From 623c5a841b351a21e82a9752d15154da3ab5a635 Mon Sep 17 00:00:00 2001 From: Bryan Newbold Date: Thu, 1 Jul 2021 18:06:47 -0700 Subject: simplify README for general audience; move some content to notes --- README.md | 278 ++++++++++++-------------------------------------- notes/old_pipeline.md | 177 ++++++++++++++++++++++++++++++++ 2 files changed, 245 insertions(+), 210 deletions(-) create mode 100644 notes/old_pipeline.md diff --git a/README.md b/README.md index c70b8f3..b01776a 100644 --- a/README.md +++ b/README.md @@ -1,240 +1,98 @@ -# fuzzycat (wip) -Fuzzy matching utilities for [fatcat](https://fatcat.wiki). +
+ + + + +
-![https://pypi.org/project/fuzzycat/](https://img.shields.io/pypi/v/fuzzycat?style=flat-square) - -To install with [pip](https://pypi.org/project/pip/), run: - -``` -$ pip install fuzzycat -``` - -![](static/192112067_046be9fd21_b.jpg) - -Photo by [Chika Watanabe](https://www.flickr.com/photos/chikawatanabe/192112067) (CC BY 2.0). - -## Overview - -The fuzzycat library currently works on [fatcat database release -dumps](https://archive.org/details/fatcat_snapshots_and_exports?&sort=-publicdate) -and can cluster similar release items, that is it can find clusters and can -verify match candidates. - -For example we can identify: - -* versions of various items (arxiv, figshare, datacite, ...) -* preprint and published pairs -* similar items from different sources - -## TODO - -* [ ] take a list of title strings and return match candidates (faster than - elasticsearch); e.g. derive a key and find similar keys some cached clusters -* [ ] take a list of title, author documents and return match candidates; e.g. - key may depend on title only, but verification can be more precise -* [ ] take a more complete, yet partial document and return match candidates +`fuzzycat`: bibliographic fuzzy matching for fatcat.wiki +======================================================== -For this to work, we will need to have cluster from fatcat precomputed and -cache. We also might want to have it sorted by key (which is a side effect of -clustering) so we can binary search into the cluster file for the above todo -items. - -## Dataset - -For development, we worked on a `release_export_expanded.json` dump (113G/700G -zstd/plain, 154,203,375 lines) and with the [fatcat -API](https://api.fatcat.wiki/). - -The development workflow looked something like the following. - -![](notes/steps.png) - -## Clustering - -Clustering derives sets of similar documents from a [fatcat database release -dump](https://archive.org/details/fatcat_snapshots_and_exports?&sort=-publicdate). - -Following algorithms are implemented (or planned): - -* [x] exact title matches (title) -* [x] normalized title matches (tnorm) -* [x] NYSIIS encoded title matches (tnysi) -* [x] extended title normalization (tsandcrawler) - -Example running clustering: - -``` -$ python -m fuzzycat cluster -t tsandcrawler < data/re.json | zstd -c -T0 > cluster.json.zst -``` - -Clustering works in a three step process: - -1. key extraction for each document (choose algorithm) -2. sorting by keys (via [GNU sort](https://www.gnu.org/software/coreutils/manual/html_node/sort-invocation.html)) -3. group by key and write out ([itertools.groupby](https://docs.python.org/3/library/itertools.html#itertools.groupby)) - -Note: For long running processes, this all-or-nothing approach is impractical; -e.g. running clustering