can be retrieved.

get_word_cloud(exclude_stop_words=False)¶

Retrieve word cloud data for the model.

Parameters:	exclude_stop_words : bool, optional Set to True if you want stopwords filtered out of response.
Returns:	WordCloud Word cloud data for the model.

open_model_browser()¶

Opens model at project leaderboard in web browser.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

request_cross_class_accuracy_scores()¶

Request data disparity insights to be computed for the model.

Returns:	status_id : str A statusId of computation request.

request_data_disparity_insights(feature, compared_class_names)¶

Request data disparity insights to be computed for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. compared_class_names : list(str) List of two classes to compare
Returns:	status_id : str A statusId of computation request.

request_external_test(dataset_id, actual_value_column=None)¶

Request external test to compute scores and insights on an external test dataset

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
Returns
——-
job : Job: a Job representing external dataset insights computation

request_fairness_insights(fairness_metrics_set=None)¶

Request fairness insights to be computed for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores.
Returns:	status_id : str A statusId of computation request.

request_feature_effect(row_count=None)¶

Request feature effects to be computed for the model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less.
Returns:	job : Job A Job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None)¶

Request Feature Effects computation for the multiclass model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int The number of rows from dataset to use for Feature Impact calculation. top_n_features : int or None Number of top features (ranked by feature impact) used to calculate Feature Effects. features : list or None The list of features used to calculate Feature Effects.
Returns:	job : Job A Job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete.

request_feature_fit()¶

Request feature fit to be computed for the model.

See get_feature_effect for more information on the result of the job.

Returns:	job : Job A Job representing the feature fit computation. To get the completed feature fit data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

request_feature_impact(row_count=None, with_metadata=False)¶

Request feature impacts to be computed for the model.

See get_feature_impact for more information on the result of the job.

Parameters:	row_count : int The sample size (specified in rows) to use for Feature Impact computation. This is not supported for unsupervised, multi-class (that has a separate method) and time series projects.
Returns:	job : Job A Job representing the feature impact computation. To get the completed feature impact data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature impacts have already been requested.

request_predictions(dataset_id, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None)¶

Requests predictions against a previously uploaded dataset.

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
include_prediction_intervals : bool, optional: (New in v2.16) For time series projects only. Specifies whether prediction intervals should be calculated for this request. Defaults to True if prediction_intervals_size is specified, otherwise defaults to False.
prediction_intervals_size : int, optional: (New in v2.16) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if include_prediction_intervals is True. Prediction intervals size must be between 1 and 100 (inclusive).
forecast_point : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
predictions_start_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
explanation_algorithm: (New in version v2.21) optional; If set to ‘shap’, the: response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).
max_explanations: (New in version v2.21) optional; specifies the maximum number of: explanation values that should be returned for each row, ordered by absolute value, greatest to least. If null, no limit. In the case of ‘shap’: if the number of features is greater than the limit, the sum of remaining values will also be returned as shapRemainingTotal. Defaults to null. Cannot be set if explanation_algorithm is omitted.

Returns:

job : PredictJob: The job computing the predictions

request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None)¶

Start a job to build training predictions

Parameters:

data_subset : str

data set definition to build predictions on. Choices are:

dr.enums.DATA_SUBSET.ALL or string all for all data available. Not valid for

models in datetime partitioned projects

dr.enums.DATA_SUBSET.VALIDATION_AND_HOLDOUT or string validationAndHoldout for

all data except training set. Not valid for models in datetime partitioned projects

dr.enums.DATA_SUBSET.HOLDOUT or string holdout for holdout data set only

dr.enums.DATA_SUBSET.ALL_BACKTESTS or string allBacktests for downloading

the predictions for all backtest validation folds. Requires the model to have successfully scored all backtests. Datetime partitioned projects only.

explanation_algorithm : dr.enums.EXPLANATIONS_ALGORITHM

(New in v2.21) Optional. If set to dr.enums.EXPLANATIONS_ALGORITHM.SHAP, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to None (no prediction explanations).

max_explanations : int

(New in v2.21) Optional. Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of dr.enums.EXPLANATIONS_ALGORITHM.SHAP: If not set, explanations are returned for all features. If the number of features is greater than the max_explanations, the sum of remaining values will also be returned as shap_remaining_total. Max 100. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Is ignored if explanation_algorithm is not set.

Returns:

Job: an instance of created async job

request_transferable_export(prediction_intervals_size=None)¶

Request generation of an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

This function does not download the exported file. Use download_export for that.

Parameters:	prediction_intervals_size : int, optional (New in v2.19) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Prediction intervals size must be between 1 and 100 (inclusive).

Examples

model = datarobot.Model.get('project-id', 'model-id')
job = model.request_transferable_export()
job.wait_for_completion()
model.download_export('my_exported_model.drmodel')

# Client must be configured to use standalone prediction server for import:
datarobot.Client(token='my-token-at-standalone-server',
                 endpoint='standalone-server-url/api/v2')

imported_model = datarobot.ImportedModel.create('my_exported_model.drmodel')

retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None)¶

Submit a job to the queue to train a blender model.

Parameters:

sample_pct: str, optional: The sample size in percents (1 to 100) to use in training. If this parameter is used then training_row_count should not be given.
featurelist_id : str, optional: The featurelist id
training_row_count : str, optional: The number of rows used to train the model. If this parameter is used, then sample_pct should not be given.
n_clusters: int, optional: (new in version 2.27) number of clusters to use in an unsupervised clustering model. This parameter is used only for unsupervised clustering models that do not determine the number of clusters automatically.

Returns:

job : ModelJob: The created job that is retraining the model

set_prediction_threshold(threshold)¶

Set a custom prediction threshold for the model.

May not be used once prediction_threshold_read_only is True for this model.

Parameters:	threshold : float only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive).

star_model()¶

Mark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

start_advanced_tuning_session()¶

Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:	AdvancedTuningSession Session for setting up and running Advanced Tuning on a model

unstar_model()¶

Unmark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

BlenderModel¶

class datarobot.models.BlenderModel(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, model_type=None, model_category=None, is_frozen=None, blueprint_id=None, metrics=None, model_ids=None, blender_method=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, model_number=None, parent_model_id=None, supports_composable_ml=None)¶

Represents blender model that combines prediction results from other models.

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

id : str: the id of the model
project_id : str: the id of the project the model belongs to
processes : list of str: the processes used by the model
featurelist_name : str: the name of the featurelist used by the model
featurelist_id : str: the id of the featurelist used by the model
sample_pct : float: the percentage of the project dataset used in training the model
training_row_count : int or None: the number of rows of the project dataset used in training the model. In a datetime partitioned project, if specified, defines the number of rows used to train the model and evaluate backtest scores; if unspecified, either training_duration or training_start_date and training_end_date was used to determine that instead.
training_duration : str or None: only present for models in datetime partitioned projects. If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
training_start_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
training_end_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
model_type : str: what model this is, e.g. ‘DataRobot Prime’
model_category : str: what kind of model this is - always ‘prime’ for DataRobot Prime models
is_frozen : bool: whether this model is a frozen model
blueprint_id : str: the id of the blueprint used in this model
metrics : dict: a mapping from each metric to the model’s scores for that metric
model_ids : list of str: List of model ids used in blender
blender_method : str: Method used to blend results from underlying models
monotonic_increasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced.
monotonic_decreasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced.
supports_monotonic_constraints : bool: optional, whether this model supports enforcing monotonic constraints
is_starred : bool: whether this model marked as starred
prediction_threshold : float: for binary classification projects, the threshold used for predictions
prediction_threshold_read_only : bool: indicated whether modification of the prediction threshold is forbidden. Threshold modification is forbidden once a model has had a deployment created or predictions made via the dedicated prediction API.
model_number : integer: model number assigned to a model
parent_model_id : str or None: (New in version v2.20) the id of the model that tuning parameters are derived from
supports_composable_ml : bool or None: (New in version v2.26) whether this model is supported in the Composable ML.

classmethod get(project_id, model_id)¶

Retrieve a specific blender.

Parameters:	project_id : str The project’s id. model_id : str The `model_id` of the leaderboard item to retrieve.
Returns:	model : BlenderModel The queried instance.

advanced_tune(params, description=None)¶

Generate a new model with the specified advanced-tuning parameters

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Parameters:	params : dict Mapping of parameter ID to parameter value. The list of valid parameter IDs for a model can be found by calling get_advanced_tuning_parameters(). This endpoint does not need to include values for all parameters. If a parameter is omitted, its current_value will be used. description : unicode Human-readable string describing the newly advanced-tuned model
Returns:	ModelJob The created job to build the model

cross_validate()¶

Run cross validation on the model.

Note

To perform Cross Validation on a new model with new parameters, use train instead.

Returns:	ModelJob The created job to build the model

delete()¶: Delete a model from the project’s leaderboard.

download_export(filepath)¶

Download an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

Parameters:	filepath : str The path at which to save the exported model file.

download_scoring_code(file_name, source_code=False)¶

Download the Scoring Code JAR.

Parameters:	file_name : str File path where scoring code will be saved. source_code : bool, optional Set to True to download source code archive. It will not be executable.

download_training_artifact(file_name)¶

Retrieve trained artifact(s) from a model containing one or more custom tasks.

Artifact(s) will be downloaded to the specified local filepath.

Parameters:	file_name : str File path where trained model artifact(s) will be saved.

classmethod fetch_resource_data(url, join_endpoint=True)¶

(Deprecated.) Used to acquire model data directly from its url.

Consider using get instead, as this is a convenience function used for development of datarobot

Parameters:	url : str The resource we are acquiring join_endpoint : boolean, optional Whether the client’s endpoint should be joined to the URL before sending the request. Location headers are returned as absolute locations, so will _not_ need the endpoint
Returns:	model_data : dict The queried model’s data

get_advanced_tuning_parameters()¶

Get the advanced-tuning parameters available for this model.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:

dict

A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuningDescription and tuningParameters.

tuningDescription an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter.

tuningParameters is a list of a dicts, each has the following keys

parameterName : (unicode) name of the parameter (unique per task, see below)
parameterId : (unicode) opaque ID string uniquely identifying parameter
defaultValue : (*) default value of the parameter for the blueprint
currentValue : (*) value of the parameter that was used for this model
taskName : (unicode) name of the task that this parameter belongs to
constraints: (dict) see the notes below

Notes

The type of defaultValue and currentValue is defined by the constraints structure. It will be a string or numeric Python type.

constraints is a dict with at least one, possibly more, of the following keys. The presence of a key indicates that the parameter may take on the specified type. (If a key is absent, this means that the parameter may not take on the specified type.) If a key on constraints is present, its value will be a dict containing all of the fields described below for that key.

"constraints": {
    "select": {
        "values": [<list(basestring or number) : possible values>]
    },
    "ascii": {},
    "unicode": {},
    "int": {
        "min": <int : minimum valid value>,
        "max": <int : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "float": {
        "min": <float : minimum valid value>,
        "max": <float : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "intList": {
        "length": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <int : minimum valid value>,
        "max_val": <int : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "floatList": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <float : minimum valid value>,
        "max_val": <float : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    }
}

The keys have meaning as follows:

select: Rather than specifying a specific data type, if present, it indicates that the parameter is permitted to take on any of the specified values. Listed values may be of any string or real (non-complex) numeric type.
ascii: The parameter may be a unicode object that encodes simple ASCII characters. (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed constraints, ASCII keys currently may not contain either newlines or semicolons.
unicode: The parameter may be any Python unicode object.
int: The value may be an object of type int within the specified range (inclusive). Please note that the value will be passed around using the JSON format, and some JSON parsers have undefined behavior with integers outside of the range [-(2**53)+1, (2**53)-1].
float: The value may be an object of type float within the specified range (inclusive).
intList, floatList: The value may be a list of int or float objects, respectively, following constraints as specified respectively by the int and float types (above).

Many parameters only specify one key under constraints. If a parameter specifies multiple keys, the parameter may take on any value permitted by any key.

get_all_confusion_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all confusion matrices available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ConfusionChart Data for all available confusion charts for model.

get_all_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_multiclass_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_residuals_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ResidualsChart Data for all available model residuals charts.

get_all_roc_curves(fallback_to_parent_insights=False)¶

Retrieve a list of all ROC curves available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of RocCurve Data for all available model ROC curves.

get_confusion_chart(source, fallback_to_parent_insights=False)¶

Retrieve them model’s confusion matrix for the specified source.

Parameters:	source : str Confusion chart source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent if the confusion chart is not available for this model and the defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	ConfusionChart Model ConfusionChart data
Raises:	ClientError If the insight is not available for this model

get_cross_class_accuracy_scores()¶

Retrieves a list of Cross Class Accuracy scores for the model.

Returns:	json

get_cross_validation_scores(partition=None, metric=None)¶

Return a dictionary, keyed by metric, showing cross validation scores per partition.

Cross Validation should already have been performed using cross_validate or train.

Note

Models that computed cross validation before this feature was added will need to be deleted and retrained before this method can be used.

Parameters:	partition : float optional, the id of the partition (1,2,3.0,4.0,etc…) to filter results by can be a whole number positive integer or float value. 0 corresponds to the validation partition. metric: unicode optional name of the metric to filter to resulting cross validation scores by
Returns:	cross_validation_scores: dict A dictionary keyed by metric showing cross validation scores per partition.

get_data_disparity_insights(feature, class_name1, class_name2)¶

Retrieve a list of Cross Class Data Disparity insights for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. class_name1 : str One of the compared classes class_name2 : str Another compared class
Returns:	json

get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100)¶

Retrieve a list of Per Class Bias insights for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores. offset : int, optional Number of items to skip. limit : int, optional Number of items to return.
Returns:	json

get_feature_effect(source, include_ice_plots=False)¶

Retrieve Feature Effects for the model.

Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Effects are retrieved for. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_effects : FeatureEffects The feature effects data.
Raises:	ClientError (404) If the feature effects have not been computed or source is not valid value.

get_feature_effect_metadata()¶

Retrieve Feature Effects metadata. Response contains status and available model sources.

Feature Fit for the training partition is always available, with the exception of older projects that only supported Feature Fit for validation.
When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout.
Feature Effects for holdout is not available when holdout was not unlocked for the project.

Use source to retrieve Feature Effects, selecting one of the provided sources.

Returns:	feature_effect_metadata: FeatureEffectMetadata

get_feature_effects_multiclass(source='training', class_=None)¶

Retrieve Feature Effects for the multiclass model.

Feature Effects provide partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : str The source Feature Effects are retrieved for. class_ : str or None The class name Feature Effects are retrieved for.
Returns:	list The list of multiclass feature effects.
Raises:	ClientError (404) If Feature Effects have not been computed or source is not valid value.

get_feature_fit(source, include_ice_plots=False)¶

Retrieve Feature Fit for the model.

Feature Fit provides partial dependence and predicted vs actual values for top-500 features ordered by feature importance score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Fit has already been computed with request_feature_effect.

See get_feature_fit_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources]. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_fit : FeatureFit The feature fit data.
Raises:	ClientError (404) If the feature fit have not been computed or source is not valid value.

get_feature_fit_metadata()¶

Retrieve Feature Fit metadata. Response contains status and available model sources.

Feature Fit of training is always available (except for the old project which supports only Feature Fit for validation).
When a model is trained into validation or holdout without stacked prediction (e.g. no out-of-sample prediction in validation or holdout), Feature Fit is not available for validation or holdout.
Feature Fit for holdout is not available when there is no holdout configured for the project.

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

Returns:	feature_effect_metadata: FeatureFitMetadata

get_feature_impact(with_metadata=False)¶

Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model.

Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features.

If a feature is a redundant feature, i.e. once other features are considered it doesn’t contribute much in addition, the ‘redundantWith’ value is the name of feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used.

Elsewhere this technique is sometimes called ‘Permutation Importance’.

Requires that Feature Impact has already been computed with request_feature_impact.

Parameters:	with_metadata : bool The flag indicating if the result should include the metadata as well.
Returns:	list or dict The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with actual data or just a list with that data. Each List item is a dict with the keys `featureName`, `impactNormalized`, and `impactUnnormalized`, `redundantWith` and `count`. For dict response available keys are: `featureImpacts` - Feature Impact data as a dictionary. Each item is a dict with keys: `featureName`, `impactNormalized`, and `impactUnnormalized`, and `redundantWith`. `shapBased` - A boolean that indicates whether Feature Impact was calculated using Shapley values. `ranRedundancyDetection` - A boolean that indicates whether redundant feature identification was run while calculating this Feature Impact. `rowCount` - An integer or None that indicates the number of rows that was used to calculate Feature Impact. For the Feature Impact calculated with the default logic, without specifying the rowCount, we return None here. `count` - An integer with the number of features under the `featureImpacts`.
Raises:	ClientError (404) If the feature impacts have not been computed.

get_features_used()¶

Query the server to determine which features were used.

Note that the data returned by this method is possibly different than the names of the features in the featurelist used by this model. This method will return the raw features that must be supplied in order for predictions to be generated on a new set of data. The featurelist, in contrast, would also include the names of derived features.

Returns:	features : list of str The names of the features used in the model.

get_frozen_child_models()¶

Retrieve the IDs for all models that are frozen from this model.

Returns:	A list of Models

get_labelwise_roc_curves(source, fallback_to_parent_insights=False)¶

Retrieve a list of LabelwiseRocCurve instances for the given source and all labels.

New in version v2.24.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	list of : class:LabelwiseRocCurve <datarobot.models.roc_curve.LabelwiseRocCurve> Labelwise ROC Curve instances for `source` and all labels
Raises:	ClientError If the insight is not available for this model

get_leaderboard_ui_permalink()¶

Returns:	url : str Permanent static hyperlink to this model at leaderboard.

get_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve the model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	LiftChart Model lift chart data
Raises:	ClientError If the insight is not available for this model

get_missing_report_info()¶

Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model.

Returns:	An iterable of MissingReportPerFeature The queried model missing report, sorted by missing count (DESCENDING order).

get_model_blueprint_chart()¶

Retrieve a diagram that can be used to understand data flow in the blueprint.

Returns:	ModelBlueprintChart The queried model blueprint chart.

get_model_blueprint_documents()¶

Get documentation for tasks used in this model.

Returns:	list of BlueprintTaskDocument All documents available for the model.

get_multiclass_feature_impact()¶

For multiclass it’s possible to calculate feature impact separately for each target class. The method for calculation is exactly the same, calculated in one-vs-all style for each target class.

Requires that Feature Impact has already been computed with request_feature_impact.

Returns:	feature_impacts : list of dict The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’.
Raises:	ClientError (404) If the multiclass feature impacts have not been computed.

get_multiclass_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_multilabel_lift_charts(source, fallback_to_parent_insights=False)¶

Retrieve model Lift charts for the specified source.

New in version v2.24.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_num_iterations_trained()¶

Retrieves the number of estimators trained by early-stopping tree-based models.

– versionadded:: v2.22

Returns:	projectId: str id of project containing the model modelId: str id of the model data: array list of numEstimatorsItem objects, one for each modeling stage. numEstimatorsItem will be of the form: stage: str indicates the modeling stage (for multi-stage models); None of single-stage models numIterations: int the number of estimators or iterations trained by the model

get_or_request_feature_effect(source, max_wait=600, row_count=None)¶

Retrieve feature effect for the model, requesting a job if it hasn’t been run previously

See get_feature_effect_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature effect job to complete before erroring row_count : int, optional (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less. source : string The source Feature Effects are retrieved for.
Returns:	feature_effects : FeatureEffects The feature effects data.

get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600)¶

Retrieve Feature Effects for the multiclass model, requesting a job if it hasn’t been run previously.

Parameters:

source : string: The source Feature Effects retrieve for.
class_ : str or None: The class name Feature Effects retrieve for.
row_count : int: The number of rows from dataset to use for Feature Impact calculation.
top_n_features : int or None: Number of top features (ranked by Feature Impact) used to calculate Feature Effects.
features : list or None: The list of features used to calculate Feature Effects.
max_wait : int, optional: The maximum time to wait for a requested Feature Effects job to complete before erroring.

Returns:

feature_effects : list of FeatureEffectsMulticlass: The list of multiclass feature effects data.

get_or_request_feature_fit(source, max_wait=600)¶

Retrieve feature fit for the model, requesting a job if it hasn’t been run previously

See get_feature_fit_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature fit job to complete before erroring source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources].
Returns:	feature_effects : FeatureFit The feature fit data.

get_or_request_feature_impact(max_wait=600, **kwargs)¶

Retrieve feature impact for the model, requesting a job if it hasn’t been run previously

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature impact job to complete before erroring **kwargs Arbitrary keyword arguments passed to `request_feature_impact`.
Returns:	feature_impacts : list or dict The feature impact data. See `get_feature_impact` for the exact schema.

get_parameters()¶

Retrieve model parameters.

Returns:	ModelParameters Model parameters for this model.

get_pareto_front()¶

Retrieve the Pareto Front for a Eureqa model.

This method is only supported for Eureqa models.

Returns:	ParetoFront Model ParetoFront data

get_prime_eligibility()¶

Check if this model can be approximated with DataRobot Prime

Returns:	prime_eligibility : dict a dict indicating whether a model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message)

get_residuals_chart(source, fallback_to_parent_insights=False)¶

Retrieve model residuals chart for the specified source.

Parameters:	source : str Residuals chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent if the residuals chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return residuals data from this model’s parent.
Returns:	ResidualsChart Model residuals chart data
Raises:	ClientError If the insight is not available for this model

get_roc_curve(source, fallback_to_parent_insights=False)¶

Retrieve the ROC curve for the specified source.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	RocCurve Model ROC curve data
Raises:	ClientError If the insight is not available for this model

get_rulesets()¶

List the rulesets approximating this model generated by DataRobot Prime

If this model hasn’t been approximated yet, will return an empty list. Note that these are rulesets approximating this model, not rulesets used to construct this model.

Returns:	rulesets : list of Ruleset

get_supported_capabilities()¶

Retrieves a summary of the capabilities supported by a model.

New in version v2.14.

Returns:

supportsBlending: bool

whether the model supports blending

supportsMonotonicConstraints: bool

whether the model supports monotonic constraints

hasWordCloud: bool

whether the model has word cloud data available

eligibleForPrime: bool

whether the model is eligible for Prime

hasParameters: bool

whether the model has parameters that can be retrieved

supportsCodeGeneration: bool

(New in version v2.18) whether the model supports code generation

supportsShap: bool

(New in version v2.18) True if the model supports Shapley package. i.e. Shapley based: feature Importance

supportsEarlyStopping: bool

(New in version v2.22) True if this is an early stopping tree-based model and number of trained iterations can be retrieved.

get_word_cloud(exclude_stop_words=False)¶

Retrieve word cloud data for the model.

Parameters:	exclude_stop_words : bool, optional Set to True if you want stopwords filtered out of response.
Returns:	WordCloud Word cloud data for the model.

open_model_browser()¶

Opens model at project leaderboard in web browser.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

request_approximation()¶

Request an approximation of this model using DataRobot Prime

This will create several rulesets that could be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally.

Returns:	job : Job the job generating the rulesets

request_cross_class_accuracy_scores()¶

Request data disparity insights to be computed for the model.

Returns:	status_id : str A statusId of computation request.

request_data_disparity_insights(feature, compared_class_names)¶

Request data disparity insights to be computed for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. compared_class_names : list(str) List of two classes to compare
Returns:	status_id : str A statusId of computation request.

request_external_test(dataset_id, actual_value_column=None)¶

Request external test to compute scores and insights on an external test dataset

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
Returns
——-
job : Job: a Job representing external dataset insights computation

request_fairness_insights(fairness_metrics_set=None)¶

Request fairness insights to be computed for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores.
Returns:	status_id : str A statusId of computation request.

request_feature_effect(row_count=None)¶

Request feature effects to be computed for the model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less.
Returns:	job : Job A Job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None)¶

Request Feature Effects computation for the multiclass model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int The number of rows from dataset to use for Feature Impact calculation. top_n_features : int or None Number of top features (ranked by feature impact) used to calculate Feature Effects. features : list or None The list of features used to calculate Feature Effects.
Returns:	job : Job A Job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete.

request_feature_fit()¶

Request feature fit to be computed for the model.

See get_feature_effect for more information on the result of the job.

Returns:	job : Job A Job representing the feature fit computation. To get the completed feature fit data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

request_feature_impact(row_count=None, with_metadata=False)¶

Request feature impacts to be computed for the model.

See get_feature_impact for more information on the result of the job.

Parameters:	row_count : int The sample size (specified in rows) to use for Feature Impact computation. This is not supported for unsupervised, multi-class (that has a separate method) and time series projects.
Returns:	job : Job A Job representing the feature impact computation. To get the completed feature impact data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature impacts have already been requested.

request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None)¶

Train a new frozen model with parameters from this model.

Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job.

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

In addition of training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified.

Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked).

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, training_duration may not be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, training_row_count may not be specified.
training_start_date : datetime.datetime, optional: the start date of the data to train to model on. Only rows occurring at or after this datetime will be used. If training_start_date is specified, training_end_date must also be specified.
training_end_date : datetime.datetime, optional: the end date of the data to train the model on. Only rows occurring strictly before this datetime will be used. If training_end_date is specified, training_start_date must also be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.

Returns:

model_job : ModelJob: the modeling job training a frozen model

request_frozen_model(sample_pct=None, training_row_count=None)¶

Train a new frozen model with parameters from this model

Note

This method only works if project the model belongs to is not datetime partitioned. If it is, use request_frozen_datetime_model instead.

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

Parameters:	sample_pct : float optional, the percentage of the dataset to use with the model. If not provided, will use the value from this model. training_row_count : int (New in version v2.9) optional, the integer number of rows of the dataset to use with the model. Only one of sample_pct and training_row_count should be specified.
Returns:	model_job : ModelJob the modeling job training a frozen model

request_predictions(dataset_id, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None)¶

Requests predictions against a previously uploaded dataset.

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
include_prediction_intervals : bool, optional: (New in v2.16) For time series projects only. Specifies whether prediction intervals should be calculated for this request. Defaults to True if prediction_intervals_size is specified, otherwise defaults to False.
prediction_intervals_size : int, optional: (New in v2.16) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if include_prediction_intervals is True. Prediction intervals size must be between 1 and 100 (inclusive).
forecast_point : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
predictions_start_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
explanation_algorithm: (New in version v2.21) optional; If set to ‘shap’, the: response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).
max_explanations: (New in version v2.21) optional; specifies the maximum number of: explanation values that should be returned for each row, ordered by absolute value, greatest to least. If null, no limit. In the case of ‘shap’: if the number of features is greater than the limit, the sum of remaining values will also be returned as shapRemainingTotal. Defaults to null. Cannot be set if explanation_algorithm is omitted.

Returns:

job : PredictJob: The job computing the predictions

request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None)¶

Start a job to build training predictions

Parameters:

data_subset : str

data set definition to build predictions on. Choices are:

dr.enums.DATA_SUBSET.ALL or string all for all data available. Not valid for

models in datetime partitioned projects

dr.enums.DATA_SUBSET.VALIDATION_AND_HOLDOUT or string validationAndHoldout for

all data except training set. Not valid for models in datetime partitioned projects

dr.enums.DATA_SUBSET.HOLDOUT or string holdout for holdout data set only

dr.enums.DATA_SUBSET.ALL_BACKTESTS or string allBacktests for downloading

the predictions for all backtest validation folds. Requires the model to have successfully scored all backtests. Datetime partitioned projects only.

explanation_algorithm : dr.enums.EXPLANATIONS_ALGORITHM

(New in v2.21) Optional. If set to dr.enums.EXPLANATIONS_ALGORITHM.SHAP, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to None (no prediction explanations).

max_explanations : int

(New in v2.21) Optional. Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of dr.enums.EXPLANATIONS_ALGORITHM.SHAP: If not set, explanations are returned for all features. If the number of features is greater than the max_explanations, the sum of remaining values will also be returned as shap_remaining_total. Max 100. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Is ignored if explanation_algorithm is not set.

Returns:

Job: an instance of created async job

request_transferable_export(prediction_intervals_size=None)¶

Request generation of an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

This function does not download the exported file. Use download_export for that.

Parameters:	prediction_intervals_size : int, optional (New in v2.19) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Prediction intervals size must be between 1 and 100 (inclusive).

Examples

model = datarobot.Model.get('project-id', 'model-id')
job = model.request_transferable_export()
job.wait_for_completion()
model.download_export('my_exported_model.drmodel')

# Client must be configured to use standalone prediction server for import:
datarobot.Client(token='my-token-at-standalone-server',
                 endpoint='standalone-server-url/api/v2')

imported_model = datarobot.ImportedModel.create('my_exported_model.drmodel')

retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None)¶

Submit a job to the queue to train a blender model.

Parameters:

sample_pct: str, optional: The sample size in percents (1 to 100) to use in training. If this parameter is used then training_row_count should not be given.
featurelist_id : str, optional: The featurelist id
training_row_count : str, optional: The number of rows used to train the model. If this parameter is used, then sample_pct should not be given.
n_clusters: int, optional: (new in version 2.27) number of clusters to use in an unsupervised clustering model. This parameter is used only for unsupervised clustering models that do not determine the number of clusters automatically.

Returns:

job : ModelJob: The created job that is retraining the model

set_prediction_threshold(threshold)¶

Set a custom prediction threshold for the model.

May not be used once prediction_threshold_read_only is True for this model.

Parameters:	threshold : float only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive).

star_model()¶

Mark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

start_advanced_tuning_session()¶

Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:	AdvancedTuningSession Session for setting up and running Advanced Tuning on a model

train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>)¶

Train the blueprint used in model on a particular featurelist or amount of data.

This method creates a new training job for worker and appends it to the end of the queue for this project. After the job has finished you can get the newly trained model by retrieving it from the project leaderboard, or by retrieving the result of the job.

Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither are specified, a default of the maximum amount of data that can safely be used to train any blueprint without going into the validation data will be selected.

In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class.

Note

For datetime partitioned projects, see train_datetime instead.

Parameters:

sample_pct : float, optional: The amount of data to use for training, as a percentage of the project dataset from 0 to 100.
featurelist_id : str, optional: The identifier of the featurelist to use. If not defined, the featurelist of this model is used.
scoring_type : str, optional: Either SCORING_TYPE.validation or SCORING_TYPE.cross_validation. SCORING_TYPE.validation is available for every partitioning type, and indicates that the default model validation should be used for the project. If the project uses a form of cross-validation partitioning, SCORING_TYPE.cross_validation can also be used to indicate that all of the available training/validation combinations should be used to evaluate the model.
training_row_count : int, optional: The number of rows to use to train the requested model.
monotonic_increasing_featurelist_id : str: (new in version 2.11) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id : str: (new in version 2.11) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.

