spaCy/spacy/tests/serialize/test_serialize_vocab_strings.py

196 lines
6.9 KiB
Python
Raw Normal View History

Reduce stored lexemes data, move feats to lookups (#5238) * Reduce stored lexemes data, move feats to lookups * Move non-derivable lexemes features (`norm / cluster / prob`) to `spacy-lookups-data` as lookups * Get/set `norm` in both lookups and `LexemeC`, serialize in lookups * Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in lookups only * Remove serialization of lexemes data as `vocab/lexemes.bin` * Remove `SerializedLexemeC` * Remove `Lexeme.to_bytes/from_bytes` * Modify normalization exception loading: * Always create `Vocab.lookups` table `lexeme_norm` for normalization exceptions * Load base exceptions from `lang.norm_exceptions`, but load language-specific exceptions from lookups * Set `lex_attr_getter[NORM]` including new lookups table in `BaseDefaults.create_vocab()` and when deserializing `Vocab` * Remove all cached lexemes when deserializing vocab to override existing normalizations with the new normalizations (as a replacement for the previous step that replaced all lexemes data with the deserialized data) * Skip English normalization test Skip English normalization test because the data is now in `spacy-lookups-data`. * Remove norm exceptions Moved to spacy-lookups-data. * Move norm exceptions test to spacy-lookups-data * Load extra lookups from spacy-lookups-data lazily Load extra lookups (currently for cluster and prob) lazily from the entry point `lg_extra` as `Vocab.lookups_extra`. * Skip creating lexeme cache on load To improve model loading times, do not create the full lexeme cache when loading. The lexemes will be created on demand when processing. * Identify numeric values in Lexeme.set_attrs() With the removal of a special case for `PROB`, also identify `float` to avoid trying to convert it with the `StringStore`. * Skip lexeme cache init in from_bytes * Unskip and update lookups tests for python3.6+ * Update vocab pickle to include lookups_extra * Update vocab serialization tests Check strings rather than lexemes since lexemes aren't initialized automatically, account for addition of "_SP". * Re-skip lookups test because of python3.5 * Skip PROB/float values in Lexeme.set_attrs * Convert is_oov from lexeme flag to lex in vectors Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether the lexeme has a vector. Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-05-19 16:59:14 +03:00
import pickle
import pytest
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 15:08:31 +03:00
from thinc.api import get_current_ops
import spacy
from spacy.lang.en import English
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
from spacy.strings import StringStore
from spacy.tokens import Doc
from spacy.util import ensure_path, load_model
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 15:08:31 +03:00
from spacy.vectors import Vectors
from spacy.vocab import Vocab
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
from ..util import make_tempdir
test_strings = [([], []), (["rats", "are", "cute"], ["i", "like", "rats"])]
test_strings_attrs = [(["rats", "are", "cute"], "Hello")]
@pytest.mark.issue(599)
def test_issue599(en_vocab):
doc = Doc(en_vocab)
doc2 = Doc(doc.vocab)
doc2.from_bytes(doc.to_bytes())
assert doc2.has_annotation("DEP")
@pytest.mark.issue(4054)
def test_issue4054(en_vocab):
"""Test that a new blank model can be made with a vocab from file,
and that serialization does not drop the language at any point."""
