Add logging to TSDataset.make_future

CHANGELOG.md

@@ -34,7 +34,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Update CONTRIBUTING.md ([#536](https://github.com/tinkoff-ai/etna/pull/536))
 - 
 - Rename `_CatBoostModel`, `_HoltWintersModel`, `_SklearnModel` ([#543](https://github.com/tinkoff-ai/etna/pull/543))
-- 
+- Add logging to TSDataset.make_future, log repr of transform instead of class name ([#555](https://github.com/tinkoff-ai/etna/pull/555))
 - Rename `_SARIMAXModel` and `_ProphetModel`, make `SARIMAXModel` and `ProphetModel` inherit from `PerSegmentPredictionIntervalModel` ([#549](https://github.com/tinkoff-ai/etna/pull/549))
 - 
 - 

etna/core/mixins.py

@@ -16,14 +16,14 @@ def __repr__(self):
                 continue
             elif param.kind == param.VAR_KEYWORD:
                 for arg_, value in self.__dict__[arg].items():
-                    args_str_representation += f"{arg_} = {value.__repr__()}, "
+                    args_str_representation += f"{arg_} = {repr(value)}, "
             else:
                 try:
                     value = self.__dict__[arg]
                 except KeyError as e:
                     value = None
                     warnings.warn(f"You haven't set all parameters inside class __init__ method: {e}")
-                args_str_representation += f"{arg} = {value.__repr__()}, "
+                args_str_representation += f"{arg} = {repr(value)}, "
         return f"{self.__class__.__name__}({args_str_representation})"
 
 

etna/datasets/tsdataset.py

@@ -134,7 +134,7 @@ def transform(self, transforms: Sequence["Transform"]):
         self._check_endings(warning=True)
         self.transforms = transforms
         for transform in self.transforms:
-            tslogger.log(f"Transform {transform.__class__.__name__} is applied to dataset")
+            tslogger.log(f"Transform {repr(transform)} is applied to dataset")
             columns_before = set(self.columns.get_level_values("feature"))
             self.df = transform.transform(self.df)
             columns_after = set(self.columns.get_level_values("feature"))
@@ -145,7 +145,7 @@ def fit_transform(self, transforms: Sequence["Transform"]):
         self._check_endings(warning=True)
         self.transforms = transforms
         for transform in self.transforms:
-            tslogger.log(f"Transform {transform.__class__.__name__} is applied to dataset")
+            tslogger.log(f"Transform {repr(transform)} is applied to dataset")
             columns_before = set(self.columns.get_level_values("feature"))
             self.df = transform.fit_transform(self.df)
             columns_after = set(self.columns.get_level_values("feature"))
@@ -288,6 +288,7 @@ def make_future(self, future_steps: int) -> "TSDataset":
 
         if self.transforms is not None:
             for transform in self.transforms:
+                tslogger.log(f"Transform {repr(transform)} is applied to dataset")
                 df = transform.transform(df)
 
         future_dataset = df.tail(future_steps).copy(deep=True)

tests/test_loggers/test_console_logger.py

@@ -1,4 +1,5 @@
 from tempfile import NamedTemporaryFile
+from typing import Sequence
 
 import pytest
 from loguru import logger as _logger
@@ -16,20 +17,50 @@
 from etna.transforms import AddConstTransform
 from etna.transforms import DateFlagsTransform
 from etna.transforms import LagTransform
+from etna.transforms import Transform
+
+
+def check_logged_transforms(log_file: str, transforms: Sequence[Transform]):
+    """Check that transforms are logged into the file."""
+    with open(log_file, "r") as in_file:
+        lines = in_file.readlines()
+        assert len(lines) == len(transforms)
+        for line, transform in zip(lines, transforms):
+            assert transform.__class__.__name__ in line
+
+
+def test_tsdataset_transform_logging(example_tsds: TSDataset):
+    """Check working of logging inside `TSDataset.transform`."""
+    transforms = [LagTransform(lags=5, in_column="target"), AddConstTransform(value=5, in_column="target")]
+    file = NamedTemporaryFile()
+    _logger.add(file.name)
+    example_tsds.fit_transform(transforms=transforms)
+    idx = tslogger.add(ConsoleLogger())
+    example_tsds.transform(transforms=example_tsds.transforms)
+    check_logged_transforms(log_file=file.name, transforms=transforms)
+    tslogger.remove(idx)
 
 
 def test_tsdataset_fit_transform_logging(example_tsds: TSDataset):
-    """Check working of logging inside fit_transform of TSDataset."""
+    """Check working of logging inside `TSDataset.fit_transform`."""
     transforms = [LagTransform(lags=5, in_column="target"), AddConstTransform(value=5, in_column="target")]
     file = NamedTemporaryFile()
     _logger.add(file.name)
     idx = tslogger.add(ConsoleLogger())
     example_tsds.fit_transform(transforms=transforms)
-    with open(file.name, "r") as in_file:
-        lines = in_file.readlines()
-        assert len(lines) == len(transforms)
-        for line, transform in zip(lines, transforms):
-            assert transform.__class__.__name__ in line
+    check_logged_transforms(log_file=file.name, transforms=transforms)
+    tslogger.remove(idx)
+
+
+def test_tsdataset_make_future_logging(example_tsds: TSDataset):
+    """Check working of logging inside `TSDataset.make_future`."""
+    transforms = [LagTransform(lags=5, in_column="target"), AddConstTransform(value=5, in_column="target")]
+    file = NamedTemporaryFile()
+    _logger.add(file.name)
+    example_tsds.fit_transform(transforms=transforms)
+    idx = tslogger.add(ConsoleLogger())
+    _ = example_tsds.make_future(5)
+    check_logged_transforms(log_file=file.name, transforms=transforms)
     tslogger.remove(idx)
 
 
@@ -88,6 +119,8 @@ def test_model_logging(example_tsds, model):
 
     with open(file.name, "r") as in_file:
         lines = in_file.readlines()
+        # filter out logs related to transforms
+        lines = [line for line in lines if lags.__class__.__name__ not in line]
         assert len(lines) == 2
         assert "fit" in lines[0]
         assert "forecast" in lines[1]