ml-mixins 0.2.0

A collection of useful mixins for machine learning development code.

ML Mixins

This package contains some useful python mixin classes for use in ML / data science, including a mixin for seeding (SeedableMixin), timing (TimeableMixin), and memory tracking (MemTrackableMixin).

Installation

this package can be installed via pip:

pip install ml-mixins

Usage

You can use these mixins either by (1) Defining your classes to inherit from them, then leveraging their included methods in your derived class, or (2) Adding them post-hoc to an existing class for use in secondary applications such as benchmarking or debugging without overhead in production code. Below, we show how to use each mixin directly first, then we show how to add them to an existing class at the end, as that process will still leverage the same decorator methods and class member variables in the resulting modified classes.

Mixin Documentation

SeedableMixin

from mixins import SeedableMixin

class MyModel(SeedableMixin):
    ...

    @SeedableMixin.WithSeed
    def fit(self, X, y):
        # This function can now be called with a seed kwarg, or it will use a pseudo-random seed which will be
        # saved to a class member variable if a seed is not passed.
...

TimeableMixin

TODO

MemTrackableMixin

TODO

Adding Mixins Post-Hoc

from mixins import TimeableMixin, add_mixin


class MyModel:
    ...

    def fit(self, X, y): ...


# Add the mixin to the class
TimedModel = add_mixin(
    MyModel, TimeableMixin, decorate_methods={"fit": TimeableMixin.TimeAs}
)

# Now, the class `TimedModel` will have the same methods as `MyModel`, but with the added timing
# functionality:

model = TimedModel()
model.fit(X, y)
model._profile_durations()  # will print durations...