mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 09:57:26 +03:00
425 lines
24 KiB
Python
425 lines
24 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import os
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import warnings
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import inspect
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def add_codes(err_cls):
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"""Add error codes to string messages via class attribute names."""
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class ErrorsWithCodes(object):
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def __getattribute__(self, code):
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msg = getattr(err_cls, code)
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return "[{code}] {msg}".format(code=code, msg=msg)
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return ErrorsWithCodes()
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# fmt: off
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@add_codes
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class Warnings(object):
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W001 = ("As of spaCy v2.0, the keyword argument `path=` is deprecated. "
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"You can now call spacy.load with the path as its first argument, "
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"and the model's meta.json will be used to determine the language "
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"to load. For example:\nnlp = spacy.load('{path}')")
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W002 = ("Tokenizer.from_list is now deprecated. Create a new Doc object "
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"instead and pass in the strings as the `words` keyword argument, "
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"for example:\nfrom spacy.tokens import Doc\n"
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"doc = Doc(nlp.vocab, words=[...])")
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W003 = ("Positional arguments to Doc.merge are deprecated. Instead, use "
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"the keyword arguments, for example tag=, lemma= or ent_type=.")
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W004 = ("No text fixing enabled. Run `pip install ftfy` to enable fixing "
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"using ftfy.fix_text if necessary.")
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W005 = ("Doc object not parsed. This means displaCy won't be able to "
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"generate a dependency visualization for it. Make sure the Doc "
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"was processed with a model that supports dependency parsing, and "
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"not just a language class like `English()`. For more info, see "
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"the docs:\nhttps://spacy.io/usage/models")
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W006 = ("No entities to visualize found in Doc object. If this is "
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"surprising to you, make sure the Doc was processed using a model "
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"that supports named entity recognition, and check the `doc.ents` "
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"property manually if necessary.")
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W007 = ("The model you're using has no word vectors loaded, so the result "
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"of the {obj}.similarity method will be based on the tagger, "
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"parser and NER, which may not give useful similarity judgements. "
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"This may happen if you're using one of the small models, e.g. "
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"`en_core_web_sm`, which don't ship with word vectors and only "
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"use context-sensitive tensors. You can always add your own word "
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"vectors, or use one of the larger models instead if available.")
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W008 = ("Evaluating {obj}.similarity based on empty vectors.")
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W009 = ("Custom factory '{name}' provided by entry points of another "
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"package overwrites built-in factory.")
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W010 = ("As of v2.1.0, the PhraseMatcher doesn't have a phrase length "
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"limit anymore, so the max_length argument is now deprecated.")
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W011 = ("It looks like you're calling displacy.serve from within a "
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"Jupyter notebook or a similar environment. This likely means "
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"you're already running a local web server, so there's no need to "
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"make displaCy start another one. Instead, you should be able to "
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"replace displacy.serve with displacy.render to show the "
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"visualization.")
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W012 = ("A Doc object you're adding to the PhraseMatcher for pattern "
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"'{key}' is parsed and/or tagged, but to match on '{attr}', you "
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"don't actually need this information. This means that creating "
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"the patterns is potentially much slower, because all pipeline "
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"components are applied. To only create tokenized Doc objects, "
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"try using `nlp.make_doc(text)` or process all texts as a stream "
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"using `list(nlp.tokenizer.pipe(all_texts))`.")
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W013 = ("As of v2.1.0, {obj}.merge is deprecated. Please use the more "
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"efficient and less error-prone Doc.retokenize context manager "
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"instead.")
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@add_codes
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class Errors(object):
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E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
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E002 = ("Can't find factory for '{name}'. This usually happens when spaCy "
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"calls `nlp.create_pipe` with a component name that's not built "
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"in - for example, when constructing the pipeline from a model's "
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"meta.json. If you're using a custom component, you can write to "
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"`Language.factories['{name}']` or remove it from the model meta "
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"and add it via `nlp.add_pipe` instead.")
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E003 = ("Not a valid pipeline component. Expected callable, but "
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"got {component} (name: '{name}').")
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E004 = ("If you meant to add a built-in component, use `create_pipe`: "
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"`nlp.add_pipe(nlp.create_pipe('{component}'))`")
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E005 = ("Pipeline component '{name}' returned None. If you're using a "
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"custom component, maybe you forgot to return the processed Doc?")
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E006 = ("Invalid constraints. You can only set one of the following: "
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"before, after, first, last.")
