automatically encode categorical features in QRF

pyforecaster/forecasting_models/randomforests.py

@@ -2,7 +2,7 @@
 import pandas as pd
 from pyforecaster.utilities import get_logger
 from tqdm import tqdm
-from pyforecaster.forecaster import ScenarioGenerator
+from pyforecaster.forecaster import ScenarioGenerator, encode_categorical
 import numpy as np
 import concurrent.futures
 from time import time
@@ -94,6 +94,7 @@ def _fit(self, i, x, y):
         model = RandomForestQuantileRegressor(**self.qrf_pars).fit(x_i, y.iloc[:, i], sparse_pickle=True)
         return model
 
+    @encode_categorical
     def fit(self, x, y):
         x, y, x_val, y_val = self.train_val_split(x, y)
         if self.parallel:
@@ -135,6 +136,7 @@ def fit(self, x, y):
         super().fit(x_val, y_val)
         return self
 
+    @encode_categorical
     def predict(self, x, **kwargs):
         preds = []
         period = kwargs['period'] if 'period' in kwargs else '24h'