on the joint references and fatcat dataset (2B records) -takes 24h+. - -Ideas: - -* [ ] make (sorted) key extraction a fast standalone thing - -> `cat data.jsonl | fuzzycat-key --algo X > data.key.tsv` - -Where `data.key` group (id, key, blob) or the like. Make this line speed (maybe -w/ rust). Need to carry the blob, as we do not want to restrict options. - - -## Verification - -Run verification (pairwise *double-check* of match candidates in a cluster). - -``` -$ time zstdcat -T0 sample_cluster.json.zst | python -m fuzzycat verify > sample_verify.txt +![https://pypi.org/project/fuzzycat/](https://img.shields.io/pypi/v/fuzzycat?style=flat-square) -real 7m56.713s -user 8m50.703s -sys 0m29.262s -``` +This Python library contains routines for finding near-duplicate bibliographic +entities (primarily research papers), and estimating whether two metadata +records describe the same work (or variations of the same work). Some routines +are designed to work "offline" with batches of billions of sorted metadata +records, and others are designed to work "online" making queries against hosted +web services and catalogs. -This is a one-pass operation. For processing 150M docs, we very much depend on -the documents being on disk in a file (we keep the complete document in the -clustering result). +`fuzzycat` was originally developed by Martin Czygan at the Internet Archive, +and is used in the construction of a citation graph and to identify duplicate +records in the [fatcat.wiki](https://fatcat.wiki) catalog and +[scholar.archive.org](https://scholar.archive.org) search index. -Example results: +**DISCLAIMER:** this tool is still under development, as indicated by the "0" +major version. The interface, semantics, and behavior are likely to be tweaked. -``` -3450874 Status.EXACT Reason.TITLE_AUTHOR_MATCH -2619990 Status.STRONG Reason.SLUG_TITLE_AUTHOR_MATCH -2487633 Status.DIFFERENT Reason.YEAR -2434532 Status.EXACT Reason.WORK_ID -2085006 Status.DIFFERENT Reason.CONTRIB_INTERSECTION_EMPTY -1397420 Status.DIFFERENT Reason.SHARED_DOI_PREFIX -1355852 Status.DIFFERENT Reason.RELEASE_TYPE -1290162 Status.AMBIGUOUS Reason.DUMMY -1145511 Status.DIFFERENT Reason.BOOK_CHAPTER -1009657 Status.DIFFERENT Reason.DATASET_DOI - 996503 Status.STRONG Reason.PMID_DOI_PAIR - 868951 Status.EXACT Reason.DATACITE_VERSION - 796216 Status.STRONG Reason.DATACITE_RELATED_ID - 704154 Status.STRONG Reason.FIGSHARE_VERSION - 534963 Status.STRONG Reason.VERSIONED_DOI - 343310 Status.STRONG Reason.TOKENIZED_AUTHORS - 334974 Status.STRONG Reason.JACCARD_AUTHORS - 293835 Status.STRONG Reason.PREPRINT_PUBLISHED - 269366 Status.DIFFERENT Reason.COMPONENT - 263626 Status.DIFFERENT Reason.SUBTITLE - 224021 Status.AMBIGUOUS Reason.SHORT_TITLE - 152990 Status.DIFFERENT Reason.PAGE_COUNT - 133811 Status.AMBIGUOUS Reason.CUSTOM_PREFIX_10_5860_CHOICE_REVIEW - 122600 Status.AMBIGUOUS Reason.CUSTOM_PREFIX_10_7916 - 79664 Status.STRONG Reason.CUSTOM_IEEE_ARXIV - 46649 Status.DIFFERENT Reason.CUSTOM_PREFIX_10_14288 - 39797 Status.DIFFERENT Reason.JSTOR_ID - 38598 Status.STRONG Reason.CUSTOM_BSI_UNDATED - 18907 Status.STRONG Reason.CUSTOM_BSI_SUBDOC - 15465 Status.EXACT Reason.DOI - 13393 Status.DIFFERENT Reason.CUSTOM_IOP_MA_PATTERN - 10378 Status.DIFFERENT Reason.CONTAINER - 3081 Status.AMBIGUOUS Reason.BLACKLISTED - 2504 Status.AMBIGUOUS Reason.BLACKLISTED_FRAGMENT - 1273 Status.AMBIGUOUS Reason.APPENDIX - 1063 Status.DIFFERENT Reason.TITLE_FILENAME - 104 Status.DIFFERENT Reason.NUM_DIFF - 4 Status.STRONG Reason.ARXIV_VERSION -``` -## A full run +## Quickstart -Single threaded, 42h. +Inside a `virtualenv` (or similar), install with [pip](https://pypi.org/project/pip/): ``` -$ time zstdcat -T0 release_export_expanded.json.zst | \ - TMPDIR=/bigger/tmp python -m fuzzycat cluster --tmpdir /bigger/tmp -t tsandcrawler | \ - zstd -c9 > cluster_tsandcrawler.json.zst -{ - "key_fail": 0, - "key_ok": 154202433, - "key_empty": 942, - "key_denylist": 0, - "num_clusters": 124321361 -} - -real 2559m7.880s -user 2605m41.347s -sys 118m38.141s -``` - -So, 29881072 (about 20%) docs in the potentially duplicated set. Verification (about 15h w/o parallel): - +pip install fuzzycat ``` -$ time zstdcat -T0 cluster_tsandcrawler.json.zst | python -m fuzzycat verify | \ - zstd -c9 > cluster_tsandcrawler_verified_3c7378.tsv.zst -... +The `fuzzycat.simple` module contains high-level helpers which query Internet +Archive hosted services: -real 927m28.631s -user 939m32.761s -sys 36m47.602s -``` + import elasticsearch + from fuzzycat.simple import * ----- + es_client = elasticsearch.Elasticsearch("https://search.fatcat.wiki:443") -# Misc + # parses reference using GROBID (at https://grobid.qa.fatcat.wiki), + # then queries Elasticsearch (at https://search.fatcat.wiki), + # then scores candidates against latest catalog record fetched from + # https://api.fatcat.wiki + best_match = closest_fuzzy_unstructured_match( + """Cunningham HB, Weis JJ, Taveras LR, Huerta S. Mesh migration following abdominal hernia repair: a comprehensive review. Hernia. 2019 Apr;23(2):235-243. doi: 10.1007/s10029-019-01898-9. Epub 2019 Jan 30. PMID: 30701369.""", + es_client=es_client) -## Use cases + print(best_match) + # FuzzyReleaseMatchResult(status=, reason=, release={...}) -* [ ] 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) + # same as above, but without the GROBID parsing, and returns multiple results + matches = close_fuzzy_biblio_matches( + dict( + title="Mesh migration following abdominal hernia repair: a comprehensive review", + first_author="Cunningham", + year=2019, + journal="Hernia", + ), + es_client=es_client, + ) -## Usage +A CLI tool is included for processing records in UNIX stdin/stdout pipelines: -Release clusters start with release entities json lines. + # print usage + python -m fuzzycat -```shell -$ cat data/sample.json | python -m fuzzycat cluster -t title > out.json -``` -Clustering 1M records (single core) takes about 64s (15K docs/s). +## Features and Use-Cases -```shell -$ head -1 out.json -{ - "k": "裏表紙", - "v": [ - ... - ] -} -``` +The **`cgraph`** system builds on top of this library to build a citation graph +by processing billions of structured and unstructured reference records +extracted from scholarly papers. -Using GNU parallel to make it faster. +Automated imports of metadata records into the fatcat catalog use fuzzycat to +filter new metadata which look like duplicates of existing records from other +sources. -``` -$ cat data/sample.json | parallel -j 8 --pipe --roundrobin python -m fuzzycat.main cluster -t title -``` +In conjunction with standard command-line tools (like `sort`), fatcat bulk +metadata snapshots can be clustered and reduced into groups to flag duplicate +records for merging. -Interestingly, the parallel variants detects fewer clusters (because data