API Reference¶

API Object¶

class datarobot.models.api_object.APIObject¶

classmethod from_data(data)¶

Instantiate an object of this class using a dict.

Parameters

datadict: Correctly snake_cased keys and their values.

Return type: TypeVar(T, bound= APIObject)

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

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: TypeVar(T, bound= APIObject)

Advanced Options¶

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

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

Parameters

weightsstring, optional: The name of a column indicating the weight of each row
response_capbool 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_thresholdint, optional: Number of hours models are permitted to run before being excluded from later autopilot stages Minimum 1
seedint, optional: a seed to use for randomization
smart_downsampledbool, 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_ratefloat, 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.
offsetlist of str, optional: (New in version v2.6) the list of the names of the columns containing the offset of each row
exposurestring, optional: (New in version v2.6) the name of a column containing the exposure of each row
accuracy_optimized_mbbool, 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_modestring, 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_countstring, optional: (New in version v2.8) the name of a column specifying events count.
monotonic_increasing_featurelist_idstring, 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_idstring, 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_blueprintsbool, 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_groupslist 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_namestr, optional: The feature from protected features that will be used in a bias mitigation task to mitigate bias
bias_mitigation_techniquestr, 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_variablebool, 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_idstr, optional: Returned from server on Project GET request - not able to be updated by user
default_monotonic_decreasing_featurelist_idstr, 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)¶

Update individual attributes of an instance of AdvancedOptions.

Return type: None

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.

Return type: List[AnomalyAssessmentRecord]

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.

Return type: AnomalyAssessmentRecord

delete()¶

Delete anomaly assessment record with preview and explanations.

Return type: None

get_predictions_preview()¶

Retrieve aggregated predictions statistics for the anomaly assessment record.

Returns

AnomalyAssessmentPredictionsPreview

Return type: AnomalyAssessmentPredictionsPreview

get_latest_explanations()¶

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

Returns

AnomalyAssessmentExplanations

Return type: 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

Return type: 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}

Return type: RegionExplanationsData

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

Return type: 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

Return type: 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

Return type: List[AnomalyAssessmentPreviewBin]

Application¶

class datarobot.Application(id, application_type_id, user_id, model_deployment_id, name, created_by, created_at, updated_at, datasets, cloud_provider, deployment_ids, pool_used, permissions, has_custom_logo, org_id, deployment_status_id=None, description=None, related_entities=None, application_template_type=None, deployment_name=None, deactivation_status_id=None, created_first_name=None, creator_last_name=None, creator_userhash=None, deployments=None)¶

An entity associated with a DataRobot Application.

Attributes

idstr: The ID of the created application.
application_type_idstr: The ID of the type of the application.
user_idstr: The ID of the user which created the application.
model_deployment_idstr: The ID of the associated model deployment.
deactivation_status_idstr or None: The ID of the status object to track the asynchronous app deactivation process status. Will be None if the app was never deactivated.
namestr: The name of the application.
created_bystr: The username of the user created the application.
created_atstr: The timestamp when the application was created.
updated_atstr: The timestamp when the application was updated.
datasetsList[str]: The list of datasets IDs associated with the application.
creator_first_nameOptional[str]: Application creator first name. Optional.
creator_last_nameOptional[str]: Application creator last name. Optional.
creator_userhashOptional[str]: Application creator userhash. Optional.
deployment_status_idstr: The ID of the status object to track the asynchronous deployment process status.
descriptionstr: A description of the application.
cloud_providerstr: The host of this application.
deploymentsOptional[List[ApplicationDeployment]]: A list of deployment details. Optional.
deployment_idsList[str]: A list of deployment IDs for this app.
deployment_nameOptional[str]: Name of the deployment. Optional.
application_template_typeOptional[str]: Application template type, purpose. Optional.
pool_usedbool: Whether the pool where used for last app deployment.
permissionsList[str]: The list of permitted actions, which the authenticated user can perform on this application. Permissions should be ApplicationPermission options.
has_custom_logobool: Whether the app has a custom logo.
related_entitiesOptional[ApplcationRelatedEntity]: IDs of entities, related to app for easy search.
org_idstr: ID of the app’s organization.

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

Retrieve a list of user applications.

Parameters

offsetOptional[int]: Optional. Retrieve applications in a list after this number.
limitOptional[int]: Optional. Retrieve only this number of applications.
use_cases: Optional[Union[UseCase, List[UseCase], str, List[str]]]: Optional. Filter available Applications by a specific Use Case or Use Cases. Accepts either the entity or the ID.

Returns

applicationsList[Application]: The requested list of user applications.

Return type: List[Application]

classmethod get(application_id)¶

Retrieve a single application.

Parameters

application_idstr: The ID of the application to retrieve.

Returns

applicationApplication: The requested application.

Return type: Application

Batch Predictions¶

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

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

Attributes

idstr: the id of the job

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

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

deploymentDeployment or string ID

Deployment which will be used for scoring.

intake_settingsdict (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_settingsdict (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_settingsdict (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_settingsdict (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_concurrentint (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_sizestring 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_columnslist[string] (optional)

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

passthrough_columns_setstring (optional)

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

max_explanationsint (optional)

Compute prediction explanations for this amount of features.

max_ngram_explanationsint 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_highfloat (optional)

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

threshold_lowfloat (optional)

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

explanations_modePredictionExplanationsMode, 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_enabledboolean (optional)

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

include_prediction_statusboolean (optional)

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

skip_drift_trackingboolean (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_instancedict (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_errorboolean (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_remappingdict (optional)

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

include_probabilitiesboolean (optional)

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

include_probabilities_classeslist (optional)

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

download_timeoutint (optional)

New in version 2.22.

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

download_read_timeoutint (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.

Return type: BatchPredictionJob

classmethod apply_time_series_data_prep_and_score(deployment, intake_settings, timeseries_settings, **kwargs)¶

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

deploymentDeployment

Deployment which will be used for scoring.

intake_settingsdict

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_settingsdict

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.

Return type: BatchPredictionJob

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

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

Will block until the entire file is scored.

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

Returns

BatchPredictionJob: Instance of BatchPredictionJob

Attributes

deploymentDeployment or string ID: Deployment which will be used for scoring.
intake_pathfile-like object/string path to file/pandas.DataFrame: Scoring data
output_pathstr: Filename to save the result under

classmethod apply_time_series_data_prep_and_score_to_file(deployment, intake_path, output_path, timeseries_settings, **kwargs)¶

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

deploymentDeployment

The deployment which will be used for scoring.

intake_pathfile-like object/string path to file/pandas.DataFrame

The scoring data.

output_pathstr

The filename under which you save the result.

timeseries_settingsdict

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.

Return type: BatchPredictionJob

classmethod score_s3(deployment, source_url, destination_url, credential=None, endpoint_url=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

deploymentDeployment or string ID: Deployment which will be used for scoring.
source_urlstring: The URL for the prediction dataset (e.g.: s3://bucket/key)
destination_urlstring: The URL for the scored dataset (e.g.: s3://bucket/key)
credentialstring or Credential (optional): The AWS Credential object or credential id
endpoint_urlstring (optional): Any non-default endpoint URL for S3 access (omit to use the default)

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

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

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

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

Returns

BatchPredictionJob: Instance of BatchPredictionJob

Attributes

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

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

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

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

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

Returns

BatchPredictionJob: Instance of BatchPredictionJob

Attributes

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

classmethod score_from_existing(batch_prediction_job_id)¶

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

Returns

BatchPredictionJob: Instance of BatchPredictionJob

Attributes

batch_prediction_job_id: str: ID of the previous batch prediction job

Return type: BatchPredictionJob

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

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

Use columnNamesRemapping to drop or rename columns in the output

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

Refer to the 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

deploymentDeployment or string ID: Deployment which will be used for scoring.
dfpandas.DataFrame: The dataframe to score

Return type: Tuple[BatchPredictionJob, DataFrame]

classmethod get(batch_prediction_job_id)¶

Get batch prediction job

Returns

BatchPredictionJob: Instance of BatchPredictionJob

Attributes

batch_prediction_job_id: str: ID of batch prediction job

Return type: BatchPredictionJob

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

Downloads the CSV result of a prediction job

Attributes

fileobj: A file-like object where the CSV prediction results will be

written to. Examples include an in-memory buffer (e.g., io.BytesIO) or a file on disk (opened for binary writing).

timeoutint (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_timeoutint (optional, default 660)

New in version 2.22.

Seconds to wait for the server to respond between chunks.

Return type: None

delete(ignore_404_errors=False)¶

Cancel this job. If this job has not finished running, it will be removed and canceled.

Return type: None

get_status()¶

Get status of batch prediction job

Returns

BatchPredictionJob status data: Dict with job status

classmethod list_by_status(statuses=None)¶

Get jobs collection for specific set of statuses

Returns

BatchPredictionJob statuses: List of job statuses dicts with specific statuses

Attributes

statuses: List of statuses to filter jobs ([ABORTED|COMPLETED…]) if statuses is not provided, returns all jobs for user

Return type: List[BatchPredictionJob]

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

classmethod get(batch_prediction_job_definition_id)¶

Get batch prediction job definition

Returns

BatchPredictionJobDefinition: Instance of BatchPredictionJobDefinition

Examples

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

Attributes

batch_prediction_job_definition_id: str: ID of batch prediction job definition

Return type: BatchPredictionJobDefinition

classmethod list()¶

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)
]

Return type: List[BatchPredictionJobDefinition]

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

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

Returns

BatchPredictionJobDefinition: Instance of BatchPredictionJobDefinition

Examples

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

Attributes

enabledbool (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.

namestring (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.

scheduledict (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.

Return type: BatchPredictionJobDefinition

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

Updates a job definition with the changed specs.

Takes the same input as create()

Returns

BatchPredictionJobDefinition: Instance of the updated BatchPredictionJobDefinition

Examples

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

Attributes

enabledbool (default False): Same as enabled in create().
batch_prediction_job: dict: Same as batch_prediction_job in create().
namestring (optional): Same as name in create().
scheduledict: Same as schedule in create().

Return type: BatchPredictionJobDefinition

run_on_schedule(schedule)¶

Sets the run schedule of an already created job definition.

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

Returns

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

Examples

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

Attributes

scheduledict: Same as schedule in create().

Return type: BatchPredictionJobDefinition

run_once()¶

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

Returns

BatchPredictionJob: Instance of BatchPredictionJob

Examples

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

Return type: BatchPredictionJob

delete()¶

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

Examples

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

Return type: None

Batch Monitoring¶

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

A Batch Monitoring Job is used to monitor data sets outside DataRobot app.

Attributes

idstr: the id of the job

classmethod get(project_id, job_id)¶

Get batch monitoring job

Returns

BatchMonitoringJob: Instance of BatchMonitoringJob

Attributes

job_id: str: ID of batch job

Return type: BatchMonitoringJob

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

Downloads the results of a monitoring job as a CSV.

