def __init__(self,
bins,
source_data_dir,
calibration_features,
config,
model_data_store_dir,
apply_old,
apply_difference,
model_type,
model_number,
player_info,
focus_actions_list=[]):
self.bins = bins
# self.bins_names = bins.keys()
self.apply_old = apply_old
self.apply_difference = apply_difference
self.data_path = source_data_dir
self.calibration_features = calibration_features
if self.apply_difference:
self.calibration_values_all_dict = {
'all': {
'cali_sum': [0],
'model_sum': [0],
'number': 0
}
}
else:
self.calibration_values_all_dict = {
'all': {
'cali_sum': [0, 0, 0],
'model_sum': [0, 0, 0],
'number': 0
}
}
self.data_store_dir = model_data_store_dir
self.config = config
self.focus_actions_list = focus_actions_list
if self.apply_difference:
self.save_calibration_dir = './calibration_results/difference-calibration_{2}_{0}_{1}{3}.txt'. \
format(str(self.focus_actions_list), datetime.date.today().strftime("%Y%B%d"), model_type, player_info)
else:
self.save_calibration_dir = './calibration_results/calibration_{2}_{0}_{1}{3}.txt'. \
format(str(self.focus_actions_list), datetime.date.today().strftime("%Y%B%d"), model_type, player_info)
self.save_calibration_file = open(self.save_calibration_dir, 'w')
if apply_difference:
self.teams = ['home-away']
else:
self.teams = ['home', 'away', 'end']
# learning_rate = tt_lstm_config.learn.learning_rate
# pass
self.data_name = get_data_name(config=config,
model_catagoery=model_type,
model_number=model_number)
self.data_name = self.data_name.replace('Qs', 'next_Qs')
print(self.data_name)
"icehockey_stats_lstm_encoder{0}" \
"_config{1}.yaml".format(predicted_target, player_info)
icehockey_model_config = EncoderConfig.load(
icehockey_encoder_config_path)
elif model_category == 'lstm_diff':
icehockey_config_path = "../../environment_settings/" \
"ice_hockey_predict_score_diff_lstm{0}.yaml".format(player_info)
icehockey_model_config = LSTMDiffCongfig.load(
icehockey_config_path)
player_id_cluster_dir = None
else:
raise ValueError(
"uknown model catagoery {0}".format(model_category))
if model_category != 'zero':
data_name = get_data_name(icehockey_model_config, model_category,
model_number)
data_name = data_name.replace('Qs', 'accumu_Qs') + "_cv"
else:
data_name = None
print(model_category + '_' + model_number + player_info)
acc_diff_mean_by_event, acc_diff_var_by_event, event_numbers, \
acc_diff_mean_by_time, acc_diff_var_by_time, game_time_list = validate_score_diff(model_data_store_dir,
data_name,
model_data_store_dir,
data_store,
model_category,
None,
cv_number=5)
event_numbers_all.append(event_numbers)
acc_diff_mean_by_event_all.append(acc_diff_mean_by_event)
model_number=model_number,
player_id_cluster_dir=player_id_cluster_dir,
model_category=model_type,
return_values_flag=False,
apply_cv=True,
running_number=running_number)
# for dir_game_index in range(0, len(dir_games_all)):
# cv_record_all_model_next_Q_values[dir_game_index].update(
# {running_number: model_next_Q_values_all[dir_game_index]})
# cv_record_all_model_accumu_Q_values[dir_game_index].update(
# {running_number: model_accumu_Q_value_all[dir_game_index]})
# dir_games_all = dir_games_all[1:]
for dir_game_index in range(0, len(dir_games_all)):
data_name = get_data_name(config=icehockey_model_config,
model_catagoery=model_type,
model_number=model_number)
game_name_dir = dir_games_all[dir_game_index]
game_store_dir = game_name_dir.split('.')[0]
game_all_next_Qs_values = {}
game_all_accumu_Qs_values = {}
for running_number in running_numbers:
if model_type != 'lstm_diff' and model_type != 'multi_agent' and model_type != 'clvrnn' and model_type != 'caernn':
with open(
data_store_dir + "/" + game_store_dir + "/" +
data_name.replace('Qs', 'next_Qs') + '_r' +
str(running_number), 'r') as outfile:
cv_next_Qs_game_values = json.load(outfile)
game_all_next_Qs_values.update(
{running_number: cv_next_Qs_game_values})
os.remove(data_store_dir + "/" + game_store_dir + "/" +
def __init__(self,
bins,
source_data_dir,
calibration_features,
config,
model_data_store_dir,
apply_old,
apply_difference,
model_type,
model_number,
player_info,
calibration_type,
embed_mode,
focus_actions_list=[],
if_apply_cv=False,
running_numbers=[None]):
self.calibration_type = calibration_type
self.bins = bins
# self.bins_names = bins.keys()
self.apply_old = apply_old
self.if_apply_difference = apply_difference
self.data_path = source_data_dir
self.calibration_features = calibration_features
self.if_apply_cv = if_apply_cv
self.running_numbers = running_numbers
if self.if_apply_difference:
if self.if_apply_cv:
self.cv_calibration_values_all_list = []
for running_number in self.running_numbers:
self.cv_calibration_values_all_list.append({
'all'.format(str(running_number)): {
'cali_sum': [0],
'model_sum': [0],
'number': 0
}
})
else:
self.calibration_values_all_dict = {
'all': {
'cali_sum': [0],
'model_sum': [0],
'number': 0
}
}
else:
if self.if_apply_cv:
self.cv_calibration_values_all_list = []
for running_number in self.running_numbers:
self.cv_calibration_values_all_list.append({
'all'.format(str(running_number)): {
'cali_sum': [0, 0, 0],
'model_sum': [0, 0, 0],
'number': 0
}
})
else:
self.calibration_values_all_dict = {
'all': {
'cali_sum': [0, 0, 0],
'model_sum': [0, 0, 0],
'number': 0
}
}
self.data_store_dir = model_data_store_dir
self.config = config
self.focus_actions_list = focus_actions_list
if self.if_apply_difference:
self.save_calibration_dir = './calibration_results/difference-calibration_{2}_{0}_{1}{3}_model{4}_{5}.txt'. \
format(str(self.focus_actions_list), datetime.date.today().strftime("%Y%B%d"), model_type,
player_info, model_number, embed_mode)
else:
self.save_calibration_dir = './calibration_results/calibration_{2}_{0}_{1}{3}_model{4}_{5}.txt'. \
format(str(self.focus_actions_list), datetime.date.today().strftime("%Y%B%d"), model_type,
player_info, model_number, embed_mode)
if self.if_apply_cv:
self.save_calibration_dir = self.save_calibration_dir.replace(
'.txt', '_cv.txt')
self.save_calibration_file = open(self.save_calibration_dir, 'w')
if apply_difference:
self.teams = ['home-away']
else:
if self.calibration_type == 'next_goal':
self.teams = ['home', 'away', 'end']
elif self.calibration_type == 'score_diff':
self.teams = ['home', 'away']
# learning_rate = tt_lstm_config.learn.learning_rate
# pass
data_name = get_data_name(config=config,
model_catagoery=model_type,
model_number=model_number)
if self.calibration_type == 'next_goal':
self.data_name = data_name.replace('Qs', 'next_Qs')
elif self.calibration_type == 'score_diff':
self.data_name = data_name.replace('Qs', 'accumu_Qs')
else:
raise ValueError('unknown calibration type {0}'.format(
self.calibration_type))
if if_apply_cv:
self.data_name = self.data_name + "_cv"
print(self.data_name)
self.calibration_type = calibration_type