def race_set_runner_trial():
#relevant_dir = 'fulldata/2005'
relevant_dir = 'test_1'
#relevant_dir = 'code_test_1_month'
#relevant_dir = 'code_test_3_months'
#relevant_dir = 'code_test_many_events'
#relevant_dir = 'code_test_single_event'
#relevant_dir = 'code_test_single_race'
data_getter = DataGetter(relevant_dir)
data_getter.format_data()
df = data_getter.get_racesdataframe()
#print df.head()
#df['Date'] = df['Date'].apply(pd.to_datetime, format='%d/%m/%Y')
#df['Race_Time'] = df['Race_Time'].apply(pd.to_datetime)
######## APPLY THIS IN DATAGETTER WHEN FORMING DATAFRAME #########
df['Race_Date_Time'] = pd.to_datetime(df.apply(lambda x: x['Date'] + ' ' + x['Race_Time'], 1),format = '%d/%m/%Y %H:%M')
# df['Race_Date_Time'] = pd.concat([df['Date'],' ',df['Race_Time']], axis = 1)
# df['Race_Date_Time'] = pd.to_datetime(df['Race_Date_Time'])
df = df.drop(['Date','Race_Time'], 1)
#print df.head()
########## END OF ROUTINE TO APPLY IN MAIN DATAFRAME ###########################
race_runner_parameter_dict = {'min_num_race_results':10000}
trial_parameters = {'Track_list':['Crayford','Coventry'],'Grade_list':['HP','A8','A7','A6','A5','A4','A3','A2','A1','IM',],'Start_Date':datetime.datetime(2005,01,01), 'End_Date':datetime.datetime(2005,01,03),'num_sims':10}
trial_parameters = {'Track_list':[],'Grade_list':[],'Start_Date':None, 'End_Date':None,'num_sims':10000}
race_runner_object = None
RSR = RaceSetRunner(df,trial_parameters, race_runner_object, race_runner_parameter_dict)
RSR.mainline()
def mainline(self):
'''
Load the relevant directory data from a file into a dataframe
If the file does not exist, create the file
'''
# Get all the relevant data into a dataframe
# - if it doesn't exist as a CSV file, then save it as a CSV
#experiment_results_table = self.create_experiment_results_table()
data_file_list = ut.get_shallow_csv_file_list(self.data_dir_path)
if len(data_file_list) != 1:
data_getter = DataGetter(self.data_dir_path)
data_getter.format_data()
data_getter.write_csv()
self.data_df = data_getter.get_racesdataframe()
else:
data_getter = DataGetter(self.data_dir_path)
data_getter.load_csv()
self.data_df = data_getter.get_racesdataframe()
# print self.data_df.tail()
trial_results = []
for trial_ref, trial_definition in self.experiment_design.items():
#print trial_ref#, trial_definition
trial_runner = TrialRunner(self.data_df, trial_definition)
trial_result_table = trial_runner.mainline()
#print trial_result_table
#print 'trial_result_table.shape' , trial_result_table.shape
trial_results.append(trial_result_table)
#print trial_result_dict
experiment_results = trial_results[0]
experiment_results['trial'] = 1
for ind, result_table in enumerate(trial_results[1:]):
result_table['trial'] = ind+2
experiment_results = experiment_results.append(result_table)
#print 'experiment_results.shape'
#print experiment_results.shape
experiment_results.to_csv(self.results_write_file_path, encoding='utf-8')
print 'Experiment Completed'
return experiment_results
def trial_RaceRunner():
#relevant_dir = 'fulldata/2005'
#relevant_dir = 'test_1'
#relevant_dir = 'code_test_1_month'
relevant_dir = 'code_test_3_months'
#relevant_dir = 'code_test_many_events'
#relevant_dir = 'code_test_single_event'
#relevant_dir = 'code_test_single_race'
data_getter = DataGetter(relevant_dir)
data_getter.format_data()
df = data_getter.get_racesdataframe()
#print df.head()
#df['Date'] = df['Date'].apply(pd.to_datetime, format='%d/%m/%Y')
#df['Race_Time'] = df['Race_Time'].apply(pd.to_datetime)
######## APPLY THIS IN DATAGETTER WHEN FORMING DATAFRAME #########
df['Race_Date_Time'] = pd.to_datetime(df.apply(lambda x: x['Date'] + ' ' + x['Race_Time'], 1),format = '%d/%m/%Y %H:%M')
# df['Race_Date_Time'] = pd.concat([df['Date'],' ',df['Race_Time']], axis = 1)
# df['Race_Date_Time'] = pd.to_datetime(df['Race_Date_Time'])
df = df.drop(['Date','Race_Time'], 1)
#print df.head()
########## END OF ROUTINE TO APPLY IN MAIN DATAFRAME ###########################
######## TO CREATE A LIST OF RACES FROM THE DATAFRAME ########
def split_2_part_tuple(some_tuple):
return some_tuple[0], some_tuple[1]
races = pd.DataFrame(pd.unique(df[[ 'Track','Race_Date_Time']].values))
#print races.head()
races['Track'], races['Race_Date_Time'] = zip(*races[0].map(split_2_part_tuple))
races = races.drop(0,1)
races = races.sort(['Race_Date_Time'], ascending = [1])
races.set_index('Race_Date_Time', inplace = True)
####### END OF CREATING A LIST OF RACES FROM THE DATAFRAME ###############
#print races.head(10)
#print list(races['Race_Date_Time'])
for k, v in races.iterrows():
#print k,v
race_date = df[df['Race_Date_Time'] == k]
race_track = v
# This returns a list
#race_track = race_date['Track'].unique()
#race_track =
# race_track = df[df['Race_Date_Time'] == k]
#print k
dog_list = list(race_date['Dog'])
#print 'dog_list' , #dog_list
#print race_dogs['Dog'].head()
# print k
# print dog_list
#print race_date.head(11)
#race_track = race_date['Track'].unique()
#print race_track, k
# race_track = list(race_track)
# print type(race_track)
# print type(k)
## k = k.to_datetime()
## print k
# #print k.to_datetime()
# race_track.extend([k])
# race_reference = race_track[:]
# #race_reference = list(race_track).append(k)
# print 'race reference' , race_reference
# print 'v', v[0]
# print
# print k
#print 'v', v
full_df = df ; race_reference_dict = {'Track':v[0],'Race_Date_Time':k} ;
num_sims = 10000 ; parameter_dict = {'min_num_race_results':10}
race_runner = RaceRunner(full_df, race_reference_dict, num_sims, parameter_dict)
results = race_runner.main_line()
print 'results_shape' , results.shape
print results.head()