Attributes

fileobj: A file-like object where the CSV monitoring results will be

written to. Examples include an in-memory buffer (e.g., io.BytesIO) or a file on disk (opened for binary writing).

timeoutint (optional, default 120)

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_timeoutint (optional, default 660)

Seconds to wait for the server to respond between chunks.

Return type: None

classmethod run(deployment, intake_settings=None, output_settings=None, csv_settings=None, num_concurrent=None, chunk_size=None, abort_on_error=True, monitoring_aggregation=None, monitoring_columns=None, monitoring_output_settings=None, download_timeout=120, download_read_timeout=660, upload_read_timeout=600)¶

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

Returns

BatchMonitoringJob: Instance of BatchMonitoringJob

Examples

>>> import datarobot as dr
>>> job_spec = {
...     "intake_settings": {
...         "type": "jdbc",
...         "data_store_id": "645043933d4fbc3215f17e34",
...         "catalog": "SANDBOX",
...         "table": "10kDiabetes_output_actuals",
...         "schema": "SCORING_CODE_UDF_SCHEMA",
...         "credential_id": "645043b61a158045f66fb329"
...     },
>>>     "monitoring_columns": {
...         "predictions_columns": [
...             {
...                 "class_name": "True",
...                 "column_name": "readmitted_True_PREDICTION"
...             },
...             {
...                 "class_name": "False",
...                 "column_name": "readmitted_False_PREDICTION"
...             }
...         ],
...         "association_id_column": "rowID",
...         "actuals_value_column": "ACTUALS"
...     }
... }
>>> deployment_id = "foobar"
>>> job = dr.BatchMonitoringJob.run(deployment_id, **job_spec)
>>> job.wait_for_completion()

Attributes

deploymentDeployment or string ID

Deployment which will be used for monitoring.

intake_settingsdict

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

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

To monitor 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 monitor 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_settingsdict (optional)

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

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

To save monitored data to a local file, add 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 monitored 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 monitored 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_settingsdict (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.

num_concurrentint (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_sizestring 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.

abort_on_errorboolean (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.

download_timeoutint (optional)

New in version 2.22.

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

download_read_timeoutint (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.

Return type: BatchMonitoringJob

cancel(ignore_404_errors=False)¶

Cancel this job. If this job has not finished running, it will be removed and canceled.

Return type: None

get_status()¶

Get status of batch monitoring job

Returns

BatchMonitoringJob status data: Dict with job status

Return type: Any

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

classmethod get(batch_monitoring_job_definition_id)¶

Get batch monitoring job definition

Returns

BatchMonitoringJobDefinition: Instance of BatchMonitoringJobDefinition

Examples

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

Attributes

batch_monitoring_job_definition_id: str: ID of batch monitoring job definition

Return type: BatchMonitoringJobDefinition

classmethod list()¶

Get job all monitoring job definitions

Returns

List[BatchMonitoringJobDefinition]: List of job definitions the user has access to see

Examples

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

Return type: List[BatchMonitoringJobDefinition]

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

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

Returns

BatchMonitoringJobDefinition: Instance of BatchMonitoringJobDefinition

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 = BatchMonitoringJobDefinition.create(
...    enabled=False,
...    batch_monitoring_job=job_spec,
...    name="some_definition_name",
...    schedule=schedule
... )
>>> definition
BatchMonitoringJobDefinition(60912e09fd1f04e832a575c1)

Attributes

enabledbool (default False)

Whether the definition should be active on a scheduled basis. If True, schedule is required.

batch_monitoring_job: dict

The job specifications for your batch monitoring job. It requires the same job input parameters as used with BatchMonitoringJob

namestring (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.

scheduledict (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 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.

Return type: BatchMonitoringJobDefinition

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

Updates a job definition with the changed specs.

Takes the same input as create()

Returns

BatchMonitoringJobDefinition: Instance of the updated BatchMonitoringJobDefinition

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 = BatchMonitoringJobDefinition.create(
...    enabled=False,
...    batch_monitoring_job=job_spec,
...    name="updated_definition_name",
...    schedule=schedule
... )
>>> definition
BatchMonitoringJobDefinition(60912e09fd1f04e832a575c1)

Attributes

enabledbool (default False): Same as enabled in create().
batch_monitoring_job: dict: Same as batch_monitoring_job in create().
namestring (optional): Same as name in create().
scheduledict: Same as schedule in create().

Return type: BatchMonitoringJobDefinition

run_on_schedule(schedule)¶

Sets the run schedule of an already created job definition.

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

Returns

BatchMonitoringJobDefinition: Instance of the updated BatchMonitoringJobDefinition with the new / updated schedule.

Examples

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

Attributes

scheduledict: Same as schedule in create().

Return type: BatchMonitoringJobDefinition

run_once()¶

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

Returns

BatchMonitoringJob: Instance of BatchMonitoringJob

Examples

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

Return type: BatchMonitoringJob

delete()¶

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

Examples

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

Return type: None

Status Check Job¶

class datarobot.models.StatusCheckJob(job_id, resource_type=None)¶

Tracks asynchronous task status

Attributes

job_idstr: The ID of the status the job belongs to.

wait_for_completion(max_wait=600)¶

Waits for job to complete.

Parameters

max_waitint, optional: How long to wait for the job to finish. If the time expires, DataRobot returns the current status.

Returns

statusJobStatusResult: Returns the current status of the job.

Return type: JobStatusResult

get_status()¶

Retrieve JobStatusResult object with the latest job status data from the server.

Return type: JobStatusResult

get_result_when_complete(max_wait=600)¶

Wait for the job to complete, then attempt to convert the resulting json into an object of type self.resource_type Returns ——- A newly created resource of type self.resource_type

Return type: APIObject

class datarobot.models.JobStatusResult(status: Optional[str], status_id: Optional[str], completed_resource_url: Optional[str])¶

This class represents a result of status check for submitted async jobs.

property status¶: Alias for field number 0

property status_id¶: Alias for field number 1

property completed_resource_url¶: Alias for field number 2

Blueprint¶

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

A Blueprint which can be used to fit models

Attributes

idstr: the id of the blueprint
processeslist of str: the processes used by the blueprint
model_typestr: the model produced by the blueprint
project_idstr: the project the blueprint belongs to
blueprint_categorystr: (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_mlbool or None: (New in version v2.26) whether this blueprint is supported in the Composable ML.

classmethod get(project_id, blueprint_id)¶

Retrieve a blueprint.

Parameters

project_idstr: The project’s id.
blueprint_idstr: Id of blueprint to retrieve.

Returns

blueprintBlueprint: The queried blueprint.

Return type: Blueprint

get_json()¶

Get the blueprint json representation used by this model.

Returns

BlueprintJson: Json representation of the blueprint stages.

Return type: Dict[str, Tuple[List[str], List[str], str]]

get_chart()¶

Retrieve a chart.

Returns

BlueprintChart: The current blueprint chart.

Return type: BlueprintChart

get_documents()¶

Get documentation for tasks used in the blueprint.

Returns

list of BlueprintTaskDocument: All documents available for blueprint.

Return type: List[BlueprintTaskDocument]

classmethod from_data(data)¶

Instantiate an object of this class using a dict.

Parameters

datadict: Correctly snake_cased keys and their values.

Return type: TypeVar(T, bound= APIObject)

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

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: TypeVar(T, bound= APIObject)

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

Document describing a task from a blueprint.

Attributes

titlestr: Title of document.
taskstr: Name of the task described in document.
descriptionstr: Task description.
parameterslist of dict(name, type, description): Parameters that task can receive in human-readable format.
linkslist of dict(name, url): External links used in document
referenceslist of dict(name, url): References used in document. When no link available url equals None.

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

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

Attributes

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

classmethod get(project_id, blueprint_id)¶

Retrieve a blueprint chart.

Parameters

project_idstr: The project’s id.
blueprint_idstr: Id of blueprint to retrieve chart.

Returns

BlueprintChart: The queried blueprint chart.

Return type: BlueprintChart

to_graphviz()¶

Get blueprint chart in graphviz DOT format.

Returns

unicode: String representation of chart in graphviz DOT language.

Return type: str

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

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

Attributes

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

classmethod get(project_id, model_id)¶

Retrieve a model blueprint chart.

Parameters

project_idstr: The project’s id.
model_idstr: Id of model to retrieve model blueprint chart.

Returns

ModelBlueprintChart: The queried model blueprint chart.

Return type: ModelBlueprintChart

to_graphviz()¶

Get blueprint chart in graphviz DOT format.

Returns

unicode: String representation of chart in graphviz DOT language.

Return type: str

Calendar File¶

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

Represents the data for a calendar file.

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

Attributes

idstr: The id of the calendar file.
calendar_start_datestr: The earliest date in the calendar.
calendar_end_datestr: The last date in the calendar.
createdstr: The date this calendar was created, i.e. uploaded to DR.
namestr: The name of the calendar.
num_event_typesint: The number of different event types.
num_eventsint: The number of events this calendar has.
project_idslist 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.
rolestr: The access role the user has for this calendar.

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

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

The provided file must be a CSV in the format:

Date,   Event,          Series ID,    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_pathstring: A string representing a path to a local csv file.
calendar_namestring, optional: A name to assign to the calendar. Defaults to the name of the file if not provided.
multiseries_id_columnslist 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_fileCalendarFile: 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']

Return type: CalendarFile

classmethod create_calendar_from_dataset(dataset_id, dataset_version_id=None, calendar_name=None, multiseries_id_columns=None, delete_on_error=False)¶

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_idstring: The identifier of the dataset from which to create the calendar.
dataset_version_idstring, optional: The identifier of the dataset version from which to create the calendar.
calendar_namestring, optional: A name to assign to the calendar. Defaults to the name of the dataset if not provided.
multiseries_id_columnslist 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_errorboolean, optional: Whether delete calendar file from Catalog if it’s not valid.

Returns

calendar_fileCalendarFile: 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

Return type: CalendarFile

classmethod create_calendar_from_country_code(country_code, start_date, end_date)¶

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

Parameters

country_codestring: The country code for the country to use for generating the calendar.
start_datedatetime.datetime: The earliest date to include in the generated calendar.
end_datedatetime.datetime: The latest date to include in the generated calendar.

Returns

calendar_fileCalendarFile: Instance with initialized data.

Return type: CalendarFile

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

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

Parameters

offsetint: Optional, defaults to 0. This many results will be skipped.
limitint: 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.

Return type: List[CountryCode]

classmethod get(calendar_id)¶

Gets the details of a calendar, given the id.

Parameters

calendar_idstr: The identifier of the calendar.

Returns

calendar_fileCalendarFile: 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

Return type: CalendarFile

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

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

Parameters

project_idstr, optional: If provided, will filter for calendars associated only with the specified project.
batch_sizeint, 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_listlist of CalendarFile: A list of CalendarFile objects.

