xenonpy.model.utils package

Submodules

xenonpy.model.utils.metrics module

xenonpy.model.utils.metrics.classification_metrics(y_true, y_pred, *, average=('weighted', 'micro', 'macro'), labels=None)

Calculate most common classification scores. See also: https://scikit-learn.org/stable/modules/model_evaluation.html

Parameters:

• y_true (Union[ndarray, DataFrame, Series]) – True results.

• y_pred (Union[ndarray, Series]) – Predicted results.

• average (Union[None, List[str], Tuple[str]]) –

  This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:

  binary:

  Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.

  micro:

  Calculate metrics globally by counting the total true positives, false negatives and false positives.

  macro:

  Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

  weighted:

  Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters macro to account for label imbalance; it can result in an F-score that is not between precision and recall.

• labels – The set of labels to include when average != binary, and their order if average is None. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order.

Returns:

An collections.OrderedDict contains classification scores. These scores will always contains accuracy, f1, precision and recall. For multilabel targets, based on the selection of the average parameter, the weighted, micro, and macro scores of f1`, ``precision, and recall will be calculated.

Return type:

OrderedDict

xenonpy.model.utils.metrics.regression_metrics(y_true, y_pred)

Calculate most common regression scores. See Also: https://scikit-learn.org/stable/modules/model_evaluation.html

Parameters:

• y_true (Union[ndarray, Series]) – True results.

• y_pred (Union[ndarray, Series]) – Predicted results.

Returns:

An collections.OrderedDict contains regression scores. These scores will be calculated: mae, mse, rmse, r2, pearsonr, spearmanr, p_value, and max_ae

Return type:

OrderedDict

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