nlp1 = English()
vocab1 = nlp1.vocab
with make_tempdir() as d:
vocab_dir = ensure_path(d / "vocab")
if not vocab_dir.exists():
vocab_dir.mkdir()
vocab1.to_disk(vocab_dir)
vocab2 = Vocab().from_disk(vocab_dir)
nlp2 = spacy.blank("en", vocab=vocab2)
nlp_dir = ensure_path(d / "nlp")
if not nlp_dir.exists():
nlp_dir.mkdir()
nlp2.to_disk(nlp_dir)
nlp3 = load_model(nlp_dir)
assert nlp3.lang == "en"
@pytest.mark.issue(4133)
def test_issue4133(en_vocab):
nlp = English()
vocab_bytes = nlp.vocab.to_bytes()
words = ["Apple", "is", "looking", "at", "buying", "a", "startup"]
pos = ["NOUN", "VERB", "ADP", "VERB", "PROPN", "NOUN", "ADP"]
doc = Doc(en_vocab, words=words)
for i, token in enumerate(doc):
token.pos_ = pos[i]
# usually this is already True when starting from proper models instead of blank English
doc_bytes = doc.to_bytes()
vocab = Vocab()
vocab = vocab.from_bytes(vocab_bytes)
doc = Doc(vocab).from_bytes(doc_bytes)
actual = []
for token in doc:
actual.append(token.pos_)
assert actual == pos
@pytest.mark.parametrize("text", ["rat"])
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
def test_serialize_vocab(en_vocab, text):
text_hash = en_vocab.strings.add(text)
vocab_bytes = en_vocab.to_bytes(exclude=["lookups"])
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
new_vocab = Vocab().from_bytes(vocab_bytes)
2019-03-10 18:36:29 +03:00
assert new_vocab.strings[text_hash] == text
assert new_vocab.to_bytes(exclude=["lookups"]) == vocab_bytes
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
@pytest.mark.parametrize("strings1,strings2", test_strings)
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
def test_serialize_vocab_roundtrip_bytes(strings1, strings2):
vocab1 = Vocab(strings=strings1)
vocab2 = Vocab(strings=strings2)
vocab1_b = vocab1.to_bytes()
vocab2_b = vocab2.to_bytes()
if strings1 == strings2:
assert vocab1_b == vocab2_b
else:
assert vocab1_b != vocab2_b
vocab1 = vocab1.from_bytes(vocab1_b)
assert vocab1.to_bytes() == vocab1_b
new_vocab1 = Vocab().from_bytes(vocab1_b)
assert new_vocab1.to_bytes() == vocab1_b
Add Lemmatizer and simplify related components (#5848) * Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
2020-08-07 16:27:13 +03:00
assert len(new_vocab1.strings) == len(strings1)
assert sorted([s for s in new_vocab1.strings]) == sorted(strings1)
@pytest.mark.parametrize("strings1,strings2", test_strings)
def test_serialize_vocab_roundtrip_disk(strings1, strings2):
vocab1 = Vocab(strings=strings1)
vocab2 = Vocab(strings=strings2)
with make_tempdir() as d:
file_path1 = d / "vocab1"
file_path2 = d / "vocab2"
vocab1.to_disk(file_path1)
vocab2.to_disk(file_path2)
vocab1_d = Vocab().from_disk(file_path1)
vocab2_d = Vocab().from_disk(file_path2)
Reduce stored lexemes data, move feats to lookups (#5238) * Reduce stored lexemes data, move feats to lookups * Move non-derivable lexemes features (`norm / cluster / prob`) to `spacy-lookups-data` as lookups * Get/set `norm` in both lookups and `LexemeC`, serialize in lookups * Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in lookups only * Remove serialization of lexemes data as `vocab/lexemes.bin` * Remove `SerializedLexemeC` * Remove `Lexeme.to_bytes/from_bytes` * Modify normalization exception loading: * Always create `Vocab.lookups` table `lexeme_norm` for normalization exceptions * Load base exceptions from `lang.norm_exceptions`, but load language-specific exceptions from lookups * Set `lex_attr_getter[NORM]` including new lookups table in `BaseDefaults.create_vocab()` and when deserializing `Vocab` * Remove all cached lexemes when deserializing vocab to override existing normalizations with the new normalizations (as a replacement for the previous step that replaced all lexemes data with the deserialized data) * Skip English normalization test Skip English normalization test because the data is now in `spacy-lookups-data`. * Remove norm exceptions Moved to spacy-lookups-data. * Move norm exceptions test to spacy-lookups-data * Load extra lookups from spacy-lookups-data lazily Load extra lookups (currently for cluster and