[FIX] Typo in initial config for TimeSeries's TFT

[marcelovca90]

Fixed a typo in the initial configs for Time Series forecasting (TemporalFusionTransformer configspace).

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• [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
• [x] Bug fix (non-breaking change which fixes an issue)
• [ ] New feature (non-breaking change which adds functionality)

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• [ ] My change requires a change to the documentation.
• [ ] I have updated the documentation accordingly.
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  Fixed a typo in the initial configs for Time Series forecasting (TemporalFusionTransformer configspace).

  Motivation and Context

  Allows the correct namespace for the config space creation.

  How has this been tested?

[marcelovca90]

Something else might be missing.

autoPyTorch/pipeline/time_series_forecasting.py, line 298 if transform_time_features in cs: this CRASHES with the following error message:

Hyperparameter data_loader:transform_time_features not found in space. Configuration space object: Hyperparameters: data_loader:backcast, Type: Categorical, Choices: {True, False}, Default: False data_loader:backcast_period, Type: UniformInteger, Range: [1, 7], Default: 2 data_loader:batch_size, Type: UniformInteger, Range: [32, 320], Default: 64 data_loader:num_batches_per_epoch, Type: UniformInteger, Range: [30, 100], Default: 50 data_loader:sample_strategy, Type: Categorical, Choices: {LengthUniform, SeqUniform}, Default: SeqUniform data_loader:window_size, Type: UniformInteger, Range: [12, 36], Default: 15 feature_encoding:choice, Type: Categorical, Choices: {NoEncoder}, Default: NoEncoder loss:DistributionLoss:aggregation, Type: Categorical, Choices: {mean, median}, Default: mean loss:DistributionLoss:dist_cls, Type: Categorical, Choices: {studentT, normal}, Default: studentT loss:DistributionLoss:forecast_strategy, Type: Categorical, Choices: {sample, mean}, Default: sample loss:DistributionLoss:num_samples, Type: UniformInteger, Range: [50, 200], Default: 100 loss:QuantileLoss:lower_quantile, Type: UniformFloat, Range: [0.0, 0.4], Default: 0.1 loss:QuantileLoss:upper_quantile, Type: UniformFloat, Range: [0.6, 1.0], Default: 0.9 loss:RegressionLoss:loss_name, Type: Categorical, Choices: {l1, mse, mase, mape}, Default: mse loss:choice, Type: Categorical, Choices: {DistributionLoss, QuantileLoss, RegressionLoss}, Default: DistributionLoss ...

If I change it to: if transform_time_features in cs.keys(): no Exception is thrown and fit/predict works.