mirror of
https://github.com/explosion/spaCy.git
synced 2025-01-11 17:56:30 +03:00
Feature/example only (#5707)
* remove _convert_examples * fix test_gold, raise TypeError if tuples are used instead of Example's * throwing proper errors when the wrong type of objects are passed * fix deprectated format in tests * fix deprectated format in parser tests * fix tests for NEL, morph, senter, tagger, textcat * update regression tests with new Example format * use make_doc * more fixes to nlp.update calls * few more small fixes for rehearse and evaluate * only import ml_datasets if really necessary
This commit is contained in:
Sofie Van Landeghem
GitHub
parent
63247cbe87
commit
fcbf899b08
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@ -33,7 +33,7 @@ def read_raw_data(nlp, jsonl_loc):
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for json_obj in srsly.read_jsonl(jsonl_loc):
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if json_obj["text"].strip():
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doc = nlp.make_doc(json_obj["text"])
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yield doc
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yield Example.from_dict(doc, {})
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def read_gold_data(nlp, gold_loc):
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@ -52,7 +52,7 @@ def main(model_name, unlabelled_loc):
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batch_size = 4
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nlp = spacy.load(model_name)
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nlp.get_pipe("ner").add_label(LABEL)
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raw_docs = list(read_raw_data(nlp, unlabelled_loc))
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raw_examples = list(read_raw_data(nlp, unlabelled_loc))
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optimizer = nlp.resume_training()
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# Avoid use of Adam when resuming training. I don't understand this well
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# yet, but I'm getting weird results from Adam. Try commenting out the
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@ -61,20 +61,24 @@ def main(model_name, unlabelled_loc):
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optimizer.learn_rate = 0.1
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optimizer.b1 = 0.0
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optimizer.b2 = 0.0
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sizes = compounding(1.0, 4.0, 1.001)
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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with nlp.select_pipes(enable="ner") and warnings.catch_warnings():
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# show warnings for misaligned entity spans once
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warnings.filterwarnings("once", category=UserWarning, module="spacy")
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(raw_docs)
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random.shuffle(train_examples)
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random.shuffle(raw_examples)
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losses = {}
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r_losses = {}
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# batch up the examples using spaCy's minibatch
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raw_batches = minibatch(raw_docs, size=4)
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for batch in minibatch(TRAIN_DATA, size=sizes):
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raw_batches = minibatch(raw_examples, size=4)
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for batch in minibatch(train_examples, size=sizes):
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nlp.update(batch, sgd=optimizer, drop=dropout, losses=losses)
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raw_batch = list(next(raw_batches))
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nlp.rehearse(raw_batch, sgd=optimizer, losses=r_losses)
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@ -20,6 +20,8 @@ from pathlib import Path
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from spacy.vocab import Vocab
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import spacy
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from spacy.kb import KnowledgeBase
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from spacy.gold import Example
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from spacy.pipeline import EntityRuler
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from spacy.util import minibatch, compounding
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@ -94,7 +96,7 @@ def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
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# Convert the texts to docs to make sure we have doc.ents set for the training examples.
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# Also ensure that the annotated examples correspond to known identifiers in the knowledge base.
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kb_ids = nlp.get_pipe("entity_linker").kb.get_entity_strings()
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TRAIN_DOCS = []
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train_examples = []
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for text, annotation in TRAIN_DATA:
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with nlp.select_pipes(disable="entity_linker"):
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doc = nlp(text)
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@ -109,17 +111,17 @@ def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
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"Removed", kb_id, "from training because it is not in the KB."
