def make_corpus(target_folder):
print(target_folder)
corpus_name = target_folder + '.txt'
f_wr = open(os.path.join(CONFIG.DATA_PATH, 'corpus', corpus_name),
'w',
encoding='utf-8')
text_path = os.path.join(CONFIG.DATA_PATH, target_folder)
text_folder_list = os.listdir(text_path)
count = 0
# languages_dic = dict()
for text_folder in text_folder_list:
# print("folder: ", text_folder)
text_files = os.listdir(os.path.join(text_path, text_folder))
for text_file in text_files:
if text_file.endswith(
'.txt') and not text_file.endswith('_location.txt'):
if count % 100 == 0:
print(count)
with open(os.path.join(text_path, text_folder, text_file),
'r',
encoding='utf-8',
newline='\n') as f:
# print("file: ", text_file)
data = f.read()
line = process_text(data)
if len(line) > 0:
f_wr.write(line + ' <EOS> <PAD>\n')
count = count + 1
f_wr.close()
# csv_name = target_folder + '_meta.csv'
# with open(os.path.join(CONFIG.CSV_PATH, csv_name), 'w', encoding='utf-8-sig', newline='') as f:
# w = csv.writer(f)
# for k,v in languages_dic.items():
# w.writerow((k, v))
print("completed to make corpus")
def crawler(key, info_tuple, max=5):
'''
max: maxmium trails allowed (suggest: 5)
'''
queue = [key]
path = []
i = 1
dic = {}
while len(queue) > 0 and i <= max:
key = queue[0]
queue.remove(key)
path.append(key)
web = util.retrieve_web(key, info_tuple)
hashtag = util.process_hashtag(web)
text = util.process_text(web)
queue.extend([
x for x in set(hashtag)
if x != key and x not in queue and x not in path
])
dic.update({key: [hashtag, text]})
i += 1
return dic
def get_expenses_for_rows(df, stor_exp_data_path, stor_data_path, budg_path,
bankconfig):
"""
Gets the expense data for stores, prompting the user when multiple expenses exist for a store
params:
df - pandas dataframe
stor_exp_data_path - filepath to expensesDB
"""
print(
"\nIterating your transactions. If you want to quit halfway, type ctrl c to save!\n"
)
# initialize the objects for tracking changes
exp_stor_db = data_help.read_jsonFile(stor_exp_data_path)
stor_db = data_help.read_jsonFile(stor_data_path)
budg_db = data_help.read_jsonFile(budg_path)
try:
for idx, row in df.iterrows():
# iterate through only the data which has no expenses declared.
if pd.isnull(row[env.EXPENSE]):
# get relevant expenses for that month set by the user.
month_end_date = util.get_month_from_timestamp(row[env.DATE],
start=False)
if type(row[env.BANK_STORENAME]) is str:
match = bankconfig.regex_str.search(
row[env.BANK_STORENAME])
if match:
processed_text = util.process_text(match.group(0))
print(
f"Was able to filter - {row[env.BANK_STORENAME]} -> {processed_text}"
)
storename = processed_text
else:
print(f"Unable to filter - {row[env.BANK_STORENAME]}")
storename = row[env.BANK_STORENAME]
else: # default case use empty str
print("No storename exists for this transaction.")
storename = ""
print(
"Curr Transaction: %-10s | %-10s | %-10s | %-10s " %
(row[env.DATE], row[env.AMOUNT], storename, row[env.TYPE]))
selected_exp, exp_stor_db, stor_db, storename = search_store_relationships(
storename, exp_stor_db, budg_db[month_end_date],
stor_exp_data_path, stor_db, stor_data_path)
df.at[idx, env.FILT_STORENAME] = storename
df.at[idx, env.EXPENSE] = selected_exp
except KeyboardInterrupt:
print(
"\n\nQuitting to main menu. Your data inputs will be saved, and you can resume where you left off by restarting and selecting 'v' for view data!\n"
)
return df
def process_dataset_text(target_dataset):
dataset_path = os.path.join(CONFIG.DATASET_PATH, target_dataset)
if not os.path.exists(dataset_path):
os.mkdir(dataset_path)
df_data = pd.read_csv(os.path.join(CONFIG.TARGET_PATH, 'posts.csv'), encoding='utf-8-sig')
print("tokenizing sentences...")
pbar = tqdm(total=df_data.shape[0])
shortcode_list = []
word_list_list = []
for index, in_row in df_data.iterrows():
pbar.update(1)
if pd.isna(in_row.iloc[2]):
continue
word_list = process_text(in_row.iloc[2])
if len(word_list) > 0:
shortcode_list.append(in_row.iloc[1])
word_list_list.append(word_list)
pbar.close()
print("counting frequencies...")