Returns:

model_job_id : str: id of created job, can be used as parameter to ModelJob.get method or wait_for_async_model_creation function

Examples

project = Project.get('project-id')
model = Model.get('project-id', 'model-id')
model_job_id = model.train(training_row_count=project.max_train_rows)

train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>, use_project_settings=False, sampling_method=None)¶

Trains this model on a different featurelist or sample size.

Requires that this model is part of a datetime partitioned project; otherwise, an error will occur.

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

featurelist_id : str, optional: the featurelist to use to train the model. If not specified, the featurelist of this model is used.
training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, neither training_duration nor use_project_settings may be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, neither training_row_count nor use_project_settings may be specified.
use_project_settings : bool, optional: (New in version v2.20) defaults to False. If True, indicates that the custom backtest partitioning settings specified by the user will be used to train the model and evaluate backtest scores. If specified, neither training_row_count nor training_duration may be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.
monotonic_increasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.

Returns:

job : ModelJob: the created job to build the model

unstar_model()¶

Unmark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

DatetimeModel¶

class datarobot.models.DatetimeModel(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None, model_type=None, model_category=None, is_frozen=None, blueprint_id=None, metrics=None, training_info=None, holdout_score=None, holdout_status=None, data_selection_method=None, backtests=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, effective_feature_derivation_window_start=None, effective_feature_derivation_window_end=None, forecast_window_start=None, forecast_window_end=None, windows_basis_unit=None, model_number=None, parent_model_id=None, use_project_settings=None, supports_composable_ml=None)¶

Represents a model from a datetime partitioned project

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Note that only one of training_row_count, training_duration, and training_start_date and training_end_date will be specified, depending on the data_selection_method of the model. Whichever method was selected determines the amount of data used to train on when making predictions and scoring the backtests and the holdout.

Attributes:

id : str: the id of the model
project_id : str: the id of the project the model belongs to
processes : list of str: the processes used by the model
featurelist_name : str: the name of the featurelist used by the model
featurelist_id : str: the id of the featurelist used by the model
sample_pct : float: the percentage of the project dataset used in training the model
training_row_count : int or None: If specified, an int specifying the number of rows used to train the model and evaluate backtest scores.
training_duration : str or None: If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
training_start_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
training_end_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
time_window_sample_pct : int or None: An integer between 1 and 99 indicating the percentage of sampling within the training window. The points kept are determined by a random uniform sample. If not specified, no sampling was done.
sampling_method : str or None: (New in v2.23) indicates the way training data has been selected (either how rows have been selected within backtest or how time_window_sample_pct has been applied).
model_type : str: what model this is, e.g. ‘Nystroem Kernel SVM Regressor’
model_category : str: what kind of model this is - ‘prime’ for DataRobot Prime models, ‘blend’ for blender models, and ‘model’ for other models
is_frozen : bool: whether this model is a frozen model
blueprint_id : str: the id of the blueprint used in this model
metrics : dict: a mapping from each metric to the model’s scores for that metric. The keys in metrics are the different metrics used to evaluate the model, and the values are the results. The dictionaries inside of metrics will be as described here: ‘validation’, the score for a single backtest; ‘crossValidation’, always None; ‘backtesting’, the average score for all backtests if all are available and computed, or None otherwise; ‘backtestingScores’, a list of scores for all backtests where the score is None if that backtest does not have a score available; and ‘holdout’, the score for the holdout or None if the holdout is locked or the score is unavailable.
backtests : list of dict: describes what data was used to fit each backtest, the score for the project metric, and why the backtest score is unavailable if it is not provided.
data_selection_method : str: which of training_row_count, training_duration, or training_start_data and training_end_date were used to determine the data used to fit the model. One of ‘rowCount’, ‘duration’, or ‘selectedDateRange’.
training_info : dict: describes which data was used to train on when scoring the holdout and making predictions. training_info` will have the following keys: holdout_training_start_date, holdout_training_duration, holdout_training_row_count, holdout_training_end_date, prediction_training_start_date, prediction_training_duration, prediction_training_row_count, prediction_training_end_date. Start and end dates will be datetimes, durations will be duration strings, and rows will be integers.
holdout_score : float or None: the score against the holdout, if available and the holdout is unlocked, according to the project metric.
holdout_status : string or None: the status of the holdout score, e.g. “COMPLETED”, “HOLDOUT_BOUNDARIES_EXCEEDED”. Unavailable if the holdout fold was disabled in the partitioning configuration.
monotonic_increasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced.
monotonic_decreasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced.
supports_monotonic_constraints : bool: optional, whether this model supports enforcing monotonic constraints
is_starred : bool: whether this model marked as starred
prediction_threshold : float: for binary classification projects, the threshold used for predictions
prediction_threshold_read_only : bool: indicated whether modification of the prediction threshold is forbidden. Threshold modification is forbidden once a model has had a deployment created or predictions made via the dedicated prediction API.
effective_feature_derivation_window_start : int or None: (New in v2.16) For time series projects only. How many units of the windows_basis_unit into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the feature_derivation_window_start set on the project due to the differencing method and period selected, or if the model is a time series native model such as ARIMA. Will be a negative integer in time series projects and None otherwise.
effective_feature_derivation_window_end : int or None: (New in v2.16) For time series projects only. How many units of the windows_basis_unit into the past relative to the forecast point the feature derivation window should end. Will be a non-positive integer in time series projects and None otherwise.
forecast_window_start : int or None: (New in v2.16) For time series projects only. How many units of the windows_basis_unit into the future relative to the forecast point the forecast window should start. Note that this field will be the same as what is shown in the project settings. Will be a non-negative integer in time series projects and None otherwise.
forecast_window_end : int or None: (New in v2.16) For time series projects only. How many units of the windows_basis_unit into the future relative to the forecast point the forecast window should end. Note that this field will be the same as what is shown in the project settings. Will be a non-negative integer in time series projects and None otherwise.
windows_basis_unit : str or None: (New in v2.16) For time series projects only. Indicates which unit is the basis for the feature derivation window and the forecast window. Note that this field will be the same as what is shown in the project settings. In time series projects, will be either the detected time unit or “ROW”, and None otherwise.
model_number : integer: model number assigned to a model
parent_model_id : str or None: (New in version v2.20) the id of the model that tuning parameters are derived from
use_project_settings : bool or None: (New in version v2.20) If True, indicates that the custom backtest partitioning settings specified by the user were used to train the model and evaluate backtest scores.
supports_composable_ml : bool or None: (New in version v2.26) whether this model is supported in the Composable ML.

classmethod get(project, model_id)¶

Retrieve a specific datetime model.

If the project does not use datetime partitioning, a ClientError will occur.

Parameters:	project : str the id of the project the model belongs to model_id : str the id of the model to retrieve
Returns:	model : DatetimeModel the model

score_backtests()¶

Compute the scores for all available backtests.

Some backtests may be unavailable if the model is trained into their validation data.

Returns:	job : Job a job tracking the backtest computation. When it is complete, all available backtests will have scores computed.

cross_validate()¶: Inherited from the model. DatetimeModels cannot request cross validation scores; use backtests instead.

get_cross_validation_scores(partition=None, metric=None)¶

Inherited from Model - DatetimeModels cannot request Cross Validation scores,

Use backtests instead.

request_training_predictions(data_subset)¶

Start a job that builds training predictions.

Parameters:	data_subset : str data set definition to build predictions on. Choices are: dr.enums.DATA_SUBSET.HOLDOUT for holdout data set only dr.enums.DATA_SUBSET.ALL_BACKTESTS for downloading the predictions for all backtest validation folds. Requires the model to have successfully scored all backtests. Returns ——- Job an instance of created async job

get_series_accuracy_as_dataframe(offset=0, limit=100, metric=None, multiseries_value=None, order_by=None, reverse=False)¶

Retrieve series accuracy results for the specified model as a pandas.DataFrame.

Parameters:

offset : int, optional: The number of results to skip. Defaults to 0 if not specified.
limit : int, optional: The maximum number of results to return. Defaults to 100 if not specified.
metric : str, optional: The name of the metric to retrieve scores for. If omitted, the default project metric will be used.
multiseries_value : str, optional: If specified, only the series containing the given value in one of the series ID columns will be returned.
order_by : str, optional: Used for sorting the series. Attribute must be one of datarobot.enums.SERIES_ACCURACY_ORDER_BY.
reverse : bool, optional: Used for sorting the series. If True, will sort the series in descending order by the attribute specified by order_by.

Returns:

data: A pandas.DataFrame with the Series Accuracy for the specified model.

download_series_accuracy_as_csv(filename, encoding='utf-8', offset=0, limit=100, metric=None, multiseries_value=None, order_by=None, reverse=False)¶

Save series accuracy results for the specified model in a CSV file.

Parameters:

filename : str or file object: The path or file object to save the data to.
encoding : str, optional: A string representing the encoding to use in the output csv file. Defaults to ‘utf-8’.
offset : int, optional: The number of results to skip. Defaults to 0 if not specified.
limit : int, optional: The maximum number of results to return. Defaults to 100 if not specified.
metric : str, optional: The name of the metric to retrieve scores for. If omitted, the default project metric will be used.
multiseries_value : str, optional: If specified, only the series containing the given value in one of the series ID columns will be returned.
order_by : str, optional: Used for sorting the series. Attribute must be one of datarobot.enums.SERIES_ACCURACY_ORDER_BY.
reverse : bool, optional: Used for sorting the series. If True, will sort the series in descending order by the attribute specified by order_by.

compute_series_accuracy(compute_all_series=False)¶

Compute series accuracy for the model.

Parameters:	compute_all_series : bool, optional Calculate accuracy for all series or only first 1000.
Returns:	Job an instance of the created async job

retrain(time_window_sample_pct=None, featurelist_id=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, sampling_method=None)¶

Retrain an existing datetime model using a new training period for the model’s training set (with optional time window sampling) or a different feature list.

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

featurelist_id : str, optional: The ID of the featurelist to use.
training_row_count : str, optional: The number of rows to train the model on. If this parameter is used then sample_pct cannot be specified.
time_window_sample_pct : int, optional: An int between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by a random uniform sample. If specified, training_row_count must not be specified and either training_duration or training_start_date and training_end_date must be specified.
training_duration : str, optional: A duration string representing the training duration for the submitted model. If specified then training_row_count, training_start_date, and training_end_date cannot be specified.
training_start_date : str, optional: A datetime string representing the start date of the data to use for training this model. If specified, training_end_date must also be specified, and training_duration cannot be specified. The value must be before the training_end_date value.
training_end_date : str, optional: A datetime string representing the end date of the data to use for training this model. If specified, training_start_date must also be specified, and training_duration cannot be specified. The value must be after the training_start_date value.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.

Returns:

job : ModelJob: The created job that is retraining the model

get_feature_effect_metadata()¶

Retrieve Feature Effect metadata for each backtest. Response contains status and available sources for each backtest of the model.

Each backtest is available for training and validation
If holdout is configured for the project it has holdout as backtestIndex. It has training and holdout sources available.

Start/stop models contain a single response item with startstop value for backtestIndex.

Feature Effect of training is always available (except for the old project which supports only Feature Effect for validation).
When a model is trained into validation or holdout without stacked prediction (e.g. no out-of-sample prediction in validation or holdout), Feature Effect is not available for validation or holdout.
Feature Effect for holdout is not available when there is no holdout configured for the project.

source is expected parameter to retrieve Feature Effect. One of provided sources shall be used.

backtestIndex is expected parameter to submit compute request and retrieve Feature Effect. One of provided backtest indexes shall be used.

Returns:	feature_effect_metadata: FeatureEffectMetadataDatetime

get_feature_fit_metadata()¶

Retrieve Feature Fit metadata for each backtest. Response contains status and available sources for each backtest of the model.

Each backtest is available for training and validation
If holdout is configured for the project it has holdout as backtestIndex. It has training and holdout sources available.

Start/stop models contain a single response item with startstop value for backtestIndex.

Feature Fit of training is always available (except for the old project which supports only Feature Effect for validation).
When a model is trained into validation or holdout without stacked prediction (e.g. no out-of-sample prediction in validation or holdout), Feature Fit is not available for validation or holdout.
Feature Fit for holdout is not available when there is no holdout configured for the project.

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

backtestIndex is expected parameter to submit compute request and retrieve Feature Fit. One of provided backtest indexes shall be used.

Returns:	feature_effect_metadata: FeatureFitMetadataDatetime

request_feature_effect(backtest_index)¶

Request feature effects to be computed for the model.

See get_feature_effect for more information on the result of the job.

See get_feature_effect_metadata for retrieving information of backtest_index.

Parameters:	backtest_index: string, FeatureEffectMetadataDatetime.backtest_index. The backtest index to retrieve Feature Effects for.
Returns:	job : Job A Job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

get_feature_effect(source, backtest_index, include_ice_plots=False)¶

Retrieve Feature Effects for the model.

Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information of source, backtest_index.

Parameters:	source: string The source Feature Effects are retrieved for. One value of [FeatureEffectMetadataDatetime.sources]. To retrieve the availiable sources for feature effect. backtest_index: string, FeatureEffectMetadataDatetime.backtest_index. The backtest index to retrieve Feature Effects for. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_effects: FeatureEffects The feature effects data.
Raises:	ClientError (404) If the feature effects have not been computed or source is not valid value.

get_or_request_feature_effect(source, backtest_index, max_wait=600)¶

Retrieve feature effect for the model, requesting a job if it hasn’t been run previously

See get_feature_effect_metadata for retrieving information of source, backtest_index.

Parameters:

max_wait : int, optional: The maximum time to wait for a requested feature effect job to complete before erroring
source : string: The source Feature Effects are retrieved for. One value of [FeatureEffectMetadataDatetime.sources]. To retrieve the availiable sources for feature effect.
backtest_index: string, FeatureEffectMetadataDatetime.backtest_index.: The backtest index to retrieve Feature Effects for.

Returns:

feature_effects : FeatureEffects: The feature effects data.

request_feature_effects_multiclass(backtest_index, row_count=None, top_n_features=None, features=None)¶

Request feature effects to be computed for the multiclass datetime model.

See get_feature_effect for more information on the result of the job.

Parameters:	backtest_index : str The backtest index to use for Feature Effects calculation. row_count : int The number of rows from dataset to use for Feature Impact calculation. top_n_features : int or None Number of top features (ranked by Feature Impact) used to calculate Feature Effects. features : list or None The list of features to use to calculate Feature Effects.
Returns:	job : Job A Job representing Feature Effects computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete.

get_feature_effects_multiclass(backtest_index, source='training', class_=None)¶

Retrieve Feature Effects for the multiclass datetime model.

Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	backtest_index : str The backtest index to retrieve Feature Effects for. source : str The source Feature Effects are retrieved for. class_ : str or None The class name Feature Effects are retrieved for.
Returns:	list The list of multiclass Feature Effects.
Raises:	ClientError (404) If the Feature Effects have not been computed or source is not valid value.

get_or_request_feature_effects_multiclass(backtest_index, source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600)¶

Retrieve Feature Effects for a datetime multiclass model, and request a job if it hasn’t been run previously.

Parameters:

backtest_index : str: The backtest index to retrieve Feature Effects for.
source : string: The source from which Feature Effects are retrieved.
class_ : str or None: The class name Feature Effects retrieve for.
row_count : int: The number of rows used from the dataset for Feature Impact calculation.
top_n_features : int or None: Number of top features (ranked by feature impact) used to calculate Feature Effects.
features : list or None: The list of features used to calculate Feature Effects.
max_wait : int, optional: The maximum time to wait for a requested feature effect job to complete before erroring.

Returns:

feature_effects : list of FeatureEffectsMulticlass: The list of multiclass feature effects data.

request_feature_fit(backtest_index)¶

Request feature fit to be computed for the model.

See get_feature_fit for more information on the result of the job.

See get_feature_fit_metadata for retrieving information of backtest_index.

Parameters:	backtest_index: string, FeatureFitMetadataDatetime.backtest_index. The backtest index to retrieve Feature Fit for.
Returns:	job : Job A Job representing the feature fit computation. To get the completed feature fit data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature fit have already been requested.

get_feature_fit(source, backtest_index, include_ice_plots=False)¶

Retrieve Feature Fit for the model.

Feature Fit provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Fit has already been computed with request_feature_fit.

See get_feature_fit_metadata for retrieving information of source, backtest_index.

Parameters:	source: string The source Feature Fit are retrieved for. One value of [FeatureFitMetadataDatetime.sources]. To retrieve the availiable sources for feature fit. backtest_index: string, FeatureFitMetadataDatetime.backtest_index. The backtest index to retrieve Feature Fit for. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_fit: FeatureFit The feature fit data.
Raises:	ClientError (404) If the feature fit have not been computed or source is not valid value.

get_or_request_feature_fit(source, backtest_index, max_wait=600)¶

Retrieve feature fit for the model, requesting a job if it hasn’t been run previously

See get_feature_fit_metadata for retrieving information of source, backtest_index.

Parameters:

max_wait : int, optional: The maximum time to wait for a requested feature fit job to complete before erroring
source : string: The source Feature Fit are retrieved for. One value of [FeatureFitMetadataDatetime.sources]. To retrieve the availiable sources for feature effect.
backtest_index: string, FeatureFitMetadataDatetime.backtest_index.: The backtest index to retrieve Feature Fit for.

Returns:

feature_fit : FeatureFit: The feature fit data.

calculate_prediction_intervals(prediction_intervals_size)¶

Calculate prediction intervals for this DatetimeModel for the specified size.

New in version v2.19.

Parameters:	prediction_intervals_size : int The prediction interval’s size to calculate for this model. See the prediction intervals documentation for more information.
Returns:	job : Job a `Job` tracking the prediction intervals computation

get_calculated_prediction_intervals(offset=None, limit=None)¶

Retrieve a list of already-calculated prediction intervals for this model

New in version v2.19.

Parameters:	offset : int, optional If provided, this many results will be skipped limit : int, optional If provided, at most this many results will be returned. If not provided, will return at most 100 results.
Returns:	list[int] A descending-ordered list of already-calculated prediction interval sizes

compute_datetime_trend_plots(backtest=0, source='validation', forecast_distance_start=None, forecast_distance_end=None)¶

Computes datetime trend plots (Accuracy over Time, Forecast vs Actual, Anomaly over Time) for this model

New in version v2.25.

Parameters:

backtest : int or string, optional: Compute plots for a specific backtest (use the backtest index starting from zero). To compute plots for holdout, use dr.enums.DATA_SUBSET.HOLDOUT
source : string, optional: The source of the data for the backtest/holdout. Attribute must be one of dr.enums.SOURCE_TYPE
forecast_distance_start : int, optional:: The start of forecast distance range (forecast window) to compute. If not specified, the first forecast distance for this project will be used. Only for time series supervised models
forecast_distance_end : int, optional:: The end of forecast distance range (forecast window) to compute. If not specified, the last forecast distance for this project will be used. Only for time series supervised models

Returns:

job : Job: a Job tracking the datetime trend plots computation

Notes

Forecast distance specifies the number of time steps between the predicted point and the origin point.
For the multiseries models only first 1000 series in alphabetical order and an average plot for them will be computed.
Maximum 100 forecast distances can be requested for calculation in time series supervised projects.

get_accuracy_over_time_plots_metadata(forecast_distance=None)¶

Retrieve Accuracy over Time plots metadata for this model.

New in version v2.25.

Parameters:	forecast_distance : int, optional Forecast distance to retrieve the metadata for. If not specified, the first forecast distance for this project will be used. Only available for time series projects.
Returns:	metadata : AccuracyOverTimePlotsMetadata a `AccuracyOverTimePlotsMetadata` representing Accuracy over Time plots metadata

get_accuracy_over_time_plot(backtest=0, source='validation', forecast_distance=None, series_id=None, resolution=None, max_bin_size=None, start_date=None, end_date=None, max_wait=600)¶

Retrieve Accuracy over Time plots for this model.

New in version v2.25.

Parameters:

backtest : int or string, optional: Retrieve plots for a specific backtest (use the backtest index starting from zero). To retrieve plots for holdout, use dr.enums.DATA_SUBSET.HOLDOUT
source : string, optional: The source of the data for the backtest/holdout. Attribute must be one of dr.enums.SOURCE_TYPE
forecast_distance : int, optional: Forecast distance to retrieve the plots for. If not specified, the first forecast distance for this project will be used. Only available for time series projects.
series_id : string, optional: The name of the series to retrieve for multiseries projects. If not provided an average plot for the first 1000 series will be retrieved.
resolution : string, optional: Specifying at which resolution the data should be binned. If not provided an optimal resolution will be used to build chart data with number of bins <= max_bin_size. One of dr.enums.DATETIME_TREND_PLOTS_RESOLUTION.
max_bin_size : int, optional: An int between 1 and 1000, which specifies the maximum number of bins for the retrieval. Default is 500.
start_date : datetime.datetime, optional: The start of the date range to return. If not specified, start date for requested plot will be used.
end_date : datetime.datetime, optional: The end of the date range to return. If not specified, end date for requested plot will be used.
max_wait : int or None, optional: The maximum time to wait for a compute job to complete before retrieving the plots. Default is dr.enums.DEFAULT_MAX_WAIT. If 0 or None, the plots would be retrieved without attempting the computation.

Returns:

plot : AccuracyOverTimePlot: a AccuracyOverTimePlot representing Accuracy over Time plot

Examples

import datarobot as dr
import pandas as pd
model = dr.DatetimeModel(project_id=project_id, id=model_id)
plot = model.get_accuracy_over_time_plot()
df = pd.DataFrame.from_dict(plot.bins)
figure = df.plot("start_date", ["actual", "predicted"]).get_figure()
figure.savefig("accuracy_over_time.png")

get_accuracy_over_time_plot_preview(backtest=0, source='validation', forecast_distance=None, series_id=None, max_wait=600)¶

Retrieve Accuracy over Time preview plots for this model.

New in version v2.25.

Parameters:

backtest : int or string, optional: Retrieve plots for a specific backtest (use the backtest index starting from zero). To retrieve plots for holdout, use dr.enums.DATA_SUBSET.HOLDOUT
source : string, optional: The source of the data for the backtest/holdout. Attribute must be one of dr.enums.SOURCE_TYPE
forecast_distance : int, optional: Forecast distance to retrieve the plots for. If not specified, the first forecast distance for this project will be used. Only available for time series projects.
series_id : string, optional: The name of the series to retrieve for multiseries projects. If not provided an average plot for the first 1000 series will be retrieved.
max_wait : int or None, optional: The maximum time to wait for a compute job to complete before retrieving the plots. Default is dr.enums.DEFAULT_MAX_WAIT. If 0 or None, the plots would be retrieved without attempting the computation.

Returns:

plot : AccuracyOverTimePlotPreview: a AccuracyOverTimePlotPreview representing Accuracy over Time plot preview

Examples

import datarobot as dr
import pandas as pd
model = dr.DatetimeModel(project_id=project_id, id=model_id)
plot = model.get_accuracy_over_time_plot_preview()
df = pd.DataFrame.from_dict(plot.bins)
figure = df.plot("start_date", ["actual", "predicted"]).get_figure()
figure.savefig("accuracy_over_time_preview.png")

get_forecast_vs_actual_plots_metadata()¶

Retrieve Forecast vs Actual plots metadata for this model.

New in version v2.25.

Returns:	metadata : ForecastVsActualPlotsMetadata a `ForecastVsActualPlotsMetadata` representing Forecast vs Actual plots metadata

get_forecast_vs_actual_plot(backtest=0, source='validation', forecast_distance_start=None, forecast_distance_end=None, series_id=None, resolution=None, max_bin_size=None, start_date=None, end_date=None, max_wait=600)¶

Retrieve Forecast vs Actual plots for this model.

New in version v2.25.

Parameters:

backtest : int or string, optional: Retrieve plots for a specific backtest (use the backtest index starting from zero). To retrieve plots for holdout, use dr.enums.DATA_SUBSET.HOLDOUT
source : string, optional: The source of the data for the backtest/holdout. Attribute must be one of dr.enums.SOURCE_TYPE
forecast_distance_start : int, optional:: The start of forecast distance range (forecast window) to retrieve. If not specified, the first forecast distance for this project will be used.
forecast_distance_end : int, optional:: The end of forecast distance range (forecast window) to retrieve. If not specified, the last forecast distance for this project will be used.
series_id : string, optional: The name of the series to retrieve for multiseries projects. If not provided an average plot for the first 1000 series will be retrieved.
resolution : string, optional: Specifying at which resolution the data should be binned. If not provided an optimal resolution will be used to build chart data with number of bins <= max_bin_size. One of dr.enums.DATETIME_TREND_PLOTS_RESOLUTION.
max_bin_size : int, optional: An int between 1 and 1000, which specifies the maximum number of bins for the retrieval. Default is 500.
start_date : datetime.datetime, optional: The start of the date range to return. If not specified, start date for requested plot will be used.
end_date : datetime.datetime, optional: The end of the date range to return. If not specified, end date for requested plot will be used.
max_wait : int or None, optional: The maximum time to wait for a compute job to complete before retrieving the plots. Default is dr.enums.DEFAULT_MAX_WAIT. If 0 or None, the plots would be retrieved without attempting the computation.

Returns:

plot : ForecastVsActualPlot: a ForecastVsActualPlot representing Forecast vs Actual plot

Examples

import datarobot as dr
import pandas as pd
import matplotlib.pyplot as plt

model = dr.DatetimeModel(project_id=project_id, id=model_id)
plot = model.get_forecast_vs_actual_plot()
df = pd.DataFrame.from_dict(plot.bins)

# As an example, get the forecasts for the 10th point
forecast_point_index = 10
# Pad the forecasts for plotting. The forecasts length must match the df length
forecasts = [None] * forecast_point_index + df.forecasts[forecast_point_index]
forecasts = forecasts + [None] * (len(df) - len(forecasts))

plt.plot(df.start_date, df.actual, label="Actual")
plt.plot(df.start_date, forecasts, label="Forecast")
forecast_point = df.start_date[forecast_point_index]
plt.title("Forecast vs Actual (Forecast Point {})".format(forecast_point))
plt.legend()
plt.savefig("forecast_vs_actual.png")

get_forecast_vs_actual_plot_preview(backtest=0, source='validation', series_id=None, max_wait=600)¶

Retrieve Forecast vs Actual preview plots for this model.

New in version v2.25.

Parameters:

backtest : int or string, optional: Retrieve plots for a specific backtest (use the backtest index starting from zero). To retrieve plots for holdout, use dr.enums.DATA_SUBSET.HOLDOUT
source : string, optional: The source of the data for the backtest/holdout. Attribute must be one of dr.enums.SOURCE_TYPE
series_id : string, optional: The name of the series to retrieve for multiseries projects. If not provided an average plot for the first 1000 series will be retrieved.
max_wait : int or None, optional: The maximum time to wait for a compute job to complete before retrieving the plots. Default is dr.enums.DEFAULT_MAX_WAIT. If 0 or None, the plots would be retrieved without attempting the computation.

Returns:

plot : ForecastVsActualPlotPreview: a ForecastVsActualPlotPreview representing Forecast vs Actual plot preview

Examples

import datarobot as dr
import pandas as pd
model = dr.DatetimeModel(project_id=project_id, id=model_id)
plot = model.get_forecast_vs_actual_plot_preview()
df = pd.DataFrame.from_dict(plot.bins)
figure = df.plot("start_date", ["actual", "predicted"]).get_figure()
figure.savefig("forecast_vs_actual_preview.png")

get_anomaly_over_time_plots_metadata()¶

Retrieve Anomaly over Time plots metadata for this model.

New in version v2.25.

Returns:	metadata : AnomalyOverTimePlotsMetadata a `AnomalyOverTimePlotsMetadata` representing Anomaly over Time plots metadata

get_anomaly_over_time_plot(backtest=0, source='validation', series_id=None, resolution=None, max_bin_size=None, start_date=None, end_date=None, max_wait=600)¶

Retrieve Anomaly over Time plots for this model.

New in version v2.25.

Parameters:

backtest : int or string, optional: Retrieve plots for a specific backtest (use the backtest index starting from zero). To retrieve plots for holdout, use dr.enums.DATA_SUBSET.HOLDOUT
source : string, optional: The source of the data for the backtest/holdout. Attribute must be one of dr.enums.SOURCE_TYPE
series_id : string, optional: The name of the series to retrieve for multiseries projects. If not provided an average plot for the first 1000 series will be retrieved.
resolution : string, optional: Specifying at which resolution the data should be binned. If not provided an optimal resolution will be used to build chart data with number of bins <= max_bin_size. One of dr.enums.DATETIME_TREND_PLOTS_RESOLUTION.
max_bin_size : int, optional: An int between 1 and 1000, which specifies the maximum number of bins for the retrieval. Default is 500.
start_date : datetime.datetime, optional: The start of the date range to return. If not specified, start date for requested plot will be used.
end_date : datetime.datetime, optional: The end of the date range to return. If not specified, end date for requested plot will be used.
max_wait : int or None, optional: The maximum time to wait for a compute job to complete before retrieving the plots. Default is dr.enums.DEFAULT_MAX_WAIT. If 0 or None, the plots would be retrieved without attempting the computation.

Returns:

plot : AnomalyOverTimePlot: a AnomalyOverTimePlot representing Anomaly over Time plot

Examples

import datarobot as dr
import pandas as pd
model = dr.DatetimeModel(project_id=project_id, id=model_id)
plot = model.get_anomaly_over_time_plot()
df = pd.DataFrame.from_dict(plot.bins)
figure = df.plot("start_date", "predicted").get_figure()
figure.savefig("anomaly_over_time.png")

get_anomaly_over_time_plot_preview(prediction_threshold=0.5, backtest=0, source='validation', series_id=None, max_wait=600)¶

Retrieve Anomaly over Time preview plots for this model.

New in version v2.25.

Parameters:

prediction_threshold: float, optional: Only bins with predictions exceeding this threshold will be returned in the response.
backtest : int or string, optional: Retrieve plots for a specific backtest (use the backtest index starting from zero). To retrieve plots for holdout, use dr.enums.DATA_SUBSET.HOLDOUT
source : string, optional: The source of the data for the backtest/holdout. Attribute must be one of dr.enums.SOURCE_TYPE
series_id : string, optional: The name of the series to retrieve for multiseries projects. If not provided an average plot for the first 1000 series will be retrieved.
max_wait : int or None, optional: The maximum time to wait for a compute job to complete before retrieving the plots. Default is dr.enums.DEFAULT_MAX_WAIT. If 0 or None, the plots would be retrieved without attempting the computation.

