ibrahimsharaf#14 added doc2vec overriden abstract class methods

models/doc2vec_builder.py

@@ -12,3 +12,42 @@ class doc2VecBuilder(ModelBuilder):
 
     def __init__(self, corpus):
         self.corpus = corpus
+
+
+    def initialize_model(self):
+        logging.info("Building Doc2Vec vocabulary")
+        self.model = doc2vec.Doc2Vec(min_count=1,  # Ignores all words with total frequency lower than this
+                              window=10,
+                              # The maximum distance between the current and predicted word within a sentence
+                              vector_size=300,  # Dimensionality of the generated feature vectors
+                              workers=5,  # Number of worker threads to train the model
+                              alpha=0.025,  # The initial learning rate
+                              min_alpha=0.00025,  # Learning rate will linearly drop to min_alpha as training progresses
+                              dm=1)  # dm defines the training algorithm. If dm=1 means 'distributed memory' (PV-DM)
+        # and dm =0 means 'distributed bag of words' (PV-DBOW)
+        self.model.build_vocab(self.corpus)
+
+    def train_model(self):
+        logging.info("Training Doc2Vec model")
+        # 10 epochs take around 10 minutes on my machine (i7), if you have more time/computational power make it 20
+        for epoch in range(10):
+            logging.info('Training iteration #{0}'.format(epoch))
+            self.model.train(self.corpus, total_examples=self.model.corpus_count, epochs=self.model.epochs)
+            # shuffle the corpus
+            random.shuffle(self.corpus)
+            # decrease the learning rate
+            self.model.alpha -= 0.0002
+            # fix the learning rate, no decay
+            self.model.min_alpha = self.model.alpha
+
+    def save_model(self):
+        logging.info("Saving trained Doc2Vec model")
+        self.model.save("./classifiers/d2v.model")
+
+    def load_model(self):
+        logging.info("Loading trained Doc2Vec model")
+        d2v = Doc2Vec.load("./classifiers/d2v.model")
+        self.model = d2v
+        return d2v
+
+