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)
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annotation_clean["links"][offset] = new_dict
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TRAIN_DOCS.append((doc, annotation_clean))
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train_examples .append(Example.from_dict(doc, annotation_clean))
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with nlp.select_pipes(enable="entity_linker"): # only train entity linker
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# reset and initialize the weights randomly
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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random.shuffle(TRAIN_DOCS)
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random.shuffle(train_examples)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DOCS, size=compounding(4.0, 32.0, 1.001))
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batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(
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batch,
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@ -23,6 +23,7 @@ import plac
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import random
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from pathlib import Path
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import spacy
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from spacy.gold import Example
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from spacy.util import minibatch, compounding
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@ -120,17 +121,19 @@ def main(model=None, output_dir=None, n_iter=15):
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parser = nlp.create_pipe("parser")
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nlp.add_pipe(parser, first=True)
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for dep in annotations.get("deps", []):
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parser.add_label(dep)
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with nlp.select_pipes(enable="parser"): # only train parser
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(train_examples)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(batch, sgd=optimizer, losses=losses)
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print("Losses", losses)
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@ -14,6 +14,7 @@ import plac
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import random
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from pathlib import Path
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import spacy
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from spacy.gold import Example
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from spacy.util import minibatch, compounding
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from spacy.morphology import Morphology
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@ -84,8 +85,10 @@ def main(lang="en", output_dir=None, n_iter=25):
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morphologizer = nlp.create_pipe("morphologizer")
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nlp.add_pipe(morphologizer)
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# add labels
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for _, annotations in TRAIN_DATA:
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# add labels and create the Example instances
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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morph_labels = annotations.get("morphs")
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pos_labels = annotations.get("pos", [""] * len(annotations.get("morphs")))
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assert len(morph_labels) == len(pos_labels)
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@ -98,10 +101,10 @@ def main(lang="en", output_dir=None, n_iter=25):
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optimizer = nlp.begin_training()
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for i in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(train_examples)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(batch, sgd=optimizer, losses=losses)
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print("Losses", losses)
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@ -17,6 +17,7 @@ import random
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import warnings
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from pathlib import Path
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import spacy
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from spacy.gold import Example
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from spacy.util import minibatch, compounding
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@ -50,8 +51,10 @@ def main(model=None, output_dir=None, n_iter=100):
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else:
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ner = nlp.get_pipe("simple_ner")
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# add labels
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for _, annotations in TRAIN_DATA:
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# add labels and create Example objects
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for ent in annotations.get("entities"):
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print("Add label", ent[2])
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ner.add_label(ent[2])
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@ -68,10 +71,10 @@ def main(model=None, output_dir=None, n_iter=100):
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"Transitions", list(enumerate(nlp.get_pipe("simple_ner").get_tag_names()))
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)
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(train_examples)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(
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batch,
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@ -80,6 +80,10 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
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print("Created blank 'en' model")
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# Add entity recognizer to model if it's not in the pipeline
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# nlp.create_pipe works for built-ins that are registered with spaCy
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train_examples = []
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for text, annotation in TRAIN_DATA:
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train_examples.append(TRAIN_DATA.from_dict(nlp(text), annotation))
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if "ner" not in nlp.pipe_names:
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ner = nlp.create_pipe("ner")
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nlp.add_pipe(ner)
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@ -102,8 +106,8 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
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sizes = compounding(1.0, 4.0, 1.001)
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# batch up the examples using spaCy's minibatch
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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batches = minibatch(TRAIN_DATA, size=sizes)
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random.shuffle(train_examples)
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batches = minibatch(train_examples, size=sizes)
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losses = {}
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for batch in batches:
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nlp.update(batch, sgd=optimizer, drop=0.35, losses=losses)
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@ -14,6 +14,7 @@ import plac
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import random
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from pathlib import Path
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import spacy
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from spacy.gold import Example
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from spacy.util import minibatch, compounding
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@ -59,18 +60,20 @@ def main(model=None, output_dir=None, n_iter=15):
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else:
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parser = nlp.get_pipe("parser")
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# add labels to the parser
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for _, annotations in TRAIN_DATA:
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# add labels to the parser and create the Example objects
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for dep in annotations.get("deps", []):
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parser.add_label(dep)
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with nlp.select_pipes(enable="parser"): # only train parser
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(train_examples)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(batch, sgd=optimizer, losses=losses)
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print("Losses", losses)
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@ -17,6 +17,7 @@ import plac
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import random
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from pathlib import Path
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import spacy
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from spacy.gold import Example
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from spacy.util import minibatch, compounding
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@ -58,12 +59,16 @@ def main(lang="en", output_dir=None, n_iter=25):
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tagger.add_label(tag, values)
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nlp.add_pipe(tagger)
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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optimizer = nlp.begin_training()
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for i in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(train_examples)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(batch, sgd=optimizer, losses=losses)
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print("Losses", losses)
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@ -31,17 +31,20 @@ def profile_cli(
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def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) -> None:
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try:
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import ml_datasets
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except ImportError:
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msg.fail(
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"This command requires the ml_datasets library to be installed:"
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"pip install ml_datasets",
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exits=1,
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)
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if inputs is not None:
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inputs = _read_inputs(inputs, msg)
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if inputs is None:
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try:
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import ml_datasets
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except ImportError:
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msg.fail(
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"This command, when run without an input file, "
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"requires the ml_datasets library to be installed: "
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"pip install ml_datasets",
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exits=1,
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)
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n_inputs = 25000
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with msg.loading("Loading IMDB dataset via Thinc..."):
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imdb_train, _ = ml_datasets.imdb()
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@ -12,7 +12,7 @@ from thinc.api import Model, use_pytorch_for_gpu_memory
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import random
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from ._app import app, Arg, Opt
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from ..gold import Corpus
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from ..gold import Corpus, Example
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from ..lookups import Lookups
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from .. import util
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from ..errors import Errors
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@ -423,9 +423,8 @@ def train_while_improving(
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if raw_text:
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random.shuffle(raw_text)
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raw_batches = util.minibatch(
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(nlp.make_doc(rt["text"]) for rt in raw_text), size=8
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)
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raw_examples = [Example.from_dict(nlp.make_doc(rt["text"]), {}) for rt in raw_text]
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raw_batches = util.minibatch(raw_examples, size=8)
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for step, (epoch, batch) in enumerate(train_data):
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dropout = next(dropouts)
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@ -547,13 +547,13 @@ class Errors(object):
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E972 = ("Example.__init__ got None for '{arg}'. Requires Doc.")
