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install editable package in codespace (#826)

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* install editable package in codespace

* fix test error in test_forecast

* fix test error in test_space

* openml version

* break tests; pre-commit

* skip on py10+win32

* install mlflow in test

* install mlflow in [test]

* skip test in windows

* import

* handle PermissionError

* skip test in windows

* skip test in windows

* skip test in windows

* skip test in windows

* remove ts_forecast_panel from doc
This commit is contained in:
Chi Wang
2022-11-27 11:22:54 -08:00
committed by GitHub
parent 586afe0d6b
commit 595af7a04f
19 changed files with 129 additions and 59 deletions
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website/docs/Use-Cases/Task-Oriented-AutoML.md
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@@ -12,7 +12,7 @@
- 'regression': regression with tabular data.
- 'ts_forecast': time series forecasting.
- 'ts_forecast_classification': time series forecasting for classification.
- 'ts_forecast_panel': time series forecasting for panel datasets (multiple time series).
<!-- - 'ts_forecast_panel': time series forecasting for panel datasets (multiple time series). -->
- 'rank': learning to rank.
- 'seq-classification': sequence classification.
- 'seq-regression': sequence regression.
@@ -120,7 +120,7 @@ The estimator list can contain one or more estimator names, each corresponding t
- 'arima': ARIMA for task "ts_forecast". Hyperparameters: p, d, q.
- 'sarimax': SARIMAX for task "ts_forecast". Hyperparameters: p, d, q, P, D, Q, s.
- 'transformer': Huggingface transformer models for task "seq-classification", "seq-regression", "multichoice-classification", "token-classification" and "summarization". Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed.
- 'temporal_fusion_transform': TemporalFusionTransformerEstimator for task "ts_forecast_panel". Hyperparameters: gradient_clip_val, hidden_size, hidden_continuous_size, attention_head_size, dropout, learning_rate.
<!-- - 'temporal_fusion_transform': TemporalFusionTransformerEstimator for task "ts_forecast_panel". Hyperparameters: gradient_clip_val, hidden_size, hidden_continuous_size, attention_head_size, dropout, learning_rate. -->
* Custom estimator. Use custom estimator for:
- tuning an estimator that is not built-in;
- customizing search space for a built-in estimator.