frequency = {}
pbar = tqdm(total=len(word_list_list))
for word_list in word_list_list:
pbar.update(1)
for word in word_list:
count = frequency.get(word, 0)
frequency[word] = count + 1
pbar.close()
print("convert too few words to UNK token...")
pbar = tqdm(total=len(word_list_list))
processed_word_list_list = []
for word_list in word_list_list:
pbar.update(1)
processed_word_list = []
for word in word_list:
if frequency[word] < CONFIG.MIN_WORD_COUNT:
processed_word_list.append('UNK')
else:
processed_word_list.append(word)
processed_word_list_list.append(processed_word_list)
pbar.close()
print("making corpus and csv files...")
f_csv = open(os.path.join(dataset_path, 'posts.csv'), 'w', encoding='utf-8-sig')
f_corpus = open(os.path.join(dataset_path, 'corpus.txt'), 'w', encoding='utf-8')
wr = csv.writer(f_csv)
pbar = tqdm(total=len(processed_word_list_list))
for index in range(len(processed_word_list_list)):
pbar.update(1)
sentence = ' '.join(processed_word_list_list[index])
if len(sentence) > 0:
out_row = []
out_row.append(shortcode_list[index])
out_row.append(sentence + ' <EOS>')
wr.writerow(out_row)
f_corpus.write(sentence + ' <EOS>\n')
pbar.close()
f_csv.close()
f_corpus.close()
te_d = c.fetchall()
c.execute("SELECT * FROM validation")
va_d = c.fetchall()
tr_x = [d[0] for d in tr_d]
tr_y_ = [d[2] for d in tr_d]
te_x = [d[0] for d in te_d]
te_y_ = [d[2] for d in te_d]
va_x = [d[0] for d in va_d]
va_y_ = [d[2] for d in va_d]
print("Done.\n")
if CLEAN_TEXT:
print("Cleaning data\n----------")
ptr_x = [process_text(x, stop_words) for x in tr_x]
pte_x = [process_text(x, stop_words) for x in te_x]
pva_x = [process_text(x, stop_words) for x in va_x]
print("Done.\n")
else:
ptr_x = tr_x
pte_x = te_x
pva_x = va_x
# Transform text to document-term matrix
print("Creating document-term matrix\n---------")
cv = CountVectorizer(min_df=2)
cv = cv.fit(ptr_x)
tr_dtmat = cv.transform(ptr_x)
if TF_IDF:
tfidf = TfidfTransformer()
def copy_selected_post(target_folder):
from util import process_text
path_to_posts = {}
data_path = os.path.join(CONFIG.DATA_PATH, target_folder)
for directory in os.listdir(data_path):
path_dir = os.path.join(data_path, directory)
path_to_posts[directory] = []
for file in os.listdir(path_dir):
if file.endswith('UTC.txt'):
path_to_posts[directory].append(file)
print("Total # of locations: ", len(path_to_posts))
data_path = os.path.join(CONFIG.DATA_PATH, target_folder)
dataset_path = os.path.join(CONFIG.DATASET_PATH, target_folder)
if not os.path.exists(dataset_path):
os.mkdir(dataset_path)
count = 0
for directory, posts in path_to_posts.items():
print(str(count), "th Location directory: ", directory)
path_dir = os.path.join(data_path, directory)
for file in os.listdir(path_dir):
if file.endswith('location.txt'):
os.remove(os.path.join(path_dir, file))
continue
if not file.endswith('.jpg') and not file.endswith(
'.txt') and not file.endswith('.json'):
os.remove(os.path.join(path_dir, file))
continue
for post in tqdm(posts):
post_name = post.replace(".txt", "")
post_dic = {"img": [], "text": "", "json": ""}
for file in os.listdir(path_dir):
if file.startswith(post_name):
if file.endswith('.jpg'):
post_dic['img'].append(file)
elif file.endswith('.json'):
post_dic['json'] = file
elif file.endswith(
'.txt') and not file.endswith('location.txt'):
post_dic['text'] = file
else:
pass
if len(post_dic["img"]
) > 0 and post_dic["text"] != "" and post_dic["json"] != "":
with open(os.path.join(path_dir, post_dic["text"]),
'r',
encoding='utf-8',
newline='\n') as f:
# print("file: ", text_file)
data = f.read()
line = process_text(data)
if len(line) > 0:
path_to_location = os.path.join(
dataset_path, directory)
if not os.path.exists(path_to_location):
os.mkdir(path_to_location)
path_to_post = os.path.join(dataset_path, directory,
post_name)
