Added new class 'BaseSklearnWrapper'

gensim/sklearn_integration/base_sklearn_wrapper.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz>
+# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
+#
+"""
+Scikit learn interface for gensim for easy use of gensim with scikit-learn
+follows on scikit learn API conventions
+"""
+from abc import ABCMeta, abstractmethod
+
+
+class BaseSklearnWrapper(object):
+    """
+    Base sklearn wrapper module
+    """
+    __metaclass__ = ABCMeta
+
+    @abstractmethod
+    def get_params(self, deep=True):
+        pass
+
+    @abstractmethod
+    def set_params(self, **parameters):
+        """
+        Set all parameters.
+        """
+        for parameter, value in parameters.items():
+            setattr(self, parameter, value)
+        return self
+
+    @abstractmethod
+    def fit(self, X, y=None):
+        pass
+
+    @abstractmethod
+    def transform(self, docs, minimum_probability=None):
+        pass
+
+    @abstractmethod
+    def partial_fit(self, X):
+        pass

gensim/sklearn_integration/sklearn_wrapper_gensim_ldamodel.py

@@ -12,11 +12,12 @@
 
 from gensim import models
 from gensim import matutils
+from gensim.sklearn_integration import base_sklearn_wrapper
 from scipy import sparse
 from sklearn.base import TransformerMixin, BaseEstimator
 
 
-class SklearnWrapperLdaModel(models.LdaModel, TransformerMixin, BaseEstimator):
+class SklearnWrapperLdaModel(models.LdaModel, base_sklearn_wrapper.BaseSklearnWrapper, TransformerMixin, BaseEstimator):
     """
     Base LDA module
     """
@@ -70,11 +71,9 @@ def set_params(self, **parameters):
         """
         Set all parameters.
         """
-        for parameter, value in parameters.items():
-            self.parameter = value
-        return self
+        super(SklearnWrapperLdaModel, self).set_params(**parameters)
 
-    def fit(self, X,  y=None):
+    def fit(self, X, y=None):
         """
         For fitting corpus into the class object.
         Calls gensim.model.LdaModel:

gensim/sklearn_integration/sklearn_wrapper_gensim_lsimodel.py

@@ -12,10 +12,12 @@
 
 from gensim import models
 from gensim import matutils
+from gensim.sklearn_integration import base_sklearn_wrapper
 from scipy import sparse
 from sklearn.base import TransformerMixin, BaseEstimator
 
-class SklearnWrapperLsiModel(models.LsiModel, TransformerMixin, BaseEstimator):
+
+class SklearnWrapperLsiModel(models.LsiModel, base_sklearn_wrapper.BaseSklearnWrapper, TransformerMixin, BaseEstimator):
     """
     Base LSI module
     """
@@ -51,11 +53,9 @@ def set_params(self, **parameters):
         """
         Set all parameters.
         """
-        for parameter, value in parameters.items():
-            self.parameter = value
-        return self
+        super(SklearnWrapperLsiModel, self).set_params(**parameters)
 
-    def fit(self, X,  y=None):
+    def fit(self, X, y=None):
         """
         For fitting corpus into the class object.
         Calls gensim.model.LsiModel:

gensim/test/test_sklearn_integration.py

@@ -114,6 +114,19 @@ def testPipeline(self):
         score = text_lda.score(corpus, data.target)
         self.assertGreater(score, 0.40)
 
+    def testSetGetParams(self):
+        # updating only one param
+        self.model.set_params(num_topics=3)
+        model_params = self.model.get_params()
+        self.assertEqual(model_params["num_topics"], 3)
+
+        # updating multiple params
+        param_dict = {"eval_every": 20, "decay": 0.7}
+        self.model.set_params(**param_dict)
+        model_params = self.model.get_params()
+        for key in param_dict.keys():
+            self.assertEqual(model_params[key], param_dict[key])
+
 
 class TestSklearnLSIWrapper(unittest.TestCase):
     def setUp(self):
@@ -165,5 +178,19 @@ def testPipeline(self):
         score = text_lda.score(corpus, data.target)
         self.assertGreater(score, 0.50)
 
+    def testSetGetParams(self):
+        # updating only one param
+        self.model.set_params(num_topics=3)
+        model_params = self.model.get_params()
+        self.assertEqual(model_params["num_topics"], 3)
+
+        # updating multiple params
+        param_dict = {"chunksize": 10000, "decay": 0.9}
+        self.model.set_params(**param_dict)
+        model_params = self.model.get_params()
+        for key in param_dict.keys():
+            self.assertEqual(model_params[key], param_dict[key])
+
+
 if __name__ == '__main__':
     unittest.main()