# self.diff_output = tf.nn.softmax(diff_output)
self.action_pred_output = tf.nn.softmax(action_pred_output)
with tf.variable_scope('action_pred_cost'):
action_pred_loss = get_action_pred_lossfunc(
self.action_pred_output,
self.action_pred_target_ph,
condition,
if_last_output=True)
self.action_pred_loss = action_pred_loss
# self.win_acc, win_acc_op = tf.metrics.accuracy(labels=tf.argmax(self.win_target_ph, 1),
# predictions=tf.argmax(self.win_output, 1))
if self.config.Learn.integral_update_flag:
tvars_action_pred = tf.trainable_variables()
else:
tvars_action_pred = tf.trainable_variables(scope='prediction')
for t in tvars_action_pred:
print('tvars_action_pred: ' + str(t.name))
action_grads = tf.gradients(tf.reduce_mean(self.action_pred_loss),
tvars_action_pred)
self.train_action_pred_op = optimizer.apply_gradients(
zip(action_grads, tvars_action_pred))
if __name__ == '__main__':
cvrnn_config_path = "../environment_settings/icehockey_cvrnn_PlayerLocalId_predict_nex_goal_config_embed_random.yaml"
cvrnn_config = CVRNNCongfig.load(cvrnn_config_path)
cvrnn = CVRNN(config=cvrnn_config)
cvrnn()
acc_diff_var_by_event_all = []
game_time_all = []
acc_diff_mean_by_time_all = []
acc_diff_var_by_time_all = []
for model_info_setting in validated_model_type:
model_category = model_info_setting.get('model_category')
model_name = model_info_setting.get('model_name')
model_number = model_info_setting.get('model_number')
player_info = model_info_setting.get('player_info')
if model_category == 'cvrnn':
embed_mode = '_embed_random'
predicted_target = '_PlayerLocalId_predict_nex_goal'
icehockey_cvrnn_config_path = "../../environment_settings/icehockey_cvrnn{0}_config{1}{2}.yaml".format(
predicted_target, player_info, embed_mode)
icehockey_model_config = CVRNNCongfig.load(
icehockey_cvrnn_config_path)
elif model_category == 'cvae':
predicted_target = '_PlayerLocalId_predict_next_goal' # playerId_
# player_id_cluster_dir = '../../sport_resource/ice_hockey_201819/local_player_id_2018_2019.json'
icehockey_config_path = "../../environment_settings/icehockey_cvae_lstm{0}_config{1}.yaml".format(
predicted_target, player_info)
icehockey_model_config = CVAECongfig.load(icehockey_config_path)
elif model_category == 'vhe':
predicted_target = '_PlayerLocalId_predict_next_goal' # playerId_
# player_id_cluster_dir = '../../sport_resource/ice_hockey_201819/local_player_id_2018_2019.json'
icehockey_config_path = "../../environment_settings/icehockey_vhe_lstm{0}_config{1}.yaml".format(
predicted_target, player_info)
icehockey_model_config = CVAECongfig.load(icehockey_config_path)
elif model_category == 'encoder':
predicted_target = '_PlayerLocalId_predict_next_goal'
# player_id_cluster_dir = '../../sport_resource/ice_hockey_201819/local_player_id_2018_2019.json'
def run_calibration():
model_type = 'lstm_Qs'
player_info = '_pid'
apply_old = False
apply_difference = False
running_numbers = [0, 1, 2, 3, 4]
if model_type == 'cvrnn':
model_number = 601
embed_mode = '_embed_random'
predicted_target = '_PlayerLocalId_predict_nex_goal'
icehockey_config_path = "../../environment_settings/" \
"icehockey_cvrnn{0}_config{1}{2}.yaml" \
.format(predicted_target, player_info, embed_mode)
config = CVRNNCongfig.load(icehockey_config_path)
elif model_type == 'cvae':
model_number = 601
embed_mode = ''
predicted_target = '_PlayerLocalId_predict_next_goal' # playerId_
icehockey_config_path = "../../environment_settings/icehockey_cvae_lstm{0}_config{1}.yaml".format(
predicted_target, player_info)
config = CVAECongfig.load(icehockey_config_path)
elif model_type == 'vhe':
model_number = 601
embed_mode = ''
predicted_target = '_PlayerLocalId_predict_next_goal' # playerId_
icehockey_config_path = "../../environment_settings/icehockey_vhe_lstm{0}_config{1}.yaml".format(
predicted_target, player_info)
config = CVAECongfig.load(icehockey_config_path)
elif model_type == 'encoder':
model_number = 901
embed_mode = ''
predicted_target = '_PlayerLocalId_predict_next_goal'
# player_id_cluster_dir = '../sport_resource/ice_hockey_201819/local_player_id_2018_2019.json'
icehockey_encoder_config_path = "../../environment_settings/" \
"icehockey_stats_lstm_encoder{0}" \
"_config{1}.yaml".format(predicted_target, player_info)
config = EncoderConfig.load(icehockey_encoder_config_path)
elif model_type == 'lstm_Qs':
embed_mode = ''
# 'model_2101_three_cut_lstm_next_Qs_featurev1_next_Qs_batch32_iterate10_lr1e-05_v1_MaxTL10_LSTM512_dense256'
model_number = 901
icehockey_config_path = "../../environment_settings/ice_hockey_predict_Qs_lstm{0}.yaml".format(
player_info)
config = LSTMQsCongfig.load(icehockey_config_path)
else:
raise ValueError('incorrect model type {0}'.format(model_type))
calibration_features = ['period', 'scoreDifferential', 'zone', 'manpowerSituation', 'home_away']
calibration_bins = {'period': {'feature_name': ('period'), 'range': (1, 2, 3, 4)},
'scoreDifferential': {'feature_name': ('scoreDifferential'), 'range': range(-10, 10)},
'zone': {'feature_name': ('zone'), 'range': ('dz', 'nz', 'oz')},
'manpowerSituation': {'feature_name': ('manpowerSituation'),
'range': ('shortHanded', 'evenStrength', 'powerPlay')},
'home_away': {'feature_name': ('home_away'), 'range': (1, 0)},
# TODO: we must add the home/away label
}
source_data_dir = '/Local-Scratch/oschulte/Galen/2018-2019/'
model_data_store_dir = '/Local-Scratch/oschulte/Galen/Ice-hockey-data/2018-2019'
Cali = Calibration(bins=calibration_bins, source_data_dir=source_data_dir,
calibration_features=calibration_features, config=config,
model_data_store_dir=model_data_store_dir, apply_old=apply_old,
apply_difference=apply_difference,
model_type=model_type, model_number=model_number,
player_info=player_info, calibration_type='next_goal',
# testing_dir_all=testing_dir_games_all[:1],
embed_mode=embed_mode,
focus_actions_list=[],
if_apply_cv=True,
running_numbers=running_numbers)
Cali.construct_bin_dicts()
for running_number in running_numbers:
saved_network_dir, log_dir = get_model_and_log_name(config=config,
model_catagoery=model_type,
running_number=running_number)
with open(saved_network_dir + '/testing_file_dirs_all.csv', 'rb') as f:
# with open('../../sport_resource/ice_hockey_201819/' + '/testing_file_dirs_all.csv', 'rb') as f:
testing_dir_all = f.readlines()
testing_dir_games_all = []
for testing_dir in testing_dir_all:
testing_dir_games_all.append(str(int(testing_dir)) + '-playsequence-wpoi.json')
Cali.aggregate_calibration_values(testing_dir_games_all=testing_dir_games_all, running_number=running_number)
Cali.compute_distance()
print Cali.save_calibration_dir
def run():
training = True
local_test_flag = False
box_msg = ''
predict_action = '_predict_nex_goal'
embed_mode = '_embed_random'
running_number = 0 # running_number is [0, 1, 2, 3, 4] for 5-fold cross validation
if len(predict_action) > 0:
extra_prediction_flag = True
else:
extra_prediction_flag = False
player_id_cluster_dir = '../sport_resource/ice_hockey_201819/local_player_id_2018_2019.json'