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E973 = ("Unexpected type for NER data")
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E974 = ("Unknown {obj} attribute: {key}")
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E975 = ("The method Example.from_dict expects a Doc as first argument, "
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E975 = ("The method 'Example.from_dict' expects a Doc as first argument, "
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"but got {type}")
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E976 = ("The method Example.from_dict expects a dict as second argument, "
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E976 = ("The method 'Example.from_dict' expects a dict as second argument, "
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"but received None.")
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E977 = ("Can not compare a MorphAnalysis with a string object. "
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"This is likely a bug in spaCy, so feel free to open an issue.")
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E978 = ("The {method} method of component {name} takes a list of Example objects, "
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E978 = ("The '{method}' method of {name} takes a list of Example objects, "
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"but found {types} instead.")
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E979 = ("Cannot convert {type} to an Example object.")
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E980 = ("Each link annotation should refer to a dictionary with at most one "
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@ -2,6 +2,7 @@ import random
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import itertools
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import weakref
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import functools
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from collections import Iterable
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from contextlib import contextmanager
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from copy import copy, deepcopy
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from pathlib import Path
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@ -529,22 +530,6 @@ class Language(object):
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def make_doc(self, text):
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return self.tokenizer(text)
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def _convert_examples(self, examples):
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converted_examples = []
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if isinstance(examples, tuple):
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examples = [examples]
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for eg in examples:
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if isinstance(eg, Example):
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converted_examples.append(eg.copy())
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elif isinstance(eg, tuple):
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doc, annot = eg
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if isinstance(doc, str):
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doc = self.make_doc(doc)
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converted_examples.append(Example.from_dict(doc, annot))
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else:
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raise ValueError(Errors.E979.format(type=type(eg)))
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return converted_examples
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def update(
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self,
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examples,
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@ -557,7 +542,7 @@ class Language(object):
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):
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"""Update the models in the pipeline.
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examples (iterable): A batch of `Example` or `Doc` objects.
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examples (iterable): A batch of `Example` objects.
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dummy: Should not be set - serves to catch backwards-incompatible scripts.
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drop (float): The dropout rate.
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sgd (callable): An optimizer.
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@ -569,10 +554,13 @@ class Language(object):
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"""
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if dummy is not None:
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raise ValueError(Errors.E989)
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if len(examples) == 0:
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return
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examples = self._convert_examples(examples)
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if not isinstance(examples, Iterable):
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raise TypeError(Errors.E978.format(name="language", method="update", types=type(examples)))
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wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)])
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if wrong_types:
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raise TypeError(Errors.E978.format(name="language", method="update", types=wrong_types))
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if sgd is None:
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if self._optimizer is None:
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@ -605,22 +593,26 @@ class Language(object):
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initial ones. This is useful for keeping a pretrained model on-track,
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even if you're updating it with a smaller set of examples.
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examples (iterable): A batch of `Doc` objects.
|
||||
examples (iterable): A batch of `Example` objects.
|
||||
drop (float): The dropout rate.
|
||||
sgd (callable): An optimizer.
|
||||
RETURNS (dict): Results from the update.