Returns:

plot : AnomalyOverTimePlotPreview: a AnomalyOverTimePlotPreview representing Anomaly over Time plot preview

Examples

import datarobot as dr
import pandas as pd
import matplotlib.pyplot as plt

model = dr.DatetimeModel(project_id=project_id, id=model_id)
plot = model.get_anomaly_over_time_plot_preview(prediction_threshold=0.01)
df = pd.DataFrame.from_dict(plot.bins)
x = pd.date_range(
    plot.start_date, plot.end_date, freq=df.end_date[0] - df.start_date[0]
)
plt.plot(x, [0] * len(x), label="Date range")
plt.plot(df.start_date, [0] * len(df.start_date), "ro", label="Anomaly")
plt.yticks([])
plt.legend()
plt.savefig("anomaly_over_time_preview.png")

initialize_anomaly_assessment(backtest, source, series_id=None)¶

Initialize the anomaly assessment insight and calculate Shapley explanations for the most anomalous points in the subset. The insight is available for anomaly detection models in time series unsupervised projects which also support calculation of Shapley values.

Parameters:	backtest: int starting with 0 or “holdout” The backtest to compute insight for. source: “training” or “validation” The source to compute insight for. series_id: string Required for multiseries projects. The series id to compute insight for. Say if there is a series column containing cities, the example of the series name to pass would be “Boston”
Returns:	AnomalyAssessmentRecord

get_anomaly_assessment_records(backtest=None, source=None, series_id=None, limit=100, offset=0, with_data_only=False)¶

Retrieve computed Anomaly Assessment records for this model. Model must be an anomaly detection model in time series unsupervised project which also supports calculation of Shapley values.

Records can be filtered by the data backtest, source and series_id. The results can be limited.

New in version v2.25.

Parameters:

backtest: int starting with 0 or “holdout”: The backtest of the data to filter records by.
source: “training” or “validation”: The source of the data to filter records by.
series_id: string: The series id to filter records by.
limit: int, optional
offset: int, optional
with_data_only: bool, optional: Whether to return only records with preview and explanations available. False by default.

Returns:

records : list of AnomalyAssessmentRecord: a AnomalyAssessmentRecord representing Anomaly Assessment Record

advanced_tune(params, description=None)¶

Generate a new model with the specified advanced-tuning parameters

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Parameters:	params : dict Mapping of parameter ID to parameter value. The list of valid parameter IDs for a model can be found by calling get_advanced_tuning_parameters(). This endpoint does not need to include values for all parameters. If a parameter is omitted, its current_value will be used. description : unicode Human-readable string describing the newly advanced-tuned model
Returns:	ModelJob The created job to build the model

delete()¶: Delete a model from the project’s leaderboard.

download_export(filepath)¶

Download an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

Parameters:	filepath : str The path at which to save the exported model file.

download_scoring_code(file_name, source_code=False)¶

Download the Scoring Code JAR.

Parameters:	file_name : str File path where scoring code will be saved. source_code : bool, optional Set to True to download source code archive. It will not be executable.

download_training_artifact(file_name)¶

Retrieve trained artifact(s) from a model containing one or more custom tasks.

Artifact(s) will be downloaded to the specified local filepath.

Parameters:	file_name : str File path where trained model artifact(s) will be saved.

classmethod fetch_resource_data(url, join_endpoint=True)¶

(Deprecated.) Used to acquire model data directly from its url.

Consider using get instead, as this is a convenience function used for development of datarobot

Parameters:	url : str The resource we are acquiring join_endpoint : boolean, optional Whether the client’s endpoint should be joined to the URL before sending the request. Location headers are returned as absolute locations, so will _not_ need the endpoint
Returns:	model_data : dict The queried model’s data

get_advanced_tuning_parameters()¶

Get the advanced-tuning parameters available for this model.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:

dict

A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuningDescription and tuningParameters.

tuningDescription an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter.

tuningParameters is a list of a dicts, each has the following keys

parameterName : (unicode) name of the parameter (unique per task, see below)
parameterId : (unicode) opaque ID string uniquely identifying parameter
defaultValue : (*) default value of the parameter for the blueprint
currentValue : (*) value of the parameter that was used for this model
taskName : (unicode) name of the task that this parameter belongs to
constraints: (dict) see the notes below

Notes

The type of defaultValue and currentValue is defined by the constraints structure. It will be a string or numeric Python type.

constraints is a dict with at least one, possibly more, of the following keys. The presence of a key indicates that the parameter may take on the specified type. (If a key is absent, this means that the parameter may not take on the specified type.) If a key on constraints is present, its value will be a dict containing all of the fields described below for that key.

"constraints": {
    "select": {
        "values": [<list(basestring or number) : possible values>]
    },
    "ascii": {},
    "unicode": {},
    "int": {
        "min": <int : minimum valid value>,
        "max": <int : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "float": {
        "min": <float : minimum valid value>,
        "max": <float : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "intList": {
        "length": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <int : minimum valid value>,
        "max_val": <int : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "floatList": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <float : minimum valid value>,
        "max_val": <float : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    }
}

The keys have meaning as follows:

select: Rather than specifying a specific data type, if present, it indicates that the parameter is permitted to take on any of the specified values. Listed values may be of any string or real (non-complex) numeric type.
ascii: The parameter may be a unicode object that encodes simple ASCII characters. (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed constraints, ASCII keys currently may not contain either newlines or semicolons.
unicode: The parameter may be any Python unicode object.
int: The value may be an object of type int within the specified range (inclusive). Please note that the value will be passed around using the JSON format, and some JSON parsers have undefined behavior with integers outside of the range [-(2**53)+1, (2**53)-1].
float: The value may be an object of type float within the specified range (inclusive).
intList, floatList: The value may be a list of int or float objects, respectively, following constraints as specified respectively by the int and float types (above).

Many parameters only specify one key under constraints. If a parameter specifies multiple keys, the parameter may take on any value permitted by any key.

get_all_confusion_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all confusion matrices available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ConfusionChart Data for all available confusion charts for model.

get_all_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_multiclass_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_residuals_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ResidualsChart Data for all available model residuals charts.

get_all_roc_curves(fallback_to_parent_insights=False)¶

Retrieve a list of all ROC curves available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of RocCurve Data for all available model ROC curves.

get_confusion_chart(source, fallback_to_parent_insights=False)¶

Retrieve them model’s confusion matrix for the specified source.

Parameters:	source : str Confusion chart source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent if the confusion chart is not available for this model and the defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	ConfusionChart Model ConfusionChart data
Raises:	ClientError If the insight is not available for this model

get_cross_class_accuracy_scores()¶

Retrieves a list of Cross Class Accuracy scores for the model.

Returns:	json

get_data_disparity_insights(feature, class_name1, class_name2)¶

Retrieve a list of Cross Class Data Disparity insights for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. class_name1 : str One of the compared classes class_name2 : str Another compared class
Returns:	json

get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100)¶

Retrieve a list of Per Class Bias insights for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores. offset : int, optional Number of items to skip. limit : int, optional Number of items to return.
Returns:	json

get_feature_impact(with_metadata=False)¶

Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model.

Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features.

If a feature is a redundant feature, i.e. once other features are considered it doesn’t contribute much in addition, the ‘redundantWith’ value is the name of feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used.

Elsewhere this technique is sometimes called ‘Permutation Importance’.

Requires that Feature Impact has already been computed with request_feature_impact.

Parameters:	with_metadata : bool The flag indicating if the result should include the metadata as well.
Returns:	list or dict The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with actual data or just a list with that data. Each List item is a dict with the keys `featureName`, `impactNormalized`, and `impactUnnormalized`, `redundantWith` and `count`. For dict response available keys are: `featureImpacts` - Feature Impact data as a dictionary. Each item is a dict with keys: `featureName`, `impactNormalized`, and `impactUnnormalized`, and `redundantWith`. `shapBased` - A boolean that indicates whether Feature Impact was calculated using Shapley values. `ranRedundancyDetection` - A boolean that indicates whether redundant feature identification was run while calculating this Feature Impact. `rowCount` - An integer or None that indicates the number of rows that was used to calculate Feature Impact. For the Feature Impact calculated with the default logic, without specifying the rowCount, we return None here. `count` - An integer with the number of features under the `featureImpacts`.
Raises:	ClientError (404) If the feature impacts have not been computed.

get_features_used()¶

Query the server to determine which features were used.

Note that the data returned by this method is possibly different than the names of the features in the featurelist used by this model. This method will return the raw features that must be supplied in order for predictions to be generated on a new set of data. The featurelist, in contrast, would also include the names of derived features.

Returns:	features : list of str The names of the features used in the model.

get_frozen_child_models()¶

Retrieve the IDs for all models that are frozen from this model.

Returns:	A list of Models

get_labelwise_roc_curves(source, fallback_to_parent_insights=False)¶

Retrieve a list of LabelwiseRocCurve instances for the given source and all labels.

New in version v2.24.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	list of : class:LabelwiseRocCurve <datarobot.models.roc_curve.LabelwiseRocCurve> Labelwise ROC Curve instances for `source` and all labels
Raises:	ClientError If the insight is not available for this model

get_leaderboard_ui_permalink()¶

Returns:	url : str Permanent static hyperlink to this model at leaderboard.

get_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve the model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	LiftChart Model lift chart data
Raises:	ClientError If the insight is not available for this model

get_missing_report_info()¶

Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model.

Returns:	An iterable of MissingReportPerFeature The queried model missing report, sorted by missing count (DESCENDING order).

get_model_blueprint_chart()¶

Retrieve a diagram that can be used to understand data flow in the blueprint.

Returns:	ModelBlueprintChart The queried model blueprint chart.

get_model_blueprint_documents()¶

Get documentation for tasks used in this model.

Returns:	list of BlueprintTaskDocument All documents available for the model.

get_multiclass_feature_impact()¶

For multiclass it’s possible to calculate feature impact separately for each target class. The method for calculation is exactly the same, calculated in one-vs-all style for each target class.

Requires that Feature Impact has already been computed with request_feature_impact.

Returns:	feature_impacts : list of dict The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’.
Raises:	ClientError (404) If the multiclass feature impacts have not been computed.

get_multiclass_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_multilabel_lift_charts(source, fallback_to_parent_insights=False)¶

Retrieve model Lift charts for the specified source.

New in version v2.24.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_num_iterations_trained()¶

Retrieves the number of estimators trained by early-stopping tree-based models.

– versionadded:: v2.22

Returns:	projectId: str id of project containing the model modelId: str id of the model data: array list of numEstimatorsItem objects, one for each modeling stage. numEstimatorsItem will be of the form: stage: str indicates the modeling stage (for multi-stage models); None of single-stage models numIterations: int the number of estimators or iterations trained by the model

get_or_request_feature_impact(max_wait=600, **kwargs)¶

Retrieve feature impact for the model, requesting a job if it hasn’t been run previously

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature impact job to complete before erroring **kwargs Arbitrary keyword arguments passed to `request_feature_impact`.
Returns:	feature_impacts : list or dict The feature impact data. See `get_feature_impact` for the exact schema.

get_parameters()¶

Retrieve model parameters.

Returns:	ModelParameters Model parameters for this model.

get_pareto_front()¶

Retrieve the Pareto Front for a Eureqa model.

This method is only supported for Eureqa models.

Returns:	ParetoFront Model ParetoFront data

get_prime_eligibility()¶

Check if this model can be approximated with DataRobot Prime

Returns:	prime_eligibility : dict a dict indicating whether a model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message)

get_residuals_chart(source, fallback_to_parent_insights=False)¶

Retrieve model residuals chart for the specified source.

Parameters:	source : str Residuals chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent if the residuals chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return residuals data from this model’s parent.
Returns:	ResidualsChart Model residuals chart data
Raises:	ClientError If the insight is not available for this model

get_roc_curve(source, fallback_to_parent_insights=False)¶

Retrieve the ROC curve for the specified source.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	RocCurve Model ROC curve data
Raises:	ClientError If the insight is not available for this model

get_rulesets()¶

List the rulesets approximating this model generated by DataRobot Prime

If this model hasn’t been approximated yet, will return an empty list. Note that these are rulesets approximating this model, not rulesets used to construct this model.

Returns:	rulesets : list of Ruleset

get_supported_capabilities()¶

Retrieves a summary of the capabilities supported by a model.

New in version v2.14.

Returns:

supportsBlending: bool

whether the model supports blending

supportsMonotonicConstraints: bool

whether the model supports monotonic constraints

hasWordCloud: bool

whether the model has word cloud data available

eligibleForPrime: bool

whether the model is eligible for Prime

hasParameters: bool

whether the model has parameters that can be retrieved

supportsCodeGeneration: bool

(New in version v2.18) whether the model supports code generation

supportsShap: bool

(New in version v2.18) True if the model supports Shapley package. i.e. Shapley based: feature Importance

supportsEarlyStopping: bool

(New in version v2.22) True if this is an early stopping tree-based model and number of trained iterations can be retrieved.

get_word_cloud(exclude_stop_words=False)¶

Retrieve word cloud data for the model.

Parameters:	exclude_stop_words : bool, optional Set to True if you want stopwords filtered out of response.
Returns:	WordCloud Word cloud data for the model.

open_model_browser()¶

Opens model at project leaderboard in web browser.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

request_approximation()¶

Request an approximation of this model using DataRobot Prime

This will create several rulesets that could be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally.

Returns:	job : Job the job generating the rulesets

request_cross_class_accuracy_scores()¶

Request data disparity insights to be computed for the model.

Returns:	status_id : str A statusId of computation request.

request_data_disparity_insights(feature, compared_class_names)¶

Request data disparity insights to be computed for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. compared_class_names : list(str) List of two classes to compare
Returns:	status_id : str A statusId of computation request.

request_external_test(dataset_id, actual_value_column=None)¶

Request external test to compute scores and insights on an external test dataset

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
Returns
——-
job : Job: a Job representing external dataset insights computation

request_fairness_insights(fairness_metrics_set=None)¶

Request fairness insights to be computed for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores.
Returns:	status_id : str A statusId of computation request.

request_feature_impact(row_count=None, with_metadata=False)¶

Request feature impacts to be computed for the model.

See get_feature_impact for more information on the result of the job.

Parameters:	row_count : int The sample size (specified in rows) to use for Feature Impact computation. This is not supported for unsupervised, multi-class (that has a separate method) and time series projects.
Returns:	job : Job A Job representing the feature impact computation. To get the completed feature impact data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature impacts have already been requested.

request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None)¶

Train a new frozen model with parameters from this model.

Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job.

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

In addition of training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified.

Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked).

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, training_duration may not be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, training_row_count may not be specified.
training_start_date : datetime.datetime, optional: the start date of the data to train to model on. Only rows occurring at or after this datetime will be used. If training_start_date is specified, training_end_date must also be specified.
training_end_date : datetime.datetime, optional: the end date of the data to train the model on. Only rows occurring strictly before this datetime will be used. If training_end_date is specified, training_start_date must also be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.

Returns:

model_job : ModelJob: the modeling job training a frozen model

request_predictions(dataset_id, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None)¶

Requests predictions against a previously uploaded dataset.

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
include_prediction_intervals : bool, optional: (New in v2.16) For time series projects only. Specifies whether prediction intervals should be calculated for this request. Defaults to True if prediction_intervals_size is specified, otherwise defaults to False.
prediction_intervals_size : int, optional: (New in v2.16) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if include_prediction_intervals is True. Prediction intervals size must be between 1 and 100 (inclusive).
forecast_point : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
predictions_start_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
explanation_algorithm: (New in version v2.21) optional; If set to ‘shap’, the: response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).
max_explanations: (New in version v2.21) optional; specifies the maximum number of: explanation values that should be returned for each row, ordered by absolute value, greatest to least. If null, no limit. In the case of ‘shap’: if the number of features is greater than the limit, the sum of remaining values will also be returned as shapRemainingTotal. Defaults to null. Cannot be set if explanation_algorithm is omitted.

Returns:

job : PredictJob: The job computing the predictions

request_transferable_export(prediction_intervals_size=None)¶

Request generation of an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

This function does not download the exported file. Use download_export for that.

Parameters:	prediction_intervals_size : int, optional (New in v2.19) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Prediction intervals size must be between 1 and 100 (inclusive).

Examples

model = datarobot.Model.get('project-id', 'model-id')
job = model.request_transferable_export()
job.wait_for_completion()
model.download_export('my_exported_model.drmodel')

# Client must be configured to use standalone prediction server for import:
datarobot.Client(token='my-token-at-standalone-server',
                 endpoint='standalone-server-url/api/v2')

imported_model = datarobot.ImportedModel.create('my_exported_model.drmodel')

set_prediction_threshold(threshold)¶

Set a custom prediction threshold for the model.

May not be used once prediction_threshold_read_only is True for this model.

Parameters:	threshold : float only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive).

star_model()¶

Mark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

start_advanced_tuning_session()¶

Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:	AdvancedTuningSession Session for setting up and running Advanced Tuning on a model

train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>, use_project_settings=False, sampling_method=None)¶

Trains this model on a different featurelist or sample size.

Requires that this model is part of a datetime partitioned project; otherwise, an error will occur.

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

featurelist_id : str, optional: the featurelist to use to train the model. If not specified, the featurelist of this model is used.
training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, neither training_duration nor use_project_settings may be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, neither training_row_count nor use_project_settings may be specified.
use_project_settings : bool, optional: (New in version v2.20) defaults to False. If True, indicates that the custom backtest partitioning settings specified by the user will be used to train the model and evaluate backtest scores. If specified, neither training_row_count nor training_duration may be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.
monotonic_increasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.

Returns:

job : ModelJob: the created job to build the model

unstar_model()¶

Unmark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

Frozen Model¶

class datarobot.models.FrozenModel(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, model_type=None, model_category=None, is_frozen=None, blueprint_id=None, metrics=None, parent_model_id=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, model_number=None, supports_composable_ml=None)¶

Represents a model tuned with parameters which are derived from another model

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

id : str: the id of the model
project_id : str: the id of the project the model belongs to
processes : list of str: the processes used by the model
featurelist_name : str: the name of the featurelist used by the model
featurelist_id : str: the id of the featurelist used by the model
sample_pct : float: the percentage of the project dataset used in training the model
training_row_count : int or None: the number of rows of the project dataset used in training the model. In a datetime partitioned project, if specified, defines the number of rows used to train the model and evaluate backtest scores; if unspecified, either training_duration or training_start_date and training_end_date was used to determine that instead.
training_duration : str or None: only present for models in datetime partitioned projects. If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
training_start_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
training_end_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
model_type : str: what model this is, e.g. ‘Nystroem Kernel SVM Regressor’
model_category : str: what kind of model this is - ‘prime’ for DataRobot Prime models, ‘blend’ for blender models, and ‘model’ for other models
is_frozen : bool: whether this model is a frozen model
parent_model_id : str: the id of the model that tuning parameters are derived from
blueprint_id : str: the id of the blueprint used in this model
metrics : dict: a mapping from each metric to the model’s scores for that metric
monotonic_increasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced.
monotonic_decreasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced.
supports_monotonic_constraints : bool: optional, whether this model supports enforcing monotonic constraints
is_starred : bool: whether this model marked as starred
prediction_threshold : float: for binary classification projects, the threshold used for predictions
prediction_threshold_read_only : bool: indicated whether modification of the prediction threshold is forbidden. Threshold modification is forbidden once a model has had a deployment created or predictions made via the dedicated prediction API.
model_number : integer: model number assigned to a model
supports_composable_ml : bool or None: (New in version v2.26) whether this model is supported in the Composable ML.

classmethod get(project_id, model_id)¶

Retrieve a specific frozen model.

Parameters:	project_id : str The project’s id. model_id : str The `model_id` of the leaderboard item to retrieve.
Returns:	model : FrozenModel The queried instance.

Imported Model¶

Note

Imported Models are used in Stand Alone Scoring Engines. If you are not an administrator of such an engine, they are not relevant to you.

class datarobot.models.ImportedModel(id, imported_at=None, model_id=None, target=None, featurelist_name=None, dataset_name=None, model_name=None, project_id=None, version=None, note=None, origin_url=None, imported_by_username=None, project_name=None, created_by_username=None, created_by_id=None, imported_by_id=None, display_name=None)¶

Represents an imported model available for making predictions. These are only relevant for administrators of on-premise Stand Alone Scoring Engines.

ImportedModels are trained in one DataRobot application, exported as a .drmodel file, and then imported for use in a Stand Alone Scoring Engine.

Attributes:

id : str: id of the import
model_name : str: model type describing the model generated by DataRobot
display_name : str: manually specified human-readable name of the imported model
note : str: manually added node about this imported model
imported_at : datetime: the time the model was imported
imported_by_username : str: username of the user who imported the model
imported_by_id : str: id of the user who imported the model
origin_url : str: URL of the application the model was exported from
model_id : str: original id of the model prior to export
featurelist_name : str: name of the featurelist used to train the model
project_id : str: id of the project the model belonged to prior to export
project_name : str: name of the project the model belonged to prior to export
target : str: the target of the project the model belonged to prior to export
version : float: project version of the project the model belonged to
dataset_name : str: filename of the dataset used to create the project the model belonged to
created_by_username : str: username of the user who created the model prior to export
created_by_id : str: id of the user who created the model prior to export

classmethod create(path, max_wait=600)¶

Import a previously exported model for predictions.

Parameters:	path : str The path to the exported model file max_wait : int, optional Time in seconds after which model import is considered unsuccessful

classmethod get(import_id)¶

Retrieve imported model info

Parameters:	import_id : str The ID of the imported model.
Returns:	imported_model : ImportedModel The ImportedModel instance

classmethod list(limit=None, offset=None)¶

List the imported models.

Parameters:	limit : int The number of records to return. The server will use a (possibly finite) default if not specified. offset : int The number of records to skip.
Returns:	imported_models : list[ImportedModel]

update(display_name=None, note=None)¶

Update the display name or note for an imported model. The ImportedModel object is updated in place.

Parameters:	display_name : str The new display name. note : str The new note.

delete()¶: Delete this imported model.

RatingTableModel¶

class datarobot.models.RatingTableModel(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, model_type=None, model_category=None, is_frozen=None, blueprint_id=None, metrics=None, rating_table_id=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, model_number=None, supports_composable_ml=None)¶

A model that has a rating table.

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

id : str: the id of the model
project_id : str: the id of the project the model belongs to
processes : list of str: the processes used by the model
featurelist_name : str: the name of the featurelist used by the model
featurelist_id : str: the id of the featurelist used by the model
sample_pct : float or None: the percentage of the project dataset used in training the model. If the project uses datetime partitioning, the sample_pct will be None. See training_row_count, training_duration, and training_start_date and training_end_date instead.
training_row_count : int or None: the number of rows of the project dataset used in training the model. In a datetime partitioned project, if specified, defines the number of rows used to train the model and evaluate backtest scores; if unspecified, either training_duration or training_start_date and training_end_date was used to determine that instead.
training_duration : str or None: only present for models in datetime partitioned projects. If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
training_start_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
training_end_date : datetime or None: only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
model_type : str: what model this is, e.g. ‘Nystroem Kernel SVM Regressor’
model_category : str: what kind of model this is - ‘prime’ for DataRobot Prime models, ‘blend’ for blender models, and ‘model’ for other models
is_frozen : bool: whether this model is a frozen model
blueprint_id : str: the id of the blueprint used in this model
metrics : dict: a mapping from each metric to the model’s scores for that metric
rating_table_id : str: the id of the rating table that belongs to this model
monotonic_increasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced.
monotonic_decreasing_featurelist_id : str: optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced.
supports_monotonic_constraints : bool: optional, whether this model supports enforcing monotonic constraints
is_starred : bool: whether this model marked as starred
prediction_threshold : float: for binary classification projects, the threshold used for predictions
prediction_threshold_read_only : bool: indicated whether modification of the prediction threshold is forbidden. Threshold modification is forbidden once a model has had a deployment created or predictions made via the dedicated prediction API.
model_number : integer: model number assigned to a model
supports_composable_ml : bool or None: (New in version v2.26) whether this model is supported in the Composable ML.

classmethod get(project_id, model_id)¶

Retrieve a specific rating table model

If the project does not have a rating table, a ClientError will occur.

Parameters:	project_id : str the id of the project the model belongs to model_id : str the id of the model to retrieve
Returns:	model : RatingTableModel the model

classmethod create_from_rating_table(project_id, rating_table_id)¶

Creates a new model from a validated rating table record. The RatingTable must not be associated with an existing model.

Parameters:	project_id : str the id of the project the rating table belongs to rating_table_id : str the id of the rating table to create this model from
Returns:	job: Job an instance of created async job
Raises:	ClientError (422) Raised if creating model from a RatingTable that failed validation JobAlreadyRequested Raised if creating model from a RatingTable that is already associated with a RatingTableModel

advanced_tune(params, description=None)¶

Generate a new model with the specified advanced-tuning parameters

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Parameters:	params : dict Mapping of parameter ID to parameter value. The list of valid parameter IDs for a model can be found by calling get_advanced_tuning_parameters(). This endpoint does not need to include values for all parameters. If a parameter is omitted, its current_value will be used. description : unicode Human-readable string describing the newly advanced-tuned model
Returns:	ModelJob The created job to build the model

cross_validate()¶

Run cross validation on the model.

Note

To perform Cross Validation on a new model with new parameters, use train instead.

Returns:	ModelJob The created job to build the model

delete()¶: Delete a model from the project’s leaderboard.

download_export(filepath)¶

Download an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

Parameters:	filepath : str The path at which to save the exported model file.

download_scoring_code(file_name, source_code=False)¶

Download the Scoring Code JAR.

Parameters:	file_name : str File path where scoring code will be saved. source_code : bool, optional Set to True to download source code archive. It will not be executable.

download_training_artifact(file_name)¶

Retrieve trained artifact(s) from a model containing one or more custom tasks.

Artifact(s) will be downloaded to the specified local filepath.

Parameters:	file_name : str File path where trained model artifact(s) will be saved.

classmethod fetch_resource_data(url, join_endpoint=True)¶

(Deprecated.) Used to acquire model data directly from its url.

Consider using get instead, as this is a convenience function used for development of datarobot

Parameters:	url : str The resource we are acquiring join_endpoint : boolean, optional Whether the client’s endpoint should be joined to the URL before sending the request. Location headers are returned as absolute locations, so will _not_ need the endpoint
Returns:	model_data : dict The queried model’s data

get_advanced_tuning_parameters()¶

Get the advanced-tuning parameters available for this model.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:

dict

A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuningDescription and tuningParameters.

tuningDescription an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter.

tuningParameters is a list of a dicts, each has the following keys

parameterName : (unicode) name of the parameter (unique per task, see below)
parameterId : (unicode) opaque ID string uniquely identifying parameter
defaultValue : (*) default value of the parameter for the blueprint
currentValue : (*) value of the parameter that was used for this model
taskName : (unicode) name of the task that this parameter belongs to
constraints: (dict) see the notes below

Notes

The type of defaultValue and currentValue is defined by the constraints structure. It will be a string or numeric Python type.

constraints is a dict with at least one, possibly more, of the following keys. The presence of a key indicates that the parameter may take on the specified type. (If a key is absent, this means that the parameter may not take on the specified type.) If a key on constraints is present, its value will be a dict containing all of the fields described below for that key.

"constraints": {
    "select": {
        "values": [<list(basestring or number) : possible values>]
    },
    "ascii": {},
    "unicode": {},
    "int": {
        "min": <int : minimum valid value>,
        "max": <int : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "float": {
        "min": <float : minimum valid value>,
        "max": <float : maximum valid value>,
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "intList": {
        "length": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <int : minimum valid value>,
        "max_val": <int : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    },
    "floatList": {
        "min_length": <int : minimum valid length>,
        "max_length": <int : maximum valid length>
        "min_val": <float : minimum valid value>,
        "max_val": <float : maximum valid value>
        "supports_grid_search": <bool : True if Grid Search may be
                                        requested for this param>
    }
}

The keys have meaning as follows:

select: Rather than specifying a specific data type, if present, it indicates that the parameter is permitted to take on any of the specified values. Listed values may be of any string or real (non-complex) numeric type.
ascii: The parameter may be a unicode object that encodes simple ASCII characters. (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed constraints, ASCII keys currently may not contain either newlines or semicolons.
unicode: The parameter may be any Python unicode object.
int: The value may be an object of type int within the specified range (inclusive). Please note that the value will be passed around using the JSON format, and some JSON parsers have undefined behavior with integers outside of the range [-(2**53)+1, (2**53)-1].
float: The value may be an object of type float within the specified range (inclusive).
intList, floatList: The value may be a list of int or float objects, respectively, following constraints as specified respectively by the int and float types (above).

Many parameters only specify one key under constraints. If a parameter specifies multiple keys, the parameter may take on any value permitted by any key.

get_all_confusion_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all confusion matrices available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ConfusionChart Data for all available confusion charts for model.

get_all_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_multiclass_lift_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of LiftChart Data for all available model lift charts.
Raises:	ClientError If the insight is not available for this model

get_all_residuals_charts(fallback_to_parent_insights=False)¶

Retrieve a list of all Lift charts available for the model.

Parameters:	fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of ResidualsChart Data for all available model residuals charts.

get_all_roc_curves(fallback_to_parent_insights=False)¶

Retrieve a list of all ROC curves available for the model.

Parameters:	fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent for any source that is not available for this model and if this model has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent.
Returns:	list of RocCurve Data for all available model ROC curves.

get_confusion_chart(source, fallback_to_parent_insights=False)¶

Retrieve them model’s confusion matrix for the specified source.

Parameters:	source : str Confusion chart source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent if the confusion chart is not available for this model and the defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	ConfusionChart Model ConfusionChart data
Raises:	ClientError If the insight is not available for this model

get_cross_class_accuracy_scores()¶

Retrieves a list of Cross Class Accuracy scores for the model.

Returns:	json

get_cross_validation_scores(partition=None, metric=None)¶

Return a dictionary, keyed by metric, showing cross validation scores per partition.

Cross Validation should already have been performed using cross_validate or train.

Note

Models that computed cross validation before this feature was added will need to be deleted and retrained before this method can be used.

Parameters:	partition : float optional, the id of the partition (1,2,3.0,4.0,etc…) to filter results by can be a whole number positive integer or float value. 0 corresponds to the validation partition. metric: unicode optional name of the metric to filter to resulting cross validation scores by
Returns:	cross_validation_scores: dict A dictionary keyed by metric showing cross validation scores per partition.

get_data_disparity_insights(feature, class_name1, class_name2)¶

Retrieve a list of Cross Class Data Disparity insights for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. class_name1 : str One of the compared classes class_name2 : str Another compared class
Returns:	json

get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100)¶

Retrieve a list of Per Class Bias insights for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores. offset : int, optional Number of items to skip. limit : int, optional Number of items to return.
Returns:	json

get_feature_effect(source, include_ice_plots=False)¶

Retrieve Feature Effects for the model.

Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Effects are retrieved for. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_effects : FeatureEffects The feature effects data.
Raises:	ClientError (404) If the feature effects have not been computed or source is not valid value.

get_feature_effect_metadata()¶

Retrieve Feature Effects metadata. Response contains status and available model sources.

Feature Fit for the training partition is always available, with the exception of older projects that only supported Feature Fit for validation.
When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout.
Feature Effects for holdout is not available when holdout was not unlocked for the project.

Use source to retrieve Feature Effects, selecting one of the provided sources.

Returns:	feature_effect_metadata: FeatureEffectMetadata

get_feature_effects_multiclass(source='training', class_=None)¶

Retrieve Feature Effects for the multiclass model.

Feature Effects provide partial dependence and predicted vs actual values for top-500 features ordered by feature impact score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Effects has already been computed with request_feature_effect.

See get_feature_effect_metadata for retrieving information the availiable sources.

Parameters:	source : str The source Feature Effects are retrieved for. class_ : str or None The class name Feature Effects are retrieved for.
Returns:	list The list of multiclass feature effects.
Raises:	ClientError (404) If Feature Effects have not been computed or source is not valid value.

get_feature_fit(source, include_ice_plots=False)¶

Retrieve Feature Fit for the model.

Feature Fit provides partial dependence and predicted vs actual values for top-500 features ordered by feature importance score.

The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction.

Requires that Feature Fit has already been computed with request_feature_effect.

See get_feature_fit_metadata for retrieving information the availiable sources.

Parameters:	source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources]. include_ice_plots: bool, optional Indicates whether Individual Conditional Expectation (ICE) plots should be returned.
Returns:	feature_fit : FeatureFit The feature fit data.
Raises:	ClientError (404) If the feature fit have not been computed or source is not valid value.

get_feature_fit_metadata()¶

Retrieve Feature Fit metadata. Response contains status and available model sources.

Feature Fit of training is always available (except for the old project which supports only Feature Fit for validation).
When a model is trained into validation or holdout without stacked prediction (e.g. no out-of-sample prediction in validation or holdout), Feature Fit is not available for validation or holdout.
Feature Fit for holdout is not available when there is no holdout configured for the project.

source is expected parameter to retrieve Feature Fit. One of provided sources shall be used.

Returns:	feature_effect_metadata: FeatureFitMetadata

get_feature_impact(with_metadata=False)¶

Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model.

Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features.

If a feature is a redundant feature, i.e. once other features are considered it doesn’t contribute much in addition, the ‘redundantWith’ value is the name of feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used.

Elsewhere this technique is sometimes called ‘Permutation Importance’.

Requires that Feature Impact has already been computed with request_feature_impact.

Parameters:	with_metadata : bool The flag indicating if the result should include the metadata as well.
Returns:	list or dict The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with actual data or just a list with that data. Each List item is a dict with the keys `featureName`, `impactNormalized`, and `impactUnnormalized`, `redundantWith` and `count`. For dict response available keys are: `featureImpacts` - Feature Impact data as a dictionary. Each item is a dict with keys: `featureName`, `impactNormalized`, and `impactUnnormalized`, and `redundantWith`. `shapBased` - A boolean that indicates whether Feature Impact was calculated using Shapley values. `ranRedundancyDetection` - A boolean that indicates whether redundant feature identification was run while calculating this Feature Impact. `rowCount` - An integer or None that indicates the number of rows that was used to calculate Feature Impact. For the Feature Impact calculated with the default logic, without specifying the rowCount, we return None here. `count` - An integer with the number of features under the `featureImpacts`.
Raises:	ClientError (404) If the feature impacts have not been computed.

get_features_used()¶

Query the server to determine which features were used.

Note that the data returned by this method is possibly different than the names of the features in the featurelist used by this model. This method will return the raw features that must be supplied in order for predictions to be generated on a new set of data. The featurelist, in contrast, would also include the names of derived features.

Returns:	features : list of str The names of the features used in the model.

get_frozen_child_models()¶

Retrieve the IDs for all models that are frozen from this model.

Returns:	A list of Models

get_labelwise_roc_curves(source, fallback_to_parent_insights=False)¶

Retrieve a list of LabelwiseRocCurve instances for the given source and all labels.

New in version v2.24.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	list of : class:LabelwiseRocCurve <datarobot.models.roc_curve.LabelwiseRocCurve> Labelwise ROC Curve instances for `source` and all labels
Raises:	ClientError If the insight is not available for this model

get_leaderboard_ui_permalink()¶

Returns:	url : str Permanent static hyperlink to this model at leaderboard.

get_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve the model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	LiftChart Model lift chart data
Raises:	ClientError If the insight is not available for this model

get_missing_report_info()¶

Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model.

Returns:	An iterable of MissingReportPerFeature The queried model missing report, sorted by missing count (DESCENDING order).

get_model_blueprint_chart()¶

Retrieve a diagram that can be used to understand data flow in the blueprint.

Returns:	ModelBlueprintChart The queried model blueprint chart.

get_model_blueprint_documents()¶

Get documentation for tasks used in this model.

Returns:	list of BlueprintTaskDocument All documents available for the model.

get_multiclass_feature_impact()¶

For multiclass it’s possible to calculate feature impact separately for each target class. The method for calculation is exactly the same, calculated in one-vs-all style for each target class.

Requires that Feature Impact has already been computed with request_feature_impact.

Returns:	feature_impacts : list of dict The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’.
Raises:	ClientError (404) If the multiclass feature impacts have not been computed.

get_multiclass_lift_chart(source, fallback_to_parent_insights=False)¶

Retrieve model Lift chart for the specified source.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_multilabel_lift_charts(source, fallback_to_parent_insights=False)¶

Retrieve model Lift charts for the specified source.

New in version v2.24.

Parameters:	source : str Lift chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return lift chart data for this model’s parent if the lift chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return insight data from this model’s parent.
Returns:	list of LiftChart Model lift chart data for each saved target class
Raises:	ClientError If the insight is not available for this model

get_num_iterations_trained()¶

Retrieves the number of estimators trained by early-stopping tree-based models.

– versionadded:: v2.22

Returns:	projectId: str id of project containing the model modelId: str id of the model data: array list of numEstimatorsItem objects, one for each modeling stage. numEstimatorsItem will be of the form: stage: str indicates the modeling stage (for multi-stage models); None of single-stage models numIterations: int the number of estimators or iterations trained by the model

get_or_request_feature_effect(source, max_wait=600, row_count=None)¶

Retrieve feature effect for the model, requesting a job if it hasn’t been run previously

See get_feature_effect_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature effect job to complete before erroring row_count : int, optional (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less. source : string The source Feature Effects are retrieved for.
Returns:	feature_effects : FeatureEffects The feature effects data.

get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600)¶

Retrieve Feature Effects for the multiclass model, requesting a job if it hasn’t been run previously.

Parameters:

source : string: The source Feature Effects retrieve for.
class_ : str or None: The class name Feature Effects retrieve for.
row_count : int: The number of rows from dataset to use for Feature Impact calculation.
top_n_features : int or None: Number of top features (ranked by Feature Impact) used to calculate Feature Effects.
features : list or None: The list of features used to calculate Feature Effects.
max_wait : int, optional: The maximum time to wait for a requested Feature Effects job to complete before erroring.

Returns:

feature_effects : list of FeatureEffectsMulticlass: The list of multiclass feature effects data.

get_or_request_feature_fit(source, max_wait=600)¶

Retrieve feature fit for the model, requesting a job if it hasn’t been run previously

See get_feature_fit_metadata for retrieving information of source.

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature fit job to complete before erroring source : string The source Feature Fit are retrieved for. One value of [FeatureFitMetadata.sources].
Returns:	feature_effects : FeatureFit The feature fit data.

get_or_request_feature_impact(max_wait=600, **kwargs)¶

Retrieve feature impact for the model, requesting a job if it hasn’t been run previously

Parameters:	max_wait : int, optional The maximum time to wait for a requested feature impact job to complete before erroring **kwargs Arbitrary keyword arguments passed to `request_feature_impact`.
Returns:	feature_impacts : list or dict The feature impact data. See `get_feature_impact` for the exact schema.

get_parameters()¶

Retrieve model parameters.

Returns:	ModelParameters Model parameters for this model.

get_pareto_front()¶

Retrieve the Pareto Front for a Eureqa model.

This method is only supported for Eureqa models.

Returns:	ParetoFront Model ParetoFront data

get_prime_eligibility()¶

Check if this model can be approximated with DataRobot Prime

Returns:	prime_eligibility : dict a dict indicating whether a model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message)

get_residuals_chart(source, fallback_to_parent_insights=False)¶

Retrieve model residuals chart for the specified source.

Parameters:	source : str Residuals chart data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. fallback_to_parent_insights : bool Optional, if True, this will return residuals chart data for this model’s parent if the residuals chart is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return residuals data from this model’s parent.
Returns:	ResidualsChart Model residuals chart data
Raises:	ClientError If the insight is not available for this model

get_roc_curve(source, fallback_to_parent_insights=False)¶

Retrieve the ROC curve for the specified source.

Parameters:	source : str ROC curve data source. Check datarobot.enums.CHART_DATA_SOURCE for possible values. (New in version v2.23) For time series and OTV models, also accepts values backtest_2, backtest_3, …, up to the number of backtests in the model. fallback_to_parent_insights : bool (New in version v2.14) Optional, if True, this will return ROC curve data for this model’s parent if the ROC curve is not available for this model and the model has a defined parent model. If omitted or False, or there is no parent model, will not attempt to return data from this model’s parent.
Returns:	RocCurve Model ROC curve data
Raises:	ClientError If the insight is not available for this model

get_rulesets()¶

List the rulesets approximating this model generated by DataRobot Prime

If this model hasn’t been approximated yet, will return an empty list. Note that these are rulesets approximating this model, not rulesets used to construct this model.

Returns:	rulesets : list of Ruleset

get_supported_capabilities()¶

Retrieves a summary of the capabilities supported by a model.

New in version v2.14.

Returns:

supportsBlending: bool

whether the model supports blending

supportsMonotonicConstraints: bool

whether the model supports monotonic constraints

hasWordCloud: bool

whether the model has word cloud data available

eligibleForPrime: bool

whether the model is eligible for Prime

hasParameters: bool

whether the model has parameters that can be retrieved

supportsCodeGeneration: bool

(New in version v2.18) whether the model supports code generation

supportsShap: bool

(New in version v2.18) True if the model supports Shapley package. i.e. Shapley based: feature Importance

supportsEarlyStopping: bool

(New in version v2.22) True if this is an early stopping tree-based model and number of trained iterations can be retrieved.

get_word_cloud(exclude_stop_words=False)¶

Retrieve word cloud data for the model.

Parameters:	exclude_stop_words : bool, optional Set to True if you want stopwords filtered out of response.
Returns:	WordCloud Word cloud data for the model.

open_model_browser()¶

Opens model at project leaderboard in web browser.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

request_approximation()¶

Request an approximation of this model using DataRobot Prime

This will create several rulesets that could be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally.

Returns:	job : Job the job generating the rulesets

request_cross_class_accuracy_scores()¶

Request data disparity insights to be computed for the model.

Returns:	status_id : str A statusId of computation request.

request_data_disparity_insights(feature, compared_class_names)¶

Request data disparity insights to be computed for the model.

Parameters:	feature : str Bias and Fairnes protected feature name. compared_class_names : list(str) List of two classes to compare
Returns:	status_id : str A statusId of computation request.

request_external_test(dataset_id, actual_value_column=None)¶

Request external test to compute scores and insights on an external test dataset

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
Returns
——-
job : Job: a Job representing external dataset insights computation

request_fairness_insights(fairness_metrics_set=None)¶

Request fairness insights to be computed for the model.

Parameters:	fairness_metrics_set : str, optional Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores.
Returns:	status_id : str A statusId of computation request.

request_feature_effect(row_count=None)¶

Request feature effects to be computed for the model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int (New in version v2.21) The sample size to use for Feature Impact computation. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less.
Returns:	job : Job A Job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None)¶

Request Feature Effects computation for the multiclass model.

See get_feature_effect for more information on the result of the job.

Parameters:	row_count : int The number of rows from dataset to use for Feature Impact calculation. top_n_features : int or None Number of top features (ranked by feature impact) used to calculate Feature Effects. features : list or None The list of features used to calculate Feature Effects.
Returns:	job : Job A Job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete.

request_feature_fit()¶

Request feature fit to be computed for the model.

See get_feature_effect for more information on the result of the job.

Returns:	job : Job A Job representing the feature fit computation. To get the completed feature fit data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature effect have already been requested.

request_feature_impact(row_count=None, with_metadata=False)¶

Request feature impacts to be computed for the model.

See get_feature_impact for more information on the result of the job.

Parameters:	row_count : int The sample size (specified in rows) to use for Feature Impact computation. This is not supported for unsupervised, multi-class (that has a separate method) and time series projects.
Returns:	job : Job A Job representing the feature impact computation. To get the completed feature impact data, use job.get_result or job.get_result_when_complete.
Raises:	JobAlreadyRequested (422) If the feature impacts have already been requested.

request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None)¶

Train a new frozen model with parameters from this model.

Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job.

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

In addition of training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified.

Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked).

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, training_duration may not be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, training_row_count may not be specified.
training_start_date : datetime.datetime, optional: the start date of the data to train to model on. Only rows occurring at or after this datetime will be used. If training_start_date is specified, training_end_date must also be specified.
training_end_date : datetime.datetime, optional: the end date of the data to train the model on. Only rows occurring strictly before this datetime will be used. If training_end_date is specified, training_start_date must also be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.

Returns:

model_job : ModelJob: the modeling job training a frozen model

request_frozen_model(sample_pct=None, training_row_count=None)¶

Train a new frozen model with parameters from this model

Note

This method only works if project the model belongs to is not datetime partitioned. If it is, use request_frozen_datetime_model instead.

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

Parameters:	sample_pct : float optional, the percentage of the dataset to use with the model. If not provided, will use the value from this model. training_row_count : int (New in version v2.9) optional, the integer number of rows of the dataset to use with the model. Only one of sample_pct and training_row_count should be specified.
Returns:	model_job : ModelJob the modeling job training a frozen model

request_predictions(dataset_id, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None)¶

Requests predictions against a previously uploaded dataset.

Parameters:

dataset_id : string: The dataset to make predictions against (as uploaded from Project.upload_dataset)
include_prediction_intervals : bool, optional: (New in v2.16) For time series projects only. Specifies whether prediction intervals should be calculated for this request. Defaults to True if prediction_intervals_size is specified, otherwise defaults to False.
prediction_intervals_size : int, optional: (New in v2.16) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if include_prediction_intervals is True. Prediction intervals size must be between 1 and 100 (inclusive).
forecast_point : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
predictions_start_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date : datetime.datetime or None, optional: (New in version v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column can be used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
explanation_algorithm: (New in version v2.21) optional; If set to ‘shap’, the: response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).
max_explanations: (New in version v2.21) optional; specifies the maximum number of: explanation values that should be returned for each row, ordered by absolute value, greatest to least. If null, no limit. In the case of ‘shap’: if the number of features is greater than the limit, the sum of remaining values will also be returned as shapRemainingTotal. Defaults to null. Cannot be set if explanation_algorithm is omitted.

Returns:

job : PredictJob: The job computing the predictions

request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None)¶

Start a job to build training predictions

Parameters:

data_subset : str

data set definition to build predictions on. Choices are:

dr.enums.DATA_SUBSET.ALL or string all for all data available. Not valid for

models in datetime partitioned projects

dr.enums.DATA_SUBSET.VALIDATION_AND_HOLDOUT or string validationAndHoldout for

all data except training set. Not valid for models in datetime partitioned projects

dr.enums.DATA_SUBSET.HOLDOUT or string holdout for holdout data set only

dr.enums.DATA_SUBSET.ALL_BACKTESTS or string allBacktests for downloading

the predictions for all backtest validation folds. Requires the model to have successfully scored all backtests. Datetime partitioned projects only.

explanation_algorithm : dr.enums.EXPLANATIONS_ALGORITHM

(New in v2.21) Optional. If set to dr.enums.EXPLANATIONS_ALGORITHM.SHAP, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to None (no prediction explanations).

max_explanations : int

(New in v2.21) Optional. Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of dr.enums.EXPLANATIONS_ALGORITHM.SHAP: If not set, explanations are returned for all features. If the number of features is greater than the max_explanations, the sum of remaining values will also be returned as shap_remaining_total. Max 100. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Is ignored if explanation_algorithm is not set.

Returns:

Job: an instance of created async job

request_transferable_export(prediction_intervals_size=None)¶

Request generation of an exportable model file for use in an on-premise DataRobot standalone prediction environment.

This function can only be used if model export is enabled, and will only be useful if you have an on-premise environment in which to import it.

This function does not download the exported file. Use download_export for that.

Parameters:	prediction_intervals_size : int, optional (New in v2.19) For time series projects only. Represents the percentile to use for the size of the prediction intervals. Prediction intervals size must be between 1 and 100 (inclusive).

Examples

model = datarobot.Model.get('project-id', 'model-id')
job = model.request_transferable_export()
job.wait_for_completion()
model.download_export('my_exported_model.drmodel')

# Client must be configured to use standalone prediction server for import:
datarobot.Client(token='my-token-at-standalone-server',
                 endpoint='standalone-server-url/api/v2')

imported_model = datarobot.ImportedModel.create('my_exported_model.drmodel')

retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None)¶

Submit a job to the queue to train a blender model.

Parameters:

sample_pct: str, optional: The sample size in percents (1 to 100) to use in training. If this parameter is used then training_row_count should not be given.
featurelist_id : str, optional: The featurelist id
training_row_count : str, optional: The number of rows used to train the model. If this parameter is used, then sample_pct should not be given.
n_clusters: int, optional: (new in version 2.27) number of clusters to use in an unsupervised clustering model. This parameter is used only for unsupervised clustering models that do not determine the number of clusters automatically.

Returns:

job : ModelJob: The created job that is retraining the model

set_prediction_threshold(threshold)¶

Set a custom prediction threshold for the model.

May not be used once prediction_threshold_read_only is True for this model.

Parameters:	threshold : float only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive).

star_model()¶

Mark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

start_advanced_tuning_session()¶

Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution.

As of v2.17, all models other than blenders, open source, prime, scaleout, baseline and user-created support Advanced Tuning.

Returns:	AdvancedTuningSession Session for setting up and running Advanced Tuning on a model

train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>)¶

Train the blueprint used in model on a particular featurelist or amount of data.

This method creates a new training job for worker and appends it to the end of the queue for this project. After the job has finished you can get the newly trained model by retrieving it from the project leaderboard, or by retrieving the result of the job.

Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither are specified, a default of the maximum amount of data that can safely be used to train any blueprint without going into the validation data will be selected.

In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class.

Note

For datetime partitioned projects, see train_datetime instead.

Parameters:

sample_pct : float, optional: The amount of data to use for training, as a percentage of the project dataset from 0 to 100.
featurelist_id : str, optional: The identifier of the featurelist to use. If not defined, the featurelist of this model is used.
scoring_type : str, optional: Either SCORING_TYPE.validation or SCORING_TYPE.cross_validation. SCORING_TYPE.validation is available for every partitioning type, and indicates that the default model validation should be used for the project. If the project uses a form of cross-validation partitioning, SCORING_TYPE.cross_validation can also be used to indicate that all of the available training/validation combinations should be used to evaluate the model.
training_row_count : int, optional: The number of rows to use to train the requested model.
monotonic_increasing_featurelist_id : str: (new in version 2.11) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id : str: (new in version 2.11) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.

Returns:

model_job_id : str: id of created job, can be used as parameter to ModelJob.get method or wait_for_async_model_creation function

Examples

project = Project.get('project-id')
model = Model.get('project-id', 'model-id')
model_job_id = model.train(training_row_count=project.max_train_rows)

train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>, use_project_settings=False, sampling_method=None)¶

Trains this model on a different featurelist or sample size.

Requires that this model is part of a datetime partitioned project; otherwise, an error will occur.

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

featurelist_id : str, optional: the featurelist to use to train the model. If not specified, the featurelist of this model is used.
training_row_count : int, optional: the number of rows of data that should be used to train the model. If specified, neither training_duration nor use_project_settings may be specified.
training_duration : str, optional: a duration string specifying what time range the data used to train the model should span. If specified, neither training_row_count nor use_project_settings may be specified.
use_project_settings : bool, optional: (New in version v2.20) defaults to False. If True, indicates that the custom backtest partitioning settings specified by the user will be used to train the model and evaluate backtest scores. If specified, neither training_row_count nor training_duration may be specified.
time_window_sample_pct : int, optional: may only be specified when the requested model is a time window (e.g. duration or start and end dates). An integer between 1 and 99 indicating the percentage to sample by within the window. The points kept are determined by a random uniform sample. If specified, training_duration must be specified otherwise, the number of rows used to train the model and evaluate backtest scores and an error will occur.
sampling_method : str, optional: (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.
monotonic_increasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id : str, optional: (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.

Returns:

job : ModelJob: the created job to build the model

unstar_model()¶

Unmark the model as starred.

Model stars propagate to the web application and the API, and can be used to filter when listing models.

Combined Model¶

See API reference for Combined Model in Segmented Modeling API Reference

Advanced Tuning¶

class datarobot.models.advanced_tuning.AdvancedTuningSession(model)¶

A session enabling users to configure and run advanced tuning for a model.

Every model contains a set of one or more tasks. Every task contains a set of zero or more parameters. This class allows tuning the values of each parameter on each task of a model, before running that model.

This session is client-side only and is not persistent. Only the final model, constructed when run is called, is persisted on the DataRobot server.

Attributes:	description : basestring Description for the new advance-tuned model. Defaults to the same description as the base model.

get_task_names()¶

Get the list of task names that are available for this model

Returns:	list(basestring) List of task names

get_parameter_names(task_name)¶

Get the list of parameter names available for a specific task

Returns:	list(basestring) List of parameter names

set_parameter(value, task_name=None, parameter_name=None, parameter_id=None)¶

Set the value of a parameter to be used

The caller must supply enough of the optional arguments to this function to uniquely identify the parameter that is being set. For example, a less-common parameter name such as ‘building_block__complementary_error_function’ might only be used once (if at all) by a single task in a model. In which case it may be sufficient to simply specify ‘parameter_name’. But a more-common name such as ‘random_seed’ might be used by several of the model’s tasks, and it may be necessary to also specify ‘task_name’ to clarify which task’s random seed is to be set. This function only affects client-side state. It will not check that the new parameter value(s) are valid.

Parameters:	task_name : basestring Name of the task whose parameter needs to be set parameter_name : basestring Name of the parameter to set parameter_id : basestring ID of the parameter to set value : int, float, list, or basestring New value for the parameter, with legal values determined by the parameter being set
Raises:	NoParametersFoundException if no matching parameters are found. NonUniqueParametersException if multiple parameters matched the specified filtering criteria

get_parameters()¶

Returns the set of parameters available to this model

The returned parameters have one additional key, “value”, reflecting any new values that have been set in this AdvancedTuningSession. When the session is run, “value” will be used, or if it is unset, “current_value”.

Returns:	parameters : dict “Parameters” dictionary, same as specified on Model.get_advanced_tuning_params. An additional field is added per parameter to the ‘tuningParameters’ list in the dictionary: value : int, float, list, or basestring The current value of the parameter. None if none has been specified.

run()¶

Submit this model for Advanced Tuning.

Returns:	datarobot.models.modeljob.ModelJob The created job to build the model

ModelJob¶

datarobot.models.modeljob.wait_for_async_model_creation(project_id, model_job_id, max_wait=600)¶

Given a Project id and ModelJob id poll for status of process responsible for model creation until model is created.

Parameters:	project_id : str The identifier of the project model_job_id : str The identifier of the ModelJob max_wait : int, optional Time in seconds after which model creation is considered unsuccessful
Returns:	model : Model Newly created model
Raises:	AsyncModelCreationError Raised if status of fetched ModelJob object is `error` AsyncTimeoutError Model wasn’t created in time, specified by `max_wait` parameter

class datarobot.models.ModelJob(data, completed_resource_url=None)¶

Tracks asynchronous work being done within a project

Attributes:

id : int: the id of the job
project_id : str: the id of the project the job belongs to
status : str: the status of the job - will be one of datarobot.enums.QUEUE_STATUS
job_type : str: what kind of work the job is doing - will be ‘model’ for modeling jobs
is_blocked : bool: if true, the job is blocked (cannot be executed) until its dependencies are resolved
sample_pct : float: the percentage of the project’s dataset used in this modeling job
model_type : str: the model this job builds (e.g. ‘Nystroem Kernel SVM Regressor’)
processes : list of str: the processes used by the model
featurelist_id : str: the id of the featurelist used in this modeling job
blueprint : Blueprint: the blueprint used in this modeling job

classmethod from_job(job)¶

Transforms a generic Job into a ModelJob

Parameters:	job: Job A generic job representing a ModelJob
Returns:	model_job: ModelJob A fully populated ModelJob with all the details of the job
Raises:	ValueError: If the generic Job was not a model job, e.g. job_type != JOB_TYPE.MODEL

classmethod get(project_id, model_job_id)¶

Fetches one ModelJob. If the job finished, raises PendingJobFinished exception.

Parameters:	project_id : str The identifier of the project the model belongs to model_job_id : str The identifier of the model_job
Returns:	model_job : ModelJob The pending ModelJob
Raises:	PendingJobFinished If the job being queried already finished, and the server is re-routing to the finished model. AsyncFailureError Querying this resource gave a status code other than 200 or 303

classmethod get_model(project_id, model_job_id)¶

Fetches a finished model from the job used to create it.

Parameters:	project_id : str The identifier of the project the model belongs to model_job_id : str The identifier of the model_job
Returns:	model : Model The finished model
Raises:	JobNotFinished If the job has not finished yet AsyncFailureError Querying the model_job in question gave a status code other than 200 or 303

cancel()¶: Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)¶

Parameters:	params : dict or None Query parameters to be added to request to get results. For featureEffects and featureFit, source param is required to define source, otherwise the default is `training`
Returns:	result : object Return type depends on the job type: for model jobs, a Model is returned for predict jobs, a pandas.DataFrame (with predictions) is returned for featureImpact jobs, a list of dicts by default (see `with_metadata` parameter of the `FeatureImpactJob` class and its `get()` method). for primeRulesets jobs, a list of Rulesets for primeModel jobs, a PrimeModel for primeDownloadValidation jobs, a PrimeFile for reasonCodesInitialization jobs, a ReasonCodesInitialization for reasonCodes jobs, a ReasonCodes for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization for predictionExplanations jobs, a PredictionExplanations for featureEffects, a FeatureEffects for featureFit, a FeatureFit
Raises:	JobNotFinished If the job is not finished, the result is not available. AsyncProcessUnsuccessfulError If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)¶

Parameters:	max_wait : int, optional How long to wait for the job to finish. params : dict, optional Query parameters to be added to request.
Returns:	result: object Return type is the same as would be returned by Job.get_result.
Raises:	AsyncTimeoutError If the job does not finish in time AsyncProcessUnsuccessfulError If the job errored or was aborted

refresh()¶: Update this object with the latest job data from the server.

wait_for_completion(max_wait=600)¶

Waits for job to complete.

Parameters:	max_wait : int, optional How long to wait for the job to finish.

Pareto Front¶

class datarobot.models.pareto_front.ParetoFront(project_id, error_metric, hyperparameters, target_type, solutions)¶

Pareto front data for a Eureqa model.

The pareto front reflects the tradeoffs between error and complexity for particular model. The solutions reflect possible Eureqa models that are different levels of complexity. By default, only one solution will have a corresponding model, but models can be created for each solution.

Attributes:

project_id : str: the ID of the project the model belongs to
error_metric : str: Eureqa error-metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do NOT correspond 1:1 with DataRobot error metrics – the available metrics are not the same, and are computed from a subset of the training data rather than from the validation data.
hyperparameters : dict: Hyperparameters used by this run of the Eureqa blueprint
target_type : str: Indicating what kind of modeling is being done in this project, either ‘Regression’, ‘Binary’ (Binary classification), or ‘Multiclass’ (Multiclass classification).
solutions : list(Solution): Solutions that Eureqa has found to model this data. Some solutions will have greater accuracy. Others will have slightly less accuracy but will use simpler expressions.

classmethod from_server_data(data, keep_attrs=None)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place keep_attrs : list List of the dotted namespace notations for attributes to keep within the object structure even if their values are None

class datarobot.models.pareto_front.Solution(eureqa_solution_id, complexity, error, expression, expression_annotated, best_model, project_id)¶

Eureqa Solution.

A solution represents a possible Eureqa model; however not all solutions have models associated with them. It must have a model created before it can be used to make predictions, etc.

Attributes:

eureqa_solution_id: str: ID of this Solution
complexity: int: Complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The Complexity calculation can be tuned via model hyperparameters.
error: float or None: Error for the current solution, as computed by Eureqa using the ‘error_metric’ error metric. It will be None if model refitted existing solution.
expression: str: Eureqa model equation string.
expression_annotated: str: Eureqa model equation string with variable names tagged for easy identification.
best_model: bool: True, if the model is determined to be the best

create_model()¶: Add this solution to the leaderboard, if it is not already present.

Partitioning¶

class datarobot.RandomCV(holdout_pct, reps, seed=0)¶

A partition in which observations are randomly assigned to cross-validation groups and the holdout set.

Parameters:	holdout_pct : int the desired percentage of dataset to assign to holdout set reps : int number of cross validation folds to use seed : int a seed to use for randomization

class datarobot.StratifiedCV(holdout_pct, reps, seed=0)¶

A partition in which observations are randomly assigned to cross-validation groups and the holdout set, preserving in each group the same ratio of positive to negative cases as in the original data.

Parameters:	holdout_pct : int the desired percentage of dataset to assign to holdout set reps : int number of cross validation folds to use seed : int a seed to use for randomization

class datarobot.GroupCV(holdout_pct, reps, partition_key_cols, seed=0)¶

A partition in which one column is specified, and rows sharing a common value for that column are guaranteed to stay together in the partitioning into cross-validation groups and the holdout set.

Parameters:	holdout_pct : int the desired percentage of dataset to assign to holdout set reps : int number of cross validation folds to use partition_key_cols : list a list containing a single string, where the string is the name of the column whose values should remain together in partitioning seed : int a seed to use for randomization

class datarobot.UserCV(user_partition_col, cv_holdout_level, seed=0)¶

A partition where the cross-validation folds and the holdout set are specified by the user.

