codes and documentation for stacking cleaned up

Author: stephenhky

docs/tutorial_stacking.rst

@@ -77,6 +77,11 @@ offered in this package. To load them, initialize it in the same way:
 >>> stacker2 = shorttext.stack.LogisticStackedGeneralization(intermediate_classifiers={'clstm': clstm_classifier, 'lda128': lda128_svm_classifier})
 >>> stacker2.load_compact_model('/path/to/logitmodel.bin')
 
+
+.. automodule:: shorttext.stack.stacking
+   :members:
+
+
 Reference
 ---------
 

shorttext/stack/stacking.py

@@ -1,4 +1,5 @@
 import pickle
+from abc import ABC, abstractmethod
 
 import numpy as np
 from keras.layers import Dense, Reshape
@@ -11,7 +12,7 @@
 
 
 # abstract class
-class StackedGeneralization:
+class StackedGeneralization(ABC):
     """
     This is an abstract class for any stacked generalization method. It is an intermediate model
     that takes the results of other classifiers as the input features, and perform another classification.
@@ -136,6 +137,7 @@ def convert_traindata_matrix(self, classdict, tobucket=True):
                 X = self.translate_shorttext_intfeature_matrix(shorttext)
                 yield X, y
 
+    @abstractmethod
     def train(self, classdict, *args, **kwargs):
         """ Train the stacked generalization.
 
@@ -152,6 +154,7 @@ def train(self, classdict, *args, **kwargs):
         """
         raise e.NotImplementedException()
 
+    @abstractmethod
     def score(self, shorttext, *args, **kwargs):
         """ Calculate the scores for each class labels.