|
||||
|
||||
EXAMPLE:
|
||||
>>> raw_text_batches = minibatch(raw_texts)
|
||||
>>> for labelled_batch in minibatch(zip(train_docs, train_golds)):
|
||||
>>> for labelled_batch in minibatch(examples):
|
||||
>>> nlp.update(labelled_batch)
|
||||
>>> raw_batch = [nlp.make_doc(text) for text in next(raw_text_batches)]
|
||||
>>> raw_batch = [Example.from_dict(nlp.make_doc(text), {}) for text in next(raw_text_batches)]
|
||||
>>> nlp.rehearse(raw_batch)
|
||||
"""
|
||||
# TODO: document
|
||||
if len(examples) == 0:
|
||||
return
|
||||
examples = self._convert_examples(examples)
|
||||
if not isinstance(examples, Iterable):
|
||||
raise TypeError(Errors.E978.format(name="language", method="rehearse", types=type(examples)))
|
||||
wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)])
|
||||
if wrong_types:
|
||||
raise TypeError(Errors.E978.format(name="language", method="rehearse", types=wrong_types))
|
||||
if sgd is None:
|
||||
if self._optimizer is None:
|
||||
self._optimizer = create_default_optimizer()
|
||||
|
|
@ -696,7 +688,7 @@ class Language(object):
|
|||
component that has a .rehearse() method. Rehearsal is used to prevent
|
||||
models from "forgetting" their initialised "knowledge". To perform
|
||||
rehearsal, collect samples of text you want the models to retain performance
|
||||
on, and call nlp.rehearse() with a batch of Doc objects.
|
||||
on, and call nlp.rehearse() with a batch of Example objects.
|
||||
"""
|
||||
if cfg.get("device", -1) >= 0:
|
||||
util.use_gpu(cfg["device"])
|
||||
|
|
@ -728,7 +720,11 @@ class Language(object):
|
|||
|
||||
DOCS: https://spacy.io/api/language#evaluate
|
||||
"""
|
||||
examples = self._convert_examples(examples)
|
||||
if not isinstance(examples, Iterable):
|
||||
raise TypeError(Errors.E978.format(name="language", method="evaluate", types=type(examples)))
|
||||
wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)])
|
||||
if wrong_types:
|
||||
raise TypeError(Errors.E978.format(name="language", method="evaluate", types=wrong_types))
|
||||
if scorer is None:
|
||||
scorer = Scorer(pipeline=self.pipeline)
|
||||
if component_cfg is None:
|
||||
|
|
|
|||
|
|
@ -295,7 +295,7 @@ class Tagger(Pipe):
|
|||
return
|
||||
except AttributeError:
|
||||
types = set([type(eg) for eg in examples])
|
||||
raise ValueError(Errors.E978.format(name="Tagger", method="update", types=types))
|
||||
raise TypeError(Errors.E978.format(name="Tagger", method="update", types=types))
|
||||
set_dropout_rate(self.model, drop)
|
||||
tag_scores, bp_tag_scores = self.model.begin_update(
|
||||
[eg.predicted for eg in examples])
|
||||
|
|
@ -321,7 +321,7 @@ class Tagger(Pipe):
|
|||
docs = [eg.predicted for eg in examples]
|
||||
except AttributeError:
|
||||
types = set([type(eg) for eg in examples])
|
||||
raise ValueError(Errors.E978.format(name="Tagger", method="rehearse", types=types))
|
||||
raise TypeError(Errors.E978.format(name="Tagger", method="rehearse", types=types))
|
||||
if self._rehearsal_model is None:
|
||||
return
|
||||
if not any(len(doc) for doc in docs):
|
||||
|
|
@ -358,7 +358,7 @@ class Tagger(Pipe):
|
|||
try:
|
||||
y = example.y
|
||||
except AttributeError:
|
||||
raise ValueError(Errors.E978.format(name="Tagger", method="begin_training", types=type(example)))
|
||||
raise TypeError(Errors.E978.format(name="Tagger", method="begin_training", types=type(example)))
|
||||
for token in y:
|
||||
tag = token.tag_
|
||||
if tag in orig_tag_map:
|
||||
|
|
@ -790,7 +790,7 @@ class ClozeMultitask(Pipe):
|
|||
predictions, bp_predictions = self.model.begin_update([eg.predicted for eg in examples])
|
||||
except AttributeError:
|
||||
types = set([type(eg) for eg in examples])
|
||||
raise ValueError(Errors.E978.format(name="ClozeMultitask", method="rehearse", types=types))
|
||||
raise TypeError(Errors.E978.format(name="ClozeMultitask", method="rehearse", types=types))
|
||||
loss, d_predictions = self.get_loss(examples, self.vocab.vectors.data, predictions)
|
||||
bp_predictions(d_predictions)
|
||||
if sgd is not None:
|
||||
|
|
@ -856,7 +856,7 @@ class TextCategorizer(Pipe):
|
|||
return
|
||||
except AttributeError:
|
||||
types = set([type(eg) for eg in examples])
|
||||
raise ValueError(Errors.E978.format(name="TextCategorizer", method="update", types=types))
|
||||
raise TypeError(Errors.E978.format(name="TextCategorizer", method="update", types=types))
|
||||
set_dropout_rate(self.model, drop)
|
||||
scores, bp_scores = self.model.begin_update(
|
||||
[eg.predicted for eg in examples]
|
||||
|
|
@ -879,7 +879,7 @@ class TextCategorizer(Pipe):
|
|||
docs = [eg.predicted for eg in examples]
|
||||
except AttributeError:
|
||||
types = set([type(eg) for eg in examples])
|
||||
raise ValueError(Errors.E978.format(name="TextCategorizer", method="rehearse", types=types))
|
||||
raise TypeError(Errors.E978.format(name="TextCategorizer", method="rehearse", types=types))
|
||||
if not any(len(doc) for doc in docs):
