nannyml.performance_estimation.confidence_based.metrics module

class nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationAUROC(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationAccuracy(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationF1(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationPrecision(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationRecall(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationSpecificity(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.Metric(display_name: str, column_name: str, estimator: AbstractEstimator)

Bases: ABC

A performance metric used to calculate realized model performance.

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

__eq__(other)

Establishes equality by comparing all properties.

estimate(data: DataFrame)

Calculates performance metrics on data.

Parameters:

data (pd.DataFrame) – The data to estimate performance metrics for. Requires presence of either the predicted labels or prediction scores/probabilities (depending on the metric to be calculated).

fit(reference_data: DataFrame)

Fits a Metric on reference data.

Parameters:

reference_data (pd.DataFrame) – The reference data used for fitting. Must have target data available.

abstract realized_performance(data: DataFrame) → float

sampling_error(data: DataFrame)

Calculates the sampling error with respect to the reference data for a given chunk of data.

Parameters:

data (pd.DataFrame) – The data to calculate the sampling error on, with respect to the reference data.

Returns:

sampling_error – The expected sampling error.

Return type:

float

class nannyml.performance_estimation.confidence_based.metrics.MetricFactory

Bases: object

A factory class that produces Metric instances based on a given magic string or a metric specification.

classmethod create(key: str, use_case: ProblemType, kwargs: Optional[Dict[str, Any]] = None) → Metric

classmethod register(metric: str, use_case: ProblemType) → Callable

registry: Dict[str, Dict[ProblemType, Metric]] = {'accuracy': {ProblemType.CLASSIFICATION_BINARY: <class 'nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationAccuracy'>, ProblemType.CLASSIFICATION_MULTICLASS: <class 'nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationAccuracy'>}, 'f1': {ProblemType.CLASSIFICATION_BINARY: <class 'nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationF1'>, ProblemType.CLASSIFICATION_MULTICLASS: <class 'nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationF1'>}, 'precision': {ProblemType.CLASSIFICATION_BINARY: <class 'nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationPrecision'>, ProblemType.CLASSIFICATION_MULTICLASS: <class 'nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationPrecision'>}, 'recall': {ProblemType.CLASSIFICATION_BINARY: <class 'nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationRecall'>, ProblemType.CLASSIFICATION_MULTICLASS: <class 'nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationRecall'>}, 'roc_auc': {ProblemType.CLASSIFICATION_BINARY: <class 'nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationAUROC'>, ProblemType.CLASSIFICATION_MULTICLASS: <class 'nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationAUROC'>}, 'specificity': {ProblemType.CLASSIFICATION_BINARY: <class 'nannyml.performance_estimation.confidence_based.metrics.BinaryClassificationSpecificity'>, ProblemType.CLASSIFICATION_MULTICLASS: <class 'nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationSpecificity'>}}

class nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationAUROC(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationAccuracy(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationF1(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationPrecision(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationRecall(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

class nannyml.performance_estimation.confidence_based.metrics.MulticlassClassificationSpecificity(estimator)

Bases: Metric

Creates a new Metric instance.

Parameters:

• display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

• column_name (str) – The name used to indicate the metric in columns of a DataFrame.

realized_performance(data: DataFrame) → float

nannyml.performance_estimation.confidence_based.metrics.estimate_f1(y_pred: DataFrame, y_pred_proba: DataFrame) → float

nannyml.performance_estimation.confidence_based.metrics.estimate_precision(y_pred: DataFrame, y_pred_proba: DataFrame) → float

nannyml.performance_estimation.confidence_based.metrics.estimate_recall(y_pred: DataFrame, y_pred_proba: DataFrame) → float

nannyml.performance_estimation.confidence_based.metrics.estimate_roc_auc(y_pred_proba: Series) → float

nannyml.performance_estimation.confidence_based.metrics.estimate_specificity(y_pred: DataFrame, y_pred_proba: DataFrame) → float