Digitally encode labels using Sklearn's Labelencoder

 fromSklearn.preprocessingImportLabelencoderdefGen_label_encoder (): Labels= ['BB','CC'] Le=Labelencoder () le.fit (labels)Print 'Le.classes_', Le.classes_ forLabelinchLe.classes_:Printlabel, Le.transform ([Label]) [0] Joblib.dump (LE,'Data/label_encoder.h5')

Description of Labelencoder:

1 classLabelencoder (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 Attributes7     ----------8 Classes_: Array of shape (N_class,)9 holds the label for each class.Ten  One Examples A     -------- - ' Labelencoder ' can is used to normalize labels. -  the >>> from Sklearn import preprocessing - >>> le = preprocessing. Labelencoder () - >>> Le.fit ([1, 2, 2, 6]) - Labelencoder () + >>> Le.classes_ - Array ([1, 2, 6]) + >>> Le.transform ([1, 1, 2, 6]) #doctest: +ellipsis A Array ([0, 0, 1, 2] ...) at >>> Le.inverse_transform ([0, 0, 1, 2]) - Array ([1, 1, 2, 6]) -  - It can also is used to transform non-numerical labels (as long as they is - hashable and comparable) to numerical labels. -  in >>> le = preprocessing. Labelencoder () - >>> Le.fit (["Paris", "Paris", "Tokyo", "Amsterdam"]) to Labelencoder () + >>> List (le.classes_) - [' Amsterdam ', ' Paris ', ' Tokyo '] the >>> Le.transform (["Tokyo", "Tokyo", "Paris"]) #doctest: +ellipsis * Array ([2, 2, 1] ...) $ >>> List (Le.inverse_transform ([2, 2, 1]))Panax Notoginseng [' Tokyo ', ' Tokyo ', ' Paris '] -  the See also +     -------- A sklearn.preprocessing.OneHotEncoder:encode categorical integer features the using a One-hot aka One-of-k scheme. +     """

Digitally encode labels using Sklearn's Labelencoder