Parameters:	user_partition_col : string the name of the column containing the partition assignments cv_holdout_level the value of the partition column indicating a row is part of the holdout set seed : int a seed to use for randomization

class datarobot.RandomTVH(holdout_pct, validation_pct, seed=0)¶

Specifies a partitioning method in which rows are randomly assigned to training, validation, and holdout.

Parameters:	holdout_pct : int the desired percentage of dataset to assign to holdout set validation_pct : int the desired percentage of dataset to assign to validation set seed : int a seed to use for randomization

class datarobot.UserTVH(user_partition_col, training_level, validation_level, holdout_level, seed=0)¶

Specifies a partitioning method in which rows are assigned by the user to training, validation, and holdout sets.

Parameters:

user_partition_col : string: the name of the column containing the partition assignments
training_level: the value of the partition column indicating a row is part of the training set
validation_level: the value of the partition column indicating a row is part of the validation set
holdout_level: the value of the partition column indicating a row is part of the holdout set (use None if you want no holdout set)
seed : int: a seed to use for randomization

class datarobot.StratifiedTVH(holdout_pct, validation_pct, seed=0)¶

A partition in which observations are randomly assigned to train, validation, and holdout sets, preserving in each group the same ratio of positive to negative cases as in the original data.

Parameters:	holdout_pct : int the desired percentage of dataset to assign to holdout set validation_pct : int the desired percentage of dataset to assign to validation set seed : int a seed to use for randomization

class datarobot.GroupTVH(holdout_pct, validation_pct, partition_key_cols, seed=0)¶

A partition in which one column is specified, and rows sharing a common value for that column are guaranteed to stay together in the partitioning into the training, validation, and holdout sets.

Parameters:	holdout_pct : int the desired percentage of dataset to assign to holdout set validation_pct : int the desired percentage of dataset to assign to validation set partition_key_cols : list a list containing a single string, where the string is the name of the column whose values should remain together in partitioning seed : int a seed to use for randomization

class datarobot.DatetimePartitioningSpecification(datetime_partition_column, autopilot_data_selection_method=None, validation_duration=None, holdout_start_date=None, holdout_duration=None, disable_holdout=None, gap_duration=None, number_of_backtests=None, backtests=None, use_time_series=False, default_to_known_in_advance=False, default_to_do_not_derive=False, feature_derivation_window_start=None, feature_derivation_window_end=None, feature_settings=None, forecast_window_start=None, forecast_window_end=None, windows_basis_unit=None, treat_as_exponential=None, differencing_method=None, periodicities=None, multiseries_id_columns=None, use_cross_series_features=None, aggregation_type=None, cross_series_group_by_columns=None, calendar_id=None, holdout_end_date=None, unsupervised_mode=False, model_splits=None, allow_partial_history_time_series_predictions=False)¶

Uniquely defines a DatetimePartitioning for some project

Includes only the attributes of DatetimePartitioning that are directly controllable by users, not those determined by the DataRobot application based on the project dataset and the user-controlled settings.

This is the specification that should be passed to Project.set_target via the partitioning_method parameter. To see the full partitioning based on the project dataset, use DatetimePartitioning.generate.

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Note that either (holdout_start_date, holdout_duration) or (holdout_start_date, holdout_end_date) can be used to specify holdout partitioning settings.

Attributes:

datetime_partition_column : str: the name of the column whose values as dates are used to assign a row to a particular partition
autopilot_data_selection_method : str: one of datarobot.enums.DATETIME_AUTOPILOT_DATA_SELECTION_METHOD. Whether models created by the autopilot should use “rowCount” or “duration” as their data_selection_method.
validation_duration : str or None: the default validation_duration for the backtests
holdout_start_date : datetime.datetime or None: The start date of holdout scoring data. If holdout_start_date is specified, either holdout_duration or holdout_end_date must also be specified. If disable_holdout is set to True, holdout_start_date, holdout_duration, and holdout_end_date may not be specified.
holdout_duration : str or None: The duration of the holdout scoring data. If holdout_duration is specified, holdout_start_date must also be specified. If disable_holdout is set to True, holdout_duration, holdout_start_date, and holdout_end_date may not be specified.
holdout_end_date : datetime.datetime or None: The end date of holdout scoring data. If holdout_end_date is specified, holdout_start_date must also be specified. If disable_holdout is set to True, holdout_end_date, holdout_start_date, and holdout_duration may not be specified.
disable_holdout : bool or None: (New in version v2.8) Whether to suppress allocating a holdout fold. If set to True, holdout_start_date, holdout_duration, and holdout_end_date may not be specified.
gap_duration : str or None: The duration of the gap between training and holdout scoring data
number_of_backtests : int or None: the number of backtests to use
backtests : list of BacktestSpecification: the exact specification of backtests to use. The indexes of the specified backtests should range from 0 to number_of_backtests - 1. If any backtest is left unspecified, a default configuration will be chosen.
use_time_series : bool: (New in version v2.8) Whether to create a time series project (if True) or an OTV project which uses datetime partitioning (if False). The default behaviour is to create an OTV project.
default_to_known_in_advance : bool: (New in version v2.11) Optional, default False. Used for time series projects only. Sets whether all features default to being treated as known in advance. Known in advance features are expected to be known for dates in the future when making predictions, e.g., “is this a holiday?”. Individual features can be set to a value different than the default using the feature_settings parameter.
default_to_do_not_derive : bool: (New in v2.17) Optional, default False. Used for time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the feature_settings parameter.
feature_derivation_window_start : int or None: (New in version v2.8) Only used for time series projects. Offset into the past to define how far back relative to the forecast point the feature derivation window should start. Expressed in terms of the windows_basis_unit and should be negative or zero.
feature_derivation_window_end : int or None: (New in version v2.8) Only used for time series projects. Offset into the past to define how far back relative to the forecast point the feature derivation window should end. Expressed in terms of the windows_basis_unit and should be a positive value.
feature_settings : list of FeatureSettings: (New in version v2.9) Optional, a list specifying per feature settings, can be left unspecified.
forecast_window_start : int or None: (New in version v2.8) Only used for time series projects. Offset into the future to define how far forward relative to the forecast point the forecast window should start. Expressed in terms of the windows_basis_unit.
forecast_window_end : int or None: (New in version v2.8) Only used for time series projects. Offset into the future to define how far forward relative to the forecast point the forecast window should end. Expressed in terms of the windows_basis_unit.
windows_basis_unit : string, optional: (New in version v2.14) Only used for time series projects. Indicates which unit is a basis for feature derivation window and forecast window. Valid options are detected time unit (one of the datarobot.enums.TIME_UNITS) or “ROW”. If omitted, the default value is the detected time unit.
treat_as_exponential : string, optional: (New in version v2.9) defaults to “auto”. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. Use values from the datarobot.enums.TREAT_AS_EXPONENTIAL enum.
differencing_method : string, optional: (New in version v2.9) defaults to “auto”. Used to specify which differencing method to apply of case if data is stationary. Use values from datarobot.enums.DIFFERENCING_METHOD enum.
periodicities : list of Periodicity, optional: (New in version v2.9) a list of datarobot.Periodicity. Periodicities units should be “ROW”, if the windows_basis_unit is “ROW”.
multiseries_id_columns : list of str or null: (New in version v2.11) a list of the names of multiseries id columns to define series within the training data. Currently only one multiseries id column is supported.
use_cross_series_features : bool: (New in version v2.14) Whether to use cross series features.
aggregation_type : str, optional: (New in version v2.14) The aggregation type to apply when creating cross series features. Optional, must be one of “total” or “average”.
cross_series_group_by_columns : list of str, optional: (New in version v2.15) List of columns (currently of length 1). Optional setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like “men’s clothing”, “sports equipment”, etc.. Can only be used in a multiseries project with use_cross_series_features set to True.
calendar_id : str, optional: (New in version v2.15) The id of the CalendarFile to use with this project.
unsupervised_mode: bool, optional: (New in version v2.20) defaults to False, indicates whether partitioning should be constructed for the unsupervised project.
model_splits: int, optional: (New in version v2.21) Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of model splits will allow for less downsampling leading to the use of more post-processed data.
allow_partial_history_time_series_predictions: bool, optional: (New in version v2.24) Wheter to allow time series models to make predictions using partial historical data.

collect_payload()¶: Set up the dict that should be sent to the server when setting the target Returns ——- partitioning_spec : dict

prep_payload(project_id, max_wait=600)¶

Run any necessary validation and prep of the payload, including async operations

Mainly used for the datetime partitioning spec but implemented in general for consistency

class datarobot.BacktestSpecification(index, gap_duration=None, validation_start_date=None, validation_duration=None, validation_end_date=None, primary_training_start_date=None, primary_training_end_date=None)¶

Uniquely defines a Backtest used in a DatetimePartitioning

Includes only the attributes of a backtest directly controllable by users. The other attributes are assigned by the DataRobot application based on the project dataset and the user-controlled settings.

There are two ways to specify an individual backtest:

Option 1: Use index, gap_duration, validation_start_date, and validation_duration. All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method.

import datarobot as dr

partitioning_spec = dr.DatetimePartitioningSpecification(
    backtests=[
        # modify the first backtest using option 1
        dr.BacktestSpecification(
            index=0,
            gap_duration=dr.partitioning_methods.construct_duration_string(),
            validation_start_date=datetime(year=2010, month=1, day=1),
            validation_duration=dr.partitioning_methods.construct_duration_string(years=1),
        )
    ],
    # other partitioning settings...
)

Option 2 (New in version v2.20): Use index, primary_training_start_date, primary_training_end_date, validation_start_date, and validation_end_date. In this case, note that setting primary_training_end_date and validation_start_date to the same timestamp will result with no gap being created.

import datarobot as dr

partitioning_spec = dr.DatetimePartitioningSpecification(
    backtests=[
        # modify the first backtest using option 2
        dr.BacktestSpecification(
            index=0,
            primary_training_start_date=datetime(year=2005, month=1, day=1),
            primary_training_end_date=datetime(year=2010, month=1, day=1),
            validation_start_date=datetime(year=2010, month=1, day=1),
            validation_end_date=datetime(year=2011, month=1, day=1),
        )
    ],
    # other partitioning settings...
)

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

index : int: the index of the backtest to update
gap_duration : str: a duration string specifying the desired duration of the gap between training and validation scoring data for the backtest
validation_start_date : datetime.datetime: the desired start date of the validation scoring data for this backtest
validation_duration : str: a duration string specifying the desired duration of the validation scoring data for this backtest
validation_end_date : datetime.datetime: the desired end date of the validation scoring data for this backtest
primary_training_start_date : datetime.datetime: the desired start date of the training partition for this backtest
primary_training_end_date : datetime.datetime: the desired end date of the training partition for this backtest

class datarobot.FeatureSettings(feature_name, known_in_advance=None, do_not_derive=None)¶

Per feature settings

Attributes:

feature_name : string: name of the feature
known_in_advance : bool: (New in version v2.11) Optional, for time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the default_to_known_in_advance flag.
do_not_derive : bool: (New in v2.17) Optional, for time series projects only. Sets whether the feature is excluded from feature derivation. If not specified, the feature uses the value from the default_to_do_not_derive flag.

class datarobot.Periodicity(time_steps, time_unit)¶

Periodicity configuration

Parameters:	time_steps : int Time step value time_unit : string Time step unit, valid options are values from datarobot.enums.TIME_UNITS

Examples

from datarobot as dr
periodicities = [
    dr.Periodicity(time_steps=10, time_unit=dr.enums.TIME_UNITS.HOUR),
    dr.Periodicity(time_steps=600, time_unit=dr.enums.TIME_UNITS.MINUTE)]
spec = dr.DatetimePartitioningSpecification(
    # ...
    periodicities=periodicities
)

class datarobot.DatetimePartitioning(project_id=None, datetime_partition_column=None, date_format=None, autopilot_data_selection_method=None, validation_duration=None, available_training_start_date=None, available_training_duration=None, available_training_row_count=None, available_training_end_date=None, primary_training_start_date=None, primary_training_duration=None, primary_training_row_count=None, primary_training_end_date=None, gap_start_date=None, gap_duration=None, gap_row_count=None, gap_end_date=None, disable_holdout=None, holdout_start_date=None, holdout_duration=None, holdout_row_count=None, holdout_end_date=None, number_of_backtests=None, backtests=None, total_row_count=None, use_time_series=False, default_to_known_in_advance=False, default_to_do_not_derive=False, feature_derivation_window_start=None, feature_derivation_window_end=None, feature_settings=None, forecast_window_start=None, forecast_window_end=None, windows_basis_unit=None, treat_as_exponential=None, differencing_method=None, periodicities=None, multiseries_id_columns=None, number_of_known_in_advance_features=0, number_of_do_not_derive_features=0, use_cross_series_features=None, aggregation_type=None, cross_series_group_by_columns=None, calendar_id=None, calendar_name=None, model_splits=None, allow_partial_history_time_series_predictions=False)¶

Full partitioning of a project for datetime partitioning.

To instantiate, use DatetimePartitioning.get(project_id).

Includes both the attributes specified by the user, as well as those determined by the DataRobot application based on the project dataset. In order to use a partitioning to set the target, call to_specification and pass the resulting DatetimePartitioningSpecification to Project.set_target via the partitioning_method parameter.

The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available.

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

project_id : str: the id of the project this partitioning applies to
datetime_partition_column : str: the name of the column whose values as dates are used to assign a row to a particular partition
date_format : str: the format (e.g. “%Y-%m-%d %H:%M:%S”) by which the partition column was interpreted (compatible with strftime)
autopilot_data_selection_method : str: one of datarobot.enums.DATETIME_AUTOPILOT_DATA_SELECTION_METHOD. Whether models created by the autopilot use “rowCount” or “duration” as their data_selection_method.
validation_duration : str or None: the validation duration specified when initializing the partitioning - not directly significant if the backtests have been modified, but used as the default validation_duration for the backtests. Can be absent if this is a time series project with an irregular primary date/time feature.
available_training_start_date : datetime.datetime: The start date of the available training data for scoring the holdout
available_training_duration : str: The duration of the available training data for scoring the holdout
available_training_row_count : int or None: The number of rows in the available training data for scoring the holdout. Only available when retrieving the partitioning after setting the target.
available_training_end_date : datetime.datetime: The end date of the available training data for scoring the holdout
primary_training_start_date : datetime.datetime or None: The start date of primary training data for scoring the holdout. Unavailable when the holdout fold is disabled.
primary_training_duration : str: The duration of the primary training data for scoring the holdout
primary_training_row_count : int or None: The number of rows in the primary training data for scoring the holdout. Only available when retrieving the partitioning after setting the target.
primary_training_end_date : datetime.datetime or None: The end date of the primary training data for scoring the holdout. Unavailable when the holdout fold is disabled.
gap_start_date : datetime.datetime or None: The start date of the gap between training and holdout scoring data. Unavailable when the holdout fold is disabled.
gap_duration : str: The duration of the gap between training and holdout scoring data
gap_row_count : int or None: The number of rows in the gap between training and holdout scoring data. Only available when retrieving the partitioning after setting the target.
gap_end_date : datetime.datetime or None: The end date of the gap between training and holdout scoring data. Unavailable when the holdout fold is disabled.
disable_holdout : bool or None: Whether to suppress allocating a holdout fold. If set to True, holdout_start_date, holdout_duration, and holdout_end_date may not be specified.
holdout_start_date : datetime.datetime or None: The start date of holdout scoring data. Unavailable when the holdout fold is disabled.
holdout_duration : str: The duration of the holdout scoring data
holdout_row_count : int or None: The number of rows in the holdout scoring data. Only available when retrieving the partitioning after setting the target.
holdout_end_date : datetime.datetime or None: The end date of the holdout scoring data. Unavailable when the holdout fold is disabled.
number_of_backtests : int: the number of backtests used.
backtests : list of Backtest: the configured backtests.
total_row_count : int: the number of rows in the project dataset. Only available when retrieving the partitioning after setting the target.
use_time_series : bool: (New in version v2.8) Whether to create a time series project (if True) or an OTV project which uses datetime partitioning (if False). The default behaviour is to create an OTV project.
default_to_known_in_advance : bool: (New in version v2.11) Optional, default False. Used for time series projects only. Sets whether all features default to being treated as known in advance. Known in advance features are expected to be known for dates in the future when making predictions, e.g., “is this a holiday?”. Individual features can be set to a value different from the default using the feature_settings parameter.
default_to_do_not_derive : bool: (New in v2.17) Optional, default False. Used for time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different from the default by using the feature_settings parameter.
feature_derivation_window_start : int or None: (New in version v2.8) Only used for time series projects. Offset into the past to define how far back relative to the forecast point the feature derivation window should start. Expressed in terms of the windows_basis_unit.
feature_derivation_window_end : int or None: (New in version v2.8) Only used for time series projects. Offset into the past to define how far back relative to the forecast point the feature derivation window should end. Expressed in terms of the windows_basis_unit.
feature_settings : list of FeatureSettings: (New in version v2.9) Optional, a list specifying per feature settings, can be left unspecified.
forecast_window_start : int or None: (New in version v2.8) Only used for time series projects. Offset into the future to define how far forward relative to the forecast point the forecast window should start. Expressed in terms of the windows_basis_unit.
forecast_window_end : int or None: (New in version v2.8) Only used for time series projects. Offset into the future to define how far forward relative to the forecast point the forecast window should end. Expressed in terms of the windows_basis_unit.
windows_basis_unit : string, optional: (New in version v2.14) Only used for time series projects. Indicates which unit is a basis for feature derivation window and forecast window. Valid options are detected time unit (one of the datarobot.enums.TIME_UNITS) or “ROW”. If omitted, the default value is detected time unit.
treat_as_exponential : string, optional: (New in version v2.9) defaults to “auto”. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. Use values from the datarobot.enums.TREAT_AS_EXPONENTIAL enum.
differencing_method : string, optional: (New in version v2.9) defaults to “auto”. Used to specify which differencing method to apply of case if data is stationary. Use values from the datarobot.enums.DIFFERENCING_METHOD enum.
periodicities : list of Periodicity, optional: (New in version v2.9) a list of datarobot.Periodicity. Periodicities units should be “ROW”, if the windows_basis_unit is “ROW”.
multiseries_id_columns : list of str or null: (New in version v2.11) a list of the names of multiseries id columns to define series within the training data. Currently only one multiseries id column is supported.
number_of_known_in_advance_features : int: (New in version v2.14) Number of features that are marked as known in advance.
number_of_do_not_derive_features : int: (New in v2.17) Number of features that are excluded from derivation.
use_cross_series_features : bool: (New in version v2.14) Whether to use cross series features.
aggregation_type : str, optional: (New in version v2.14) The aggregation type to apply when creating cross series features. Optional, must be one of “total” or “average”.
cross_series_group_by_columns : list of str, optional: (New in version v2.15) List of columns (currently of length 1). Optional setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like “men’s clothing”, “sports equipment”, etc.. Can only be used in a multiseries project with use_cross_series_features set to True.
calendar_id : str, optional: (New in version v2.15) Only available for time series projects. The id of the CalendarFile to use with this project.
calendar_name : str, optional: (New in version v2.17) Only available for time series projects. The name of the CalendarFile used with this project.
model_splits: int, optional: (New in version v2.21) Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of model splits will allow for less downsampling leading to the use of more post-processed data.
allow_partial_history_time_series_predictions: bool, optional: (New in version v2.24) Wheter to allow time series models to make predictions using partial historical data.

classmethod generate(project_id, spec, max_wait=600, target=None)¶

Preview the full partitioning determined by a DatetimePartitioningSpecification

Based on the project dataset and the partitioning specification, inspect the full partitioning that would be used if the same specification were passed into Project.set_target.

Parameters:

project_id : str: the id of the project
spec : DatetimePartitioningSpec: the desired partitioning
max_wait : int, optional: For some settings (e.g. generating a partitioning preview for a multiseries project for the first time), an asynchronous task must be run to analyze the dataset. max_wait governs the maximum time (in seconds) to wait before giving up. In all non-multiseries projects, this is unused.
target : str, optional: the name of the target column. For unsupervised projects target may be None. Providing a target will ensure that partitions are correctly optimized for your dataset.

Returns:

DatetimePartitioning :: the full generated partitioning

classmethod get(project_id)¶

Retrieve the DatetimePartitioning from a project

Only available if the project has already set the target as a datetime project.

Parameters:	project_id : str the id of the project to retrieve partitioning for
Returns:	DatetimePartitioning : the full partitioning for the project

classmethod generate_optimized(project_id, spec, target, max_wait=600)¶

Preview the full partitioning determined by a DatetimePartitioningSpecification

Based on the project dataset and the partitioning specification, inspect the full partitioning that would be used if the same specification were passed into Project.set_target.

Parameters:	project_id : str the id of the project spec : DatetimePartitioningSpec the desired partitioning target : str the name of the target column. For unsupervised projects target may be None. max_wait : int, optional Governs the maximum time (in seconds) to wait before giving up.
Returns:	DatetimePartitioning : the full generated partitioning

classmethod get_optimized(project_id, datetime_partitioning_id)¶

Retrieve an Optimized DatetimePartitioning from a project for the specified datetime_partitioning_id. A datetime_partitioning_id is created by using the generate_optimized function.

Parameters:	project_id : str the id of the project to retrieve partitioning for datetime_partitioning_id : ObjectId the ObjectId associated with the project to retrieve from mongo
Returns:	DatetimePartitioning : the full partitioning for the project

classmethod feature_log_list(project_id, offset=None, limit=None)¶

Retrieve the feature derivation log content and log length for a time series project.

The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority, as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected.

This route is only supported for time series projects that have finished partitioning.

The feature derivation log will include information about:

Detected stationarity of the series:

e.g. ‘Series detected as non-stationary’
Detected presence of multiplicative trend in the series:

e.g. ‘Multiplicative trend detected’
Detected presence of multiplicative trend in the series:

e.g. ‘Detected periodicities: 7 day’
Maximum number of feature to be generated:

e.g. ‘Maximum number of feature to be generated is 1440’
Window sizes used in rolling statistics / lag extractors

e.g. ‘The window sizes chosen to be: 2 months

(because the time step is 1 month and Feature Derivation Window is 2 months)’
Features that are specified as known-in-advance

e.g. ‘Variables treated as apriori: holiday’
Details about why certain variables are transformed in the input data

e.g. ‘Generating variable “y (log)” from “y” because multiplicative trend

is detected’
Details about features generated as timeseries features, and their priority

e.g. ‘Generating feature “date (actual)” from “date” (priority: 1)’

Parameters:	project_id : str project id to retrieve a feature derivation log for. offset : int optional, defaults is 0, this many results will be skipped. limit : int optional, defaults to 100, at most this many results are returned. To specify no limit, use 0. The default may change without notice.

classmethod feature_log_retrieve(project_id)¶

Retrieve the feature derivation log content and log length for a time series project.

The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority, as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected.

This route is only supported for time series projects that have finished partitioning.

The feature derivation log will include information about:

Detected stationarity of the series:

e.g. ‘Series detected as non-stationary’
Detected presence of multiplicative trend in the series:

e.g. ‘Multiplicative trend detected’
Detected presence of multiplicative trend in the series:

e.g. ‘Detected periodicities: 7 day’
Maximum number of feature to be generated:

e.g. ‘Maximum number of feature to be generated is 1440’
Window sizes used in rolling statistics / lag extractors

e.g. ‘The window sizes chosen to be: 2 months

(because the time step is 1 month and Feature Derivation Window is 2 months)’
Features that are specified as known-in-advance

e.g. ‘Variables treated as apriori: holiday’
Details about why certain variables are transformed in the input data

e.g. ‘Generating variable “y (log)” from “y” because multiplicative trend

is detected’
Details about features generated as timeseries features, and their priority

e.g. ‘Generating feature “date (actual)” from “date” (priority: 1)’

Parameters:	project_id : str project id to retrieve a feature derivation log for.

to_specification(use_holdout_start_end_format=False, use_backtest_start_end_format=False)¶

Render the DatetimePartitioning as a DatetimePartitioningSpecification

The resulting specification can be used when setting the target, and contains only the attributes directly controllable by users.

Parameters:

use_holdout_start_end_format : bool, optional: Defaults to False. If True, will use holdout_end_date when configuring the holdout partition. If False, will use holdout_duration instead.
use_backtest_start_end_format : bool, optional: Defaults to False. If False, will use a duration-based approach for specifying backtests (gap_duration, validation_start_date, and validation_duration). If True, will use a start/end date approach for specifying backtests (primary_training_start_date, primary_training_end_date, validation_start_date, validation_end_date).

Returns:

DatetimePartitioningSpecification: the specification for this partitioning

to_dataframe()¶

Render the partitioning settings as a dataframe for convenience of display

Excludes project_id, datetime_partition_column, date_format, autopilot_data_selection_method, validation_duration, and number_of_backtests, as well as the row count information, if present.

Also excludes the time series specific parameters for use_time_series, default_to_known_in_advance, default_to_do_not_derive, and defining the feature derivation and forecast windows.

class datarobot.helpers.partitioning_methods.Backtest(index=None, available_training_start_date=None, available_training_duration=None, available_training_row_count=None, available_training_end_date=None, primary_training_start_date=None, primary_training_duration=None, primary_training_row_count=None, primary_training_end_date=None, gap_start_date=None, gap_duration=None, gap_row_count=None, gap_end_date=None, validation_start_date=None, validation_duration=None, validation_row_count=None, validation_end_date=None, total_row_count=None)¶

A backtest used to evaluate models trained in a datetime partitioned project

When setting up a datetime partitioning project, backtests are specified by a BacktestSpecification.

The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available.

All durations are specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Attributes:

index : int: the index of the backtest
available_training_start_date : datetime.datetime: the start date of the available training data for this backtest
available_training_duration : str: the duration of available training data for this backtest
available_training_row_count : int or None: the number of rows of available training data for this backtest. Only available when retrieving from a project where the target is set.
available_training_end_date : datetime.datetime: the end date of the available training data for this backtest
primary_training_start_date : datetime.datetime: the start date of the primary training data for this backtest
primary_training_duration : str: the duration of the primary training data for this backtest
primary_training_row_count : int or None: the number of rows of primary training data for this backtest. Only available when retrieving from a project where the target is set.
primary_training_end_date : datetime.datetime: the end date of the primary training data for this backtest
gap_start_date : datetime.datetime: the start date of the gap between training and validation scoring data for this backtest
gap_duration : str: the duration of the gap between training and validation scoring data for this backtest
gap_row_count : int or None: the number of rows in the gap between training and validation scoring data for this backtest. Only available when retrieving from a project where the target is set.
gap_end_date : datetime.datetime: the end date of the gap between training and validation scoring data for this backtest
validation_start_date : datetime.datetime: the start date of the validation scoring data for this backtest
validation_duration : str: the duration of the validation scoring data for this backtest
validation_row_count : int or None: the number of rows of validation scoring data for this backtest. Only available when retrieving from a project where the target is set.
validation_end_date : datetime.datetime: the end date of the validation scoring data for this backtest
total_row_count : int or None: the number of rows in this backtest. Only available when retrieving from a project where the target is set.

to_specification(use_start_end_format=False)¶

Render this backtest as a BacktestSpecification.

The resulting specification includes only the attributes users can directly control, not those indirectly determined by the project dataset.

Parameters:	use_start_end_format : bool Default `False`. If `False`, will use a duration-based approach for specifying backtests (`gap_duration`, `validation_start_date`, and `validation_duration`). If `True`, will use a start/end date approach for specifying backtests (`primary_training_start_date`, `primary_training_end_date`, `validation_start_date`, `validation_end_date`).
Returns:	BacktestSpecification the specification for this backtest

to_dataframe()¶

Render this backtest as a dataframe for convenience of display

Returns:	backtest_partitioning : pandas.Dataframe the backtest attributes, formatted into a dataframe

datarobot.helpers.partitioning_methods.construct_duration_string(years=0, months=0, days=0, hours=0, minutes=0, seconds=0)¶

Construct a valid string representing a duration in accordance with ISO8601

A duration of six months, 3 days, and 12 hours could be represented as P6M3DT12H.

Parameters:	years : int the number of years in the duration months : int the number of months in the duration days : int the number of days in the duration hours : int the number of hours in the duration minutes : int the number of minutes in the duration seconds : int the number of seconds in the duration
Returns:	duration_string: str The duration string, specified compatibly with ISO8601

PayoffMatrix¶

class datarobot.models.PayoffMatrix(project_id, id, name=None, true_positive_value=None, true_negative_value=None, false_positive_value=None, false_negative_value=None)¶

Represents a Payoff Matrix, a costs/benefit scenario used for creating a profit curve.

Examples

import datarobot as dr

# create a payoff matrix
payoff_matrix = dr.PayoffMatrix.create(project_id, name, true_positive_value=100,
                true_negative_value=10, false_positive_value=0, false_negative_value=-10)

# list available payoff matrices
payoff_matrices = dr.PayoffMatrix.list(project_id)
payoff_matrix = payoff_matrices[0]

Attributes:

project_id : str: id of the project with which the payoff matrix is associated.
id : str: id of the payoff matrix.
name : str: User-supplied label for the payoff matrix.
true_positive_value : float: Cost or benefit of a true positive classification
true_negative_value: float: Cost or benefit of a true negative classification
false_positive_value: float: Cost or benefit of a false positive classification
false_negative_value: float: Cost or benefit of a false negative classification

classmethod create(project_id, name, true_positive_value=1, true_negative_value=1, false_positive_value=-1, false_negative_value=-1)¶

Create a payoff matrix associated with a specific project.

Parameters:	project_id : str id of the project with which the payoff matrix will be associated
Returns:	payoff_matrix : `PayoffMatrix` The newly created payoff matrix

classmethod list(project_id)¶

Fetch all the payoff matrices for a project.

Parameters:	project_id : str id of the project Returns ——- List of PayoffMatrix A list of `PayoffMatrix` objects Raises —— datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(project_id, id)¶

Retrieve a specified payoff matrix.

Parameters:	project_id : str id of the project the model belongs to id : str id of the payoff matrix
Returns:	`PayoffMatrix` object representing specified payoff matrix
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod update(project_id, id, name, true_positive_value, true_negative_value, false_positive_value, false_negative_value)¶

Update (replace) a payoff matrix. Note that all data fields are required.

Parameters:	project_id : str id of the project to which the payoff matrix belongs id : str id of the payoff matrix name : str User-supplied label for the payoff matrix true_positive_value : float True positive payoff value to use for the profit curve true_negative_value : float True negative payoff value to use for the profit curve false_positive_value : float False positive payoff value to use for the profit curve false_negative_value : float False negative payoff value to use for the profit curve
Returns:	payoff_matrix PayoffMatrix with updated values
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod delete(project_id, id)¶

Delete a specified payoff matrix.

