ium_434704/linear_regression.py
Wojciech Jarmosz 9aa2b7d4ad
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Fixes for lab5, lab6
2021-05-15 03:50:19 +02:00

46 lines
1.4 KiB
Python

import pandas as pd
import numpy as np
import tensorflow as tf
import os.path
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
pd.set_option("display.max_columns", None)
# Wczytanie danych
train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")
test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")
# Stworzenie modelu
columns_to_use = ['Year', 'Runtime', 'Netflix']
train_X = tf.convert_to_tensor(train_data[columns_to_use])
train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
test_X = tf.convert_to_tensor(test_data[columns_to_use])
test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
normalizer = preprocessing.Normalization(input_shape=[3,])
normalizer.adapt(train_X)
model = keras.Sequential([
keras.Input(shape=(len(columns_to_use),)),
normalizer,
layers.Dense(30, activation='relu'),
layers.Dense(10, activation='relu'),
layers.Dense(25, activation='relu'),
layers.Dense(1)
])
model.compile(loss='mean_absolute_error',
optimizer=tf.keras.optimizers.Adam(0.001))
model.fit(train_X, train_Y, verbose=0, epochs=100)
# Predykcja na danych testowych
results = model.predict(test_X)
# Zapis danych do pliku
with open("results_lab5.txt", 'w') as file:
for result in results:
file.writelines(str(result[0]) + "\n")