42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import tensorflow as tf
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import pandas as pd
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import numpy as np
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import sklearn
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import sklearn.model_selection
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from tensorflow.keras.models import load_model
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from sklearn.metrics import mean_absolute_error, mean_squared_error
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import mlflow
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# Wskazujemy ścieżkę do folderu, gdzie zostaną zapisane wyniki MLflow
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mlflow.set_tracking_uri("file:/mlflow")
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# Ustawiamy nazwę eksperymentu
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mlflow.set_experiment("nazwa eksperymentu")
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feature_cols = ['year', 'mileage', 'vol_engine']
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feature_cols = ['year', 'mileage', 'vol_engine']
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model = load_model('model.h5')
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test_data = pd.read_csv('test.csv')
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predictions = model.predict(test_data[feature_cols])
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predicted_prices = [p[0] for p in predictions]
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results = pd.DataFrame({'id': test_data['id'], 'year': test_data['year'], 'mileage': test_data['mileage'], 'vol_engine': test_data['vol_engine'], 'predicted_price': predicted_prices})
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results.to_csv('predictions.csv', index=False)
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y_true = test_data['price']
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y_pred = [round(p[0]) for p in predictions]
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mae = mean_absolute_error(y_true, y_pred)
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mse = mean_squared_error(y_true, y_pred)
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rmse = np.sqrt(mse)
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with open('metrics.txt', 'w') as f:
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f.write(f"MAE: {mae:.4f}\n")
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f.write(f"MSE: {mse:.4f}\n")
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f.write(f"RMSE: {rmse:.4f}\n") |