Fix errors when importing modules without torch extras (#382)

* Revert "Add `get_anomalies_isolation_forest` method (#375)"

This reverts commit 93ae431.

* fix imports

* fix imports

* fix imports hard

* Revert "fix imports hard"

This reverts commit 10f34c4.

* chore: update changelog

---------

Co-authored-by: Egor Baturin <egoriyaa@github.com>

CHANGELOG.md

@@ -7,7 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 ### Added
-- Add `get_anomalies_isolation_forest` method for anomaly detection ([#375](https://github.com/etna-team/etna/pull/375))
+- 
+- 
+- 
+- 
 - 
 - 
 - 
@@ -28,7 +31,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 
 ### Fixed
-- 
+- Fix errors when importing modules without `torch` extras ([#382](https://github.com/etna-team/etna/pull/382))
 - 
 - 
 - 

docs/source/api_reference/analysis.rst

@@ -101,4 +101,3 @@ Outliers analysis utilities:
    get_anomalies_hist
    get_anomalies_median
    get_anomalies_prediction_interval
-   get_anomalies_isolation_forest

etna/analysis/__init__.py

@@ -40,6 +40,5 @@
 from etna.analysis.outliers.density_outliers import absolute_difference_distance
 from etna.analysis.outliers.density_outliers import get_anomalies_density
 from etna.analysis.outliers.hist_outliers import get_anomalies_hist
-from etna.analysis.outliers.isolation_forest_outliers import get_anomalies_isolation_forest
 from etna.analysis.outliers.median_outliers import get_anomalies_median
 from etna.analysis.outliers.prediction_interval_outliers import get_anomalies_prediction_interval

etna/analysis/outliers/__init__.py

@@ -1,7 +1,6 @@
 from etna.analysis.outliers.density_outliers import absolute_difference_distance
 from etna.analysis.outliers.density_outliers import get_anomalies_density
 from etna.analysis.outliers.hist_outliers import get_anomalies_hist
-from etna.analysis.outliers.isolation_forest_outliers import get_anomalies_isolation_forest
 from etna.analysis.outliers.median_outliers import get_anomalies_median
 from etna.analysis.outliers.plots import plot_anomalies
 from etna.analysis.outliers.plots import plot_anomalies_interactive

etna/models/nn/deepstate/__init__.py

@@ -1,8 +1,11 @@
-from etna.models.nn.deepstate.linear_dynamic_system import LDS
-from etna.models.nn.deepstate.state_space_model import CompositeSSM
-from etna.models.nn.deepstate.state_space_model import DaylySeasonalitySSM
-from etna.models.nn.deepstate.state_space_model import LevelSSM
-from etna.models.nn.deepstate.state_space_model import LevelTrendSSM
-from etna.models.nn.deepstate.state_space_model import SeasonalitySSM
-from etna.models.nn.deepstate.state_space_model import WeeklySeasonalitySSM
-from etna.models.nn.deepstate.state_space_model import YearlySeasonalitySSM
+from etna import SETTINGS
+
+if SETTINGS.torch_required:
+    from etna.models.nn.deepstate.linear_dynamic_system import LDS
+    from etna.models.nn.deepstate.state_space_model import CompositeSSM
+    from etna.models.nn.deepstate.state_space_model import DaylySeasonalitySSM
+    from etna.models.nn.deepstate.state_space_model import LevelSSM
+    from etna.models.nn.deepstate.state_space_model import LevelTrendSSM
+    from etna.models.nn.deepstate.state_space_model import SeasonalitySSM
+    from etna.models.nn.deepstate.state_space_model import WeeklySeasonalitySSM
+    from etna.models.nn.deepstate.state_space_model import YearlySeasonalitySSM

etna/models/nn/deepstate/deepstate.py

@@ -13,13 +13,13 @@
 from etna.distributions import IntDistribution
 from etna.models.base import DeepBaseModel
 from etna.models.base import DeepBaseNet
-from etna.models.nn.deepstate import LDS
-from etna.models.nn.deepstate import CompositeSSM
 
 if SETTINGS.torch_required:
     import torch
     import torch.nn as nn
 
+    from etna.models.nn.deepstate import LDS
+    from etna.models.nn.deepstate import CompositeSSM
     from etna.models.nn.utils import MultiEmbedding
 
 

etna/models/nn/deepstate/linear_dynamic_system.py

@@ -1,9 +1,12 @@
 from typing import Tuple
 
-import torch
-from torch import Tensor
-from torch.distributions.multivariate_normal import MultivariateNormal
-from torch.distributions.normal import Normal
+from etna import SETTINGS
+
+if SETTINGS.torch_required:
+    import torch
+    from torch import Tensor
+    from torch.distributions.multivariate_normal import MultivariateNormal
+    from torch.distributions.normal import Normal
 
 from etna.core import BaseMixin
 

etna/models/nn/deepstate/state_space_model.py

@@ -7,9 +7,13 @@
 
 import numpy as np
 import pandas as pd
-import torch
-from torch import Tensor
-from torch.nn.functional import one_hot
+
+from etna import SETTINGS
+
+if SETTINGS.torch_required:
+    import torch
+    from torch import Tensor
+    from torch.nn.functional import one_hot
 
 from etna.core import BaseMixin
 

etna/transforms/embeddings/models/__init__.py

@@ -1,3 +1,6 @@
+from etna import SETTINGS
 from etna.transforms.embeddings.models.base import BaseEmbeddingModel
-from etna.transforms.embeddings.models.ts2vec import TS2VecEmbeddingModel
-from etna.transforms.embeddings.models.tstcc import TSTCCEmbeddingModel
+
+if SETTINGS.torch_required:
+    from etna.transforms.embeddings.models.ts2vec import TS2VecEmbeddingModel
+    from etna.transforms.embeddings.models.tstcc import TSTCCEmbeddingModel

etna/transforms/embeddings/models/ts2vec.py

@@ -6,9 +6,12 @@
 
 import numpy as np
 
-from etna.libs.ts2vec import TS2Vec
+from etna import SETTINGS
 from etna.transforms.embeddings.models import BaseEmbeddingModel
 
+if SETTINGS.torch_required:
+    from etna.libs.ts2vec import TS2Vec
+
 
 class TS2VecEmbeddingModel(BaseEmbeddingModel):
     """TS2Vec embedding model.

etna/transforms/embeddings/models/tstcc.py

@@ -5,9 +5,12 @@
 
 import numpy as np
 
-from etna.libs.tstcc import TSTCC
+from etna import SETTINGS
 from etna.transforms.embeddings.models import BaseEmbeddingModel
 
+if SETTINGS.torch_required:
+    from etna.libs.tstcc import TSTCC
+
 
 class TSTCCEmbeddingModel(BaseEmbeddingModel):
     """TSTCC embedding model.