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score

method score(X, y, metric=None, sample_weights=None, pipeline=None, verbose=None) [source]

Transform new data through all transformers in the current branch and return a metric score. If called from a trainer, the best model in the pipeline (under the winner attribute) is used. If called from a model, that model is used.

Parameters:

X: dict, list, tuple, np.ndarray or pd.DataFrame
Feature set with shape=(n_samples, n_features).

y: int, str or sequence
  • If int: Position of the target column in X.
  • If str: Name of the target column in X.
  • Else: Target column with shape=(n_samples,).

metric: str, func, scorer or None, optional (default=None)
Metric to calculate. Choose from any of sklearn's SCORERS, a function with signature metric(y_true, y_pred) or a scorer object. If None, it returns mean accuracy for classification tasks and r2 for regression tasks.

sample_weights: sequence or None, optional (default=None)
Sample weights corresponding to y.

pipeline: bool, sequence or None, optional (default=None)
Transformers to use on the data before predicting.
  • If None: Only transformers that are applied on the whole dataset are used.
  • If False: Don't use any transformers.
  • If True: Use all transformers in the pipeline.
  • If sequence: Transformers to use, selected by their index in the pipeline.

verbose: int or None, optional (default=None)
Verbosity level of the output. If None, it uses the transformer's own verbosity.

Returns: score: np.float64
Metric score of X with respect to y.

Note

If the metric parameter is left to its default value, the method outputs the same value as sklearn's score method for an estimator.

Info

This method is intended to calculate metric scores on new data. To get the metric results on the train or test set, use the scoring method.


Example

from atom import ATOMClassifier

atom = ATOMClassifier(X, y)
atom.run(["MNB", "KNN", "kSVM"], metric="precision")

# Get the mean accuracy on new data
predictions = atom.mnb.score(X_new, y_new)
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