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main.py
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import numpy as np
import pandas as pd
from geo import getGeoLocation, getGasStationWithinRadius, getSocialMediaWithinRadius
from bayesian_model import RetreiveData
import datetime
from test_data import created_data, random_data
from MIP_models import minimise_expected_time, maximise_probability
from plot import plot_coordinates, plot_path
from objectives import expected_time_find_gas, expected_time_travel, probability, distance
from heurestic import greedy1
import googlemaps
# Tetsting the Bayesian Inference Model
key = pd.read_csv('key.csv')
gmaps = googlemaps.Client(key= key.columns[0])
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# Location of an arriving searcher
lat = 25.815
lon = -80.193
#Getting gas stations near the searcher within a radius
r1 = 1
df = pd.read_csv('gas_stations_florida.csv', usecols = ['NAME', 'ADDRESS', 'CITY','COUNTY'])
df_miami = getGeoLocation(df, 'MIAMI', gmaps)
Gas_stations = getGasStationWithinRadius(df_miami, lat, lon, r1, gmaps)
# Getting social posts within a radius of the searcher and the gas stations
r2 = 2
Path1 = "xxxxx"
Path2 = "xxxxx"
df_label = pd.read_csv(Path1, encoding = "ISO-8859-1")
df_post = pd.read_csv(Path2, encoding = "ISO-8859-1")
df_post = df_post.rename(columns = {'Unnamed: 0': 'SNO', 'TWEET_TEXT' : 'TWEET_TEXT_MAIN' })
df_merged = df_post.merge(df_label, on = 'SNO')[['SNO', 'TWEET_TEXT', 'DATE', 'TIME', 'TIMEZONE', 'LATITUDE', 'LONGITUDE', 'label_s']]
Post_data = df_merged[df_merged['LATITUDE'].notnull() & df_merged['label_s'] == 1]
Post_data['DATE_TIME'] = Post_data['DATE'] + " " + Post_data['TIME']
Post_data['DATE_TIME'] = Post_data['DATE_TIME'].apply(lambda x: x.replace("2017", "17"))
Post_data['DATE_TIME'] = Post_data['DATE_TIME'].apply(lambda x: datetime.datetime.strptime(x, '%m/%d/%y %H:%M:%S'))
Post_data = getSocialMediaWithinRadius(Post_data, lat, lon, r2, gmaps)
# Retreive relevant parameters for the Bayesian Model
Date = '09/07/17'
Obs_times = None
nV, nT, nP, Gas_stations_dict, Post_dict, Obs_times_dict, Obs_times, TravelDuration_data, Distance_data, Time_lag = RetreiveData(Post_data, Gas_stations, Obs_times, Date )
# Created data to test
n = 3
xc = np.array([0, 0, 1, 1])
yc = np.array([0, 1, 0, 1])
p = {0: 0.0000001,
1: 0.3,
2: 0.2,
3: 0.7}
f = 2
mi = 10
test_data = created_data(n, p, xc, yc, f, mi)
test_data.generate_parmaters()
test_data.create_paths()
exp_time_find_gas =[]
exp_time_travel = []
probability_find_gas =[]
path_distance = []
# Comparing the objectives of different paths (Brute force approach)
for x in test_data.paths:
exp_time_find_gas.append(expected_time_find_gas(x, test_data))
exp_time_travel.append(expected_time_travel(x, test_data))
probability_find_gas.append(probability(x, test_data))
path_distance.append(distance(x,test_data))
path_quality = pd.DataFrame(list(zip(test_data.paths, path_distance ,exp_time_travel, exp_time_find_gas, probability_find_gas)),
columns = ['path', 'path_distance', 'expected_time_travel', 'expected_time_findgas',
'proabability_findgas'] )
path_quality.to_csv()
# Modelling and soliving models using Gurobi slover and Heurestic
arc_orders = minimise_expected_time(test_data)
arc_orders2 = maximise_probability(test_data)
arc_orders3 = greedy1(test_data)
# Plotting
plot_coordinates(xc, yc)
plot_path(xc, yc, arc_orders)
plot_path(xc, yc, arc_orders2)
plot_path(xc, yc, arc_orders3)
expected_time_find_gas(arc_orders, test_data)
expected_time_travel(arc_orders, test_data)
probability(arc_orders, test_data)
# Random data testing
n = [5]
seeds = [x for x in range(1,2)]
data = [[i, j, random_data(i,j)] for i in n for j in seeds]
for x in data:
x[2].create_paths()
comparison_list = [[x[0], x[1], y, distance(y,x[2]), expected_time_find_gas(y,x[2]), expected_time_travel(y,x[2]), probability(y,x[2])]
for x in data for y in x[2].paths]
comparison_df = pd.DataFrame(comparison_list, columns = ['n','seed', 'path', 'path_distance', 'expected_time_travel', 'expected_time_findgas',
'proabability_findgas'])