fixed QRF quantile format

pyforecaster/forecaster.py

@@ -104,9 +104,9 @@ def anti_transform(self, x, y_hat):
     def _predict_quantiles(self, x, **kwargs):
         pass
 
-    def quantiles_to_df(self, q_hat:np.ndarray, index):
+    def quantiles_to_df(self, q_hat:np.ndarray, index, q_vect=None):
         level_0_labels = self.target_cols
-        level_1_labels = self.q_vect
+        level_1_labels = self.q_vect if q_vect is None else q_vect
         q_hat = np.swapaxes(q_hat, 1, 2)
         q_hat = np.reshape(q_hat, (q_hat.shape[0], q_hat.shape[1] * q_hat.shape[2]))
         q_hat = pd.DataFrame(q_hat, index=index, columns=pd.MultiIndex.from_product([level_0_labels, level_1_labels]))

pyforecaster/forecasting_models/randomforests.py

@@ -59,7 +59,6 @@ def __init__(self, n_estimators=100, q_vect=None, val_ratio=None, nodes_at_step=
         self.max_features = max_features
         self.max_leaf_nodes = max_leaf_nodes
         self.min_impurity_decrease = min_impurity_decrease
-        self.default_quantiles = q_vect
         self.criterion = criterion
         self.ccp_alpha = ccp_alpha
         self.parallel = parallel
@@ -81,7 +80,7 @@ def __init__(self, n_estimators=100, q_vect=None, val_ratio=None, nodes_at_step=
             "max_features": max_features ,
             "max_leaf_nodes": max_leaf_nodes,
             "min_impurity_decrease": min_impurity_decrease,
-            "default_quantiles":q_vect,
+            "default_quantiles":q_vect if q_vect is not None else 'mean',
             "criterion":criterion,
              "ccp_alpha":ccp_alpha
         }
@@ -159,7 +158,7 @@ def predict(self, x, **kwargs):
                 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)
+                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)
         y_hat = self.anti_transform(x, preds)
@@ -171,12 +170,17 @@ def _predict(self, i, x, period, **kwargs):
                                         keep_last_seconds=self.keep_last_seconds,
                                         tol_period=self.tol_period, period=period)
         p = self.models[i].predict(x_i, quantiles=list(kwargs['quantiles']) if 'quantiles' in kwargs else 'mean')
+        if len(p.shape) == 1:
+            p = np.expand_dims(p, 1)
         return p
 
     def predict_single(self, i, x, quantiles='mean', add_step=True):
         if add_step:
             x = pd.concat([x.reset_index(drop=True), pd.Series(np.ones(len(x)) * i, name='sa')], axis=1)
-        return self.multi_step_model.predict(x, quantiles)
+        preds = self.multi_step_model.predict(x, quantiles)
+        if len(preds.shape) == 1:
+            preds = np.expand_dims(preds, 1)
+        return preds
 
     def predict_parallel(self, x, quantiles='mean', add_step=True):
         with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_parallel_workers) as executor:

tests/test_models.py

@@ -4,6 +4,9 @@
 import pandas as pd
 import numpy as np
 import logging
+
+from ray.tune import quniform
+
 from pyforecaster.forecasting_models.holtwinters import HoltWinters, HoltWintersMulti
 from pyforecaster.forecasting_models.fast_adaptive_models import Fourier_es, FK, FK_multi
 from pyforecaster.forecasting_models.random_fourier_features import RFFRegression, AdditiveRFFRegression, BrutalRegressor
@@ -212,7 +215,8 @@ def test_qrf(self):
 
         qrf = QRF(val_ratio=0.2, formatter=formatter, n_jobs=4, n_single=2).fit(x_tr, y_tr)
         y_hat = qrf.predict(x_te)
-        q = qrf.predict_quantiles(x_te)
+        q = qrf.predict_quantiles(x_te, quantiles=[0.1, 0.9])
+        q = qrf.predict_quantiles(x_te, quantiles=[0.5])
 
         #plot_quantiles([y_te, y_hat], q, ['y_te', 'y_hat', 'y_hat_qrf'])