mirror of https://github.com/explosion/spaCy.git
593 lines
35 KiB
Python
593 lines
35 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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W014 = ("As of v2.1.0, the `disable` keyword argument on the serialization "
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"methods is and should be replaced with `exclude`. This makes it "
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"consistent with the other objects serializable.")
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W015 = ("As of v2.1.0, the use of keyword arguments to exclude fields from "
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"being serialized or deserialized is deprecated. Please use the "
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"`exclude` argument instead. For example: exclude=['{arg}'].")
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W016 = ("The keyword argument `n_threads` on the is now deprecated, as "
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"the v2.x models cannot release the global interpreter lock. "
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"Future versions may introduce a `n_process` argument for "
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"parallel inference via multiprocessing.")
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W017 = ("Alias '{alias}' already exists in the Knowledge base.")
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W018 = ("Entity '{entity}' already exists in the Knowledge base.")
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W019 = ("Changing vectors name from {old} to {new}, to avoid clash with "
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"previously loaded vectors. See Issue #3853.")
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W020 = ("Unnamed vectors. This won't allow multiple vectors models to be "
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"loaded. (Shape: {shape})")
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W021 = ("Unexpected hash collision in PhraseMatcher. Matches may be "
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"incorrect. Modify PhraseMatcher._terminal_hash to fix.")
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W022 = ("Training a new part-of-speech tagger using a model with no "
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"lemmatization rules or data. This means that the trained model "
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"may not be able to lemmatize correctly. If this is intentional "
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"or the language you're using doesn't have lemmatization data, "
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"you can ignore this warning by setting SPACY_WARNING_IGNORE=W022. "
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"If this is surprising, make sure you have the spacy-lookups-data "
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"package installed.")
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W023 = ("Multiprocessing of Language.pipe is not supported in Python2. 'n_process' will be set to 1.")
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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 = ("Unknown 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}'. This usually "
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"refers to an issue with the `Vocab` or `StringStore`.")
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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 that the model can't be updated in a way that's valid "
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"and satisfies the correct annotations specified in the GoldParse. "
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"For example, are all labels added to the model? If you're "
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"training a named entity recognizer, also make sure that none of "
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"your annotated entity spans have leading or trailing whitespace. "
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"You can also use the experimental `debug-data` command to "
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"validate your JSON-formatted training data. For details, run:\n"
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"python -m spacy debug-data --help")
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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} (tokens: {cycle_tokens}) in the document starting "
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"with tokens: {doc_tokens}.")
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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 "
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"tokens to merge. If you want to find the longest non-overlapping "
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"spans, you can use the util.filter_spans helper:\n"
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"https://spacy.io/api/top-level#util.filter_spans")
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E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A "
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"token can only be part of one entity, so make sure the entities "
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"you're 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 "
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"load 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 "
|
|
"of the parent Doc and can't exist on their own. A pickled span "
|
|
"would always have to include its Doc and Vocab, which has "
|
|
"practically no advantage over pickling the parent Doc directly. "
|
|
"So instead of pickling the span, pickle the Doc it belongs to or "
|
|
"use Span.as_doc to convert the span to a standalone Doc object.")
|
|
E113 = ("The newly split token can only have one root (head = 0).")
|
|
E114 = ("The newly split token needs to have a root (head = 0).")
|
|
E115 = ("All subtokens must have associated heads.")
|
|
E116 = ("Cannot currently add labels to pretrained text classifier. Add "
|
|
"labels before training begins. This functionality was available "
|
|
"in previous versions, but had significant bugs that led to poor "
|
|
"performance.")
|
|
E117 = ("The newly split tokens must match the text of the original token. "
|
|
"New orths: {new}. Old text: {old}.")
|
|
E118 = ("The custom extension attribute '{attr}' is not registered on the "
|
|
"Token object so it can't be set during retokenization. To "
|
|
"register an attribute, use the Token.set_extension classmethod.")
|
|
E119 = ("Can't set custom extension attribute '{attr}' during "
|
|
"retokenization because it's not writable. This usually means it "
|
|
"was registered with a getter function (and no setter) or as a "
|
|
"method extension, so the value is computed dynamically. To "
|
|
"overwrite a custom attribute manually, it should be registered "
|
|
"with a default value or with a getter AND setter.")
|
|
E120 = ("Can't set custom extension attributes during retokenization. "
|
|
"Expected dict mapping attribute names to values, but got: {value}")
|
|
E121 = ("Can't bulk merge spans. Attribute length {attr_len} should be "
|
|
"equal to span length ({span_len}).")
|
|
E122 = ("Cannot find token to be split. Did it get merged?")
|
|
E123 = ("Cannot find head of token to be split. Did it get merged?")
