import sys
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Input, Dense, Activation,Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.models import Sequential

# odczytanie danych z plików
vgsales_train = pd.read_csv('train.csv')
vgsales_test = pd.read_csv('test.csv')
vgsales_dev = pd.read_csv('dev.csv')

vgsales_train['Nintendo'] = vgsales_train['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
vgsales_dev['Nintendo'] = vgsales_dev['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)

# podzial na X i y
X_train = vgsales_train.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
y_train = vgsales_train[['Nintendo']]
X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
y_test = vgsales_test[['Nintendo']]

print(X_train.shape[1])
# keras model
model = Sequential()
model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))

early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)

# kompilacja
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# model fit
epochs = int(sys.argv[1])
batch_size = int(sys.argv[2])

# trenowanie modelu
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))

# zapisanie modelu
model.save('vgsales_model.h5')