automated-machine-learning

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

• A better name date of birth column in our mock dataset would be birthday.

Building the doc fails for example 40_advanced/example_single_configurations on the current development branch

Logs here

...
generating gallery for examples/40_advanced... [ 50%] example_debug_logging.py

Warning, treated as error:
/home/runner/work/auto-sklearn/auto-sklearn/examples/40_advanced/example_single_configu

When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?

Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =

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.

Details in discussion mljar/mljar-supervised#421

A possible improvement of the time series smoothing operation is proposed. Below is example of applying smoothed operation

Currently, the implementation of smoothing allows modifying only the "features" time series (features field in InputData/OutputData). But it does not change the