Parameters:	project_id : str id of the project the model belongs to id : str id of the payoff matrix
Returns:	response : requests.Response Empty response (204)
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod from_server_data(data, keep_attrs=None)¶

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:	data : dict The directly translated dict of JSON from the server. No casing fixes have taken place keep_attrs : list List of the dotted namespace notations for attributes to keep within the object structure even if their values are None

PredictJob¶

datarobot.models.predict_job.wait_for_async_predictions(project_id, predict_job_id, max_wait=600)¶

Given a Project id and PredictJob id poll for status of process responsible for predictions generation until it’s finished

Parameters:	project_id : str The identifier of the project predict_job_id : str The identifier of the PredictJob max_wait : int, optional Time in seconds after which predictions creation is considered unsuccessful
Returns:	predictions : pandas.DataFrame Generated predictions.
Raises:	AsyncPredictionsGenerationError Raised if status of fetched PredictJob object is `error` AsyncTimeoutError Predictions weren’t generated in time, specified by `max_wait` parameter

class datarobot.models.PredictJob(data, completed_resource_url=None)¶

Tracks asynchronous work being done within a project

Attributes:

id : int: the id of the job
project_id : str: the id of the project the job belongs to
status : str: the status of the job - will be one of datarobot.enums.QUEUE_STATUS
job_type : str: what kind of work the job is doing - will be ‘predict’ for predict jobs
is_blocked : bool: if true, the job is blocked (cannot be executed) until its dependencies are resolved
message : str: a message about the state of the job, typically explaining why an error occurred

classmethod from_job(job)¶

Transforms a generic Job into a PredictJob

Parameters:	job: Job A generic job representing a PredictJob
Returns:	predict_job: PredictJob A fully populated PredictJob with all the details of the job
Raises:	ValueError: If the generic Job was not a predict job, e.g. job_type != JOB_TYPE.PREDICT

classmethod create(model, sourcedata)¶

Note

Deprecated in v2.3 in favor of Project.upload_dataset and Model.request_predictions. That workflow allows you to reuse the same dataset for predictions from multiple models within one project.

Starts predictions generation for provided data using previously created model.

Parameters:	model : Model Model to use for predictions generation sourcedata : str, file or pandas.DataFrame Data to be used for predictions. If this parameter is a str, it can be either a path to a local file or raw file content. If using a file on disk, the filename must consist of ASCII characters only. The file must be a CSV, and cannot be compressed
Returns:	predict_job_id : str id of created job, can be used as parameter to `PredictJob.get` or `PredictJob.get_predictions` methods or `wait_for_async_predictions` function
Raises:	InputNotUnderstoodError If the parameter for sourcedata didn’t resolve into known data types

Examples

model = Model.get('project-id', 'model-id')
predict_job = PredictJob.create(model, './data_to_predict.csv')

classmethod get(project_id, predict_job_id)¶

Fetches one PredictJob. If the job finished, raises PendingJobFinished exception.

Parameters:	project_id : str The identifier of the project the model on which prediction was started belongs to predict_job_id : str The identifier of the predict_job
Returns:	predict_job : PredictJob The pending PredictJob
Raises:	PendingJobFinished If the job being queried already finished, and the server is re-routing to the finished predictions. AsyncFailureError Querying this resource gave a status code other than 200 or 303

classmethod get_predictions(project_id, predict_job_id, class_prefix='class_')¶

Fetches finished predictions from the job used to generate them.

Note

The prediction API for classifications now returns an additional prediction_values dictionary that is converted into a series of class_prefixed columns in the final dataframe. For example, <label> = 1.0 is converted to ‘class_1.0’. If you are on an older version of the client (prior to v2.8), you must update to v2.8 to correctly pivot this data.

Parameters:	project_id : str The identifier of the project to which belongs the model used for predictions generation predict_job_id : str The identifier of the predict_job class_prefix : str The prefix to append to labels in the final dataframe (e.g., apple -> class_apple)
Returns:	predictions : pandas.DataFrame Generated predictions
Raises:	JobNotFinished If the job has not finished yet AsyncFailureError Querying the predict_job in question gave a status code other than 200 or 303

cancel()¶: Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)¶

Parameters:	params : dict or None Query parameters to be added to request to get results. For featureEffects and featureFit, source param is required to define source, otherwise the default is `training`
Returns:	result : object Return type depends on the job type: for model jobs, a Model is returned for predict jobs, a pandas.DataFrame (with predictions) is returned for featureImpact jobs, a list of dicts by default (see `with_metadata` parameter of the `FeatureImpactJob` class and its `get()` method). for primeRulesets jobs, a list of Rulesets for primeModel jobs, a PrimeModel for primeDownloadValidation jobs, a PrimeFile for reasonCodesInitialization jobs, a ReasonCodesInitialization for reasonCodes jobs, a ReasonCodes for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization for predictionExplanations jobs, a PredictionExplanations for featureEffects, a FeatureEffects for featureFit, a FeatureFit
Raises:	JobNotFinished If the job is not finished, the result is not available. AsyncProcessUnsuccessfulError If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)¶

Parameters:	max_wait : int, optional How long to wait for the job to finish. params : dict, optional Query parameters to be added to request.
Returns:	result: object Return type is the same as would be returned by Job.get_result.
Raises:	AsyncTimeoutError If the job does not finish in time AsyncProcessUnsuccessfulError If the job errored or was aborted

refresh()¶: Update this object with the latest job data from the server.

wait_for_completion(max_wait=600)¶

Waits for job to complete.

Parameters:	max_wait : int, optional How long to wait for the job to finish.

Prediction Dataset¶

class datarobot.models.PredictionDataset(project_id, id, name, created, num_rows, num_columns, forecast_point=None, predictions_start_date=None, predictions_end_date=None, relax_known_in_advance_features_check=None, data_quality_warnings=None, forecast_point_range=None, data_start_date=None, data_end_date=None, max_forecast_date=None, actual_value_column=None, detected_actual_value_columns=None, contains_target_values=None, secondary_datasets_config_id=None)¶

A dataset uploaded to make predictions

Typically created via project.upload_dataset

Attributes:

id : str

the id of the dataset

project_id : str

the id of the project the dataset belongs to

created : str

the time the dataset was created

name : str

the name of the dataset

num_rows : int

the number of rows in the dataset

num_columns : int

the number of columns in the dataset

forecast_point : datetime.datetime or None

For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series predictions documentation for more information.

predictions_start_date : datetime.datetime or None, optional

For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.

predictions_end_date : datetime.datetime or None, optional

For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.

relax_known_in_advance_features_check : bool, optional

(New in version v2.15) For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed.

data_quality_warnings : dict, optional

(New in version v2.15) A dictionary that contains available warnings about potential problems in this prediction dataset. Available warnings include:

has_kia_missing_values_in_forecast_window : bool: Applicable for time series projects. If True, known in advance features have missing values in forecast window which may decrease prediction accuracy.
insufficient_rows_for_evaluating_models : bool: Applicable for datasets which are used as external test sets. If True, there is not enough rows in dataset to calculate insights.
single_class_actual_value_column : bool: Applicable for datasets which are used as external test sets. If True, actual value column has only one class and such insights as ROC curve can not be calculated. Only applies for binary classification projects or unsupervised projects.

forecast_point_range : list[datetime.datetime] or None, optional

(New in version v2.20) For time series projects only. Specifies the range of dates available for use as a forecast point.

data_start_date : datetime.datetime or None, optional

(New in version v2.20) For time series projects only. The minimum primary date of this prediction dataset.

data_end_date : datetime.datetime or None, optional

(New in version v2.20) For time series projects only. The maximum primary date of this prediction dataset.

max_forecast_date : datetime.datetime or None, optional

(New in version v2.20) For time series projects only. The maximum forecast date of this prediction dataset.

actual_value_column : string, optional

(New in version v2.21) Optional, only available for unsupervised projects, in case dataset was uploaded with actual value column specified. Name of the column which will be used to calculate the classification metrics and insights.

detected_actual_value_columns : list of dict, optional

(New in version v2.21) For unsupervised projects only, list of detected actual value columns information containing missing count and name for each column.

contains_target_values : bool, optional

(New in version v2.21) Only for supervised projects. If True, dataset contains target values and can be used to calculate the classification metrics and insights.

secondary_datasets_config_id: string or None, optional

(New in version v2.23) The Id of the alternative secondary dataset config to use during prediction for Feature discovery project.

classmethod get(project_id, dataset_id)¶

Retrieve information about a dataset uploaded for predictions

Parameters:	project_id: the id of the project to query dataset_id: the id of the dataset to retrieve
Returns:	dataset: PredictionDataset A dataset uploaded to make predictions

delete()¶

Delete a dataset uploaded for predictions

Will also delete predictions made using this dataset and cancel any predict jobs using this dataset.

Prediction Explanations¶

class datarobot.PredictionExplanationsInitialization(project_id, model_id, prediction_explanations_sample=None)¶

Represents a prediction explanations initialization of a model.

Attributes:	project_id : str id of the project the model belongs to model_id : str id of the model the prediction explanations initialization is for prediction_explanations_sample : list of dict a small sample of prediction explanations that could be generated for the model

classmethod get(project_id, model_id)¶

Retrieve the prediction explanations initialization for a model.

Prediction explanations initializations are a prerequisite for computing prediction explanations, and include a sample what the computed prediction explanations for a prediction dataset would look like.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model the prediction explanations initialization is for
Returns:	prediction_explanations_initialization : PredictionExplanationsInitialization The queried instance.
Raises:	ClientError (404) If the project or model does not exist or the initialization has not been computed.

classmethod create(project_id, model_id)¶

Create a prediction explanations initialization for the specified model.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model for which initialization is requested
Returns:	job : Job an instance of created async job

delete()¶: Delete this prediction explanations initialization.

class datarobot.PredictionExplanations(id, project_id, model_id, dataset_id, max_explanations, num_columns, finish_time, prediction_explanations_location, threshold_low=None, threshold_high=None)¶

Represents prediction explanations metadata and provides access to computation results.

Examples

prediction_explanations = dr.PredictionExplanations.get(project_id, explanations_id)
for row in prediction_explanations.get_rows():
    print(row)  # row is an instance of PredictionExplanationsRow

Attributes:

id : str: id of the record and prediction explanations computation result
project_id : str: id of the project the model belongs to
model_id : str: id of the model the prediction explanations are for
dataset_id : str: id of the prediction dataset prediction explanations were computed for
max_explanations : int: maximum number of prediction explanations to supply per row of the dataset
threshold_low : float: the lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset
threshold_high : float: the high threshold, above which a prediction must score in order for prediction explanations to be computed for a row in the dataset
num_columns : int: the number of columns prediction explanations were computed for
finish_time : float: timestamp referencing when computation for these prediction explanations finished
prediction_explanations_location : str: where to retrieve the prediction explanations

classmethod get(project_id, prediction_explanations_id)¶

Retrieve a specific prediction explanations metadata.

Parameters:	project_id : str id of the project the explanations belong to prediction_explanations_id : str id of the prediction explanations
Returns:	prediction_explanations : PredictionExplanations The queried instance.

classmethod create(project_id, model_id, dataset_id, max_explanations=None, threshold_low=None, threshold_high=None)¶

Create prediction explanations for the specified dataset.

In order to create PredictionExplanations for a particular model and dataset, you must first:

Compute feature impact for the model via datarobot.Model.get_feature_impact()

Compute a PredictionExplanationsInitialization for the model via datarobot.PredictionExplanationsInitialization.create(project_id, model_id)

Compute predictions for the model and dataset via datarobot.Model.request_predictions(dataset_id)

threshold_high and threshold_low are optional filters applied to speed up computation. When at least one is specified, only the selected outlier rows will have prediction explanations computed. Rows are considered to be outliers if their predicted value (in case of regression projects) or probability of being the positive class (in case of classification projects) is less than threshold_low or greater than thresholdHigh. If neither is specified, prediction explanations will be computed for all rows.

Parameters:

project_id : str: id of the project the model belongs to
model_id : str: id of the model for which prediction explanations are requested
dataset_id : str: id of the prediction dataset for which prediction explanations are requested
threshold_low : float, optional: the lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither threshold_high nor threshold_low is specified, prediction explanations will be computed for all rows.
threshold_high : float, optional: the high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither threshold_high nor threshold_low is specified, prediction explanations will be computed for all rows.
max_explanations : int, optional: the maximum number of prediction explanations to supply per row of the dataset, default: 3.

Returns:

job: Job: an instance of created async job

classmethod list(project_id, model_id=None, limit=None, offset=None)¶

List of prediction explanations metadata for a specified project.

Parameters:	project_id : str id of the project to list prediction explanations for model_id : str, optional if specified, only prediction explanations computed for this model will be returned limit : int or None at most this many results are returned, default: no limit offset : int or None this many results will be skipped, default: 0
Returns:	prediction_explanations : list[PredictionExplanations]

get_rows(batch_size=None, exclude_adjusted_predictions=True)¶

Retrieve prediction explanations rows.

Parameters:	batch_size : int or None, optional maximum number of prediction explanations rows to retrieve per request exclude_adjusted_predictions : bool Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.
Yields:	prediction_explanations_row : PredictionExplanationsRow Represents prediction explanations computed for a prediction row.

get_all_as_dataframe(exclude_adjusted_predictions=True)¶

Retrieve all prediction explanations rows and return them as a pandas.DataFrame.

Returned dataframe has the following structure:

row_id : row id from prediction dataset

prediction : the output of the model for this row

adjusted_prediction : adjusted prediction values (only appears for projects that utilize prediction adjustments, e.g. projects with an exposure column)

class_0_label : a class level from the target (only appears for classification projects)

class_0_probability : the probability that the target is this class (only appears for classification projects)

class_1_label : a class level from the target (only appears for classification projects)

class_1_probability : the probability that the target is this class (only appears for classification projects)

explanation_0_feature : the name of the feature contributing to the prediction for this explanation

explanation_0_feature_value : the value the feature took on

explanation_0_label : the output being driven by this explanation. For regression projects, this is the name of the target feature. For classification projects, this is the class label whose probability increasing would correspond to a positive strength.

explanation_0_qualitative_strength : a human-readable description of how strongly the feature affected the prediction (e.g. ‘+++’, ‘–’, ‘+’) for this explanation

explanation_0_strength : the amount this feature’s value affected the prediction

…

explanation_N_feature : the name of the feature contributing to the prediction for this explanation

explanation_N_feature_value : the value the feature took on

explanation_N_label : the output being driven by this explanation. For regression projects, this is the name of the target feature. For classification projects, this is the class label whose probability increasing would correspond to a positive strength.

explanation_N_qualitative_strength : a human-readable description of how strongly the feature affected the prediction (e.g. ‘+++’, ‘–’, ‘+’) for this explanation

explanation_N_strength : the amount this feature’s value affected the prediction

For classification projects, the server does not guarantee any ordering on the prediction values, however within this function we sort the values so that class_X corresponds to the same class from row to row.

Parameters:	exclude_adjusted_predictions : bool Optional, defaults to True. Set this to False to include adjusted prediction values in the returned dataframe.
Returns:	dataframe: pandas.DataFrame

download_to_csv(filename, encoding='utf-8', exclude_adjusted_predictions=True)¶

Save prediction explanations rows into CSV file.

Parameters:

filename : str or file object: path or file object to save prediction explanations rows
encoding : string, optional: A string representing the encoding to use in the output file, defaults to ‘utf-8’
exclude_adjusted_predictions : bool: Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.

get_prediction_explanations_page(limit=None, offset=None, exclude_adjusted_predictions=True)¶

Get prediction explanations.

If you don’t want use a generator interface, you can access paginated prediction explanations directly.

Parameters:

limit : int or None: the number of records to return, the server will use a (possibly finite) default if not specified
offset : int or None: the number of records to skip, default 0
exclude_adjusted_predictions : bool: Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.

Returns:

prediction_explanations : PredictionExplanationsPage

delete()¶: Delete these prediction explanations.

class datarobot.models.prediction_explanations.PredictionExplanationsRow(row_id, prediction, prediction_values, prediction_explanations=None, adjusted_prediction=None, adjusted_prediction_values=None)¶

Represents prediction explanations computed for a prediction row.

Notes

PredictionValue contains:

label : describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature.
value : the output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.

PredictionExplanation contains:

label : described what output was driven by this explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation.
feature : the name of the feature contributing to the prediction
feature_value : the value the feature took on for this row
strength : the amount this feature’s value affected the prediction
qualitative_strength : a human-readable description of how strongly the feature affected the prediction (e.g. ‘+++’, ‘–’, ‘+’)

Attributes:

row_id : int: which row this PredictionExplanationsRow describes
prediction : float: the output of the model for this row
adjusted_prediction : float or None: adjusted prediction value for projects that provide this information, None otherwise
prediction_values : list: an array of dictionaries with a schema described as PredictionValue
adjusted_prediction_values : list: same as prediction_values but for adjusted predictions
prediction_explanations : list: an array of dictionaries with a schema described as PredictionExplanation

class datarobot.models.prediction_explanations.PredictionExplanationsPage(id, count=None, previous=None, next=None, data=None, prediction_explanations_record_location=None, adjustment_method=None)¶

Represents a batch of prediction explanations received by one request.

Attributes:

id : str: id of the prediction explanations computation result
data : list[dict]: list of raw prediction explanations; each row corresponds to a row of the prediction dataset
count : int: total number of rows computed
previous_page : str: where to retrieve previous page of prediction explanations, None if current page is the first
next_page : str: where to retrieve next page of prediction explanations, None if current page is the last
prediction_explanations_record_location : str: where to retrieve the prediction explanations metadata
adjustment_method : str: Adjustment method that was applied to predictions, or ‘N/A’ if no adjustments were done.

classmethod get(project_id, prediction_explanations_id, limit=None, offset=0, exclude_adjusted_predictions=True)¶

Retrieve prediction explanations.

Parameters:

project_id : str: id of the project the model belongs to
prediction_explanations_id : str: id of the prediction explanations
limit : int or None: the number of records to return; the server will use a (possibly finite) default if not specified
offset : int or None: the number of records to skip, default 0
exclude_adjusted_predictions : bool: Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.

Returns:

prediction_explanations : PredictionExplanationsPage: The queried instance.

class datarobot.models.ShapMatrix(project_id, id, model_id=None, dataset_id=None)¶

Represents SHAP based prediction explanations and provides access to score values.

Examples

import datarobot as dr

# request SHAP matrix calculation
shap_matrix_job = dr.ShapMatrix.create(project_id, model_id, dataset_id)
shap_matrix = shap_matrix_job.get_result_when_complete()

# list available SHAP matrices
shap_matrices = dr.ShapMatrix.list(project_id)
shap_matrix = shap_matrices[0]

# get SHAP matrix as dataframe
shap_matrix_values = shap_matrix.get_as_dataframe()

Attributes:	project_id : str id of the project the model belongs to shap_matrix_id : str id of the generated SHAP matrix model_id : str id of the model used to dataset_id : str id of the prediction dataset SHAP values were computed for

classmethod create(project_id, model_id, dataset_id)¶

Calculate SHAP based prediction explanations against previously uploaded dataset.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model for which prediction explanations are requested dataset_id : str id of the prediction dataset for which prediction explanations are requested (as uploaded from Project.upload_dataset)
Returns:	job : ShapMatrixJob The job computing the SHAP based prediction explanations
Raises:	ClientError If the server responded with 4xx status. Possible reasons are project, model or dataset don’t exist, user is not allowed or model doesn’t support SHAP based prediction explanations ServerError If the server responded with 5xx status

classmethod list(project_id)¶

Fetch all the computed SHAP prediction explanations for a project.

Parameters:	project_id : str id of the project
Returns:	List of ShapMatrix A list of `ShapMatrix` objects
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(project_id, id)¶

Retrieve the specific SHAP matrix.

Parameters:	project_id : str id of the project the model belongs to id : str id of the SHAP matrix
Returns:	`ShapMatrix` object representing specified record

get_as_dataframe()¶

Retrieve SHAP matrix values as dataframe.

Returns:	dataframe : pandas.DataFrame A dataframe with SHAP scores
Raises:	datarobot.dse.errors.ClientError if the server responded with 4xx status. datarobot.dse.errors.ServerError if the server responded with 5xx status.

Predictions¶

class datarobot.models.Predictions(project_id, prediction_id, model_id=None, dataset_id=None, includes_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None, shap_warnings=None)¶

Represents predictions metadata and provides access to prediction results.

Examples

List all predictions for a project

import datarobot as dr

# Fetch all predictions for a project
all_predictions = dr.Predictions.list(project_id)

# Inspect all calculated predictions
for predictions in all_predictions:
    print(predictions)  # repr includes project_id, model_id, and dataset_id

Retrieve predictions by id

import datarobot as dr

# Getting predictions by id
predictions = dr.Predictions.get(project_id, prediction_id)

# Dump actual predictions
df = predictions.get_all_as_dataframe()
print(df)

Attributes:

project_id : str: id of the project the model belongs to
model_id : str: id of the model
prediction_id : str: id of generated predictions
includes_prediction_intervals : bool, optional: (New in v2.16) For time series projects only. Indicates if prediction intervals will be part of the response. Defaults to False.
prediction_intervals_size : int, optional: (New in v2.16) For time series projects only. Indicates the percentile used for prediction intervals calculation. Will be present only if includes_prediction_intervals is True.
forecast_point : datetime.datetime, optional: (New in v2.20) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
predictions_start_date : datetime.datetime or None, optional: (New in v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date : datetime.datetime or None, optional: (New in v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column : string, optional: (New in version v2.21) For time series unsupervised projects only. Actual value column which was used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the forecast_point parameter.
explanation_algorithm : datarobot.enums.EXPLANATIONS_ALGORITHM, optional: (New in version v2.21) If set to ‘shap’, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).
max_explanations : int, optional: (New in version v2.21) The maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. If null, no limit. In the case of ‘shap’: if the number of features is greater than the limit, the sum of remaining values will also be returned as shapRemainingTotal. Defaults to null. Cannot be set if explanation_algorithm is omitted.
shap_warnings : dict, optional: (New in version v2.21) Will be present if explanation_algorithm was set to datarobot.enums.EXPLANATIONS_ALGORITHM.SHAP and there were additivity failures during SHAP values calculation.

classmethod list(project_id, model_id=None, dataset_id=None)¶

Fetch all the computed predictions metadata for a project.

Parameters:	project_id : str id of the project model_id : str, optional if specified, only predictions metadata for this model will be retrieved dataset_id : str, optional if specified, only predictions metadata for this dataset will be retrieved
Returns:	A list of : py:class:Predictions <datarobot.models.Predictions> objects

classmethod get(project_id, prediction_id)¶

Retrieve the specific predictions metadata

Parameters:	project_id : str id of the project the model belongs to prediction_id : str id of the prediction set
Returns:	`Predictions` object representing specified predictions

get_all_as_dataframe(class_prefix='class_', serializer='json')¶

Retrieve all prediction rows and return them as a pandas.DataFrame.

Parameters:	class_prefix : str, optional The prefix to append to labels in the final dataframe. Default is `class_` (e.g., apple -> class_apple) serializer : str, optional Serializer to use for the download. Options: `json` (default) or `csv`.
Returns:	dataframe: pandas.DataFrame
Raises:	datarobot.dse.errors.ClientError if the server responded with 4xx status. datarobot.dse.errors.ServerError if the server responded with 5xx status.

download_to_csv(filename, encoding='utf-8', serializer='json')¶

Save prediction rows into CSV file.

Parameters:	filename : str or file object path or file object to save prediction rows encoding : string, optional A string representing the encoding to use in the output file, defaults to ‘utf-8’ serializer : str, optional Serializer to use for the download. Options: `json` (default) or `csv`.

PredictionServer¶

class datarobot.PredictionServer(id=None, url=None, datarobot_key=None)¶

A prediction server can be used to make predictions

Attributes:	id : str the id of the prediction server url : str the url of the prediction server datarobot_key : str the datarobot-key header used in requests to this prediction server

classmethod list()¶

Returns a list of prediction servers a user can use to make predictions.

New in version v2.17.

Returns:	prediction_servers : list of PredictionServer instances Contains a list of prediction servers that can be used to make predictions.

Examples

prediction_servers = PredictionServer.list()
prediction_servers
>>> [PredictionServer('https://example.com')]

PrimeFile¶

class datarobot.models.PrimeFile(id=None, project_id=None, parent_model_id=None, model_id=None, ruleset_id=None, language=None, is_valid=None)¶

Represents an executable file available for download of the code for a DataRobot Prime model

Attributes:

id : str: the id of the PrimeFile
project_id : str: the id of the project this PrimeFile belongs to
parent_model_id : str: the model being approximated by this PrimeFile
model_id : str: the prime model this file represents
ruleset_id : int: the ruleset being used in this PrimeFile
language : str: the language of the code in this file - see enums.LANGUAGE for possibilities
is_valid : bool: whether the code passed basic validation

download(filepath)¶

Download the code and save it to a file

Parameters:	filepath: string the location to save the file to

Project¶

class datarobot.models.Project(id=None, project_name=None, mode=None, quickrun=None, target=None, target_type=None, holdout_unlocked=None, metric=None, stage=None, partition=None, positive_class=None, created=None, advanced_options=None, recommender=None, max_train_pct=None, max_train_rows=None, scaleout_max_train_pct=None, scaleout_max_train_rows=None, file_name=None, credentials=None, feature_engineering_prediction_point=None, unsupervised_mode=None, use_feature_discovery=None, relationships_configuration_id=None, project_description=None, query_generator_id=None, segmentation=None)¶

A project built from a particular training dataset

Attributes:

id : str: the id of the project
project_name : str: the name of the project
project_description : str: an optional description for the project
mode : int: The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 4: Comprehenisve Autopilot. null: Mode not set. Note: Quick Autopilot is defined by the quickrun field.
quickrun: bool: If the Autopilot mode is set to quick.
target : str: the name of the selected target features
target_type : str: Indicating what kind of modeling is being done in this project Options are: ‘Regression’, ‘Binary’ (Binary classification), ‘Multiclass’ (Multiclass classification), ‘Multilabel’ (Multilabel classification)
holdout_unlocked : bool: whether the holdout has been unlocked
metric : str: the selected project metric (e.g. LogLoss)
stage : str: the stage the project has reached - one of datarobot.enums.PROJECT_STAGE
partition : dict: information about the selected partitioning options
positive_class : str: for binary classification projects, the selected positive class; otherwise, None
created : datetime: the time the project was created
advanced_options : dict: information on the advanced options that were selected for the project settings, e.g. a weights column or a cap of the runtime of models that can advance autopilot stages
recommender : dict: information on the recommender settings of the project (i.e. whether it is a recommender project, or the id columns)
max_train_pct : float: the maximum percentage of the project dataset that can be used without going into the validation data or being too large to submit any blueprint for training
max_train_rows : int: the maximum number of rows that can be trained on without going into the validation data or being too large to submit any blueprint for training
scaleout_max_train_pct : float: the maximum percentage of the project dataset that can be used to successfully train a scaleout model without going into the validation data. May exceed max_train_pct, in which case only scaleout models can be trained up to this point.
scaleout_max_train_rows : int: the maximum number of rows that can be used to successfully train a scaleout model without going into the validation data. May exceed max_train_rows, in which case only scaleout models can be trained up to this point.
file_name : str: the name of the file uploaded for the project dataset
credentials : list, optional: a list of credentials for the datasets used in relationship configuration (previously graphs).
feature_engineering_prediction_point : str, optional: for Feature Discovery projects that are not time-aware, this parameter identifies the column in the primary dataset which should be used as the prediction point.
unsupervised_mode : bool, optional: (New in version v2.20) defaults to False, indicates whether this is an unsupervised project.
relationships_configuration_id : str, optional: (New in version v2.21) id of the relationships configuration to use
query_generator_id: str, optional: (New in version v2.27) id of the query generator applied for time series data prep
segmentation : dict, optional: information on the segmentation options for segmented project

classmethod get(project_id)¶

Gets information about a project.

Parameters:	project_id : str The identifier of the project you want to load.
Returns:	project : Project The queried project

Examples

import datarobot as dr
p = dr.Project.get(project_id='54e639a18bd88f08078ca831')
p.id
>>>'54e639a18bd88f08078ca831'
p.project_name
>>>'Some project name'

classmethod create(sourcedata, project_name='Untitled Project', max_wait=600, read_timeout=600, dataset_filename=None)¶

Creates a project with provided data.

Project creation is asynchronous process, which means that after initial request we will keep polling status of async process that is responsible for project creation until it’s finished. For SDK users this only means that this method might raise exceptions related to it’s async nature.

Parameters:	sourcedata : basestring, file, pathlib.Path or pandas.DataFrame Dataset to use for the project. If string can be either a path to a local file, url to publicly available file or raw file content. If using a file, the filename must consist of ASCII characters only. project_name : str, unicode, optional The name to assign to the empty project. max_wait : int, optional Time in seconds after which project creation is considered unsuccessful read_timeout: int The maximum number of seconds to wait for the server to respond indicating that the initial upload is complete dataset_filename : string or None, optional (New in version v2.14) File name to use for dataset. Ignored for url and file path sources.
Returns:	project : Project Instance with initialized data.
Raises:	InputNotUnderstoodError Raised if sourcedata isn’t one of supported types. AsyncFailureError Polling for status of async process resulted in response with unsupported status code. Beginning in version 2.1, this will be ProjectAsyncFailureError, a subclass of AsyncFailureError AsyncProcessUnsuccessfulError Raised if project creation was unsuccessful AsyncTimeoutError Raised if project creation took more time, than specified by `max_wait` parameter

Examples

p = Project.create('/home/datasets/somedataset.csv',
                   project_name="New API project")
p.id
>>> '5921731dkqshda8yd28h'
p.project_name
>>> 'New API project'

classmethod encrypted_string(plaintext)¶

Sends a string to DataRobot to be encrypted

This is used for passwords that DataRobot uses to access external data sources

Parameters:	plaintext : str The string to encrypt
Returns:	ciphertext : str The encrypted string

classmethod create_from_hdfs(url, port=None, project_name=None, max_wait=600)¶

Create a project from a datasource on a WebHDFS server.

Parameters:

url : str: The location of the WebHDFS file, both server and full path. Per the DataRobot specification, must begin with hdfs://, e.g. hdfs:///tmp/10kDiabetes.csv
port : int, optional: The port to use. If not specified, will default to the server default (50070)
project_name : str, optional: A name to give to the project
max_wait : int: The maximum number of seconds to wait before giving up.

