API Reference¶

Advanced Options¶

class datarobot.helpers.AdvancedOptions(weights: Optional[str] = None, response_cap: Union[bool, float, None] = None, blueprint_threshold: Optional[int] = None, seed: Optional[int] = None, smart_downsampled: Optional[bool] = None, majority_downsampling_rate: Optional[float] = None, offset: Optional[List[str]] = None, exposure: Optional[str] = None, accuracy_optimized_mb: Optional[bool] = None, scaleout_modeling_mode: Optional[str] = None, events_count: Optional[str] = None, monotonic_increasing_featurelist_id: Optional[str] = None, monotonic_decreasing_featurelist_id: Optional[str] = None, only_include_monotonic_blueprints: Optional[bool] = None, allowed_pairwise_interaction_groups: Optional[List[Tuple[str, ...]]] = None, blend_best_models: Optional[bool] = None, scoring_code_only: Optional[bool] = None, prepare_model_for_deployment: Optional[bool] = None, consider_blenders_in_recommendation: Optional[bool] = None, min_secondary_validation_model_count: Optional[int] = None, shap_only_mode: Optional[bool] = None, autopilot_data_sampling_method: Optional[str] = None, run_leakage_removed_feature_list: Optional[bool] = None, autopilot_with_feature_discovery: Optional[bool] = False, feature_discovery_supervised_feature_reduction: Optional[bool] = None, exponentially_weighted_moving_alpha: Optional[float] = None, external_time_series_baseline_dataset_id: Optional[str] = None, use_supervised_feature_reduction: Optional[bool] = True, primary_location_column: Optional[str] = None, protected_features: Optional[List[str]] = None, preferable_target_value: Optional[str] = None, fairness_metrics_set: Optional[str] = None, fairness_threshold: Optional[str] = None, bias_mitigation_feature_name: Optional[str] = None, bias_mitigation_technique: Optional[str] = None, include_bias_mitigation_feature_as_predictor_variable: Optional[bool] = None, default_monotonic_increasing_featurelist_id: Optional[str] = None, default_monotonic_decreasing_featurelist_id: Optional[str] = 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 : bool or float in [0.5, 1), optional: Defaults to none here, but server defaults to False. If specified, it is the 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, optional: a seed to use for randomization
smart_downsampled : bool, optional: 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, optional: 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: (Deprecated in 2.28. Will be removed in 2.30) DataRobot no longer supports scaleout models. Please remove any usage of this parameter as it will be removed from the API soon.
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 the 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, default ``False``, 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, 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: list of 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
bias_mitigation_feature_name : str, optional: The feature from protected features that will be used in a bias mitigation task to mitigate bias
bias_mitigation_technique : str, optional: One of datarobot.enums.BiasMitigationTechnique Options: - ‘preprocessingReweighing’ - ‘postProcessingRejectionOptionBasedClassification’ The technique by which we’ll mitigate bias, which will inform which bias mitigation task we insert into blueprints
include_bias_mitigation_feature_as_predictor_variable : bool, optional: Whether we should also use the mitigation feature as in input to the modeler just like any other categorical used for training, i.e. do we want the model to “train on” this feature in addition to using it for bias mitigation
default_monotonic_increasing_featurelist_id : str, optional: Returned from server on Project GET request - not able to be updated by user
default_monotonic_decreasing_featurelist_id : str, optional: Returned from server on Project GET request - not able to be updated by user

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)

update_individual_options(**kwargs) → None¶: Update individual attributes of an instance of AdvancedOptions.

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: Dict[str, Any], completed_resource_url: Optional[str] = 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: DeploymentType, intake_settings: Optional[IntakeSettings] = None, output_settings: Optional[OutputSettings] = None, csv_settings: Optional[CsvSettings] = None, timeseries_settings: Optional[TimeSeriesSettings] = None, num_concurrent: Optional[int] = None, chunk_size: Optional[Union[int, str]] = None, passthrough_columns: Optional[List[str]] = None, passthrough_columns_set: Optional[str] = None, max_explanations: Optional[int] = None, max_ngram_explanations: Optional[Union[int, str]] = None, threshold_high: Optional[float] = None, threshold_low: Optional[float] = None, prediction_warning_enabled: Optional[bool] = None, include_prediction_status: bool = False, skip_drift_tracking: bool = False, prediction_instance: Optional[PredictionInstance] = None, abort_on_error: bool = True, column_names_remapping: Optional[Dict[str, str]] = None, include_probabilities: bool = True, include_probabilities_classes: Optional[List[str]] = None, download_timeout: Optional[int] = 120, download_read_timeout: Optional[int] = 660, upload_read_timeout: Optional[int] = 600, explanations_mode: Optional[PredictionExplanationsMode] = None) → BatchPredictionJob¶

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

Note that to pass a dataset, you not only need to specify the type parameter as dataset, but you must also set the dataset parameter as a dr.Dataset object.

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)

endpoint_url : string (optional), any non-default endpoint URL for S3 access (omit to use the default)

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)

endpoint_url : string (optional), any non-default endpoint URL for S3 access (omit to use the default)

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.

max_ngram_explanations : int or str (optional)

Compute text explanations for this amount of ngrams. Set to all to return all ngram explanations, or set to a positive integer value to limit the amount of ngram explanations returned. By default no ngram explanations will be computed and returned.

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.

explanations_mode : PredictionExplanationsMode, optional

Mode of prediction explanations calculation for multiclass and clustering models, if not specified - server default is to explain only the predicted class, identical to passing TopPredictionsMode(1).

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 behavior 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 apply_time_series_data_prep_and_score(deployment: Deployment, intake_settings: IntakeSettings, timeseries_settings: TimeSeriesSettings, **kwargs) → BatchPredictionJob¶

Prepare the dataset with time series data prep, create new batch prediction job, upload the scoring dataset, and return a batch prediction job.

The supported intake_settings are of type localFile or dataset.

For timeseries_settings of type forecast the forecast_point must be specified.

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

New in version v3.1.

Returns:	BatchPredictionJob Instance of BatchPredictionJob
Raises:	InvalidUsageError If the deployment does not support time series data prep. If the intake type is not supported for time series data prep.
Attributes:	deployment : Deployment Deployment which will be used for scoring. intake_settings : dict A dict configuring where data is coming from. Supported options: type : string, either localFile, dataset Note that to pass a dataset, you not only need to specify the type parameter as dataset, but you must also set the dataset parameter as a `Dataset` object. To score from a local file, add this parameter to the settings: file : file-like object, string path to file or a pandas.DataFrame of scoring data. timeseries_settings : dict 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. 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. Must 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.

classmethod score_to_file(deployment: DeploymentType, intake_path, output_path: str, **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 datarobot.models.BatchPredictionJob.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 apply_time_series_data_prep_and_score_to_file(deployment: Deployment, intake_path: Union[str, pd.DataFrame, io.IOBase], output_path: str, timeseries_settings: TimeSeriesSettings, **kwargs) → BatchPredictionJob¶

Prepare the input dataset with time series data prep. Then, create a new batch prediction job using the prepared AI catalog item as input and concurrently download the scored CSV file.

The function call will return when the entire file is scored.

For timeseries_settings of type forecast the forecast_point must be specified.

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

New in version v3.1.

