Merge branch 'master' into issue-532

CHANGELOG.md

@@ -20,7 +20,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add plot_residuals ([#539](https://github.com/tinkoff-ai/etna/pull/539))
 - 
 - Create `PerSegmentBaseModel`, `PerSegmentPredictionIntervalModel` ([#537](https://github.com/tinkoff-ai/etna/pull/537))
-- 
+- Create `MultisegmentModel` ([#551](https://github.com/tinkoff-ai/etna/pull/551))
 ### Changed
 - Change the way `ProphetModel` works with regressors ([#383](https://github.com/tinkoff-ai/etna/pull/383))
 - Change the way `SARIMAXModel` works with regressors ([#380](https://github.com/tinkoff-ai/etna/pull/380)) 
@@ -46,7 +46,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fix regressors handling for `OneHotEncoderTransform` and `HolidayTransform` ([#518](https://github.com/tinkoff-ai/etna/pull/518))
 - 
 - 
-- 
+- Fix import Literal in plotters ([#558](https://github.com/tinkoff-ai/etna/pull/558))
 - 
 - 
 - 

etna/analysis/plotters.py

@@ -5,7 +5,6 @@
 from typing import Callable
 from typing import Dict
 from typing import List
-from typing import Literal
 from typing import Optional
 from typing import Sequence
 from typing import Tuple
@@ -18,6 +17,7 @@
 import plotly
 import plotly.graph_objects as go
 import seaborn as sns
+from typing_extensions import Literal
 
 from etna.transforms import Transform
 

etna/models/base.py

@@ -204,7 +204,7 @@ def fit(self, ts: TSDataset) -> "PerSegmentBaseModel":
 
         for segment, model in self._models.items():
             segment_features = ts[:, segment, :]
-            segment_features = segment_features.dropna()
+            segment_features = segment_features.dropna()  # TODO: https://github.com/tinkoff-ai/etna/issues/557
             segment_features = segment_features.droplevel("segment", axis=1)
             segment_features = segment_features.reset_index()
             model.fit(df=segment_features, regressors=ts.regressors)
@@ -336,3 +336,72 @@ def forecast(
         ts.df = df
         ts.inverse_transform()
         return ts
+
+
+class MultiSegmentModel(FitAbstractModel, ForecastAbstractModel, BaseMixin):
+    """Class for holding specific models for per-segment prediction."""
+
+    def __init__(self, base_model: Any):
+        """
+        Init MultiSegmentModel.
+
+        Parameters
+        ----------
+        base_model:
+            Internal model which will be used to forecast segments, expected to have fit/predict interface
+        """
+        self._base_model = base_model
+
+    @log_decorator
+    def fit(self, ts: TSDataset) -> "MultiSegmentModel":
+        """Fit model.
+
+        Parameters
+        ----------
+        ts:
+            Dataset with features
+
+        Returns
+        -------
+        self:
+            Model after fit
+        """
+        df = ts.to_pandas(flatten=True)
+        df = df.dropna()  # TODO: https://github.com/tinkoff-ai/etna/issues/557
+        df = df.drop(columns="segment")
+        self._base_model.fit(df=df, regressors=ts.regressors)
+        return self
+
+    @log_decorator
+    def forecast(self, ts: TSDataset) -> TSDataset:
+        """Make predictions.
+
+        Parameters
+        ----------
+        ts:
+            Dataset with features
+
+        Returns
+        -------
+        forecast:
+            Dataset with predictions
+        """
+        horizon = len(ts.df)
+        x = ts.to_pandas(flatten=True).drop(["segment"], axis=1)
+        y = self._base_model.predict(x).reshape(-1, horizon).T
+        ts.loc[:, pd.IndexSlice[:, "target"]] = y
+        ts.inverse_transform()
+        return ts
+
+    def get_model(self) -> Any:
+        """Get internal model that is used inside etna class.
+
+        Internal model is a model that is used inside etna to forecast segments, e.g. `catboost.CatBoostRegressor`
+        or `sklearn.linear_model.Ridge`.
+
+        Returns
+        -------
+        result:
+           Internal model
+        """
+        return self._base_model

etna/models/catboost.py

@@ -6,10 +6,8 @@
 from catboost import CatBoostRegressor
 from catboost import Pool
 
-from etna.datasets.tsdataset import TSDataset
-from etna.models.base import Model
+from etna.models.base import MultiSegmentModel
 from etna.models.base import PerSegmentModel
-from etna.models.base import log_decorator
 
 
 class _CatBoostAdapter:
@@ -190,7 +188,7 @@ def __init__(
         )
 
 
-class CatBoostModelMultiSegment(Model):
+class CatBoostModelMultiSegment(MultiSegmentModel):
     """Class for holding Catboost model for all segments.
 
