Metadata-Version: 2.1
Name: stc
Version: 0.2.0
Summary: Sparse Tensor Classifier
Home-page: https://sparsetensorclassifier.org
Author: Emanuele Guidotti
Author-email: emanuele.guidotti@unine.ch
License: GPLv3
Project-URL: Documentation, https://sparsetensorclassifier.org/docs/
Project-URL: Source, https://github.com/sparsetensorclassifier
Project-URL: Tracker, https://github.com/sparsetensorclassifier/stc/issues
Description: # 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. 
        
        The algorithm is implemented in SQL and made available via the Python module ``stc`` on [PyPI](https://pypi.org/project/stc/). By default, the library uses an in-memory SQLite database, shipped with Python standard library, that require no configuration by the user. However, it is possible to configure STC to run on alternative DBMS in order to take advantage of persistent storage and scalability.
        
        ## Quickstart
        
        Install ``stc`` from [PyPI](https://pypi.org/project/stc/) and make sure to be running ``Python >=3.7``
        
        ```python
        pip install stc
        ```
        
        ## Example
        
        Use the Sparse Tensor Classifier to classify animals. The [dataset](https://archive.ics.uci.edu/ml/datasets/Zoo) consists of 101 animals from a zoo.
        There are 16 variables with various traits to describe the animals. The 7 Class Types are: Mammal, Bird, Reptile, Fish, Amphibian, Bug and Invertebrate. The purpose for this dataset is to be able to predict the classification of the animals.
        
        ```python
        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:])
        ```
        
        ## Documentation
        
        Discover the flexibility of the library in the [documentation](https://sparsetensorclassifier.org/docs.html).
        
        ## Tutorials
        
        Get started with more advanced [tutorials](https://github.com/SparseTensorClassifier/tutorial).
        
        ## Cite as
        
        ```latex
        @Misc{stc2021,
          title = {An Explainable Probabilistic Classifier for Categorical Data Inspired to Quantum Physics},
          author = {Guidotti, Emanuele and Ferrara, Alfio},
          year = {2021},
        }
        ```
        
        ___
        
        
        
        ![](./docs/source/_static/img/logo.svg)
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