Returns:	BatchPredictionJob Instance of BatchPredictionJob.
Raises:	InvalidUsageError If the deployment does not support time series data prep.
Attributes:	deployment : Deployment The deployment which will be used for scoring. intake_path : file-like object/string path to file/pandas.DataFrame The scoring data. output_path : str The filename under which you save the result. timeseries_settings : dict 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. 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. Must 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.

classmethod score_s3(deployment: DeploymentType, source_url: str, destination_url: str, credential=None, endpoint_url: Optional[str] = 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 datarobot.models.BatchPredictionJob.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
endpoint_url : string (optional): Any non-default endpoint URL for S3 access (omit to use the default)

classmethod score_azure(deployment: DeploymentType, source_url: str, destination_url: str, 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 datarobot.models.BatchPredictionJob.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: DeploymentType, source_url: str, destination_url: str, 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 datarobot.models.BatchPredictionJob.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: str) → datarobot.models.batch_prediction_job.BatchPredictionJob¶

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: DeploymentType, df: pd.DataFrame, read_timeout: int = 660, **kwargs) → Tuple[BatchPredictionJob, pd.DataFrame]¶

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 datarobot.models.BatchPredictionJob.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: str) → datarobot.models.batch_prediction_job.BatchPredictionJob¶

Get batch prediction job

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

download(fileobj, timeout: int = 120, read_timeout: int = 660) → None¶

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: bool = False) → None¶: 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: Optional[List[str]] = None) → List[datarobot.models.batch_prediction_job.BatchPredictionJob]¶

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: Optional[str] = None, name: Optional[str] = None, enabled: Optional[bool] = None, schedule: Optional[Schedule] = None, batch_prediction_job=None, created: Optional[str] = None, updated: Optional[str] = None, created_by=None, updated_by=None, last_failed_run_time: Optional[str] = None, last_successful_run_time: Optional[str] = None, last_started_job_status: Optional[str] = None, last_scheduled_run_time: Optional[str] = None)¶

classmethod get(batch_prediction_job_definition_id: str) → datarobot.models.batch_prediction_job.BatchPredictionJobDefinition¶

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() → List[datarobot.models.batch_prediction_job.BatchPredictionJobDefinition]¶

Get job all definitions

Returns:	List[BatchPredictionJobDefinition] 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: bool, batch_prediction_job, name: Optional[str] = None, schedule: Optional[Schedule] = None) → BatchPredictionJobDefinition¶

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 asterisk ["*"] 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: bool, batch_prediction_job=None, name: Optional[str] = None, schedule: Optional[Schedule] = None) → BatchPredictionJobDefinition¶

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: Schedule) → BatchPredictionJobDefinition¶

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() → datarobot.models.batch_prediction_job.BatchPredictionJob¶

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() → None¶

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: Optional[str] = None, processes: Optional[List[str]] = None, model_type: Optional[str] = None, project_id: Optional[str] = None, blueprint_category: Optional[str] = None, monotonic_increasing_featurelist_id: Optional[str] = None, monotonic_decreasing_featurelist_id: Optional[str] = None, supports_monotonic_constraints: Optional[bool] = None, recommended_featurelist_id: Optional[str] = None, supports_composable_ml: Optional[bool] = 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: str, blueprint_id: str) → datarobot.models.blueprint.Blueprint¶

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() → datarobot.models.blueprint.BlueprintChart¶

Retrieve a chart.

Returns:	BlueprintChart The current blueprint chart.

get_documents() → List[datarobot.models.blueprint.BlueprintTaskDocument]¶

Get documentation for tasks used in the blueprint.

Returns:	list of BlueprintTaskDocument All documents available for blueprint.

classmethod from_data(data: Union[Dict[str, Any], List[Dict[str, Any]]]) → T¶

Instantiate an object of this class using a dict.

Parameters:	data : dict Correctly snake_cased keys and their values.

classmethod from_server_data(data: Union[Dict[str, Any], List[Dict[str, Any]]], keep_attrs: Optional[Iterable[str]] = None) → T¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

class datarobot.models.BlueprintTaskDocument(title: Optional[str] = None, task: Optional[str] = None, description: Optional[str] = None, parameters: Optional[List[ParameterType]] = None, links: Optional[List[LinkType]] = None, references: Optional[List[ReferenceType]] = 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: List[Dict[str, str]], edges: List[Tuple[str, str]])¶

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: str, blueprint_id: str) → datarobot.models.blueprint.BlueprintChart¶

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() → str¶

Get blueprint chart in graphviz DOT format.

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

class datarobot.models.ModelBlueprintChart(nodes: List[Dict[str, str]], edges: List[Tuple[str, str]])¶

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: str, model_id: str) → datarobot.models.blueprint.ModelBlueprintChart¶

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() → str¶

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: Optional[str] = None, calendar_start_date: Optional[str] = None, created: Optional[str] = None, id: Optional[str] = None, name: Optional[str] = None, num_event_types: Optional[int] = None, num_events: Optional[int] = None, project_ids: Optional[List[str]] = None, role: Optional[str] = None, multiseries_id_columns: Optional[List[str]] = 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: str, calendar_name: Optional[str] = None, multiseries_id_columns: Optional[List[str]] = None) → datarobot.models.calendar_file.CalendarFile¶

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,    Event Duration
<date>, <event_type>,   <series id>,  <event duration>
<date>, <event_type>,              ,  <event duration>

A header row is required, and the “Series ID” and “Event Duration” columns are 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_dataset(dataset_id: str, dataset_version_id: Optional[str] = None, calendar_name: Optional[str] = None, multiseries_id_columns: Optional[List[str]] = None, delete_on_error: Optional[bool] = False) → datarobot.models.calendar_file.CalendarFile¶

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

The provided dataset have the following format:

Date,   Event,          Series ID,    Event Duration
<date>, <event_type>,   <series id>,  <event duration>
<date>, <event_type>,              ,  <event duration>

The “Series ID” and “Event Duration” columns are 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:	dataset_id : string The identifier of the dataset from which to create the calendar. dataset_version_id : string, optional The identifier of the dataset version from which to create the calendar. calendar_name : string, optional A name to assign to the calendar. Defaults to the name of the dataset if not provided. multiseries_id_columns : list of str, optional 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. delete_on_error : boolean, optional Whether delete calendar file from Catalog if it’s not valid.
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 from a dataset
dataset = dr.Dataset.create_from_file('/home/calendars/somecalendar.csv')
cal = dr.CalendarFile.create_calendar_from_dataset(
    dataset.id, calendar_name='Some Calendar Name'
)
cal.id
>>> 5c1d4904211c0a061bc93013
cal.name
>>> Some Calendar Name

# Creating a calendar from a new dataset version
new_dataset_version = dr.Dataset.create_version_from_file(
    dataset.id, '/home/calendars/anothercalendar.csv'
)
cal = dr.CalendarFile.create(
    new_dataset_version.id, dataset_version_id=new_dataset_version.version_id
)
cal.id
>>> 5c1d4904211c0a061bc93012
cal.name
>>> anothercalendar.csv

classmethod create_calendar_from_country_code(country_code: str, start_date: datetime.datetime, end_date: datetime.datetime) → datarobot.models.calendar_file.CalendarFile¶

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: Optional[int] = None, limit: Optional[int] = None) → List[CountryCode]¶

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: str) → datarobot.models.calendar_file.CalendarFile¶

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: Optional[str] = None, batch_size: Optional[int] = None) → List[datarobot.models.calendar_file.CalendarFile]¶

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: str) → None¶

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: str, new_calendar_name: str) → int¶

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: str, access_list: List[datarobot.models.sharing.SharingAccess]) → int¶

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: str, batch_size: Optional[int] = None) → List[datarobot.models.sharing.SharingAccess]¶

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()

is_model_compliance_initialized¶

Check if model compliance documentation pre-processing is initialized. Model compliance documentation pre-processing must be initialized before generating documentation for a custom model.

