fixed bug in QRF predict_quantiles

supsi-dacd-isaac · Sep 9, 2024 · d522cab · d522cab
1 parent d7a20c4
commit d522cab
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diff --git a/pyforecaster/forecasting_models/randomforests.py b/pyforecaster/forecasting_models/randomforests.py
@@ -157,7 +157,6 @@ def predict(self, x, **kwargs):
             else:
                 if len(preds.shape) == 2:
                     preds = np.expand_dims(preds, 0)
-                preds = np.swapaxes(preds, 1, 2)
                 preds = self.quantiles_to_df(preds, index=x.index, q_vect=kwargs['quantiles'])
         else:
             preds = pd.DataFrame(np.atleast_2d(np.squeeze(preds)), index=x.index, columns=self.target_cols)

diff --git a/tests/test_models.py b/tests/test_models.py
@@ -188,17 +188,6 @@ def test_qrf(self):
         formatter.add_target_transform(['all'], lags=-np.arange(6))
         formatter.add_target_normalizer(['all'], 'mean', agg_freq='3d', name='a_movingavg')
         formatter.add_target_normalizer(['all'], 'std', agg_freq='3d', name='a_movingstd')
-
-        #m_lin = LinearForecaster(val_ratio=0.2, formatter=formatter).fit(x_tr, y_tr)
-        #y_hat_nonorm = m_lin.predict(x_te)
-        #q_nonorm = m_lin.predict_quantiles(x_te)
-
-        #m_lgb = LGBForecaster(val_ratio=0.5, lgb_pars={'num_leaves':20}, formatter=formatter).fit(x_tr, y_tr)
-        #y_hat_lgb = m_lgb.predict(x_te)
-        #mae = lambda x, y: np.abs(x-y).mean().mean()
-        #print('MAE lin:', mae(y_te, y_hat_nonorm))
-
-
         formatter.add_normalizing_fun(expr="(df[t] - df['a_movingavg']) / (df['a_movingstd'] + 1)",
                                       inv_expr="df[t]*(df['a_movingstd']+1) + df['a_movingavg']")
         x, y_norm = formatter.transform(self.data.iloc[:1000])