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import pandas as pd
import numpy as np
import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
import tensorflow as tf
import keras
from keras import layers

import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
# from sklearn.metrics import accuracy_score

# Random seed
seed = 42
tf.config.experimental.enable_op_determinism()
tf.random.set_seed(seed)
keras.utils.set_random_seed(812)

# get the generated data into train & test folder
df = pd.read_csv('./nominal_data/train.csv')
df_train, df_test = train_test_split(df, test_size=0.2)

# Constant Flow mode
# input will be rpm & flow & dp_sidecar
# output will be supply_temp & dp_rpm_available

#Train Data
x_train = df_train[['pump1_rpm', 'liquid_flow', 'dp_sidecar']]
y_train = df_train[['supply_temp', 'dp_rpm_available']]
# Test Data
x_test = df_test[['pump1_rpm', 'liquid_flow', 'dp_sidecar']]
y_test = df_test[['supply_temp','dp_rpm_available']]

#print (x_train)
# scaler = MinMaxScaler(feature_range=(0,1))
# scaled_x_train = scaler.fit_transform(x_train)
# scaled_y_train = scaler.fit_transform(y_train)

model = keras.Sequential(name="my_sequential")
model.add(layers.Dense(32, activation="relu", name="layer1", input_shape = (3,)))
model.add(layers.Dense(64, activation="relu", name="layer2"))
model.add(layers.Dense(64, activation="relu", name="layer3"))
model.add(layers.Dense(32, activation="relu", name="layer4"))
model.add(layers.Dense(2, activation="linear", name="layer5"))
#
model.compile(loss='mse', optimizer= 'adam', metrics = ['mse','mae'])
model.fit(x_train, y_train, epochs = 1000, batch_size = 1200, verbose = 0,  validation_data = (x_test, y_test))
#
results = model.evaluate(x_test, y_test)
print("test loss, test acc:", results)