Returns:	Tuple of (boolean, string) boolean flag is whether model compliance documentation pre-processing is initialized string value is the initialization status

initialize_model_compliance()¶

Initialize model compliance documentation pre-processing. Must be called before generating documentation for a custom model.

Returns:	Tuple of (boolean, string) boolean flag is whether model compliance documentation pre-processing is initialized string value is the initialization status

Examples

import datarobot as dr

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

# NOTE: entity_id is either a model id or a model package id
doc = dr.AutomatedDocument(
        document_type="MODEL_COMPLIANCE",
        entity_id="6f50cdb77cc4f8d1560c3ed5",
        output_format="docx",
        locale="EN_US")

doc.initialize_model_compliance()

generate(max_wait: int = 600) → requests.models.Response¶

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: Optional[int] = None, min_class_support: Optional[int] = None, excluded_from_aggregation: Optional[List[str]] = None, aggregation_class_name: Optional[str] = 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.

Client Configuration¶

datarobot.client.Client(token: Optional[str] = None, endpoint: Optional[str] = None, config_path: Optional[str] = None, connect_timeout: Optional[int] = None, user_agent_suffix: Optional[str] = None, ssl_verify: bool = True, max_retries: Union[int, urllib3.util.retry.Retry, None] = None, token_type: str = 'Token') → datarobot.rest.RESTClientObject¶

Configures the global API client for the Python SDK with optional configuration. Missing configuration will be read from env or config file.

Parameters:

token : str, optional: API token
endpoint : str, optional: Base url of API
config_path : str, optional: Alternate location of config file
connect_timeout : int, optional: How long the client should be willing to wait before establishing a connection with the server.
user_agent_suffix : str, optional: Additional text that is appended to the User-Agent HTTP header when communicating with the DataRobot REST API. This can be useful for identifying different applications that are built on top of the DataRobot Python Client, which can aid debugging and help track usage.
ssl_verify : bool or str, optional: Whether to check SSL certificate. Could be set to path with certificates of trusted certification authorities.
max_retries : int or datarobot.rest.Retry, optional: Either an integer number of times to retry connection errors, or a urllib3.util.retry.Retry object to configure retries.
token_type: str, “Token” by default: Authentication token type: Token, Bearer. “Bearer” is for DataRobot OAuth2 token, “Token” for token generated in Developer Tools.

Returns:

The RESTClientObject instance created.

datarobot.client.set_client(client: datarobot.rest.RESTClientObject) → Optional[datarobot.rest.RESTClientObject]¶: Configure the global HTTP client for the Python SDK. Returns previous instance.

datarobot.client.client_configuration(*args, **kwargs)¶

This context manager can be used to temporarily change the global HTTP client.

In multithreaded scenarios, it is highly recommended to use a fresh manager object per thread.

DataRobot does not recommend nesting these contexts.

Parameters:	args : Parameters passed to `datarobot.client.Client()` kwargs : Keyword arguments passed to `datarobot.Client()`

Examples

from datarobot.client import client_configuration
from datarobot.models import Project

with client_configuration(token="api-key-here", endpoint="https://host-name.com"):
    Project.list()

from datarobot.client import Client, client_configuration
from datarobot.models import Project

Client()  # Interact with DataRobot using the default configuration.
Project.list()

with client_configuration(config_path="/path/to/a/drconfig.yaml"):
    # Interact with DataRobot using a different configuration.
    Project.list()

class datarobot.rest.RESTClientObject(auth: str, endpoint: str, connect_timeout: Optional[int] = 6.05, verify: bool = True, user_agent_suffix: Optional[str] = None, max_retries: Union[int, urllib3.util.retry.Retry, None] = None, authentication_type: Optional[str] = None)¶

Parameters

connect_timeout: timeout for http request and connection
headers: headers for outgoing requests

open_in_browser() → None¶: Opens the DataRobot app in a web browser, or logs the URL if a browser is not available.

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, 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: int = 600) → List[datarobot.models.cluster_insight.ClusterInsight]¶

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: List[Tuple[str, str]]) → List[datarobot.models.cluster.Cluster]¶

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: str, new_name: str) → List[datarobot.models.cluster.Cluster]¶

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: str, model_id: str) → List[datarobot.models.cluster.Cluster]¶

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: str, model_id: str, cluster_name_mappings: List[Tuple[str, str]]) → List[datarobot.models.cluster.Cluster]¶

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: str, model_id: str, current_name: str, new_name: str) → List[datarobot.models.cluster.Cluster]¶

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: str, model_id: str, max_wait: int = 600) → List[datarobot.models.cluster_insight.ClusterInsight]¶

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:	List[ClusterInsight]
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 AutomatedDocument.

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.

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: Optional[str] = None, name: Optional[str] = None, credential_type: Optional[str] = None, creation_date: Optional[datetime.datetime] = None, description: Optional[str] = None)¶

classmethod list() → List[datarobot.models.credential.Credential]¶

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: str) → datarobot.models.credential.Credential¶

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() → None¶

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: str, user: str, password: str, description: Optional[str] = None) → datarobot.models.credential.Credential¶

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: str, token: str, refresh_token: str, description: Optional[str] = None) → datarobot.models.credential.Credential¶

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: str, aws_access_key_id: Optional[str] = None, aws_secret_access_key: Optional[str] = None, aws_session_token: Optional[str] = None, description: Optional[str] = None) → datarobot.models.credential.Credential¶

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: str, azure_connection_string: str, description: Optional[str] = None) → datarobot.models.credential.Credential¶

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: str, gcp_key: Union[str, Dict[str, str], None] = None, description: Optional[str] = None) → datarobot.models.credential.Credential¶

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.CustomInferenceModel(**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, 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. 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.

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 : iterable List, set or tuple 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_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_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_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_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_values=None)¶

Update custom model version properties.

New in version v2.21.

Parameters:	description: str new custom model version description 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.models.CustomModelVersionConversion(**kwargs)¶

A conversion of a DataRobot custom model version.

New in version v2.27.

Attributes:

id: str: ID of the custom model version conversion.
custom_model_version_id: str: ID of the custom model version.
created: str: ISO-8601 timestamp of when the custom model conversion created.
main_program_item_id: str or None: ID of the main program item.
log_message: str or None: The conversion output log message.
generated_metadata: dict or None: The dict contains two items: ‘outputDataset’ & ‘outputColumns’.
conversion_succeeded: bool: Whether the conversion succeeded or not.
conversion_in_progress: bool: Whether a given conversion is in progress or not.
should_stop: bool: Whether the user asked to stop a conversion.

classmethod run_conversion(custom_model_id, custom_model_version_id, main_program_item_id, max_wait=None)¶

Initiate a new custom model version conversion.

Parameters:	custom_model_id : str The associated custom model ID. custom_model_version_id : str The associated custom model version ID. main_program_item_id : str The selected main program item ID. This should be one of the SAS items in the associated custom model version. max_wait: int or None Max wait time in seconds. If None, than don’t wait.
Returns:	conversion_id : str The ID of the newly created conversion entity.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status datarobot.errors.ServerError if the server responded with 5xx statuscustom model conversion

classmethod stop_conversion(custom_model_id, custom_model_version_id, conversion_id)¶

Stop a conversion that is in progress.