prob) lazily from the entry point `lg_extra` as `Vocab.lookups_extra`. * Skip creating lexeme cache on load To improve model loading times, do not create the full lexeme cache when loading. The lexemes will be created on demand when processing. * Identify numeric values in Lexeme.set_attrs() With the removal of a special case for `PROB`, also identify `float` to avoid trying to convert it with the `StringStore`. * Skip lexeme cache init in from_bytes * Unskip and update lookups tests for python3.6+ * Update vocab pickle to include lookups_extra * Update vocab serialization tests Check strings rather than lexemes since lexemes aren't initialized automatically, account for addition of "_SP". * Re-skip lookups test because of python3.5 * Skip PROB/float values in Lexeme.set_attrs * Convert is_oov from lexeme flag to lex in vectors Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether the lexeme has a vector. Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-05-19 16:59:14 +03:00
# check strings rather than lexemes, which are only reloaded on demand
assert set(strings1) == set([s for s in vocab1_d.strings])
assert set(strings2) == set([s for s in vocab2_d.strings])
if set(strings1) == set(strings2):
Add Lemmatizer and simplify related components (#5848) * Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
2020-08-07 16:27:13 +03:00
assert [s for s in vocab1_d.strings] == [s for s in vocab2_d.strings]
else:
Add Lemmatizer and simplify related components (#5848) * Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
2020-08-07 16:27:13 +03:00
assert [s for s in vocab1_d.strings] != [s for s in vocab2_d.strings]
@pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
def test_serialize_vocab_lex_attrs_bytes(strings, lex_attr):
vocab1 = Vocab(strings=strings)
vocab2 = Vocab()
vocab1[strings[0]].norm_ = lex_attr
assert vocab1[strings[0]].norm_ == lex_attr
assert vocab2[strings[0]].norm_ != lex_attr
vocab2 = vocab2.from_bytes(vocab1.to_bytes())
assert vocab2[strings[0]].norm_ == lex_attr
2019-03-10 18:36:29 +03:00
@pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
def test_deserialize_vocab_seen_entries(strings, lex_attr):
# Reported in #2153
vocab = Vocab(strings=strings)
vocab.from_bytes(vocab.to_bytes())
Add Lemmatizer and simplify related components (#5848) * Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
2020-08-07 16:27:13 +03:00
assert len(vocab.strings) == len(strings)
2019-03-10 18:36:29 +03:00
@pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
def test_serialize_vocab_lex_attrs_disk(strings, lex_attr):
vocab1 = Vocab(strings=strings)
vocab2 = Vocab()
vocab1[strings[0]].norm_ = lex_attr
assert vocab1[strings[0]].norm_ == lex_attr
assert vocab2[strings[0]].norm_ != lex_attr
with make_tempdir() as d:
file_path = d / "vocab"
vocab1.to_disk(file_path)
vocab2 = vocab2.from_disk(file_path)
assert vocab2[strings[0]].norm_ == lex_attr
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
@pytest.mark.parametrize("strings1,strings2", test_strings)
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
def test_serialize_stringstore_roundtrip_bytes(strings1, strings2):
sstore1 = StringStore(strings=strings1)
sstore2 = StringStore(strings=strings2)
sstore1_b = sstore1.to_bytes()
sstore2_b = sstore2.to_bytes()
if set(strings1) == set(strings2):
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
assert sstore1_b == sstore2_b
else:
assert sstore1_b != sstore2_b
sstore1 = sstore1.from_bytes(sstore1_b)
assert sstore1.to_bytes() == sstore1_b
new_sstore1 = StringStore().from_bytes(sstore1_b)
assert new_sstore1.to_bytes() == sstore1_b
assert set(new_sstore1) == set(strings1)
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
@pytest.mark.parametrize("strings1,strings2", test_strings)
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
def test_serialize_stringstore_roundtrip_disk(strings1, strings2):
sstore1 = StringStore(strings=strings1)
sstore2 = StringStore(strings=strings2)
with make_tempdir() as d:
file_path1 = d / "strings1"
file_path2 = d / "strings2"
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