Returns:

Project

Examples

p = Project.create_from_hdfs('hdfs:///tmp/somedataset.csv',
                             project_name="New API project")
p.id
>>> '5921731dkqshda8yd28h'
p.project_name
>>> 'New API project'

classmethod create_from_data_source(data_source_id, username, password, project_name=None, max_wait=600)¶

Create a project from a data source. Either data_source or data_source_id should be specified.

Parameters:

data_source_id : str: the identifier of the data source.
username : str: the username for database authentication.
password : str: the password for database authentication. The password is encrypted at server side and never saved / stored.
project_name : str, optional: optional, a name to give to the project.
max_wait : int: optional, the maximum number of seconds to wait before giving up.

Returns:

Project

classmethod create_from_dataset(dataset_id, dataset_version_id=None, project_name=None, user=None, password=None, credential_id=None, use_kerberos=None, credential_data=None)¶

Create a Project from a datarobot.Dataset

Parameters:

dataset_id: string: The ID of the dataset entry to user for the project’s Dataset
dataset_version_id: string, optional: The ID of the dataset version to use for the project dataset. If not specified - uses latest version associated with dataset_id
project_name: string, optional: The name of the project to be created. If not specified, will be “Untitled Project” for database connections, otherwise the project name will be based on the file used.
user: string, optional: The username for database authentication.
password: string, optional: The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored
credential_id: string, optional: The ID of the set of credentials to use instead of user and password.
use_kerberos: bool, optional: Server default is False. If true, use kerberos authentication for database authentication.
credential_data: dict, optional: The credentials to authenticate with the database, to use instead of user/password or credential ID.

Returns:

Project

classmethod from_async(async_location, max_wait=600)¶

Given a temporary async status location poll for no more than max_wait seconds until the async process (project creation or setting the target, for example) finishes successfully, then return the ready project

Parameters:	async_location : str The URL for the temporary async status resource. This is returned as a header in the response to a request that initiates an async process max_wait : int The maximum number of seconds to wait before giving up.
Returns:	project : Project The project, now ready
Raises:	ProjectAsyncFailureError If the server returned an unexpected response while polling for the asynchronous operation to resolve AsyncProcessUnsuccessfulError If the final result of the asynchronous operation was a failure AsyncTimeoutError If the asynchronous operation did not resolve within the time specified

classmethod start(sourcedata, target=None, project_name='Untitled Project', worker_count=None, metric=None, autopilot_on=True, blueprint_threshold=None, response_cap=None, partitioning_method=None, positive_class=None, target_type=None, unsupervised_mode=False, blend_best_models=None, prepare_model_for_deployment=None, consider_blenders_in_recommendation=None, scoring_code_only=None, min_secondary_validation_model_count=None, shap_only_mode=None, relationships_configuration_id=None, autopilot_with_feature_discovery=None, feature_discovery_supervised_feature_reduction=None, unsupervised_type=None, autopilot_cluster_list=None)¶

Chain together project creation, file upload, and target selection.

Note

While this function provides a simple means to get started, it does not expose all possible parameters. For advanced usage, using create and set_target directly is recommended.

Other Parameters:
Parameters:	sourcedata : str or pandas.DataFrame The path to the file to upload. Can be either a path to a local file or a publicly accessible URL (starting with `http://`, `https://`, `file://`, or `s3://`). If the source is a DataFrame, it will be serialized to a temporary buffer. If using a file, the filename must consist of ASCII characters only. target : str, optional The name of the target column in the uploaded file. Should not be provided if `unsupervised_mode` is `True`. project_name : str The project name.
Returns:	project : Project The newly created and initialized project.
	worker_count : int, optional The number of workers that you want to allocate to this project. metric : str, optional The name of metric to use. autopilot_on : boolean, default `True` Whether or not to begin modeling automatically. blueprint_threshold : int, optional Number of hours the model is permitted to run. Minimum 1 response_cap : float, optional Quantile of the response distribution to use for response capping Must be in range 0.5 .. 1.0 partitioning_method : PartitioningMethod object, optional Instance of one of the Partition Classes defined in `datarobot.helpers.partitioning_methods`. positive_class : str, float, or int; optional Specifies a level of the target column that should be treated as the positive class for binary classification. May only be specified for binary classification targets. target_type : str, optional Override the automatically selected target_type. An example usage would be setting the target_type=’Multiclass’ when you want to preform a multiclass classification task on a numeric column that has a low cardinality. You can use `TARGET_TYPE` enum. unsupervised_mode : boolean, default `False` Specifies whether to create an unsupervised project. blend_best_models: bool, optional blend best models during Autopilot run scoring_code_only: bool, optional Keep only models that can be converted to scorable java code during Autopilot run. shap_only_mode: bool, optional Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. Defaults to False. prepare_model_for_deployment: bool, optional Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label. consider_blenders_in_recommendation: bool, optional Include blenders when selecting a model to prepare for deployment in an Autopilot Run. Defaults to False. min_secondary_validation_model_count: int, optional Compute “All backtest” scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default. relationships_configuration_id : str, optional (New in version v2.23) id of the relationships configuration to use autopilot_with_feature_discovery: bool, optional. (New in version v2.23) If true, autopilot will run on a feature list that includes features found via search for interactions. feature_discovery_supervised_feature_reduction: bool, default ``True`` optional (New in version v2.23) Run supervised feature reduction for feature discovery projects. unsupervised_type : UnsupervisedTypeEnum, optional (New in version v2.27) Specifies whether an unsupervised project is anomaly detection or clustering. autopilot_cluster_list : list(int), optional (New in version v2.27) Specifies the list of clusters to build for each model during Autopilot. Specifying multiple values in a list will build models with each number of clusters for the Leaderboard.
Raises:	AsyncFailureError Polling for status of async process resulted in response with unsupported status code AsyncProcessUnsuccessfulError Raised if project creation or target setting was unsuccessful AsyncTimeoutError Raised if project creation or target setting timed out

Examples

Project.start("./tests/fixtures/file.csv",
              "a_target",
              project_name="test_name",
              worker_count=4,
              metric="a_metric")

This is an example of using a URL to specify the datasource:

Project.start("https://example.com/data/file.csv",
              "a_target",
              project_name="test_name",
              worker_count=4,
              metric="a_metric")

classmethod list(search_params=None)¶

Returns the projects associated with this account.

Parameters:	search_params : dict, optional. If not None, the returned projects are filtered by lookup. Currently you can query projects by: `project_name`
Returns:	projects : list of Project instances Contains a list of projects associated with this user account.
Raises:	TypeError Raised if `search_params` parameter is provided, but is not of supported type.

Examples

List all projects .. code-block:: python

p_list = Project.list() p_list >>> [Project(‘Project One’), Project(‘Two’)]

Search for projects by name .. code-block:: python

Project.list(search_params={‘project_name’: ‘red’}) >>> [Project(‘Predtime’), Project(‘Fred Project’)]

refresh()¶

Fetches the latest state of the project, and updates this object with that information. This is an inplace update, not a new object.

Returns:	self : Project the now-updated project

delete()¶: Removes this project from your account.

set_target(target=None, mode='quick', metric=None, quickrun=None, worker_count=None, positive_class=None, partitioning_method=None, featurelist_id=None, advanced_options=None, max_wait=600, target_type=None, credentials=None, feature_engineering_prediction_point=None, unsupervised_mode=False, relationships_configuration_id=None, class_mapping_aggregation_settings=None, segmentation_task_id=None, unsupervised_type=None, autopilot_cluster_list=None)¶

Set target variable of an existing project and begin the autopilot process (unless manual mode is specified).

Target setting is an asynchronous process, which means that after initial request we will keep polling status of async process that is responsible for target setting until it’s finished. For SDK users this only means that this method might raise exceptions related to it’s async nature.

When execution returns to the caller, the autopilot process will already have commenced (again, unless manual mode is specified).

Parameters:	target : str, optional The name of the target column in the uploaded file. Should not be provided if `unsupervised_mode` is `True`. mode : str, optional You can use `AUTOPILOT_MODE` enum to choose between `AUTOPILOT_MODE.FULL_AUTO` `AUTOPILOT_MODE.MANUAL` `AUTOPILOT_MODE.QUICK` `AUTOPILOT_MODE.COMPREHENSIVE`: Runs all blueprints in the repository (warning: this may be extremely slow). If unspecified, `QUICK` is used. If the `MANUAL` value is used, the model creation process will need to be started by executing the `start_autopilot` function with the desired featurelist. It will start immediately otherwise. metric : str, optional Name of the metric to use for evaluating models. You can query the metrics available for the target by way of `Project.get_metrics`. If none is specified, then the default recommended by DataRobot is used. quickrun : bool, optional Deprecated - pass `AUTOPILOT_MODE.QUICK` as mode instead. Sets whether project should be run in `quick run` mode. This setting causes DataRobot to recommend a more limited set of models in order to get a base set of models and insights more quickly. worker_count : int, optional The number of concurrent workers to request for this project. If None, then the default is used. (New in version v2.14) Setting this to -1 will request the maximum number available to your account. partitioning_method : PartitioningMethod object, optional Instance of one of the Partition Classes defined in `datarobot.helpers.partitioning_methods`. positive_class : str, float, or int; optional Specifies a level of the target column that should be treated as the positive class for binary classification. May only be specified for binary classification targets. featurelist_id : str, optional Specifies which feature list to use. advanced_options : AdvancedOptions, optional Used to set advanced options of project creation. max_wait : int, optional Time in seconds after which target setting is considered unsuccessful. target_type : str, optional Override the automatically selected target_type. An example usage would be setting the target_type=’Mutliclass’ when you want to preform a multiclass classification task on a numeric column that has a low cardinality. You can use `TARGET_TYPE` enum. credentials: list, optional, a list of credentials for the datasets used in relationship configuration (previously graphs). feature_engineering_prediction_point : str, optional additional aim parameter. unsupervised_mode : boolean, default `False` (New in version v2.20) Specifies whether to create an unsupervised project. If `True`, `target` may not be provided. relationships_configuration_id : str, optional (New in version v2.21) ID of the relationships configuration to use. segmentation_task_id : str or SegmentationTask, optional (New in version v2.28) The segmentation task that should be used to split the project for segmented modeling. unsupervised_type : UnsupervisedTypeEnum, optional (New in version v2.27) Specifies whether an unsupervised project is anomaly detection or clustering. autopilot_cluster_list : list(int), optional (New in version v2.27) Specifies the list of clusters to build for each model during Autopilot. Specifying multiple values in a list will build models with each number of clusters for the Leaderboard.
Returns:	project : Project The instance with updated attributes.
Raises:	AsyncFailureError Polling for status of async process resulted in response with unsupported status code AsyncProcessUnsuccessfulError Raised if target setting was unsuccessful AsyncTimeoutError Raised if target setting took more time, than specified by `max_wait` parameter TypeError Raised if `advanced_options`, `partitioning_method` or `target_type` is provided, but is not of supported type

Rating Table¶

class datarobot.models.RatingTable(id, rating_table_name, original_filename, project_id, parent_model_id, model_id=None, model_job_id=None, validation_job_id=None, validation_error=None)¶

Interface to modify and download rating tables.

Attributes:

id : str: The id of the rating table.
project_id : str: The id of the project this rating table belongs to.
rating_table_name : str: The name of the rating table.
original_filename : str: The name of the file used to create the rating table.
parent_model_id : str: The model id of the model the rating table was validated against.
model_id : str: The model id of the model that was created from the rating table. Can be None if a model has not been created from the rating table.
model_job_id : str: The id of the job to create a model from this rating table. Can be None if a model has not been created from the rating table.
validation_job_id : str: The id of the created job to validate the rating table. Can be None if the rating table has not been validated.
validation_error : str: Contains a description of any errors caused during validation.

classmethod get(project_id, rating_table_id)¶

Retrieve a single rating table

Parameters:	project_id : str The ID of the project the rating table is associated with. rating_table_id : str The ID of the rating table
Returns:	rating_table : RatingTable The queried instance

classmethod create(project_id, parent_model_id, filename, rating_table_name='Uploaded Rating Table')¶

Uploads and validates a new rating table CSV

Parameters:	project_id : str id of the project the rating table belongs to parent_model_id : str id of the model for which this rating table should be validated against filename : str The path of the CSV file containing the modified rating table. rating_table_name : str, optional A human friendly name for the new rating table. The string may be truncated and a suffix may be added to maintain unique names of all rating tables.
Returns:	job: Job an instance of created async job
Raises:	InputNotUnderstoodError Raised if filename isn’t one of supported types. ClientError (400) Raised if parent_model_id is invalid.

download(filepath)¶

Download a csv file containing the contents of this rating table

Parameters:	filepath : str The path at which to save the rating table file.

rename(rating_table_name)¶

Renames a rating table to a different name.

Parameters:	rating_table_name : str The new name to rename the rating table to.

create_model()¶

Creates a new model from this rating table record. This rating table must not already be associated with a model and must be valid.

Returns:	job: Job an instance of created async job
Raises:	ClientError (422) Raised if creating model from a RatingTable that failed validation JobAlreadyRequested Raised if creating model from a RatingTable that is already associated with a RatingTableModel

Reason Codes (Deprecated)¶

This interface is considered deprecated. Please use PredictionExplanations instead.

class datarobot.ReasonCodesInitialization(project_id, model_id, reason_codes_sample=None)¶

Represents a reason codes initialization of a model.

Attributes:	project_id : str id of the project the model belongs to model_id : str id of the model reason codes initialization is for reason_codes_sample : list of dict a small sample of reason codes that could be generated for the model

classmethod get(project_id, model_id)¶

Retrieve the reason codes initialization for a model.

Reason codes initializations are a prerequisite for computing reason codes, and include a sample what the computed reason codes for a prediction dataset would look like.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model reason codes initialization is for
Returns:	reason_codes_initialization : ReasonCodesInitialization The queried instance.
Raises:	ClientError (404) If the project or model does not exist or the initialization has not been computed.

classmethod create(project_id, model_id)¶

Create a reason codes initialization for the specified model.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model for which initialization is requested
Returns:	job : Job an instance of created async job

delete()¶: Delete this reason codes initialization.

class datarobot.ReasonCodes(id, project_id, model_id, dataset_id, max_codes, num_columns, finish_time, reason_codes_location, threshold_low=None, threshold_high=None)¶

Represents reason codes metadata and provides access to computation results.

Examples

reason_codes = dr.ReasonCodes.get(project_id, reason_codes_id)
for row in reason_codes.get_rows():
    print(row)  # row is an instance of ReasonCodesRow

Attributes:

id : str: id of the record and reason codes computation result
project_id : str: id of the project the model belongs to
model_id : str: id of the model reason codes initialization is for
dataset_id : str: id of the prediction dataset reason codes were computed for
max_codes : int: maximum number of reason codes to supply per row of the dataset
threshold_low : float: the lower threshold, below which a prediction must score in order for reason codes to be computed for a row in the dataset
threshold_high : float: the high threshold, above which a prediction must score in order for reason codes to be computed for a row in the dataset
num_columns : int: the number of columns reason codes were computed for
finish_time : float: timestamp referencing when computation for these reason codes finished
reason_codes_location : str: where to retrieve the reason codes

classmethod get(project_id, reason_codes_id)¶

Retrieve a specific reason codes.

Parameters:	project_id : str id of the project the model belongs to reason_codes_id : str id of the reason codes
Returns:	reason_codes : ReasonCodes The queried instance.

classmethod create(project_id, model_id, dataset_id, max_codes=None, threshold_low=None, threshold_high=None)¶

Create a reason codes for the specified dataset.

In order to create ReasonCodesPage for a particular model and dataset, you must first:

Compute feature impact for the model via datarobot.Model.get_feature_impact()

Compute a ReasonCodesInitialization for the model via datarobot.ReasonCodesInitialization.create(project_id, model_id)

Compute predictions for the model and dataset via datarobot.Model.request_predictions(dataset_id)

threshold_high and threshold_low are optional filters applied to speed up computation. When at least one is specified, only the selected outlier rows will have reason codes computed. Rows are considered to be outliers if their predicted value (in case of regression projects) or probability of being the positive class (in case of classification projects) is less than threshold_low or greater than thresholdHigh. If neither is specified, reason codes will be computed for all rows.

Parameters:

project_id : str: id of the project the model belongs to
model_id : str: id of the model for which reason codes are requested
dataset_id : str: id of the prediction dataset for which reason codes are requested
threshold_low : float, optional: the lower threshold, below which a prediction must score in order for reason codes to be computed for a row in the dataset. If neither threshold_high nor threshold_low is specified, reason codes will be computed for all rows.
threshold_high : float, optional: the high threshold, above which a prediction must score in order for reason codes to be computed. If neither threshold_high nor threshold_low is specified, reason codes will be computed for all rows.
max_codes : int, optional: the maximum number of reason codes to supply per row of the dataset, default: 3.

Returns:

job: Job: an instance of created async job

classmethod list(project_id, model_id=None, limit=None, offset=None)¶

List of reason codes for a specified project.

Parameters:	project_id : str id of the project to list reason codes for model_id : str, optional if specified, only reason codes computed for this model will be returned limit : int or None at most this many results are returned, default: no limit offset : int or None this many results will be skipped, default: 0
Returns:	reason_codes : list[ReasonCodes]

get_rows(batch_size=None, exclude_adjusted_predictions=True)¶

Retrieve reason codes rows.

Parameters:	batch_size : int maximum number of reason codes rows to retrieve per request exclude_adjusted_predictions : bool Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.
Yields:	reason_codes_row : ReasonCodesRow Represents reason codes computed for a prediction row.

get_all_as_dataframe(exclude_adjusted_predictions=True)¶

Retrieve all reason codes rows and return them as a pandas.DataFrame.

Returned dataframe has the following structure:

row_id : row id from prediction dataset

prediction : the output of the model for this row

adjusted_prediction : adjusted prediction values (only appears for projects that utilize prediction adjustments, e.g. projects with an exposure column)

class_0_label : a class level from the target (only appears for classification projects)

class_0_probability : the probability that the target is this class (only appears for classification projects)

class_1_label : a class level from the target (only appears for classification projects)

class_1_probability : the probability that the target is this class (only appears for classification projects)

reason_0_feature : the name of the feature contributing to the prediction for this reason

reason_0_feature_value : the value the feature took on

reason_0_label : the output being driven by this reason. For regression projects, this is the name of the target feature. For classification projects, this is the class label whose probability increasing would correspond to a positive strength.

reason_0_qualitative_strength : a human-readable description of how strongly the feature affected the prediction (e.g. ‘+++’, ‘–’, ‘+’) for this reason

reason_0_strength : the amount this feature’s value affected the prediction

…

reason_N_feature : the name of the feature contributing to the prediction for this reason

reason_N_feature_value : the value the feature took on

reason_N_label : the output being driven by this reason. For regression projects, this is the name of the target feature. For classification projects, this is the class label whose probability increasing would correspond to a positive strength.

reason_N_qualitative_strength : a human-readable description of how strongly the feature affected the prediction (e.g. ‘+++’, ‘–’, ‘+’) for this reason

reason_N_strength : the amount this feature’s value affected the prediction

Parameters:	exclude_adjusted_predictions : bool Optional, defaults to True. Set this to False to include adjusted prediction values in the returned dataframe.
Returns:	dataframe: pandas.DataFrame

download_to_csv(filename, encoding='utf-8', exclude_adjusted_predictions=True)¶

Save reason codes rows into CSV file.

Parameters:

filename : str or file object: path or file object to save reason codes rows
encoding : string, optional: A string representing the encoding to use in the output file, defaults to ‘utf-8’
exclude_adjusted_predictions : bool: Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.

get_reason_codes_page(limit=None, offset=None, exclude_adjusted_predictions=True)¶

Get reason codes.

If you don’t want use a generator interface, you can access paginated reason codes directly.

Parameters:

limit : int or None: the number of records to return, the server will use a (possibly finite) default if not specified
offset : int or None: the number of records to skip, default 0
exclude_adjusted_predictions : bool: Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.

Returns:

reason_codes : ReasonCodesPage

delete()¶: Delete this reason codes.

class datarobot.models.reason_codes.ReasonCodesRow(row_id, prediction, prediction_values, reason_codes=None, adjusted_prediction=None, adjusted_prediction_values=None)¶

Represents reason codes computed for a prediction row.

Notes

PredictionValue contains:

label : describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature.
value : the output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.

ReasonCode contains:

label : described what output was driven by this reason code. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this reason code.
feature : the name of the feature contributing to the prediction
feature_value : the value the feature took on for this row
strength : the amount this feature’s value affected the prediction
qualitativate_strength : a human-readable description of how strongly the feature affected the prediction (e.g. ‘+++’, ‘–’, ‘+’)

Attributes:

row_id : int: which row this ReasonCodeRow describes
prediction : float: the output of the model for this row
adjusted_prediction : float or None: adjusted prediction value for projects that provide this information, None otherwise
prediction_values : list: an array of dictionaries with a schema described as PredictionValue
adjusted_prediction_values : list: same as prediction_values but for adjusted predictions
reason_codes : list: an array of dictionaries with a schema described as ReasonCode

class datarobot.models.reason_codes.ReasonCodesPage(id, count=None, previous=None, next=None, data=None, reason_codes_record_location=None, adjustment_method=None)¶

Represents batch of reason codes received by one request.

Attributes:

id : str: id of the reason codes computation result
data : list[dict]: list of raw reason codes, each row corresponds to a row of the prediction dataset
count : int: total number of rows computed
previous_page : str: where to retrieve previous page of reason codes, None if current page is the first
next_page : str: where to retrieve next page of reason codes, None if current page is the last
reason_codes_record_location : str: where to retrieve the reason codes metadata
adjustment_method : str: Adjustment method that was applied to predictions, or ‘N/A’ if no adjustments were done.

classmethod get(project_id, reason_codes_id, limit=None, offset=0, exclude_adjusted_predictions=True)¶

Retrieve reason codes.

Parameters:

project_id : str: id of the project the model belongs to
reason_codes_id : str: id of the reason codes
limit : int or None: the number of records to return, the server will use a (possibly finite) default if not specified
offset : int or None: the number of records to skip, default 0
exclude_adjusted_predictions : bool: Optional, defaults to True. Set to False to include adjusted predictions, which will differ from the predictions on some projects, e.g. those with an exposure column specified.

Returns:

reason_codes : ReasonCodesPage: The queried instance.

Recommended Models¶

class datarobot.models.ModelRecommendation(project_id, model_id, recommendation_type)¶

A collection of information about a recommended model for a project.

Attributes:	project_id : str the id of the project the model belongs to model_id : str the id of the recommended model recommendation_type : str the type of model recommendation

classmethod get(project_id, recommendation_type=None)¶

Retrieves the default or specified by recommendation_type recommendation.

Parameters:	project_id : str The project’s id. recommendation_type : enums.RECOMMENDED_MODEL_TYPE The type of recommendation to get. If None, returns the default recommendation.
Returns:	recommended_model : ModelRecommendation

classmethod get_all(project_id)¶

Retrieves all of the current recommended models for the project.

Parameters:	project_id : str The project’s id.
Returns:	recommended_models : list of ModelRecommendation

classmethod get_recommendation(recommended_models, recommendation_type)¶

Returns the model in the given list with the requested type.

Parameters:	recommended_models : list of ModelRecommendation recommendation_type : enums.RECOMMENDED_MODEL_TYPE the type of model to extract from the recommended_models list
Returns:	recommended_model : ModelRecommendation or None if no model with the requested type exists

get_model()¶

Returns the Model associated with this ModelRecommendation.

Returns:	recommended_model : Model or DatetimeModel if the project is datetime-partitioned

ROC Curve¶

class datarobot.models.roc_curve.RocCurve(source, roc_points, negative_class_predictions, positive_class_predictions, source_model_id)¶

ROC curve data for model.

Attributes:

source : str: ROC curve data source. Can be ‘validation’, ‘crossValidation’ or ‘holdout’.
roc_points : list of dict: List of precalculated metrics associated with thresholds for ROC curve.
negative_class_predictions : list of float: List of predictions from example for negative class
positive_class_predictions : list of float: List of predictions from example for positive class
source_model_id : str: ID of the model this ROC curve represents; in some cases, insights from the parent of a frozen model may be used

class datarobot.models.roc_curve.LabelwiseRocCurve(source, roc_points, negative_class_predictions, positive_class_predictions, source_model_id, label, kolmogorov_smirnov_metric, auc)¶

Labelwise ROC curve data for one label and one source.

Attributes:

source : str: ROC curve data source. Can be ‘validation’, ‘crossValidation’ or ‘holdout’.
roc_points : list of dict: List of precalculated metrics associated with thresholds for ROC curve.
negative_class_predictions : list of float: List of predictions from example for negative class
positive_class_predictions : list of float: List of predictions from example for positive class
source_model_id : str: ID of the model this ROC curve represents; in some cases, insights from the parent of a frozen model may be used
label : str: Label name for
kolmogorov_smirnov_metric : float: Kolmogorov-Smirnov metric value for label
auc : float: AUC metric value for label

Ruleset¶

class datarobot.models.Ruleset(project_id=None, parent_model_id=None, model_id=None, ruleset_id=None, rule_count=None, score=None)¶

Represents an approximation of a model with DataRobot Prime

Attributes:

id : str: the id of the ruleset
rule_count : int: the number of rules used to approximate the model
score : float: the validation score of the approximation
project_id : str: the project the approximation belongs to
parent_model_id : str: the model being approximated
model_id : str or None: the model using this ruleset (if it exists). Will be None if no such model has been trained.

request_model()¶

Request training for a model using this ruleset

Training a model using a ruleset is a necessary prerequisite for being able to download the code for a ruleset.

Returns:	job: Job the job fitting the new Prime model

Segmented Modeling¶

API Reference for entities used in Segmented Modeling. See dedicated User Guide for examples.

class datarobot.CombinedModel(id=None, project_id=None, segmentation_task_id=None)¶

A model from a segmented project. Combination of ordinary models in child segments projects.

Attributes:	id : str the id of the model project_id : str the id of the project the model belongs to segmentation_task_id : str the id of a segmentation task used in this model

classmethod get(project_id, combined_model_id)¶

Retrieve combined model

Parameters:	project_id : str The project’s id. combined_model_id : str Id of the combined model.
Returns:	CombinedModel The queried combined model.

classmethod set_segment_champion(project_id, model_id)¶

Update a segment champion in a combined model by setting the model_id that belongs to the child project_id as the champion.

Parameters:	project_id : str The project id for the child model that contains the model id. model_id : str Id of the model to mark as the champion
Returns:	combined_model_id : str Id of the combined model that was updated

get_segments_info()¶

Retrieve Combined Model segments info

Returns:	list[SegmentInfo] List of segments

get_segments_as_dataframe(encoding='utf-8')¶

Retrieve Combine Models segments as a DataFrame.

Parameters:	encoding : str, optional A string representing the encoding to use in the output csv file. Defaults to ‘utf-8’.
Returns:	DataFrame Combined model segments

get_segments_as_csv(filename, encoding='utf-8')¶

Save the Combine Models segments to a csv.

Parameters:	filename : str or file object The path or file object to save the data to. encoding : str, optional A string representing the encoding to use in the output csv file. Defaults to ‘utf-8’.

train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>)¶: Inherited from Model - CombinedModels cannot be retrained directly

train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>, use_project_settings=False, sampling_method=None)¶: Inherited from Model - CombinedModels cannot be retrained directly

retrain(time_window_sample_pct=None, featurelist_id=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, sampling_method=None)¶: Inherited from Model - CombinedModels cannot be retrained directly

request_frozen_model(sample_pct=None, training_row_count=None)¶: Inherited from Model - CombinedModels cannot be retrained as frozen

request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None)¶: Inherited from Model - CombinedModels cannot be retrained as frozen

cross_validate()¶: Inherited from Model - CombinedModels cannot request cross validation

class datarobot.SegmentationTask(id=None, project_id=None, name=None, type=None, created=None, segments_count=None, segments=None, metadata=None, data=None)¶

A Segmentation Task is used for segmenting an existing project into multiple child projects. Each child project (or segment) will be a separate autopilot run. Currently only user defined segmentation is supported.

Example for creating a new SegmentationTask for Time Series segmentation with a user defined id column:

from datarobot import SegmentationTask

# Create the SegmentationTask
segmentation_task_results = SegmentationTask.create(
    project_id=project.id,
    target=target,
    use_time_series=True,
    datetime_partition_column=datetime_partition_column,
    multiseries_id_columns=[multiseries_id_column],
    user_defined_segment_id_columns=[user_defined_segment_id_column]
)

# Retrieve the completed SegmentationTask object from the job results
segmentation_task = segmentation_task_results['completedJobs'][0]

Attributes:

id : ObjectId: The id of the segmentation task.
project_id : ObjectId: The associated id of the parent project.
type : str: What type of job the segmentation task is associated with, e.g. auto_ml or auto_ts.
created : datetime: The date this segmentation task was created.
segments_count : int: The number of segments the segmentation task generated.
segments : list of strings: The segment names that the segmentation task generated.
metadata : dict: List of features that help to identify the parameters used by the segmentation task.
data : dict: Optional parameters that are associated with enabled metadata for the segmentation task.

collect_payload()¶: Convert the record to a dictionary

classmethod create(project_id, target, use_time_series=False, datetime_partition_column=None, multiseries_id_columns=None, user_defined_segment_id_columns=None, max_wait=600)¶

Creates segmentation tasks for the project based on the defined parameters.

Parameters:

project_id : basestring: The associated id of the parent project.
target : basestring: The column that represents the target in the dataset.
use_time_series : bool: Whether AutoTS or AutoML segmentations should be generated.
datetime_partition_column : basestring or null: Required for Time Series. The name of the column whose values as dates are used to assign a row to a particular partition.
multiseries_id_columns : list of str or null: Required for Time Series. A list of the names of multiseries id columns to define series within the training data. Currently only one multiseries id column is supported.
user_defined_segment_id_columns : list of str or null: Required when using a column for segmentation. A list of the segment id columns to use to define what columns are used to manually segment data. Currently only one user defined segment id column is supported.
max_wait : integer: The number of seconds to wait

Returns:

segmentation_tasks : dict: Dictionary containing the numberOfJobs, completedJobs, and failedJobs. completedJobs is a list of SegmentationTask objects, while failed jobs is a list of dictionaries indicating problems with submitted tasks.

classmethod list(project_id)¶

List all of the segmentation tasks that have been created for a specific project_id.

Parameters:	project_id : basestring The id of the parent project
Returns:	segmentation_tasks : list of SegmentationTask List of instances with initialized data.

classmethod get(project_id, segmentation_task_id)¶

Retrieve information for a single segmentation task associated with a project_id.

