instancelib 0.6.0

A generic interface for datasets and Machine Learning models

A generic interface for datasets and Machine Learning models

---

instancelib provides a generic architecture for datasets and machine learning algorithms such as classification algorithms.

© Michiel Bron, 2021

Quick tour

Load dataset: Load the dataset in an environment

import instancelib as il
text_env = il.read_excel_dataset("./datasets/testdataset.xlsx",
                                  data_cols=["fulltext"],
                                  label_cols=["label"])

ds = text_env.dataset # A `dict-like` interface for instances
labels = text_env.labels # An object that stores all labels
labelset = labels.labelset # All labels that can be given to instances

ins = ds[20] # Get instance with identifier key  `20`
ins_data = ins.data # Get the raw data for instance 20
ins_vector = ins.vector # Get the vector representation for 20 if any

ins_labels = labels.get_labels(ins)

Dataset manipulation: Divide the dataset in a train and test set

train, test = text_env.train_test_split(ds, train_size=0.70)

print(20 in train) # May be true or false, because of random sampling

Train a model:

from sklearn.pipeline import Pipeline 
from sklearn.naive_bayes import MultinomialNB 
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer

pipeline = Pipeline([
     ('vect', CountVectorizer()),
     ('tfidf', TfidfTransformer()),
     ('clf', MultinomialNB()),
     ])

model = il.SkLearnDataClassifier.build(pipeline, text_env)
model.fit_provider(train, labels)
predictions = model.predict(test)

Installation

See installation.md for an extended installation guide.

Method	Instructions
pip	Install from PyPI via pip install instancelib.
uv	Add to your project via uv add instancelib.
Local	Clone this repository and install via pip install -e . or uv sync.

Documentation

Full documentation of the latest version is provided at https://instancelib.readthedocs.org.

Example usage

See usage.py to see an example of how the package can be used.

Releases

instancelib is officially released through PyPI.

See CHANGELOG.md for a full overview of the changes for each version.

Citation

@misc{instancelib,
  title = {Python package instancelib},
  author = {Michiel Bron},
  howpublished = {\url{https://github.com/mpbron/instancelib}},
  year = {2021}
}

Library usage

This library is used in the following projects:

• python-allib. A typed Active Learning framework for Python for both Classification and Technology-Assisted Review systems.
• text_explainability. A generic explainability architecture for explaining text machine learning models
• text_sensitivity. Sensitivity testing (fairness & robustness) for text machine learning models.

Maintenance

Contributors

• Michiel Bron (@mpbron)