def format_review(raw_data):
formatted_data = []
for review in raw_data:
formatted_review = []
tokenized_review = sent_tokenize(review)
tokenized_review = list(flatten(tokenized_review))
tokenized_review[0] = tokenized_review[0].partition(" ")[2]
for sentence in tokenized_review:
formatted_review.append(sentence.split())
formatted_data.append(formatted_review)
debug()
return formatted_data
def main():
input_dictionary = {}
try:
input_reviews = open(sys.argv[1])
except:
print("Please supply an input file to be classified as the first argument and try again")
try:
input_dictionary = pickle.load(open(sys.argv[2], 'rb'))
except:
print("Please supply a pickled data set as the second argument and try again ")
data_dictionary = input_dictionary["freq"]
prior_dictionary = input_dictionary["prior"]
TCount = 0
FCount = 0
correct = 0
incorrect = 0
test_list = list(flatten((('T',)*20,('F',)*20)))
debug()
for review in format_review(input_reviews):
intermediate = classify(data_dictionary, prior_dictionary, review)
probable_class = (max(intermediate, key=lambda k: (intermediate[k])))
if probable_class == 'final_T':
TCount += 1
else:
FCount += 1
if probable_class[6:] == test_list.pop(0):
correct += 1
else:
incorrect += 1
print(TCount)
print(FCount)
print(TCount/(TCount + FCount))
print(FCount/(TCount + FCount))
if TCount > FCount:
print('T')
else:
print('F')
print(correct/(correct + incorrect))
def main():
global input_files
global output_file
processed_dictionary = {}
output_dictionary = {}
prior_counts = {}
#Populate input_files
for i in range(1, len(sys.argv)-1): input_files.append(open(sys.argv[i]))
#Populate output_file
output_file = open(sys.argv[len(sys.argv)-1], 'wb')
process_counts(input_files[0], 'T', processed_dictionary, prior_counts)
debug()
process_counts(input_files[1], 'F', processed_dictionary, prior_counts)
debug()
output_dictionary["freq"] = processed_dictionary
output_dictionary["prior"] = prior_counts
pickle.dump(output_dictionary, output_file)
def main():
global input_files
global output_file
processed_dictionary = {}
output_dictionary = {}
prior_counts = {}
input_corpi = []
corpus = {}
corpus["text"] = []
corpus["id"] = []
corpus["class"] = []
# Populate input_files
for i in range(1, len(sys.argv) - 2):
input_files.append(open(sys.argv[i]))
debug()
test_file = open(sys.argv[len(sys.argv) - 2])
for i, corpus_file in enumerate(input_files):
input_corpi.append([line for line in corpus_file])
test_set = {}
test_set["text"] = []
test_set["id"] = []
for line in test_file:
split_line = line.split()
for k, word in enumerate(split_line):
if k is 0:
continue
split_line[k] = stem_word(word)
line = " ".join(x for x in split_line)
review_id = line[0:7]
test_set["text"].append(line[8:])
test_set["id"].append(review_id)
for i, corpus_file in enumerate(input_corpi):
current_class = input_files[i].name[0]
for line in corpus_file:
split_line = line.split()
for k, word in enumerate(split_line):
if k is 0:
continue
split_line[k] = stem_word(word)
line = " ".join(x for x in split_line)
review_id = line[0:7]
corpus["text"].append(line[8:])
corpus["id"].append(review_id)
corpus["class"].append(current_class)
selector = SelectKBest(chi2, k=100)
training_data = vectorize_for_training(corpus["text"])
test_data = vectorize_for_testing(test_set["text"])
other_training_data = selector.fit_transform(training_data, corpus["class"])
filtered_test_data = selector.transform(test_data)
linear_svm = svm.LinearSVC()
linear_svm.fit(other_training_data, corpus["class"])
predictions = linear_svm.predict(filtered_test_data)
print(predictions)
t_count = 0
f_count = 0
for c in predictions:
if c == "T":
t_count += 1
else:
f_count += 1
print("True Count: " + str(t_count))
print("False Count: " + str(f_count))
# Populate output_file
output_file = open(sys.argv[len(sys.argv) - 1], "w")
for i, prediction in enumerate(predictions):
output_file.write(prediction + "\t" + test_set["id"][i])