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E007 = ("'{name}' already exists in pipeline. Existing names: {opts}")
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E008 = ("Some current components would be lost when restoring previous "
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"pipeline state. If you added components after calling "
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"`nlp.disable_pipes()`, you should remove them explicitly with "
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"`nlp.remove_pipe()` before the pipeline is restored. Names of "
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"the new components: {names}")
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E009 = ("The `update` method expects same number of docs and golds, but "
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"got: {n_docs} docs, {n_golds} golds.")
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E010 = ("Word vectors set to length 0. This may be because you don't have "
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"a model installed or loaded, or because your model doesn't "
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"include word vectors. For more info, see the docs:\n"
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"https://spacy.io/usage/models")
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E011 = ("Unknown operator: '{op}'. Options: {opts}")
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E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
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E013 = ("Error selecting action in matcher")
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E014 = ("Uknown tag ID: {tag}")
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E015 = ("Conflicting morphology exception for ({tag}, {orth}). Use "
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"`force=True` to overwrite.")
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E016 = ("MultitaskObjective target should be function or one of: dep, "
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"tag, ent, dep_tag_offset, ent_tag.")
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E017 = ("Can only add unicode or bytes. Got type: {value_type}")
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E018 = ("Can't retrieve string for hash '{hash_value}'.")
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E019 = ("Can't create transition with unknown action ID: {action}. Action "
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"IDs are enumerated in spacy/syntax/{src}.pyx.")
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E020 = ("Could not find a gold-standard action to supervise the "
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"dependency parser. The tree is non-projective (i.e. it has "
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"crossing arcs - see spacy/syntax/nonproj.pyx for definitions). "
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"The ArcEager transition system only supports projective trees. "
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"To learn non-projective representations, transform the data "
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"before training and after parsing. Either pass "
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"`make_projective=True` to the GoldParse class, or use "
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"spacy.syntax.nonproj.preprocess_training_data.")
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E021 = ("Could not find a gold-standard action to supervise the "
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"dependency parser. The GoldParse was projective. The transition "
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"system has {n_actions} actions. State at failure: {state}")
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E022 = ("Could not find a transition with the name '{name}' in the NER "
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"model.")
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E023 = ("Error cleaning up beam: The same state occurred twice at "
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"memory address {addr} and position {i}.")
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E024 = ("Could not find an optimal move to supervise the parser. Usually, "
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"this means the GoldParse was not correct. For example, are all "
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"labels added to the model?")
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E025 = ("String is too long: {length} characters. Max is 2**30.")
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E026 = ("Error accessing token at position {i}: out of bounds in Doc of "
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"length {length}.")
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E027 = ("Arguments 'words' and 'spaces' should be sequences of the same "
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"length, or 'spaces' should be left default at None. spaces "
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"should be a sequence of booleans, with True meaning that the "
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"word owns a ' ' character following it.")
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E028 = ("orths_and_spaces expects either a list of unicode string or a "
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"list of (unicode, bool) tuples. Got bytes instance: {value}")
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E029 = ("noun_chunks requires the dependency parse, which requires a "
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"statistical model to be installed and loaded. For more info, see "
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"the documentation:\nhttps://spacy.io/usage/models")
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E030 = ("Sentence boundaries unset. You can add the 'sentencizer' "
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"component to the pipeline with: "
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"nlp.add_pipe(nlp.create_pipe('sentencizer')) "
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"Alternatively, add the dependency parser, or set sentence "
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"boundaries by setting doc[i].is_sent_start.")
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E031 = ("Invalid token: empty string ('') at position {i}.")
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E032 = ("Conflicting attributes specified in doc.from_array(): "
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"(HEAD, SENT_START). The HEAD attribute currently sets sentence "
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"boundaries implicitly, based on the tree structure. This means "
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"the HEAD attribute would potentially override the sentence "
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"boundaries set by SENT_START.")
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E033 = ("Cannot load into non-empty Doc of length {length}.")
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E034 = ("Doc.merge received {n_args} non-keyword arguments. Expected "
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"either 3 arguments (deprecated), or 0 (use keyword arguments).\n"
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"Arguments supplied:\n{args}\nKeyword arguments:{kwargs}")
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E035 = ("Error creating span with start {start} and end {end} for Doc of "
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"length {length}.")
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E036 = ("Error calculating span: Can't find a token starting at character "
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"offset {start}.")
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E037 = ("Error calculating span: Can't find a token ending at character "
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"offset {end}.")
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E038 = ("Error finding sentence for span. Infinite loop detected.")
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E039 = ("Array bounds exceeded while searching for root word. This likely "
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"means the parse tree is in an invalid state. Please report this "
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"issue here: http://github.com/explosion/spaCy/issues")
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E040 = ("Attempt to access token at {i}, max length {max_length}.")