Parameters:	custom_model_id : str ID of the associated custom model. custom_model_version_id : str ID of the associated custom model version. conversion_id ID of a conversion that is in-progress.
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, conversion_id)¶

Get custom model version conversion by id.

New in version v2.27.

Parameters:	custom_model_id: str The ID of the custom model. custom_model_version_id: str The ID of the custom model version. conversion_id: str The ID of the conversion to retrieve.
Returns:	CustomModelVersionConversion Retrieved custom model version conversion.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

classmethod get_latest(custom_model_id, custom_model_version_id)¶

Get latest custom model version conversion for a given custom model version.

New in version v2.27.

Parameters:	custom_model_id: str The ID of the custom model. custom_model_version_id: str The ID of the custom model version.
Returns:	CustomModelVersionConversion or None Retrieved latest conversion for a given 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, custom_model_version_id)¶

Get custom model version conversions list per custom model version.

New in version v2.27.

Parameters:	custom_model_id: str The ID of the custom model. custom_model_version_id: str The ID of the custom model version.
Returns:	List[CustomModelVersionConversion] Retrieved conversions for a given custom model version.
Raises:	datarobot.errors.ClientError if the server responded with 4xx status. datarobot.errors.ServerError if the server responded with 5xx status.

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: str, target_type: datarobot.enums.CUSTOM_TASK_TARGET_TYPE, latest_version: Optional[datarobot.models.custom_task_version.CustomTaskVersion], created_at: str, updated_at: str, name: str, description: str, language: datarobot.enums.Enum, created_by: str, calibrate_predictions: Optional[bool] = 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: Union[Dict[str, Any], List[Dict[str, Any]]], keep_attrs: Optional[Iterable[str]] = None) → datarobot.models.custom_task.CustomTask¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

classmethod list(order_by: Optional[str] = None, search_for: Optional[str] = None) → List[datarobot.models.custom_task.CustomTask]¶

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: str) → datarobot.models.custom_task.CustomTask¶

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: str) → datarobot.models.custom_task.CustomTask¶

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: str, target_type: datarobot.enums.CUSTOM_TASK_TARGET_TYPE, language: Optional[datarobot.enums.Enum] = None, description: Optional[str] = None, calibrate_predictions: Optional[bool] = None, **kwargs) → datarobot.models.custom_task.CustomTask¶

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: Optional[str] = None, language: Optional[datarobot.enums.Enum] = None, description: Optional[str] = None, **kwargs) → None¶

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() → None¶

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() → None¶

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: str) → None¶

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() → List[datarobot.models.sharing.SharingAccess]¶

Retrieve access control settings of this custom task.

New in version v2.27.

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

share(access_list: List[datarobot.models.sharing.SharingAccess]) → None¶

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, 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
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 : iterable List, set or tuple 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_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_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_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_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_values=None)¶

Update custom task version properties.

New in version v2.26.

Parameters:	description: str new custom task version description 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: Optional[str] = None, creator: Optional[str] = None, base_names: Optional[List[str]] = None, class_name: Optional[str] = None, canonical_name: Optional[str] = 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() → List[datarobot.models.driver.DataDriver]¶

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: str) → datarobot.models.driver.DataDriver¶

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: str, canonical_name: str, files: List[str]) → datarobot.models.driver.DataDriver¶

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: Optional[str] = None, canonical_name: Optional[str] = None) → 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() → None¶

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: Optional[str] = None, creator_id: Optional[str] = None, configuration_id: Optional[str] = None, base_name: Optional[str] = None, canonical_name: Optional[str] = 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() → List[datarobot.models.connector.Connector]¶

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: str) → datarobot.models.connector.Connector¶

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: str) → datarobot.models.connector.Connector¶

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: str) → datarobot.models.connector.Connector¶

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() → None¶

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: Optional[str] = None, data_store_type: Optional[str] = None, canonical_name: Optional[str] = None, creator: Optional[str] = None, updated: Optional[datetime.datetime] = None, params: Optional[datarobot.models.data_store.DataStoreParameters] = None, role: Optional[str] = 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.
role : str: Your access role for this data store.

classmethod list() → List[datarobot.models.data_store.DataStore]¶

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: str) → datarobot.models.data_store.DataStore¶

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: str, canonical_name: str, driver_id: str, jdbc_url: str) → datarobot.models.data_store.DataStore¶

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: Optional[str] = None, driver_id: Optional[str] = None, jdbc_url: Optional[str] = None) → 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() → None¶: Removes the DataStore

test(username: str, password: str) → TestResponse¶

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: str, password: str) → SchemasResponse¶

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: str, password: str, schema: Optional[str] = None) → TablesResponse¶

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: Union[Dict[str, Any], List[Dict[str, Any]]], keep_attrs: Optional[List[str]] = None) → datarobot.models.data_store.DataStore¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

get_access_list() → List[datarobot.models.sharing.SharingAccess]¶

Retrieve what users have access to this data store

New in version v2.14.

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

share(access_list: List[datarobot.models.sharing.SharingAccess]) → None¶

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: Optional[str] = None, data_source_type: Optional[str] = None, canonical_name: Optional[str] = None, creator: Optional[str] = None, updated: Optional[datetime.datetime] = None, params: Optional[datarobot.models.data_source.DataSourceParameters] = None, role: Optional[str] = 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.
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.

classmethod list() → List[datarobot.models.data_source.DataSource]¶

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: str) → TDataSource¶

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: str, canonical_name: str, params: datarobot.models.data_source.DataSourceParameters) → TDataSource¶

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: Optional[str] = None, params: Optional[datarobot.models.data_source.DataSourceParameters] = None) → 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() → None¶: Removes the DataSource

classmethod from_server_data(data: ServerDataType, keep_attrs: Optional[Iterable[str]] = None) → TDataSource¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

get_access_list() → List[datarobot.models.sharing.SharingAccess]¶

Retrieve what users have access to this data source

New in version v2.14.

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

share(access_list: List[datarobot.models.sharing.SharingAccess]) → None¶

Modify the ability of users to access this data source

New in version v2.14.

Parameters:	access_list: list of : class:SharingAccess <datarobot.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

from datarobot.enums import SHARING_ROLE
from datarobot.models.data_source import DataSource
from datarobot.models.sharing import SharingAccess

new_access = SharingAccess(
    "[email protected]",
    SHARING_ROLE.OWNER,
    can_share=True,
)
access_list = [
    SharingAccess("[email protected]", SHARING_ROLE.OWNER, can_share=True),
    new_access,
]

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

create_dataset(username: Optional[str] = None, password: Optional[str] = None, do_snapshot: Optional[bool] = None, persist_data_after_ingestion: Optional[bool] = None, categories: Optional[List[str]] = None, credential_id: Optional[str] = None, use_kerberos: Optional[bool] = None) → datarobot.models.dataset.Dataset¶

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: Optional[str] = None, table: Optional[str] = None, schema: Optional[str] = None, partition_column: Optional[str] = None, query: Optional[str] = None, fetch_size: Optional[int] = 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.models.Dataset(dataset_id: str, version_id: str, name: str, categories: List[str], created_at: str, is_data_engine_eligible: bool, is_latest_version: bool, is_snapshot: bool, processing_state: str, created_by: Optional[str] = None, data_persisted: Optional[bool] = None, size: Optional[int] = None, row_count: Optional[int] = 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, optional: 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.

get_uri() → str¶

Returns:	url : str Permanent static hyperlink to this dataset in AI Catalog.

classmethod upload(source: Union[str, pandas.core.frame.DataFrame, io.IOBase]) → TDataset¶

This method covers Dataset creation from local materials (file & DataFrame) and a URL.

Parameters:	source: str, pd.DataFrame or file object Pass a URL, filepath, file or DataFrame to create and return a Dataset.
Returns:	response: Dataset The Dataset created from the uploaded data source.
Raises:	InvalidUsageError If the source parameter cannot be determined to be a URL, filepath, file or DataFrame.

Examples

# Upload a local file
dataset_one = Dataset.upload("./data/examples.csv")

# Create a dataset via URL
dataset_two = Dataset.upload(
    "https://raw.githubusercontent.com/curran/data/gh-pages/dbpedia/cities/data.csv"
)

# Create dataset with a pandas Dataframe
dataset_three = Dataset.upload(my_df)

# Create dataset using a local file
with open("./data/examples.csv", "rb") as file_pointer:
    dataset_four = Dataset.create_from_file(filelike=file_pointer)

classmethod create_from_file(file_path: Optional[str] = None, filelike: Optional[io.IOBase] = None, categories: Optional[List[str]] = None, read_timeout: int = 600, max_wait: int = 600) → TDataset¶

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 dataset creation is considered unsuccessful

Returns:

response: Dataset: A fully armed and operational Dataset

classmethod create_from_in_memory_data(data_frame: Optional[pandas.core.frame.DataFrame] = None, records: Optional[List[Dict[str, Any]]] = None, categories: Optional[List[str]] = None, read_timeout: int = 600, max_wait: int = 600, fname: Optional[str] = None) → TDataset¶

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 dataset creation is considered unsuccessful fname: string, optional The file name, “data.csv” by default
Returns:	response: Dataset The Dataset created from the uploaded data.
Raises:	InvalidUsageError If neither a DataFrame or list of records is passed.

classmethod create_from_url(url: str, do_snapshot: Optional[bool] = None, persist_data_after_ingestion: Optional[bool] = None, categories: Optional[List[str]] = None, max_wait: int = 600) → TDataset¶

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 dataset creation is considered unsuccessful.

Returns:

response: Dataset: The Dataset created from the uploaded data

classmethod create_from_data_source(data_source_id: str, username: Optional[str] = None, password: Optional[str] = None, do_snapshot: Optional[bool] = None, persist_data_after_ingestion: Optional[bool] = None, categories: Optional[List[str]] = None, credential_id: Optional[str] = None, use_kerberos: Optional[bool] = None, credential_data: Optional[Dict[str, str]] = None, max_wait: int = 600) → TDataset¶

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: str, dataset_id: Optional[str] = None, dataset_version_id: Optional[str] = None, max_wait: int = 600) → TDataset¶

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: str) → TDataset¶

Get information about a dataset.

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

classmethod delete(dataset_id: str) → None¶

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: str) → None¶

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: Optional[str] = None, filter_failed: Optional[bool] = None, order_by: Optional[str] = None) → List[TDataset]¶

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: Optional[int] = None, limit: Optional[int] = None, category: Optional[str] = None, order_by: Optional[str] = None, filter_failed: Optional[bool] = None) → Generator[TDataset, None, 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() → None¶

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

Returns:	None

modify(name: Optional[str] = None, categories: Optional[List[str]] = None) → 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

share(access_list: List[datarobot.models.sharing.SharingAccess], apply_grant_to_linked_objects: bool = False) → None¶

Modify the ability of users to access this dataset

Parameters:	access_list: list of : class:SharingAccess <datarobot.SharingAccess> The modifications to make. apply_grant_to_linked_objects: bool If true for any users being granted access to the dataset, grant the user read access to any linked objects such as DataSources and DataStores that may be used by this dataset. Ignored if no such objects are relevant for dataset, defaults to False.
Raises:	datarobot.ClientError: If you do not have permission to share this dataset, 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 dataset without an owner.

Examples

Transfer access to the dataset from old_user@datarobot.com to new_user@datarobot.com

from datarobot.enums import SHARING_ROLE
from datarobot.models.dataset import Dataset
from datarobot.models.sharing import SharingAccess

new_access = SharingAccess(
    "[email protected]",
    SHARING_ROLE.OWNER,
    can_share=True,
)
access_list = [
    SharingAccess(
        "[email protected]",
        SHARING_ROLE.OWNER,
        can_share=True,
        can_use_data=True,
    ),
    new_access,
]

Dataset.get('my-dataset-id').share(access_list)

get_details() → datarobot.models.dataset.DatasetDetails¶

Gets the details for this Dataset

Returns:	DatasetDetails

get_all_features(order_by: Optional[str] = None) → List[datarobot.models.feature.DatasetFeature]¶

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: Optional[int] = None, limit: Optional[int] = None, order_by: Optional[str] = None) → Generator[datarobot.models.feature.DatasetFeature, None, 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() → List[datarobot.models.featurelist.DatasetFeaturelist]¶

Get DatasetFeaturelists created on this Dataset

Returns:	feature_lists: list[DatasetFeaturelist]

create_featurelist(name: str, features: List[str]) → datarobot.models.featurelist.DatasetFeaturelist¶

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: Optional[str] = None, filelike: Optional[io.IOBase] = None) → 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_as_dataframe() → pandas.core.frame.DataFrame¶

Retrieves all the originally uploaded data in a pandas DataFrame.

New in version v3.0.

Returns:	pd.DataFrame

get_projects() → List[datarobot.models.dataset.ProjectLocation]¶

Retrieves the Dataset’s projects as ProjectLocation named tuples.

Returns:	locations: list[ProjectLocation]

create_project(project_name: Optional[str] = None, user: Optional[str] = None, password: Optional[str] = None, credential_id: Optional[str] = None, use_kerberos: Optional[bool] = None, credential_data: Optional[Dict[str, str]] = None) → datarobot.models.project.Project¶

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: str, file_path: Optional[str] = None, filelike: Optional[io.IOBase] = None, categories: Optional[List[str]] = None, read_timeout: int = 600, max_wait: int = 600) → TDataset¶

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: str, data_frame: Optional[pandas.core.frame.DataFrame] = None, records: Optional[List[Dict[str, Any]]] = None, categories: Optional[List[str]] = None, read_timeout: int = 600, max_wait: int = 600) → TDataset¶

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
Raises:	InvalidUsageError If neither a DataFrame or list of records is passed.

classmethod create_version_from_url(dataset_id: str, url: str, categories: Optional[List[str]] = None, max_wait: int = 600) → TDataset¶

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: str, data_source_id: str, username: Optional[str] = None, password: Optional[str] = None, categories: Optional[List[str]] = None, credential_id: Optional[str] = None, use_kerberos: Optional[bool] = None, credential_data: Optional[Dict[str, str]] = None, max_wait: int = 600) → TDataset¶

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

classmethod from_data(data: Union[Dict[str, Any], List[Dict[str, Any]]]) → T¶

Instantiate an object of this class using a dict.

