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

Bug/Feature Request Description

In [1]: import featuretools as ft                                                                                                                             

In [2]: es = ft.demo.load_mock_customer(return_entityset=True)                                                                                                

In [3]: import pandas as pd    

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
Since Java 8 was introduced there is no need to use Joda as it has been replaced the native Date-Time API.

Solution
Ideally greping and replacing the text should work (mostly)

Additional context
Need to check if de/serializing will still work.

Details in discussion mljar/mljar-supervised#421

Feedback from sebhrusen (from the automlbenchmark)
CatboostEstimator is creating and filling a catboost_info subfolder in the running directory. We should be able to pass a 'train_dir' param to Catboost to avoid that.

For example at AutoML level, accept a tmpdir and pass it to each algo supporting an equivalent property (or pass a dedicated subfolder, for example tmpdir/catboost for Catboost a

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