57 lines
2.2 KiB
Python
Executable File
57 lines
2.2 KiB
Python
Executable File
from sklearn.metrics import explained_variance_score,r2_score
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#from sklearn.metrics import mean_squared_error
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import pandas as pd
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class Backtesting:
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def __init__(self, original_ytrain, train_predict, original_ytest, test_predict):
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self.original_ytrain = original_ytrain
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self.train_predict = train_predict
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self.original_ytest = original_ytest
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self.test_predict = test_predict
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def run(self):
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'''
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Explained variance regression score:
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The explained variance score explains the dispersion of errors of a given dataset,
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and the formula is written as follows:
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Here, and Var(y) is the variance of prediction errors and actual values respectively.
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Scores close to 1.0 are highly desired, indicating better squares of standard deviations of errors.
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print('Variance score:')
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print("Train data explained variance regression score:", explained_variance_score(self.original_ytrain, self.train_predict))
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print("Test data explained variance regression score:", explained_variance_score(self.original_ytest, self.test_predict))
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print('=================')
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R2 score for regression
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R-squared (R2) is a statistical measure that represents the proportion of the variance
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for a dependent variable that's explained by an independent variable or variables in
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a regression model
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1 = Best
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0 or < 0 = worse
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print('R2 score:')
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print("Train data R2 score:", r2_score(self.original_ytrain, self.train_predict))
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print("Test data R2 score:", r2_score(self.original_ytest, self.test_predict))
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print('=================')
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print('Mean squared error:')
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print("Train data accuracy regression score:", mean_squared_error(self.original_ytrain, self.train_predict))
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print("Test data accuracy regression score:", mean_squared_error(self.original_ytest, self.test_predict))
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print('=================')
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'''
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res = pd.DataFrame(index=['metrics'])
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res['train_variance_score'] = explained_variance_score(self.original_ytrain, self.train_predict)
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res['test_variance_score'] = explained_variance_score(self.original_ytest, self.test_predict)
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res['train_r2_score'] = r2_score(self.original_ytrain, self.train_predict)
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res['test_r2_score'] = r2_score(self.original_ytest, self.test_predict)
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res=res.reset_index(drop=True)
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return res |