Describe the issue:
During computing Channel Dependencies reshape_break_channel_dependency does following code to ensure that the number of input channels equals the number of output channels:
in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel
This is correct
This issue was originally reported by @fukatani.
When I clicked the Edit on GitHub in some reference page, I got 404 errors.
This i
I have been trying to use AutoSklearn with Multi-class classification
so my labels are like this
0 1 2 3 4 ... 200
1 0 1 1 1 ... 1
0 1 0 0 1 ... 0
1 0 0 1 0 ... 0
1 1 0 1 0 ... 1
0 1 1 0 1 ... 0
1 1 1 0 0 ... 1
1 0 1 0 1 ... 0
I used this code
`
y = y[:, (65,67,54,133,122,63,102
Related: awslabs/autogluon#1479
Add a scikit-learn compatible API wrapper of TabularPredictor:
Required functionality (may need more than listed):
When using r2 as eval metric for regression task (with 'Explain' mode) the metric values reported in Leaderboard (at README.md file) are multiplied by -1.
For instance, the metric value for some model shown in the Leaderboard is -0.41, while when clicking the model name leads to the detailed results page - and there the value of r2 is 0.41.
I've noticed that when one of R2 metric values in the L
When I set "split_type" as GroupKFold, then use fit() with setting "groups", I got the issue: AttributeError: 'GroupKFold' object has no attribute 'groups' and
ml.py:460, in evaluate_model_CV(config, estimator, X_train_all, y_train_all, budget, kf, task, eval_metric, best_val_loss, log_training_metric, fit_kwargs)
458 kf = kf.split(X_train_split, y_train_split)
459 elif is
If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.
We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing
Describe the bug
Code could be more conform to pep8 and so forth.
Expected behavior
Less code st
Add a description, image, and links to the hyperparameter-optimization topic page so that developers can more easily learn about it.
To associate your repository with the hyperparameter-optimization topic, visit your repo's landing page and select "manage topics."
Description
There are multiple user requests of using GraphNN data (node and edge lists) as sample batches into a custom RLlib model.
https://discuss.ray.io/t/rllib-variable-length-observation-spaces-without-padding/726
https://discuss.ray.io/t/working-with-graph-neural-networks-varying-state-space/5730/2
The recommended method today is to use Repeated observation space and VariableVal