# coding: utf8 from __future__ import unicode_literals import os import warnings import inspect def add_codes(err_cls): """Add error codes to string messages via class attribute names.""" class ErrorsWithCodes(object): def __getattribute__(self, code): msg = getattr(err_cls, code) return '[{code}] {msg}'.format(code=code, msg=msg) return ErrorsWithCodes() @add_codes class Warnings(object): W001 = ("As of spaCy v2.0, the keyword argument `path=` is deprecated. " "You can now call spacy.load with the path as its first argument, " "and the model's meta.json will be used to determine the language " "to load. For example:\nnlp = spacy.load('{path}')") W002 = ("Tokenizer.from_list is now deprecated. Create a new Doc object " "instead and pass in the strings as the `words` keyword argument, " "for example:\nfrom spacy.tokens import Doc\n" "doc = Doc(nlp.vocab, words=[...])") W003 = ("Positional arguments to Doc.merge are deprecated. Instead, use " "the keyword arguments, for example tag=, lemma= or ent_type=.") W004 = ("No text fixing enabled. Run `pip install ftfy` to enable fixing " "using ftfy.fix_text if necessary.") W005 = ("Doc object not parsed. This means displaCy won't be able to " "generate a dependency visualization for it. Make sure the Doc " "was processed with a model that supports dependency parsing, and " "not just a language class like `English()`. For more info, see " "the docs:\nhttps://spacy.io/usage/models") W006 = ("No entities to visualize found in Doc object. If this is " "surprising to you, make sure the Doc was processed using a model " "that supports named entity recognition, and check the `doc.ents` " "property manually if necessary.") @add_codes class Errors(object): E001 = ("No component '{name}' found in pipeline. Available names: {opts}") E002 = ("Can't find factory for '{name}'. This usually happens when spaCy " "calls `nlp.create_pipe` with a component name that's not built " "in - for example, when constructing the pipeline from a model's " "meta.json. If you're using a custom component, you can write to " "`Language.factories['{name}']` or remove it from the model meta " "and add it via `nlp.add_pipe` instead.") E003 = ("Not a valid pipeline component. Expected callable, but " "got {component} (name: '{name}').") E004 = ("If you meant to add a built-in component, use `create_pipe`: " "`nlp.add_pipe(nlp.create_pipe('{component}'))`") E005 = ("Pipeline component '{name}' returned None. If you're using a " "custom component, maybe you forgot to return the processed Doc?") E006 = ("Invalid constraints. You can only set one of the following: " "before, after, first, last.") E007 = ("'{name}' already exists in pipeline. Existing names: {opts}") E008 = ("Some current components would be lost when restoring previous " "pipeline state. If you added components after calling " "`nlp.disable_pipes()`, you should remove them explicitly with " "`nlp.remove_pipe()` before the pipeline is restored. Names of " "the new components: {names}") E009 = ("The `update` method expects same number of docs and golds, but " "got: {n_docs} docs, {n_golds} golds.") E010 = ("Word vectors set to length 0. This may be because you don't have " "a model installed or loaded, or because your model doesn't " "include word vectors. For more info, see the docs:\n" "https://spacy.io/usage/models") E011 = ("Unknown operator: '{op}'. Options: {opts}") E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}") E013 = ("Error selecting action in matcher") E014 = ("Uknown tag ID: {tag}") E015 = ("Conflicting morphology exception for ({tag}, {orth}). Use " "`force=True` to overwrite.") E016 = ("MultitaskObjective target should be function or one of: dep, " "tag, ent, dep_tag_offset, ent_tag.") E017 = ("Can only add unicode or bytes. Got type: {value_type}") E018 = ("Can't retrieve string for hash '{hash_value}'.") E019 = ("Can't create transition with unknown action ID: {action}. Action " "IDs are enumerated in spacy/syntax/{src}.pyx.") E020 = ("Could not find a gold-standard action to supervise the " "dependency parser. The tree is non-projective (i.e. it has " "crossing arcs - see spacy/syntax/nonproj.pyx for definitions). " "The ArcEager transition system only supports projective trees. " "To learn non-projective representations, transform the data " "before training and after parsing. Either pass " "`make_projective=True` to the GoldParse class, or use " "spacy.syntax.nonproj.preprocess_training_data.") E021 = ("Could not find a gold-standard action to supervise the " "dependency parser. The GoldParse was projective. The transition " "system has {n_actions} actions. State at failure: {state}") E022 = ("Could not find a transition with the name '{name}' in the NER " "model.") E023 = ("Error cleaning up beam: The same state occurred twice at " "memory address {addr} and position {i}.") E024 = ("Could not find an optimal move to supervise the parser. Usually, " "this means the GoldParse was not correct. For example, are all " "labels added to the model?") E025 = ("String is too long: {length} characters. Max is 2**30.") E026 = ("Error accessing token at position {i}: out of bounds in Doc of " "length {length}.") E027 = ("Arguments 'words' and 'spaces' should be sequences of the same " "length, or 'spaces' should be left default at None. spaces " "should be a sequence of booleans, with True meaning that the " "word owns a ' ' character following it.") E028 = ("orths_and_spaces expects either a list of unicode string or a " "list of (unicode, bool) tuples. Got bytes instance: {value}") E029 = ("noun_chunks requires the dependency parse, which requires a " "statistical model to be installed and loaded. For more info, see " "the documentation:\nhttps://spacy.io/usage/models") E030 = ("Sentence boundaries unset. You can add the 'sentencizer' " "component to the pipeline with: " "nlp.add_pipe(nlp.create_pipe('sentencizer')) " "Alternatively, add the dependency parser, or set sentence " "boundaries by setting doc[i].is_sent_start.") E031 = ("Invalid token: empty string ('') at position {i}.") E032 = ("Conflicting attributes specified in doc.from_array(): " "(HEAD, SENT_START). The HEAD attribute currently sets sentence " "boundaries implicitly, based on the tree structure. This means " "the HEAD attribute would potentially override the sentence " "boundaries set by SENT_START.") E033 = ("Cannot load into non-empty Doc of length {length}.") E034 = ("Doc.merge received {n_args} non-keyword arguments. Expected " "either 3 arguments (deprecated), or 0 (use keyword arguments).\n" "Arguments supplied:\n{args}\nKeyword arguments:{kwargs}") E035 = ("Error creating span with start {start} and end {end} for Doc of " "length {length}.") E036 = ("Error calculating span: Can't find a token starting at character " "offset {start}.") E037 = ("Error calculating span: Can't find a token ending at character " "offset {end}.") E038 = ("Error finding sentence for span. Infinite loop detected.") E039 = ("Array bounds exceeded while searching for root word. This likely " "means the parse tree is in an invalid state. Please report this " "issue here: http://github.com/explosion/spaCy/issues") E040 = ("Attempt to access token at {i}, max length {max_length}.") E041 = ("Invalid comparison operator: {op}. Likely a Cython bug?") E042 = ("Error accessing doc[{i}].nbor({j}), for doc of length {length}.") E043 = ("Refusing to write to token.sent_start if its document is parsed, " "because this may cause inconsistent state.") E044 = ("Invalid value for token.sent_start: {value}. Must be one of: " "None, True, False") E045 = ("Possibly infinite loop encountered while looking for {attr}.") E046 = ("Can't retrieve unregistered extension attribute '{name}'. Did " "you forget to call the `set_extension` method?") E047 = ("Can't assign a value to unregistered extension attribute " "'{name}'. Did you forget to call the `set_extension` method?") E048 = ("Can't import language {lang} from spacy.lang.") E049 = ("Can't find spaCy data directory: '{path}'. Check your " "installation and permissions, or use spacy.util.set_data_path " "to customise the location if necessary.") E050 = ("Can't find model '{name}'. It doesn't seem to be a shortcut " "link, a Python package or a valid path to a data directory.") E051 = ("Cant' load '{name}'. If you're using a shortcut link, make sure " "it points to a valid package (not just a data directory).") E052 = ("Can't find model directory: {path}") E053 = ("Could not read meta.json from {path}") E054 = ("No valid '{setting}' setting found in model meta.json.") E055 = ("Invalid ORTH value in exception:\nKey: {key}\nOrths: {orths}") E056 = ("Invalid tokenizer exception: ORTH values combined don't match " "original string.\nKey: {key}\nOrths: {orths}") E057 = ("Stepped slices not supported in Span objects. Try: " "list(tokens)[start:stop:step] instead.") E058 = ("Could not retrieve vector for key {key}.") E059 = ("One (and only one) keyword arg must be set. Got: {kwargs}") E060 = ("Cannot add new key to vectors: the table is full. Current shape: " "({rows}, {cols}).") E061 = ("Bad file name: {filename}. Example of a valid file name: " "'vectors.128.f.bin'") E062 = ("Cannot find empty bit for new lexical flag. All bits between 0 " "and 63 are occupied. You can replace one by specifying the " "`flag_id` explicitly, e.g. " "`nlp.vocab.add_flag(your_func, flag_id=IS_ALPHA`.") E063 = ("Invalid value for flag_id: {value}. Flag IDs must be between 1 " "and 63 (inclusive).") E064 = ("Error fetching a Lexeme from the Vocab. When looking up a " "string, the lexeme returned had an orth ID that did not match " "the query string. This means that the cached lexeme structs are " "mismatched to the string encoding table. The mismatched:\n" "Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}") E065 = ("Only one of the vector table's width and shape can be specified. " "Got width {width} and shape {shape}.") E066 = ("Error creating model helper for extracting columns. Can only " "extract columns by positive integer. Got: {value}.") E067 = ("Invalid BILUO tag sequence: Got a