Update pipelines to handle integer timestamp

CHANGELOG.md

@@ -64,6 +64,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Update `FoldMask` to work with integer timestamp, in `validate_on_dataset` method add validation on presence of `FoldMask` parameters in `ts.index`, add tests for `FoldMask` ([#226](https://github.com/etna-team/etna/pull/226))
 - Fix `FourierTransform` on integer index, add inference tests ([#230](https://github.com/etna-team/etna/pull/230))
 - Update outliers transforms to handle integer timestamp ([#229](https://github.com/etna-team/etna/pull/229))
+- Update pipelines to handle integer timestamp ([#241](https://github.com/etna-team/etna/pull/241))
 
 ### Fixed
 - 

etna/datasets/tsdataset.py

@@ -585,8 +585,8 @@ def plot(
         n_segments: int = 10,
         column: str = "target",
         segments: Optional[Sequence[str]] = None,
-        start: Optional[Union[pd.Timestamp, int, str]] = None,
-        end: Optional[Union[pd.Timestamp, int, str]] = None,
+        start: Union[pd.Timestamp, int, str, None] = None,
+        end: Union[pd.Timestamp, int, str, None] = None,
         seed: int = 1,
         figsize: Tuple[int, int] = (10, 5),
     ):
@@ -930,10 +930,10 @@ def to_hierarchical_dataset(
 
     def _find_all_borders(
         self,
-        train_start: Optional[Union[pd.Timestamp, int, str]],
-        train_end: Optional[Union[pd.Timestamp, int, str]],
-        test_start: Optional[Union[pd.Timestamp, int, str]],
-        test_end: Optional[Union[pd.Timestamp, int, str]],
+        train_start: Union[pd.Timestamp, int, str, None],
+        train_end: Union[pd.Timestamp, int, str, None],
+        test_start: Union[pd.Timestamp, int, str, None],
+        test_end: Union[pd.Timestamp, int, str, None],
         test_size: Optional[int],
     ) -> Tuple[Union[pd.Timestamp, int], Union[pd.Timestamp, int], Union[pd.Timestamp, int], Union[pd.Timestamp, int]]:
         """Find borders for train_test_split if some values wasn't specified."""
@@ -1005,10 +1005,10 @@ def _find_all_borders(
 
     def train_test_split(
         self,
-        train_start: Optional[Union[pd.Timestamp, int, str]] = None,
-        train_end: Optional[Union[pd.Timestamp, int, str]] = None,
-        test_start: Optional[Union[pd.Timestamp, int, str]] = None,
-        test_end: Optional[Union[pd.Timestamp, int, str]] = None,
+        train_start: Union[pd.Timestamp, int, str, None] = None,
+        train_end: Union[pd.Timestamp, int, str, None] = None,
+        test_start: Union[pd.Timestamp, int, str, None] = None,
+        test_end: Union[pd.Timestamp, int, str, None] = None,
         test_size: Optional[int] = None,
     ) -> Tuple["TSDataset", "TSDataset"]:
         """Split given df with train-test timestamp indices or size of test set.

etna/datasets/utils.py

@@ -298,19 +298,24 @@
 
 
 def _check_timestamp_param(
-    param: Optional[Union[pd.Timestamp, int, str]], param_name: str, freq: Optional[str]
-) -> Optional[Union[pd.Timestamp, int]]:
+    param: Union[pd.Timestamp, int, str, None], param_name: str, freq: Optional[str]
+) -> Union[pd.Timestamp, int, None]:
     if param is None:
         return param
 
     if freq is None:
-        if not isinstance(param, int):
+        if not (isinstance(param, int) or isinstance(param, np.integer)):
             raise ValueError(
                 f"Parameter {param_name} has incorrect type! For integer timestamp only integer parameter type is allowed."
             )
 
         return param
     else:
+        if not isinstance(param, str) and not isinstance(param, pd.Timestamp):
+            raise ValueError(
+                f"Parameter {param_name} has incorrect type! For datetime timestamp only pd.Timestamp or str parameter type is allowed."
+            )
+
         new_param = pd.Timestamp(param)
         return new_param
 

