利用sklearn的LabelEncoder對標籤進行數字化編碼

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from sklearn.preprocessing import LabelEncoderdef gen_label_encoder():    labels = [‘BB‘, ‘CC‘]      le = LabelEncoder()    le.fit(labels)    print ‘le.classes_‘, le.classes_    for label in le.classes_:        print label, le.transform([label])[0]    joblib.dump(le, ‘data/label_encoder.h5‘)

 

LabelEncoder的說明：

 1 class LabelEncoder(BaseEstimator, TransformerMixin): 2     """Encode labels with value between 0 and n_classes-1. 3  4     Read more in the :ref:`User Guide <preprocessing_targets>`. 5  6     Attributes 7     ---------- 8     classes_ : array of shape (n_class,) 9         Holds the label for each class.10 11     Examples12     --------13     `LabelEncoder` can be used to normalize labels.14 15     >>> from sklearn import preprocessing16     >>> le = preprocessing.LabelEncoder()17     >>> le.fit([1, 2, 2, 6])18     LabelEncoder()19     >>> le.classes_20     array([1, 2, 6])21     >>> le.transform([1, 1, 2, 6]) #doctest: +ELLIPSIS22     array([0, 0, 1, 2]...)23     >>> le.inverse_transform([0, 0, 1, 2])24     array([1, 1, 2, 6])25 26     It can also be used to transform non-numerical labels (as long as they are27     hashable and comparable) to numerical labels.28 29     >>> le = preprocessing.LabelEncoder()30     >>> le.fit(["paris", "paris", "tokyo", "amsterdam"])31     LabelEncoder()32     >>> list(le.classes_)33     [‘amsterdam‘, ‘paris‘, ‘tokyo‘]34     >>> le.transform(["tokyo", "tokyo", "paris"]) #doctest: +ELLIPSIS35     array([2, 2, 1]...)36     >>> list(le.inverse_transform([2, 2, 1]))37     [‘tokyo‘, ‘tokyo‘, ‘paris‘]38 39     See also40     --------41     sklearn.preprocessing.OneHotEncoder : encode categorical integer features42         using a one-hot aka one-of-K scheme.43     """

 

利用sklearn的LabelEncoder對標籤進行數字化編碼