| Name | textknnassifier JSON |
| Version |
0.0.1rc1
JSON |
| download |
| home_page | |
| Summary | TextKNNClassifier is a k-nearest neighbors classifier for text data. It uses a compression algorithm to compute the distance between texts and predicts the label of a test entry based on the labels of the k-nearest neighbors in the training data. |
| upload_time | 2023-07-13 19:31:23 |
| maintainer | |
| docs_url | None |
| author | Reinder Vos de Wael |
| requires_python | >=3.8,<4.0 |
| license | LGPL-2.1 |
| keywords |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# TextKNNClassifier
[](https://github.com/cmi-dair/text-knnassifier/actions/workflows/test.yaml?query=branch%3Amain)
[](https://codecov.io/gh/cmi-dair/text-knnassifier)
[](https://github.com/psf/black)
[](LICENSE)
[](https://cmi-dair.github.io/text-knnassifier)
`TextKNNClassifier` is a k-nearest neighbors classifier for text data. It uses a compression algorithm to compute the distance between texts and predicts the label of a test entry based on the labels of the k-nearest neighbors in the training data.
## Installation
You can install `TextKNNassifier` using pip:
```bash
pip install textknnassifier
```
## Usage
Here's an example of how to use `TextKNNClassifier`:
```python
from textknnassifier import classifier
training_text = [
"This is a test",
"Another test",
"General Tarkin",
"General Grievous",
]
training_labels = ["test", "test", "star_wars", "star_wars"]
testing_data = [
"This is a test",
"Testing here too!",
"General Kenobi",
"General Skywalker",
]
KNN = classifier.TextKNNClassifier(n_neighbors=2)
KNN.fit(training_data, training_labels)
predicted_labels = KNN.predict(testing_data)
print(predicted_labels)
# Output: ['test1', 'test1', 'star_wars', 'star_wars']
```
In this example, we create a `TextKNNClassifier` instance and use it to predict the labels of the test entries. The initialization is given `n_neighbors=2`, this denotes the number of training datapoints to consider for predicting the testing label. The `fit` method takes two arguments: the training data, and the training labels. It simply stores these values for later use. The `predict` method takes the testing data as an argument and returns the predicted labels.
## References
- Jiang, Z., Yang, M., Tsirlin, M., Tang, R., Dai, Y., & Lin, J. (2023, July). “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors. In Findings of the Association for Computational Linguistics: ACL 2023 (pp. 6810-6828).
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