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E041 = ("Invalid comparison operator: {op}. Likely a Cython bug?")
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E042 = ("Error accessing doc[{i}].nbor({j}), for doc of length {length}.")
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E043 = ("Refusing to write to token.sent_start if its document is parsed, "
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"because this may cause inconsistent state.")
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E044 = ("Invalid value for token.sent_start: {value}. Must be one of: "
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"None, True, False")
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E045 = ("Possibly infinite loop encountered while looking for {attr}.")
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E046 = ("Can't retrieve unregistered extension attribute '{name}'. Did "
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"you forget to call the `set_extension` method?")
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E047 = ("Can't assign a value to unregistered extension attribute "
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"'{name}'. Did you forget to call the `set_extension` method?")
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E048 = ("Can't import language {lang} from spacy.lang: {err}")
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E049 = ("Can't find spaCy data directory: '{path}'. Check your "
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"installation and permissions, or use spacy.util.set_data_path "
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"to customise the location if necessary.")
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E050 = ("Can't find model '{name}'. It doesn't seem to be a shortcut "
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"link, a Python package or a valid path to a data directory.")
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E051 = ("Cant' load '{name}'. If you're using a shortcut link, make sure "
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"it points to a valid package (not just a data directory).")
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E052 = ("Can't find model directory: {path}")
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E053 = ("Could not read meta.json from {path}")
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E054 = ("No valid '{setting}' setting found in model meta.json.")
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E055 = ("Invalid ORTH value in exception:\nKey: {key}\nOrths: {orths}")
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E056 = ("Invalid tokenizer exception: ORTH values combined don't match "
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"original string.\nKey: {key}\nOrths: {orths}")
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E057 = ("Stepped slices not supported in Span objects. Try: "
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"list(tokens)[start:stop:step] instead.")
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E058 = ("Could not retrieve vector for key {key}.")
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E059 = ("One (and only one) keyword arg must be set. Got: {kwargs}")
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E060 = ("Cannot add new key to vectors: the table is full. Current shape: "
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"({rows}, {cols}).")
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E061 = ("Bad file name: {filename}. Example of a valid file name: "
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"'vectors.128.f.bin'")
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E062 = ("Cannot find empty bit for new lexical flag. All bits between 0 "
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"and 63 are occupied. You can replace one by specifying the "
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"`flag_id` explicitly, e.g. "
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"`nlp.vocab.add_flag(your_func, flag_id=IS_ALPHA`.")
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E063 = ("Invalid value for flag_id: {value}. Flag IDs must be between 1 "
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"and 63 (inclusive).")
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E064 = ("Error fetching a Lexeme from the Vocab. When looking up a "
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"string, the lexeme returned had an orth ID that did not match "
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"the query string. This means that the cached lexeme structs are "
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"mismatched to the string encoding table. The mismatched:\n"
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"Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}")
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E065 = ("Only one of the vector table's width and shape can be specified. "
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"Got width {width} and shape {shape}.")
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E066 = ("Error creating model helper for extracting columns. Can only "
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"extract columns by positive integer. Got: {value}.")
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E067 = ("Invalid BILUO tag sequence: Got a tag starting with 'I' (inside "
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"an entity) without a preceding 'B' (beginning of an entity). "
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"Tag sequence:\n{tags}")
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E068 = ("Invalid BILUO tag: '{tag}'.")
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E069 = ("Invalid gold-standard parse tree. Found cycle between word "
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"IDs: {cycle}")
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E070 = ("Invalid gold-standard data. Number of documents ({n_docs}) "
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"does not align with number of annotations ({n_annots}).")
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E071 = ("Error creating lexeme: specified orth ID ({orth}) does not "
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"match the one in the vocab ({vocab_orth}).")
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E072 = ("Error serializing lexeme: expected data length {length}, "
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"got {bad_length}.")
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E073 = ("Cannot assign vector of length {new_length}. Existing vectors "
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"are of length {length}. You can use `vocab.reset_vectors` to "
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"clear the existing vectors and resize the table.")
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E074 = ("Error interpreting compiled match pattern: patterns are expected "
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"to end with the attribute {attr}. Got: {bad_attr}.")
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E075 = ("Error accepting match: length ({length}) > maximum length "
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"({max_len}).")
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E076 = ("Error setting tensor on Doc: tensor has {rows} rows, while Doc "
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"has {words} words.")
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E077 = ("Error computing {value}: number of Docs ({n_docs}) does not "
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"equal number of GoldParse objects ({n_golds}) in batch.")
