70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
import tensorflow as tf
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from keras import layers
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from keras.models import save_model
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import sys
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# Pobranie przykładowego argumentu trenowania
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EPOCHS_NUM = int(sys.argv[1])
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# Wczytanie danych
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data_train = pd.read_csv('lego_sets_clean_train.csv')
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data_test = pd.read_csv('lego_sets_clean_test.csv')
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# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
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train_piece_counts = np.array(data_train['piece_count'])
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train_prices = np.array(data_train['list_price'])
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test_piece_counts = np.array(data_test['piece_count'])
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test_prices = np.array(data_test['list_price'])
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# Normalizacja
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normalizer = layers.Normalization(input_shape=[1, ], axis=None)
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normalizer.adapt(train_piece_counts)
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# Inicjalizacja
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model = tf.keras.Sequential([
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normalizer,
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layers.Dense(units=1)
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])
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# Kompilacja
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model.compile(
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optimizer=tf.optimizers.Adam(learning_rate=0.1),
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loss='mean_absolute_error'
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)
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# Trening
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history = model.fit(
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train_piece_counts,
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train_prices,
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epochs=EPOCHS_NUM,
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verbose=0,
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validation_split=0.2
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)
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# Wykonanie predykcji na danych ze zbioru testującego
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y_pred = model.predict(test_piece_counts)
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# Zapis predykcji do pliku
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results = pd.DataFrame({'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
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results.to_csv('lego_reg_results.csv', index=False, header=True)
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# Zapis modelu do pliku
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model.save('lego_reg_model')
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# Opcjonalne statystyki, wykresy
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'''
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hist = pd.DataFrame(history.history)
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hist['epoch'] = history.epoch
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print(hist.tail())
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plt.scatter(train_piece_counts, train_prices, label='Data')
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plt.plot(x, y_pred, color='k', label='Predictions')
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plt.xlabel('pieces')
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plt.ylabel('price')
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plt.legend()
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plt.show()
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'''
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