sklearn API

scorer.scorer.sklearn_api.score_metrics(y_true, y_pred, metrics=['accuracy_score'])

Scikit-learn compatibility API for Scorer usage. Evaluate the required score-metric using the Scorer object.

Parameters

• y_true (array-like) – List of true labels

• y_pred (array-like) – List of predicted labels

• metrics (str or array-like) – List of metric-names to evaluate

Returns

metrics – The required metrics

Return type

float or array-like

Example

>>> from scorer import sklearn_api
>>>
>>> y_true = ['a', 'b', 'a', 'a', 'b', 'c', 'c', 'a', 'a', 'b', 'c', 'a']
>>> y_pred = ['b', 'b', 'a', 'c', 'b', 'a', 'c', 'b', 'a', 'b', 'a', 'a']
>>>
>>> metrics = sklearn_api.score_metrics(y_true, y_pred, metrics='accuracy_score')

Or you can use the scorer metrics inside a sklearn pipeline like

Example

>>> from scorer import sklearn_api
>>> from sklearn.svm import SVC
>>> from sklearn.metrics import make_scorer
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.datasets import load_iris
>>>
>>> X, y = load_iris(return_X_y=True)
>>> clf = SVC(kernel='linear', C=1.)
>>> my_scorer = make_scorer(sklearn_api.score_metrics, metrics='accuracy_score')
>>>
>>>  scores = cross_val_score(clf,  # classifier
>>>                           X,  # training data
>>>                           y,  # training labels
>>>                           cv=5,  # split data randomly into 10 parts: 9 for training, 1 for scoring
>>>                           scoring=my_scorer,  # which scoring metric?
>>>                          )