resource_p, data_p, transition)
testset_dir = "../testsets"
log_config = {
"control_flow_p": control_flow_p,
"time_p": time_p,
"resource_p": resource_p,
"data_p": data_p,
"transition": transition
}
# load data
print("flag: loading data")
fg = FeatureGenerator()
df = fg.create_initial_log(filename, log_config)
print("done")
num_events = len(df)
num_cases = len(set(df["id"]))
# feature generation
print("flag: generating features")
if task == 'next_activity':
loss = 'categorical_crossentropy'
regression = False
feature_type_list = ["activity_history"]
df = fg.add_activity_history(df)
df = fg.add_next_activity(df)
elif task == 'next_timestamp':
model_name = args.data_set + args.task
contextual_info = args.contextual_info
if args.task == 'next_activity':
loss = 'categorical_crossentropy'
regression = False
elif args.task == 'next_timestamp':
loss = 'mae'
regression = True
batch_size = args.batch_size_train
num_folds = args.num_folds
# load data
FG = FeatureGenerator()
df = FG.create_initial_log(filename)
#split train and test
#train_df, test_df = FG.train_test_split(df, 0.7, 0.3)
train_df = df
test_df = train_df
#create train
train_df = FG.order_csv_time(train_df)
train_df = FG.queue_level(train_df)
train_df.to_csv('./training_data.csv')
state_list = FG.get_states(train_df)
train_X, train_Y_Event, train_Y_Time = FG.one_hot_encode_history(
train_df, args.checkpoint_dir + args.data_set)
if contextual_info:
train_context_X = FG.generate_context_feature(train_df, state_list)
model = net()