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 all-subsequences

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scoring_functions.py

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import numpy as np

""" Here I implemented the scoring functions.

MAE, MSE, RMSE, RMSLE are included.

Those are used for calculating differences between

predicted values and actual values.

Metrics are slightly differentiated. Sometimes squared, rooted,

even log is used.

Using log and roots can be perceived as tools for penalizing big

erors. However, using appropriate metrics depends on the situations,

and types of data

"""

#Mean Absolute Error

def mae(predict, actual):

predict = np.array(predict)

actual = np.array(actual)

difference = abs(predict - actual)

score = difference.mean()

return score

#Mean Squared Error

def mse(predict, actual):

predict = np.array(predict)

actual = np.array(actual)

difference = predict - actual

square_diff = np.square(difference)

score = square_diff.mean()

return score

#Root Mean Squared Error

def rmse(predict, actual):

predict = np.array(predict)

actual = np.array(actual)

difference = predict - actual

square_diff = np.square(difference)

mean_square_diff = square_diff.mean()

score = np.sqrt(mean_square_diff)

return score

#Root Mean Square Logarithmic Error

def rmsle(predict, actual):

predict = np.array(predict)

actual = np.array(actual)

log_predict = np.log(predict+1)

log_actual = np.log(actual+1)

difference = log_predict - log_actual

square_diff = np.square(difference)

mean_square_diff = square_diff.mean()

score = np.sqrt(mean_square_diff)

return score

#Mean Bias Deviation

def mbd(predict, actual):

predict = np.array(predict)

actual = np.array(actual)

difference = predict - actual

numerator = np.sum(difference) / len(predict)

denumerator = np.sum(actual) / len(predict)

print(numerator)

print(denumerator)

score = float(numerator) / denumerator * 100

return score