84c6230896e6fee31ecca27fa9fe4a4306b89b60,skll/data.py,,write_feature_file,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,1094

Before Change

    // Check for valid features
    if feat_vectorizer is None and features and not isinstance(features[0],
                                                               dict):
        raise ValueError("If `feat_vectorizer` is unspecified, you must pass " +
                         "a list of dicts for `features`.")

    // Convert features to list of dicts if given an array-like and a vectorizer
    if feat_vectorizer is not None:
        features = feat_vectorizer.inverse_transform(features)

After Change

    logger.debug("Feature vectorizer: %s", feat_vectorizer)
    logger.debug("Features: %s", features)

    is_libsvm = os.path.splitext(path)[1].lower() == ".libsvm"

    // Check for valid features
    if isinstance(features, np.ndarray):
        if feat_vectorizer is None:
            raise ValueError("If `feat_vectorizer` is unspecified, you must "
                             "pass a list of dicts for `features`.")
        // Convert features to list of dicts if given an array-like & vectorizer
        else:
            features = feat_vectorizer.inverse_transform(features)
    // Create feature vectorizer if unspecified and writing libsvm
    elif is_libsvm:
        feat_vectorizer = DictVectorizer(sparse=True)
        feat_vectorizer.fit(features)
    // Create label vectorizer if writing libsvm

Instances