is -split and clusters are searched within each batch). TODO(miku): sort out sharding bug. +Extracted reference strings from any source (webpages, books, papers, wikis, +databases, etc) can be resolved against the fatcat catalog of scholarly papers. -# Notes on Refs -* technique from fuzzycat ported in parts to - [skate](https://github.com/miku/skate) - to go from refs and release dataset -to a number of clusters, relating references to releases -* need to verify, but not the references against each other, only refs againt the release +## Support and Acknowledgements -# Notes on Performance +Work on this software received support from the Andrew W. Mellon Foundation +through multiple phases of the ["Ensuring the Persistent Access of Open Access +Journal Literature"](https://mellon.org/grants/grants-database/advanced-search/?amount-low=&amount-high=&year-start=&year-end=&city=&state=&country=&q=%22Ensuring+the+Persistent+Access%22&per_page=25) project (see [original announcement](http://blog.archive.org/2018/03/05/andrew-w-mellon-foundation-awards-grant-to-the-internet-archive-for-long-tail-journal-preservation/)). -While running bulk (1B+) clustering and verification, even with parallel, -fuzzycat got slow. The citation graph project therefore contains a -reimplementation of `fuzzycat.verify` and related functions in Go, which in -this case is an order of magnitude faster. See: -[skate](https://git.archive.org/martin/cgraph/-/tree/master/skate). +Additional acknowledgements [at fatcat.wiki](https://fatcat.wiki/about). diff --git a/notes/old_pipeline.md b/notes/old_pipeline.md new file mode 100644 index 0000000..2f84d66 --- /dev/null +++ b/notes/old_pipeline.md @@ -0,0 +1,177 @@ + +## Performance + +For development, we worked on a `release_export_expanded.json` dump (113G/700G zstd/plain, 154,203,375 lines) and with the [fatcat API](https://api.fatcat.wiki/). + + +### Clustering + +Clustering derives sets of similar documents from a [fatcat database release +dump](https://archive.org/details/fatcat_snapshots_and_exports?&sort=-publicdate). + + +Example running clustering: + +``` +$ python -m fuzzycat cluster -t tsandcrawler < data/re.json | zstd -c -T0 > cluster.json.zst +``` + +Clustering works in a three step process: + +1. key extraction for each document (choose algorithm) +2. sorting by keys (via [GNU sort](https://www.gnu.org/software/coreutils/manual/html_node/sort-invocation.html)) +3. group by key and write out ([itertools.groupby](https://docs.python.org/3/library/itertools.html#itertools.groupby)) + +Note: For long running processes, this all-or-nothing approach is impractical; +e.g. running clustering on the joint references and fatcat dataset (2B records) +takes 24h+. + +Ideas: + +* [ ] make (sorted) key extraction a fast standalone thing + +> `cat data.jsonl | fuzzycat-key --algo X > data.key.tsv` + +Where `data.key` group (id, key, blob) or the like. Make this line speed (maybe +w/ rust). Need to carry the blob, as we do not want to restrict options. + + +## Verification + +Run verification (pairwise *double-check* of match candidates in a cluster). + +``` +$ time zstdcat -T0 sample_cluster.json.zst | python -m fuzzycat verify > sample_verify.txt + +real 7m56.713s +user 8m50.703s +sys 0m29.262s +``` + +This is a one-pass operation. For processing 150M docs, we very much depend on +the documents being on disk in a file (we keep the complete