Examples

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

Return type: List[CalendarFile]

classmethod delete(calendar_id)¶

Deletes the calendar specified by calendar_id.

Parameters

calendar_idstr: 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

Return type: None

classmethod update_name(calendar_id, new_calendar_name)¶

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

Parameters

calendar_idstr: The id of the calendar to update.
new_calendar_namestr: The new name to set for the specified calendar.

Returns

status_codeint: 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

Return type: int

classmethod share(calendar_id, access_list)¶

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

Parameters

calendar_idstr: 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_codeint: 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

Return type: int

classmethod get_access_list(calendar_id, batch_size=None)¶

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

Parameters

calendar_idstr: The id of the calendar to retrieve the access list for.
batch_sizeint, 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_listlist of SharingAccess: A list of SharingAccess objects.

Raises

ClientError: Raised if user does not have access to calendar or calendar does not exist.

Return type: List[SharingAccess]

class datarobot.models.calendar_file.CountryCode() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)¶

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_typestr or None: Type of automated document. You can specify: MODEL_COMPLIANCE, AUTOPILOT_SUMMARY depending on your account settings. Required for document generation.
entity_idstr or None: ID of the entity to generate the document for. It can be model ID or project ID. Required for document generation.
output_formatstr or None: Format of the generate document, either docx or html. Required for document generation.
localestr or None: Localization of the document, dependent on your account settings. Default setting is EN_US.
template_idstr or None: Template ID to use for the document outline. Defaults to standard DataRobot template. See the documentation for ComplianceDocTemplate for more information.
idstr or None: ID of the document. Required to download or delete a document.
filepathstr 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_atdatetime 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()

Return type: List[DocumentOption]

property is_model_compliance_initialized: Tuple[bool, str]¶

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

Return type: Tuple[bool, str]

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()

Return type: Tuple[bool, str]

generate(max_wait=600)¶

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()

Return type: Response

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()

Return type: Response

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.

Return type: Response

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_typesList of str or None: Query for one or more document types.
entity_idsList of str or None: Query generated documents by one or more entity IDs.
output_formatsList of str or None: Query for one or more output formats.
localesList 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()

Return type: List[AutomatedDocument]

class datarobot.models.automated_documentation.DocumentOption() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)¶

Class Mapping Aggregation Settings¶

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

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

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

Attributes

max_unaggregated_class_valuesint, optional: Maximum amount of unique values allowed before aggregation kicks in.
min_class_supportint, optional: Minimum number of instances necessary for each target value in the dataset. All values with less instances will be aggregated.
excluded_from_aggregationlist, optional: List of target values that should be guaranteed to kept as is, regardless of other settings.
aggregation_class_namestr, 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=None, endpoint=None, config_path=None, connect_timeout=None, user_agent_suffix=None, ssl_verify=True, max_retries=None, token_type='Token', default_use_case=None, enable_api_consumer_tracking=None, trace_context=None)¶

Configures the global API client for the Python SDK. The client will be configured in one of the following ways, in order of priority.

From call args iff token and endpoint kwargs are specified;

From a YAML file at the path specified in the config_path kwarg;

From a YAML file at the path specified in the env var DATAROBOT_CONFIG_FILE;

From env vars, iff DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN are specified;

From a YAML file at the path $HOME/.config/datarobot/drconfig.yaml.

Note

All client configuration must be done via a single method; there is no fall back to lower priority methods.

This can also have the side effect of setting a default Use Case for client API requests.

Parameters

tokenstr, optional: API token
endpointstr, optional: Base url of API
config_pathstr, optional: Alternate location of config file
connect_timeoutint, optional: How long the client should be willing to wait before establishing a connection with the server.
user_agent_suffixstr, 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_verifybool or str, optional: Whether to check SSL certificate. Could be set to path with certificates of trusted certification authorities.
max_retriesint 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.
default_use_case: str, optional: The entity ID of the default Use Case to use with any requests made by the client.
enable_api_consumer_tracking: bool, optional: Enable and disable user metrics tracking within the datarobot module. Default: False.
trace_context: str, optional: An ID or other string for identifying which code template or AI Accelerator was used to make a request.
Returns
——-: The RESTClientObject instance created.

Return type: RESTClientObject

datarobot.client.get_client()¶

Returns the global HTTP client for the Python SDK, instantiating it if necessary.

Return type: RESTClientObject

datarobot.client.set_client(client)¶

Configure the global HTTP client for the Python SDK. Returns previous instance.

Return type: Optional[RESTClientObject]

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

argsParameters passed to datarobot.client.Client()
kwargsKeyword arguments passed to datarobot.client.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, endpoint, connect_timeout=6.05, verify=True, user_agent_suffix=None, max_retries=None, authentication_type=None)¶

Parameters

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

open_in_browser()¶

Opens the DataRobot app in a web browser, or logs the URL if a browser is not available.

Return type: None

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=600)¶

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

Parameters

project_id: str: Project to start creation in.
model_id: str: Project’s model to start creation in.
max_wait: int: Maximum number of seconds to wait before giving up

Returns

List of ClusterInsight

Raises

ClientError: Server rejected creation due to client error. Most likely cause is bad project_id or model_id.
AsyncFailureError: If any of the responses from the server are unexpected
AsyncProcessUnsuccessfulError: If the cluster insights computation has failed or was cancelled.
AsyncTimeoutError: If the cluster insights computation did not resolve in time

Return type: List[ClusterInsight]

property insights: List[datarobot.models.cluster_insight.ClusterInsight]¶

Return actual list of cluster insights if already computed.

Returns

List of ClusterInsight

Return type: List[ClusterInsight]

property clusters: List[datarobot.models.cluster.Cluster]¶

Return actual list of Clusters.

Returns

List of Cluster

Return type: List[Cluster]

update_cluster_names(cluster_name_mappings)¶

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

Parameters

cluster_name_mappings: List of tuples

Cluster names mapping consisting of current cluster name and old cluster name. Example:

cluster_name_mappings = [
    ("current cluster name 1", "new cluster name 1"),
    ("current cluster name 2", "new cluster name 2")]

Returns

List of Cluster

Raises

datarobot.errors.ClientError: Server rejected update of cluster names. Possible reasons include: incorrect format of mapping, mapping introduces duplicates.

Return type: List[Cluster]

update_cluster_name(current_name, new_name)¶

Change cluster name from current_name to new_name.

Parameters

current_name: str: Current cluster name.
new_name: str: New cluster name.

Returns

List of Cluster

Raises

datarobot.errors.ClientError: Server rejected update of cluster names.

Return type: List[Cluster]

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

Representation of a single cluster.

Attributes

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

classmethod list(project_id, model_id)¶

Retrieve a list of clusters in the model.

Parameters

project_id: str: ID of the project that the model is part of.
model_id: str: ID of the model.

Returns

List of clusters

Return type: List[Cluster]

classmethod update_multiple_names(project_id, model_id, cluster_name_mappings)¶

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

Parameters

project_id: str

ID of the project that the model is part of.

model_id: str

ID of the model.

cluster_name_mappings: List of tuples

Cluster name mappings, consisting of current and previous names for each cluster. Example:

cluster_name_mappings = [
    ("current cluster name 1", "new cluster name 1"),
    ("current cluster name 2", "new cluster name 2")]

Returns

List of clusters

Raises

datarobot.errors.ClientError: Server rejected update of cluster names.
ValueError: Invalid cluster name mapping provided.

Return type: List[Cluster]

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

Change cluster name from current_name to new_name

Parameters

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

Returns

List of Cluster

Return type: List[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.
insightsList of classes (ClusterInsight): List provides information regarding the importance of a specific feature in relation to each cluster. Results help understand how the model is grouping data and what each cluster represents.
feature_impact: float: Impact of a feature ranging from 0 to 1.

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

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

Parameters

project_id: str: ID of the project to begin creation of cluster insights for.
model_id: str: ID of the project model to begin creation of cluster insights for.
max_wait: int: Maximum number of seconds to wait canceling the request.

Returns

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).

Return type: List[ClusterInsight]

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

idstr: the id of the template
namestr: the name of the template.
creator_idstr: the id of the user who created the template
creator_usernamestr: username of the user who created the template
org_idstr: the id of the organization the template belongs to
sectionslist 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_typestr or None: Type of the template. Currently supported values are “normal” and “time_series”

Returns

templateComplianceDocTemplate: the default template object with sections attribute populated with default sections.

Return type: ComplianceDocTemplate

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

namestr: the name of the template. Must be unique for your user.
pathstr: the path to find the JSON file at

Returns

templateComplianceDocTemplate: the created template

Return type: ComplianceDocTemplate

classmethod create(name, sections)¶

Create a template with the specified name and sections.

Parameters

namestr: the name of the template. Must be unique for your user.
sectionslist: list of section objects

Returns

templateComplianceDocTemplate: the created template

Return type: ComplianceDocTemplate

classmethod get(template_id)¶

Retrieve a specific template.

Parameters

template_idstr: the id of the template to retrieve

Returns

templateComplianceDocTemplate: the retrieved template

Return type: ComplianceDocTemplate

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

Get a paginated list of compliance documentation template objects.

Parameters

name_partstr or None: Return only the templates with names matching specified string. The matching is case-insensitive.
limitint: The number of records to return. The server will use a (possibly finite) default if not specified.
offsetint: The number of records to skip.

Returns

templateslist of ComplianceDocTemplate: the list of template objects

Return type: List[ComplianceDocTemplate]

sections_to_json_file(path, indent=2)¶

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

Parameters

pathstr: the path to save the file to
indentint: indentation to use in the json file.

Return type: None

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

namestr, optional: the new name for the template
sectionslist of dicts: list of sections

Return type: None

delete()¶

Delete the compliance documentation template.

Return type: None

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

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

Credentials¶

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

classmethod list()¶

Returns list of available credentials.

Returns

credentialslist 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'),
]

Return type: List[Credential]

classmethod get(credential_id)¶

Gets the Credential.

Parameters

credential_idstr: the identifier of the credential.

Returns

credentialCredential: the requested credential.

Examples

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

Return type: Credential

delete()¶

Deletes the Credential the store.

Parameters

credential_idstr: the identifier of the credential.

Returns

credentialCredential: the requested credential.

Examples

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

Return type: None

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

Creates the credentials.

Parameters

namestr: the name to use for this set of credentials.
userstr: the username to store for this set of credentials.
passwordstr: the password to store for this set of credentials.
descriptionstr, optional: the description to use for this set of credentials.

Returns

credentialCredential: 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'),

Return type: Credential

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

Creates the OAUTH credentials.

Parameters

namestr: the name to use for this set of credentials.
token: str: the OAUTH token
refresh_token: str: The OAUTH token
descriptionstr, optional: the description to use for this set of credentials.

Returns

credentialCredential: 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'),

Return type: Credential

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

Creates the S3 credentials.

