42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn import metrics
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import matplotlib.pyplot as plt
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import tensorflow as tf
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import math
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from tensorflow import keras
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from process_dataset import process_data_and_get_x_y
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def show_result(x, y):
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plt.title('Usage kWh Model', fontsize=15, color='g', pad=12)
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plt.plot(x, y, 'o', color='r')
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m, b = np.polyfit(x, y, 1)
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plt.plot(x, m * x + b, color='darkblue')
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plt.xlabel('Actual')
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plt.ylabel('Predicted')
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plt.show()
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model = keras.models.load_model('steel_industry_model')
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energy_data_test = pd.read_csv('Steel_industry_data_test.csv')
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energy_data_test, x_test, y_test = process_data_and_get_x_y(energy_data_test)
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y_predicted = model.predict(x_test)
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test_results = {}
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test_results['usage_model'] = model.evaluate(
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x_test,
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y_test, verbose=0)
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print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
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print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_predicted))
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print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
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print(test_results['usage_model'])
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show_result(y_test, y_predicted)
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