from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import sys
from sklearn.preprocessing import StandardScaler, LabelEncoder
from tensorflow.keras.optimizers import Adam
from sacred.observers import FileStorageObserver
from sacred import Experiment
from datetime import datetime


wine=pd.read_csv('train.csv')

y = wine['quality']
x = wine.drop('quality', axis=1)

citricacid = x['fixed acidity'] * x['citric acid']
citric_acidity = pd.DataFrame(citricacid, columns=['citric_accidity'])

density_acidity = x['fixed acidity'] * x['density']
density_acidity = pd.DataFrame(density_acidity, columns=['density_acidity'])


x = wine.join(citric_acidity).join(density_acidity)

bins = (2, 5, 8)
gnames = ['bad', 'nice']
y = pd.cut(y, bins = bins, labels = gnames)

enc = LabelEncoder()
yenc = enc.fit_transform(y)

scale = StandardScaler()
scaled_x = scale.fit_transform(x)

NeuralModel = Sequential([
                          Dense(128, activation='relu', input_shape=(14,)),
                          Dense(32, activation='relu'),
                          Dense(64, activation='relu'),
                          Dense(64, activation='relu'),
                          Dense(64, activation='relu'),
                          Dense(1, activation='sigmoid')
])

rms = Adam(lr=0.0003)

NeuralModel.compile(optimizer=rms, loss='binary_crossentropy', metrics=['accuracy'])

NeuralModel.fit(scaled_x, yenc, batch_size=int(sys.argv[1]), epochs = int(sys.argv[2])) #verbose = 1

NeuralModel.save('wine_model.h5')