nannyml.performance_calculation.calculator module

Calculates realized performance metrics when target data is available.

class nannyml.performance_calculation.calculator.PerformanceCalculator(metrics: Union[str, List[str]], y_true: str, y_pred: str, problem_type: Union[str, nannyml._typing.ProblemType], y_pred_proba: Optional[Union[str, Dict[str, str]]] = None, timestamp_column_name: Optional[str] = None, thresholds: Optional[Dict[str, nannyml.thresholds.Threshold]] = None, chunk_size: Optional[int] = None, chunk_number: Optional[int] = None, chunk_period: Optional[str] = None, chunker: Optional[nannyml.chunk.Chunker] = None)[source]

Bases: nannyml.base.AbstractCalculator

Calculates realized performance metrics when target data is available.

Creates a new performance calculator.

Parameters

  • y_true (str) – The name of the column containing target values.

  • 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.

  • thresholds (dict, default={ 'roc_auc': StandardDeviationThreshold(), 'f1': StandardDeviationThreshold(), 'precision': StandardDeviationThreshold(), 'recall': StandardDeviationThreshold(), 'specificity': StandardDeviationThreshold(), 'accuracy': StandardDeviationThreshold(), 'mae': StandardDeviationThreshold(), 'mape': StandardDeviationThreshold(), 'mse': StandardDeviationThreshold(), 'msle': StandardDeviationThreshold(), 'rmse': StandardDeviationThreshold(), 'rmsle': StandardDeviationThreshold(), }) –

    A dictionary allowing users to set a custom threshold for each method. It links a Threshold subclass to a method name. This dictionary is optional. When a dictionary is given its values will override the default values. If no dictionary is given a default will be applied. The default method thresholds are as follows:

    • roc_auc: StandardDeviationThreshold()

    • f1: StandardDeviationThreshold()

    • precision: StandardDeviationThreshold()

    • recall: StandardDeviationThreshold()

    • specificity: StandardDeviationThreshold()

    • accuracy: StandardDeviationThreshold()

    • mae: StandardDeviationThreshold()

    • mape: StandardDeviationThreshold()

    • mse: StandardDeviationThreshold()

    • msle: StandardDeviationThreshold()

    • rmse: StandardDeviationThreshold()

    • rmsle: StandardDeviationThreshold()

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]
>>> analysis_target_df = nml.load_synthetic_binary_classification_dataset()[2]
>>> analysis_df = analysis_df.merge(analysis_target_df, on='identifier')
>>> display(reference_df.head(3))
>>> calc = nml.PerformanceCalculator(
...     y_pred_proba='y_pred_proba',
...     y_pred='y_pred',
...     y_true='work_home_actual',
...     timestamp_column_name='timestamp',
...     problem_type='classification_binary',
...     metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'],
...     chunk_size=5000)
>>> calc.fit(reference_df)
>>> results = calc.calculate(analysis_df)
>>> display(results.data)
>>> display(results.calculator.previous_reference_results)
>>> for metric in calc.metrics:
...     figure = results.plot(kind='performance', plot_reference=True, metric=metric)
...     figure.show()