TapPy/make_prediction.py at master · gnatman/TapPy · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
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'))