Parameters:	data : dict Correctly snake_cased keys and their values.

classmethod from_server_data(data: Union[Dict[str, Any], List[Dict[str, Any]]], keep_attrs: Optional[Iterable[str]] = None) → T¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

open_in_browser() → None¶

Opens class’ relevant web browser location. If default browser is not available the URL is logged.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

class datarobot.DatasetDetails(dataset_id: str, version_id: str, categories: List[str], created_by: str, created_at: str, data_source_type: str, error: str, is_latest_version: bool, is_snapshot: bool, is_data_engine_eligible: bool, last_modification_date: str, last_modifier_full_name: str, name: str, uri: str, processing_state: str, data_persisted: Optional[bool] = None, data_engine_query_id: Optional[str] = None, data_source_id: Optional[str] = None, description: Optional[str] = None, eda1_modification_date: Optional[str] = None, eda1_modifier_full_name: Optional[str] = None, feature_count: Optional[int] = None, feature_count_by_type: Optional[List[datarobot.models.dataset.FeatureTypeCount]] = None, row_count: Optional[int] = None, size: Optional[int] = None, tags: Optional[List[str]] = 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: str) → TDatasetDetails¶

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() → datarobot.models.dataset.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

prepare_prediction_dataset_from_catalog(project_id: str, dataset_id: str, dataset_version_id: Optional[str] = None, max_wait: Optional[int] = 600, relax_known_in_advance_features_check: Optional[bool] = None) → datarobot.models.prediction_dataset.PredictionDataset¶

Apply time series data prep to a catalog dataset and upload it to the project as a PredictionDataset.

New in version v3.1.

Parameters:

project_id : str: The id of the project to which you upload the prediction dataset.
dataset_id : str: The identifier of the dataset.
dataset_version_id : str, optional: The version id of the dataset to use.
max_wait : int, optional: Optional, the maximum number of seconds to wait before giving up.
relax_known_in_advance_features_check : bool, optional: 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.

Returns:

dataset : PredictionDataset: The newly uploaded dataset.

prepare_prediction_dataset(sourcedata: Union[str, pandas.core.frame.DataFrame, io.IOBase], project_id: str, max_wait: Optional[int] = 600, relax_known_in_advance_features_check: Optional[bool] = None) → datarobot.models.prediction_dataset.PredictionDataset¶

Apply time series data prep and upload the PredictionDataset to the project.

New in version v3.1.

Parameters:

sourcedata : str, file or pandas.DataFrame: Data to be used for predictions. If it is a string, 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.
project_id : str: The id of the project to which you upload the prediction dataset.
max_wait : int, optional: The maximum number of seconds to wait for the uploaded dataset to be processed before raising an error.
relax_known_in_advance_features_check : bool, optional: 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.
Returns
——-
dataset : PredictionDataset: The newly uploaded dataset.

Raises:

InputNotUnderstoodError: Raised if sourcedata isn’t one of supported types.
AsyncFailureError: Raised if polling for the status of an async process resulted in a response with an unsupported status code.
AsyncProcessUnsuccessfulError: Raised if project creation was unsuccessful (i.e. the server reported an error in uploading the dataset).
AsyncTimeoutError: Raised if processing the uploaded dataset took more time than specified by the max_wait parameter.

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.models.Deployment(id: str, label: Optional[str] = None, description: Optional[str] = None, status: Optional[str] = None, default_prediction_server: Optional[PredictionServer] = None, model: Optional[ModelDict] = None, capabilities: Optional[Dict[str, Any]] = None, prediction_usage: Optional[PredictionUsage] = None, permissions: Optional[List[str]] = None, service_health: Optional[Health] = None, model_health: Optional[Health] = None, accuracy_health: Optional[Health] = None, importance: Optional[str] = None, fairness_health: Optional[Health] = None, governance: Optional[Dict[str, Any]] = None, owners: Optional[Dict[str, Any]] = None, prediction_environment: Optional[Dict[str, Any]] = 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

status : str

(New in version v2.29) deployment status

default_prediction_server : dict

Information about the default prediction server for the deployment. Accepts the following values:

id: str. Prediction server ID.
url: str, optional. Prediction server URL.
datarobot-key: str. Corresponds the to the PredictionServer’s “snake_cased” datarobot_key parameter that allows you to verify and access the prediction server.

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: str, label: str, description: Optional[str] = None, default_prediction_server_id: Optional[str] = None, importance: Optional[str] = None, prediction_threshold: Optional[float] = None, status: Optional[str] = None) → TDeployment¶

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
status : str, optional: deployment status

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_version(custom_model_version_id: str, label: str, description: Optional[str] = None, default_prediction_server_id: Optional[str] = None, max_wait: int = 600, importance: Optional[str] = None) → TDeployment¶

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: Optional[str] = None, search: Optional[str] = None, filters: Optional[datarobot.models.deployment.DeploymentListFilters] = None) → List[TDeployment]¶

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: str) → TDeployment¶

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'

predict_batch(source: Union[str, pandas.core.frame.DataFrame, io.IOBase], passthrough_columns: Optional[List[str]] = None, download_timeout: Optional[int] = None, download_read_timeout: Optional[int] = None, upload_read_timeout: Optional[int] = None) → pandas.core.frame.DataFrame¶

Using a deployment, make batch predictions and return results as a DataFrame.

If a DataFrame is passed as source, then the prediction results are merged with the original DataFrame and a new DataFrame is returned.

New in version v3.0.

Parameters:	source: str, pd.DataFrame or file object Pass a filepath, file, or DataFrame for making batch predictions. 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. download_timeout: int, optional Wait this many seconds for the download to become available. See `datarobot.models.BatchPredictionJob.score()`. download_read_timeout: int, optional Wait this many seconds for the server to respond between chunks. See `datarobot.models.BatchPredictionJob.score()`. upload_read_timeout: int, optional Wait this many seconds for the server to respond after a whole dataset upload. See `datarobot.models.BatchPredictionJob.score()`.
Returns:	pd.DataFrame Prediction results in a pandas DataFrame.
Raises:	InvalidUsageError If the source parameter cannot be determined to be a filepath, file, or DataFrame.

Examples

from datarobot.models.deployment import Deployment

deployment = Deployment.get("<MY_DEPLOYMENT_ID>")
prediction_results_as_dataframe = deployment.predict_batch(
    source="./my_local_file.csv",
)

get_uri() → str¶

Returns:	url : str Deployment’s overview URI

update(label: Optional[str] = None, description: Optional[str] = None, importance: Optional[str] = None) → None¶: Update the label and description of this deployment.

New in version v2.19.

delete() → None¶: Delete this deployment.

New in version v2.17.

activate(max_wait: int = 600) → None¶

Activates this deployment. When succeeded, deployment status become active.

New in version v2.29.

Parameters:	max_wait : int, optional The maximum time to wait for deployment activation to complete before erroring

deactivate(max_wait: int = 600) → None¶

Deactivates this deployment. When succeeded, deployment status become inactive.

New in version v2.29.