|
||||
# Handle cases where there are no tokens in any docs.
|
||||
return
|
||||
|
|
@ -940,7 +940,7 @@ class TextCategorizer(Pipe):
|
|||
try:
|
||||
y = example.y
|
||||
except AttributeError:
|
||||
raise ValueError(Errors.E978.format(name="TextCategorizer", method="update", types=type(example)))
|
||||
raise TypeError(Errors.E978.format(name="TextCategorizer", method="update", types=type(example)))
|
||||
for cat in y.cats:
|
||||
self.add_label(cat)
|
||||
self.require_labels()
|
||||
|
|
@ -1105,7 +1105,7 @@ class EntityLinker(Pipe):
|
|||
docs = [eg.predicted for eg in examples]
|
||||
except AttributeError:
|
||||
types = set([type(eg) for eg in examples])
|
||||
raise ValueError(Errors.E978.format(name="EntityLinker", method="update", types=types))
|
||||
raise TypeError(Errors.E978.format(name="EntityLinker", method="update", types=types))
|
||||
if set_annotations:
|
||||
# This seems simpler than other ways to get that exact output -- but
|
||||
# it does run the model twice :(
|
||||
|
|
|
|||
|
|
@ -209,6 +209,10 @@ def test_train_empty():
|
|||
]
|
||||
|
||||
nlp = English()
|
||||
train_examples = []
|
||||
for t in train_data:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
||||
|
||||
ner = nlp.create_pipe("ner")
|
||||
ner.add_label("PERSON")
|
||||
nlp.add_pipe(ner, last=True)
|
||||
|
|
@ -216,10 +220,9 @@ def test_train_empty():
|
|||
nlp.begin_training()
|
||||
for itn in range(2):
|
||||
losses = {}
|
||||
batches = util.minibatch(train_data)
|
||||
batches = util.minibatch(train_examples)
|
||||
for batch in batches:
|
||||
texts, annotations = zip(*batch)
|
||||
nlp.update(train_data, losses=losses)
|
||||
nlp.update(batch, losses=losses)
|
||||
|
||||
|
||||
def test_overwrite_token():
|
||||
|
|
@ -328,7 +331,9 @@ def test_overfitting_IO():
|
|||
# Simple test to try and quickly overfit the NER component - ensuring the ML models work correctly
|
||||
nlp = English()
|
||||
ner = nlp.create_pipe("ner")
|
||||
for _, annotations in TRAIN_DATA:
|
||||
train_examples = []
|
||||
for text, annotations in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
||||
for ent in annotations.get("entities"):
|
||||
ner.add_label(ent[2])
|
||||
nlp.add_pipe(ner)
|
||||
|
|
@ -336,7 +341,7 @@ def test_overfitting_IO():
|
|||
|
||||
for i in range(50):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
assert losses["ner"] < 0.00001
|
||||
|
||||
# test the trained model
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import pytest
|
|||
from spacy.lang.en import English
|
||||
from ..util import get_doc, apply_transition_sequence, make_tempdir
|
||||
from ... import util
|
||||
from ...gold import Example
|
||||
|
||||
TRAIN_DATA = [
|
||||
(
|
||||
|
|
@ -189,7 +190,9 @@ def test_overfitting_IO():
|
|||
# Simple test to try and quickly overfit the dependency parser - ensuring the ML models work correctly
|
||||
nlp = English()
|
||||
parser = nlp.create_pipe("parser")
|
||||
for _, annotations in TRAIN_DATA:
|
||||
train_examples = []
|
||||
for text, annotations in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
||||
for dep in annotations.get("deps", []):
|
||||
parser.add_label(dep)
|
||||
nlp.add_pipe(parser)
|
||||
|
|
@ -197,7 +200,7 @@ def test_overfitting_IO():
|
|||
|
||||
for i in range(50):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
assert losses["parser"] < 0.00001
|
||||
|
||||
# test the trained model
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import pytest
|
|||
from spacy.kb import KnowledgeBase
|
||||
|
||||
from spacy import util
|
||||
from spacy.gold import Example
|
||||
from spacy.lang.en import English
|
||||
from spacy.pipeline import EntityRuler
|
||||
from spacy.tests.util import make_tempdir
|
||||
|
|
@ -283,11 +284,10 @@ def test_overfitting_IO():
|
|||
nlp.add_pipe(ruler)
|
||||
|
||||
# Convert the texts to docs to make sure we have doc.ents set for the training examples
|
||||
TRAIN_DOCS = []
|
||||
train_examples = []
|
||||
for text, annotation in TRAIN_DATA:
|
||||
doc = nlp(text)
|
||||
annotation_clean = annotation
|
||||
TRAIN_DOCS.append((doc, annotation_clean))
|
||||
train_examples.append(Example.from_dict(doc, annotation))
|
||||
|
||||
# create artificial KB - assign same prior weight to the two russ cochran's
|
||||
# Q2146908 (Russ Cochran): American golfer
|
||||
|
|
@ -309,7 +309,7 @@ def test_overfitting_IO():
|
|||
optimizer = nlp.begin_training()
|
||||
