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benchmark.py
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benchmark.py
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import pandas as pd
import utilities
import matching
def create():
results_fix = []
results_shuffle = []
shuffle_time = []
results_fix_waiting = []
results_shuffle_waiting = []
n = [20, 40, 60, 80, 100]
s = [25, 50, 100, 200]
i = 1000
for size in n:
for k in range(i):
# Generate features and preferences for this run
profits = utilities.generateFeatures(size, "driver", 1)
eta = utilities.generateFeatures(size, "passenger", 1)
waiting_time = utilities.generateFeatures(size, "waiting time", 1)
eta_waiting_time = utilities.calculateWaitingTime(eta, waiting_time)
driver_gender = utilities.generateFeatures(size, "driver", 2)
passenger_gender = utilities.generateFeatures(size, "passenger", 2)
driver_g_preferences = utilities.generatePreferences(size)
passenger_g_preferences = utilities.generatePreferences(size)
driver_l1 = utilities.calculatePreferencesNumerical(profits, 'Profit')
driver_l2 = utilities.calculatePreferences(driver_g_preferences, passenger_gender)
passenger_l1 = utilities.calculatePreferencesNumerical(eta, 'ETA')
passenger_l1_waiting_time = utilities.calculatePreferencesNumerical(eta_waiting_time, 'ETA')
passenger_l2 = utilities.calculatePreferences(passenger_g_preferences, driver_gender)
# Run Fix Order
inter_results_fix = matching.run(size, driver_l1, passenger_l1, driver_l2, passenger_l2, profits, eta,
driver_gender)
results_fix.append(inter_results_fix[0])
results_fix.append(inter_results_fix[1])
# Enable for driver-optimal results
# results_fix.append(inter_results_fix[2])
# results_fix.append(inter_results_fix[3])
inter_results_fix.clear()
# Run Fix Order with Waiting Time
inter_results_fix_waiting = matching.run(size, driver_l1, passenger_l1_waiting_time, driver_l2, passenger_l2,
profits, eta_waiting_time, driver_gender)
results_fix_waiting.append(inter_results_fix_waiting[0])
results_fix_waiting.append(inter_results_fix_waiting[1])
# results_fix_waiting.append(inter_results_fix_waiting[2])
# results_fix_waiting.append(inter_results_fix_waiting[3])
inter_results_fix_waiting.clear()
for shuffle in s:
for ss in range(shuffle):
# Run Shuffle
inter_results_shuffle = matching.shuffle_run(size, shuffle, driver_l1, passenger_l1, driver_l2, passenger_l2,
profits, eta, driver_gender, shuffle)
results_shuffle.append(inter_results_shuffle[0])
results_shuffle.append(inter_results_shuffle[1])
shuffle_time.append(inter_results_shuffle[2])
shuffle_time.append(inter_results_shuffle[3])
inter_results_shuffle.clear()
# Run Shuffle Waiting Time
inter_results_shuffle_waiting = matching.shuffle_run(size, shuffle, driver_l1, passenger_l1_waiting_time,
driver_l2, passenger_l2, profits,
eta_waiting_time, driver_gender, shuffle)
results_shuffle_waiting.append(inter_results_shuffle_waiting[0])
results_shuffle_waiting.append(inter_results_shuffle_waiting[1])
shuffle_time.append(inter_results_shuffle_waiting[2])
shuffle_time.append(inter_results_shuffle_waiting[3])
inter_results_shuffle_waiting.clear()
print(k)
print(size)
df_results_fix = pd.DataFrame(results_fix, columns=[
'ALG',
'Optimum',
'Blocking Pairs',
'Sum_Profit',
'Sum_ETA',
'Sum_Max_Possible_Profit',
'Min_Profit',
'Max_Profit',
'Sum_Min_Possible_ETA',
'Min_ETA',
'Max_ETA'
])
df_results_fix.to_csv(f'./results/Fix/data/data_fix_n{size}.csv')
del df_results_fix
results_fix.clear()
df_results_shuffle = pd.DataFrame(results_shuffle, columns=[
'ALG',
'Optimum',
'Shuffle',
'Blocking Pairs',
'Sum_Profit',
'Sum_ETA',
'Sum_Max_Possible_Profit',
'Min_Profit',
'Max_Profit',
'Sum_Min_Possible_ETA',
'Min_ETA',
'Max_ETA'
])
df_results_shuffle.to_csv(f'./results/Shuffle/data/data_shuffle_n{size}.csv')
del df_results_shuffle
results_shuffle.clear()
# Waiting Time Fix Order
df_results_fix_waiting = pd.DataFrame(results_fix_waiting, columns=[
'ALG',
'Optimum',
'Blocking Pairs',
'Sum_Profit',
'Sum_ETA',
'Sum_Max_Possible_Profit',
'Min_Profit',
'Max_Profit',
'Sum_Min_Possible_ETA',
'Min_ETA',
'Max_ETA'
])
df_results_fix_waiting.to_csv(f'./results/Fix/data/data_fix_waiting_n{size}.csv')
del df_results_fix_waiting
results_fix_waiting.clear()
# Waiting Time Shuffle Order
df_results_shuffle_waiting = pd.DataFrame(results_shuffle_waiting, columns=[
'ALG',
'Optimum',
'Shuffle',
'Blocking Pairs',
'Sum_Profit',
'Sum_ETA',
'Sum_Max_Possible_Profit',
'Min_Profit',
'Max_Profit',
'Sum_Min_Possible_ETA',
'Min_ETA',
'Max_ETA'
])
df_results_shuffle_waiting.to_csv(f'./results/Shuffle/data/data_shuffle_waiting_n{size}.csv')
del df_results_shuffle_waiting
results_shuffle_waiting.clear()
df_shuffle_time = pd.DataFrame(shuffle_time, columns=[
'Type',
'ALG',
'N=',
'Shuffle',
'Time'
])
df_shuffle_time.to_csv(f'./results/Shuffle/data/shuffle_timen{size}.csv')
del df_shuffle_time
shuffle_time.clear()