predicted_target = '_PlayerLocalId' # playerId_
icehockey_cvrnn_config_path = "../environment_settings/" \
"icehockey_cvrnn{0}{2}_config{1}{3}.yaml".format(predicted_target,
box_msg,
predict_action,
embed_mode)
name_training_record_file = "record_training_cvrnn{0}{2}_config{1}{3}{4}.yaml".format(
predicted_target, box_msg, predict_action, embed_mode,
datetime.date.today().strftime('%Y-%m-%d-%H'))
icehockey_cvrnn_config = CVRNNCongfig.load(icehockey_cvrnn_config_path)
Prediction_MemoryBuffer.set_cache_memory(
cache_number=icehockey_cvrnn_config.Arch.Predict.output_size)
saved_network_dir, log_dir = get_model_and_log_name(
config=icehockey_cvrnn_config,
model_catagoery='cvrnn',
running_number=running_number,
date_msg='')
source_data_dir = icehockey_cvrnn_config.Learn.save_mother_dir + '/oschulte/Galen/2018-2019/' # you source data (before pre-preprocessing)
data_store_dir = icehockey_cvrnn_config.Learn.save_mother_dir + '/oschulte/Galen/Ice-hockey-data/2018-2019/' # your source data (after pre-preprocessing)
dir_games_all = os.listdir(data_store_dir)
# shuffle(dir_games_all) # randomly shuffle the list
if running_number == 0:
training_dir_games_all = dir_games_all[0:len(dir_games_all) / 5 * 4 -
running_number *
len(dir_games_all) / 5]
else:
training_dir_games_all = dir_games_all[0: len(dir_games_all) / 5 * 4 - running_number * len(dir_games_all) / 5] \
+ dir_games_all[-running_number * len(dir_games_all) / 5:]
test_validate_dir_games_all = [
item for item in dir_games_all if item not in training_dir_games_all
]
testing_dir_games_all = test_validate_dir_games_all[:len(
test_validate_dir_games_all) / 2]
validate_dir_games_all = test_validate_dir_games_all[
len(test_validate_dir_games_all) / 2:]
tmp_testing_dir_games_all = testing_dir_games_all[
-10:] # TODO: it is a small running testing, not the real one
number_of_total_game = len(dir_games_all)
icehockey_cvrnn_config.Learn.number_of_total_game = number_of_total_game
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
sess = tf.Session()
cvrnn = CVRNN(config=icehockey_cvrnn_config,
extra_prediction_flag=extra_prediction_flag,
deterministic_decoder=True)
cvrnn()
sess.run(tf.global_variables_initializer())
if not local_test_flag:
if not os.path.exists(saved_network_dir):
os.mkdir(saved_network_dir)
# save the training and testing dir list
if os.path.exists(saved_network_dir + '/training_file_dirs_all.csv'):
os.rename(
saved_network_dir + '/training_file_dirs_all.csv',
saved_network_dir +
'/bak_training_file_dirs_all_{0}.csv'.format(
datetime.date.today().strftime("%Y%B%d")))
if os.path.exists(saved_network_dir + '/testing_file_dirs_all.csv'):
os.rename(
saved_network_dir + '/testing_file_dirs_all.csv',
saved_network_dir +
'/bak_testing_file_dirs_all_{0}.csv'.format(
datetime.date.today().strftime("%Y%B%d")))
if os.path.exists(saved_network_dir + '/validate_file_dirs_all.csv'):
os.rename(
saved_network_dir + '/validate_file_dirs_all.csv',
saved_network_dir +
'/bak_validate_file_dirs_all_{0}.csv'.format(
datetime.date.today().strftime("%Y%B%d")))
with open(saved_network_dir + '/training_file_dirs_all.csv',
'wb') as f:
for dir in training_dir_games_all:
f.write(dir + '\n')
with open(saved_network_dir + '/validate_file_dirs_all.csv',
'wb') as f:
for dir in validate_dir_games_all:
f.write(dir + '\n')
with open(saved_network_dir + '/testing_file_dirs_all.csv', 'wb') as f:
for dir in testing_dir_games_all:
f.write(dir + '\n')
print('training the model.')
