File size: 8,810 Bytes
9be4956 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
from tools.accommodations.apis import Accommodations
from tools.flights.apis import Flights
from tools.restaurants.apis import Restaurants
from tools.rank.apis import Rank
from tools.filter.apis import Filter
from tools.googleDistanceMatrix.apis import GoogleDistanceMatrix
import pandas as pd
hotel = Accommodations()
flight = Flights()
flight.load_db()
restaurant = Restaurants()
rank = Rank()
filter = Filter()
distanceMatrix = GoogleDistanceMatrix()
def estimate_budget(data, mode):
"""
Estimate the budget based on the mode (lowest, highest, average) for flight, hotel, or restaurant data.
"""
if mode == "lowest":
return min(data)
elif mode == "highest":
return max(data)
elif mode == "average":
# filter the nan values
data = [x for x in data if str(x) != 'nan']
return sum(data) / len(data)
def budget_calc(org, dest, days, date:list , people_number=None, local_constraint = None):
"""
Calculate the estimated budget for all three modes: lowest, highest, average.
grain: city, state
"""
if days == 3:
grain = "city"
elif days in [5,7]:
grain = "state"
if grain not in ["city", "state"]:
raise ValueError("grain must be one of city, state")
# Multipliers based on days
multipliers = {
3: {"flight": 2, "hotel": 3, "restaurant": 9},
5: {"flight": 3, "hotel": 5, "restaurant": 15},
7: {"flight": 4, "hotel": 7, "restaurant": 21}
}
if grain == "city":
hotel_data = hotel.run(dest)
restaurant_data = restaurant.run(dest)
flight_data = flight.data[(flight.data["DestCityName"] == dest) & (flight.data["OriginCityName"] == org)]
elif grain == "state":
city_set = open('../database/background/citySet_with_states.txt').read().strip().split('\n')
all_hotel_data = []
all_restaurant_data = []
all_flight_data = []
for city in city_set:
if dest == city.split('\t')[1]:
candidate_city = city.split('\t')[0]
# Fetch data for the current city
current_hotel_data = hotel.run(candidate_city)
current_restaurant_data = restaurant.run(candidate_city)
current_flight_data = flight.data[(flight.data["DestCityName"] == candidate_city) & (flight.data["OriginCityName"] == org)]
# Append the dataframes to the lists
all_hotel_data.append(current_hotel_data)
all_restaurant_data.append(current_restaurant_data)
all_flight_data.append(current_flight_data)
# Use concat to combine all dataframes in the lists
hotel_data = pd.concat(all_hotel_data, axis=0)
restaurant_data = pd.concat(all_restaurant_data, axis=0)
flight_data = pd.concat(all_flight_data, axis=0)
# flight_data should be in the range of supported date
flight_data = flight_data[flight_data['FlightDate'].isin(date)]
if people_number:
hotel_data = hotel_data[hotel_data['maximum occupancy'] >= people_number]
if local_constraint:
if local_constraint['transportation'] == 'no self-driving':
if grain == "city":
if len(flight_data[flight_data['FlightDate'] == date[0]]) < 2:
raise ValueError("No flight data available for the given constraints.")
elif grain == "state":
if len(flight_data[flight_data['FlightDate'] == date[0]]) < 10:
raise ValueError("No flight data available for the given constraints.")
elif local_constraint['transportation'] == 'no flight':
if len(flight_data[flight_data['FlightDate'] == date[0]]) < 2 or flight_data.iloc[0]['Distance'] > 800:
raise ValueError("Impossible")
# if local_constraint['flgiht time']:
# if local_constraint['flgiht time'] == 'morning':
# flight_data = flight_data[flight_data['DepTime'] < '12:00']
# elif local_constraint['flgiht time'] == 'afternoon':
# flight_data = flight_data[(flight_data['DepTime'] >= '12:00') & (flight_data['DepTime'] < '18:00')]
# elif local_constraint['flgiht time'] == 'evening':
# flight_data = flight_data[flight_data['DepTime'] >= '18:00']
if local_constraint['room type']:
if local_constraint['room type'] == 'shared room':
hotel_data = hotel_data[hotel_data['room type'] == 'Shared room']
elif local_constraint['room type'] == 'not shared room':
hotel_data = hotel_data[(hotel_data['room type'] == 'Private room') | (hotel_data['room type'] == 'Entire home/apt')]
elif local_constraint['room type'] == 'private room':
hotel_data = hotel_data[hotel_data['room type'] == 'Private room']
elif local_constraint['room type'] == 'entire room':
hotel_data = hotel_data[hotel_data['room type'] == 'Entire home/apt']
if days == 3:
if len(hotel_data) < 3:
raise ValueError("No hotel data available for the given constraints.")
elif days == 5:
if len(hotel_data) < 5:
raise ValueError("No hotel data available for the given constraints.")
elif days == 7:
if len(hotel_data) < 7:
raise ValueError("No hotel data available for the given constraints.")
if local_constraint['house rule']:
if local_constraint['house rule'] == 'parties':
# the house rule should not contain 'parties'
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No parties')]
elif local_constraint['house rule'] == 'smoking':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No smoking')]
elif local_constraint['house rule'] == 'children under 10':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No children under 10')]
elif local_constraint['house rule'] == 'pets':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No pets')]
elif local_constraint['house rule'] == 'visitors':
hotel_data = hotel_data[~hotel_data['house_rules'].str.contains('No visitors')]
if days == 3:
if len(hotel_data) < 3:
raise ValueError("No hotel data available for the given constraints.")
elif days == 5:
if len(hotel_data) < 5:
raise ValueError("No hotel data available for the given constraints.")
elif days == 7:
if len(hotel_data) < 7:
raise ValueError("No hotel data available for the given constraints.")
if local_constraint['cuisine']:
# judge whether the cuisine is in the cuisine list
restaurant_data = restaurant_data[restaurant_data['Cuisines'].str.contains('|'.join(local_constraint['cuisine']))]
if days == 3:
if len(restaurant_data) < 3:
raise ValueError("No restaurant data available for the given constraints.")
elif days == 5:
if len(restaurant_data) < 5:
raise ValueError("No restaurant data available for the given constraints.")
elif days == 7:
if len(restaurant_data) < 7:
raise ValueError("No restaurant data available for the given constraints.")
# hotel_data = filter.run(hotel_data, local_constraint)
# restaurant_data = filter.run(restaurant_data, local_constraint)
# flight_data = filter.run(flight_data, local_constraint)
# Calculate budgets for all three modes
budgets = {}
for mode in ["lowest", "highest", "average"]:
if local_constraint and local_constraint['transportation'] == 'self driving':
flight_budget = eval(distanceMatrix.run(org, dest)['cost'].replace("$","")) * multipliers[days]["flight"]
else:
flight_budget = estimate_budget(flight_data["Price"].tolist(), mode) * multipliers[days]["flight"]
hotel_budget = estimate_budget(hotel_data["price"].tolist(), mode) * multipliers[days]["hotel"]
restaurant_budget = estimate_budget(restaurant_data["Average Cost"].tolist(), mode) * multipliers[days]["restaurant"]
total_budget = flight_budget + hotel_budget + restaurant_budget
budgets[mode] = total_budget
return budgets
|