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/"):