with open(name_training_record_file, 'w') as training_file:
run_network(sess=sess,
model=cvrnn,
config=icehockey_cvrnn_config,
log_dir=log_dir,
save_network_dir=saved_network_dir,
data_store=data_store_dir,
source_data_dir=source_data_dir,
training_dir_games_all=training_dir_games_all,
testing_dir_games_all=tmp_testing_dir_games_all,
player_id_cluster_dir=player_id_cluster_dir,
training_file=training_file)
sess.close()
def run_calibration():
model_type = 'cvrnn'
player_info = '_box'
apply_old = False
apply_difference = False
if model_type == 'cvrnn':
model_number = 2101
predicted_target = '_PlayerLocalId'
icehockey_config_path = "../../environment_settings/icehockey_cvrnn{0}_config{1}.yaml".format(
predicted_target, player_info)
config = CVRNNCongfig.load(icehockey_config_path)
elif model_type == 'lstm_Qs':
model_number = 2101
icehockey_config_path = "../../environment_settings/ice_hockey_predict_Qs_lstm{0}.yaml".format(
player_info)
config = LSTMQsCongfig.load(icehockey_config_path)
else:
raise ValueError('incorrect model type {0}'.format(model_type))
calibration_features = [
'period', 'scoreDifferential', 'zone', 'manpowerSituation', 'home_away'
]
calibration_bins = {
'period': {
'feature_name': ('period'),
'range': (1, 2, 3, 4)
},
'scoreDifferential': {
'feature_name': ('scoreDifferential'),
'range': range(-10, 10)
},
'zone': {
'feature_name': ('zone'),
'range': ('dz', 'nz', 'oz')
},
'manpowerSituation': {
'feature_name': ('manpowerSituation'),
'range': ('shortHanded', 'evenStrength', 'powerPlay')
},
'home_away': {
'feature_name': ('home_away'),
'range': (1, 0)
},
# TODO: we must add the home/away label
}
source_data_dir = '/Local-Scratch/oschulte/Galen/2018-2019/'
model_data_store_dir = '/Local-Scratch/oschulte/Galen/Ice-hockey-data/2018-2019'
Cali = Calibration(bins=calibration_bins,
source_data_dir=source_data_dir,
calibration_features=calibration_features,
config=config,
model_data_store_dir=model_data_store_dir,
apply_old=apply_old,
apply_difference=apply_difference,
model_type=model_type,
model_number=model_number,
player_info=player_info,
focus_actions_list=[])
Cali.construct_bin_dicts()
Cali.aggregate_calibration_values()
Cali.compute_distance()
print Cali.save_calibration_dir
def run_calibration():
model_type = 'cvrnn'
player_info = '_box'
apply_old = False
apply_difference = True
if model_type == 'cvrnn':
model_number = 2101
predicted_target = '_PlayerLocalId'
icehockey_config_path = "../../environment_settings/icehockey_cvrnn{0}_config{1}.yaml".format(
predicted_target, player_info)
config = CVRNNCongfig.load(icehockey_config_path)
elif model_type == 'lstm_diff':
model_number = 2101
icehockey_config_path = "../../environment_settings/ice_hockey_predict_score_diff_lstm{0}.yaml".format(
player_info)
config = LSTMQsCongfig.load(icehockey_config_path)
else:
raise ValueError('incorrect model type {0}'.format(model_type))
calibration_features = [
'period', 'scoreDifferential', 'zone', 'manpowerSituation', 'home_away'
]
calibration_bins = {
'period': {
'feature_name': ('period'),
'range': (1, 2, 3, 4)
},
'scoreDifferential': {
'feature_name': ('scoreDifferential'),
'range': range(-10, 10)
},
'zone': {
'feature_name': ('zone'),
'range': ('dz', 'nz', 'oz')
},
'manpowerSituation': {
'feature_name': ('manpowerSituation'),
'range': ('shortHanded', 'evenStrength', 'powerPlay')
},
'home_away': {
'feature_name': ('home_away'),
'range': (1, 0)
},
# TODO: we must add the home/away label
}
source_data_dir = '/Local-Scratch/oschulte/Galen/2018-2019/'
model_data_store_dir = '/Local-Scratch/oschulte/Galen/Ice-hockey-data/2018-2019'
testing_dir_games_all = []
# with open('../../sport_resource/ice_hockey_201819/testing_file_dirs_all.csv', 'rb') as f:
saved_network_dir, log_dir = get_model_and_log_name(
config=config, model_catagoery=model_type)
# with open(saved_network_dir + '/testing_file_dirs_all.csv', 'rb') as f:
with open(
'../../sport_resource/ice_hockey_201819/' +
'/testing_file_dirs_all.csv', 'rb') as f:
testing_dir_all = f.readlines()
for testing_dir in testing_dir_all:
testing_dir_games_all.append(
str(int(testing_dir)) + '-playsequence-wpoi.json')
Cali = Calibration(bins=calibration_bins,
source_data_dir=source_data_dir,
calibration_features=calibration_features,
config=config,
model_data_store_dir=model_data_store_dir,
apply_old=apply_old,
apply_difference=apply_difference,
model_type=model_type,
model_number=model_number,
player_info=player_info,
calibration_type='score_diff',
testing_dir_all=testing_dir_games_all,
focus_actions_list=[])
Cali.construct_bin_dicts()
Cali.aggregate_calibration_values()
Cali.compute_distance()
print Cali.save_calibration_dir
