added sample_weight to QRF

pyforecaster/forecasting_models/randomforests.py

@@ -85,23 +85,23 @@ def __init__(self, n_estimators=100, q_vect=None, val_ratio=None, nodes_at_step=
              "ccp_alpha":ccp_alpha
         }
 
-    def _fit(self, i, x, y):
+    def _fit(self, i, x, y, sample_weight=None):
         x_i = self.dataset_at_stepahead(x, i, self.metadata_features, formatter=self.formatter,
                                         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])
+        model = RandomForestQuantileRegressor(**self.qrf_pars).fit(x_i, y.iloc[:, i], sample_weight=sample_weight)
         return model
 
     @encode_categorical
-    def fit(self, x, y):
+    def fit(self, x, y, sample_weight=None):
         x, y, x_val, y_val = self.train_val_split(x, y)
         if self.parallel:
             with concurrent.futures.ProcessPoolExecutor(max_workers=self.max_parallel_workers) as executor:
-                self.models = [i for i in tqdm(executor.map(partial(self._fit, x=x, y=y), range(self.n_single)),total=self.n_single)]
+                self.models = [i for i in tqdm(executor.map(partial(self._fit, x=x, y=y, sample_weight=sample_weight), range(self.n_single)),total=self.n_single)]
         else:
             for i in tqdm(range(self.n_single)):
-                model = self._fit(i, x, y)
+                model = self._fit(i, x, y, sample_weight=sample_weight)
                 self.models.append(model)
 
         n_sa = y.shape[1]