featureforge
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machinalis
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
``` root@5da98a0113fa:/# pip install featureforge bash: pip: command not found root@5da98a0113fa:/# pip3 install featureforge Downloading/unpacking featureforge Downloading featureforge-0.1.6.tar.gz Running setup.py (path:/tmp/pip_build_root/featureforge/setup.py) egg_info for package featureforge Traceback (most recent call last):...
This code from the documentation is not working because of this: ``` >>> from featureforge.experimentation.stats_manager import StatsManager >>> sm = StatsManager(None, 'Your-database-name') Traceback (most recent call last): File "", line...
In a very long experiment, I would like to be able to incrementally submit results. This is useful if the experiment fails later, or if I want to make queries...
Whenever a feature output / input check fails there's no indication as to which feature has the blame. It's necesary to know this in an environment with tens of features...
Default behavior is to just log some info about each error, and proceed. For debugging, it's useful to be able to have errors raised up to the face.
It's customary when providing APIs for runners to provide an optional argv argument to use instead of sys.argv. This allows building custom runners more easily or overriding/defaulting arguments. It also...
If you have a Feature producing instances of long integers, the flattener will fail to process it.
If you have a Feature producing instances of decimal.Decimal, the flattener will fail to process it.
At https://github.com/machinalis/featureforge/blob/develop/featureforge/flattener.py#L197 , `TypeError`s are caught. The call to `next()` triggers feature evaluation, and when the call to a feature raises a `TypeError`, that exception is caught and a `ValueError("Cannot...
When testing, if you assign a plain function to BaseFeatureFixture.feature it gets transformed[0] to a bound method and therefore gets called with a "self" argument (along with the fixture). This...
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A set of tools for creating and testing machine learning features, with a scikit-learn compatible API