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# BlockingPy
BlockingPy is a Python package that implements efficient blocking methods for record linkage and data deduplication using Approximate Nearest Neighbor (ANN) algorithms. It is based on [R blocking package](https://github.com/ncn-foreigners/blocking).
## Purpose
When performing record linkage or deduplication on large datasets, comparing all possible record pairs becomes computationally infeasible. Blocking helps reduce the comparison space by identifying candidate record pairs that are likely to match, using efficient approximate nearest neighbor search algorithms.
## Installation
BlockingPy requires Python 3.10 or later. Installation is handled via PIP as follows:
```bash
pip install blockingpy
```
or i.e. with poetry:
```bash
poetry add blockingpy
```
### Note
You may need to run the following beforehand:
```bash
sudo apt-get install -y libmlpack-dev # on Linux
brew install mlpack # on MacOS
```
## Basic Usage
### Record Linkage
```python
from blockingpy.blocker import Blocker
import pandas as pd
# Example data for record linkage
x = pd.DataFrame({
"txt": [
"johnsmith",
"smithjohn",
"smiithhjohn",
"smithjohnny",
"montypython",
"pythonmonty",
"errmontypython",
"monty",
]})
y = pd.DataFrame({
"txt": [
"montypython",
"smithjohn",
"other",
]})
# Initialize blocker instance
blocker = Blocker()
# Perform blocking with default ANN : FAISS
block_result = blocker.block(x = x['txt'], y = y['txt'])
```
Printing `block_result` contains:
- The method used (`faiss` - refers to Facebook AI Similarity Search)
- Number of blocks created (`3` in this case)
- Number of columns (features) used for blocking (intersecting n-grams generated from both datasets, `17` in this example)
- Reduction ratio, i.e. how large is the reduction of comparison pairs (here `0.8750` which means blocking reduces comparison by over 87.5%).
```python
print(block_result)
# ========================================================
# Blocking based on the faiss method.
# Number of blocks: 3
# Number of columns used for blocking: 17
# Reduction ratio: 0.8750
# ========================================================
# Distribution of the size of the blocks:
# Block Size | Number of Blocks
# 1 | 3
```
By printing `block_result.result` we can take a look at the results table containing:
- row numbers from the original data,
- block number (integers),
- distance (from the ANN algorithm).
```python
print(block_result.result)
# x y block dist
# 0 4 0 0 0.0
# 1 1 1 1 0.0
# 2 7 2 2 6.0
```
### Deduplication
We can perform deduplication by putting previously created DataFrame in the `block()` method.
```python
dedup_result = blocker.block(x = x['txt'])
```
```python
print(dedup_result)
# ========================================================
# Blocking based on the faiss method.
# Number of blocks: 2
# Number of columns used for blocking: 25
# Reduction ratio: 0.5714
# ========================================================
# Distribution of the size of the blocks:
# Block Size | Number of Blocks
# 3 | 2
```
```python
print(dedup_result.result)
# x y block dist
# 0 0 1 0 2.0
# 1 1 2 0 2.0
# 2 1 3 0 2.0
# 3 4 5 1 2.0
# 4 4 6 1 3.0
# 5 4 7 1 6.0
```
## Features
- Multiple ANN algorithms available:
- [FAISS](https://github.com/facebookresearch/faiss) (Facebook AI Similarity Search)
- [Voyager](https://github.com/spotify/voyager) (Spotify)
- [HNSW](https://github.com/nmslib/hnswlib) (Hierarchical Navigable Small World)
- [MLPACK](https://github.com/mlpack/mlpack) (both LSH and k-d tree)
- [NND](https://github.com/lmcinnes/pynndescent) (Nearest Neighbor Descent)
- [Annoy](https://github.com/spotify/annoy) (Spotify)
- Multiple distance metrics such as:
- Euclidean
- Cosine
- Inner Product
and more...
- Comprehensive algorithm parameters customization with `control_ann` and `control_txt`
- Support for already created Document-Term-Matrices (as `np.ndarray` or `csr_matrix`)
- Support for both record linkage and deduplication
- Evaluation metrics when true blocks are known
You can find detailed information about BlockingPy in [documentation](https://blockingpy.readthedocs.io/en/latest/).
## Disclaimer
BlockingPy is still under development, API and features may change. Also bugs or errors can occur.
## License
BlockingPy is released under [MIT license](https://github.com/T-Strojny/BlockingPy/blob/main/LICENSE).
## Third Party
BlockingPy benefits from many open-source packages such as [Faiss](https://github.com/facebookresearch/faiss) or [Annoy](https://github.com/spotify/annoy). For detailed information see [third party notice](https://github.com/T-Strojny/BlockingPy/blob/main/THIRD_PARTY).