document in the +clustering result). + +Example results: + +``` +3450874 Status.EXACT Reason.TITLE_AUTHOR_MATCH +2619990 Status.STRONG Reason.SLUG_TITLE_AUTHOR_MATCH +2487633 Status.DIFFERENT Reason.YEAR +2434532 Status.EXACT Reason.WORK_ID +2085006 Status.DIFFERENT Reason.CONTRIB_INTERSECTION_EMPTY +1397420 Status.DIFFERENT Reason.SHARED_DOI_PREFIX +1355852 Status.DIFFERENT Reason.RELEASE_TYPE +1290162 Status.AMBIGUOUS Reason.DUMMY +1145511 Status.DIFFERENT Reason.BOOK_CHAPTER +1009657 Status.DIFFERENT Reason.DATASET_DOI + 996503 Status.STRONG Reason.PMID_DOI_PAIR + 868951 Status.EXACT Reason.DATACITE_VERSION + 796216 Status.STRONG Reason.DATACITE_RELATED_ID + 704154 Status.STRONG Reason.FIGSHARE_VERSION + 534963 Status.STRONG Reason.VERSIONED_DOI + 343310 Status.STRONG Reason.TOKENIZED_AUTHORS + 334974 Status.STRONG Reason.JACCARD_AUTHORS + 293835 Status.STRONG Reason.PREPRINT_PUBLISHED + 269366 Status.DIFFERENT Reason.COMPONENT + 263626 Status.DIFFERENT Reason.SUBTITLE + 224021 Status.AMBIGUOUS Reason.SHORT_TITLE + 152990 Status.DIFFERENT Reason.PAGE_COUNT + 133811 Status.AMBIGUOUS Reason.CUSTOM_PREFIX_10_5860_CHOICE_REVIEW + 122600 Status.AMBIGUOUS Reason.CUSTOM_PREFIX_10_7916 + 79664 Status.STRONG Reason.CUSTOM_IEEE_ARXIV + 46649 Status.DIFFERENT Reason.CUSTOM_PREFIX_10_14288 + 39797 Status.DIFFERENT Reason.JSTOR_ID + 38598 Status.STRONG Reason.CUSTOM_BSI_UNDATED + 18907 Status.STRONG Reason.CUSTOM_BSI_SUBDOC + 15465 Status.EXACT Reason.DOI + 13393 Status.DIFFERENT Reason.CUSTOM_IOP_MA_PATTERN + 10378 Status.DIFFERENT Reason.CONTAINER + 3081 Status.AMBIGUOUS Reason.BLACKLISTED + 2504 Status.AMBIGUOUS Reason.BLACKLISTED_FRAGMENT + 1273 Status.AMBIGUOUS Reason.APPENDIX + 1063 Status.DIFFERENT Reason.TITLE_FILENAME + 104 Status.DIFFERENT Reason.NUM_DIFF + 4 Status.STRONG Reason.ARXIV_VERSION +``` + +## A full run + +Single threaded, 42h. + +``` +$ time zstdcat -T0 release_export_expanded.json.zst | \ + TMPDIR=/bigger/tmp python -m fuzzycat cluster --tmpdir /bigger/tmp -t tsandcrawler | \ + zstd -c9 > cluster_tsandcrawler.json.zst +{ + "key_fail": 0, + "key_ok": 154202433, + "key_empty": 942, + "key_denylist": 0, + "num_clusters": 124321361 +} + +real 2559m7.880s +user 2605m41.347s +sys 118m38.141s +``` + +So, 29881072 (about 20%) docs in the potentially duplicated set. Verification (about 15h w/o parallel): + +``` +$ time zstdcat -T0 cluster_tsandcrawler.json.zst | python -m fuzzycat verify | \ + zstd -c9 > cluster_tsandcrawler_verified_3c7378.tsv.zst + +... + +real 927m28.631s +user 939m32.761s +sys 36m47.602s +``` + +---- + +# Misc + + +## 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 +{ + "k": "裏表紙", + "v": [ + ... + ] +} +``` + +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. + +# Notes on Refs + +* technique from fuzzycat ported in parts to [skate](https://github.com/miku/skate) - to go from refs and release dataset to a number of clusters, relating references to releases +* need to verify, but not the references against each other, only refs againt the release + +# Notes on Performance + +While running bulk (1B+) clustering and verification, even with parallel, +fuzzycat got slow. The citation graph project therefore contains a +reimplementation of `fuzzycat.verify` and related functions in Go, which in +this case is an order of magnitude faster. See: +[skate](https://git.archive.org/martin/cgraph/-/tree/master/skate). -- cgit v1.2.3