Commit Graph

14 Commits

Author SHA1 Message Date
Ines Montani
8283df80e9 Tidy up and auto-format 2020-06-20 14:15:04 +02:00
adrianeboyd
b71a11ff6d
Update morphologizer (#5108)
* Add pos and morph scoring to Scorer

Add pos, morph, and morph_per_type to `Scorer`. Report pos and morph
accuracy in `spacy evaluate`.

* Update morphologizer for v3

* switch to tagger-based morphologizer
* use `spacy.HashCharEmbedCNN` for morphologizer defaults
* add `Doc.is_morphed` flag

* Add morphologizer to train CLI

* Add basic morphologizer pipeline tests

* Add simple morphologizer training example

* Remove subword_features from CharEmbed models

Remove `subword_features` argument from `spacy.HashCharEmbedCNN.v1` and
`spacy.HashCharEmbedBiLSTM.v1` since in these cases `subword_features`
is always `False`.

* Rename setting in morphologizer example

Use `with_pos_tags` instead of `without_pos_tags`.

* Fix kwargs for spacy.HashCharEmbedBiLSTM.v1

* Remove defaults for spacy.HashCharEmbedBiLSTM.v1

Remove default `nM/nC` for `spacy.HashCharEmbedBiLSTM.v1`.

* Set random seed for textcat overfitting test
2020-04-02 14:46:32 +02:00
Ines Montani
db55577c45
Drop Python 2.7 and 3.5 (#4828)
* Remove unicode declarations

* Remove Python 3.5 and 2.7 from CI

* Don't require pathlib

* Replace compat helpers

* Remove OrderedDict

* Use f-strings

* Set Cython compiler language level

* Fix typo

* Re-add OrderedDict for Table

* Update setup.cfg

* Revert CONTRIBUTING.md

* Revert lookups.md

* Revert top-level.md

* Small adjustments and docs [ci skip]
2019-12-22 01:53:56 +01:00
Ines Montani
158b98a3ef Merge branch 'master' into develop 2019-12-21 18:55:03 +01:00
adrianeboyd
392c4880d9 Restructure Example with merged sents as default (#4632)
* Switch to train_dataset() function in train CLI

* Fixes for pipe() methods in pipeline components

* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`

* Update Parser.pipe() for Examples

* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s

* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)

* Fixes to Example implementation in spacy.gold

* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`

* Head of 0 are not treated as unset

* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)

* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example

* Don't clobber `examples` variable in `iter_gold_docs()`

* Add/modify gold tests for handling projectivity

* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)

* Add test for projective train vs. nonprojective dev versions of the
same `Doc`

* Handle ignore_misaligned as arg rather than attr

Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.

Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).

* Remove unused attrs from gold.pxd

Remove `ignore_misaligned` and `make_projective` from `gold.pxd`

* Restructure Example with merged sents as default

An `Example` now includes a single `TokenAnnotation` that includes all
the information from one `Doc` (=JSON `paragraph`). If required, the
individual sentences can be returned as a list of examples with
`Example.split_sents()` with no raw text available.

* Input/output a single `Example.token_annotation`

* Add `sent_starts` to `TokenAnnotation` to handle sentence boundaries

* Replace `Example.merge_sents()` with `Example.split_sents()`

* Modify components to use a single `Example.token_annotation`

  * Pipeline components
  * conllu2json converter

* Rework/rename `add_token_annotation()` and `add_doc_annotation()` to
`set_token_annotation()` and `set_doc_annotation()`, functions that set
rather then appending/extending.

* Rename `morphology` to `morphs` in `TokenAnnotation` and `GoldParse`

* Add getters to `TokenAnnotation` to supply default values when a given
attribute is not available

* `Example.get_gold_parses()` in `spacy.gold._make_golds()` is only
applied on single examples, so the `GoldParse` is returned saved in the
provided `Example` rather than creating a new `Example` with no other
internal annotation

* Update tests for API changes and `merge_sents()` vs. `split_sents()`

* Refer to Example.goldparse in iter_gold_docs()

Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError

* Fix make_orth_variants()

Fix bug in make_orth_variants() related to conversion from multiple to
one TokenAnnotation per Example.

