# Module `miplearn.classifiers.evaluator`

Expand source code

    #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
    #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
    #  Released under the modified BSD license. See COPYING.md for more details.

    from sklearn.metrics import roc_auc_score

    class ClassifierEvaluator:
        def __init__(self) -> None:
            pass

        def evaluate(self, clf, x_train, y_train):
            # FIXME: use cross-validation
            proba = clf.predict_proba(x_train)
            return roc_auc_score(y_train, proba[:, 1])

## Classes

`class ClassifierEvaluator`
  Expand source code

      class ClassifierEvaluator:
          def __init__(self) -> None:
              pass

          def evaluate(self, clf, x_train, y_train):
              # FIXME: use cross-validation
              proba = clf.predict_proba(x_train)
              return roc_auc_score(y_train, proba[:, 1])

  ### Methods

  `def evaluate(self, clf, x_train, y_train)`
    Expand source code

        def evaluate(self, clf, x_train, y_train):
            # FIXME: use cross-validation
            proba = clf.predict_proba(x_train)
            return roc_auc_score(y_train, proba[:, 1])