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1f8a21d727
* Tighten up psf/black and flake8
* Fix some tests
* Fix some E741
* Fix some E741
* updating DIRECTORY.md
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
138 lines
3.2 KiB
Python
Executable File
138 lines
3.2 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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errors. 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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"""
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Examples(rounded for precision):
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>>> actual = [1,2,3];predict = [1,4,3]
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>>> np.around(mae(predict,actual),decimals = 2)
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0.67
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>>> actual = [1,1,1];predict = [1,1,1]
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>>> mae(predict,actual)
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0.0
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"""
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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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"""
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Examples(rounded for precision):
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>>> actual = [1,2,3];predict = [1,4,3]
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>>> np.around(mse(predict,actual),decimals = 2)
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1.33
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>>> actual = [1,1,1];predict = [1,1,1]
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>>> mse(predict,actual)
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0.0
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"""
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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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"""
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Examples(rounded for precision):
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>>> actual = [1,2,3];predict = [1,4,3]
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>>> np.around(rmse(predict,actual),decimals = 2)
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1.15
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>>> actual = [1,1,1];predict = [1,1,1]
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>>> rmse(predict,actual)
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0.0
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"""
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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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"""
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Examples(rounded for precision):
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>>> actual = [10,10,30];predict = [10,2,30]
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>>> np.around(rmsle(predict,actual),decimals = 2)
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0.75
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>>> actual = [1,1,1];predict = [1,1,1]
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>>> rmsle(predict,actual)
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0.0
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"""
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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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"""
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This value is Negative, if the model underpredicts,
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positive, if it overpredicts.
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Example(rounded for precision):
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Here the model overpredicts
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>>> actual = [1,2,3];predict = [2,3,4]
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>>> np.around(mbd(predict,actual),decimals = 2)
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50.0
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Here the model underpredicts
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>>> actual = [1,2,3];predict = [0,1,1]
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>>> np.around(mbd(predict,actual),decimals = 2)
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-66.67
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"""
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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(numerator, denumerator)
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score = float(numerator) / denumerator * 100
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return score
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