updated jupyter notebook

src/BagOfWords.py

@@ -68,7 +68,7 @@ class BagOfWords:
         returns list of lists of all extracted words, one row per article
         '''
         extracted_words = []
-        print('# extracting all words from articles...')
+        print('# BOW: extracting all words from articles...')
         print()
         for text in corpus:
             extracted_words.append(BagOfWords.extract_words(text, stemming))
@@ -88,8 +88,7 @@ class BagOfWords:
 
         # total number of words in bag of words
         word_count = 0
-        print('# counting number of features in corpus...')
+
-        print()
         for list in extracted_words:
             word_count += len(list)
 
@@ -102,7 +101,7 @@ class BagOfWords:
         array = np.zeros(shape=(n_articles, l_vocab))
         df_matrix = pd.DataFrame(array, columns=vocab)
 
-        print('# calculating frequencies...')
+        print('# BOW: calculating frequencies...')
         print()
 
         # for every text in series
@@ -142,7 +141,7 @@ class BagOfWords:
             for word in e_list:
                 # add every single word to vocabulary
                 vocab.add(word)
-        print('# vocabulary consists of {} features.'.format(len(vocab)))
+        print('# BOW: vocabulary consists of {} features.'.format(len(vocab)))
         print()
         # transform set to list
         return list(vocab)
@@ -259,7 +258,7 @@ class BagOfWords:
     def count_features(texts, stemming=True):
         ''' count total number of features in textual corpus
         '''
-        print('# counting all features in corpus...')
+        print('# BOW: counting all features in corpus...')
         print()
         vocab = BagOfWords.make_vocab(texts, stemming)
         return len(vocab)

src/MNBInteractive.py

@@ -23,7 +23,7 @@ class MNBInteractive:
         # chose BagOfWords implementation (own if false)
         sklearn_cv = False
 
-        print('# starting multinomial naives bayes...')
+        print('# MNB: starting multinomial naives bayes...')
         print()
 
         # split labeled data into text and label set
@@ -40,7 +40,7 @@ class MNBInteractive:
 
         # fit_prior=False: a uniform prior will be used instead
         # of learning class prior probabilities
-        classifier = MultinomialNB(alpha=0.5,
+        classifier = MultinomialNB(alpha=1.0e-10,
                                    fit_prior=False,
                                    class_prior=None)
 
@@ -69,10 +69,14 @@ class MNBInteractive:
             vocab = BagOfWords.make_vocab(extracted_words)
 
             # fit the training data and then return the matrix
+            print('# MNB: fit training data and calculate matrix...')
+            print()
             training_data = BagOfWords.make_matrix(extracted_words,
                             vocab, rel_freq, stemming)
 
             # transform testing data and return the matrix
+            print('# MNB: transform testing data to matrix...')
+            print()
             extracted_words = BagOfWords.extract_all_words(U)
             testing_data = BagOfWords.make_matrix(extracted_words,
                             vocab, rel_freq, stemming)
@@ -85,7 +89,12 @@ class MNBInteractive:
 
         # number of samples encountered for each class during fitting
         # this value is weighted by the sample weight when provided
-        class_count = classifier.class_count_
+        # class_count = classifier.class_count_
+
+        # classes in order used
+        classes = classifier.classes_
+
+        print('# MNB: ending multinomial naive bayes')
 
         # return classes and vector of class estimates
-        return class_count, class_probs
+        return classes, class_probs

src/NER.py

@@ -156,6 +156,27 @@ class NER:
         # print(max(dict_com, key=dict_com.get))
         return list(dict_com.values())
 
+    def make_article_orgs_dict(texts):
+        '''param: list of all article texts
+        returns: dict of article indices with company names in it
+        '''
+        print('# searching company names...')
+        print()
+
+        # dict of article indices with company names in it
+        dict_art_orgs = {}
+
+        for i, text in enumerate(texts):
+            # list of found companies in article
+            print('# article no. {}:'.format(i))
+            dict_art_orgs[i] = NER.find_companies(text)
+
+        # save coms_list
+        with open('../obj/'+ 'dict_articles_organizations' + '.pkl', 'wb') as f:
+            pickle.dump(dict_art_orgs, f, pickle.HIGHEST_PROTOCOL)
+
+        print(dict_art_orgs)
+
     def show_most_common_companies(n_commons=50):
         # load pickle object
         with open('../obj/dict_organizations.pkl', 'rb') as input:
@@ -189,6 +210,6 @@ if __name__ == '__main__':
                      quotechar='\'')
     #print(df)
     texts = df[1] + '. ' + df[2]
-    NER.count_companies(texts)
+    NER.make_article_orgs_dict(texts)
     # NER.show_most_common_companies()
     # print(NER.tag_words('On Monday, Github and Microsoft announced their merger.'))