Parameters

namestr: the name to use for this set of credentials.
aws_access_key_idstr, optional: the AWS access key id.
aws_secret_access_keystr, optional: the AWS secret access key.
aws_session_tokenstr, optional: the AWS session token.
descriptionstr, optional: the description to use for this set of credentials.

Returns

credentialCredential: 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'),

Return type: Credential

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

Creates the Azure storage credentials.

Parameters

namestr: the name to use for this set of credentials.
azure_connection_stringstr: the Azure connection string.
descriptionstr, optional: the description to use for this set of credentials.

Returns

credentialCredential: 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'),

Return type: Credential

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

Creates the GCP credentials.

Parameters

namestr: the name to use for this set of credentials.
gcp_keystr | dict: the GCP key in json format or parsed as dict.
descriptionstr, optional: the description to use for this set of credentials.

Returns

credentialCredential: 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'),

Return type: Credential

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

Update the credential values of an existing credential. Updates this object in place.

New in version v3.2.

Parameters

namestr: The name to use for this set of credentials.
descriptionstr, optional: The description to use for this set of credentials; if omitted, and name is not omitted, then it clears any previous description for that name.
kwargsKeyword arguments specific to the given credential_type that should be updated.

Return type: None

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: The ID of the file item.
file_name: str: The name of the file item.
file_path: str: The path of the file item.
file_source: str: The 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: The ID of the custom model.
name: str: The name of the custom model.
language: str: The programming language of the custom inference model. Can be “python”, “r”, “java” or “other”.
description: str: The description 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, 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: The 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: The 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: The ID of a dataset assigned to the custom model.
training_dataset_version_id: str, optional: The ID of a dataset version assigned to the custom model.
training_data_file_name: str, optional: The name of assigned training data file.
training_data_partition_column: str, optional: The name of a partition column in a training dataset assigned to the custom model.
created_by: str: The username of a 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. Values: [datarobot.NETWORK_EGRESS_POLICY.NONE, datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS, datarobot.NETWORK_EGRESS_POLICY.PUBLIC]. Note: datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS value is only supported by the SaaS (cloud) environment.
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
is_training_data_for_versions_permanently_enabled: bool, optional: Whether training data assignment on the version level is permanently enabled for the model.

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

Return type: List[CustomInferenceModel]

classmethod get(custom_model_id)¶

Get custom inference model by id.

New in version v2.21.

Parameters

custom_model_id: str: The ID of the custom inference model.

Returns

CustomInferenceModel: Retrieved custom inference model.

Raises

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

Return type: CustomInferenceModel

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.

Return type: None

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, is_training_data_for_versions_permanently_enabled=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. Values: [datarobot.NETWORK_EGRESS_POLICY.NONE, datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS, datarobot.NETWORK_EGRESS_POLICY.PUBLIC] Note: datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS value is only supported by the SaaS (cloud) environment.
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.
is_training_data_for_versions_permanently_enabled: bool, optional: Permanently enable training data assignment on the version level for the current model, instead of training data assignment on the model level.

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.

Return type: CustomInferenceModel

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: The 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.

Return type: CustomInferenceModel

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, is_training_data_for_versions_permanently_enabled=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
is_training_data_for_versions_permanently_enabled: bool, optional: Permanently enable training data assignment on the version level for the current model, instead of training data assignment on the model level.

Raises

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

Return type: None

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.

Return type: None

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.

Return type: None

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

Return type: None

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. Values: [datarobot.NETWORK_EGRESS_POLICY.NONE, datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS, datarobot.NETWORK_EGRESS_POLICY.PUBLIC]. Note: datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS value is only supported by the SaaS (cloud) environment.
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. Values: [datarobot.NETWORK_EGRESS_POLICY.NONE, datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS, datarobot.NETWORK_EGRESS_POLICY.PUBLIC]. Note: datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS value is only supported by the SaaS (cloud) environment.
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: The ID of the custom model version.
custom_model_id: str: The ID of the custom model.
version_minor: int: A minor version number of the custom model version.
version_major: int: A major version number of the 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: The ID of the environment to use with the model.
base_environment_version_id: str: The ID of the environment version to use with the model.
label: str, optional: A short human readable string to label the version.
description: str, optional: The 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. Values: [datarobot.NETWORK_EGRESS_POLICY.NONE, datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS, datarobot.NETWORK_EGRESS_POLICY.PUBLIC]. Note: datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS value is only supported by the SaaS (cloud) environment.
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.
training_data: TrainingData, optional: The information about the training data assigned to the model version.
holdout_data: HoldoutData, optional: The information about the holdout data assigned to the model version.

classmethod from_server_data(data, keep_attrs=None)¶

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

Parameters

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: CustomModelVersion

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, training_dataset_id=None, partition_column=None, holdout_dataset_id=None, keep_training_holdout_data=None, max_wait=600)¶

Create a custom model version without files from previous versions.

Create a version with training or holdout data: If training/holdout data related parameters are provided, the training data is assigned asynchronously. In this case: * if max_wait is not None, the function returns once the job is finished. * if max_wait is None, the function returns immediately. Progress can be polled by the user (see examples).

If training data assignment fails, new version is still created, but it is not allowed to create a model package for the model version and to deploy it. To check for training data assignment error, check version.training_data.assignment_error[“message”].

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 the 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. Values: [datarobot.NETWORK_EGRESS_POLICY.NONE, datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS, datarobot.NETWORK_EGRESS_POLICY.PUBLIC]. Note: datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS value is only supported by the SaaS (cloud) environment.
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.
training_dataset_id: str, optional: The ID of the training dataset to assign to the custom model.
partition_column: str, optional: Name of a partition column in a training dataset assigned to the custom model. Can only be assigned for structured models.
holdout_dataset_id: str, optional: The ID of the holdout dataset to assign to the custom model. Can only be assigned for unstructured models.
keep_training_holdout_data: bool, optional: If the version should inherit training and holdout data from the previous version. Defaults to True. This field is only applicable if the model has training data for versions enabled, otherwise the field value will be ignored.
max_wait: int, optional: Max time to wait for training data assignment. If set to None - method will return without waiting. Defaults to 10 minutes.

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.
datarobot.errors.InvalidUsageError: If wrong parameters are provided.
datarobot.errors.TrainingDataAssignmentError: If training data assignment fails.

Examples

Create a version with blocking (default max_wait=600) training data assignment:

import datarobot as dr
from datarobot.errors import TrainingDataAssignmentError

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

try:
    version = dr.CustomModelVersion.create_from_previous(
        custom_model_id="6444482e5583f6ee2e572265",
        base_environment_id="642209acc563893014a41e24",
        training_dataset_id="6421f2149a4f9b1bec6ad6dd",
    )
except TrainingDataAssignmentError as e:
    print(e)

Create a version with non-blocking training data assignment:

import datarobot as dr

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

version = dr.CustomModelVersion.create_from_previous(
    custom_model_id="6444482e5583f6ee2e572265",
    base_environment_id="642209acc563893014a41e24",
    training_dataset_id="6421f2149a4f9b1bec6ad6dd",
    max_wait=None,
)

while version.training_data.assignment_in_progress:
    time.sleep(10)
    version.refresh()
if version.training_data.assignment_error:
    print(version.training_data.assignment_error["message"])

Return type: CustomModelVersion

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, training_dataset_id=None, partition_column=None, holdout_dataset_id=None, keep_training_holdout_data=None, max_wait=600)¶

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

Create a version with training/holdout data: If training/holdout data related parameters are provided, the training data is assigned asynchronously. In this case: * if max_wait is not None, function returns once job is finished. * if max_wait is None, function returns immediately, progress can be polled by the user, see examples.

If training data assignment fails, new version is still created, but it is not allowed to create a model package for the model version and to deploy it. To check for training data assignment error, check version.training_data.assignment_error[“message”].

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. Defaults 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. Values: [datarobot.NETWORK_EGRESS_POLICY.NONE, datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS, datarobot.NETWORK_EGRESS_POLICY.PUBLIC]. Note: datarobot.NETWORK_EGRESS_POLICY.DR_API_ACCESS value is only supported by the SaaS (cloud) environment.
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.
training_dataset_id: str, optional: The ID of the training dataset to assign to the custom model.
partition_column: str, optional: Name of a partition column in a training dataset assigned to the custom model. Can only be assigned for structured models.
holdout_dataset_id: str, optional: The ID of the holdout dataset to assign to the custom model. Can only be assigned for unstructured models.
keep_training_holdout_data: bool, optional: If the version should inherit training and holdout data from the previous version. Defaults to True. This field is only applicable if the model has training data for versions enabled, otherwise the field value will be ignored.
max_wait: int, optional: Max time to wait for training data assignment. If set to None - method will return without waiting. Defaults to 10 minutes.

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.
datarobot.errors.InvalidUsageError: If wrong parameters are provided.
datarobot.errors.TrainingDataAssignmentError: If training data assignment fails.

Examples

Create a version with blocking (default max_wait=600) training data assignment:

import datarobot as dr
from datarobot.errors import TrainingDataAssignmentError

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

try:
    version = dr.CustomModelVersion.create_from_previous(
        custom_model_id="6444482e5583f6ee2e572265",
        base_environment_id="642209acc563893014a41e24",
        training_dataset_id="6421f2149a4f9b1bec6ad6dd",
    )
except TrainingDataAssignmentError as e:
    print(e)

Create a version with non-blocking training data assignment:

import datarobot as dr

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

version = dr.CustomModelVersion.create_from_previous(
    custom_model_id="6444482e5583f6ee2e572265",
    base_environment_id="642209acc563893014a41e24",
    training_dataset_id="6421f2149a4f9b1bec6ad6dd",
    max_wait=None,
)

while version.training_data.assignment_in_progress:
    time.sleep(10)
    version.refresh()
if version.training_data.assignment_error:
    print(version.training_data.assignment_error["message"])

Return type: CustomModelVersion

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.

Return type: List[CustomModelVersion]

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.

Return type: CustomModelVersion

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.

Return type: None

update(description=None, required_metadata_values=None)¶

Update custom model version properties.

New in version v2.21.

Parameters

description: str, optional: New custom model version description.
required_metadata_values: List[RequiredMetadataValue], optional: 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.

Return type: None

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.

Return type: None

get_feature_impact(with_metadata=False)¶

Get custom model feature impact.

New in version v2.23.

Parameters

with_metadatabool: The flag indicating if the result should include the metadata as well.

Returns

feature_impactslist 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.

Return type: List[Dict[str, Any]]

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

Return type: None

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: The ID of the custom model version conversion.
custom_model_version_id: str: The 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: The 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_idstr: The associated custom model ID.
custom_model_version_idstr: The associated custom model version ID.
main_program_item_idstr: 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, then don’t wait.

Returns

conversion_idstr: 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 status.

Return type: str

classmethod stop_conversion(custom_model_id, custom_model_version_id, conversion_id)¶

Stop a conversion that is in progress.

