def make_prediction(classifier, vectorizer, test_file_name, out_file_name):
# Read the test data
test = pd.read_csv(test_file_name, header=0, delimiter="\t", quoting=3)
clean_test_reviews = clean_reviews(test)
# Get a bag of words for the test set, and convert to a numpy array
test_data_features = vectorizer.transform(clean_test_reviews)
test_data_features = test_data_features.toarray()
# Use the random forest to make sentiment label predictions
result = classifier.predict(test_data_features)
# Copy the results to a pandas dataframe with an "id" column and
# a "sentiment" column
output = pd.DataFrame(data={"id": test["id"], "sentiment": result})
# Use pandas to write the comma-separated output file
output.to_csv(out_file_name, index=False, quoting=3)
# Use the random forest to make sentiment label predictions
result = classifier.predict(test_data_features)
# Copy the results to a pandas dataframe with an "id" column and
# a "sentiment" column
output = pd.DataFrame(data={"id": test["id"], "sentiment": result})
# Use pandas to write the comma-separated output file
output.to_csv(out_file_name, index=False, quoting=3)
train = pd.read_csv("F:\Data Mining\word2vec-nlp-tutorial\Data\labeledTrainData.tsv", header=0, delimiter="\t",
quoting=3)
clean_train_reviews = clean_reviews(train)
count_vectorizer = TfidfVectorizer(analyzer="word",
tokenizer=None,
preprocessor=None,
stop_words=None,
max_features=5000)
train_data_features = count_vectorizer.fit_transform(clean_train_reviews)
# Numpy arrays are easy to work with, so convert the result to an array
train_data_features = train_data_features.toarray()
# vocab = count_vectorizer.get_feature_names()
# show_word_statistic(vocab, train_data_features)
