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def _leaderboard_compute_overall_score(self, N=100):
"""Based on NULL distribution, compute overall score of model1
Not finalised.
"""
self._compute_pvalues_pred1(N=N)
self._compute_pvalues_param1(N=N)
import fitter
fit_param1 = fitter.Fitter(self.rdistance_param1)
fit_param1.distributions = ['beta']
fit_param1.fit()
fit_pred1 = fitter.Fitter(self.rdistance_pred1)
fit_pred1.distributions = ['beta']
fit_pred1.fit()
import scipy.stats
self.pvalues_param1 = scipy.stats.beta.cdf(self.scores['param1'].scores,
*fit_param1.fitted_param['beta'])
self.pvalues_pred1 = scipy.stats.beta.cdf(self.scores['pred1'].scores,
*fit_pred1.fitted_param['beta'])
self.scores['pred1']['pvalues'] = self.pvalues_pred1
self.scores['param1']['pvalues'] = self.pvalues_param1
def _leaderboard_compute_overall_score(self, N=100):
"""Based on NULL distribution, compute overall score of model1
Not finalised.
"""
self._compute_pvalues_pred1(N=N)
self._compute_pvalues_param1(N=N)
import fitter
fit_param1 = fitter.Fitter(self.rdistance_param1)
fit_param1.distributions = ['beta']
fit_param1.fit()
fit_pred1 = fitter.Fitter(self.rdistance_pred1)
fit_pred1.distributions = ['beta']
fit_pred1.fit()
import scipy.stats
self.pvalues_param1 = scipy.stats.beta.cdf(self.scores['param1'].scores,
*fit_param1.fitted_param['beta'])
self.pvalues_pred1 = scipy.stats.beta.cdf(self.scores['pred1'].scores,
*fit_pred1.fitted_param['beta'])
self.scores['pred1']['pvalues'] = self.pvalues_pred1
self.scores['param1']['pvalues'] = self.pvalues_param1