sstore1.to_disk(file_path1)
sstore2.to_disk(file_path2)
sstore1_d = StringStore().from_disk(file_path1)
sstore2_d = StringStore().from_disk(file_path2)
assert set(sstore1_d) == set(sstore1)
assert set(sstore2_d) == set(sstore2)
if set(strings1) == set(strings2):
assert set(sstore1_d) == set(sstore2_d)
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
2018-07-25 00:38:44 +03:00
else:
assert set(sstore1_d) != set(sstore2_d)
Reduce stored lexemes data, move feats to lookups (#5238) * Reduce stored lexemes data, move feats to lookups * Move non-derivable lexemes features (`norm / cluster / prob`) to `spacy-lookups-data` as lookups * Get/set `norm` in both lookups and `LexemeC`, serialize in lookups * Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in lookups only * Remove serialization of lexemes data as `vocab/lexemes.bin` * Remove `SerializedLexemeC` * Remove `Lexeme.to_bytes/from_bytes` * Modify normalization exception loading: * Always create `Vocab.lookups` table `lexeme_norm` for normalization exceptions * Load base exceptions from `lang.norm_exceptions`, but load language-specific exceptions from lookups * Set `lex_attr_getter[NORM]` including new lookups table in `BaseDefaults.create_vocab()` and when deserializing `Vocab` * Remove all cached lexemes when deserializing vocab to override existing normalizations with the new normalizations (as a replacement for the previous step that replaced all lexemes data with the deserialized data) * Skip English normalization test Skip English normalization test because the data is now in `spacy-lookups-data`. * Remove norm exceptions Moved to spacy-lookups-data. * Move norm exceptions test to spacy-lookups-data * Load extra lookups from spacy-lookups-data lazily Load extra lookups (currently for cluster and prob) lazily from the entry point `lg_extra` as `Vocab.lookups_extra`. * Skip creating lexeme cache on load To improve model loading times, do not create the full lexeme cache when loading. The lexemes will be created on demand when processing. * Identify numeric values in Lexeme.set_attrs() With the removal of a special case for `PROB`, also identify `float` to avoid trying to convert it with the `StringStore`. * Skip lexeme cache init in from_bytes * Unskip and update lookups tests for python3.6+ * Update vocab pickle to include lookups_extra * Update vocab serialization tests Check strings rather than lexemes since lexemes aren't initialized automatically, account for addition of "_SP". * Re-skip lookups test because of python3.5 * Skip PROB/float values in Lexeme.set_attrs * Convert is_oov from lexeme flag to lex in vectors Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether the lexeme has a vector. Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-05-19 16:59:14 +03:00
2020-05-21 15:14:01 +03:00
Reduce stored lexemes data, move feats to lookups (#5238) * Reduce stored lexemes data, move feats to lookups * Move non-derivable lexemes features (`norm / cluster / prob`) to `spacy-lookups-data` as lookups * Get/set `norm` in both lookups and `LexemeC`, serialize in lookups * Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in lookups only * Remove serialization of lexemes data as `vocab/lexemes.bin` * Remove `SerializedLexemeC` * Remove `Lexeme.to_bytes/from_bytes` * Modify normalization exception loading: * Always create `Vocab.lookups` table `lexeme_norm` for normalization exceptions * Load base exceptions from `lang.norm_exceptions`, but load language-specific exceptions from lookups * Set `lex_attr_getter[NORM]` including new lookups table in `BaseDefaults.create_vocab()` and when deserializing `Vocab` * Remove all cached lexemes when deserializing vocab to override existing normalizations with the new normalizations (as a replacement for the previous step that replaced all lexemes data with the deserialized data) * Skip English normalization test Skip English normalization test because the data is now in `spacy-lookups-data`. * Remove norm exceptions Moved to spacy-lookups-data. * Move norm exceptions test to