* Add basic test for make_orth_variants()

* Replace try/except with conditionals

* Replace default morph value with set
2019-11-25 16:03:28 +01:00
Ines Montani
6e303de717 Auto-format 2019-11-20 13:15:24 +01:00
Sofie Van Landeghem
e48a09df4e Example class for training data (#4543)
* OrigAnnot class instead of gold.orig_annot list of zipped tuples

* from_orig to replace from_annot_tuples

* rename to RawAnnot

* some unit tests for GoldParse creation and internal format

* removing orig_annot and switching to lists instead of tuple

* rewriting tuples to use RawAnnot (+ debug statements, WIP)

* fix pop() changing the data

* small fixes

* pop-append fixes

* return RawAnnot for existing GoldParse to have uniform interface

* clean up imports

* fix merge_sents

* add unit test for 4402 with new structure (not working yet)

* introduce DocAnnot

* typo fixes

* add unit test for merge_sents

* rename from_orig to from_raw

* fixing unit tests

* fix nn parser

* read_annots to produce text, doc_annot pairs

* _make_golds fix

* rename golds_to_gold_annots

* small fixes

* fix encoding

* have golds_to_gold_annots use DocAnnot

* missed a spot

* merge_sents as function in DocAnnot

* allow specifying only part of the token-level annotations

* refactor with Example class + underlying dicts

* pipeline components to work with Example objects (wip)

* input checking

* fix yielding

* fix calls to update

* small fixes

* fix scorer unit test with new format

* fix kwargs order

* fixes for ud and conllu scripts

* fix reading data for conllu script

* add in proper errors (not fixed numbering yet to avoid merge conflicts)

* fixing few more small bugs

* fix EL script
2019-11-11 17:35:27 +01:00
adrianeboyd
56ad3a3988 Add LAS per dependency to Scorer (#4560) 2019-10-31 21:18:16 +01:00
Ines Montani
3d8fd4b461 Revert #4334 2019-09-29 17:32:12 +02:00
Ines Montani
c9cd516d96 Move tests out of package (#4334)
* Move tests out of package

* Fix typo
2019-09-28 18:05:00 +02:00
Ines Montani
00a8cbc306 Tidy up and auto-format 2019-09-18 20:27:03 +02:00
adrianeboyd
b5d999e510 Add textcat to train CLI (#4226)
* Add doc.cats to spacy.gold at the paragraph level

Support `doc.cats` as `"cats": [{"label": string, "value": number}]` in
the spacy JSON training format at the paragraph level.

* `spacy.gold.docs_to_json()` writes `docs.cats`

* `GoldCorpus` reads in cats in each `GoldParse`

* Update instances of gold_tuples to handle cats

Update iteration over gold_tuples / gold_parses to handle addition of
cats at the paragraph level.

* Add textcat to train CLI

* Add textcat options to train CLI
* Add textcat labels in `TextCategorizer.begin_training()`
* Add textcat evaluation to `Scorer`:
  * For binary exclusive classes with provided label: F1 for label
  * For 2+ exclusive classes: F1 macro average
  * For multilabel (not exclusive): ROC AUC macro average (currently
relying on sklearn)
* Provide user info on textcat evaluation settings, potential
incompatibilities
* Provide pipeline to Scorer in `Language.evaluate` for textcat config
* Customize train CLI output to include only metrics relevant to current
pipeline
* Add textcat evaluation to evaluate CLI

* Fix handling of unset arguments and config params

Fix handling of unset arguments and model confiug parameters in Scorer
initialization.

* Temporarily add sklearn requirement

* Remove sklearn version number

* Improve Scorer handling of models without textcats

* Fixing Scorer handling of models without textcats

* Update Scorer output for python 2.7

* Modify inf in Scorer for python 2.7

* Auto-format

Also make small adjustments to make auto-formatting with black easier and produce nicer results

* Move error message to Errors

* Update documentation

* Add cats to annotation JSON format [ci skip]

* Fix tpl flag and docs [ci skip]

* Switch to internal roc_auc_score

Switch to internal `roc_auc_score()` adapted from scikit-learn.

* Add AUCROCScore tests and improve errors/warnings

* Add tests for AUCROCScore and roc_auc_score
* Add missing error for only positive/negative values
* Remove unnecessary warnings and errors

* Make reduced roc_auc_score functions private

Because most of the checks and warnings have been stripped for the
internal functions and access is only intended through `ROCAUCScore`,
make the functions for roc_auc_score adapted from scikit-learn private.

* Check that data corresponds with multilabel flag

Check that the training instances correspond with the multilabel flag,
adding the multilabel flag if required.

* Add textcat score to early stopping check

* Add more checks to debug-data for textcat

* Add example training data for textcat

* Add more checks to textcat train CLI

* Check configuration when extending base model
* Fix typos

* Update textcat example data

* Provide licensing details and licenses for data
* Remove two labels with no positive instances from jigsaw-toxic-comment
data.


Co-authored-by: Ines Montani <ines@ines.io>
2019-09-15 22:31:31 +02:00
Ines Montani
009280fbc5 Tidy up and auto-format 2019-08-18 15:09:16 +02:00
adrianeboyd
925a852bb6 Improve NER per type scoring (#4052)
* Improve NER per type scoring

* include all gold labels in per type scoring, not only when recall > 0
* improve efficiency of per type scoring

* Create Scorer tests, initially with NER tests

* move regression test #3968 (per type NER scoring) to Scorer tests

* add new test for per type NER scoring with imperfect P/R/F and per
type P/R/F including a case where R == 0.0
2019-08-01 17:15:36 +02:00