ium_434788/Zadanie_05_ML.py

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 wget
import numpy as np
import os

url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
wget.download(url, out='winequality-red.csv', bar=None)

wine=pd.read_csv('winequality-red.csv')
wine

y = wine.quality
y.head()

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

x=((x-x.min())/(x.max()-x.min())) #Normalizacja

x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)

def regression_model():
    model = Sequential()
    model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
    model.add(Dense(64,activation = "relu"))
    model.add(Dense(1,activation = "relu"))
    
    model.compile(optimizer = "adam", loss = "mean_squared_error")
    return model

model = regression_model()
model.fit(x_train, y_train, epochs = 600, verbose = 1)

y_pred = model.predict(x_test)

y_pred = np.around(y_pred, decimals=0)


dirpath = os.getcwd()
print("dirpath = ", dirpath, "\n")

output_path = os.path.join(dirpath,'output.csv')
print(output_path,"\n")

pd.DataFrame(y_pred).to_csv(output_path)



print(accuracy_score(y_test, y_pred))
print(classification_report(y_test,y_pred))