hiclass 3.1.6

Hierarchical Classification Library.

HiClass

HiClass is an open-source Python library for hierarchical classification compatible with scikit-learn.

✨ Here are a couple of demos that show HiClass in action on hierarchical datasets:

• Classify a consumer complaints dataset from the consumer financial protection bureau: consumer-complaints
• Classify a 16S rRNA dataset from the TAXXI benchmark: 16s-rrna

Quick links

• Features
• Benchmarks
• Roadmap
• Who is using HiClass
• Install
• Quick start
• Step-by-step- walk-through
• API documentation
• FAQ
• Support
• Contributing
• Getting the latest updates
• Citation

Features

• Python lists and NumPy arrays: Handles Python lists and NumPy arrays elegantly, out-of-the-box.
• Pandas Series and DataFrames: If you prefer to use pandas, that is not an issue as HiClass also works with Pandas.
• Sparse matrices: HiClass also supports features (X_train and X_test) built with sparse matrices, both for training and predicting, which can save you heaps of memory.
• Parallel training: Training can be performed in parallel on the hierarchical classifiers, which allows parallelization regardless of the implementations available on scikit-learn.
• Build pipelines: Since the hierarchical classifiers inherit from the BaseEstimator of scikit-learn, pipelines can be built to automate machine learning workflows.
• Hierarchical metrics: HiClass supports the computation of hierarchical precision, recall and f-score, which are more appropriate for hierarchical data than traditional metrics.
• Compatible with pickle: Easily store trained models on disk for future use.

Don't see a feature on this list? Search our issue tracker if someone has already requested it and add a comment to it explaining your use-case, or open a new issue if not. We prioritize our roadmap based on user feedback, so we'd love to hear from you.

Install

Option 1: Conda

HiClass and its dependencies can be easily installed with conda:

conda install -c conda-forge hiclass

Option 2: Pip

Alternatively, HiClass and its dependencies can also be installed with pip:

pip install hiclass

Quick start

Here's a quick example showcasing how you can train and predict using a local classifier per node, with a RandomForestClassifier for each node:

from hiclass import LocalClassifierPerNode
from sklearn.ensemble import RandomForestClassifier

# define data
X_train = [[1], [2], [3], [4]]
X_test = [[4], [3], [2], [1]]
Y_train = [
    ['Animal', 'Mammal', 'Sheep'],
    ['Animal', 'Mammal', 'Cow'],
    ['Animal', 'Reptile', 'Snake'],
    ['Animal', 'Reptile', 'Lizard'],
]

# Use random forest classifiers for every node
rf = RandomForestClassifier()
classifier = LocalClassifierPerNode(local_classifier=rf)

# Train local classifier per node
classifier.fit(X_train, Y_train)

# Predict
predictions = classifier.predict(X_test)

HiClass can also be adopted in scikit-learn pipelines, and fully supports sparse matrices as input. In order to demonstrate the use of both of these features, we will use the following consumer complaints dataset:

from hiclass import LocalClassifierPerParentNode
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

# define data
X_train = [
    'Struggling to repay loan',
    'Unable to get annual report',
]
X_test = [
    'Unable to get annual report',
    'Struggling to repay loan',
]
Y_train = [
    ['Loan', 'Student loan'],
    ['Credit reporting', 'Reports']
]

Now, let's build a pipeline that will use CountVectorizer and TfidfTransformer to extract features as sparse matrices:

# Use logistic regression classifiers for every parent node
lr = LogisticRegression()
pipeline = Pipeline([
    ('count', CountVectorizer()),
    ('tfidf', TfidfTransformer()),
    ('lcppn', LocalClassifierPerParentNode(local_classifier=lr)),
])

Finally, let's train and predict with the pipeline we just created:

# Train local classifier per parent node
pipeline.fit(X_train, Y_train)

# Predict
predictions = pipeline.predict(X_test)

Step-by-step walk-through

A step-by-step walk-through is available on our interactive notebook hosted on Google Colab.

This will guide you through the process of installing hiclass with conda, training and predicting a small dataset.

API documentation

Here's our official API documentation, available on Read the Docs.

If you notice any issues with the documentation or walk-through, please let us know by opening an issue here: https://github.com/mirand863/hiclass/issues.

Support

If you run into any problems or issues, please create a Github issue and we'll try our best to help.

We strive to provide good support through our issue tracker on Github. However, if you'd like to receive private support with:

• Phone / video calls to discuss your specific use case and get recommendations
• Private discussions over Slack or Mattermost

Please reach out to us at [e-mail].

Contributing

We are a small team on a mission to democratize hierarchical classification, and we'll take all the help we can get! If you'd like to get involved, here's information on where we could use your help: Contributing.md

Getting the latest updates

If you'd like to get updates when we release new versions, please click on the "Watch" button on the top and select "Releases only". Github will then send you notifications along with a changelog with each new release.

Citation

If you use HiClass, please cite:

Miranda, Fábio M., Niklas Köehnecke, and Bernhard Y. Renard. "HiClass: a Python library for local hierarchical classification compatible with scikit-learn." arXiv preprint arXiv:2112.06560 (2021).

@article{miranda2021hiclass,
  title={HiClass: a Python library for local hierarchical classification compatible with scikit-learn},
  author={Miranda, F{\'a}bio M and K{\"o}ehnecke, Niklas and Renard, Bernhard Y},
  journal={arXiv preprint arXiv:2112.06560},
  year={2021}
}

In addition, we would like to list publications that use HiClass to solve hierarchical problems. If you would like your manuscript to be added to this list, please email the reference, the name of your lab, department and institution to [e-mail]