39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
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import pandas as pd
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import numpy as np
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import sys
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import os
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from keras.models import load_model
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from helper import prepare_tensors
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build_number = int(sys.argv[1])
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hp_test = pd.read_csv('hp_test.csv')
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X_test, Y_test = prepare_tensors(hp_test)
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model = load_model('hp_model.h5')
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test_predictions = model.predict(X_test)
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predictions_df = pd.DataFrame(test_predictions, columns=["Predicted_Price"])
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predictions_df.to_csv('hp_test_predictions.csv', index=False)
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rmse = np.sqrt(mean_squared_error(Y_test, test_predictions))
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mae = mean_absolute_error(Y_test, test_predictions)
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r2 = r2_score(Y_test, test_predictions)
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metrics_df = pd.DataFrame({
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'Build_Number': [build_number],
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'RMSE': [rmse],
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'MAE': [mae],
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'R2': [r2]
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})
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metrics_file = 'hp_test_metrics.csv'
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if os.path.isfile(metrics_file):
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existing_metrics_df = pd.read_csv(metrics_file)
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updated_metrics_df = pd.concat([existing_metrics_df, metrics_df], ignore_index=True)
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else:
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updated_metrics_df = metrics_df
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updated_metrics_df.to_csv(metrics_file, index=False)
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