7e9c3b7fe153b5f533f94aabaa1e35a6c58a5540,tpot/base.py,TPOTBase,fit,#TPOTBase#Any#Any#Any#,371

Before Change

        try:
            clf = clf.fit(features, classes)
        except Exception:
            raise ValueError("Error: Input data is not in a valid format. "
                             "Please confirm that the input data is scikit-learn compatible. "
                             "For example, the features must be a 2-D array and target labels "
                             "must be a 1-D array.")

        // Set the seed for the GP run
        if self.random_state is not None:
            random.seed(self.random_state)  // deap uses random

After Change

        features = features.astype(np.float64)

        if self._contains_nan(features):
            features = Imputer(strategy="most_frequent").fit_transform(features)

        self._check_dataset(features, classes)

        // Set the seed for the GP run

Instances