hyperparameter-optimization

What happened + What you expected to happen

The shim tune.create_scheduler() does not properly parse the keyword parameters passed in a dictionary for the pb2 scheduler. For this call

pb2_parm_dict = {"time_attr": "time_total_s", "metric": "metric_score", "mode": "min",
                 "hyperparam_bounds": {"param1": [0, 1]}}

pb2_scheduler = create_scheduler("pb2", **pb2_pa

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

Expected behavior

GridSampler should stop the optimization when all grids are evaluated.

Environment

• Optuna version: 3.0.0b1.dev
• Python version: 3.8.6
• OS: macOS-10.16-x86_64-i386-64bit
• (Optional) Other libraries and their versions:

Error messages, stack traces, or logs

See steps to reproduce.

Steps to reproduce

In the following code, optimize s

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:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

Use case

Polyaxon tracking can be used to automatically log all information generated by Ludwig.

We should probably think about adding support for Polyaxon's tracking module in Ludwig contrib.

In order to reduce overfitting, I would like to ask for a new parameter: "n_repetitions". This parameter sets the number of complete sets of folds to compute for repeated k-fold cross-validation.

Cross-validation example:

{
    "validation_type": "kfold",
    "k_folds": 5,
    "n_repetitions": 3, # new
    "shuffle": True,
    "stratify": True,
    "random_seed": 123
}

The indentation of fit_kwargs_by_estimator is not consistent with custom_hp:

https://github.com/microsoft/FLAML/blob/main/flaml/automl.py#L2203

which appears inconsistent in:

https://microsoft.github.io/FLAML/docs/reference/automl

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.

To Reproduce
https://app.codacy.com/gh/Neuraxio/Neuraxle/issues?&filters=W3siaWQiOiJMYW5ndWFnZSIsInZhbHVlcyI6W119LHsiaWQiOiJDYXRlZ29yeSIsInZhbHVlcyI6WyJDb2RlU3R5bGUiXX0seyJpZCI6IkxldmVsIiwidmFsdWVzIjpbXX0seyJpZCI6IlBhdHRlcm4iLCJ2YWx1ZXMiOltdfSx7ImlkIjoiQXV0aG9yIiwidmFsdWVzIjpbXX1d

Expected behavior
Less code st