# initial the explanatory variables
UTILITY_VARIABLES_ALTERNATIVES = [
# Alternative 2
melbourne,
# Alternative 3
brisbane,
# Alternative 4
adelaide,
# Alternative 5
hobart,
# Alternative 6
darwin,
]
UTILITY_VARIABLES = get_utility_variables(UTILITY_VARIABLES_ALTERNATIVES)
case_config_list = [1, 4, 7]
start_time = datetime.now()
for case_config in case_config_list:
# write the file
input_file_path = INPUT_DIR_PATH + '/NVS2007_trimed_v3.csv'
estimation_output_file = RESULTS_PATH + '/results' + '_{}'.format(
case_config) + '_{}'.format('MDCEV_2') + '.txt'
forecast_output_file = RESULTS_PATH + '/results' + '_{}'.format(
case_config) + '_{}'.format('MDCEV_2') + '.txt'
print estimation_output_file
class ModelRun(object):
# R script
r_estimation = 'mdcev/runner_mdcev_nooutside.r'
r_alpha_forecast = 'mdcev/forecast/run_mdcev_alpha_forecast.r'
r_gamma_forecast = 'mdcev/forecast/run_mdcev_gamma_forecast.r'
# CSV data file
original_source_file = '../Data/ivs/2012/IVS_2012.csv'
model_data_file = '../Data/ivs/filted_data.csv'
halton_filepath = INPUT_DIR_PATH + '/Halton.csv'
# fixed value
compulsory_fields = COMPULSORY_FIELDS
alternative_list = STATE_LISTS
alternative_code = STATE_CODES
distance_destination_list = DISTANCE_DESTINATION_LIST
utility_parameters = [
'GENDER', 'MARITAL', 'AGEGROUP', 'PARTYPE', 'NUMVISIT', 'TRIP_PURPOSE',
'CUSTOMS', 'COUNTRY', 'OTHPURP1'
]
# , 'GROUPTYPE' , 'RANDOMSTOP'
## OK ##
# 'MARITAL_SINGLE' 'GENDER_MALE'
### NOT CONVERGE ###
# local_variable = ['GENDER_MALE', 'GENDER_FEMALE', 'MARITAL_SINGLE', 'MARITAL_COUPLE', 'AGEGROUP_15_29',
# 'AGEGROUP_30_39', 'AGEGROUP_40_49', 'AGEGROUP_50_59', 'AGEGROUP_60_69', 'AGEGROUP_70_MORE',
# 'PARTYPE_UNACCOMPANIED', 'PARTYPE_ADULT_COUPLE', 'PARTYPE_FAMILY_GROUP',
# 'PARTYPE_FREIEND_RELATIVES', 'PARTYPE_BUSINESS_ASSOCIATES', 'PARTYPE_SCHOOL_TOUR',
# 'TRIP_PURPOSE_HOLIDAY', 'TRIP_PURPOSE_VISITING_FR', 'TRIP_PURPOSE_BUSINESS',
# 'TRIP_PURPOSE_EMPLOYMENT', 'TRIP_PURPOSE_EDUCATION', 'TRIP_PURPOSE_OTHER', 'CUSTOMS_ENTRY_NSW',
# 'CUSTOMS_ENTRY_VIC', 'CUSTOMS_ENTRY_QLD', 'CUSTOMS_ENTRY_SA', 'CUSTOMS_ENTRY_NT',
# 'CUSTOMS_ENTRY_OTHER', 'OTHPURP1']
local_variable = ['GENDER_MALE']
# Alternatives
vic = local_variable
nt = local_variable
qld = local_variable
sa = local_variable
wa = local_variable
tas = local_variable
act = local_variable
constant = []
alternatives_utility_variables = [
# Alternative 2
vic + constant,
# Alternative 3
qld + constant,
# Alternative 4
sa + constant,
# Alternative 5
wa + constant,
# Alternative 6
tas + constant,
# Alternative 7
nt + constant,
# Alternative 8
act + constant,
]
utility_variables = get_utility_variables(alternatives_utility_variables)
# case_config_list = [1, 4, 7]
case_config_list = [4]
def _create_estimation_output_filename(self, case_config):
return RESULTS_PATH + '/results' + '_{}'.format(
case_config) + '_{}'.format('MDCEV_TEMP') + '.txt'
def _create_coef_filename(self, case_config):
return RESULTS_PATH + '/coef' + '_{}'.format(case_config) + '.csv'
def _create_forecast_filename(self, case_config):
return RESULTS_PATH + '/results' + '_{}'.format(
case_config) + '_{}'.format('MDCEV_forecasting') + '.csv'
def _get_coef(self, case_config):
# get the filename by case_config number
txt_filepath = self._create_estimation_output_filename(case_config)
csv_filepath = convert_txt_to_csv(txt_filepath)
coef_data = get_coef_file(csv_filepath)
coef_file = self._create_coef_filename(case_config)
flag = write_data_to_csv(filename=coef_file, data=coef_data)
return flag
def read_the_data(self):
flag = read_ivs_state_combinations(
input_file=self.original_source_file,
output_file=self.model_data_file,
compulsory_fields=self.compulsory_fields,
state_list=self.alternative_list,
state_codes=self.alternative_code,
utility_parameters=self.utility_parameters,
distance_destination_list=self.distance_destination_list,
)
return flag
