nannyml.sampling_error.multiclass_classification module
- nannyml.sampling_error.multiclass_classification.accuracy_sampling_error(sampling_error_components: Tuple, data) float [source]
Calculate the accuracy sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.multiclass_classification.accuracy_sampling_error_components(y_true_reference: List[Series], y_pred_reference: List[Series])[source]
Calculate sampling error components for accuracy using reference data.
The
y_true_reference
andy_pred_proba_reference
lists represent the binarized target values and model probabilities. The order of the Series in both lists should both match the list of class labels present.- Parameters:
y_true_reference (List[pd.Series]) – Target values for the reference dataset.
y_pred_reference (List[pd.Series]) – Prediction values for the reference dataset.
- Returns:
sampling_error_components
- Return type:
Tuple
- nannyml.sampling_error.multiclass_classification.auroc_sampling_error(sampling_error_components, data) float [source]
Calculate the AUROC sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.multiclass_classification.auroc_sampling_error_components(y_true_reference: List[Series], y_pred_proba_reference: List[Series])[source]
Calculate sampling error components for AUROC using reference data.
The
y_true_reference
andy_pred_proba_reference
lists represent the binarized target values and model probabilities. The order of the Series in both lists should both match the list of class labels present.- Parameters:
y_true_reference (List[pd.Series]) – Target values for the reference dataset.
y_pred_proba_reference (List[pd.Series]) – Prediction probability values for the reference dataset.
- Returns:
sampling_error_components
- Return type:
List[Tuple]
- nannyml.sampling_error.multiclass_classification.f1_sampling_error(sampling_error_components: List[Tuple], data) float [source]
Calculate the F1 sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.multiclass_classification.f1_sampling_error_components(y_true_reference: List[Series], y_pred_reference: List[Series])[source]
Calculate sampling error components for F1 using reference data.
The
y_true_reference
andy_pred_proba_reference
lists represent the binarized target values and model probabilities. The order of the Series in both lists should both match the list of class labels present.- Parameters:
y_true_reference (List[pd.Series]) – Target values for the reference dataset.
y_pred_reference (List[pd.Series]) – Prediction values for the reference dataset.
- Returns:
sampling_error_components
- Return type:
List[Tuple]
- nannyml.sampling_error.multiclass_classification.multiclass_confusion_matrix_sampling_error(sampling_error_components: Tuple, data)[source]
- nannyml.sampling_error.multiclass_classification.multiclass_confusion_matrix_sampling_error_components(y_true_reference: List[Series], y_pred_reference: List[Series], normalize_confusion_matrix: Optional[str])[source]
- nannyml.sampling_error.multiclass_classification.precision_sampling_error(sampling_error_components: List[Tuple], data) float [source]
Calculate the precision sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.multiclass_classification.precision_sampling_error_components(y_true_reference: List[Series], y_pred_reference: List[Series])[source]
Calculate sampling error components for precision using reference data.
The
y_true_reference
andy_pred_proba_reference
lists represent the binarized target values and model probabilities. The order of the Series in both lists should both match the list of class labels present.- Parameters:
y_true_reference (List[pd.Series]) – Target values for the reference dataset.
y_pred_reference (List[pd.Series]) – Prediction values for the reference dataset.
- Returns:
sampling_error_components
- Return type:
List[Tuple]
- nannyml.sampling_error.multiclass_classification.recall_sampling_error(sampling_error_components: List[Tuple], data) float [source]
Calculate the recall sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.multiclass_classification.recall_sampling_error_components(y_true_reference: List[Series], y_pred_reference: List[Series])[source]
Calculate sampling error components for recall using reference data.
The
y_true_reference
andy_pred_proba_reference
lists represent the binarized target values and model probabilities. The order of the Series in both lists should both match the list of class labels present.- Parameters:
y_true_reference (List[pd.Series]) – Target values for the reference dataset.
y_pred_reference (List[pd.Series]) – Prediction values for the reference dataset.
- Returns:
sampling_error_components
- Return type:
List[Tuple]
- nannyml.sampling_error.multiclass_classification.specificity_sampling_error(sampling_error_components: List[Tuple], data) float [source]
Calculate the specificity sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.multiclass_classification.specificity_sampling_error_components(y_true_reference: List[Series], y_pred_reference: List[Series])[source]
Calculate sampling error components for specificity using reference data.
The
y_true_reference
andy_pred_proba_reference
lists represent the binarized target values and model probabilities. The order of the Series in both lists should both match the list of class labels present.- Parameters:
y_true_reference (List[pd.Series]) – Target values for the reference dataset.
y_pred_reference (List[pd.Series]) – Prediction values for the reference dataset.
- Returns:
sampling_error_components
- Return type:
List[Tuple]