|
|
E124 = ("Cannot read from file: {path}. Supported formats: {formats}")
|
|
E125 = ("Unexpected value: {value}")
|
|
E126 = ("Unexpected matcher predicate: '{bad}'. Expected one of: {good}. "
|
|
"This is likely a bug in spaCy, so feel free to open an issue.")
|
|
E127 = ("Cannot create phrase pattern representation for length 0. This "
|
|
"is likely a bug in spaCy.")
|
|
E128 = ("Unsupported serialization argument: '{arg}'. The use of keyword "
|
|
"arguments to exclude fields from being serialized or deserialized "
|
|
"is now deprecated. Please use the `exclude` argument instead. "
|
|
"For example: exclude=['{arg}'].")
|
|
E129 = ("Cannot write the label of an existing Span object because a Span "
|
|
"is a read-only view of the underlying Token objects stored in the "
|
|
"Doc. Instead, create a new Span object and specify the `label` "
|
|
"keyword argument, for example:\nfrom spacy.tokens import Span\n"
|
|
"span = Span(doc, start={start}, end={end}, label='{label}')")
|
|
E130 = ("You are running a narrow unicode build, which is incompatible "
|
|
"with spacy >= 2.1.0. To fix this, reinstall Python and use a wide "
|
|
"unicode build instead. You can also rebuild Python and set the "
|
|
"--enable-unicode=ucs4 flag.")
|
|
E131 = ("Cannot write the kb_id of an existing Span object because a Span "
|
|
"is a read-only view of the underlying Token objects stored in "
|
|
"the Doc. Instead, create a new Span object and specify the "
|
|
"`kb_id` keyword argument, for example:\nfrom spacy.tokens "
|
|
"import Span\nspan = Span(doc, start={start}, end={end}, "
|
|
"label='{label}', kb_id='{kb_id}')")
|
|
E132 = ("The vectors for entities and probabilities for alias '{alias}' "
|
|
"should have equal length, but found {entities_length} and "
|
|
"{probabilities_length} respectively.")
|
|
E133 = ("The sum of prior probabilities for alias '{alias}' should not "
|
|
"exceed 1, but found {sum}.")
|
|
E134 = ("Alias '{alias}' defined for unknown entity '{entity}'.")
|
|
E135 = ("If you meant to replace a built-in component, use `create_pipe`: "
|
|
"`nlp.replace_pipe('{name}', nlp.create_pipe('{name}'))`")
|
|
E136 = ("This additional feature requires the jsonschema library to be "
|
|
"installed:\npip install jsonschema")
|
|
E137 = ("Expected 'dict' type, but got '{type}' from '{line}'. Make sure "
|
|
"to provide a valid JSON object as input with either the `text` "
|
|
"or `tokens` key. For more info, see the docs:\n"
|
|
"https://spacy.io/api/cli#pretrain-jsonl")
|
|
E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input "
|
|
"includes either the `text` or `tokens` key. For more info, see "
|
|
"the docs:\nhttps://spacy.io/api/cli#pretrain-jsonl")
|
|
E139 = ("Knowledge base for component '{name}' not initialized. Did you "
|
|
"forget to call set_kb()?")
|
|
E140 = ("The list of entities, prior probabilities and entity vectors "
|
|
"should be of equal length.")
|
|
E141 = ("Entity vectors should be of length {required} instead of the "
|
|
"provided {found}.")
|
|
E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or "
|
|
"'cosine'.")
|
|
E143 = ("Labels for component '{name}' not initialized. Did you forget to "
|
|
"call add_label()?")
|
|
E144 = ("Could not find parameter `{param}` when building the entity "
|
|
"linker model.")
|
|
E145 = ("Error reading `{param}` from input file.")
|
|
E146 = ("Could not access `{path}`.")
|
|
E147 = ("Unexpected error in the {method} functionality of the "
|
|
"EntityLinker: {msg}. This is likely a bug in spaCy, so feel free "
|
|
"to open an issue.")
|
|
E148 = ("Expected {ents} KB identifiers but got {ids}. Make sure that "
|
|
"each entity in `doc.ents` is assigned to a KB identifier.")
|
|
E149 = ("Error deserializing model. Check that the config used to create "
|
|
"the component matches the model being loaded.")
|
|
E150 = ("The language of the `nlp` object and the `vocab` should be the "
|
|
"same, but found '{nlp}' and '{vocab}' respectively.")
|
|
E151 = ("Trying to call nlp.update without required annotation types. "
|
|
"Expected top-level keys: {exp}. Got: {unexp}.")
|
|
E152 = ("The attribute {attr} is not supported for token patterns. "
|
|
"Please use the option validate=True with Matcher, PhraseMatcher, "
|
|
"or EntityRuler for more details.")
