ium_434765/neural_network.py

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error

from tensorflow import keras


def normalize_data(data):
    return (data - np.min(data)) / (np.max(data) - np.min(data))


data = pd.read_csv("data_train", sep=',', error_bad_lines=False).dropna()
X = data.loc[:,data.columns == "2805317"].astype(int)
y = data.loc[:,data.columns == "198909"].astype(int)

min_val_sub = np.min(X)
max_val_sub = np.max(X)
X = (X - min_val_sub) / (max_val_sub - min_val_sub)
print(min_val_sub)
print(max_val_sub)

min_val_like = np.min(y)
max_val_like = np.max(y)
y = (y - min_val_like) / (max_val_like - min_val_like)

print(min_val_like)
print(max_val_like)


model = keras.Sequential([
    keras.layers.Dense(512,input_dim = X.shape[1], activation='relu'),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(1,activation='linear'),
])

model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])

model.fit(X, y, epochs=30, validation_split = 0.3)

data = pd.read_csv("data_dev", sep=',', error_bad_lines=False).dropna()
X_test = data.loc[:,data.columns == "440265"].astype(int)
y_test = data.loc[:,data.columns == "21629"].astype(int)

min_val_sub = np.min(X_test)
max_val_sub = np.max(X_test)
X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
print(min_val_sub)
print(max_val_sub)

min_val_like = np.min(y_test)
max_val_like = np.max(y_test)
print(min_val_like)
print(max_val_like)

prediction = model.predict(X_test)

prediction_denormalized = []
for pred in prediction:
    denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
    prediction_denormalized.append(denorm)

f = open("predictions.txt", "w")
for (pred, test) in zip(prediction_denormalized, y_test.values):
    f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))

error = mean_squared_error(y_test, prediction_denormalized)
print(error)