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    

The problem I want to use auto-sklearn on is a time-series. Can we modify sklearn to include cv with time series?

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.

Right now there is an error when computing prediction on a single sample, see #156 for details.

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

It would help to have download option to get a list of used packages:

For example:
Download list of ML Packages used in Model Training, with all corresponding citations in CSV Format.

In current version, out-of-sample-foracast for time series is implemented in out_of_sample_ts_forecast function.
However, it cannot be used with FEDOT API.

The support of in-sample and out-of-sample forecasting should for simple and multivariate time series should be implemented in forecast method of API.