From 1561e26aae27dfe567173e669dfa2b49ce3d7e01 Mon Sep 17 00:00:00 2001 From: vascomedici Date: Tue, 10 Sep 2024 11:18:43 +0200 Subject: [PATCH] sparse_pickle False by default --- pyforecaster/forecasting_models/randomforests.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyforecaster/forecasting_models/randomforests.py b/pyforecaster/forecasting_models/randomforests.py index eeafff1..d678f5f 100644 --- a/pyforecaster/forecasting_models/randomforests.py +++ b/pyforecaster/forecasting_models/randomforests.py @@ -90,7 +90,7 @@ def _fit(self, i, x, y): logger=self.logger, method='periodic', keep_last_n_lags=self.keep_last_n_lags, keep_last_seconds=self.keep_last_seconds, tol_period=self.tol_period) - model = RandomForestQuantileRegressor(**self.qrf_pars).fit(x_i, y.iloc[:, i], sparse_pickle=True) + model = RandomForestQuantileRegressor(**self.qrf_pars).fit(x_i, y.iloc[:, i]) return model @encode_categorical @@ -111,7 +111,7 @@ def fit(self, x, y): qrf_pars_global = self.qrf_pars.copy() if 'n_jobs' in qrf_pars_global and qrf_pars_global['n_jobs'] is not None and qrf_pars_global['n_jobs'] > 0: qrf_pars_global['n_jobs'] *= self.max_parallel_workers - self.multi_step_model = RandomForestQuantileRegressor(**qrf_pars_global).fit(x, y.iloc[:, -self.n_multistep:], sparse_pickle=True) + self.multi_step_model = RandomForestQuantileRegressor(**qrf_pars_global).fit(x, y.iloc[:, -self.n_multistep:]) self.logger.info('QRF multistep fitted in {:0.2e} s, x shape: [{}, {}]'.format(time() - t_0, x.shape[0], x.shape[1]))