def estimation(self):
for case_config in self.case_config_list:
estimation_output_file = self._create_estimation_output_filename(
case_config)
print('This is the estimation file', estimation_output_file)
process = subprocess.call([
'Rscript --vanilla {r_script} {input_file} '
'{number_of_alternatives} {case_config} {utility_parameters} {state_list} {results_file} '
'{alternative_2_variables} {alternative_3_variables} {alternative_4_variables} '
'{alternative_5_variables} {alternative_6_variables} {alternative_7_variables} {alternative_8_variables}'
.format(
r_script=self.r_estimation,
input_file=self.model_data_file,
number_of_alternatives=self.alternative_list.__len__(),
case_config=case_config,
utility_parameters=convert_list_to_str(
self.utility_variables),
state_list=convert_list_to_str(self.alternative_list),
results_file=estimation_output_file,
alternative_2_variables=convert_list_to_str(
self.alternatives_utility_variables[0]),
alternative_3_variables=convert_list_to_str(
self.alternatives_utility_variables[1]),
alternative_4_variables=convert_list_to_str(
self.alternatives_utility_variables[2]),
alternative_5_variables=convert_list_to_str(
self.alternatives_utility_variables[3]),
alternative_6_variables=convert_list_to_str(
self.alternatives_utility_variables[4]),
alternative_7_variables=convert_list_to_str(
self.alternatives_utility_variables[5]),
alternative_8_variables=convert_list_to_str(
self.alternatives_utility_variables[6]),
)
],
shell=True)
return
def forecast(self):
for case_config in self.case_config_list:
self._get_coef(case_config)
r_file = self.r_alpha_forecast
if case_config == 4:
r_file = self.r_gamma_forecast
forecast(r_file=r_file,
data_filepath=self.model_data_file,
case_config=case_config,
results_file=self._create_forecast_filename(case_config),
halton_filepath=self.halton_filepath,
coef_file=self._create_coef_filename(case_config))
evaluate(data_file=self.model_data_file,
result_file=self._create_forecast_filename(case_config),
alternative_list=STATE_LISTS)
return
def plot(self):
for case_config in self.case_config_list:
# Read the result from csv file and get the index number of the data
data = read_csv_to_data(self.model_data_file)
data_alternative_index = map(data[0].index, STATE_LISTS)
result = read_csv_to_data(
self._create_forecast_filename(case_config))
result_alternative_index = map(result[0].index, STATE_LISTS)
# print data_alternative_index, result_alternative_index
# Get the pure data
pure_data = get_pure_data(data, data_alternative_index)
pure_result = get_pure_data(result, result_alternative_index)
# Initial some list to storing values
number_of_alternatives = data_alternative_index.__len__(
) # == 6 in this model
data_duration_counts, data_alternative_counts, data_number_of_chosen_alternative_counts = count_on_pure_data(
pure_data, number_of_alternatives)
result_duration_counts, result_alternative_counts, result_number_of_chosen_alternative_counts = count_on_pure_data(
pure_result, number_of_alternatives)
print data_duration_counts, result_duration_counts
print data_alternative_counts, result_alternative_counts
print data_number_of_chosen_alternative_counts, result_number_of_chosen_alternative_counts
# Plotting part
fig = plt.figure(case_config)
fig = plot_bar_graph_within(STATE_LISTS, data_duration_counts,
result_duration_counts,
'Duration counts in days', fig, 221)
fig = plot_bar_graph_within(STATE_LISTS, data_alternative_counts,
result_alternative_counts,
'Alternative counts as correct ratio',
fig, 222)
fig = plot_bar_graph_within(
[1, 2, 3, 4, 5, 6], data_number_of_chosen_alternative_counts,
result_number_of_chosen_alternative_counts,
"Number of chosen alternative hit", fig, 223)
# This will show all the figures generated by the plot function.
plt.show()
return
def full(self):
# self.read_the_data()
self.estimation()
# self.forecast()
# self.plot()
return