test_classifier.py 文件源码

def test_predict_from_file():
    from microtc.wrappers import ClassifierWrapper
    from microtc.textmodel import TextModel
    from microtc.utils import read_data_labels
    from sklearn.preprocessing import LabelEncoder

    import os
    fname = os.path.dirname(__file__) + '/text.json'
    corpus, labels = read_data_labels(fname)
    t = TextModel(corpus)
    le = LabelEncoder()
    le.fit(labels)
    y = le.transform(labels)
    c = ClassifierWrapper()
    X = [t[x] for x in corpus]
    c.fit(X, y)
    hy = le.inverse_transform(c.predict(X))
    for i in hy:
        assert i in ['POS', 'NEU', 'NEG']