automl

According to FastAPI's docs, response_model can accept type annotations that are not pydantic models. However, the code referenced below is checking for the __fields__ attribute, which won't be on type annotations such as list[float], for example.

https://github.com/ray-project/ray/blob/e60a5f52eb93c851b186cb78fa1f70d

Feature Description

We want to enable the users to specify the value ranges for any argument in the blocks.
The following code example shows a typical use case.
The users can specify the number of units in a DenseBlock to be either 10 or 20.

Code Example

import au

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

• transform_primitive.py is becoming very large. I suggest splitting out into separate files.
• We could split this file up by groups (such as LatLong transform primitives in 1 file).

We need to increase the number of datasources that MindsDB supports. This task should add a new datasource for connecting to Redash. You can get more info on Redash docs.
If possible, please include a test for the datasource. You can check the example here.

Note: if you are familiar with another datasource tha

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

We would like to forward a particular 'key' column which is part of the features to appear alongside the predictions - this is to be able to identify to which set of features a particular prediction belongs to. Here is an example of predictions output using the tensorflow.contrib.estimator.multi_class_head:

{"classes": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
 "scores": [0.068196

Can we have an example of REST API calls in the documentation?
Examples with CURL, HTTPie or another client and the results would be better for newbies.
Thanks again for your good work.

Problem
Some of our transformers & estimators are not thoroughly tested or not tested at all.

Solution
Use OpTransformerSpec and OpEstimatorSpec base test specs to provide tests for all existing transformers & estimators.

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

Contact Details [Optional]

support@zenml.io

Describe the feature you'd like

Currently our CLI offers a way to install the python packages that are required for a given integration. However, some of our integrations also have system requirements that are necessary to make them work (graphviz, kubectl, etc. ).
All system requirements should be listed on an integration level, just

https://www.usenix.org/conference/opml19/program

Prepare to read papers