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E078 = ("Error computing score: number of words in Doc ({words_doc}) does "
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"not equal number of words in GoldParse ({words_gold}).")
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E079 = ("Error computing states in beam: number of predicted beams "
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"({pbeams}) does not equal number of gold beams ({gbeams}).")
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E080 = ("Duplicate state found in beam: {key}.")
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E081 = ("Error getting gradient in beam: number of histories ({n_hist}) "
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"does not equal number of losses ({losses}).")
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E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), "
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"projective heads ({n_proj_heads}) and labels ({n_labels}) do not "
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"match.")
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E083 = ("Error setting extension: only one of `default`, `method`, or "
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"`getter` (plus optional `setter`) is allowed. Got: {nr_defined}")
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E084 = ("Error assigning label ID {label} to span: not in StringStore.")
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E085 = ("Can't create lexeme for string '{string}'.")
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E086 = ("Error deserializing lexeme '{string}': orth ID {orth_id} does "
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"not match hash {hash_id} in StringStore.")
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E087 = ("Unknown displaCy style: {style}.")
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E088 = ("Text of length {length} exceeds maximum of {max_length}. The "
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"v2.x parser and NER models require roughly 1GB of temporary "
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"memory per 100,000 characters in the input. This means long "
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"texts may cause memory allocation errors. If you're not using "
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"the parser or NER, it's probably safe to increase the "
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"`nlp.max_length` limit. The limit is in number of characters, so "
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"you can check whether your inputs are too long by checking "
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"`len(text)`.")
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E089 = ("Extensions can't have a setter argument without a getter "
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"argument. Check the keyword arguments on `set_extension`.")
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E090 = ("Extension '{name}' already exists on {obj}. To overwrite the "
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"existing extension, set `force=True` on `{obj}.set_extension`.")
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E091 = ("Invalid extension attribute {name}: expected callable or None, "
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"but got: {value}")
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E092 = ("Could not find or assign name for word vectors. Ususally, the "
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"name is read from the model's meta.json in vector.name. "
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"Alternatively, it is built from the 'lang' and 'name' keys in "
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"the meta.json. Vector names are required to avoid issue #1660.")
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E093 = ("token.ent_iob values make invalid sequence: I without B\n{seq}")
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E094 = ("Error reading line {line_num} in vectors file {loc}.")
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E095 = ("Can't write to frozen dictionary. This is likely an internal "
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"error. Are you writing to a default function argument?")
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E096 = ("Invalid object passed to displaCy: Can only visualize Doc or "
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"Span objects, or dicts if set to manual=True.")
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E097 = ("Invalid pattern: expected token pattern (list of dicts) or "
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"phrase pattern (string) but got:\n{pattern}")
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E098 = ("Invalid pattern specified: expected both SPEC and PATTERN.")
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E099 = ("First node of pattern should be a root node. The root should "
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"only contain NODE_NAME.")
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E100 = ("Nodes apart from the root should contain NODE_NAME, NBOR_NAME and "
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"NBOR_RELOP.")
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E101 = ("NODE_NAME should be a new node and NBOR_NAME should already have "
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"have been declared in previous edges.")
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E102 = ("Can't merge non-disjoint spans. '{token}' is already part of tokens to merge")
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E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A token"
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" can only be part of one entity, so make sure the entities you're "
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"setting don't overlap.")
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E104 = ("Can't find JSON schema for '{name}'.")
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E105 = ("The Doc.print_tree() method is now deprecated. Please use "
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"Doc.to_json() instead or write your own function.")
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E106 = ("Can't find doc._.{attr} attribute specified in the underscore "
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"settings: {opts}")
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E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
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E108 = ("As of spaCy v2.1, the pipe name `sbd` has been deprecated "
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"in favor of the pipe name `sentencizer`, which does the same "
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"thing. For example, use `nlp.create_pipeline('sentencizer')`")
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E109 = ("Model for component '{name}' not initialized. Did you forget to load "
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"a model, or forget to call begin_training()?")
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E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
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E111 = ("Pickling a token is not supported, because tokens are only views "
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"of the parent Doc and can't exist on their own. A pickled token "
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"would always have to include its Doc and Vocab, which has "
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"practically no advantage over pickling the parent Doc directly. "
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"So instead of pickling the token, pickle the Doc it belongs to.")
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E112 = ("Pickling a span is not supported, because spans are only views "
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"of the parent Doc and can't exist on their own. A pickled span "
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"would always have to include its Doc and Vocab, which has "
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"practically no advantage over pickling the parent Doc directly. "
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"So instead of pickling the span, pickle the Doc it belongs to or "
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"use Span.as_doc to convert the span to a standalone Doc object.")
