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Jul 2, 2021 - Python
automated-machine-learning
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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
TimeSeries Split
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.
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Feb 10, 2021 - Python
Details in discussion mljar/mljar-supervised#421
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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
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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.
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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