import pandas as pd import numpy as np from model_manager import ModelManager import matplotlib.pyplot as plt import matplotlib.patches as mpatches import os from sklearn.externals import joblib manager = ModelManager() train = pd.concat( manager.read_data(global_dirs.splitted_data_path, formats=["hdf"], type="train", verbose=False)[1].values()) validation = pd.concat( manager.read_data(global_dirs.splitted_data_path, formats=["hdf"], type="validation", verbose=False)[1].values()) manager.assign_sets(train=train) tup = manager.create_mask( train.iloc[:, :-1], global_dirs.variable_selection[0], select=global_dirs.variable_selection[1] ) # This tuple shouldn't take care about y_column index scalers = manager.preprocess_train(tup, scale_Y=True)
import global_dirs import pandas as pd import numpy as np from model_manager import ModelManager import matplotlib.pyplot as plt import matplotlib.patches as mpatches import os from sklearn.externals import joblib manager=ModelManager() train=pd.concat(manager.read_data(global_dirs.splitted_data_path, formats=["hdf"], type="train",verbose=False)[1].values()) validation=pd.concat(manager.read_data(global_dirs.splitted_data_path, formats=["hdf"], type="validation",verbose=False)[1].values()) manager.assign_sets(train=train, val=validation) tup = manager.create_mask(train.iloc[:,:-1], global_dirs.variable_selection[0], select=global_dirs.variable_selection[1]) #This tuple shouldn't take care about y_column index scalers=manager.preprocess_train(tup,scale_Y=False) mlp_model = manager.fit_mlp_regression() if not os.path.isdir(global_dirs.results_path): os.mkdir(global_dirs.results_path) if not os.path.isdir(global_dirs.mlp_path): os.mkdir(global_dirs.mlp_path) if not os.path.isdir(global_dirs.mlp_path+"scalers/"): os.mkdir(global_dirs.mlp_path+"scalers/") if not os.path.isdir(global_dirs.mlp_path+"model/"):