etna/ensembles/direct_ensemble.py

@@ -4,6 +4,7 @@
 from typing import List
 from typing import Optional
 from typing import Sequence
+from typing import Union
 
 import numpy as np
 import pandas as pd
@@ -148,8 +149,8 @@
     def _predict(
         self,
         ts: TSDataset,
-        start_timestamp: pd.Timestamp,
-        end_timestamp: pd.Timestamp,
+        start_timestamp: Union[pd.Timestamp, int],
+        end_timestamp: Union[pd.Timestamp, int],
         prediction_interval: bool,
         quantiles: Sequence[float],
         return_components: bool,

etna/ensembles/mixins.py

@@ -4,6 +4,7 @@
 from copy import deepcopy
 from typing import List
 from typing import Optional
+from typing import Union
 
 import pandas as pd
 from typing_extensions import Self
@@ -52,8 +53,8 @@
     def _predict_pipeline(
         ts: TSDataset,
         pipeline: BasePipeline,
-        start_timestamp: Optional[pd.Timestamp],
-        end_timestamp: Optional[pd.Timestamp],
+        start_timestamp: Union[pd.Timestamp, int, str, None],
+        end_timestamp: Union[pd.Timestamp, int, str, None],
     ) -> TSDataset:
         """Make predict with given pipeline."""
         tslogger.log(msg=f"Start prediction with {pipeline}.")

etna/ensembles/stacking_ensemble.py

@@ -256,8 +256,8 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
     def _predict(
         self,
         ts: TSDataset,
-        start_timestamp: pd.Timestamp,
-        end_timestamp: pd.Timestamp,
+        start_timestamp: Union[pd.Timestamp, int],
+        end_timestamp: Union[pd.Timestamp, int],
         prediction_interval: bool,
         quantiles: Sequence[float],
         return_components: bool,

etna/ensembles/voting_ensemble.py

@@ -214,8 +214,8 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
     def _predict(
         self,
         ts: TSDataset,
-        start_timestamp: pd.Timestamp,
-        end_timestamp: pd.Timestamp,
+        start_timestamp: Union[pd.Timestamp, int],
+        end_timestamp: Union[pd.Timestamp, int],
         prediction_interval: bool,
         quantiles: Sequence[float],
         return_components: bool,

etna/pipeline/autoregressive_pipeline.py

@@ -2,6 +2,7 @@
 from typing import Sequence
 from typing import cast
 
+import numpy as np
 import pandas as pd
 from typing_extensions import get_args
 
@@ -106,10 +107,12 @@
 
     def _create_predictions_template(self, ts: TSDataset) -> pd.DataFrame:
         """Create dataframe to fill with forecasts."""
-        prediction_df = ts[:, :, "target"]
-        future_dates = pd.date_range(
-            start=prediction_df.index.max(), periods=self.horizon + 1, freq=ts.freq, closed="right"
-        )
+        prediction_df = ts.to_pandas(features=["target"])
+        last_timestamp = prediction_df.index[-1]
+        if ts.freq is None:
+            future_dates = pd.Index(np.arange(last_timestamp, last_timestamp + self.horizon + 1))[1:]
+        else:
+            future_dates = pd.date_range(start=last_timestamp, periods=self.horizon + 1, freq=ts.freq, closed="right")
         prediction_df = prediction_df.reindex(prediction_df.index.append(future_dates))
         prediction_df.index.name = "timestamp"
         return prediction_df
@@ -172,21 +175,3 @@
             prediction_ts.add_target_components(target_components_df=target_components_df)
 
         return prediction_ts
-
-    def _predict(
-        self,
-        ts: TSDataset,
-        start_timestamp: pd.Timestamp,
-        end_timestamp: pd.Timestamp,
-        prediction_interval: bool,
-        quantiles: Sequence[float],
-        return_components: bool = False,
-    ) -> TSDataset:
-        return super()._predict(
-            ts=ts,
-            start_timestamp=start_timestamp,
-            end_timestamp=end_timestamp,
-            prediction_interval=prediction_interval,
-            quantiles=quantiles,
-            return_components=return_components,
-        )

