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adc9745718
* Restructure tag maps for MorphAnalysis changes
Prepare tag maps for upcoming MorphAnalysis changes that allow
arbritrary features.
* Use default tag map rather than duplicating for ca / uk / vi
* Import tag map into defaults for ga
* Modify tag maps so all morphological fields and features are strings
* Move features from `"Other"` to the top level
* Rewrite tuples as strings separated by `","`
* Rewrite morph symbols for fr lemmatizer as strings
* Export MorphAnalysis under spacy.tokens
* Modify morphology to support arbitrary features
Modify `Morphology` and `MorphAnalysis` so that arbitrary features are
supported.
* Modify `MorphAnalysisC` so that it can support arbitrary features and
multiple values per field. `MorphAnalysisC` is redesigned to contain:
* key: hash of UD FEATS string of morphological features
* array of `MorphFeatureC` structs that each contain a hash of `Field`
and `Field=Value` for a given morphological feature, which makes it
possible to:
* find features by field
* represent multiple values for a given field
* `get_field()` is renamed to `get_by_field()` and is no longer `nogil`.
Instead a new helper function `get_n_by_field()` is `nogil` and returns
`n` features by field.
* `MorphAnalysis.get()` returns all possible values for a field as a
list of individual features such as `["Tense=Pres", "Tense=Past"]`.
* `MorphAnalysis`'s `str()` and `repr()` are the UD FEATS string.
* `Morphology.feats_to_dict()` converts a UD FEATS string to a dict
where:
* Each field has one entry in the dict
* Multiple values remain separated by a separator in the value string
* `Token.morph_` returns the UD FEATS string and you can set
`Token.morph_` with a UD FEATS string or with a tag map dict.
* Modify get_by_field to use np.ndarray
Modify `get_by_field()` to use np.ndarray. Remove `max_results` from
`get_n_by_field()` and always iterate over all the fields.
* Rewrite without MorphFeatureC
* Add shortcut for existing feats strings as keys
Add shortcut for existing feats strings as keys in `Morphology.add()`.
* Check for '_' as empty analysis when adding morphs
* Extend helper converters in Morphology
Add and extend helper converters that convert and normalize between:
* UD FEATS strings (`"Case=dat,gen|Number=sing"`)
* per-field dict of feats (`{"Case": "dat,gen", "Number": "sing"}`)
* list of individual features (`["Case=dat", "Case=gen",
"Number=sing"]`)
All converters sort fields and values where applicable.
27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
import pytest
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from spacy.morphology import Morphology
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def test_feats_converters():
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feats = "Case=dat,gen|Number=sing"
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feats_dict = {"Case": "dat,gen", "Number": "sing"}
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feats_list = feats.split(Morphology.FEATURE_SEP)
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# simple conversions
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assert Morphology.list_to_feats(feats_list) == feats
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assert Morphology.dict_to_feats(feats_dict) == feats
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assert Morphology.feats_to_dict(feats) == feats_dict
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# roundtrips
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assert Morphology.dict_to_feats(Morphology.feats_to_dict(feats)) == feats
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assert Morphology.feats_to_dict(Morphology.dict_to_feats(feats_dict)) == feats_dict
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# unsorted input is normalized
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unsorted_feats = "Number=sing|Case=gen,dat"
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unsorted_feats_dict = {"Case": "gen,dat", "Number": "sing"}
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unsorted_feats_list = feats.split(Morphology.FEATURE_SEP)
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assert Morphology.feats_to_dict(unsorted_feats) == feats_dict
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assert Morphology.dict_to_feats(unsorted_feats_dict) == feats
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assert Morphology.list_to_feats(unsorted_feats_list) == feats
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assert Morphology.dict_to_feats(Morphology.feats_to_dict(unsorted_feats)) == feats
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