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.
Added in version v2.25.
Notes
Recordcontains: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:
- 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:
- get_latest_explanations()
Retrieve latest predictions along with shap explanations for the most anomalous records.
- Returns:
- AnomalyAssessmentExplanations
- Return type:
- 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:
- 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:
- 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.
Added in version v2.25.
Notes
AnomalyAssessmentExplanationscontains: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.
DataPointcontains: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.
ShapleyFeatureContributioncontains: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:
- 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.
Added in version v2.25.
Notes
AnomalyAssessmentPredictionsPreviewcontains: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.
PreviewBincontains: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:
- find_anomalous_regions(max_prediction_threshold=0.0)
- Return type:
- 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