etna/pipeline/base.py

@@ -25,6 +25,7 @@
 from etna.core import AbstractSaveable
 from etna.core import BaseMixin
 from etna.datasets import TSDataset
+from etna.datasets.utils import _check_timestamp_param
 from etna.distributions import BaseDistribution
 from etna.loggers import tslogger
 from etna.metrics import Metric
@@ -269,8 +270,8 @@
     def predict(
         self,
         ts: TSDataset,
-        start_timestamp: Optional[pd.Timestamp] = None,
-        end_timestamp: Optional[pd.Timestamp] = None,
+        start_timestamp: Union[pd.Timestamp, int, str, None] = None,
+        end_timestamp: Union[pd.Timestamp, int, str, None] = None,
         prediction_interval: bool = False,
         quantiles: Sequence[float] = (0.025, 0.975),
         return_components: bool = False,
@@ -280,6 +281,8 @@
         Currently, in situation when segments start with different timestamps
         we only guarantee to work with ``start_timestamp`` >= beginning of all segments.
 
+        Parameters ``start_timestamp`` and ``end_timestamp`` of type ``str`` are converted into ``pd.Timestamp``.
+
         Parameters
         ----------
         ts:
@@ -305,6 +308,10 @@
 
         Raises
         ------
+        ValueError:
+            Value of ``start_timestamp`` doesn't match the type of timestamp in ``ts``.
+        ValueError:
+            Value of ``end_timestamp`` doesn't match the type of timestamp in ``ts``.
         ValueError:
             Value of ``end_timestamp`` is less than ``start_timestamp``.
         ValueError:
@@ -553,9 +560,12 @@
     @staticmethod
     def _make_predict_timestamps(
         ts: TSDataset,
-        start_timestamp: Optional[pd.Timestamp] = None,
-        end_timestamp: Optional[pd.Timestamp] = None,
-    ) -> Tuple[pd.Timestamp, pd.Timestamp]:
+        start_timestamp: Union[pd.Timestamp, int, str, None],
+        end_timestamp: Union[pd.Timestamp, int, str, None],
+    ) -> Union[Tuple[pd.Timestamp, pd.Timestamp], Tuple[int, int]]:
+        start_timestamp = _check_timestamp_param(param=start_timestamp, param_name="start_timestamp", freq=ts.freq)
+        end_timestamp = _check_timestamp_param(param=end_timestamp, param_name="end_timestamp", freq=ts.freq)
+
         min_timestamp = ts.describe()["start_timestamp"].max()
         max_timestamp = ts.index[-1]
 
@@ -569,7 +579,7 @@
         if end_timestamp > max_timestamp:
             raise ValueError("Value of end_timestamp is more than ending of dataset!")
 
-        if start_timestamp > end_timestamp:
+        if start_timestamp > end_timestamp:  # type: ignore
             raise ValueError("Value of end_timestamp is less than start_timestamp!")
 
         return start_timestamp, end_timestamp
@@ -578,8 +588,8 @@
     def _predict(
         self,
         ts: TSDataset,
-        start_timestamp: Optional[pd.Timestamp],
-        end_timestamp: Optional[pd.Timestamp],
+        start_timestamp: Union[pd.Timestamp, int],
+        end_timestamp: Union[pd.Timestamp, int],
         prediction_interval: bool,
         quantiles: Sequence[float],
         return_components: bool,
@@ -589,8 +599,8 @@
     def predict(
         self,
         ts: TSDataset,
-        start_timestamp: Optional[pd.Timestamp] = None,
-        end_timestamp: Optional[pd.Timestamp] = None,
+        start_timestamp: Union[pd.Timestamp, int, str, None] = None,
+        end_timestamp: Union[pd.Timestamp, int, str, None] = None,
         prediction_interval: bool = False,
         quantiles: Sequence[float] = (0.025, 0.975),
         return_components: bool = False,
@@ -600,6 +610,8 @@
         Currently, in situation when segments start with different timestamps
         we only guarantee to work with ``start_timestamp`` >= beginning of all segments.
 
