def test_LearnerSCCS_fit_KFold_CV(self): lrn = ConvSCCS(n_lags=self.n_lags, penalized_features=np.arange(self.n_features), random_state=self.seed, C_tv=1e-1, C_group_l1=1e-1) lrn.fit(self.features, self.labels, self.censoring) score = lrn.score() tv_range = (-5, -1) groupl1_range = (-5, -1) lrn.fit_kfold_cv(self.features, self.labels, self.censoring, C_tv_range=tv_range, C_group_l1_range=groupl1_range, n_cv_iter=4) self.assertTrue(lrn.score() <= score)
session.merge(sim_log) session.commit() n_features = n_base_features n_lags = np.repeat(49, n_features).astype("uint64") start = time() lrn = ConvSCCS( n_lags=n_lags, penalized_features=np.arange(n_features), verbose=False ) C_tv_range = (1, 5) C_group_l1_range = (1, 5) fitted_coeffs, cv_track = lrn.fit_kfold_cv( censored_features, labels, censoring, C_tv_range=C_tv_range, C_group_l1_range=C_group_l1_range, confidence_intervals=False, ) # WARNING: no bootstrap in this simulation elapsed_time = time() - start model_id = "ConvSCCS" model_log = ConvSCCSModel( experiment_id=experiment_id, version=version, seed=seed, model_id=model_id, run_time=elapsed_time, model_params=str(cv_track.model_params), cv_track=dumps(cv_track), )
features = [hstack([f, feat_agegrp]).tocsr() for f in features] censored_features = [ hstack([f, feat_agegrp]).tocsr() for f in censored_features ] n_lags = np.hstack([n_lags, np.zeros(n_agegrps)]) start = time() lrn = ConvSCCS( n_lags=n_lags, penalized_features=np.arange(n_features), verbose=False ) C_tv_range = (1, 5) C_group_l1_range = (1, 5) fitted_coeffs, cv_track = lrn.fit_kfold_cv( censored_features, labels, censoring, C_tv_range=C_tv_range, C_group_l1_range=C_group_l1_range, confidence_intervals=True ) elapsed_time = time() - start model_id = "ConvSCCS" model_log = ConvSCCSModel( experiment_id=experiment_id, version=version, seed=seed, model_id=model_id, run_time=elapsed_time, model_params=str(cv_track.model_params), cv_track=dumps(cv_track), )