mlens.metrics package

Module contents

ML-ENSEMBLE

author:Sebastian Flennerhag
copyright:2017
license:MIT
mlens.metrics.rmse(y, p)[source]

Root Mean Square Error.

\[RMSE(\mathbf{y}, \mathbf{p}) = \sqrt{MSE(\mathbf{y}, \mathbf{p})},\]

with

\[MSE(\mathbf{y}, \mathbf{p}) = |S| \sum_{i \in S} (y_i - p_i)^2\]
Parameters:
  • y (array-like of shape [n_samples, ]) – ground truth.
  • p (array-like of shape [n_samples, ]) – predicted labels.
Returns:

z – root mean squared error.

Return type:

float

mlens.metrics.mape(y, p)[source]

Mean Average Percentage Error.

\[MAPE(\mathbf{y}, \mathbf{p}) = |S| \sum_{i \in S} | \frac{y_i - p_i}{y_i} |\]
Parameters:
  • y (array-like of shape [n_samples, ]) – ground truth.
  • p (array-like of shape [n_samples, ]) – predicted labels.
Returns:

z – mean average percentage error.

Return type:

float

mlens.metrics.wape(y, p)[source]

Weighted Mean Average Percentage Error.

\[WAPE(\mathbf{y}, \mathbf{p}) = \frac{\sum_{i \in S} | y_i - p_i|}{ \sum_{i \in S} |y_i|}\]
Parameters:
  • y (array-like of shape [n_samples, ]) – ground truth.
  • p (array-like of shape [n_samples, ]) – predicted labels.
Returns:

z – weighted mean average percentage error.

Return type:

float

mlens.metrics.make_scorer(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs)[source]

Make a scorer from a performance metric or loss function.

This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. It takes a score function, such as accuracy_score, mean_squared_error, adjusted_rand_index or average_precision and returns a callable that scores an estimator’s output.

Read more in the User Guide.

Parameters:
  • score_func (callable,) – Score function (or loss function) with signature score_func(y, y_pred, **kwargs).
  • greater_is_better (boolean, default=True) – Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the score_func.
  • needs_proba (boolean, default=False) – Whether score_func requires predict_proba to get probability estimates out of a classifier.
  • needs_threshold (boolean, default=False) –

    Whether score_func takes a continuous decision certainty. This only works for binary classification using estimators that have either a decision_function or predict_proba method.

    For example average_precision or the area under the roc curve can not be computed using discrete predictions alone.

  • **kwargs (additional arguments) – Additional parameters to be passed to score_func.
Returns:

scorer – Callable object that returns a scalar score; greater is better.

Return type:

callable

Examples

>>> from sklearn.metrics import fbeta_score, make_scorer
>>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
>>> ftwo_scorer
make_scorer(fbeta_score, beta=2)
>>> from sklearn.model_selection import GridSearchCV
>>> from sklearn.svm import LinearSVC
>>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]},
...                     scoring=ftwo_scorer)