Feature extraction and vectorizer three sentences

@@ -58,15 +58,16 @@ if __name__ == "__main__":
     print("Reading documents...")
     documents = []
     # Read documents from input path
-    for path, dirs, files in os.walk(args.outputPath):
+    for path, dirs, files in os.walk(args.inputPath):
         for file in files:
             with open(os.path.join(args.inputPath, file), mode="r", encoding="utf-8") as iFile:
                 print("...{}".format(file))
                 # Add file to document list
                 documents.append(iFile.read())
+    print("  Documents: {}".format(len(documents)))
 
     # Create vectorizer
-    print('Vectorizer: {}'.format(args.vectorizer))
+    print('  Vectorizer: {}'.format(args.vectorizer))
     if args.vectorizer == "b":
         # Binary vectorizer
         vectorizer = CountVectorizer(ngram_range=(1, 1), binary=True)
@@ -78,7 +79,7 @@ if __name__ == "__main__":
         vectorizer = TfidfVectorizer(ngram_range=(1, 1))
 
     matrix = csr_matrix(vectorizer.fit_transform(documents), dtype='double')
-    print('     matrix.shape: ', matrix.shape)
+    print('   matrix.shape: ', matrix.shape)
 
     with open(os.path.join(args.outputPath, "report-vectorizer.{}.txt".format(args.vectorizer)), encoding="utf-8", mode="w") as oFile:
         oFile.write("Vectorizer: {}".format(args.vectorizer))