for i in range(50):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DOCS, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
assert losses["entity_linker"] < 0.001
|
||||
|
||||
# test the trained model
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import pytest
|
||||
|
||||
from spacy import util
|
||||
from spacy.gold import Example
|
||||
from spacy.lang.en import English
|
||||
from spacy.language import Language
|
||||
from spacy.tests.util import make_tempdir
|
||||
|
|
@ -33,7 +34,9 @@ def test_overfitting_IO():
|
|||
# Simple test to try and quickly overfit the morphologizer - ensuring the ML models work correctly
|
||||
nlp = English()
|
||||
morphologizer = nlp.create_pipe("morphologizer")
|
||||
train_examples = []
|
||||
for inst in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(inst[0]), inst[1]))
|
||||
for morph, pos in zip(inst[1]["morphs"], inst[1]["pos"]):
|
||||
morphologizer.add_label(morph + "|POS=" + pos)
|
||||
nlp.add_pipe(morphologizer)
|
||||
|
|
@ -41,7 +44,7 @@ def test_overfitting_IO():
|
|||
|
||||
for i in range(50):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
assert losses["morphologizer"] < 0.00001
|
||||
|
||||
# test the trained model
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import pytest
|
||||
|
||||
from spacy import util
|
||||
from spacy.gold import Example
|
||||
from spacy.lang.en import English
|
||||
from spacy.language import Language
|
||||
from spacy.tests.util import make_tempdir
|
||||
|
|
@ -34,12 +35,15 @@ def test_overfitting_IO():
|
|||
# Simple test to try and quickly overfit the senter - ensuring the ML models work correctly
|
||||
nlp = English()
|
||||
senter = nlp.create_pipe("senter")
|
||||
train_examples = []
|
||||
for t in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
||||
nlp.add_pipe(senter)
|
||||
optimizer = nlp.begin_training()
|
||||
|
||||
for i in range(200):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
assert losses["senter"] < 0.001
|
||||
|
||||
# test the trained model
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import pytest
|
||||
|
||||
from spacy import util
|
||||
from spacy.gold import Example
|
||||
from spacy.lang.en import English
|
||||
from spacy.language import Language
|
||||
from spacy.tests.util import make_tempdir
|
||||
|
|
@ -28,12 +29,15 @@ def test_overfitting_IO():
|
|||
tagger = nlp.create_pipe("tagger")
|
||||
for tag, values in TAG_MAP.items():
|
||||
tagger.add_label(tag, values)
|
||||
train_examples = []
|
||||
for t in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
||||
nlp.add_pipe(tagger)
|
||||
optimizer = nlp.begin_training()
|
||||
|
||||
for i in range(50):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
assert losses["tagger"] < 0.00001
|
||||
|
||||
# test the trained model
|
||||
|
|
|
|||
|
|
@ -85,7 +85,9 @@ def test_overfitting_IO():
|
|||
fix_random_seed(0)
|
||||
nlp = English()
|
||||
textcat = nlp.create_pipe("textcat")
|
||||
for _, annotations in TRAIN_DATA:
|
||||
train_examples = []
|
||||
for text, annotations in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
||||
for label, value in annotations.get("cats").items():
|
||||
textcat.add_label(label)
|
||||
nlp.add_pipe(textcat)
|
||||
|
|
@ -93,7 +95,7 @@ def test_overfitting_IO():
|
|||
|
||||
for i in range(50):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
assert losses["textcat"] < 0.01
|
||||
|
||||
# test the trained model
|
||||
|
|
@ -134,11 +136,13 @@ def test_textcat_configs(textcat_config):
|
|||
pipe_config = {"model": textcat_config}
|
||||
nlp = English()
|
||||
textcat = nlp.create_pipe("textcat", pipe_config)
|
||||
for _, annotations in TRAIN_DATA:
|
||||
train_examples = []
|
||||
for text, annotations in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
||||
for label, value in annotations.get("cats").items():
|
||||
textcat.add_label(label)
|
||||
nlp.add_pipe(textcat)
|
||||
optimizer = nlp.begin_training()
|
||||
for i in range(5):
|
||||
losses = {}
|
||||
nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
|
||||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
import pytest
|
||||
from spacy import displacy
|
||||
from spacy.gold import Example
|
||||
from spacy.lang.en import English
|
||||
from spacy.lang.ja import Japanese
|
||||
from spacy.lang.xx import MultiLanguage
|
||||
|
|
@ -141,10 +142,10 @@ def test_issue2800():
|
|||
"""Test issue that arises when too many labels are added to NER model.
