Poc: base classes for deep models and rnn and deepstate with examples #776

martins0n · 2022-06-30T22:42:49Z

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Did you read the contribution guide?
Did you update the docs? We use Numpy format for all the methods and classes.
Did you write any new necessary tests?
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Closing issues

github-actions · 2022-06-30T22:47:30Z

🚀 Deployed on https://deploy-preview-776--etna-docs.netlify.app

martins0n · 2022-07-01T14:17:32Z

https://wandb.ai/martins0n/native_test?workspace=user-martins0n

codecov-commenter · 2022-07-01T14:29:25Z

Codecov Report

Merging #776 (0ed6428) into master (62737e0) will decrease coverage by 34.16%.
The diff coverage is 37.91%.

@@             Coverage Diff             @@
##           master     #776       +/-   ##
===========================================
- Coverage   83.69%   49.52%   -34.17%     
===========================================
  Files         124      125        +1     
  Lines        6959     7193      +234     
===========================================
- Hits         5824     3562     -2262     
- Misses       1135     3631     +2496

Impacted Files	Coverage Δ
etna/models/nn/rnn.py	`31.03% <31.03%> (ø)`
etna/models/base.py	`60.22% <36.44%> (-27.51%)`	⬇️
etna/datasets/tsdataset.py	`66.92% <48.00%> (-23.19%)`	⬇️
etna/datasets/utils.py	`41.66% <57.14%> (-58.34%)`	⬇️
etna/pipeline/pipeline.py	`83.78% <66.66%> (-13.19%)`	⬇️
etna/models/nn/__init__.py	`100.00% <100.00%> (ø)`
etna/commands/__init__.py	`0.00% <0.00%> (-100.00%)`	⬇️
etna/commands/backtest_command.py	`0.00% <0.00%> (-97.06%)`	⬇️
etna/commands/forecast_command.py	`0.00% <0.00%> (-94.88%)`	⬇️
etna/commands/__main__.py	`0.00% <0.00%> (-87.50%)`	⬇️
... and 79 more

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etna/datasets/tsdataset.py

etna/models/base.py

Mr-Geekman · 2022-07-04T15:16:03Z

etna/models/base.py

+        pass
+
+
+class DeepBaseModel(LightningModule, FitAbstractModel, DeepBaseAbstractModel, BaseMixin):


Why do we need inheritance from LightningModule here? I thought that in general composition is better than inheritance.

general composition is better than inheritance.

I don't think that passing LightningModule as parameter make any difference in our case because we don't have any coupled logic and deep hierarchy. But we will write a little bit extra code for wrappers with duplicated docs.

What downsides do you think of about?

Am I correctly understand that you decided to make dependency injection here?

etna/datasets/tsdataset.py

etna/models/base.py

etna/models/nn/__init__.py

alex-hse-repository · 2022-07-05T04:44:30Z

Notebook seems to be outdated

alex-hse-repository · 2022-07-11T05:32:32Z

Improve description in the notebook:

pytorch-lightning -> pytorch-forecasting
We'll used -> We'll use

docs/source/conf.py

etna/datasets/tsdataset.py

etna/models/base.py

etna/models/nn/rnn.py

alex-hse-repository · 2022-07-12T05:15:59Z

I guess we need some more tests:

Test for make_future with tails_steps!=0
Tests for tsdataset_idx_slice
Tests for to_torch_dataset
Tests for DeepBaseModel interface
Test for make_samples in RNN
May be tests for forward/step methods, don't know how to create them without pain

tests/test_datasets/test_dataset.py

tests/test_models/test_base.py

alex-hse-repository

Almost done)
There are some unresolved threads left

alex-hse-repository

👍

alex-hse-repository

Waiting for some "little" changes)

etna/datasets/tsdataset.py

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FIX: changelog

8c83571

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FIX: examples gitignore

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Implement in-sample and out-sample predictions in nn models without exceptions #805

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FIX: test cases

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FIX: mocks

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FIX: tests

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martins0n added 2 commits July 21, 2022 11:54

Poc rnn logic in models part1 (#807)

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Merge branch 'master' into poc-rnn-logic-in-models

0ed6428

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POC: LSTM net and new methods for tsdataset #620

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Mr-Geekman mentioned this pull request Aug 14, 2023

Implement in-sample and out-sample predictions in nn models without exceptions etna-team/etna#17

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Poc: base classes for deep models and rnn and deepstate with examples #776

Poc: base classes for deep models and rnn and deepstate with examples #776

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		pass


		class DeepBaseModel(LightningModule, FitAbstractModel, DeepBaseAbstractModel, BaseMixin):

Poc: base classes for deep models and rnn and deepstate with examples #776

Poc: base classes for deep models and rnn and deepstate with examples #776

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martins0n commented Jun 30, 2022 • edited Loading

Before submitting (must do checklist)

Proposed Changes

Closing issues

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martins0n commented Jun 30, 2022 •

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