Parameters

custom_model_idstr: The ID of the associated custom model.
custom_model_version_idstr: The ID of the associated custom model version.
conversion_id: THe 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.

Return type: Response

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.

Return type: CustomModelVersionConversion

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.

Return type: Optional[CustomModelVersionConversion]

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.

Return type: List[CustomModelVersionConversion]

class datarobot.CustomModelVersionDependencyBuild(**kwargs)¶

Metadata about a DataRobot custom model version’s dependency build

New in version v2.22.

Attributes

custom_model_id: str: The ID of the custom model.
custom_model_version_id: str: The 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.

Return type: CustomModelVersionDependencyBuild

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.

Return type: Optional[CustomModelVersionDependencyBuild]

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.

Return type: str

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.

Return type: None

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.

Return type: None

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

class datarobot.models.custom_model_version.HoldoutData(dataset_id=None, dataset_version_id=None, dataset_name=None, partition_column=None)¶

Holdout data assigned to a DataRobot custom model version.

New in version v3.2.

Attributes

dataset_id: str: The ID of the dataset.
dataset_version_id: str: The ID of the dataset version.
dataset_name: str: The name of the dataset.
partition_column: str: The name of the partitions column.

class datarobot.models.custom_model_version.TrainingData(dataset_id=None, dataset_version_id=None, dataset_name=None, assignment_in_progress=None, assignment_error=None)¶

Training data assigned to a DataRobot custom model version.

New in version v3.2.

Attributes

dataset_id: str: The ID of the dataset.
dataset_version_id: str: The ID of the dataset version.
dataset_name: str: The name of the dataset.
assignment_in_progress: bool: The status of the assignment in progress.
assignment_error: dict: The assignment error message.

Custom Tasks¶

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

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

New in version v2.26.

Attributes

id: str

id of the custom task

name: str

name of the custom task

language: str

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

description: str

description of the custom task

target_type: datarobot.enums.CUSTOM_TASK_TARGET_TYPE

the target type of the custom task. One of:

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

latest_version: datarobot.CustomTaskVersion or None

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

created_by: str

The username of the user who created the custom task.

updated_at: str

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

created_at: str

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

calibrate_predictions: bool

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

classmethod from_server_data(data, keep_attrs=None)¶

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

Parameters

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: CustomTask

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

List custom tasks available to the user.

New in version v2.26.

Parameters

search_for: str, optional: string for filtering custom tasks - only tasks that contain the string in name or description will be returned. If not specified, all custom task will be returned
order_by: str, optional: property to sort custom tasks by. Supported properties are “created” and “updated”. Prefix the attribute name with a dash to sort in descending order, e.g. order_by=’-created’. By default, the order_by parameter is None which will result in custom tasks being returned in order of creation time descending

Returns

List[CustomTask]: a list of custom tasks.

Raises

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

Return type: List[CustomTask]

classmethod get(custom_task_id)¶

Get custom task by id.

New in version v2.26.

Parameters

custom_task_id: str: id of the custom task

Returns

CustomTask: retrieved custom task

Raises

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

Return type: CustomTask

classmethod copy(custom_task_id)¶

Create a custom task by copying existing one.

New in version v2.26.

Parameters

custom_task_id: str: id of the custom task to copy

Returns

CustomTask

Raises

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

Return type: CustomTask

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

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

New in version v2.26.

Parameters

name: str

name of the custom task

target_type: datarobot.enums.CUSTOM_TASK_TARGET_TYPE

the target typed based on the following values. Anything else will raise an error

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

language: str, optional

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

description: str, optional

description of the custom task

calibrate_predictions: bool, optional

whether anomaly predictions should be calibrated to be between 0 and 1 by DR. if None, uses default value from DR app (True). only applies to custom estimators with target type datarobot.enums.CUSTOM_TASK_TARGET_TYPE.ANOMALY

Returns

CustomTask

Raises

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

Return type: CustomTask

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

Update custom task properties.

New in version v2.26.

Parameters

name: str, optional: new custom task name
language: str, optional: new custom task programming language
description: str, optional: new custom task description

Raises

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

Return type: None

refresh()¶

Update custom task with the latest data from server.

New in version v2.26.

Raises

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

Return type: None

delete()¶

Delete custom task.

New in version v2.26.

Raises

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

Return type: None

download_latest_version(file_path)¶

Download the latest custom task version.

New in version v2.26.

Parameters

file_path: str: the full path of the target zip file

Raises

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

Return type: None

get_access_list()¶

Retrieve access control settings of this custom task.

New in version v2.27.

Returns

list ofclass:SharingAccess <datarobot.SharingAccess>

Return type: List[SharingAccess]

share(access_list)¶

Update the access control settings of this custom task.

New in version v2.27.

Parameters

access_listlist 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)

Return type: None

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

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: 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=None, creator=None, base_names=None, class_name=None, canonical_name=None)¶

A data driver

Attributes

idstr: the id of the driver.
class_namestr: the Java class name for the driver.
canonical_namestr: the user-friendly name of the driver.
creatorstr: the id of the user who created the driver.
base_nameslist of str: a list of the file name(s) of the jar files.

classmethod list()¶

Returns list of available drivers.

Returns

driverslist 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')]

Return type: List[DataDriver]

classmethod get(driver_id)¶

Gets the driver.

Parameters

driver_idstr: the identifier of the driver.

Returns

driverDataDriver: the required driver.

Examples

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

Return type: DataDriver

classmethod create(class_name, canonical_name, files)¶

Creates the driver. Only available to admin users.

Parameters

class_namestr: the Java class name for the driver.
canonical_namestr: the user-friendly name of the driver.
fileslist of str: a list of the file paths on file system file_path(s) for the driver.

Returns

driverDataDriver: 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')

Return type: DataDriver

update(class_name=None, canonical_name=None)¶

Updates the driver. Only available to admin users.

Parameters

class_namestr: the Java class name for the driver.
canonical_namestr: 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'

Return type: None

delete()¶

Removes the driver. Only available to admin users.

Raises

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

Return type: None

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

A connector

Attributes

idstr: the id of the connector.
creator_idstr: the id of the user who created the connector.
base_namestr: the file name of the jar file.
canonical_namestr: the user-friendly name of the connector.
configuration_idstr: the id of the configuration of the connector.

classmethod list()¶

Returns list of available connectors.

Returns

connectorslist 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')]

Return type: List[Connector]

classmethod get(connector_id)¶

Gets the connector.

Parameters

connector_idstr: the identifier of the connector.

Returns

connectorConnector: the required connector.

Examples

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

Return type: Connector

classmethod create(file_path)¶

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

Parameters

file_pathstr: the file path on file system file_path(s) for the connector.

Returns

connectorConnector: 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')

Return type: Connector

update(file_path)¶

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

Parameters

file_pathstr: the file path on file system file_path(s) for the connector.

Returns

connectorConnector: 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'

Return type: Connector

delete()¶

Removes the connector. Only available to admin users.

Raises

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

Return type: None

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

A data store. Represents database

Attributes

idstr: The id of the data store.
data_store_typestr: The type of data store.
canonical_namestr: The user-friendly name of the data store.
creatorstr: The id of the user who created the data store.
updateddatetime.datetime: The time of the last update
paramsDataStoreParameters: A list specifying data store parameters.
rolestr: Your access role for this data store.

classmethod list()¶

Returns list of available data stores.

Returns

data_storeslist 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')]

Return type: List[DataStore]

classmethod get(data_store_id)¶

Gets the data store.

Parameters

data_store_idstr: the identifier of the data store.

Returns

data_storeDataStore: the required data store.

Examples

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

Return type: DataStore

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

Creates the data store.

Parameters

data_store_typestr: the type of data store.
canonical_namestr: the user-friendly name of the data store.
driver_idstr: the identifier of the DataDriver.
jdbc_urlstr: the full JDBC url, for example jdbc:postgresql://my.dbaddress.org:5432/my_db.

Returns

data_storeDataStore: 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')

Return type: DataStore

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

Updates the data store.

Parameters

canonical_namestr: optional, the user-friendly name of the data store.
driver_idstr: optional, the identifier of the DataDriver.
jdbc_urlstr: 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')

Return type: None

delete()¶

Removes the DataStore

Return type: None

test(username=None, password=None, credential_id=None, use_kerberos=None, credential_data=None)¶

Tests database connection.

Changed in version v3.2: Added credential_id, use_kerberos and credential_data optional params and made username and password optional.

Parameters

usernamestr: optional, the username for database authentication.
passwordstr: optional, the password for database authentication. The password is encrypted at server side and never saved / stored
credential_idstr: optional, id of the set of credentials to use instead of username and password
use_kerberosbool: optional, whether to use Kerberos for data store authentication
credential_datadict: optional, the credentials to authenticate with the database, to use instead of user/password or credential ID

Returns

messagedict: 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'}

Return type: TestResponse

schemas(username, password)¶

Returns list of available schemas.

Parameters

usernamestr: the username for database authentication.
passwordstr: the password for database authentication. The password is encrypted at server side and never saved / stored

Returns

responsedict: 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']}

Return type: SchemasResponse

tables(username, password, schema=None)¶

Returns list of available tables in schema.

Parameters

usernamestr: optional, the username for database authentication.
passwordstr: optional, the password for database authentication. The password is encrypted at server side and never saved / stored
schemastr: optional, the schema name.

Returns

responsedict: 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'}

Return type: TablesResponse

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

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: DataStore

get_access_list()¶

Retrieve what users have access to this data store

New in version v2.14.

Returns

list ofclass:SharingAccess <datarobot.SharingAccess>

Return type: List[SharingAccess]

get_shared_roles()¶

Retrieve what users have access to this data store

New in version v3.2.

Returns

list ofclass:SharingRole <datarobot.models.sharing.SharingRole>

Return type: List[SharingRole]

share(access_list)¶

Modify the ability of users to access this data store

New in version v2.14.

Parameters

access_listlist of SharingRole: 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

The SharingRole class is needed in order to share a Data Store with one or more users.

For example, suppose you had a list of user IDs you wanted to share this DataStore with. You could use a loop to generate a list of SharingRole objects for them, and bulk share this Data Store.

>>> import datarobot as dr
>>> from datarobot.models.sharing import SharingRole
>>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE
>>>
>>> user_ids = ["60912e09fd1f04e832a575c1", "639ce542862e9b1b1bfa8f1b", "63e185e7cd3a5f8e190c6393"]
>>> sharing_roles = []
>>> for user_id in user_ids:
...     new_sharing_role = SharingRole(
...         role=SHARING_ROLE.CONSUMER,
...         share_recipient_type=SHARING_RECIPIENT_TYPE.USER,
...         id=user_id,
...         can_share=True,
...     )
...     sharing_roles.append(new_sharing_role)
>>> dr.DataStore.get('my-data-store-id').share(access_list)

Similarly, a SharingRole instance can be used to remove a user’s access if the role is set to SHARING_ROLE.NO_ROLE, like in this example:

>>> import datarobot as dr
>>> from datarobot.models.sharing import SharingRole
>>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE
>>>
>>> user_to_remove = "[email protected]"
... remove_sharing_role = SharingRole(
...     role=SHARING_ROLE.NO_ROLE,
...     share_recipient_type=SHARING_RECIPIENT_TYPE.USER,
...     username=user_to_remove,
...     can_share=False,
... )
>>> dr.DataStore.get('my-data-store-id').share(roles=[remove_sharing_role])

Return type: None

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

A data source. Represents data request

Attributes

idstr: the id of the data source.
typestr: the type of data source.
canonical_namestr: the user-friendly name of the data source.
creatorstr: the id of the user who created the data source.
updateddatetime.datetime: the time of the last update.
paramsDataSourceParameters: a list specifying data source parameters.
rolestr 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()¶

Returns list of available data sources.

Returns

data_sourceslist 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')]

Return type: List[DataSource]

classmethod get(data_source_id)¶

Gets the data source.

Parameters

data_source_idstr: the identifier of the data source.

Returns

data_sourceDataSource: the requested data source.

Examples

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

Return type: TypeVar(TDataSource, bound= DataSource)

classmethod create(data_source_type, canonical_name, params)¶

Creates the data source.

Parameters

data_source_typestr: the type of data source.
canonical_namestr: the user-friendly name of the data source.
paramsDataSourceParameters: a list specifying data source parameters.

Returns

data_sourceDataSource: 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')

Return type: TypeVar(TDataSource, bound= DataSource)

update(canonical_name=None, params=None)¶

Creates the data source.

Parameters

canonical_namestr: optional, the user-friendly name of the data source.
paramsDataSourceParameters: 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')

Return type: None

delete()¶

Removes the DataSource

Return type: None

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

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: TypeVar(TDataSource, bound= DataSource)

get_access_list()¶

Retrieve what users have access to this data source

New in version v2.14.

Returns

list ofclass:SharingAccess <datarobot.SharingAccess>

Return type: List[SharingAccess]

share(access_list)¶

Modify the ability of users to access this data source

New in version v2.14.

Parameters

access_list: list ofclass: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)

Return type: None

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

Create a Dataset from this data source.

New in version v2.22.

Parameters

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

Returns

response: Dataset: The Dataset created from the uploaded data

Return type: Dataset

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

Data request configuration

Attributes

data_store_idstr: the id of the DataStore.
tablestr: optional, the name of specified database table.
schemastr: optional, the name of the schema associated with the table.
partition_columnstr: optional, the name of the partition column.
querystr: optional, the user specified SQL query.
fetch_sizeint: 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, version_id, name, categories, created_at, is_data_engine_eligible, is_latest_version, is_snapshot, processing_state, created_by=None, data_persisted=None, size=None, row_count=None, recipe_id=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()¶

Returns

urlstr: Permanent static hyperlink to this dataset in AI Catalog.

Return type: str

classmethod upload(source)¶

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)

Return type: TypeVar(TDataset, bound= Dataset)

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

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
use_cases: list[UseCase] | UseCase | list[string] | string, optional: A list of UseCase objects, UseCase object, list of Use Case ids or a single Use Case id to add this new Dataset to. Must be a kwarg.

Returns

response: Dataset: A fully armed and operational Dataset

Return type: TypeVar(TDataset, bound= Dataset)

classmethod create_from_in_memory_data(cls, data_frame=None, records=None, categories=None, read_timeout=600, max_wait=600, fname=None, *, use_cases=None)¶

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
use_cases: list[UseCase] | UseCase | list[string] | string, optional: A list of UseCase objects, UseCase object, list of Use Case IDs or a single Use Case ID to add this new dataset to. Must be a kwarg.

Returns

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

Raises

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

Return type: TypeVar(TDataset, bound= Dataset)

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

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.
use_cases: list[UseCase] | UseCase | list[string] | string, optional: A list of UseCase objects, UseCase object, list of Use Case IDs or a single Use Case ID to add this new dataset to. Must be a kwarg.

Returns

response: Dataset: The Dataset created from the uploaded data

Return type: TypeVar(TDataset, bound= Dataset)

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

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.
use_cases: list[UseCase] | UseCase | list[string] | string, optional: A list of UseCase objects, UseCase object, list of Use Case IDs or a single Use Case ID to add this new dataset to. Must be a kwarg.

Returns

response: Dataset: The Dataset created from the uploaded data

Return type: TypeVar(TDataset, bound= Dataset)

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

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_waitint: optional, the maximum number of seconds to wait before giving up.
use_cases: list[UseCase] | UseCase | list[string] | string, optional: A list of UseCase objects, UseCase object, list of Use Case IDs or a single Use Case ID to add this new dataset to. Must be a kwarg.

Returns

response: Dataset: The Dataset created from the query generator

Return type: TypeVar(TDataset, bound= Dataset)

classmethod get(dataset_id)¶

Get information about a dataset.

Parameters

dataset_idstring: the id of the dataset

Returns

datasetDataset: the queried dataset

Return type: TypeVar(TDataset, bound= Dataset)

classmethod delete(dataset_id)¶

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

Parameters

dataset_id: string: The id of the dataset to mark for deletion

Returns

None

Return type: None

classmethod un_delete(dataset_id)¶

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

Parameters

dataset_id: string: The id of the dataset to un-delete

Returns

None

Return type: None

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

List all datasets a user can view.

Parameters

category: string, optional: Optional. If specified, only dataset versions that have the specified category will be included in the results. Categories identify the intended use of the dataset; supported categories are “TRAINING” and “PREDICTION”.
filter_failed: bool, optional: If unset, uses the server default: False. Whether datasets that failed during import should be excluded from the results. If True invalid datasets will be excluded.
order_by: string, optional: If unset, uses the server default: “-created”. Sorting order which will be applied to catalog list, valid options are: - “created” – ascending order by creation datetime; - “-created” – descending order by creation datetime.
use_cases: Union[UseCase, List[UseCase], str, List[str]], optional: Filter available datasets by a specific Use Case or Cases. Accepts either the entity or the ID.

Returns

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

Return type: List[TypeVar(TDataset, bound= Dataset)]

classmethod iterate(offset=None, limit=None, category=None, order_by=None, filter_failed=None, use_cases=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.
use_cases: Union[UseCase, List[UseCase], str, List[str]], optional: Filter available datasets by a specific Use Case or Cases. Accepts either the entity or the ID.

Yields

Dataset: An iterator of the datasets the user can view

Return type: Generator[TypeVar(TDataset, bound= Dataset), None, None]

update()¶

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

Returns

None

Return type: None

modify(name=None, categories=None)¶

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

Parameters

name: string, optional: The new name of the dataset
categories: list[string], optional: A list of strings describing the intended use of the dataset. The supported options are “TRAINING” and “PREDICTION”. If any categories were previously specified for the dataset, they will be overwritten.

Returns

None

Return type: None

share(access_list, apply_grant_to_linked_objects=False)¶

Modify the ability of users to access this dataset

Parameters

access_list: list ofclass: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)

Return type: None

get_details()¶

Gets the details for this Dataset

Returns

DatasetDetails

Return type: DatasetDetails

get_all_features(order_by=None)¶

Get a list of all the features for this dataset.

Parameters

order_by: string, optional: If unset, uses the server default: ‘name’. How the features should be ordered. Can be ‘name’ or ‘featureType’.

Returns

list[DatasetFeature]

Return type: List[DatasetFeature]

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

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

Parameters

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

Yields

DatasetFeature

Return type: Generator[DatasetFeature, None, None]

get_featurelists()¶

Get DatasetFeaturelists created on this Dataset

Returns

feature_lists: list[DatasetFeaturelist]

Return type: List[DatasetFeaturelist]

create_featurelist(name, features)¶

Create a new dataset featurelist

Parameters

namestr: the name of the modeling featurelist to create. Names must be unique within the dataset, or the server will return an error.
featureslist of str: the names of the features to include in the dataset featurelist. Each feature must be a dataset feature.

Returns

featurelistDatasetFeaturelist: 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)

Return type: DatasetFeaturelist

get_file(file_path=None, filelike=None)¶

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

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

The user must also have permission to download data.

Parameters

file_path: string, optional: The destination to write the file to.
filelike: file, optional: A file-like object to write to. The object must be able to write bytes. The user is responsible for closing the object

Returns

None

Return type: None

get_as_dataframe(low_memory=False)¶

Retrieves all the originally uploaded data in a pandas DataFrame.

New in version v3.0.

Parameters

low_memory: bool, optional: If True, use local files to reduce memory usage which will be slower.

Returns

pd.DataFrame

Return type: DataFrame

get_projects()¶

Retrieves the Dataset’s projects as ProjectLocation named tuples.

Returns

locations: list[ProjectLocation]

Return type: List[ProjectLocation]

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

Create a datarobot.models.Project from this dataset

Parameters

project_name: string, optional: The name of the project to be created. If not specified, will be “Untitled Project” for database connections, otherwise the project name will be based on the file used.
user: string, optional: The username for database authentication.
password: string, optional: The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored
credential_id: string, optional: The ID of the set of credentials to use instead of user and password.
use_kerberos: bool, optional: Server default is False. If true, use kerberos authentication for database authentication.
credential_data: dict, optional: The credentials to authenticate with the database, to use instead of user/password or credential ID.
use_cases: list[UseCase] | UseCase | list[string] | string, optional: A list of UseCase objects, UseCase object, list of Use Case ids or a single Use Case id to add this new Dataset to. Must be a kwarg.

Returns

Project

Return type: Project

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

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

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

New in version v2.23.

Parameters

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

Returns

response: Dataset: A fully armed and operational Dataset version

Return type: TypeVar(TDataset, bound= Dataset)

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

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

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

New in version v2.23.

Parameters

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

Returns

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

Raises

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

Return type: TypeVar(TDataset, bound= Dataset)

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

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

New in version v2.23.

Parameters

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

Returns

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

Return type: TypeVar(TDataset, bound= Dataset)

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

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

New in version v2.23.

Parameters

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

Returns

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

Return type: TypeVar(TDataset, bound= Dataset)

classmethod from_data(data)¶

Instantiate an object of this class using a dict.

Parameters

datadict: Correctly snake_cased keys and their values.

Return type: TypeVar(T, bound= APIObject)

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

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: TypeVar(T, bound= APIObject)

open_in_browser()¶

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.

Return type: None

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

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

Attributes

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

classmethod get(dataset_id)¶

Get details for a Dataset from the server

Parameters

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

Returns

DatasetDetails

Return type: TypeVar(TDatasetDetails, bound= DatasetDetails)

to_dataset()¶

Build a Dataset object from the information in this object

Returns

Dataset

Return type: Dataset

class datarobot.models.dataset.ProjectLocation(url, id)¶

property id¶: Alias for field number 1

property url¶: Alias for field number 0

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_typestr: Type of data engine query generator
datasetsList[QueryGeneratorDataset]: Source datasets in the Data Engine workspace.
generator_settingsdict: Data engine generator settings of the given generator_type.