|
||||
Used to cause segfault.
|
||||
"""
|
||||
train_data = []
|
||||
train_data.extend([("One sentence", {"entities": []})])
|
||||
entity_types = [str(i) for i in range(1000)]
|
||||
nlp = English()
|
||||
train_data = []
|
||||
train_data.extend([Example.from_dict(nlp.make_doc("One sentence"), {"entities": []})])
|
||||
entity_types = [str(i) for i in range(1000)]
|
||||
ner = nlp.create_pipe("ner")
|
||||
nlp.add_pipe(ner)
|
||||
for entity_type in list(entity_types):
|
||||
|
|
@ -153,8 +154,8 @@ def test_issue2800():
|
|||
for i in range(20):
|
||||
losses = {}
|
||||
random.shuffle(train_data)
|
||||
for statement, entities in train_data:
|
||||
nlp.update((statement, entities), sgd=optimizer, losses=losses, drop=0.5)
|
||||
for example in train_data:
|
||||
nlp.update([example], sgd=optimizer, losses=losses, drop=0.5)
|
||||
|
||||
|
||||
def test_issue2822(it_tokenizer):
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import spacy
|
||||
from spacy.gold import Example
|
||||
from spacy.util import minibatch, compounding
|
||||
|
||||
|
||||
|
|
@ -12,15 +13,15 @@ def test_issue3611():
|
|||
]
|
||||
y_train = ["offensive", "offensive", "inoffensive"]
|
||||
|
||||
# preparing the data
|
||||
pos_cats = list()
|
||||
for train_instance in y_train:
|
||||
pos_cats.append({label: label == train_instance for label in unique_classes})
|
||||
train_data = list(zip(x_train, [{"cats": cats} for cats in pos_cats]))
|
||||
|
||||
# set up the spacy model with a text categorizer component
|
||||
nlp = spacy.blank("en")
|
||||
|
||||
# preparing the data
|
||||
train_data = []
|
||||
for text, train_instance in zip(x_train, y_train):
|
||||
cat_dict = {label: label == train_instance for label in unique_classes}
|
||||
train_data.append(Example.from_dict(nlp.make_doc(text), {"cats": cat_dict}))
|
||||
|
||||
# add a text categorizer component
|
||||
textcat = nlp.create_pipe(
|
||||
"textcat",
|
||||
config={"exclusive_classes": True, "architecture": "bow", "ngram_size": 2},
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import spacy
|
||||
from spacy.gold import Example
|
||||
from spacy.util import minibatch, compounding
|
||||
|
||||
|
||||
|
|
@ -12,15 +13,15 @@ def test_issue4030():
|
|||
]
|
||||
y_train = ["offensive", "offensive", "inoffensive"]
|
||||
|
||||
# preparing the data
|
||||
pos_cats = list()
|
||||
for train_instance in y_train:
|
||||
pos_cats.append({label: label == train_instance for label in unique_classes})
|
||||
train_data = list(zip(x_train, [{"cats": cats} for cats in pos_cats]))
|
||||
|
||||
# set up the spacy model with a text categorizer component
|
||||
nlp = spacy.blank("en")
|
||||
|
||||
# preparing the data
|
||||
train_data = []
|
||||
for text, train_instance in zip(x_train, y_train):
|
||||
cat_dict = {label: label == train_instance for label in unique_classes}
|
||||
train_data.append(Example.from_dict(nlp.make_doc(text), {"cats": cat_dict}))
|
||||
|
||||
# add a text categorizer component
|
||||
textcat = nlp.create_pipe(
|
||||
"textcat",
|
||||
config={"exclusive_classes": True, "architecture": "bow", "ngram_size": 2},
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
from spacy.gold import Example
|
||||
from spacy.lang.en import English
|
||||
from spacy.util import minibatch, compounding
|
||||
import pytest
|
||||
|
|
@ -7,9 +8,10 @@ import pytest
|
|||
def test_issue4348():
|
||||
"""Test that training the tagger with empty data, doesn't throw errors"""
|
||||
|
||||
TRAIN_DATA = [("", {"tags": []}), ("", {"tags": []})]
|
||||
|
||||
nlp = English()
|
||||
example = Example.from_dict(nlp.make_doc(""), {"tags": []})
|
||||
TRAIN_DATA = [example, example]
