Teach AutoARIMAModel to work with out-sample predictions

CHANGELOG.md

@@ -27,7 +27,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - 
 - 
-- 
+- Teach AutoARIMAModel to work with out-sample predictions ([#830](https://github.com/tinkoff-ai/etna/pull/830))
 - 
 - 
 - 

etna/models/autoarima.py

@@ -1,16 +1,12 @@
 import warnings
-from typing import List
-from typing import Optional
-from typing import Sequence
 
-import numpy as np
 import pandas as pd
 import pmdarima as pm
-from pmdarima.arima import ARIMA
 from statsmodels.tools.sm_exceptions import ValueWarning
+from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
 
-from etna.models.base import BaseAdapter
 from etna.models.base import PerSegmentPredictionIntervalModel
+from etna.models.sarimax import _SARIMAXBaseAdapter
 
 warnings.filterwarnings(
     message="No frequency information was provided, so inferred frequency .* will be used",
@@ -20,7 +16,7 @@
 )
 
 
-class _AutoARIMAAdapter(BaseAdapter):
+class _AutoARIMAAdapter(_SARIMAXBaseAdapter):
     """
     Class for holding auto arima model.
 
@@ -45,126 +41,11 @@ def __init__(
             Training parameters for auto_arima from pmdarima package.
         """
         self.kwargs = kwargs
-        self._model: Optional[ARIMA] = None
-        self.regressor_columns: List[str] = []
+        super().__init__()
 
-    def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_AutoARIMAAdapter":
-        """
-        Fits auto ARIMA model.
-
-        Parameters
-        ----------
-        df:
-            Features dataframe
-        regressors:
-            List of the columns with regressors
-
-        Returns
-        -------
-        :
-            Fitted model
-        """
-        self.regressor_columns = regressors
-        categorical_cols = df.select_dtypes(include=["category"]).columns.tolist()
-        try:
-            df.loc[:, categorical_cols] = df[categorical_cols].astype(int)
-        except ValueError:
-            raise ValueError(
-                f"Categorical columns {categorical_cols} can not been converted to int.\n "
-                "Try to encode this columns manually."
-            )
-
-        self._check_df(df)
-
-        targets = df["target"]
-        targets.index = df["timestamp"]
-
-        exog_train = self._select_regressors(df)
-
-        self._model = pm.auto_arima(df["target"], X=exog_train, **self.kwargs)
-        return self
-
-    def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Sequence[float]) -> pd.DataFrame:
-        """
-        Compute predictions from auto ARIMA model.
-
-        Parameters
-        ----------
-        df:
-            Features dataframe
-        prediction_interval:
-             If True returns prediction interval for forecast
-        quantiles:
-            Levels of prediction distribution
-
-        Returns
-        -------
-        :
-            DataFrame with predictions
-        """
-        if self._model is None:
-            raise ValueError("AutoARIMA model is not fitted! Fit the model before calling predict method!")
-        horizon = len(df)
-        self._check_df(df, horizon)
-
-        categorical_cols = df.select_dtypes(include=["category"]).columns.tolist()
-        try:
-            df.loc[:, categorical_cols] = df[categorical_cols].astype(int)
-        except ValueError:
-            raise ValueError(
-                f"Categorical columns {categorical_cols} can not been converted to int.\n "
-                "Try to encode this columns manually."
-            )
-
-        exog_future = self._select_regressors(df)
-        if prediction_interval:
-            confints = np.unique([2 * i if i < 0.5 else 2 * (1 - i) for i in quantiles])
-
-            y_pred = pd.DataFrame({"target": self._model.predict(len(df), X=exog_future), "timestamp": df["timestamp"]})
-
-            for confint in confints:
-                forecast = self._model.predict(len(df), X=exog_future, return_conf_int=True, alpha=confint)
-                if confint / 2 in quantiles:
-                    y_pred[f"target_{confint/2:.4g}"] = forecast[1][:, :1]
-                if 1 - confint / 2 in quantiles:
-                    y_pred[f"target_{1 - confint/2:.4g}"] = forecast[1][:, 1:]
-        else:
-            y_pred = pd.DataFrame({"target": self._model.predict(len(df), X=exog_future), "timestamp": df["timestamp"]})
-        y_pred = y_pred.reset_index(drop=True, inplace=False)
-        return y_pred
-
-    def _check_df(self, df: pd.DataFrame, horizon: Optional[int] = None):
-        column_to_drop = [col for col in df.columns if col not in ["target", "timestamp"] + self.regressor_columns]
-        if column_to_drop:
-            warnings.warn(
-                message=f"AutoARIMA model does not work with exogenous features (features unknown in future).\n "
-                f"{column_to_drop} will be dropped"
-            )
-        if horizon:
-            short_regressors = [regressor for regressor in self.regressor_columns if df[regressor].count() < horizon]
-            if short_regressors:
-                raise ValueError(
-                    f"Regressors {short_regressors} are too short for chosen horizon value.\n "
-                    "Try lower horizon value, or drop this regressors."
-                )
-
-    def _select_regressors(self, df: pd.DataFrame) -> Optional[pd.DataFrame]:
-        if self.regressor_columns:
-            exog_future = df[self.regressor_columns]
-            exog_future.index = df["timestamp"]
-        else:
-            exog_future = None
-        return exog_future
-
-    def get_model(self) -> ARIMA:
-        """Get internal pmdarima.arima.arima.ARIMA model that is used inside etna class.
-
-        Returns
-        -------
-        :
-           Internal model
-        """
-        return self._model
+    def _get_fit_results(self, endog: pd.Series, exog: pd.DataFrame) -> SARIMAXResultsWrapper:
+        model = pm.auto_arima(endog, X=exog, **self.kwargs)
+        return model.arima_res_
 
 
 class AutoARIMAModel(PerSegmentPredictionIntervalModel):