Returns

query_generatorDataEngineQueryGenerator: 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_idstr: The identifier of the query generator you want to load.

Returns

query_generatorDataEngineQueryGenerator: 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, dataset_id, dataset_version_id=None, max_wait=600, relax_known_in_advance_features_check=None)¶

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_idstr: The id of the project to which you upload the prediction dataset.
dataset_idstr: The identifier of the dataset.
dataset_version_idstr, optional: The version id of the dataset to use.
max_waitint, optional: Optional, the maximum number of seconds to wait before giving up.
relax_known_in_advance_features_checkbool, 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

datasetPredictionDataset: The newly uploaded dataset.

Return type: PredictionDataset

prepare_prediction_dataset(sourcedata, project_id, max_wait=600, relax_known_in_advance_features_check=None)¶

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

New in version v3.1.

Parameters

sourcedatastr, 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_idstr: The id of the project to which you upload the prediction dataset.
max_waitint, optional: The maximum number of seconds to wait for the uploaded dataset to be processed before raising an error.
relax_known_in_advance_features_checkbool, 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
——-
datasetPredictionDataset: 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.

Return type: PredictionDataset

Data Store¶

class datarobot.models.data_store.TestResponse() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)¶

class datarobot.models.data_store.SchemasResponse() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)¶

class datarobot.models.data_store.TablesResponse() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)¶

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, label=None, description=None, status=None, default_prediction_server=None, model=None, capabilities=None, prediction_usage=None, permissions=None, service_health=None, model_health=None, accuracy_health=None, importance=None, fairness_health=None, governance=None, owners=None, prediction_environment=None)¶

A deployment created from a DataRobot model.

Attributes

idstr

the id of the deployment

labelstr

the label of the deployment

descriptionstr

the description of the deployment

statusstr

(New in version v2.29) deployment status

default_prediction_serverdict

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.

importancestr, optional

deployment importance

modeldict

information on the model of the deployment

capabilitiesdict

information on the capabilities of the deployment

prediction_usagedict

information on the prediction usage of the deployment

permissionslist

(New in version v2.18) user’s permissions on the deployment

service_healthdict

information on the service health of the deployment

model_healthdict

information on the model health of the deployment

accuracy_healthdict

information on the accuracy health of the deployment

fairness_healthdict

information on the fairness health of a deployment

governancedict

information on approval and change requests of a deployment

ownersdict

information on the owners of a deployment

prediction_environmentdict

information on the prediction environment of a deployment

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

Create a deployment from a DataRobot model.

New in version v2.17.

Parameters

model_idstr: id of the DataRobot model to deploy
labelstr: a human-readable label of the deployment
descriptionstr, optional: a human-readable description of the deployment
default_prediction_server_idstr, optional: an identifier of a prediction server to be used as the default prediction server
importancestr, optional: deployment importance
prediction_thresholdfloat, optional: threshold used for binary classification in predictions
statusstr, optional: deployment status

Returns

deploymentDeployment: 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')

Return type: TypeVar(TDeployment, bound= Deployment)

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

Create a deployment from a DataRobot custom model image.

Parameters

custom_model_version_idstr: id of the DataRobot custom model version to deploy The version must have a base_environment_id.
labelstr: a human readable label of the deployment
descriptionstr, optional: a human readable description of the deployment
default_prediction_server_idstr, optional: an identifier of a prediction server to be used as the default prediction server
max_waitint, 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
importancestr, optional: deployment importance

Returns

deploymentDeployment: The created deployment

Return type: TypeVar(TDeployment, bound= Deployment)

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

List all deployments a user can view.

New in version v2.17.

Parameters

order_bystr, 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.

searchstr, optional

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

filtersdatarobot.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

deploymentslist: 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')]

Return type: List[TypeVar(TDeployment, bound= Deployment)]

classmethod get(deployment_id)¶

Get information about a deployment.

New in version v2.17.

Parameters

deployment_idstr: the id of the deployment

Returns

deploymentDeployment: the queried deployment

Examples

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

Return type: TypeVar(TDeployment, bound= Deployment)

predict_batch(source, passthrough_columns=None, download_timeout=None, download_read_timeout=None, upload_read_timeout=None)¶

A convenience method for making predictions with csv file or pandas DataFrame using a batch prediction job.

For advanced usage, use datarobot.models.BatchPredictionJob directly.

New in version v3.0.

Parameters

source: str, pd.DataFrame or file object: Pass a filepath, file, or DataFrame for making batch predictions.
passthrough_columnslist[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",
)

Return type: DataFrame

get_uri()¶

Returns

urlstr: Deployment’s overview URI

Return type: str

update(label=None, description=None, importance=None)¶

Update the label and description of this deployment.

New in version v2.19.

Return type: None

delete()¶

Delete this deployment.

New in version v2.17.

Return type: None

activate(max_wait=600)¶

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

New in version v2.29.

Parameters

max_waitint, optional: The maximum time to wait for deployment activation to complete before erroring

Return type: None

deactivate(max_wait=600)¶

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

New in version v2.29.

Parameters

max_waitint, optional: The maximum time to wait for deployment deactivation to complete before erroring

Return type: None

replace_model(new_model_id, reason, max_wait=600)¶

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

New in version v2.17.

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

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

Parameters

new_model_idstr: The id of the new model to use. If replacing the deployment’s model with a CustomInferenceModel, a specific CustomModelVersion ID must be used.
reasonMODEL_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_waitint, 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)')

Return type: None

validate_replacement_model(new_model_id)¶

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

New in version v2.17.

Parameters

new_model_idstr: the id of the new model to validate

Returns

statusstr: 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.
messagestr: message for the validation result
checksdict: explain why the new model can or cannot replace the deployment’s current model

Return type: Tuple[str, str, Dict[str, Any]]

get_features()¶

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

Returns

features: list: a list of feature dict

Notes

Each feature dict contains the following structure:

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

Examples

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

Return type: List[FeatureDict]

submit_actuals(data, batch_size=10000)¶

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

Parameters

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

Raises

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

Examples

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

Return type: None

submit_actuals_from_catalog_async(dataset_id, actual_value_column, association_id_column, dataset_version_id=None, timestamp_column=None, was_acted_on_column=None)¶

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

Parameters

dataset_id: str,: The ID of the source dataset.
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 is used.
association_id_column: str,: The name of the column that contains a unique identifier used with a prediction.
actual_value_column: str,: The name of the column that contains the actual value of a prediction.
was_acted_on_column: str, optional,: The name of the column that indicates if the prediction was acted on in a way that could have affected the actual outcome.
timestamp_column: str, optional,: The name of the column that contains datetime or string in RFC3339 format.

Returns

status_check_jobStatusCheckJob: Object contains all needed logic for a periodical status check of an async job.

Raises

ValueError: if dataset_id not provided if actual_value_column not provided if association_id_column not provided

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
status_check_job = deployment.submit_actuals_from_catalog_async(data)

Return type: StatusCheckJob

get_predictions_by_forecast_date_settings()¶

Retrieve predictions by forecast date settings of this deployment.

New in version v2.27.

Returns

settingsdict

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

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

Return type: ForecastDateSettings

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

Update predictions by forecast date settings of this deployment.

New in version v2.27.

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

Parameters

enable_predictions_by_forecast_datebool: 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_waitint, 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",
)

Return type: None

get_challenger_models_settings()¶

Retrieve challenger models settings of this deployment.

New in version v2.27.

Returns

settingsdict

Challenger models settings of the deployment is a dict with the following format:

enabledbool: Is ‘’True’’ if challenger models is enabled for this deployment. To update existing ‘’challenger_models’’ settings, see update_challenger_models_settings()

Return type: ChallengerModelsSettings

update_challenger_models_settings(challenger_models_enabled, max_wait=600)¶

Update challenger models settings of this deployment.

New in version v2.27.

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

Parameters

challenger_models_enabledbool: set to ‘’True’’ if challenger models is to be turned on or set to ‘’False’’ if challenger models is to be turned off
max_waitint, optional: seconds to wait for successful resolution

Return type: None

get_segment_analysis_settings()¶

Retrieve segment analysis settings of this deployment.

New in version v2.27.

Returns

settingsdict

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

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

Return type: SegmentAnalysisSettings

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

Update segment analysis settings of this deployment.

New in version v2.27.

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

Parameters

segment_analysis_enabledbool: 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_waitint, optional: seconds to wait for successful resolution

Return type: None

get_bias_and_fairness_settings()¶

Retrieve bias and fairness settings of this deployment.

..versionadded:: v3.2.0

Returns

settingsdict in the following format:

protected_featuresList[str]: A list of features to mark as protected.
preferable_target_valuebool: A target value that should be treated as a positive outcome for the prediction.
fairness_metric_setstr: Can be one of <datarobot.enums.FairnessMetricsSet>. A set of fairness metrics to use for calculating fairness.
fairness_thresholdfloat: Threshold value of the fairness metric. Cannot be less than 0 or greater than 1.

Return type: Optional[BiasAndFairnessSettings]

update_bias_and_fairness_settings(protected_features, fairness_metric_set, fairness_threshold, preferable_target_value, max_wait=600)¶

Update bias and fairness settings of this deployment.

..versionadded:: v3.2.0

Updating bias and fairness 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

protected_featuresList[str]: A list of features to mark as protected.
preferable_target_valuebool: A target value that should be treated as a positive outcome for the prediction.
fairness_metric_setstr: Can be one of <datarobot.enums.FairnessMetricsSet>. The fairness metric used to calculate the fairness scores.
fairness_thresholdfloat: Threshold value of the fairness metric. Cannot be less than 0 or greater than 1.
max_waitint, optional: seconds to wait for successful resolution

Return type: None

get_drift_tracking_settings()¶

Retrieve drift tracking settings of this deployment.

New in version v2.17.

Returns

settingsdict

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:

enabledbool: 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:

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

Return type: DriftTrackingSettings

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

Update drift tracking settings of this deployment.

New in version v2.17.

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

Parameters

target_drift_enabledbool, optional: if target drift tracking is to be turned on
feature_drift_enabledbool, optional: if feature drift tracking is to be turned on
max_waitint, optional: seconds to wait for successful resolution

Return type: None

get_association_id_settings()¶

Retrieve association ID setting for this deployment.

New in version v2.19.

Returns

association_id_settingsdict in the following format:

column_nameslist[string], optional: name of the columns to be used as association ID,
required_in_prediction_requestsbool, optional: whether the association ID column is required in prediction requests

Return type: str

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

Update association ID setting for this deployment.

New in version v2.19.

