nannyml.performance_estimation.confidence_based.cbpe module¶
Implementation of the CBPE estimator.
- class nannyml.performance_estimation.confidence_based.cbpe.CBPE(metrics: str | List[str], y_pred: str, y_pred_proba: str | Dict[str, str], y_true: str, problem_type: str | ProblemType, timestamp_column_name: str | None = None, chunk_size: int | None = None, chunk_number: int | None = None, chunk_period: str | None = None, chunker: Chunker | None = None, calibration: str | None = None, calibrator: Calibrator | None = None)[source]¶
Bases:
AbstractEstimator
Performance estimator using the Confidence Based Performance Estimation (CBPE) technique.
Initializes a new CBPE performance estimator.
- Parameters:
y_true (str) – The name of the column containing target values (that are provided in reference data during fitting).
y_pred_proba (ModelOutputsType) – Name(s) of the column(s) containing your model output. Pass a single string when there is only a single model output column, e.g. in binary classification cases. Pass a dictionary when working with multiple output columns, e.g. in multiclass classification cases. The dictionary maps a class/label string to the column name containing model outputs for that class/label.
y_pred (str) – The name of the column containing your model predictions.
timestamp_column_name (str, default=None) – The name of the column containing the timestamp of the model prediction.
metrics (Union[str, List[str]]) – A metric or list of metrics to calculate.
chunk_size (int, default=None) – Splits the data into chunks containing chunks_size observations. Only one of chunk_size, chunk_number or chunk_period should be given.
chunk_number (int, default=None) – Splits the data into chunk_number pieces. Only one of chunk_size, chunk_number or chunk_period should be given.
chunk_period (str, default=None) – Splits the data according to the given period. Only one of chunk_size, chunk_number or chunk_period should be given.
chunker (Chunker, default=None) – The Chunker used to split the data sets into a lists of chunks.
calibration (str, default='isotonic') – Determines which calibration will be applied to the model predictions. Defaults to
isotonic
, currently the only supported value.calibrator (Calibrator, default=None) – A specific instance of a Calibrator to be applied to the model predictions. If not set NannyML will use the value of the
calibration
variable instead.problem_type (Union[str, ProblemType]) – Determines which CBPE implementation to use. Allowed problem type values are ‘classification_binary’ and ‘classification_multiclass’.
Examples
>>> import nannyml as nml >>> from IPython.display import display >>> reference_df = nml.load_synthetic_binary_classification_dataset()[0] >>> analysis_df = nml.load_synthetic_binary_classification_dataset()[1] >>> display(reference_df.head(3)) >>> estimator = nml.CBPE( ... y_pred_proba='y_pred_proba', ... y_pred='y_pred', ... y_true='work_home_actual', ... timestamp_column_name='timestamp', ... metrics=['roc_auc', 'f1'], ... chunk_size=5000, ... problem_type='classification_binary', >>> ) >>> estimator.fit(reference_df) >>> results = estimator.estimate(analysis_df) >>> display(results.data) >>> for metric in estimator.metrics: ... metric_fig = results.plot(kind='performance', metric=metric) ... metric_fig.show() >>> for metric in estimator.metrics: ... metric_fig = results.plot(kind='performance', plot_reference=True, metric=metric) ... metric_fig.show()