def main():
data = process_data.read_file(file)
print("%%%%%%%%% BEGIN RUN " + str(len(data)) + " %%%%%%%%% " + file)
if False:
print_tw(data)
data = process_data.preprocess_data(data)
print("%%%%%%%%% Pre-precessing completed %%%%%%%%% ")
out = {}
ideas = {
'winners': {}, # List of candidate winner names
'awards': {}, # List of candidate award names
'links': {}, # List of candidate winner=award pairing
'xx': {}
}
award_names = [
'best motion picture', 'best director', 'best actor', 'best actress',
'best supporting actor', 'best supporting actress', 'best screenplay',
'best original score', 'best original song', 'cecil b demille'
]
for award_name in award_names:
ideas['winners'][award_name] = {}
for tw in data:
# eval_tw(tw, ideas)
guess_award_names(tw, ideas)
ideas['winners'] = guess_winners(tw, ideas, award_names)
answers = {}
answers['awards'] = []
answers['winners'] = []
award_candidates = print_votes(ideas['awards'], detailed=False)
for i in range(1, 11):
answers['awards'].append(award_candidates[-i])
for award in award_names:
candidates = print_votes(ideas['winners'][award], detailed=False)
if len(candidates) < 1:
answers['winners'].append("None Found")
else:
answers['winners'].append(candidates[-1])
output_readable(answers, award_names)
import sys
sys.path.append('./')
import process_data
import reformat_data
import find_traffic_speed
from datetime import datetime
from pathlib import Path
from helper.global_var import SAVE_TYPE_PICKLE
from helper.graph_reader import graph_reader
if __name__ == '__main__':
date_str = datetime.today().strftime('%Y%m%d')
# date_str = "20220131"
data_root = Path(".") / 'data'
process_data.preprocess_data(date_str, overwrite=True, min_file_size=10)
reformat_data.reformat_by_bus(date_str)
reformat_data.sort_reformat_data(date_str)
save_filename_list = [
"final_node_table", "final_way_table", "final_relation_table"
]
map_dates = graph_reader(Path("graph"), SAVE_TYPE_PICKLE,
save_filename_list)
final_node_table = map_dates[0]
final_way_table = map_dates[1]
final_relation_table = map_dates[2]
time_slot_intervals = [5, 15]
for interval in time_slot_intervals:
print("Processing interval:", interval)
find_traffic_speed.find_traffic_speed(date_str,
import json
import nltk
import re
import sys
import process_data
file = sys.argv[1]
data = process_data.read_file(file)
data = process_data.preprocess_data(data)
print("%%%%%%%%% Pre-precessing completed %%%%%%%%% ")
while True:
query = input("Search tweets using RegEx. To quit, enter 'quit'.>>")
if query == "quit":
break
else:
count = 0
for tw in data:
'''
for tk in tw['tokens']:
if re.match(query, tk):
try:
print(tw['text'] + "\n")
count += 1
except OSError:
print("OSError occured")
'''
try:
map_dates = graph_reader(Path("graph"), SAVE_TYPE_PICKLE,
save_filename_list)
final_node_table = map_dates[0]
final_way_table = map_dates[1]
final_relation_table = map_dates[2]
today_str = (datetime.today()).strftime('%Y%m%d')
for date_str in tqdm(os.listdir(data_root), unit="folder", position=-1):
re_result = re.match(r"[0-9]{8}", str(date_str))
if re_result is not None:
if date_str != today_str:
# print("python3.6 find_traffic_speed.py {}".format(date_str))
# process_data part
process_data.preprocess_data(date_str,
overwrite=True,
min_file_size=10,
archive_after_preprocess=True,
skip_if_archived=False)
# reformat_data part
reformat_data.reformat_by_bus(date_str)
reformat_data.sort_reformat_data(date_str)
# find_traffic_speed part
time_slot_intervals = [5, 15]
for interval in time_slot_intervals:
find_traffic_speed.find_traffic_speed(
date_str,
final_node_table,
final_way_table,
final_relation_table,
from sklearn.preprocessing import MinMaxScaler
import process_data as pro
from ml_scripts import train_models
from pandas.tools.plotting import radviz
import matplotlib.pyplot as plt
# creating a mongo client object and setting up the db
client = MongoClient()
db = client.precog
# creating a getting a mongo curser with the data we need
# PLEASE REPLACE complete_remove WITH THE NAME OF YOUR MONGO COLLECTION
dic = db.precog.find()
# process the data using preprocess_data in process_data
# this function returns a pandas dataframe
df = pro.preprocess_data(dic)
# now we will normalize the data using mix max scaler
# mms = MinMaxScaler()
# for i in df.columns:
# df[i] = pd.DataFrame(mms.fit_transform(df[i].values))
# df.describe()
# train_models(df.drop('likes_count', axis=1), df['likes_count'])
def plot_all(df):
f, axarr = plt.subplots(13, 2)
for i in range(13):
axarr[i, 0].scatter(df['likes_count'], df[df.columns[i]])
axarr[i, 0].set_title(df.columns[i])
if i == 0: