import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from keras.layers import Dense, Dropout, LSTM
import matplotlib.pyplot as plt


df = pd.read_csv('aat.us.txt', names=["Data","Open","High","Low","Close"], usecols=[0,1,2,3,4] , parse_dates=True, index_col=0)


t_train, val_train, t_target, val_target = train_test_split(df, df['Open'] , test_size=0.2 , shuffle=False)



t_target = np.array(t_target[1:])
val_train =  np.array(val_train[0:(len(val_train)-1)])
t_train = np.array(t_train[0:(len(t_train)-1)])
val_target = np.array(val_target[1:])

t_target = np.reshape(t_target,(len(t_target),1))

val_target = np.reshape(val_target,(len(val_target),1))


scaler = MinMaxScaler(feature_range=(0, 1))
t_target = scaler.fit_transform(t_target)
val_train = scaler.fit_transform(val_train)
t_train = scaler.fit_transform(t_train)
val_target = scaler.fit_transform(val_target)


clf = MLPRegressor(hidden_layer_sizes=(5, ), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001)
clf.fit(t_train,t_target.ravel())
clf.predict(t_train)
plt.plot(clf.predict(t_train))
plt.show()
print(clf.score(t_train,t_target))