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166 lines (142 loc) · 9.22 KB
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import datetime
import pickle
import numpy as np
import urllib2
from bs4 import BeautifulSoup
#from create_classifier import heat_index_calculator
import pandas as pd
import json
import tappy_settings
import tappy_sql_connection
def heat_index_calculator(temperature, humidity):
if humidity < 13:
if 80 < temperature < 112:
adjustment = ((13-humidity)/4)*np.sqrt((17-np.abs(temperature-95.))/17)
humidity = humidity-adjustment
if humidity > 85:
if 80 < temperature < 87:
adjustment = ((humidity-85)/10) * ((87-temperature)/5)
humidity = humidity + adjustment
heat_ind = -42.379 + 2.04901523*temperature + 10.14333127*humidity - .22475541*temperature*humidity - .00683783*temperature*temperature - .05481717*humidity*humidity + .00122874*temperature*temperature*humidity + .00085282*temperature*humidity*humidity - .00000199*temperature*temperature*humidity*humidity
return heat_ind
all_possible_variables = [ 'mean_temp', 'avg_mean_temp', 'min_temp', 'avg_min_temp', 'max_temp', 'avg_max_temp', 'mean_humidity', 'min_humidity', 'max_humidity', 'dew_point', 'precipitation', 'wind_speed', 'max_wind_speed', 'max_gust_speed', 'heating_degree_days', 'avg_heating_degree_days', 'mtd_heating_degree_days', 'avg_mtd_heating_degree_days', 'july_one_heating_degree_days', 'avg_july_one_heating_degree_days', 'cooling_degree_days', 'avg_cooling_degree_days', 'mtd_cooling_degree_days', 'avg_mtd_cooling_degree_days', 'ytd_cooling_degree_days', 'avg_ytd_cooling_degree_days', 'avg_precipitation', 'mtd_precipitation', 'avg_mtd_precipitation', 'ytd_precipitation', 'avg_ytd_precipitation', 'sea_level_pressure', 'visibility', 'problem_flag', 'temp_deviation_from_avg', 'mean_temp_deviation_from_75', 'max_temp_deviation_from_75', 'heat_index', 'temp_max_minus_mean', 'dew_point_high', 'cumulative_precipitation', 'fog_event', 'no_adverse_weather_event', 'rain_event', 'snow_event', 'thunderstorm_event', 'in_school', 'days_since_open', 'brewery_events', 'days_since_school_year_start', 'first_week_of_month', 'fourth_week_of_month', 'second_week_of_month', 'third_week_of_month', 'Big Daddy Zs', 'DBQ', 'Fusion Peru', 'Goats Catering', 'In-between girls', 'Into the Fire Pizza', 'Many', "Mickey's Sliders", 'Napa Flats', 'Pik Pak Push', 'Pin-Toh Thai', 'Potato Shack', 'Raspas', 'Ronin Cooking', 'Sugba', 'Taz', 'Wafology', 'none', 'any_truck', 'band', 'band_fans', 'special_event', 'number_of_bartenders', 'daily_prior', 'yearly_prior', 'yesterday_prior', 'all_sports_games', 'all_sports_home_away', 'football_games', 'football_home_away', 'baseball_games', 'baseball_home_away', 'mens_basketball_games', 'mens_basketball_home_away', 'first_friday', 'holidays', 'in_january', 'in_february', 'in_march', 'in_april', 'in_may', 'in_june', 'in_july', 'in_august', 'in_september', 'in_october', 'in_november', 'in_december', 'in_2013', 'in_2014', 'in_2015', 'in_2016']
def predict_by_weekday(day_name):
maxes = pd.read_csv('maxes.csv')
test_day = {}
if day_name == 'friday':
day_number = 4
if day_name == 'saturday':
day_number = 5
#given todays date, find the datekey for the upcoming friday and saturday
day_of_week_today = (datetime.datetime.today().weekday())
if day_of_week_today == 6:
day_of_week_today = -1
days_until = day_number-day_of_week_today
upcoming_day = ((datetime.date.today() + datetime.timedelta(days=days_until)))
week_of_year_today = upcoming_day.isocalendar()[1]
day = upcoming_day.day
month = upcoming_day.month
year = upcoming_day.year
if datetime.date(int(year), int(month), int(day)) == datetime.date.today():
today = 1
else:
today = 0
test_day['month'] = month
test_day['day'] = day
test_day['year'] = year
test_day['day_of_week'] = datetime.date(year, month, day).weekday()
test_day['day_of_year'] = datetime.date((year), (month), (day)).timetuple().tm_yday
test_day['week_of_year'] = datetime.date(year, month, day).isocalendar()[1]
if test_day['day_of_week'] == 4:
test_day['is_friday'] = 1
else:
test_day['is_friday'] = 0
if test_day['day_of_week'] == 5:
test_day['is_saturday'] = 1
else:
test_day['is_saturday'] = 0
url = 'http://www.wunderground.com/history/airport/KCLL/'+str(year)+'/'+str(month)+'/'+str(day)+'/DailyHistory.html'
page = urllib2.urlopen(url)
#print(url)
# Get temperature from page
###soup = BeautifulSoup(page, "html.parser")
#working
test_day['days_since_open'] = ((datetime.date(year, month, day)-datetime.date(2013, 9, 6)).days)
test_day['days_since_school_year_start'] = (datetime.date(year, month, day) - datetime.date(2015,8,31)).days
if month == 4:
test_day['in_april'] = 1
else:
test_day['in_april'] = 0
if year == 2016:
test_day['in_2016'] = 1
else:
test_day['in_2016'] = 0
f = urllib2.urlopen('http://api.wunderground.com/api/'+tappy_settings.wunderground_key+'/forecast10day/q/TX/College_Station.json')
json_string = f.read()
parsed_json = json.loads(json_string)
#print(parsed_json)
#print(day)
#print(month)
#print(year)
for forecast in (parsed_json['forecast']['simpleforecast']['forecastday']):
if (forecast['date']['day'] == day) & (forecast['date']['month'] == month) & (forecast['date']['year'] == year):
test_day['min_temp'] = int(forecast['low']['fahrenheit'])
test_day['max_temp'] = int(forecast['high']['fahrenheit'])
test_day['mean_temp'] = (test_day['min_temp']+test_day['max_temp'])/2
#print(test_day['mean_temp'])
test_day['mean_humidity'] = (forecast['avehumidity'])
test_day['no_adverse_weather_event'] = 1
if (forecast['conditions'] != 'Clear') & (forecast['conditions'] != 'Partly Cloudy'):
test_day['no_adverse_weather_event'] = 0
test_day['thunderstorm_event'] = 0
if (forecast['conditions'] == 'Chance of a Thunderstorm'):
test_day['thunderstorm_event'] = 1
test_day['in_school'] = 0
#Fall 2016 schedule
beginning_range = datetime.date(2016, 8, 29)
end_range = datetime.date(2016, 12, 14)
if beginning_range <= upcoming_day <= end_range:
test_day['in_school'] = 1
f.close()
#currently assuming there will always be a band
test_day['band'] = 1
#currently assuming there will not be a special event
test_day['brewery_events'] = 0
#currently assuming there will be a food truck
test_day['any_truck'] = 1
#currently assuming there will not be a football game
test_day['football_games'] = 0
sql_query = """
SELECT * FROM daily_summary;
"""
daily_df = pd.read_sql_query(sql_query,tappy_sql_connection.conn)
day_array = np.array([])
#print(test_day['day_of_week'])
for prior_index, row in daily_df.iterrows():
if row['week_of_year'] == test_day['week_of_year']:
if row['day_of_week'] == test_day['day_of_week']:
day_array = np.append(day_array,row['daily_subtotal'])
test_day['daily_prior'] = np.median(day_array)
day_array = np.array([])
for prior_index, row in daily_df.iterrows():
if row['year'] == year:
day_array = np.append(day_array,row['daily_subtotal'])
test_day['yearly_prior'] = np.median(day_array)
test_day['mean_temp_deviation_from_75'] = np.abs(test_day['mean_temp'] - 75)
test_day['max_temp_deviation_from_75'] = np.abs(test_day['max_temp'] - 75)
test_day['heat_index'] = heat_index_calculator(test_day['mean_temp'], test_day['mean_humidity'] )
#features = ['is_friday', 'no_adverse_weather_event', 'thunderstorm_event', 'days_since_open', 'brewery_events', 'days_since_school_year_start', 'any_truck', 'band', 'daily_prior', 'football_home_away', 'in_april', 'in_2016']
#features = ['is_friday', 'is_saturday', 'week_of_year', 'mean_humidity', 'precipitation', 'mtd_precipitation', 'mean_temp_deviation_from_75', 'max_temp_deviation_from_75', 'heat_index', 'dew_point_high', 'fog_event', 'no_adverse_weather_event', 'rain_event', 'thunderstorm_event', 'in_school', 'days_since_open', 'brewery_events', 'days_since_school_year_start', 'any_truck', 'band', 'band_fans', 'special_event', 'daily_prior', 'yearly_prior', 'yesterday_prior', 'all_sports_games', 'all_sports_home_away', 'football_games', 'football_home_away', 'baseball_games', 'baseball_home_away', 'mens_basketball_games', 'mens_basketball_home_away', 'first_friday', 'in_february', 'in_march', 'in_april', 'in_may', 'in_june', 'in_july', 'in_august', 'in_september', 'in_october', 'in_november', 'in_december', 'in_2013', 'in_2014', 'in_2015', 'in_2016', ]
#features = ['is_friday', 'no_adverse_weather_event', 'thunderstorm_event', 'in_school', 'days_since_open', 'brewery_events', 'band', 'daily_prior', 'yearly_prior', 'in_april', 'in_2016', ]
features = ['is_friday', 'no_adverse_weather_event', 'thunderstorm_event', 'in_school', 'days_since_open', 'brewery_events', 'days_since_school_year_start', 'any_truck', 'band', 'daily_prior', 'yearly_prior', 'football_games', 'in_april', 'in_2016', ]
test_day_list = []
for index, feature in enumerate(features):
#print(test_day[feature])
test_day_list.append(test_day[feature]/maxes[feature])
#date_of_interest = np.array([(datetime.date(2016, 6, 10).weekday()),(datetime.date(2016, 6, 10).isocalendar()[1]),85])
clf = pickle.load( open( "good_clf.p", "rb" ) )
tomorrow_pred = clf.predict(np.array(test_day_list).reshape(1, -1))
#print(np.array(test_day_list).reshape(1, -1))
return (upcoming_day, round(tomorrow_pred[0]*(maxes['daily_subtotal'])[0],2))
print(predict_by_weekday('friday'))
print(predict_by_weekday('saturday'))