automl

Apache Arrow has a first-class tabular file format, Feather, that the Ray Datasets IO layer should support. Combined with Ray Datasets' existing .from_arrow() and .to_arrow() APIs, this would round out our "all-Arrow" experience, which should be as nice as possible given our "distributed Arrow dataset" positioning.

Implementation Note

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 =

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

We want to add support for RNNs. I will let this issue open for people who want to contribute to this project.
My suggestion would be just to write RNN (or any other RNN-like model) in the algorithm field in order to use an RNN model. Here is an example to illustrate what I mean:

model:
     type: classification
     algorithm: RNN
    .
    . 

**If you a

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