spacy-lookups-data * Load extra lookups from spacy-lookups-data lazily Load extra lookups (currently for cluster and prob) lazily from the entry point `lg_extra` as `Vocab.lookups_extra`. * Skip creating lexeme cache on load To improve model loading times, do not create the full lexeme cache when loading. The lexemes will be created on demand when processing. * Identify numeric values in Lexeme.set_attrs() With the removal of a special case for `PROB`, also identify `float` to avoid trying to convert it with the `StringStore`. * Skip lexeme cache init in from_bytes * Unskip and update lookups tests for python3.6+ * Update vocab pickle to include lookups_extra * Update vocab serialization tests Check strings rather than lexemes since lexemes aren't initialized automatically, account for addition of "_SP". * Re-skip lookups test because of python3.5 * Skip PROB/float values in Lexeme.set_attrs * Convert is_oov from lexeme flag to lex in vectors Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether the lexeme has a vector. Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-05-19 16:59:14 +03:00
@pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
def test_pickle_vocab(strings, lex_attr):
vocab = Vocab(strings=strings)
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 15:08:31 +03:00
ops = get_current_ops()
vectors = Vectors(data=ops.xp.zeros((10, 10)), mode="floret", hash_count=1)
vocab.vectors = vectors
Reduce stored lexemes data, move feats to lookups (#5238) * Reduce stored lexemes data, move feats to lookups * Move non-derivable lexemes features (`norm / cluster / prob`) to `spacy-lookups-data` as lookups * Get/set `norm` in both lookups and `LexemeC`, serialize in lookups * Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in lookups only * Remove serialization of lexemes data as `vocab/lexemes.bin` * Remove `SerializedLexemeC` * Remove `Lexeme.to_bytes/from_bytes` * Modify normalization exception loading: * Always create `Vocab.lookups` table `lexeme_norm` for normalization exceptions * Load base exceptions from `lang.norm_exceptions`, but load language-specific exceptions from lookups * Set `lex_attr_getter[NORM]` including new lookups table in `BaseDefaults.create_vocab()` and when deserializing `Vocab` * Remove all cached lexemes when deserializing vocab to override existing normalizations with the new normalizations (as a replacement for the previous step that replaced all lexemes data with the deserialized data) * Skip English normalization test Skip English normalization test because the data is now in `spacy-lookups-data`. * Remove norm exceptions Moved to spacy-lookups-data. * Move norm exceptions test to spacy-lookups-data * Load extra lookups from spacy-lookups-data lazily Load extra lookups (currently for cluster and prob) lazily from the entry point `lg_extra` as `Vocab.lookups_extra`. * Skip creating lexeme cache on load To improve model loading times, do not create the full lexeme cache when loading. The lexemes will be created on demand when processing. * Identify numeric values in Lexeme.set_attrs() With the removal of a special case for `PROB`, also identify `float` to avoid trying to convert it with the `StringStore`. * Skip lexeme cache init in from_bytes * Unskip and update lookups tests for python3.6+ * Update vocab pickle to include lookups_extra * Update vocab serialization tests Check strings rather than lexemes since lexemes aren't initialized automatically, account for addition of "_SP". * Re-skip lookups test because of python3.5 * Skip PROB/float values in Lexeme.set_attrs * Convert is_oov from lexeme flag to lex in vectors Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether the lexeme has a vector. Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-05-19 16:59:14 +03:00
vocab[strings[0]].norm_ = lex_attr
vocab_pickled = pickle.dumps(vocab)
vocab_unpickled = pickle.loads(vocab_pickled)
assert vocab.to_bytes() == vocab_unpickled.to_bytes()
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 15:08:31 +03:00
assert vocab_unpickled.vectors.mode == "floret"