hyperparameter-optimization

After training a model, we should expose utilities so that the user can load their best checkpoint in memory.

1. Trainer.load_checkpoint(path) to load an arbitrary checkpoint.
2. trainer.best_checkpoint to load trainer.best_checkpoint_path into memory.

It seems there is no validation on fit_ensemble when ensemble size is 0, causing an issue to appear as seen in #1327

What is an issue?

Optuna's CIs start to use Python 3.8 by default optuna/optuna#3026. However, optuna still supports older Python versions, precisely, 3.6 and 3.7, so users still develop Optuna with one of the other versions. In some settings, the recommended procedure by https://github.com/optuna/optuna/blob/master/CONTRIBUTING.md#documentation might not work accord

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.

This issue has been coming up when I use,
automl.predict_proba(input)

I am using the requirements.txt in venv. Shouldn't input have feature names?
This message did not used to come up and I don't know why.

In principle it seems getting the parameters from FLAML to C# LightGBM seems to work, but I dont have any metrics yet. The names of parameters are slightly different but documentation is adequate to match them. Microsoft.ML seems to have version 2.3.1 of LightGBM.

Another approach that might be useful, especially for anyone working with .NET, would be having some samples about conversion to ONN

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