+        Parameters ``start_timestamp`` and ``end_timestamp`` of type ``str`` are converted into ``pd.Timestamp``.
+
         Parameters
         ----------
         ts:
@@ -625,6 +637,10 @@
 
         Raises
         ------
+        ValueError:
+            Value of ``start_timestamp`` doesn't match the type of timestamp in ``ts``.
+        ValueError:
+            Value of ``end_timestamp`` doesn't match the type of timestamp in ``ts``.
         ValueError:
             Value of ``end_timestamp`` is less than ``start_timestamp``.
         ValueError:
@@ -721,10 +737,15 @@
             min_train, max_train = dataset_timestamps[min_train_idx], dataset_timestamps[max_train_idx]
             min_test, max_test = dataset_timestamps[min_test_idx], dataset_timestamps[max_test_idx]
 
+            if ts.freq is None:
+                target_timestamps = np.arange(min_test, max_test + 1).tolist()
+            else:
+                target_timestamps = list(pd.date_range(start=min_test, end=max_test, freq=ts.freq))
+
             mask = FoldMask(
                 first_train_timestamp=min_train,
                 last_train_timestamp=max_train,
-                target_timestamps=list(pd.date_range(start=min_test, end=max_test, freq=ts.freq)),
+                target_timestamps=target_timestamps,
             )
             masks.append(mask)
 

etna/pipeline/mixins.py

@@ -5,6 +5,7 @@
 from typing import Dict
 from typing import Optional
 from typing import Sequence
+from typing import Union
 
 import numpy as np
 import pandas as pd
@@ -27,7 +28,9 @@
 class ModelPipelinePredictMixin:
     """Mixin for pipelines with model inside with implementation of ``_predict`` method."""
 
-    def _create_ts(self, ts: TSDataset, start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp) -> TSDataset:
+    def _create_ts(
+        self, ts: TSDataset, start_timestamp: Union[pd.Timestamp, int], end_timestamp: Union[pd.Timestamp, int]
+    ) -> TSDataset:
         """Create ``TSDataset`` to make predictions on."""
         self.model: ModelType
         self.transforms: Sequence[Transform]
@@ -37,7 +40,7 @@
         freq = deepcopy(ts.freq)
         known_future = deepcopy(ts.known_future)
 
-        df_to_transform = df[:end_timestamp]
+        df_to_transform = df.loc[:end_timestamp]
 
         cur_ts = TSDataset(
             df=df_to_transform,
@@ -51,25 +54,25 @@
 
         # correct start_timestamp taking into account context size
         timestamp_indices = pd.Series(np.arange(len(df.index)), index=df.index)
-        start_idx = timestamp_indices[start_timestamp]
+        start_idx = timestamp_indices.loc[start_timestamp]
         start_idx = max(0, start_idx - self.model.context_size)
         start_timestamp = timestamp_indices.index[start_idx]
 
-        cur_ts.df = cur_ts.df[start_timestamp:end_timestamp]
+        cur_ts.df = cur_ts.df.loc[start_timestamp:end_timestamp]
         return cur_ts
 
     def _determine_prediction_size(
-        self, ts: TSDataset, start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp
+        self, ts: TSDataset, start_timestamp: Union[pd.Timestamp, int], end_timestamp: Union[pd.Timestamp, int]
     ) -> int:
         timestamp_indices = pd.Series(np.arange(len(ts.index)), index=ts.index)
-        timestamps = timestamp_indices[start_timestamp:end_timestamp]
+        timestamps = timestamp_indices.loc[start_timestamp:end_timestamp]
         return len(timestamps)
 
     def _predict(
         self,
         ts: TSDataset,
-        start_timestamp: pd.Timestamp,
-        end_timestamp: pd.Timestamp,
+        start_timestamp: Union[pd.Timestamp, int],
+        end_timestamp: Union[pd.Timestamp, int],
         prediction_interval: bool,
         quantiles: Sequence[float],
         return_components: bool = False,

tests/conftest.py

@@ -717,6 +717,11 @@ def product_level_constant_hierarchical_ts(product_level_constant_hierarchical_d
     return ts
 
 
+@pytest.fixture
+def product_level_constant_hierarchical_ts_int_timestamp(product_level_constant_hierarchical_ts):
+    return convert_ts_to_int_timestamp(product_level_constant_hierarchical_ts)
+
+
 @pytest.fixture
 def product_level_constant_hierarchical_ts_with_exog(
     product_level_constant_hierarchical_df, market_level_constant_hierarchical_df_exog, hierarchical_structure