     Examples
@@ -290,42 +288,14 @@ def __init__(
         self.l2_leaf_reg = l2_leaf_reg
         self.thread_count = thread_count
         self.kwargs = kwargs
-        super(CatBoostModelMultiSegment, self).__init__()
-        self._base_model = _CatBoostAdapter(
-            iterations=iterations,
-            depth=depth,
-            learning_rate=learning_rate,
-            logging_level=logging_level,
-            thread_count=thread_count,
-            l2_leaf_reg=l2_leaf_reg,
-            **kwargs,
+        super().__init__(
+            base_model=_CatBoostAdapter(
+                iterations=iterations,
+                depth=depth,
+                learning_rate=learning_rate,
+                logging_level=logging_level,
+                thread_count=thread_count,
+                l2_leaf_reg=l2_leaf_reg,
+                **kwargs,
+            )
         )
-
-    @log_decorator
-    def fit(self, ts: TSDataset) -> "CatBoostModelMultiSegment":
-        """Fit model."""
-        df = ts.to_pandas(flatten=True)
-        df = df.dropna()
-        df = df.drop(columns="segment")
-        self._base_model.fit(df=df, regressors=ts.regressors)
-        return self
-
-    @log_decorator
-    def forecast(self, ts: TSDataset) -> TSDataset:
-        """Make predictions.
-
-        Parameters
-        ----------
-        ts:
-            Dataframe with features
-        Returns
-        -------
-        DataFrame
-            Models result
-        """
-        horizon = len(ts.df)
-        x = ts.to_pandas(flatten=True).drop(["segment"], axis=1)
-        y = self._base_model.predict(x).reshape(-1, horizon).T
-        ts.loc[:, pd.IndexSlice[:, "target"]] = y
-        ts.inverse_transform()
-        return ts

etna/models/sklearn.py

@@ -5,10 +5,8 @@
 import pandas as pd
 from sklearn.base import RegressorMixin
 
-from etna.datasets.tsdataset import TSDataset
-from etna.models.base import Model
+from etna.models.base import MultiSegmentModel
 from etna.models.base import PerSegmentModel
-from etna.models.base import log_decorator
 
 
 class _SklearnAdapter:
@@ -77,7 +75,7 @@ def __init__(self, regressor: RegressorMixin):
         super().__init__(base_model=_SklearnAdapter(regressor=regressor))
 
 
-class SklearnMultiSegmentModel(Model):
+class SklearnMultiSegmentModel(MultiSegmentModel):
     """Class for holding Sklearn model for all segments."""
 
     def __init__(self, regressor: RegressorMixin):
@@ -87,36 +85,6 @@ def __init__(self, regressor: RegressorMixin):
         Parameters
         ----------
         regressor:
-            sklearn model for regression
-        """
-        super().__init__()
-        self._base_model = _SklearnAdapter(regressor=regressor)
-
-    @log_decorator
-    def fit(self, ts: TSDataset) -> "SklearnMultiSegmentModel":
-        """Fit model."""
-        df = ts.to_pandas(flatten=True)
-        df = df.dropna()
-        df = df.drop(columns="segment")
-        self._base_model.fit(df=df, regressors=ts.regressors)
-        return self
-
-    @log_decorator
-    def forecast(self, ts: TSDataset) -> TSDataset:
-        """Make predictions.
-
-        Parameters
-        ----------
-        ts:
-            Dataframe with features
-        Returns
-        -------
-        DataFrame
-            Models result
+            Sklearn model for regression
         """
-        horizon = len(ts.df)
-        x = ts.to_pandas(flatten=True).drop(["segment"], axis=1)
-        y = self._base_model.predict(x).reshape(-1, horizon).T
-        ts.loc[:, pd.IndexSlice[:, "target"]] = y
-        ts.inverse_transform()
-        return ts
+        super().__init__(base_model=_SklearnAdapter(regressor=regressor))