evaluate_print

imbens.utils.evaluate_print(y_true, y_pred, head: str = '', eval_metrics: dict = {'balanced Acc': (<function balanced_accuracy_score>, {}), 'macro Fscore': (<function f1_score>, {'average': 'macro'}), 'macro Gmean': (<function geometric_mean_score>, {'average': 'macro'})}, print_str: bool = True, return_str: bool = False)

Evaluate and print the predictive performance with respect to the given metrics.

Returns a string of evaluation results.

Parameters:

y_true1d array-like, or label indicator array / sparse matrix

Ground truth (correct) target values.

y_pred1d array-like, or label indicator array / sparse matrix

Estimated targets as returned by a classifier.

headstring, default=””

Head of the returned string, for example, the name of the predictor.

eval_metricsdict, default=None

Metric(s) used for evaluation during the ensemble training process.

• If None

  use 3 default metrics.

  • 'balanced Acc': sklearn.metrics.balanced_accuracy_score()

  • 'macro F1': sklearn.metrics.f1_score(average='macro')

  • 'macro Gmean': imbens.metrics.geometric_mean_score(average='macro')

• If dict, { metric_name: (metric_func, func_kwargs) }

  the keys should be strings corresponding to evaluation metrics’ names (str). The values should be tuple corresponding to the metric function (callable) and additional kwargs (dict).

  • metric_name: (str), metric name

    String that specify the name of the metric.

  • metric_func: (callable), metric function

    It should at least take 2 named/keyword arguments, y_true and one of [y_pred, y_score], and returns a float as the evaluation score. Keyword arguments:

    • y_true

      1d-array of shape (n_samples,), true labels or binary label indicators corresponds to ground truth (correct) labels.

    • y_pred

      input will be 1d-array of shape (n_samples,) corresponds to predicted labels, as returned by a classifier.

    • y_score

      input will be 2d-array of shape (n_samples, n_classes,) corresponds to probability estimates provided by the predict_proba method. In addition, the order of the class scores must correspond to the order of labels, if provided in the metric function, or else to the numerical or lexicographical order of the labels in y_true.

  • func_kwargs: (dict), additional keyword arguments

    The metric additional kwargs should be a dictionary that specifies the additional arguments that need to be passed into the metric function.

print_strbool, defaul=True

Whether to print the results to stdout. If False, disable print.

return_strbool, defaul=False

Whether to return the result string. If True, returns it.

Returns:

result_strstring or NoneType