refactoring

DecisionTree.py

@@ -111,7 +111,7 @@ class DecisionTree:
         print('# starting decision tree')
         print('# ...')
 
-        file = 'classification_labelled_corrected.csv'
+        file = 'data\\classification_labelled_corrected.csv'
 
         # read csv file
         print('# reading dataset')

FileHandler.py

@@ -1,8 +1,8 @@
 '''
-Json Handler
+File Handler
 ============
 
-JsonHandler reads articles from JSON files,
+FileHandler reads articles from JSON files,
 extracts relevant information and
 writes it to a csv file.
 '''
@@ -16,9 +16,9 @@ import json
 import numpy as np
 import pandas as pd
 
-class JsonHandler:
+class FileHandler:
 
-    # string for every month of the year
+    # strings for every month of the year
     months = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10',
               '11', '12']
 
@@ -28,7 +28,6 @@ class JsonHandler:
         n number of items to select randomly,
         return new DataFrame with only selected items.
         '''
-
         # initialize random => reproducible sequence
         np.random.seed(5)
         # add new column 'Random'
@@ -39,29 +38,44 @@ class JsonHandler:
         return df.iloc[0:n]
 
     def create_labeling_dataset():
-        # number of articles to select from each month:
-        # 10.000 / 12 = 833,33
+        # output file
+        o_file = 'data\\interactive_labeling_dataset.csv'
+        # create file and write header
+        with open(o_file, 'w', newline='') as csvfile:
+            writer = csv.writer(csvfile, 
+                                delimiter='|',
+                                quotechar='\'', 
+                                quoting=csv.QUOTE_NONNUMERIC)
+            writer.writerow(['Uuid',        #0
+                             'Title',       #1
+                             'Text',        #2
+                             'Site',        #3
+                             'SiteSection', #4
+                             'Url',         #5
+                             'Timestamp'])  #6
+        # number of articles to select from each month (10000/12=833,33)
         n_select = 833
-        # except every third month:
-        every_third_month = ['03', '06', '09', '12']
-        for m in JsonHandler.month:
-            df = pandas.read_csv('all_{}.csv'.format(m),
-                                  delimiter='|',
-                                  header=0,
-                                  index_col=None,
-                                  engine='python',
-                                  quotechar='\'',
-                                  quoting=0,
-                                  encoding='utf-8')
+        for m in FileHandler.months:
+            df = pd.read_csv('data\\articles\\all_{}.csv'.format(m),
+                             delimiter='|',
+                             header=0,
+                             index_col=None,
+                             engine='python',
+                             quoting=csv.QUOTE_NONNUMERIC,
+                             quotechar='\'')
             # pick one more from every third article
-            if m in every_third_month:
+            if m in ['03', '06', '09', '12']:
                 n_select = 834
-            JsonHandler.select_randoms(df, n_select).to_csv('labeling_dataset.csv', 
-                                                            header=True, 
-                                                            mode='a', 
-                                                            encoding='python', 
-                                                            quoting=QUOTE_MINIMAL, 
-                                                            quotechar='\'')
+            random_articles = FileHandler.select_randoms(df, n_select)
+            del random_articles['Random']
+            random_articles.to_csv(o_file,
+                                   header=False,
+                                   index=False,
+                                   sep='|',
+                                   mode='a',
+                                   encoding='utf-8',
+                                   quoting=csv.QUOTE_NONNUMERIC,
+                                   quotechar='\'')
 
     def write_articles_to_csv_files():
         '''read JSON files, select articles and write them to csv.
@@ -69,14 +83,14 @@ class JsonHandler:
         # reliable sources (site_sections)
         site_sections = []
         # read list from 'sections.txt' file
-        with open('sections.txt', 'r') as s_list:
+        with open('data\\sections.txt', 'r') as s_list:
             site_sections = s_list.read().split('\n')
 
         # article counter
         a = 0
-        for m in JsonHandler.months:
+        for m in FileHandler.months:
             # 1 output file per month
-            output_file = 'all_{}.csv'.format(m)
+            output_file = 'data\\articles\\all_{}.csv'.format(m)
             # path of input JSON files per month
             path = 'C:\\Users\\anne.lorenz\\Bachelorarbeit\\01_Datensatz'\
                    '\\new_dataset\\2017_{}_ccc517fd45024a87c12318299efc50a4'\
@@ -146,7 +160,8 @@ class JsonHandler:
             print('#')
         print('# saved {} articles in total'.format(a))
         print('#')
+    def join_all_csv_files():
 
 if __name__ == '__main__':
-    JsonHandler.write_articles_to_csv_files()
-    #JsonHandler.create_labeling_dataset()
+    # FileHandler.write_articles_to_csv_files()
+    # FileHandler.create_labeling_dataset()

NER.py

@@ -5,15 +5,15 @@ Named Entity Recognition (NER)
 Stanford NER takes a text as input and returns a list of entities
 like persons, organizations and countries, e.g.
 '''
+import csv
 import os
 
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 from nltk.tag import StanfordNERTagger
 from nltk.tokenize import word_tokenize
 
-from CsvHandler import CsvHandler
-
 class NER:
 
     # toDo: complete lists:
@@ -118,8 +118,13 @@ class NER:
 
 if __name__ == '__main__':
 
-    filepath = 'classification_labelled_corrected.csv'
-    df = CsvHandler.read_csv(filepath)
+    filepath = 'data\\classification_labelled_corrected.csv'
+    df = pd.read_csv(filepath,
+                     sep='|',
+                     engine='python',
+                     decimal='.',
+                     quotechar='\'',
+                     quoting=csv.QUOTE_NONE)
 
     # only articles with label==1
     df_hits = df[df['Label'] == 1]

NaiveBayes.py

@@ -177,7 +177,7 @@ class NaiveBayes:
         print('# starting naive bayes')
         print('# ...')
 
-        file = 'classification_labelled_corrected.csv'
+        file = 'data\\classification_labelled_corrected.csv'
 
         # read csv file
         print('# reading dataset')

NaiveBayes_Interactive.py

@@ -153,7 +153,7 @@ class NaiveBayes_Interactive:
         print('# starting naive bayes')
         print('# ...')
 
-        file = 'classification_labelled_corrected.csv'
+        file = 'data\\classification_labelled_corrected.csv'
 
         # read csv file
         print('# reading dataset')

SVM.py

@@ -91,7 +91,7 @@ class SVM:
         print('# starting svm')
         print('# ...')
 
-        file = 'classification_labelled_corrected.csv'
+        file = 'data\\classification_labelled_corrected.csv'
 
         # read csv file
         print('# reading dataset')