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78 lines
1.9 KiB
Python
Executable File
78 lines
1.9 KiB
Python
Executable File
import numpy as np
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""" Here I implemented the scoring functions.
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MAE, MSE, RMSE, RMSLE are included.
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Those are used for calculating differences between
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predicted values and actual values.
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Metrics are slightly differentiated. Sometimes squared, rooted,
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even log is used.
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Using log and roots can be perceived as tools for penalizing big
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erors. However, using appropriate metrics depends on the situations,
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and types of data
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"""
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#Mean Absolute Error
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def mae(predict, actual):
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predict = np.array(predict)
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actual = np.array(actual)
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difference = abs(predict - actual)
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score = difference.mean()
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return score
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#Mean Squared Error
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def mse(predict, actual):
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predict = np.array(predict)
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actual = np.array(actual)
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difference = predict - actual
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square_diff = np.square(difference)
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score = square_diff.mean()
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return score
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#Root Mean Squared Error
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def rmse(predict, actual):
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predict = np.array(predict)
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actual = np.array(actual)
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difference = predict - actual
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square_diff = np.square(difference)
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mean_square_diff = square_diff.mean()
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score = np.sqrt(mean_square_diff)
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return score
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#Root Mean Square Logarithmic Error
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def rmsle(predict, actual):
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predict = np.array(predict)
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actual = np.array(actual)
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log_predict = np.log(predict+1)
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log_actual = np.log(actual+1)
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difference = log_predict - log_actual
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square_diff = np.square(difference)
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mean_square_diff = square_diff.mean()
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score = np.sqrt(mean_square_diff)
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return score
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#Mean Bias Deviation
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def mbd(predict, actual):
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predict = np.array(predict)
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actual = np.array(actual)
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difference = predict - actual
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numerator = np.sum(difference) / len(predict)
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denumerator = np.sum(actual) / len(predict)
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print str(numerator)
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print str(denumerator)
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score = float(numerator) / denumerator * 100
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return score |