Parameters:	max_wait : int, optional The maximum time to wait for deployment deactivation to complete before erroring

replace_model(new_model_id: str, reason: str, max_wait: int = 600) → None¶

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 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: str) → Tuple[str, str, Dict[str, Any]]¶

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() → List[FeatureDict]¶

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: Union[pd.DataFrame, List[Actual]], batch_size: int = 10000) → None¶

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() → ForecastDateSettings¶

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: bool, forecast_date_column_name: Optional[str] = None, forecast_date_format: Optional[str] = None, max_wait: int = 600) → None¶

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

Examples

# To set predictions by forecast date settings to the same default settings you see when using
# the DataRobot web application, you use your 'Deployment' object like this:
deployment.update_predictions_by_forecast_date_settings(
   enable_predictions_by_forecast_date=True,
   forecast_date_column_name="date (actual)",
   forecast_date_format="%Y-%m-%d",
)

get_challenger_models_settings() → ChallengerModelsSettings¶

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: bool, max_wait: int = 600) → None¶

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() → SegmentAnalysisSettings¶

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: bool, segment_analysis_attributes: Optional[List[str]] = None, max_wait: int = 600) → None¶

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() → DriftTrackingSettings¶

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: Optional[bool] = None, feature_drift_enabled: Optional[bool] = None, max_wait: int = 600) → None¶

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() → str¶

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: Optional[List[str]] = None, required_in_prediction_requests: Optional[bool] = None, max_wait: int = 600) → None¶

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() → Dict[str, bool]¶

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: bool, max_wait: int = 600) → None¶

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() → PredictionWarningSettings¶

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: bool, use_default_boundaries: Optional[bool] = None, lower_boundary: Optional[float] = None, upper_boundary: Optional[float] = None, max_wait: int = 600) → None¶

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() → PredictionIntervalsSettings¶

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: List[int], enabled: bool = True, max_wait: int = 600) → None¶

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: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, execution_time_quantile: Optional[float] = None, response_time_quantile: Optional[float] = None, slow_requests_threshold: Optional[float] = None) → datarobot.models.service_stats.ServiceStats¶

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: Optional[str] = None, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, bucket_size: Optional[str] = None, quantile: Optional[float] = None, threshold: Optional[int] = None) → datarobot.models.service_stats.ServiceStatsOverTime¶

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: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, metric: Optional[str] = None) → datarobot.models.data_drift.TargetDrift¶

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: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, metric: Optional[str] = None) → List[datarobot.models.data_drift.FeatureDrift]¶

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: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, start: Optional[datetime.datetime] = None, end: Optional[datetime.datetime] = None, target_classes: Optional[List[str]] = None) → datarobot.models.accuracy.Accuracy¶

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 target_classes : list[str], optional Optional list of target class strings
Returns:	accuracy : Accuracy the queried accuracy metrics information

get_accuracy_over_time(metric: Optional[datarobot.enums.ACCURACY_METRIC] = None, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, bucket_size: Optional[str] = None, target_classes: Optional[List[str]] = None) → datarobot.models.accuracy.AccuracyOverTime¶

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 target_classes : list[str], optional Optional list of target class strings
Returns:	accuracy_over_time : AccuracyOverTime the queried accuracy metric over time information

update_secondary_dataset_config(secondary_dataset_config_id: str, credential_ids: Optional[List[str]] = None) → str¶

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() → str¶

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: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, actuals_present: Optional[bool] = None, offset: Optional[int] = None, limit: Optional[int] = None) → List[Dict[str, Any]]¶

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: str, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, actuals_present: Optional[bool] = None, offset: Optional[int] = None, limit: Optional[int] = None) → 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: str, source_code: bool = False, include_agent: bool = False, include_prediction_explanations: bool = False, include_prediction_intervals: bool = False) → None¶

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')

delete_monitoring_data(model_id: str, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, max_wait: int = 600) → None¶

Delete deployment monitoring data.

Parameters:	model_id : str id of the model to delete monitoring data start_time : datetime, optional start of the time period to delete monitoring data end_time : datetime, optional end of the time period to delete monitoring data max_wait : int, optional seconds to wait for successful resolution

classmethod from_data(data: Union[Dict[str, Any], List[Dict[str, Any]]]) → T¶

Instantiate an object of this class using a dict.

Parameters:	data : dict Correctly snake_cased keys and their values.

classmethod from_server_data(data: Union[Dict[str, Any], List[Dict[str, Any]]], keep_attrs: Optional[Iterable[str]] = None) → T¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

open_in_browser() → None¶

Opens class’ relevant web browser location. If default browser is not available the URL is logged.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

class datarobot.models.deployment.DeploymentListFilters(role: Optional[str] = None, service_health: Optional[List[str]] = None, model_health: Optional[List[str]] = None, accuracy_health: Optional[List[str]] = None, execution_environment_type: Optional[List[str]] = None, importance: Optional[List[str]] = None)¶

class datarobot.models.ServiceStats(period: Optional[Period] = None, metrics: Optional[Metrics] = None, model_id: Optional[str] = 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: str, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, execution_time_quantile: Optional[float] = None, response_time_quantile: Optional[float] = None, slow_requests_threshold: Optional[float] = None) → datarobot.models.service_stats.ServiceStats¶

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: Optional[List[Bucket]] = None, summary: Optional[Bucket] = None, metric: Optional[str] = None, model_id: Optional[str] = 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: str, metric: Optional[str] = None, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, bucket_size: Optional[str] = None, quantile: Optional[float] = None, threshold: Optional[int] = None) → datarobot.models.service_stats.ServiceStatsOverTime¶

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: str, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, metric: Optional[str] = None) → datarobot.models.data_drift.TargetDrift¶

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: str, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, metric: Optional[str] = None) → List[datarobot.models.data_drift.FeatureDrift]¶

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: Optional[Period] = None, metrics: Optional[Dict[str, Metric]] = None, model_id: Optional[str] = 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: str, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, target_classes: Optional[List[str]] = None) → datarobot.models.accuracy.Accuracy¶

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 target_classes : list[str], optional Optional list of target class strings
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: Dict

metric_baselines¶

The baseline value for all metrics, keyed by metric name.

Returns:	metric_baselines: Dict

percent_changes¶

The percent change of value over baseline for all metrics, keyed by metric name.

Returns:	percent_changes: Dict

class datarobot.models.AccuracyOverTime(buckets: Optional[List[Bucket]] = None, summary: Optional[Summary] = None, baseline: Optional[Bucket] = None, metric: Optional[str] = None, model_id: Optional[str] = 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: str, metric: Optional[datarobot.enums.ACCURACY_METRIC] = None, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, bucket_size: Optional[str] = None, target_classes: Optional[List[str]] = None) → datarobot.models.accuracy.AccuracyOverTime¶

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
target_classes : list[str], optional: Optional list of target class strings

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: str, metrics: Optional[List[datarobot.enums.ACCURACY_METRIC]] = None, model_id: Optional[str] = None, start_time: Optional[datetime.datetime] = None, end_time: Optional[datetime.datetime] = None, bucket_size: Optional[str] = None) → pandas.core.frame.DataFrame¶

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: Dict

bucket_sample_sizes¶

The sample size for all time buckets, keyed by start time of the bucket.