## Contributing
Please see [CONTRIBUTING.md](https://github.com/T-Strojny/BlockingPy/blob/main/CONTRIBUTING.md) for more information.
## Citation
TODO ?
## Acknowledgements
This package is based on the R [blocking](https://github.com/ncn-foreigners/blocking/tree/main) package developed by [BERENZ](https://github.com/BERENZ). Special thanks to the original author for his foundational work in this area.
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Blocking helps reduce the comparison space by identifying candidate record pairs that are likely to match, using efficient approximate nearest neighbor search algorithms.\n\n## Installation\n\nBlockingPy requires Python 3.10 or later. Installation is handled via PIP as follows:\n```bash\npip install blockingpy\n```\nor i.e. with poetry:\n\n```bash\npoetry add blockingpy\n```\n### Note\nYou may need to run the following beforehand:\n```bash\nsudo apt-get install -y libmlpack-dev # on Linux\nbrew install mlpack # on MacOS\n```\n## Basic Usage\n### Record Linkage\n```python\nfrom blockingpy.blocker import Blocker\nimport pandas as pd\n\n# Example data for record linkage\nx = pd.DataFrame({\n \"txt\": [\n \"johnsmith\",\n \"smithjohn\",\n \"smiithhjohn\",\n \"smithjohnny\",\n \"montypython\",\n \"pythonmonty\",\n \"errmontypython\",\n \"monty\",\n ]})\n\ny = pd.DataFrame({\n \"txt\": [\n \"montypython\",\n \"smithjohn\",\n \"other\",\n ]})\n\n# Initialize blocker instance\nblocker = Blocker()\n\n# Perform blocking with default ANN : FAISS\nblock_result = blocker.block(x = x['txt'], y = y['txt'])\n```\nPrinting `block_result` contains:\n\n- The method used (`faiss` - refers to Facebook AI Similarity Search)\n- Number of blocks created (`3` in this case)\n- Number of columns (features) used for blocking (intersecting n-grams generated from both datasets, `17` in this example)\n- Reduction ratio, i.e. how large is the reduction of comparison pairs (here `0.8750` which means blocking reduces comparison by over 87.5%).\n```python\nprint(block_result)\n# ========================================================\n# Blocking based on the faiss method.\n# Number of blocks: 3\n# Number of columns used for blocking: 17\n# Reduction ratio: 0.8750\n# ========================================================\n# Distribution of the size of the blocks:\n# Block Size | Number of Blocks\n# 1 | 3\n```\nBy printing `block_result.result` we can take a look at the results table containing:\n\n- row numbers from the original data,\n- block number (integers),\n- distance (from the ANN algorithm).\n\n```python\nprint(block_result.result)\n# x y block dist\n# 0 4 0 0 0.0\n# 1 1 1 1 0.0\n# 2 7 2 2 6.0\n```\n### Deduplication\nWe can perform deduplication by putting previously created DataFrame in the `block()` method.\n```python\ndedup_result = blocker.block(x = x['txt'])\n```\n```python\nprint(dedup_result)\n# ========================================================\n# Blocking based on the faiss method.\n# Number of blocks: 2\n# Number of columns used for blocking: 25\n# Reduction ratio: 0.5714\n# ========================================================\n# Distribution of the size of the blocks:\n# Block Size | Number of Blocks\n# 3 | 2\n```\n```python\nprint(dedup_result.result)\n# x y block dist\n# 0 0 1 0 2.0\n# 1 1 2 0 2.0\n# 2 1 3 0 2.0\n# 3 4 5 1 2.0\n# 4 4 6 1 3.0\n# 5 4 7 1 6.0\n```\n## Features\n- Multiple ANN algorithms available:\n - [FAISS](https://github.com/facebookresearch/faiss) (Facebook AI Similarity Search)\n - [Voyager](https://github.com/spotify/voyager) (Spotify)\n - [HNSW](https://github.com/nmslib/hnswlib) (Hierarchical Navigable Small World)\n - [MLPACK](https://github.com/mlpack/mlpack) (both LSH and k-d tree)\n - [NND](https://github.com/lmcinnes/pynndescent) (Nearest Neighbor Descent)\n - [Annoy](https://github.com/spotify/annoy) (Spotify)\n\n- Multiple distance metrics such as:\n - Euclidean\n - Cosine\n - Inner Product\n \n and more...\n- Comprehensive algorithm parameters customization with `control_ann` and `control_txt`\n- Support for already created Document-Term-Matrices (as `np.ndarray` or `csr_matrix`)\n- Support for both record linkage and deduplication\n- Evaluation metrics when true blocks are known\n\nYou can find detailed information about BlockingPy in [documentation](https://blockingpy.readthedocs.io/en/latest/).\n\n## Disclaimer\nBlockingPy is still under development, API and features may change. 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