Comments (4)
I think this object should also support a fit_summary()
method which returns something like the current Results
object, with more informative/comprehensive instance-variable names as described here:
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Also I'd make sure all task.fit()
calls automatically call save
at the end before returning. We do not want to burden the user with having to explicitly call save
.
By the way, load()
should be a class method, so the user can call task.load(filepath)
without any predictor object.
In contrast, save()
should be an instance method, so the user calls predictor.save(filepath)
.
from autogluon.
Just to be clear, we should also try to ensure predictor.predict()
is overloaded to be able to handle:
- single example (e.g. single image, single document)
- batch of many examples
- MXNet Dataset object (ie. already preprocessed examples with a defined iterator)
For classification tasks, the sklearn convention is to use: predict_proba
underneath the hood to produce predicted class probabilities, and then predict
is just a wrapper around this that computes the argmax of the class probabilities.
from autogluon.
Just to be clear, we should also try to ensure
predictor.predict()
is overloaded to be able to handle:
- single example (e.g. single image, single document)
- batch of many examples
- MXNet Dataset object (ie. already preprocessed examples with a defined iterator)
For classification tasks, the sklearn convention is to use:
predict_proba
underneath the hood to produce predicted class probabilities, and thenpredict
is just a wrapper around this that computes the argmax of the class probabilities.
This is great suggestion. I will work on it 👍
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Related Issues (20)
- [BUG] Pip install with python 3.9 and 3.10 fails on windows at ray download
- Documentation Update Request: Issues with Specifying Hyperparameters and Tuning Them Example
- [BUG] Object Detection doesn't handle purely background negative images with no annotations HOT 1
- [BUG] Setting `ag_args_ensemble` `num_folds` to 0 results in error when `num_bag_folds >= 2` is set in the `.fit()`.
- Update on 2023 Roadmap Tasks and Request for 2024 Roadmap
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- [BUG] Kaggle Autogluon Installation Not Working for MultiModalPredictor
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- Conda does not install `ray` by default HOT 1
- During inference, multi-GPUs are not used with DDP strategy. Only single GPU is used.
- Unable to Affect Model Training with Different random_seed Values in Time-series Module HOT 1
- Request: Implement Feature Importance Explainability for Time-Series Module HOT 1
- [timeseries] DirectTabular & RecursiveTabular models fail if eval_metric = "WAPE" HOT 7
- `estimate_memory_usage` throws `TypeError` when using HPO for `GBM` model in `TabularPredictor` HOT 2
- [BUG] export_onnx not working for some Multimodal models
- [BUG] refit_full does not expand memory allowance when `use_bag_holdout=True` (`good_quality` preset) HOT 2
- Disable dynamic_stacking when `use_bag_holdout=True`
- [BUG] [Errno 2] No such file or directory: c:\\...\\prediction-net-state.pt HOT 5
- [BUG] Missing example code automm/Conv-Lora HOT 1
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