def tf_idf_vect_feature_vector():
token_array = text_processed()
training_token_array, test_token_array = split_string_2_data_array(
token_array, 0.8)
# print("token: ", token_array)
vectorizer = TfidfVectorizer(stop_words='english', analyzer="word")
# print(vectorizer)
vec = vectorizer.fit(training_token_array)
vec_matrix = vectorizer.transform(training_token_array)
# data_frame = pd.DataFrame(matrix.toarray(), columns=vectorizer.get_feature_names())
# print(data_frame)
return (test_token_array, vec, vec_matrix)
def compute_dissimalrity_matrix():
token_array = text_processed()
vectorizer = TfidfVectorizer(stop_words='english', analyzer="word")
td_if = vectorizer.fit_transform(token_array)
x = td_if.toarray()
y = vectorizer.get_feature_names()
print(x)
print(y)
matrix = euclidean_distances(td_if)
# print(matrix)
return matrix
def tf_idf_vect_feature_vector():
df = text_processed()
vectorizer = TfidfVectorizer()
vec_train = vectorizer.fit_transform(df.Tweets)
df['tweets_vec'] = list(vec_train.toarray())
#df.to_csv('tfidf_vector.csv')
train, test = train_test_split(df, test_size=0.2)
print(vectorizer.get_feature_names())
print(df)
return test, train, df
def tf_idf_trans_feature_vector():
token_array = text_processed()
training_token_array, test_token_array = split_string_2_data_array(
token_array, 0.8)
print(token_array)
vectorizer = TfidfTransformer(stop_words='english', analyzer="word")
# tokenize and build vocab
X = vectorizer.fit_transform(token_array)
analyze = vectorizer.build_analyzer()
print(analyze("subject is not the case"))
# summarize
print(vectorizer.get_feature_names())
# summarize encoded vector
print(X.toarray())
return X
def count_vectorizer_feature_vector():
token_array = text_processed()
training_token_array, test_token_array = split_string_2_data_array(
token_array, 0.8)
vectorizer = CountVectorizer(encoding='utf-8',
analyzer='word',
stop_words='english',
binary='false',
min_df=0.01)
# tokenize and build vocab
vec = vectorizer.fit(training_token_array)
vec_matrix = vectorizer.fit_transform(training_token_array)
# print(vectorizer.get_feature_names())
f_vector = vectorizer.transform(training_token_array)
# print(f_vector.shape)
# print(f_vector.toarray())
return (test_token_array, vec, vec_matrix)
def word2vec_feature_vector():
token_array = text_processed()
print(token_array)
model = models.Word2Vec(token_array, min_count=1)
print(model)
return