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E113 = ("The newly split token can only have one root (head = 0).")
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E114 = ("The newly split token needs to have a root (head = 0)")
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E115 = ("All subtokens must have associated heads")
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E116 = ("Cannot currently add labels to pre-trained text classifier. Add "
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"labels before training begins. This functionality was available "
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"in previous versions, but had significant bugs that led to poor "
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"performance")
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E117 = ("The newly split tokens must match the text of the original token. "
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"New orths: {new}. Old text: {old}.")
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@add_codes
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class TempErrors(object):
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T001 = ("Max length currently 10 for phrase matching")
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T002 = ("Pattern length ({doc_len}) >= phrase_matcher.max_length "
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"({max_len}). Length can be set on initialization, up to 10.")
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T003 = ("Resizing pre-trained Tagger models is not currently supported.")
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T004 = ("Currently parser depth is hard-coded to 1. Received: {value}.")
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T005 = ("Currently history size is hard-coded to 0. Received: {value}.")
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T006 = ("Currently history width is hard-coded to 0. Received: {value}.")
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T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
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"issue tracker: http://github.com/explosion/spaCy/issues")
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T008 = ("Bad configuration of Tagger. This is probably a bug within "
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"spaCy. We changed the name of an internal attribute for loading "
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"pre-trained vectors, and the class has been passed the old name "
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"(pretrained_dims) but not the new name (pretrained_vectors).")
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# fmt: on
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class MatchPatternError(ValueError):
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def __init__(self, key, errors):
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"""Custom error for validating match patterns.
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key (unicode): The name of the matcher rule.
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errors (dict): Validation errors (sequence of strings) mapped to pattern
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ID, i.e. the index of the added pattern.
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"""
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msg = "Invalid token patterns for matcher rule '{}'\n".format(key)
|
|
for pattern_idx, error_msgs in errors.items():
|
|
pattern_errors = "\n".join(["- {}".format(e) for e in error_msgs])
|
|
msg += "\nPattern {}:\n{}\n".format(pattern_idx, pattern_errors)
|
|
ValueError.__init__(self, msg)
|
|
|
|
|
|
class ModelsWarning(UserWarning):
|
|
pass
|
|
|
|
|
|
WARNINGS = {
|
|
"user": UserWarning,
|
|
"deprecation": DeprecationWarning,
|
|
"models": ModelsWarning,
|
|
}
|
|
|
|
|
|
def _get_warn_types(arg):
|
|
if arg == "": # don't show any warnings
|
|
return []
|
|
if not arg or arg == "all": # show all available warnings
|
|
return WARNINGS.keys()
|
|
return [w_type.strip() for w_type in arg.split(",") if w_type.strip() in WARNINGS]
|
|
|
|
|
|
def _get_warn_excl(arg):
|
|
if not arg:
|
|
return []
|
|
return [w_id.strip() for w_id in arg.split(",")]
|
|
|
|
|
|
SPACY_WARNING_FILTER = os.environ.get("SPACY_WARNING_FILTER")
|
|
SPACY_WARNING_TYPES = _get_warn_types(os.environ.get("SPACY_WARNING_TYPES"))
|
|
SPACY_WARNING_IGNORE = _get_warn_excl(os.environ.get("SPACY_WARNING_IGNORE"))
|
|
|
|
|
|
def user_warning(message):
|
|
_warn(message, "user")
|
|
|
|
|
|
def deprecation_warning(message):
|
|
_warn(message, "deprecation")
|
|
|
|
|
|
def models_warning(message):
|
|
_warn(message, "models")
|
|
|
|
|
|
def _warn(message, warn_type="user"):
|
|
"""
|
|
message (unicode): The message to display.
|
|
category (Warning): The Warning to show.
|
|
"""
|
|
if message.startswith("["):
|
|
w_id = message.split("[", 1)[1].split("]", 1)[0] # get ID from string
|
|
else:
|
|
w_id = None
|
|
ignore_warning = w_id and w_id in SPACY_WARNING_IGNORE
|
|
if warn_type in SPACY_WARNING_TYPES and not ignore_warning:
|
|
category = WARNINGS[warn_type]
|
|
stack = inspect.stack()[-1]
|
|
with warnings.catch_warnings():
|
|
if SPACY_WARNING_FILTER:
|
|
warnings.simplefilter(SPACY_WARNING_FILTER, category)
|
|
warnings.warn_explicit(message, category, stack[1], stack[2])
|