Parameters

column_nameslist[string], optional: name of the columns to be used as association ID, currently only support a list of one string
required_in_prediction_requestsbool, optional: whether the association ID column is required in prediction requests
max_waitint, optional: seconds to wait for successful resolution

Return type: None

get_predictions_data_collection_settings()¶

Retrieve predictions data collection settings of this deployment.

New in version v2.21.

Returns

predictions_data_collection_settingsdict in the following format:

enabledbool: If predictions data collection is enabled for this deployment. To update existing ‘’predictions_data_collection’’ settings, see update_predictions_data_collection_settings()

Return type: Dict[str, bool]

update_predictions_data_collection_settings(enabled, max_wait=600)¶

Update predictions data collection settings of this deployment.

New in version v2.21.

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

Parameters

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

Return type: None

get_prediction_warning_settings()¶

Retrieve prediction warning settings of this deployment.

New in version v2.19.

Returns

settingsdict in the following format:

enabledbool

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

custom_boundariesdict or None

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

upperfloat: All predictions greater than provided value are considered anomalous
lowerfloat: All predictions less than provided value are considered anomalous

Return type: PredictionWarningSettings

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

Update prediction warning settings of this deployment.

New in version v2.19.

Parameters

prediction_warning_enabledbool: If prediction warnings should be turned on.
use_default_boundariesbool, optional: If default boundaries of the model should be used for the deployment.
upper_boundaryfloat, optional: All predictions greater than provided value will be considered anomalous
lower_boundaryfloat, optional: All predictions less than provided value will be considered anomalous
max_waitint, optional: seconds to wait for successful resolution

Return type: None

get_prediction_intervals_settings()¶

Retrieve prediction intervals settings for this deployment.

New in version v2.19.

Returns

dict in the following format:

enabledbool: Whether prediction intervals are enabled for this deployment
percentileslist[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.

Return type: PredictionIntervalsSettings

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

Update prediction intervals settings for this deployment.

New in version v2.19.

Parameters

percentileslist[int]: The prediction intervals percentiles to enable for this deployment. Currently we only support setting one percentile at a time.
enabledbool, optional (defaults to True): Whether to enable showing prediction intervals in the results of predictions requested using this deployment.
max_waitint, 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.

Return type: None

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

Retrieves values of many service stat metrics aggregated over a time period.

New in version v2.18.

Parameters

model_idstr, optional: the id of the model
start_timedatetime, optional: start of the time period
end_timedatetime, optional: end of the time period
execution_time_quantilefloat, optional: quantile for executionTime, defaults to 0.5
response_time_quantilefloat, optional: quantile for responseTime, defaults to 0.5
slow_requests_thresholdfloat, optional: threshold for slowRequests, defaults to 1000

Returns

service_statsServiceStats: the queried service stats metrics information

Return type: ServiceStats

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

Retrieves values of a single service stat metric over a time period.

New in version v2.18.

Parameters

metricSERVICE_STAT_METRIC, optional: the service stat metric to retrieve
model_idstr, optional: the id of the model
start_timedatetime, optional: start of the time period
end_timedatetime, optional: end of the time period
bucket_sizestr, optional: time duration of a bucket, in ISO 8601 time duration format
quantilefloat, optional: quantile for ‘executionTime’ or ‘responseTime’, ignored when querying other metrics
thresholdint, optional: threshold for ‘slowQueries’, ignored when querying other metrics

Returns

service_stats_over_timeServiceStatsOverTime: the queried service stats metric over time information

Return type: ServiceStatsOverTime

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

Retrieve target drift information over a certain time period.

New in version v2.21.

Parameters

model_idstr: the id of the model
start_timedatetime: start of the time period
end_timedatetime: end of the time period
metricstr: (New in version v2.22) metric used to calculate the drift score

Returns

target_driftTargetDrift: the queried target drift information

Return type: TargetDrift

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

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

New in version v2.21.

Parameters

model_idstr: the id of the model
start_timedatetime: start of the time period
end_timedatetime: end of the time period
metricstr: (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

Return type: List[FeatureDrift]

get_predictions_over_time(model_ids=None, start_time=None, end_time=None, bucket_size=None, target_classes=None, include_percentiles=False)¶

Retrieve stats of deployment’s prediction response over a certain time period.

New in version v3.2.

Parameters

model_idslist[str]: ID of models to retrieve prediction stats
start_timedatetime: start of the time period
end_timedatetime: end of the time period
bucket_sizeBUCKET_SIZE: time duration of each bucket
target_classeslist[str]: class names of target, only for deployments with multiclass target
include_percentilesbool: if the returned data includes percentiles, only for a deployment with a binary and regression target

Returns

predictions_over_timePredictionsOverTime: the queried predictions over time information

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
predictions_over_time = deployment.get_predictions_over_time()
predictions_over_time.buckets[0]['mean_predicted_value']
>>>0.3772
predictions_over_time.buckets[0]['row_count']
>>>2000

Return type: PredictionsOverTime

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

Retrieves values of many accuracy metrics aggregated over a time period.

New in version v2.18.

Parameters

model_idstr: the id of the model
start_timedatetime: start of the time period
end_timedatetime: end of the time period
target_classeslist[str], optional: Optional list of target class strings

Returns

accuracyAccuracy: the queried accuracy metrics information

Return type: Accuracy

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

Retrieves values of a single accuracy metric over a time period.

New in version v2.18.

Parameters

metricACCURACY_METRIC: the accuracy metric to retrieve
model_idstr: the id of the model
start_timedatetime: start of the time period
end_timedatetime: end of the time period
bucket_sizestr: time duration of a bucket, in ISO 8601 time duration format
target_classeslist[str], optional: Optional list of target class strings

Returns

accuracy_over_timeAccuracyOverTime: the queried accuracy metric over time information

Return type: AccuracyOverTime

get_fairness_scores_over_time(start_time=None, end_time=None, bucket_size=None, model_id=None, protected_feature=None, fairness_metric=None)¶

Retrieves values of a single fairness score over a time period.

New in version v3.2.

Parameters

model_idstr: the id of the model
start_timedatetime: start of the time period
end_timedatetime: end of the time period
bucket_sizestr: time duration of a bucket, in ISO 8601 time duration format
protected_featurestr: name of protected feature
fairness_metricstr: A consolidation of the fairness metrics by the use case.

Returns

fairness_scores_over_timeFairnessScoresOverTime: the queried fairness score over time information

Return type: FairnessScoresOverTime

update_secondary_dataset_config(secondary_dataset_config_id, credential_ids=None)¶

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

New in version v2.23.

Parameters

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

Examples

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

Return type: str

get_secondary_dataset_config()¶

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

New in version v2.23.

Returns

secondary_dataset_configSecondaryDatasetConfigurations: 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'

Return type: str

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

Retrieve a list of prediction results of the deployment.

New in version v2.24.

Parameters

model_idstr: the id of the model
start_timedatetime: start of the time period
end_timedatetime: end of the time period
actuals_presentbool: filters predictions results to only those who have actuals present or with missing actuals
offsetint: this many results will be skipped
limitint: 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()

Return type: List[Dict[str, Any]]

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

Download prediction results of the deployment as a CSV file.

New in version v2.24.

Parameters

filepathstr: path of the csv file
model_idstr: the id of the model
start_timedatetime: start of the time period
end_timedatetime: end of the time period
actuals_presentbool: filters predictions results to only those who have actuals present or with missing actuals
offsetint: this many results will be skipped
limitint: 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')

Return type: None

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

Retrieve scoring code of the current deployed model.

New in version v2.24.

Parameters

filepathstr: path of the scoring code file
source_codebool: whether source code or binary of the scoring code will be retrieved
include_agentbool: whether the scoring code retrieved will include tracking agent
include_prediction_explanationsbool: whether the scoring code retrieved will include prediction explanations
include_prediction_intervalsbool: 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')

Return type: None

delete_monitoring_data(model_id, start_time=None, end_time=None, max_wait=600)¶

Delete deployment monitoring data.

Parameters

model_idstr: id of the model to delete monitoring data
start_timedatetime, optional: start of the time period to delete monitoring data
end_timedatetime, optional: end of the time period to delete monitoring data
max_waitint, optional: seconds to wait for successful resolution

Return type: None

list_shared_roles(id=None, name=None, share_recipient_type=None, limit=100, offset=0)¶

Get a list of users, groups and organizations that have an access to this user blueprint

Parameters

id: str, Optional: Only return the access control information for a organization, group or user with this ID.
name: string, Optional: Only return the access control information for a organization, group or user with this name.
share_recipient_type: enum(‘user’, ‘group’, ‘organization’), Optional: Only returns results with the given recipient type.
limit: int (Default=0): At most this many results are returned.
offset: int (Default=0): This many results will be skipped.

Returns

list(DeploymentSharedRole)

Return type: List[DeploymentSharedRole]

update_shared_roles(roles)¶

Share a deployment with a user, group, or organization

Parameters

roles: list(or(GrantAccessControlWithUsernameValidator, GrantAccessControlWithIdValidator)): Array of GrantAccessControl objects, up to maximum 100 objects.

Return type: None

classmethod from_data(data)¶

Instantiate an object of this class using a dict.

Parameters

datadict: Correctly snake_cased keys and their values.

Return type: TypeVar(T, bound= APIObject)

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

datadict: The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrsiterable: List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: TypeVar(T, bound= APIObject)

open_in_browser()¶

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.

Return type: None

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

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

Deployment service stats information.

Attributes

model_idstr: the model used to retrieve service stats metrics
perioddict: the time period used to retrieve service stats metrics
metricsdict: the service stats metrics

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

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

New in version v2.18.

Parameters

deployment_idstr: the id of the deployment
model_idstr, optional: the id of the model
start_timedatetime, optional: start of the time period
end_timedatetime, optional: end of the time period
execution_time_quantilefloat, optional: quantile for executionTime, defaults to 0.5
response_time_quantilefloat, optional: quantile for responseTime, defaults to 0.5
slow_requests_thresholdfloat, optional: threshold for slowRequests, defaults to 1000

Returns

service_statsServiceStats: the queried service stats metrics

Return type: ServiceStats

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

Deployment service stats over time information.

Attributes

model_idstr: the model used to retrieve accuracy metric
metricstr: the service stat metric being retrieved
bucketsdict: how the service stat metric changes over time
summarydict: summary for the service stat metric

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

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

New in version v2.18.

Parameters

deployment_idstr: the id of the deployment
metricSERVICE_STAT_METRIC, optional: the service stat metric to retrieve
model_idstr, optional: the id of the model
start_timedatetime, optional: start of the time period
end_timedatetime, optional: end of the time period
bucket_sizestr, optional: time duration of a bucket, in ISO 8601 time duration format
quantilefloat, optional: quantile for ‘executionTime’ or ‘responseTime’, ignored when querying other metrics
thresholdint, optional: threshold for ‘slowQueries’, ignored