Initial feature encoder 'OneHotEncoder' is not allowed to use in time-series forecasting task

[RobbyW551]

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Issue Description

• When Issue Happens Running time-series forecasting example.
• Steps To Reproduce
  1. Install autopytorch-v0.2 and run time-series forecasting example with user warning displayed.
  2. Copy https://github.com/automl/Auto-PyTorch/blob/master/autoPyTorch/configs/forecasting_init_cfgs.json to corresponding auto-pytorch repo in python site-package directory.
  3. Run time-series forecasting example with user warning allowed.

Expected Behavior

Traverse 9 initial designs and do evaluation on each, then different random search configurations will be evaluated.

Current Behavior

After the user warning is raised, only a single default configuration is evaluated before random searching begins.

Your Code

same as https://github.com/automl/Auto-PyTorch/blob/master/examples/20_basics/example_time_series_forecasting.py except allowing user warnings.

Error Message

/home/robby/miniconda3/envs/auto-pytorch/lib/python3.8/site-packages/autoPyTorch/optimizer/utils.py:97: UserWarning: Failed to convert {'data_loader:batch_size': 32, 'data_loader:backcast': False, 'data_loader:sample_strategy': 'SeqUniform', 'data_loader:num_batches_per_epoch': 50, 'data_loader:transform_time_features': False, 'lr_scheduler:__choice__': 'ReduceLROnPlateau', 'lr_scheduler:ReduceLROnPlateau:mode': 'max', 'lr_scheduler:ReduceLROnPlateau:factor': 0.5, 'lr_scheduler:ReduceLROnPlateau:patience': 10, 'optimizer:__choice__': 'AdamOptimizer', 'optimizer:AdamOptimizer:lr': 0.001, 'optimizer:AdamOptimizer:weight_decay': 1e-08, 'optimizer:AdamOptimizer:beta1': 0.9, 'optimizer:AdamOptimizer:beta2': 0.999, 'network_init:__choice__': 'XavierInit', 'network_init:XavierInit:bias_strategy': 'Normal', 'target_scaler:scaling_mode': 'mean_abs', 'trainer:__choice__': 'ForecastingStandardTrainer', 'network_embedding:__choice__': 'NoEmbedding', 'data_loader:window_size': 2, 'loss:__choice__': 'DistributionLoss', 'loss:DistributionLoss:dist_cls': 'studentT', 'loss:DistributionLoss:forecast_strategy': 'sample', 'loss:DistributionLoss:aggregation': 'median', 'loss:DistributionLoss:num_samples': 100, 'network_backbone:__choice__': 'flat_encoder', 'network_backbone:flat_encoder:__choice__': 'MLPEncoder', 'network_backbone:flat_encoder:MLPEncoder:num_groups': 1, 'network_backbone:flat_encoder:MLPEncoder:num_units_1': 40, 'network_backbone:flat_encoder:MLPEncoder:activation': 'relu', 'network_backbone:flat_encoder:MLPEncoder:use_dropout': False, 'network_backbone:flat_encoder:MLPEncoder:normalization': 'NoNorm', 'network_backbone:flat_encoder:MLPDecoder:num_layers': 0, 'network_backbone:flat_encoder:MLPDecoder:has_local_layer': True, 'network_backbone:flat_encoder:MLPDecoder:units_local_layer': 40, 'feature_encoding:__choice__': 'OneHotEncoder', 'scaler:scaling_mode': 'standard'} into a Configuration with error Trying to set illegal value 'OneHotEncoder' (type '<class 'str'>') for hyperparameter 'feature_encoding:__choice__, Type: Categorical, Choices: {NoEncoder}, Default: NoEncoder' (default-value has type '<class 'str'>').. Therefore, it can't be used as an initial configuration as it does not match the current config space.
  warnings.warn(f"Failed to convert {configuration_dict} into"

Your Local Environment

• Operating System, version Ubuntu 20.04
• Python, version Python 3.8
• Outputs of pip freeze or conda list pytorch=1.12+cu116

[dengdifan]

Thanks for the reporting! We will add another function to change the illegal values to the default configuration values and add it to the next release ASAP.

[ravinkohli]

Thanks for the reporting! We will add another function to change the illegal values to the default configuration values and add it to the next release ASAP.

I think it would not be included in the next release as that is coming soon with bug fixes. We'll take a look at this in the next major release.

[8W9aG]

I'm currently seeing this issue as well, is there a workaround I can use right now?

[dengdifan]

@8W9aG , This error happens if no categorical feature is available. If you are sure that no categorical feature exists in your codebase, you can replace this line with "feature_encoding:__choice__": "NoEncoder", hope that works