fix ResampleWithDistributionTransform with holidays transform

CHANGELOG.md

@@ -31,7 +31,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Update `CONTRIBUTING.md` with scenarios of documentation updates and release instruction ([#77](https://github.com/etna-team/etna/pull/77))
 
 ### Fixed
--
+- Fix `ResampleWithDistributionTransform` working with categorical columns ([#82](https://github.com/etna-team/etna/pull/82))
 -
 - Fix links from tinkoff-ai/etna to etna-team/etna ([#47](https://github.com/etna-team/etna/pull/47))
 - 

etna/transforms/missing_values/resample.py

@@ -96,6 +96,7 @@
         :
             result dataframe
         """
+        df = df.apply(pd.to_numeric)
         df["fold"] = self._get_folds(df)
         df = df.reset_index().merge(self.distribution, on="fold").set_index("timestamp").sort_index()
         df[self.out_column] = df[self.in_column].ffill() * df["distribution"]

tests/test_transforms/test_missing_values/test_resample_transform.py

@@ -1,6 +1,10 @@
 import pytest
 
-from etna.transforms.missing_values import ResampleWithDistributionTransform
+from etna.metrics import MSE
+from etna.models import NaiveModel
+from etna.pipeline import Pipeline
+from etna.transforms import HolidayTransform
+from etna.transforms import ResampleWithDistributionTransform
 from tests.test_transforms.utils import assert_transformation_equals_loaded_original
 
 
@@ -132,3 +136,16 @@ def test_get_regressors_info_not_fitted():
 def test_params_to_tune():
     transform = ResampleWithDistributionTransform(in_column="regressor_exog", distribution_column="target")
     assert len(transform.params_to_tune()) == 0
+
+
+def test_working_with_categorical_columns(example_tsds):
+    model = NaiveModel()
+    holiday = HolidayTransform(out_column="holiday_regressor")
+    resample = ResampleWithDistributionTransform(distribution_column="target", in_column="holiday_regressor")
+    transforms = [holiday, resample]
+    pipeline = Pipeline(
+        model=model,
+        transforms=transforms,
+        horizon=2,
+    )
+    pipeline.backtest(ts=example_tsds, metrics=[MSE()], n_folds=2)