hyperparameter-optimization

Description

Per https://discuss.ray.io/t/how-do-i-sample-from-a-ray-datasets/5308, we should add a random_sample(N) API that returns N records from a Dataset. This can be implemented via a map_batches() followed by a take().

cc @simon-mo @clarkzinzow

Use case

Random sample is useful for a variety of scenarios, including creating training batches, and downsampling the dataset for

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 does not have a seed argument, but it randomly shuffles an evaluation order of trials. It makes optimization results unreproducible if objective functions depend on previous trials, e.g., pruning. We should add a seed argument like TPESampler and RandomSampler.

Environment

• Optuna version: 3.0.0b1.dev
• Python version: 3.8.6
• OS: macOS-10.16-x

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.

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

@HuangChiEn From the console msg, it is stuck at the step of building the ensemble model (sorry for not making that explicit in the msg). You can verify it by removing "ensemble": True from the settings.

Originally posted by @sonichi in microsoft/FLAML#536 (comment)

Suggestion: Modify https://github.com/microsoft/FLAML/blob/c1e1299855dcea378591628a

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