Sparse Tensor Classifier¶
Sparse Tensor Classifier (STC) is a supervised classification algorithm for categorical data inspired by the notion of superposition of states in quantum physics. It supports multiclass and multilabel classification, online learning, prior knowledge, automatic dataset balancing, missing data, and provides a native explanation of its predictions both for single instances and for each target class label globally. Read more at https://arxiv.org/pdf/2105.13988.pdf
The algorithm is implemented in SQL and made available via the Python module
stc on PyPI. By default, the library uses an in-memory SQLite database, shipped with Python standard library, that require no configuration by the user. It is also possible to configure STC to run on alternative DBMS in order to take advantage of persistent storage and scalability.
stc from PyPI. We recommend using
SQLite>=3.24.0 for better performance.
pip install stc
Initialize, fit, and predict. Get started in 3 lines on code!
Example: use Sparse Tensor Classifier to classify animals. The dataset consists of 101 animals from a zoo. There are 16 variables with various traits to describe the animals. The 7 Class Types are:
Invertebrate. The purpose for this dataset is to be able to predict the classification of the animals. STC returns a tuple with (1) the predicted classes, (2) the probability for each class, and (3) the contribution of each feature to the target class labels (explainability).
import pandas as pd from stc import SparseTensorClassifier # Read the dataset zoo = pd.read_csv('https://git.io/Jss6f') # Initialize the class STC = SparseTensorClassifier(targets=['class_type'], features=zoo.columns[1:-1]) # Fit the training data STC.fit(zoo[0:70]) # Predict the test data labels, probability, explainability = STC.predict(zoo[70:])
Discover the flexibility of the library in the documentation.
Get started with step-by-step tutorials and use-cases.
Open an issue on GitHub.
Guidotti E., Ferrara A., (2021). “An Explainable Probabilistic Classifier for Categorical Data Inspired to Quantum Physics” arXiv:2105.13988