https://github.com/evamaxfield/lazy-text-classifiers

Build and test a variety of text multi-class classification models.
https://github.com/evamaxfield/lazy-text-classifiers

Last synced: about 2 months ago
JSON representation

Build and test a variety of text multi-class classification models.

• Host: GitHub
• URL: https://github.com/evamaxfield/lazy-text-classifiers
• Owner: evamaxfield
• License: mit
• Created: 2023-02-05T03:50:03.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-01T17:53:03.000Z (11 months ago)
• Last Synced: 2024-10-18T13:49:36.982Z (3 months ago)
• Language: Python
• Size: 629 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
# lazy-text-classifiers

[![Build Status](https://github.com/evamaxfield/lazy-text-classifiers/workflows/CI/badge.svg)](https://github.com/evamaxfield/lazy-text-classifiers/actions)

[![Documentation](https://github.com/evamaxfield/lazy-text-classifiers/workflows/Documentation/badge.svg)](https://evamaxfield.github.io/lazy-text-classifiers)

Build and test a variety of text binary or multi-class classification models.

---

## Installation

**Stable Release:** `pip install lazy-text-classifiers`


**Development Head:** `pip install git+https://github.com/evamaxfield/lazy-text-classifiers.git`

## Quickstart

```python

from lazy_text_classifiers import LazyTextClassifiers

from sklearn.datasets import fetch_20newsgroups

from sklearn.model_selection import train_test_split

# Example data from sklearn

# `x` should be an iterable of strings

# `y` should be an iterable of string labels

data = fetch_20newsgroups(subset="all", remove=("header", "footers", "quotes"))

x = data.data[:1000]

y = data.target[:1000]

y = [data.target_names[id_] for id_ in y]

# Split the data into train and test

x_train, x_test, y_train, y_test = train_test_split(

    x,

    y,

    test_size=0.4,

    random_state=12,

)

# Init and fit all models

ltc = LazyTextClassifiers(random_state=12)

results = ltc.fit(x_train, x_test, y_train, y_test)

# Results is a dataframe

# | model                  |   accuracy |   balanced_accuracy |   precision |   recall |       f1 |    time |

# |:-----------------------|-----------:|--------------------:|------------:|---------:|---------:|--------:|

# | semantic-logit         |    0.73    |            0.725162 |    0.734887 |  0.73    | 0.728247 |  13.742 |

# | tfidf-logit            |    0.70625 |            0.700126 |    0.709781 |  0.70625 | 0.702073 | 187.217 |

# | fine-tuned-transformer |    0.11125 |            0.1118   |    0.10998  |  0.11125 | 0.109288 | 220.105 |

# Get a specific model

semantic_logit = ltc.fit_models["semantic-logit"]

# either an scikit-learn Pipeline or a custom Transformer wrapper class

# All models have a `save` function which will store into the normal format

# * pickle for scikit-learn pipelines

# * torch model directory for Transformers

```

## Documentation

For full package documentation please visit [evamaxfield.github.io/lazy-text-classifiers](https://evamaxfield.github.io/lazy-text-classifiers).

## Acknowledgements

This package was heavily inspired by [lazypredict](https://github.com/shankarpandala/lazypredict).

## Development

See [CONTRIBUTING.md](CONTRIBUTING.md) for information related to developing the code.

**MIT License**