Pipeline components that support partial_fit.
The goal of scikit-partial is to offer a pipeline that can run partial_fit. This allows of online learning on an entire pipeline.
You can install everything with pip:
python -m pip install --upgrade pip
python -m pip install scikit-partial
Assuming that you use a stateless featurizer in your pipeline, such as HashingVectorizer or language models from whatlies, you choose to pre-train your scikit-learn model beforehand and fine-tune it later using models that offer the .partial_fit()-api. If you're unfamiliar with this api, you might appreciate this course on calmcode.
import pandas as pd
from sklearn.linear_model import SGDClassifier
from sklearn.feature_extraction.text import HashingVectorizer
from skpartial.pipeline import make_partial_pipeline
url = "https://raw.githubusercontent.com/koaning/icepickle/main/datasets/imdb_subset.csv"
df = pd.read_csv(url)
X, y = list(df['text']), df['label']
# Construct a pipeline with components that are `.partial_fit()` compatible
pipe = make_partial_pipeline(HashingVectorizer(), SGDClassifier(loss="log"))
# Run the learning algorithm on batches of data
for i in range(10):
# We could also do a whole bunch of data augmentation here!
pipe.partial_fit(X, y, classes=[0, 1])
When is this pattern useful? Let's consider spelling errors. Suppose that we'd like our algorithm to be robust against typos. Then we can simulate typos on our X inside of our learning loop.
The following pipeline components are added.
from skpartial.pipeline import (
PartialPipeline,
PartialFeatureUnion,
make_partial_pipeline,
make_partial_union,
)
These tools allow you to declare pipelines that support .partial_fit(). Note that components used in these pipelines all need to have .partial_fit() implemented.
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