Add examples to CatBoost models (#181)

* Add examples to CatBoost models

* Update changelog

* Fix errors in doctest

* Align example sections correctly

CHANGELOG.md

@@ -31,6 +31,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Example for ProphetModel ([#178](https://github.com/tinkoff-ai/etna-ts/pull/178))
 - Instruction notebook for custom model and transform creation ([#180](https://github.com/tinkoff-ai/etna-ts/pull/180))
 - Add inverse_transform in *OutliersTransform ([#160](https://github.com/tinkoff-ai/etna-ts/pull/160))
+- Examples for CatBoostModelMultiSegment and CatBoostModelPerSegment ([#181](https://github.com/tinkoff-ai/etna-ts/pull/181))
 
 ### Changed
 - Delete offset from WindowStatisticsTransform ([#111](https://github.com/tinkoff-ai/etna-ts/pull/111))

etna/models/catboost.py

@@ -49,7 +49,47 @@ def predict(self, df: pd.DataFrame) -> list:
 
 
 class CatBoostModelPerSegment(PerSegmentModel):
-    """Class for holding per segment Catboost model."""
+    """Class for holding per segment Catboost model.
+
+    Examples
+    --------
+    >>> from etna.datasets import generate_periodic_df
+    >>> from etna.datasets import TSDataset
+    >>> from etna.models import CatBoostModelPerSegment
+    >>> from etna.transforms import LagTransform
+    >>> classic_df = generate_periodic_df(
+    ...     periods=100,
+    ...     start_time="2020-01-01",
+    ...     n_segments=4,
+    ...     period=7,
+    ...     sigma=3
+    ... )
+    >>> df = TSDataset.to_dataset(df=classic_df)
+    >>> ts = TSDataset(df, freq="D")
+    >>> horizon = 7
+    >>> transforms = [
+    ...     LagTransform(in_column="target", lags=[horizon, horizon+1, horizon+2])
+    ... ]
+    >>> ts.fit_transform(transforms=transforms)
+    >>> future = ts.make_future(horizon)
+    >>> model = CatBoostModelPerSegment()
+    >>> model.fit(ts=ts)
+    CatBoostModelPerSegment(iterations = None, depth = None, learning_rate = None,
+    logging_level = 'Silent', l2_leaf_reg = None, thread_count = None, )
+    >>> forecast = model.forecast(future)
+    >>> pd.options.display.float_format = '{:,.2f}'.format
+    >>> forecast[:, :, "target"]
+    segment    segment_0 segment_1 segment_2 segment_3
+    feature       target    target    target    target
+    timestamp
+    2020-04-10      9.00      9.00      4.00      6.00
+    2020-04-11      5.00      2.00      7.00      9.00
+    2020-04-12      0.00      4.00      7.00      9.00
+    2020-04-13      0.00      5.00      9.00      7.00
+    2020-04-14      1.00      2.00      1.00      6.00
+    2020-04-15      5.00      7.00      4.00      7.00
+    2020-04-16      8.00      6.00      2.00      0.00
+    """
 
     def __init__(
         self,
@@ -122,7 +162,47 @@ def __init__(
 
 
 class CatBoostModelMultiSegment(Model):
-    """Class for holding Catboost model for all segments."""
+    """Class for holding Catboost model for all segments.
+
+    Examples
+    --------
+    >>> from etna.datasets import generate_periodic_df
+    >>> from etna.datasets import TSDataset
+    >>> from etna.models import CatBoostModelMultiSegment
+    >>> from etna.transforms import LagTransform
+    >>> classic_df = generate_periodic_df(
+    ...     periods=100,
+    ...     start_time="2020-01-01",
+    ...     n_segments=4,
+    ...     period=7,
+    ...     sigma=3
+    ... )
+    >>> df = TSDataset.to_dataset(df=classic_df)
+    >>> ts = TSDataset(df, freq="D")
+    >>> horizon = 7
+    >>> transforms = [
+    ...     LagTransform(in_column="target", lags=[horizon, horizon+1, horizon+2])
+    ... ]
+    >>> ts.fit_transform(transforms=transforms)
+    >>> future = ts.make_future(horizon)
+    >>> model = CatBoostModelMultiSegment()
+    >>> model.fit(ts=ts)
+    CatBoostModelMultiSegment(iterations = None, depth = None, learning_rate = None,
+    logging_level = 'Silent', l2_leaf_reg = None, thread_count = None, )
+    >>> forecast = model.forecast(future)
+    >>> pd.options.display.float_format = '{:,.2f}'.format
+    >>> forecast[:, :, "target"].round()
+    segment    segment_0 segment_1 segment_2 segment_3
+    feature       target    target    target    target
+    timestamp
+    2020-04-10      9.00      9.00      4.00      6.00
+    2020-04-11      5.00      2.00      7.00      9.00
+    2020-04-12     -0.00      4.00      7.00      9.00
+    2020-04-13      0.00      5.00      9.00      7.00
+    2020-04-14      1.00      2.00      1.00      6.00
+    2020-04-15      5.00      7.00      4.00      7.00
+    2020-04-16      8.00      6.00      2.00      0.00
+    """
 
     def __init__(
         self,