# fuzzycat (wip)

Fuzzy matching publications for [fatcat](https://fatcat.wiki).

* [fuzzycat](https://pypi.org/project/fuzzycat/)

Note: This is currently work-in-progress.

# Example Run

Run any clustering algorithm.

```
$ time python -m fuzzycat cluster -t tsandcrawler < data/sample10m.json | \
    zstd -c9 > sample_cluster.json.zst
2020-11-18 00:19:48.194 DEBUG __main__ - run_cluster:
    {"key_fail": 0, "key_ok": 9999938, "key_empty": 62, "key_denylist": 0, "num_clusters": 9040789}

real    75m23.045s
user    95m14.455s
sys     3m39.121s
```

Run verification.

```
$ time zstdcat -T0 sample_cluster.json.zst | python -m fuzzycat verify > sample_verify.txt

real    7m56.713s
user    8m50.703s
sys     0m29.262s
```


Example results over 10M docs:

```json
{
  "miss.appendix": 176,
  "miss.arxiv_version": 25,
  "miss.blacklisted": 12082,
  "miss.blacklisted_fragment": 5,
  "miss.book_chapter": 46733,
  "miss.component": 1567,
  "miss.contrib_intersection_empty": 47691,
  "miss.dataset_doi": 30806,
  "miss.num_diff": 1,
  "miss.release_type": 157718,
  "miss.short_title": 16263,
  "miss.subtitle": 6013,
  "miss.title_filename": 57,
  "miss.year": 148755,
  "ok.arxiv_version": 93,
  "ok.dummy": 88294,
  "ok.preprint_published": 110,
  "ok.slug_title_author_match": 15818,
  "ok.title_author_match": 93240,
  "skip.container_name_blacklist": 20,
  "skip.publisher_blacklist": 456,
  "skip.too_large": 7430,
  "skip.unique": 8808462,
  "total": 9481815
}
```


# Use cases

* [ ] take a release entity database dump as JSON lines and cluster releases
  (according to various algorithms)
* [ ] take cluster information and run a verification step (misc algorithms)
* [ ] create a dataset that contains grouping of releases under works
* [ ] command line tools to generate cache keys, e.g. to match reference
  strings to release titles (this needs some transparent setup, e.g. filling of
a cache before ops)

# Usage

Release clusters start with release entities json lines.

```shell
$ cat data/sample.json | python -m fuzzycat cluster -t title > out.json
```

Clustering 1M records (single core) takes about 64s (15K docs/s).

```shell
$ head -1 out.json
{
  "c": "release_key_title",
  "v": [
    "7ufkzsjywzejvjzsyegugradoa",
    "harjqexl5vagxc54zjfen5zlve",
    "i5jrdoxqmjfs3fk2dcpnqxqb2e",
    "i62bo63qqzggjjk7pf77z26djm",
    "omo3z5y7qvh6hbl7wjacinsfiq",
    "prkik3s5vzejnfe4u26g2vt2wu",
    "pyqss6ifnvgqjeqohlampswvkm",
    "spr2b23fk5asph7v6shrd6okt4",
    "togokylwfvcvzilhnx4jir2hfm",
    "us4artv2hbc5bljuwaopquicfu",
    "ycargjj4lzddnmyzbh2e22wsii"
  ],
  "k": "裏表紙"
}
```

Using GNU parallel to make it faster.

```
$ cat data/sample.json | parallel -j 8 --pipe --roundrobin python -m fuzzycat.main cluster -t title
```

Interestingly, the parallel variants detects fewer clusters (because data is
split and clusters are searched within each batch). TODO(miku): sort out sharding bug.


## Cluster

```shell
usage: fuzzycat command [options] cluster [-h] [--prefix PREFIX]
                                          [--tmpdir TMPDIR] [-P] [-f FILES]
                                          [-t TYPE]
                                          {cluster,verify} ...

positional arguments:
  {cluster,verify}
    cluster             group entities
    verify              verify groups

optional arguments:
  -h, --help            show this help message and exit
  --prefix PREFIX       temp file prefix
  --tmpdir TMPDIR       temporary directory
  -P, --profile         profile program
  -f FILES, --files FILES
                        output files
  -t TYPE, --type TYPE  cluster algorithm: title, tnorm, tnysi
```