automl

Trying out a simple example using TuneSearchCV with LGBMClassifier and it fails on start.

Environment:

Python 3.8.3
tune-sklearn 0.3.0
ray 1.3.0
macos mojave 10.14.6

Code:

from ray.tune.sklearn import TuneSearchCV

from lightgbm import LGBMClassifier

lgmb_param_dists = dict(

                boosting_type=['gbdt','dart','rf'],
                num_leaves=(10,500),
 

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 =

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

Description

I'm the creator and only maintainer of the project at the moment. I'm working on adding new features and thus I would like to let this issue open for newcomers who want to contribute to the project.

Basically, I wrote the cli using argparse since it is part of the standard language already. However, I'm starting to rethin

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

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

Prepare to read papers