if not os.path.exists(path_to_post):
os.mkdir(path_to_post)
shutil.move(os.path.join(path_dir, post_dic["json"]),
os.path.join(path_to_post, "meta.json"))
os.mkdir(os.path.join(path_to_post, "images"))
for idx, img in enumerate(post_dic["img"]):
img_name = "image_" + str(idx) + ".jpg"
shutil.move(
os.path.join(path_dir, img),
os.path.join(path_to_post, "images", img_name))
f_wr = open(os.path.join(path_to_post, "text.txt"),
'w',
encoding='utf-8')
f_wr.write(line + ' <EOS>\n')
f_wr.close()
f.close()
shutil.rmtree(path_dir)
count = count + 1
print("Copy completed")
def test(target_dataset):
# toy_path = os.path.join(CONFIG.DATASET_PATH, 'instagram0830')
# full_data = []
# full_data_norm = []
# for image_path in os.listdir(os.path.join(toy_path, 'resnext101_32x8d')):
# with open(os.path.join(toy_path, 'resnext101_32x8d', image_path), "rb") as f:
# image_data = cPickle.load(f)
# # print(data)
# # print(np.max(data))
# # print(np.min(data))
# # print(np.mean(data))
# # print(data.shape)
# full_data.append(image_data)
# image_data_norm = np.linalg.norm(image_data, axis=1, ord=2)
# full_data_norm.append(image_data_norm)
# #df_data = pd.read_csv(os.path.join(CONFIG.DATASET_PATH, target_dataset, 'posts.csv'), header=None, encoding='utf-8')
# #print(df_data)
# full_data = np.array(full_data, dtype=np.float32)
# full_data_norm = np.array(full_data_norm, dtype=np.float32)
# temp = np.mean(np.mean(full_data, axis=2), axis=1)
# print(temp.shape)
# print("mean: ", np.mean(np.mean(full_data, axis=2), axis=1))
# print("std: ", np.mean(np.std(full_data, axis=2), axis=1))
# print("max: ", np.mean(np.max(full_data, axis=2), axis=1))
# print("min: ", np.mean(np.min(full_data, axis=2), axis=1))
# print("norm: ", full_data_norm)
dataset_path = os.path.join(CONFIG.DATASET_PATH, target_dataset)
if not os.path.exists(dataset_path):
os.mkdir(dataset_path)
with open(os.path.join('./data', 'pickle', 'hotel_reviews.p'), 'rb') as f:
dataset = cPickle.load(f, encoding="latin1")
f.close()
print("tokenizing sentences...")
shortcode_list = []
word_list_list = []
pbar = tqdm(total=len(dataset[0]))
for pg in dataset[0]:
pbar.update(1)
data = " ".join([dataset[3][idx] for idx in pg])
data = data.replace("END_TOKEN", "")
word_list = process_text(data)
if len(word_list) > 0:
word_list_list.append(word_list)
pbar.close()
pbar = tqdm(total=len(dataset[1]))
for pg in dataset[1]:
pbar.update(1)
data = " ".join([dataset[3][idx] for idx in pg])
data = data.replace("END_TOKEN", "")
word_list = process_text(data)
if len(word_list) > 0:
word_list_list.append(word_list)
pbar.close()
print("making corpus and csv files...")
f_csv = open(os.path.join(dataset_path, 'posts.csv'), 'w', encoding='utf-8')
f_corpus = open(os.path.join(dataset_path, 'corpus.txt'), 'w', encoding='utf-8')
wr = csv.writer(f_csv)
pbar = tqdm(total=len(word_list_list))
for word_list in word_list_list:
pbar.update(1)
sentence = ' '.join(word_list) + ' <EOS>'
out_row = []
out_row.append('asd')
out_row.append(sentence)
wr.writerow(out_row)
f_corpus.write(sentence + '\n')
pbar.close()
f_csv.close()
f_corpus.close()
for start, stop in pairwise(path):
track = graph.edge[start][stop]['track']
playlist.append((track['name'], track['artists'][0]['name'],
track['href']))
return playlist
# TODO turn this thing into a webapp :)
def _print_playlist(playlist):
for track in playlist:
print "{!s:<20} by {!s:<30} {!s:<30}".format(*track)
# TODO improve text conditioning
def spoetify(text):
words = input_string.split()
graph = _build_graph(words)
playlist = _build_playlist(graph, words)
if not playlist:
raise SystemExit
_print_playlist(playlist)
if __name__ == '__main__':
import fileinput
input_parts = []
for line in fileinput.input():
input_parts.append(process_text(line))
input_string = u' '.join(input_parts)
spoetify(input_string)
