semvec/pypi-packages huggingface dataset:
names-matcher 2.0.13
Fuzzily biject people's names between two lists.
License: Apache-2.0
Keywords: fuzzy matching

# names-matcher
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Fuzzily biject people's names between two lists.

Let's define an identity as a series of names belonging to the same person. The algorithm is:

1. Parse, normalize, and split names in each identity. The result is a set of strings per each.
2. Define the similarity between identities as `max(ratio, token_set_ratio)`, where `ratio` \
   and `token_set_ratio` are inspired by string comparison functions from [rapidfuzz](https://github.com/maxbachmann/RapidFuzz).
3. Construct the distance matrix between identities in two specified lists.
4. Solve the Linear Assignment Problem (LAP) on that matrix.

Our LAP's solution scales up to ~1000-s of identities.

Example:
```
>>> from names_matcher import NamesMatcher
>>> NamesMatcher()([["Vadim Markovtsev", "vmarkovtsev"], ["Long, Waren", "warenlg"]], \
                    [["Warren"], ["VMarkovtsev"], ["Eiso Kant"]])
(array([1, 0], dtype=int32), array([0.75      , 0.57142857]))
```
The first resulting tuple element is the mapping indexes: of same length as the first sequence,
with indexes in the second sequence. The second element is the corresponding confidence values
from 0 to 1.

### Installation

```
pip3 install names-matcher
```

### Command line interface

Given one identity per line in two files, print the matches to standard output:

```
python3 -m names_matcher path/to/file/1 path/to/file/2
```

Each identity is several names merged with `|`, for example:

```
Vadim Markovtsev|vmarkovtsev|vadim
```

### Contributing

Contributions are very welcome and desired! Please follow the [code of conduct](CODE_OF_CONDUCT.md) and read the [contribution guidelines](CONTRIBUTING.md).

### License

Apache-2.0, see [LICENSE](LICENSE).