Returns:	bucket_sample_sizes: Dict

External Scores and Insights¶

class datarobot.ExternalScores(project_id: str, scores: List[Score], model_id: Optional[str] = None, dataset_id: Optional[str] = None, actual_value_column: Optional[str] = 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: str, model_id: str, dataset_id: str, actual_value_column: Optional[str] = None) → Job¶

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: str, model_id: Optional[str] = None, dataset_id: Optional[str] = None, offset: int = 0, limit: int = 100) → List[datarobot.models.external_dataset_scores_insights.external_scores.ExternalScores]¶

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: str, model_id: str, dataset_id: str) → datarobot.models.external_dataset_scores_insights.external_scores.ExternalScores¶

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: str, bins: List[Bin])¶

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: str, model_id: str, dataset_id: Optional[str] = None, offset: int = 0, limit: int = 100) → List[datarobot.models.external_dataset_scores_insights.external_lift_chart.ExternalLiftChart]¶

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: str, model_id: str, dataset_id: str) → datarobot.models.external_dataset_scores_insights.external_lift_chart.ExternalLiftChart¶

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: str, roc_points: List[EstimatedMetric], negative_class_predictions: List[float], positive_class_predictions: List[float])¶

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: str, model_id: str, dataset_id: Optional[str] = None, offset: int = 0, limit: int = 100) → List[datarobot.models.external_dataset_scores_insights.external_roc_curve.ExternalRocCurve]¶

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: str, model_id: str, dataset_id: str) → datarobot.models.external_dataset_scores_insights.external_roc_curve.ExternalRocCurve¶

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

classmethod from_data(data: Union[Dict[str, Any], List[Dict[str, Any]]]) → T¶

Instantiate an object of this class using a dict.

Parameters:	data : dict Correctly snake_cased keys and their values.

classmethod from_server_data(data: Union[Dict[str, Any], List[Dict[str, Any]]], keep_attrs: Optional[Iterable[str]] = None) → T¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

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 : FeatureHistogram The requested histogram with desired number or bins

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: Optional[List[Strength]] = None, features: Optional[List[Feature]] = None, project_id: Optional[str] = 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: str, metric: Optional[str] = None, association_type: Optional[str] = None, featurelist_id: Optional[str] = None) → datarobot.models.feature_association_matrix.feature_association_matrix.FeatureAssociationMatrix¶

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: Optional[str] = None, chart_type: Optional[str] = None, values: Optional[List[Tuple[Any, Any, float]]] = None, features: Optional[List[str]] = None, types: Optional[List[str]] = None, featurelist_id: Optional[str] = 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: str, feature1: str, feature2: str, featurelist_id: Optional[str] = None) → datarobot.models.feature_association_matrix.feature_association_matrix_details.FeatureAssociationMatrixDetails¶

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: Optional[str] = None, featurelists: Optional[List[FeatureListType]] = 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: str) → datarobot.models.feature_association_matrix.feature_association_featurelists.FeatureAssociationFeaturelists¶

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 identifier 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,
    feature_discovery_settings = [
        {'name': 'enable_categorical_statistics', 'value': True},
        {'name': 'enable_numeric_skewness', 'value': True},
    ]
)
>>> 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: str, catalog_id: Optional[str], catalog_version_id: str, snapshot_policy: str = 'latest', feature_list_id: Optional[str] = None, primary_temporal_key: Optional[str] = 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: str, dataset1_keys: List[str], dataset2_keys: List[str], dataset1_identifier: Optional[str] = None, feature_derivation_window_start: Optional[int] = None, feature_derivation_window_end: Optional[int] = None, feature_derivation_window_time_unit: Optional[int] = None, feature_derivation_windows: Optional[List[Dict[str, Union[int, str]]]] = None, prediction_point_rounding: Optional[int] = None, prediction_point_rounding_time_unit: Optional[str] = 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 identifier 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: str, project_id: str, config: Optional[List[DatasetConfiguration]] = None, secondary_datasets: Optional[List[SecondaryDataset]] = None, name: Optional[str] = None, creator_full_name: Optional[str] = None, creator_user_id: Optional[str] = None, created: Optional[datetime] = None, featurelist_id: Optional[str] = None, credential_ids: Optional[StoredCredentials] = None, is_default: Optional[bool] = None, project_version: Optional[str] = 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 discovery aim
project_version: str, optional: Version of project when its created (Release version)

classmethod create(project_id: str, secondary_datasets: List[datarobot.helpers.feature_discovery.SecondaryDataset], name: str, featurelist_id: Optional[str] = None) → datarobot.models.secondary_dataset.SecondaryDatasetConfigurations¶

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() → None¶

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() → datarobot.models.secondary_dataset.SecondaryDatasetConfigurations¶

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: str, featurelist_id: Optional[str] = None, limit: Optional[int] = None, offset: Optional[int] = None) → List[datarobot.models.secondary_dataset.SecondaryDatasetConfigurations]¶

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: str, catalog_id: str, catalog_version_id: str, snapshot_policy: str = '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, *args, **kwargs)¶

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, *args, **kwargs)¶

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: Optional[str] = None, name: Optional[str] = None, features: Optional[List[str]] = None, dataset_id: Optional[str] = None, dataset_version_id: Optional[str] = None, creation_date: Optional[datetime.datetime] = None, created_by: Optional[str] = None, user_created: Optional[bool] = None, description: Optional[str] = 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 : str, optional: the description of the featurelist. Only present on DataRobot-created featurelists.

classmethod get(dataset_id: str, featurelist_id: str) → TDatasetFeaturelist¶

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() → None¶

Delete a dataset featurelist

Featurelists configured into the dataset as a default featurelist cannot be deleted.

update(name: Optional[str] = None) → 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: Optional[str] = None, name: Optional[str] = None, features: Optional[List[str]] = None, project_id: Optional[str] = None, created: Optional[datetime.datetime] = None, is_user_created: Optional[bool] = None, num_models: Optional[int] = None, description: Optional[str] = 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 : str: (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 from_data(data: ServerDataDictType) → TFeaturelist¶

Overrides the parent method to ensure description is always populated

Parameters:	data : dict the data from the server, having gone through processing

classmethod get(project_id: str, featurelist_id: str) → TFeaturelist¶

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
Raises:	ValueError passed `project_id` parameter value is of not supported type

delete(dry_run: bool = False, delete_dependencies: bool = False) → DeleteFeatureListResult¶

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

classmethod from_server_data(data: Union[Dict[str, Any], List[Dict[str, Any]]], keep_attrs: Optional[Iterable[str]] = None) → T¶

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 : iterable List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

update(name: Optional[str] = None, description: Optional[str] = None) → 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: Optional[str] = None, name: Optional[str] = None, features: Optional[List[str]] = None, project_id: Optional[str] = None, created: Optional[datetime.datetime] = None, is_user_created: Optional[bool] = None, num_models: Optional[int] = None, description: Optional[str] = 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 : str: (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: str, featurelist_id: str) → TModelingFeaturelist¶

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: bool = False, delete_dependencies: bool = False) → DeleteFeatureListResult¶

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: Optional[str] = None, description: Optional[str] = None) → 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: int, remaining_restore_limit: int, count: int, features: List[str])¶

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: str, features_to_restore: List[str], max_wait: int = 600) → datarobot.models.restore_discarded_features.FeatureRestorationStatus¶

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: str) → datarobot.models.restore_discarded_features.DiscardedFeaturesInfo¶

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: Dict[str, Any], completed_resource_url: Optional[str] = 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: str, job_id: str) → datarobot.models.job.Job¶

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 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: int = 600) → None¶

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 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: int = 600) → None¶

Waits for job to complete.

Parameters:	max_wait : int, optional How long to wait for the job to finish.

class datarobot.models.ShapMatrixJob(data: Dict[str, Any], model_id: Optional[str] = None, dataset_id: Optional[str] = None, **kwargs)¶

classmethod get(project_id: str, job_id: str, model_id: Optional[str] = None, dataset_id: Optional[str] = None) → datarobot.models.shap_matrix_job.ShapMatrixJob¶

Fetches one SHAP matrix job.

Parameters:	project_id : str The identif