|
|
E153 = ("The value type {vtype} is not supported for token patterns. "
|
|
"Please use the option validate=True with Matcher, PhraseMatcher, "
|
|
"or EntityRuler for more details.")
|
|
E154 = ("One of the attributes or values is not supported for token "
|
|
"patterns. Please use the option validate=True with Matcher, "
|
|
"PhraseMatcher, or EntityRuler for more details.")
|
|
E155 = ("The pipeline needs to include a tagger in order to use "
|
|
"Matcher or PhraseMatcher with the attributes POS, TAG, or LEMMA. "
|
|
"Try using nlp() instead of nlp.make_doc() or list(nlp.pipe()) "
|
|
"instead of list(nlp.tokenizer.pipe()).")
|
|
E156 = ("The pipeline needs to include a parser in order to use "
|
|
"Matcher or PhraseMatcher with the attribute DEP. Try using "
|
|
"nlp() instead of nlp.make_doc() or list(nlp.pipe()) instead of "
|
|
"list(nlp.tokenizer.pipe()).")
|
|
E157 = ("Can't render negative values for dependency arc start or end. "
|
|
"Make sure that you're passing in absolute token indices, not "
|
|
"relative token offsets.\nstart: {start}, end: {end}, label: "
|
|
"{label}, direction: {dir}")
|
|
E158 = ("Can't add table '{name}' to lookups because it already exists.")
|
|
E159 = ("Can't find table '{name}' in lookups. Available tables: {tables}")
|
|
E160 = ("Can't find language data file: {path}")
|
|
E161 = ("Found an internal inconsistency when predicting entity links. "
|
|
"This is likely a bug in spaCy, so feel free to open an issue.")
|
|
E162 = ("Cannot evaluate textcat model on data with different labels.\n"
|
|
"Labels in model: {model_labels}\nLabels in evaluation "
|
|
"data: {eval_labels}")
|
|
E163 = ("cumsum was found to be unstable: its last element does not "
|
|
"correspond to sum")
|
|
E164 = ("x is neither increasing nor decreasing: {}.")
|
|
E165 = ("Only one class present in y_true. ROC AUC score is not defined in "
|
|
"that case.")
|
|
E166 = ("Can only merge DocBins with the same pre-defined attributes.\n"
|
|
"Current DocBin: {current}\nOther DocBin: {other}")
|
|
E167 = ("Unknown morphological feature: '{feat}' ({feat_id}). This can "
|
|
"happen if the tagger was trained with a different set of "
|
|
"morphological features. If you're using a pretrained model, make "
|
|
"sure that your models are up to date:\npython -m spacy validate")
|
|
E168 = ("Unknown field: {field}")
|
|
E169 = ("Can't find module: {module}")
|
|
E170 = ("Cannot apply transition {name}: invalid for the current state.")
|
|
E171 = ("Matcher.add received invalid on_match callback argument: expected "
|
|
"callable or None, but got: {arg_type}")
|
|
E172 = ("The Lemmatizer.load classmethod is deprecated. To create a "
|
|
"Lemmatizer, initialize the class directly. See the docs for "
|
|
"details: https://spacy.io/api/lemmatizer")
|
|
E173 = ("As of v2.2, the Lemmatizer is initialized with an instance of "
|
|
"Lookups containing the lemmatization tables. See the docs for "
|
|
"details: https://spacy.io/api/lemmatizer#init")
|
|
E174 = ("Architecture '{name}' not found in registry. Available "
|
|
"names: {names}")
|
|
E175 = ("Can't remove rule for unknown match pattern ID: {key}")
|
|
|
|
|
|
@add_codes
|
|
class TempErrors(object):
|
|
T003 = ("Resizing pretrained Tagger models is not currently supported.")
|
|
T004 = ("Currently parser depth is hard-coded to 1. Received: {value}.")
|
|
T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
|
|
"issue tracker: http://github.com/explosion/spaCy/issues")
|
|
T008 = ("Bad configuration of Tagger. This is probably a bug within "
|
|
"spaCy. We changed the name of an internal attribute for loading "
|
|
"pretrained vectors, and the class has been passed the old name "
|
|
"(pretrained_dims) but not the new name (pretrained_vectors).")
|
|
|
|
|
|
# fmt: on
|
|
|
|
|
|
class MatchPatternError(ValueError):
|
|
def __init__(self, key, errors):
|
|
"""Custom error for validating match patterns.
|
|
|
|
key (unicode): The name of the matcher rule.
|
|
errors (dict): Validation errors (sequence of strings) mapped to pattern
|
|
ID, i.e. the index of the added pattern.
|
|
"""
|
|
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])
|