evaluation
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print('test')
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evaluation.py
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evaluation.py
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import pandas as pd
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import numpy as np
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from tensorflow import keras
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import matplotlib.pyplot as plt
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from sklearn.metrics import mean_squared_error
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vgsales_model = 'vgsales_model.h5'
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model = keras.models.load_model(vgsales_model)
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vgsales_test = pd.read_csv('test.csv')
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vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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y_test = vgsales_test[['Nintendo']]
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predictions = model.predict(X_test)
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error = mean_squared_error(y_test, predictions)
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print('Error: ', error)
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with open('results.txt', 'a') as f:
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f.write(str(error) + "\n")
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with open('results.txt', 'r') as f:
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lines = f.readlines()
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fig = plt.figure(figsize=(5,5))
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chart = fig.add_subplot()
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chart.set_ylabel("MSE")
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chart.set_xlabel("Build")
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x = np.arange(0, len(lines), 1)
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y = [float(x) for x in lines]
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plt.plot(x, y, "ro")
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plt.savefig("plot.png")
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