Update ruptures version (#141)

* Update ruptures version

* Fix doccstring

* Upd CHANGELOG

CHANGELOG.md

@@ -27,6 +27,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Update EDA notebook ([#96](https://github.com/tinkoff-ai/etna-ts/pull/96), [#114](https://github.com/tinkoff-ai/etna-ts/pull/114))
 - Delete offset from WindowStatisticsTransform ([#111](https://github.com/tinkoff-ai/etna-ts/pull/111))
 - Add Pipeline example in Get started notebook ([#115](https://github.com/tinkoff-ai/etna-ts/pull/115))
+- Internal implementation of BinsegTrendTransform ([#141](https://github.com/tinkoff-ai/etna-ts/pull/141))
 
 ### Fixed
 - Add more obvious Exception Error for forecasting with unfitted model ([#102](https://github.com/tinkoff-ai/etna-ts/pull/102))

etna/transforms/binseg.py

@@ -1,55 +1,15 @@
-from functools import lru_cache
-from typing import Any
 from typing import Optional
 
 from ruptures.base import BaseCost
-from ruptures.costs import cost_factory
 from ruptures.detection import Binseg
 from sklearn.linear_model import LinearRegression
 
 from etna.transforms.change_points_trend import ChangePointsTrendTransform
 from etna.transforms.change_points_trend import TDetrendModel
 
 
-class _Binseg(Binseg):
-    """Binary segmentation with lru_cache."""
-
-    def __init__(
-        self,
-        model: str = "l2",
-        custom_cost: Optional[BaseCost] = None,
-        min_size: int = 2,
-        jump: int = 5,
-        params: Any = None,
-    ):
-        """Initialize a Binseg instance.
-
-        Args:
-            model (str, optional): segment model, ["l1", "l2", "rbf",...]. Not used if ``'custom_cost'`` is not None.
-            custom_cost (BaseCost, optional): custom cost function. Defaults to None.
-            min_size (int, optional): minimum segment length. Defaults to 2 samples.
-            jump (int, optional): subsample (one every *jump* points). Defaults to 5 samples.
-            params (dict, optional): a dictionary of parameters for the cost instance.
-        """
-        if custom_cost is not None and isinstance(custom_cost, BaseCost):
-            self.cost = custom_cost
-        elif params is None:
-            self.cost = cost_factory(model=model)
-        else:
-            self.cost = cost_factory(model=model, **params)
-        self.min_size = max(min_size, self.cost.min_size)
-        self.jump = jump
-        self.n_samples = None
-        self.signal = None
-
-    @lru_cache(maxsize=None)
-    def single_bkp(self, start: int, end: int) -> Any:
-        """Run _single_bkp with lru_cache decorator."""
-        return self._single_bkp(start=start, end=end)
-
-
 class BinsegTrendTransform(ChangePointsTrendTransform):
-    """BinsegTrendTransform uses _Binseg model as a change point detection model in ChangePointsTrendTransform transform."""
+    """BinsegTrendTransform uses Binseg model as a change point detection model in ChangePointsTrendTransform transform."""
 
     def __init__(
         self,
@@ -95,7 +55,7 @@ def __init__(
         self.epsilon = epsilon
         super().__init__(
             in_column=in_column,
-            change_point_model=_Binseg(
+            change_point_model=Binseg(
                 model=self.model, custom_cost=self.custom_cost, min_size=self.min_size, jump=self.jump
             ),
             detrend_model=detrend_model,

etna/transforms/change_points_trend.py

@@ -123,9 +123,7 @@ def fit(self, df: pd.DataFrame) -> "OneSegmentChangePointsTransform":
         -------
         self
         """
-        # we need copy here because Binseg with CostAR (model="ar") changes given signal inplace; if it is fixed
-        # @TODO: delete copy
-        series = df.loc[df[self.in_column].first_valid_index() :, self.in_column].copy(deep=True)
+        series = df.loc[df[self.in_column].first_valid_index() :, self.in_column]
         change_points = self._get_change_points(series=series)
         self.intervals = self._build_trend_intervals(change_points=change_points)
         self.per_interval_models = self._init_detrend_models(intervals=self.intervals)

pyproject.toml

@@ -42,7 +42,7 @@ scikit-learn = "^0.24.1"
 prophet = "^1.0"
 pandas = "^1"
 catboost = "^0.25"
-ruptures = "1.1.3"
+ruptures = "1.1.5"
 torch = "1.8.*"
 pytorch-forecasting = "0.8.5"
 numba = "^0.53.1"