tag starting with 'I' (inside " "an entity) without a preceding 'B' (beginning of an entity). " "Tag sequence:\n{tags}") E068 = ("Invalid BILUO tag: '{tag}'.") E069 = ("Invalid gold-standard parse tree. Found cycle between word " "IDs: {cycle}") E070 = ("Invalid gold-standard data. Number of documents ({n_docs}) " "does not align with number of annotations ({n_annots}).") E071 = ("Error creating lexeme: specified orth ID ({orth}) does not " "match the one in the vocab ({vocab_orth}).") E072 = ("Error serializing lexeme: expected data length {length}, " "got {bad_length}.") E073 = ("Cannot assign vector of length {new_length}. Existing vectors " "are of length {length}. You can use `vocab.reset_vectors` to " "clear the existing vectors and resize the table.") E074 = ("Error interpreting compiled match pattern: patterns are expected " "to end with the attribute {attr}. Got: {bad_attr}.") E075 = ("Error accepting match: length ({length}) > maximum length " "({max_len}).") E076 = ("Error setting tensor on Doc: tensor has {rows} rows, while Doc " "has {words} words.") E077 = ("Error computing {value}: number of Docs ({n_docs}) does not " "equal number of GoldParse objects ({n_golds}) in batch.") E078 = ("Error computing score: number of words in Doc ({words_doc}) does " "not equal number of words in GoldParse ({words_gold}).") E079 = ("Error computing states in beam: number of predicted beams " "({pbeams}) does not equal number of gold beams ({gbeams}).") E080 = ("Duplicate state found in beam: {key}.") E081 = ("Error getting gradient in beam: number of histories ({n_hist}) " "does not equal number of losses ({losses}).") E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), " "projective heads ({n_proj_heads}) and labels ({n_labels}) do not " "match.") E083 = ("Error setting extension: only one of `default`, `method`, or " "`getter` (plus optional `setter`) is allowed. Got: {nr_defined}") E084 = ("Error assigning label ID {label} to span: not in StringStore.") E085 = ("Can't create lexeme for string '{string}'.") E086 = ("Error deserializing lexeme '{string}': orth ID {orth_id} does " "not match hash {hash_id} in StringStore.") E087 = ("Unknown displaCy style: {style}.") E088 = ("Text of length {length} exceeds maximum of {max_length}. The " "v2.x parser and NER models require roughly 1GB of temporary " "memory per 100,000 characters in the input. This means long " "texts may cause memory allocation errors. If you're not using " "the parser or NER, it's probably safe to increase the " "`nlp.max_length` limit. The limit is in number of characters, so " "you can check whether your inputs are too long by checking " "`len(text)`.") E089 = ("Extensions can't have a setter argument without a getter " "argument. Check the keyword arguments on `set_extension`.") E090 = ("Extension '{name}' already exists on {obj}. To overwrite the " "existing extension, set `force=True` on `{obj}.set_extension`.") E091 = ("Invalid extension attribute {name}: expected callable or None, " "but got: {value}") E092 = ("Could not find or assign name for word vectors. Ususally, the " "name is read from the model's meta.json in vector.name. " "Alternatively, it is built from the 'lang' and 'name' keys in " "the meta.json. Vector names are required to avoid issue #1660.") E093 = ("token.ent_iob values make invalid sequence: I without B\n{seq}") E094 = ("Error reading line {line_num} in vectors file {loc}.") E095 = ("Can't write to frozen dictionary. This is likely an internal " "error. Are you writing to a default function argument?") E096 = ("Invalid object passed to displaCy: Can only visualize Doc or " "Span objects, or dicts if set to manual=True.") E097 = ("Can't merge non-disjoint spans. '{token}' is already part of tokens to merge") E098 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A token" " can only be part of one entity, so make sure the entities you're " "setting don't overlap.") E099 = ("The newly split token can only have one root (head = 0).") E100 = ("The newly split token needs to have a root (head = 0)") E101 = ("All subtokens must have associated heads") @add_codes class TempErrors(object): T001 = ("Max length currently 10 for phrase matching") T002 = ("Pattern length ({doc_len}) >= phrase_matcher.max_length " "({max_len}). Length can be set on initialization, up to 10.") T003 = ("Resizing pre-trained Tagger models is not currently supported.") T004 = ("Currently parser depth is hard-coded to 1. Received: {value}.") T005 = ("Currently history size is hard-coded to 0. Received: {value}.") T006 = ("Currently history width is hard-coded to 0. 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 " "pre-trained vectors, and the class has been passed the old name " "(pretrained_dims) but not the new name (pretrained_vectors).") 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. """ w_id = message.split('[', 1)[1].split(']', 1)[0] # get ID from string if warn_type in SPACY_WARNING_TYPES and w_id not in SPACY_WARNING_IGNORE: 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])