Parameters:	project_id : basestring The id of the parent project segmentation_task_id : basestring The id of the segmentation task
Returns:	segmentation_task : SegmentationTask Instance with initialized data.

class datarobot.SegmentInfo(project_id=None, segment=None, project_stage=None, project_status_error=None, autopilot_done=None, model_count=None, model_id=None)¶

A SegmentInfo is an object containing information about the combined model segments

Attributes:

project_id : ObjectId: The associated id of the child project.
segment : str: the name of the segment
project_stage : str: A description of the current stage of the project
project_status_error : str: Project status error message.
autopilot_done : str: Is autopilot done for the project.
model_count : int: Count of trained models in project.
model_id : str: ID of segment champion model.

classmethod list(project_id, model_id)¶

List all of the segments that have been created for a specific project_id.

Parameters:	project_id : basestring The id of the parent project
Returns:	segments : list of datarobot._experimental.models.segmentation.Segment List of instances with initialized data.

SHAP¶

class datarobot.models.ShapImpact(count, shap_impacts, row_count=None)¶

Represents SHAP impact score for a feature in a model.

New in version v2.21.

Notes

SHAP impact score for a feature has the following structure:

feature_name : (str) the feature name in dataset
impact_normalized : (float) normalized impact score value (largest value is 1)
impact_unnormalized : (float) raw impact score value

Attributes:	count : int the number of SHAP Impact object returned row_count: int or None the sample size (specified in rows) to use for Shap Impact computation shap_impacts : list a list which contains SHAP impact scores for top 1000 features used by a model

classmethod create(project_id, model_id, row_count=None)¶

Create SHAP impact for the specified model.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model to calculate shap impact for row_count : int the sample size (specified in rows) to use for Feature Impact computation
Returns:	job : Job an instance of created async job

classmethod get(project_id, model_id)¶

Retrieve SHAP impact scores for features in a model.

Parameters:	project_id : str id of the project the model belongs to model_id : str id of the model the SHAP impact is for
Returns:	shap_impact : ShapImpact The queried instance.
Raises:	ClientError (404) If the project or model does not exist or the SHAP impact has not been computed.

SharingAccess¶

class datarobot.SharingAccess(username, role, can_share=None, user_id=None)¶

Represents metadata about whom a entity (e.g. a data store) has been shared with

New in version v2.14.

Currently DataStores, DataSources, Projects (new in version v2.15) and CalendarFiles (new in version 2.15) can be shared.

This class can represent either access that has already been granted, or be used to grant access to additional users.

Attributes:

username : str: a particular user
role : str or None: if a string, represents a particular level of access and should be one of datarobot.enums.SHARING_ROLE. For more information on the specific access levels, see the sharing documentation. If None, can be passed to a share function to revoke access for a specific user.
can_share : bool or None: if a bool, indicates whether this user is permitted to further share. When False, the user has access to the entity, but can only revoke their own access but not modify any user’s access role. When True, the user can share with any other user at a access role up to their own. May be None if the SharingAccess was not retrieved from the DataRobot server but intended to be passed into a share function; this will be equivalent to passing True.
user_id : str: the id of the user

Training Predictions¶

class datarobot.models.training_predictions.TrainingPredictionsIterator(client, path, limit=None)¶

Lazily fetches training predictions from DataRobot API in chunks of specified size and then iterates rows from responses as named tuples. Each row represents a training prediction computed for a dataset’s row. Each named tuple has the following structure:

Notes

Each PredictionValue dict contains these keys:

label

describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification and multiclass projects, it is a label from the target feature.

value

the output of the prediction. For regression projects, it is the predicted value of the target. For classification and multiclass projects, it is the predicted probability that the row belongs to the class identified by the label.

Each PredictionExplanations dictionary contains these keys:

label : string

describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation.

feature : string

the name of the feature contributing to the prediction

feature_value : object

the value the feature took on for this row. The type corresponds to the feature (boolean, integer, number, string)

strength : float

algorithm-specific explanation value attributed to feature in this row

ShapMetadata dictionary contains these keys:

shap_remaining_total : float

The total of SHAP values for features beyond the max_explanations. This can be identically 0 in all rows, if max_explanations is greater than the number of features and thus all features are returned.

shap_base_value : float

the model’s average prediction over the training data. SHAP values are deviations from the base value.

warnings : dict or None

SHAP values calculation warnings (e.g. additivity check failures in XGBoost models). Schema described as ShapWarnings.

ShapWarnings dictionary contains these keys:

mismatch_row_count : int

the count of rows for which additivity check failed

max_normalized_mismatch : float

the maximal relative normalized mismatch value

Examples

import datarobot as dr

# Fetch existing training predictions by their id
training_predictions = dr.TrainingPredictions.get(project_id, prediction_id)

# Iterate over predictions
for row in training_predictions.iterate_rows()
    print(row.row_id, row.prediction)

Attributes:

row_id : int: id of the record in original dataset for which training prediction is calculated
partition_id : str or float: id of the data partition that the row belongs to. “0.0” corresponds to the validation partition or backtest 1.
prediction : float: the model’s prediction for this data row
prediction_values : list of dictionaries: an array of dictionaries with a schema described as PredictionValue
timestamp : str or None: (New in version v2.11) an ISO string representing the time of the prediction in time series project; may be None for non-time series projects
forecast_point : str or None: (New in version v2.11) an ISO string representing the point in time used as a basis to generate the predictions in time series project; may be None for non-time series projects
forecast_distance : str or None: (New in version v2.11) how many time steps are between the forecast point and the timestamp in time series project; None for non-time series projects
series_id : str or None: (New in version v2.11) the id of the series in a multiseries project; may be NaN for single series projects; None for non-time series projects
prediction_explanations : list of dict or None: (New in version v2.21) The prediction explanations for each feature. The total elements in the array are bounded by max_explanations and feature count. Only present if prediction explanations were requested. Schema described as PredictionExplanations.
shap_metadata : dict or None: (New in version v2.21) The additional information necessary to understand SHAP based prediction explanations. Only present if explanation_algorithm equals datarobot.enums.EXPLANATIONS_ALGORITHM.SHAP was added in compute request. Schema described as ShapMetadata.

class datarobot.models.training_predictions.TrainingPredictions(project_id, prediction_id, model_id=None, data_subset=None, explanation_algorithm=None, max_explanations=None, shap_warnings=None)¶

Represents training predictions metadata and provides access to prediction results.

Notes

Each element in shap_warnings has the following schema:

partition_name : str: the partition used for the prediction record.
value : object: the warnings related to this partition.

The objects in value are:

mismatch_row_count : int: the count of rows for which additivity check failed.
max_normalized_mismatch : float: the maximal relative normalized mismatch value.

Examples

Compute training predictions for a model on the whole dataset

import datarobot as dr

# Request calculation of training predictions
training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.ALL)
training_predictions = training_predictions_job.get_result_when_complete()
print('Training predictions {} are ready'.format(training_predictions.prediction_id))

# Iterate over actual predictions
for row in training_predictions.iterate_rows():
    print(row.row_id, row.partition_id, row.prediction)

List all training predictions for a project

import datarobot as dr

# Fetch all training predictions for a project
all_training_predictions = dr.TrainingPredictions.list(project_id)

# Inspect all calculated training predictions
for training_predictions in all_training_predictions:
    print(
        'Prediction {} is made for data subset "{}"'.format(
            training_predictions.prediction_id,
            training_predictions.data_subset,
        )
    )

Retrieve training predictions by id

import datarobot as dr

# Getting training predictions by id
training_predictions = dr.TrainingPredictions.get(project_id, prediction_id)

# Iterate over actual predictions
for row in training_predictions.iterate_rows():
    print(row.row_id, row.partition_id, row.prediction)

Attributes:

project_id : str

id of the project the model belongs to

model_id : str

id of the model

prediction_id : str

id of generated predictions

data_subset : datarobot.enums.DATA_SUBSET

data set definition used to build predictions. Choices are:

datarobot.enums.DATA_SUBSET.ALL

for all data available. Not valid for models in datetime partitioned projects.
datarobot.enums.DATA_SUBSET.VALIDATION_AND_HOLDOUT

for all data except training set. Not valid for models in datetime partitioned projects.
datarobot.enums.DATA_SUBSET.HOLDOUT

for holdout data set only.
datarobot.enums.DATA_SUBSET.ALL_BACKTESTS

for downloading the predictions for all backtest validation folds. Requires the model to have successfully scored all backtests. Datetime partitioned projects only.

explanation_algorithm : datarobot.enums.EXPLANATIONS_ALGORITHM

(New in version v2.21) Optional. If set to shap, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).

max_explanations : int

(New in version v2.21) The number of top contributors that are included in prediction explanations. Max 100. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns.

shap_warnings : list

(New in version v2.21) Will be present if explanation_algorithm was set to datarobot.enums.EXPLANATIONS_ALGORITHM.SHAP and there were additivity failures during SHAP values calculation.

classmethod list(project_id)¶

Fetch all the computed training predictions for a project.

Parameters:	project_id : str id of the project
Returns:	A list of : py:class:TrainingPredictions objects

classmethod get(project_id, prediction_id)¶

Retrieve training predictions on a specified data set.

Parameters:	project_id : str id of the project the model belongs to prediction_id : str id of the prediction set
Returns:	`TrainingPredictions` object which is ready to operate with specified predictions

iterate_rows(batch_size=None)¶

Retrieve training prediction rows as an iterator.

Parameters:	batch_size : int, optional maximum number of training prediction rows to fetch per request
Returns:	iterator : `TrainingPredictionsIterator` an iterator which yields named tuples representing training prediction rows

get_all_as_dataframe(class_prefix='class_', serializer='json')¶

Retrieve all training prediction rows and return them as a pandas.DataFrame.

Returned dataframe has the following structure:

row_id : row id from the original dataset
prediction : the model’s prediction for this row
class_<label> : the probability that the target is this class (only appears for classification and multiclass projects)
timestamp : the time of the prediction (only appears for out of time validation or time series projects)
forecast_point : the point in time used as a basis to generate the predictions (only appears for time series projects)
forecast_distance : how many time steps are between timestamp and forecast_point (only appears for time series projects)
series_id : he id of the series in a multiseries project or None for a single series project (only appears for time series projects)

Parameters:	class_prefix : str, optional The prefix to append to labels in the final dataframe. Default is `class_` (e.g., apple -> class_apple) serializer : str, optional Serializer to use for the download. Options: `json` (default) or `csv`.
Returns:	dataframe: pandas.DataFrame

download_to_csv(filename, encoding='utf-8', serializer='json')¶

Save training prediction rows into CSV file.

Parameters:	filename : str or file object path or file object to save training prediction rows encoding : string, optional A string representing the encoding to use in the output file, defaults to ‘utf-8’ serializer : str, optional Serializer to use for the download. Options: `json` (default) or `csv`.

User Blueprints¶

class datarobot.UserBlueprint(blender, blueprint_id, diagram, features, features_text, icons, insights, model_type, supported_target_types, user_blueprint_id, user_id, is_time_series=False, reference_model=False, shap_support=False, supports_gpu=False, blueprint=None, custom_task_version_metadata=None, hex_column_name_lookup=None, project_id=None, vertex_context=None, blueprint_context=None, **kwargs)¶

A representation of a blueprint which may be modified by the user, saved to a user’s AI Catalog, trained on projects, and shared with others.

It is recommended to install the python library called datarobot_bp_workshop, available via pip, for the best experience when building blueprints.

Please refer to http://blueprint-workshop.datarobot.com for tutorials, examples, and other documentation.

Parameters:

blender: bool: Whether the blueprint is a blender.
blueprint_id: string: The deterministic id of the blueprint, based on its content.
custom_task_version_metadata: list(list(string)), Optional: An association of custom entity ids and task ids.
diagram: string: The diagram used by the UI to display the blueprint.
features: list(string): A list of the names of tasks used in the blueprint.
features_text: string: A description of the blueprint via the names of tasks used.
hex_column_name_lookup: list(UserBlueprintsHexColumnNameLookupEntry), Optional: A lookup between hex values and data column names used in the blueprint.
icons: list(int): The icon(s) associated with the blueprint.
insights: string: An indication of the insights generated by the blueprint.
is_time_series: bool (Default=False): Whether the blueprint contains time-series tasks.
model_type: string: The generated or provided title of the blueprint.
project_id: string, Optional: The id of the project the blueprint was originally created with, if applicable.
reference_model: bool (Default=False): Whether the blueprint is a reference model.
shap_support: bool (Default=False): Whether the blueprint supports shapley additive explanations.
supported_target_types: list(enum(‘binary’, ‘multiclass’, ‘multilabel’, ‘nonnegative’,
‘regression’, ‘unsupervised’, ‘unsupervisedclustering’)): The list of supported targets of the current blueprint.
supports_gpu: bool (Default=False): Whether the blueprint supports execution on the GPU.
user_blueprint_id: string: The unique id associated with the user blueprint.
user_id: string: The id of the user who owns the blueprint.
blueprint: list(dict) or list(UserBlueprintTask), Optional: The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.
vertex_context: list(VertexContextItem), Optional: Info about, warnings about, and errors with a specific vertex in the blueprint.
blueprint_context: VertexContextItemMessages: Warnings and errors which may describe or summarize warnings or errors in the blueprint’s vertices

classmethod list(limit=100, offset=0, project_id=None)¶

Fetch a list of the user blueprints the current user created

Parameters:	limit: int (Default=100) The max number of results to return. offset: int (Default=0) The number of results to skip (for pagination). project_id: string, Optional The id of the project, used to filter for original project_id.
Returns:	list(UserBlueprint)
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get(user_blueprint_id, project_id=None)¶

Retrieve a user blueprint

Parameters:	user_blueprint_id: string Used to identify a specific user-owned blueprint. project_id: string (optional, default is None) String representation of ObjectId for a given project. Used to validate selected columns in the user blueprint.
Returns:	UserBlueprint
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod create(blueprint, model_type=None, project_id=None, save_to_catalog=True)¶

Create a user blueprint

Parameters:	blueprint: list(dict) or list(UserBlueprintTask) A list of tasks in the form of dictionaries which define a blueprint. model_type: string, Optional The title to give to the blueprint. project_id: string, Optional The project associated with the blueprint. Necessary in the event of project specific tasks, such as column selection tasks. save_to_catalog: bool, (Default=True) Whether the blueprint being created should be saved to the catalog.
Returns:	UserBlueprint
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod create_from_custom_task_version_id(custom_task_version_id, save_to_catalog=True, description=None)¶

Create a user blueprint with a single custom task version

Parameters:	custom_task_version_id: string Id of custom task version from which the user blueprint is created save_to_catalog: bool, (Default=True) Whether the blueprint being created should be saved to the catalog description: string (Default=None) The description for the user blueprint that will be created from the custom task version.
Returns:	UserBlueprint
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod clone_project_blueprint(blueprint_id, project_id, model_type=None, save_to_catalog=True)¶

Clone a blueprint from a project.

Parameters:	blueprint_id: string The id associated with the blueprint to create the user blueprint from. model_type: string, Optional The title to give to the blueprint. project_id: string The id of the project which the blueprint to copy comes from. save_to_catalog: bool, (Default=True) Whether the blueprint being created should be saved to the catalog.
Returns:	UserBlueprint
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod clone_user_blueprint(user_blueprint_id, model_type=None, project_id=None, save_to_catalog=True)¶

Clone a user blueprint.

Parameters:	model_type: string, Optional The title to give to the blueprint. project_id: string, Optional String representation of ObjectId for a given project. Used to validate selected columns in the user blueprint. user_blueprint_id: string The id of the existing user blueprint to copy. save_to_catalog: bool, (Default=True) Whether the blueprint being created should be saved to the catalog.
Returns:	UserBlueprint
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod update(blueprint, user_blueprint_id, model_type=None, project_id=None, include_project_id_if_none=False)¶

Update a user blueprint

Parameters:	blueprint: list(dict) or list(UserBlueprintTask) A list of tasks in the form of dictionaries which define a blueprint. If None, will not be passed. model_type: string, Optional The title to give to the blueprint. If None, will not be passed. project_id: string, Optional The project associated with the blueprint. Necessary in the event of project specific tasks, such as column selection tasks. If None, will not be passed. To explicitly pass None, pass True to include_project_id_if_none (useful if unlinking a blueprint from a project) user_blueprint_id: string Used to identify a specific user-owned blueprint. include_project_id_if_none: bool (Default=False) Allows project_id to be passed as None, instead of ignored. If set to False, will not pass project_id in the API request if it is set to None. If True, the project id will be passed even if it is set to None.
Returns:	UserBlueprint
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod delete(user_blueprint_id)¶

Delete a user blueprint, specified by the userBlueprintId.

Parameters:	user_blueprint_id: string Used to identify a specific user-owned blueprint.
Returns:	requests.models.Response
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get_input_types()¶

Retrieve the input types which can be used with User Blueprints.

Returns:	UserBlueprintAvailableInput
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod add_to_project(project_id, user_blueprint_ids)¶

Add a list of user blueprints, by id, to a specified (by id) project’s repository.

Parameters:	project_id: string The projectId of the project for the repository to add the specified user blueprints to. user_blueprint_ids: list(string) or string The ids of the user blueprints to add to the specified project’s repository.
Returns:	UserBlueprintAddToProjectMenu
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod get_available_tasks(project_id=None, user_blueprint_id=None)¶

Retrieve the available tasks, organized into categories, which can be used to create or modify User Blueprints.

Parameters:	project_id: string, Optional user_blueprint_id: string, Optional
Returns:	UserBlueprintAvailableTasks
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod validate_task_parameters(output_method, task_code, task_parameters, project_id=None)¶

Validate that each value assigned to specified task parameters are valid.

Parameters:	output_method: enum(‘P’, ‘Pm’, ‘S’, ‘Sm’, ‘T’, ‘TS’) The method representing how the task will output data. task_code: string The task code representing the task to validate parameter values. task_parameters: list(UserBlueprintTaskParameterValidationRequestParamItem) A list of task parameters and proposed values to be validated. project_id: string (optional, default is None) The projectId representing the project where this user blueprint is edited.
Returns:	UserBlueprintValidateTaskParameters
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod list_shared_roles(user_blueprint_id, limit=100, offset=0, id=None, name=None, share_recipient_type=None)¶

Get a list of users, groups and organizations that have an access to this user blueprint

Parameters:	id: str, Optional Only return the access control information for a organization, group or user with this ID. limit: int (Default=100) At most this many results are returned. name: string, Optional Only return the access control information for a organization, group or user with this name. offset: int (Default=0) This many results will be skipped. share_recipient_type: enum(‘user’, ‘group’, ‘organization’), Optional Describes the recipient type, either user, group, or organization. user_blueprint_id: str Used to identify a specific user-owned blueprint.
Returns:	list(UserBlueprintSharedRolesResponseValidator)
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod validate_blueprint(blueprint, project_id=None)¶

Validate a user blueprint and return information about the inputs expected and outputs provided by each task.

Parameters:	blueprint: list(dict) or list(UserBlueprintTask) The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. project_id: string (optional, default is None) The projectId representing the project where this user blueprint is edited.
Returns:	list(VertexContextItem)
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod update_shared_roles(user_blueprint_id, roles)¶

Share a user blueprint with a user, group, or organization

Parameters:	user_blueprint_id: str Used to identify a specific user-owned blueprint. roles: list(or(GrantAccessControlWithUsernameValidator, GrantAccessControlWithIdValidator)) Array of GrantAccessControl objects., up to maximum 100 objects.
Returns:	requests.models.Response
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx status

classmethod search_catalog(search=None, tag=None, limit=100, offset=0, owner_user_id=None, owner_username=None, order_by='-created')¶

Fetch a list of the user blueprint catalog entries the current user has access to based on an optional search term, tags, owner user info, or sort order.

Parameters:

search: string, Optional.: A value to search for in the dataset’s name, description, tags, column names, categories, and latest error. The search is case insensitive. If no value is provided for this parameter, or if the empty string is used, or if the string contains only whitespace, no filtering will be done. Partial matching is performed on dataset name and description fields while all other fields will only match if the search matches the whole value exactly.
tag: string, Optional.: If provided, the results will be filtered to include only items with the specified tag.
limit: int, Optional. (default: 0), at most this many results are returned. To specify no: limit, use 0. The default may change and a maximum limit may be imposed without notice.
offset: int, Optional. (default: 0), this many results will be skipped.
owner_user_id: string, Optional.: Filter results to those owned by one or more owner identified by UID.
owner_username: string, Optional.: Filter results to those owned by one or more owner identified by username.
order_by: string, Optional. Defaults to ‘-created’.: Sort order which will be applied to catalog list, valid options are “catalogName”, “originalName”, “description”, “created”, and “relevance”. For all options other than relevance, you may prefix the attribute name with a dash to sort in descending order. e.g. orderBy=’-catalogName’.

VisualAI¶

class datarobot.models.visualai.Image(**kwargs)¶

An image stored in a project’s dataset.

Attributes:

id: str: Image ID for this image.
image_type: str: Image media type. Accessing this may require a server request and an associated delay in returning.
image_bytes: [octet]: Raw octets of this image. Accessing this may require a server request and an associated delay in returning.
height: int: Height of the image in pixels (72 pixels per inch).
width: int: Width of the image in pixels (72 pixels per inch).

classmethod get(project_id, image_id)¶

Get a single image object from project.

Parameters:	project_id: str Project that contains the images. image_id: str ID of image to load from the project.

class datarobot.models.visualai.SampleImage(**kwargs)¶

A sample image in a project’s dataset.

If Project.stage is datarobot.enums.PROJECT_STAGE.EDA2 then the target_* attributes of this class will have values, otherwise the values will all be None.

Attributes:	image: Image Image object. target_value: str Value associated with the `feature_name`.

classmethod list(project_id, feature_name, target_value=None, offset=None, limit=None)¶

Get sample images from a project.

Parameters:	project_id: str Project that contains the images. feature_name: str Name of feature column that contains images. target_value: str Target value to filter images. offset: int Number of images to be skipped. limit: int Number of images to be returned.

class datarobot.models.visualai.DuplicateImage(**kwargs)¶

An image that was duplicated in the project dataset.

Attributes:	image: Image Image object. count: int Number of times the image was duplicated.

classmethod list(project_id, feature_name, offset=None, limit=None)¶

Get all duplicate images in a project.

Parameters:	project_id: str Project that contains the images. feature_name: str Name of feature column that contains images. offset: int Number of images to be skipped. limit: int Number of images to be returned.

class datarobot.models.visualai.ImageEmbedding(**kwargs)¶

Vector representation of an image in an embedding space.

A vector in an embedding space will allow linear computations to be carried out between images: for example computing the Euclidean distance of the images.

Attributes:	image: Image Image object used to create this map. feature_name: str Name of the feature column this embedding is associated with. position_x: int X coordinate of the image in the embedding space. position_y: int Y coordinate of the image in the embedding space. actual_target_value: object Actual target value of the dataset row.

classmethod compute(project_id, model_id)¶

Start creation of image embeddings for the model.

Parameters:	project_id: str Project to start creation in. model_id: str Project’s model to start creation in.
Returns:	str URL to check for image embeddings progress.
Raises:	datarobot.errors.ClientError Server rejected creation due to client error. Most likely cause is bad `project_id` or `model_id`.

classmethod models(project_id)¶

List the models in a project.

Parameters:	project_id: str Project that contains the models.
Returns:	list( tuple(model_id, feature_name) ) List of model and feature name pairs.

classmethod list(project_id, model_id, feature_name)¶

Return a list of ImageEmbedding objects.

Parameters:	project_id: str Project that contains the images. model_id: str Model that contains the images. feature_name: str Name of feature column that contains images.

class datarobot.models.visualai.ImageActivationMap(**kwargs)¶

Mark areas of image with weight of impact on training.

This is a technique to display how various areas of the region were used in training, and their effect on predictions. Larger values in activation_values indicates a larger impact.

Attributes:

image: Image: Image object used to create this map.
overlay_image: Image: Image object composited with activation heat map.
feature_name: str: Name of the feature column that contains the value this map is based on.
height: int: Height of the original image in pixels.
width: int: Width of the original image in pixels.
actual_target_value: object: Actual target value of the dataset row.
predicted_target_value: object: Predicted target value of the dataset row that contains this image.
activation_values: [ [ int ] ]: A row-column matrix that contains the activation strengths for image regions. Values are integers in the range [0, 255].

classmethod compute(project_id, model_id)¶

Start creation of a activation map in the given model.

Parameters:	project_id: str Project to start creation in. model_id: str Project’s model to start creation in.
Returns:	str URL to check for image embeddings progress.
Raises:	datarobot.errors.ClientError Server rejected creation due to client error. Most likely cause is bad `project_id` or `model_id`.

classmethod models(project_id)¶

List the models in a project.

Parameters:	project_id: str Project that contains the models.
Returns:	list( tuple(model_id, feature_name) ) List of model and feature name pairs.

classmethod list(project_id, model_id, feature_name, offset=None, limit=None)¶

Return a list of ImageActivationMap objects.

Parameters:	project_id: str Project that contains the images. model_id: str Model that contains the images. feature_name: str Name of feature column that contains images. offset: int Number of images to be skipped. limit: int Number of images to be returned.

class datarobot.models.visualai.ImageAugmentationOptions(id, name, project_id, min_transformation_probability, current_transformation_probability, max_transformation_probability, min_number_of_new_images, current_number_of_new_images, max_number_of_new_images, transformations=None)¶

A List of all supported Image Augmentation Transformations for a project. Includes additional information about minimum, maximum, and default values for a transformation.

Attributes:

name: string: The name of the augmentation list
project_id: string: The project containing the image data to be augmented
min_transformation_probability: float: The minimum allowed value for transformation probability.
current_transformation_probability: float: Default setting for probability that each transformation will be applied to an image.
max_transformation_probability: float: The maximum allowed value for transformation probability.
min_number_of_new_images: int: The minimum allowed number of new rows to add for each existing row
current_number_of_new_images: int: The default number of new rows to add for each existing row
max_number_of_new_images: int: The maximum allowed number of new rows to add for each existing row
transformations: array: List of transformations to possibly apply to each image

classmethod get(project_id)¶

Returns a list of all supported transformations for the given project

Parameters:	project_id – sting The id of the project for which to return the list of supported transformations.
Returns:	ImageAugmentationOptions A list containing all the supported transformations for the project.

class datarobot.models.visualai.ImageAugmentationList(id, name, project_id, feature_name=None, in_use=False, initial_list=False, transformation_probability=0.0, number_of_new_images=1, transformations=None, samples_id=None)¶

A List of Image Augmentation Transformations

Attributes:

name: string: The name of the augmentation list
project_id: string: The project containing the image data to be augmented
feature_name: string (optional): name of the feature that the augmentation list is associated with
in_use: boolean: Whether this is the list that will passed in to every blueprint during blueprint generation before autopilot
initial_list: boolean: True if this is the list to be used during training to produce augmentations
transformation_probability: float: Probability that each transformation will be applied to an image. Value should be between 0.01 - 1.0.
number_of_new_images: int: Number of new rows to add for each existing row
transformations: array: List of transformations to possibly apply to each image
samples_id: str: Id of last image augmentation sample generated for image augmentation list.

classmethod create(name, project_id, feature_name=None, in_use=False, initial_list=False, transformation_probability=0.0, number_of_new_images=1, transformations=None, samples_id=None)¶: create a new image augmentation list

retrieve_samples()¶

Lists already computed image augmentation sample for image augmentation list. Returns samples only if they have been already computed. It does not initialize computation.

Returns:	List of class ImageAugmentationSample

compute_samples(max_wait=600)¶

Initializes computation and retrieves list of image augmentation samples for image augmentation list. If samples exited prior to this call method, this will compute fresh samples and return latest version of samples.

Returns:	List of class ImageAugmentationSample

class datarobot.models.visualai.ImageAugmentationSample(image_id, project_id, height, width, original_image_id=None, sample_id=None)¶

A preview of the type of images that augmentations will create during training.

Attributes:

sample_id : ObjectId: The id of the augmentation sample, used to group related images together
image_id : ObjectId: A reference to the Image which can be used to retrieve the image binary
project_id : ObjectId: A reference to the project containing the image
original_image_id : ObjectId: A reference to the original image that generated this image in the case of an augmented image. If this is None it signifies this is an original image
height : int: Image height in pixels
width : int: Image width in pixels

classmethod compute(augmentation_list, number_of_rows=5)¶

Start creation of image augmentation samples.

Parameters:	number_of_rows: int The number of rows from the original dataset to use as input data for the augmentation samples. Defaults to 5. augmentation_list: ImageAugmentationList An Image Augmentation list that specifies the transformations to apply to each image during augmentation.
Returns:	str URL to check for image augmentation samples generation progress.
Raises:	datarobot.errors.ClientError Server rejected creation due to client error. Most likely cause is bad invalid `augmentation_list`.

classmethod list(sample_id=None, auglist_id=None)¶

Return a list of ImageAugmentationSample objects.

If both sample_id and auglist_id are specified, auglist_id will take precedence.

Parameters:	sample_id: str Unique ID for the set of sample images auglist_id: str ID for augmentation list to retrieve samples for
Returns:	List of class ImageAugmentationSample

Word Cloud¶

class datarobot.models.word_cloud.WordCloud(ngrams)¶

Word cloud data for the model.

Notes

WordCloudNgram is a dict containing the following:

ngram (str) Word or ngram value.

coefficient (float) Value from [-1.0, 1.0] range, describes effect of this ngram on the target. Large negative value means strong effect toward negative class in classification and smaller target value in regression models. Large positive - toward positive class and bigger value respectively.

count (int) Number of rows in the training sample where this ngram appears.

frequency (float) Value from (0.0, 1.0] range, relative frequency of given ngram to most frequent ngram.

is_stopword (bool) True for ngrams that DataRobot evaluates as stopwords.

class (str or None) For classification - values of the target class for corresponding word or ngram. For regression - None.

Attributes:	ngrams : list of dicts List of dicts with schema described as `WordCloudNgram` above.

most_frequent(top_n=5)¶

Return most frequent ngrams in the word cloud.

Parameters:	top_n : int Number of ngrams to return
Returns:	list of dict Up to top_n top most frequent ngrams in the word cloud. If top_n bigger then total number of ngrams in word cloud - return all sorted by frequency in descending order.

most_important(top_n=5)¶

Return most important ngrams in the word cloud.

Parameters:	top_n : int Number of ngrams to return
Returns:	list of dict Up to top_n top most important ngrams in the word cloud. If top_n bigger then total number of ngrams in word cloud - return all sorted by absolute coefficient value in descending order.

ngrams_per_class()¶

Split ngrams per target class values. Useful for multiclass models.

Returns:	dict Dictionary in the format of (class label) -> (list of ngrams for that class)