|
||||
|
||||
tagger = nlp.create_pipe("tagger")
|
||||
nlp.add_pipe(tagger)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,8 @@
|
|||
from spacy.gold import Example
|
||||
from spacy.language import Language
|
||||
|
||||
|
||||
def test_issue4924():
|
||||
nlp = Language()
|
||||
docs_golds = [("", {})]
|
||||
nlp.evaluate(docs_golds)
|
||||
example = Example.from_dict(nlp.make_doc(""), {})
|
||||
nlp.evaluate([example])
|
||||
|
|
|
|||
|
|
@ -589,7 +589,7 @@ def test_tuple_format_implicit():
|
|||
("Google rebrands its business apps", {"entities": [(0, 6, "ORG")]}),
|
||||
]
|
||||
|
||||
_train(train_data)
|
||||
_train_tuples(train_data)
|
||||
|
||||
|
||||
def test_tuple_format_implicit_invalid():
|
||||
|
|
@ -605,20 +605,24 @@ def test_tuple_format_implicit_invalid():
|
|||
]
|
||||
|
||||
with pytest.raises(KeyError):
|
||||
_train(train_data)
|
||||
_train_tuples(train_data)
|
||||
|
||||
|
||||
def _train(train_data):
|
||||
def _train_tuples(train_data):
|
||||
nlp = English()
|
||||
ner = nlp.create_pipe("ner")
|
||||
ner.add_label("ORG")
|
||||
ner.add_label("LOC")
|
||||
nlp.add_pipe(ner)
|
||||
|
||||
train_examples = []
|
||||
for t in train_data:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
||||
|
||||
optimizer = nlp.begin_training()
|
||||
for i in range(5):
|
||||
losses = {}
|
||||
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
|
||||
batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
|
||||
for batch in batches:
|
||||
nlp.update(batch, sgd=optimizer, losses=losses)
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ from spacy.tokens import Doc, Span
|
|||
from spacy.vocab import Vocab
|
||||
|
||||
from .util import add_vecs_to_vocab, assert_docs_equal
|
||||
from ..gold import Example
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -23,26 +24,45 @@ def test_language_update(nlp):
|
|||
annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
|
||||
wrongkeyannots = {"LABEL": True}
|
||||
doc = Doc(nlp.vocab, words=text.split(" "))
|
||||
# Update with text and dict
|
||||
nlp.update((text, annots))
|
||||
example = Example.from_dict(doc, annots)
|
||||
nlp.update([example])
|
||||
|
||||
# Not allowed to call with just one Example
|
||||
with pytest.raises(TypeError):
|
||||
nlp.update(example)
|
||||
|
||||
# Update with text and dict: not supported anymore since v.3
|
||||
with pytest.raises(TypeError):
|
||||
nlp.update((text, annots))
|
||||
# Update with doc object and dict
|
||||
nlp.update((doc, annots))
|
||||
# Update badly
|
||||
with pytest.raises(TypeError):
|
||||
nlp.update((doc, annots))
|
||||
|
||||
# Create examples badly
|
||||
with pytest.raises(ValueError):
|
||||
nlp.update((doc, None))
|
||||
example = Example.from_dict(doc, None)
|
||||
with pytest.raises(KeyError):
|
||||
nlp.update((text, wrongkeyannots))
|
||||
example = Example.from_dict(doc, wrongkeyannots)
|
||||
|
||||
|
||||
def test_language_evaluate(nlp):
|
||||
text = "hello world"
|
||||
annots = {"doc_annotation": {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}}
|
||||
doc = Doc(nlp.vocab, words=text.split(" "))
|
||||
# Evaluate with text and dict
|
||||
nlp.evaluate([(text, annots)])
|
||||
example = Example.from_dict(doc, annots)
|
||||
nlp.evaluate([example])
|
||||
|
||||
# Not allowed to call with just one Example
|
||||
with pytest.raises(TypeError):
|
||||
nlp.evaluate(example)
|
||||
|
||||
# Evaluate with text and dict: not supported anymore since v.3
|
||||
with pytest.raises(TypeError):
|
||||
nlp.evaluate([(text, annots)])
|
||||
# Evaluate with doc object and dict
|
||||
nlp.evaluate([(doc, annots)])
|
||||
with pytest.raises(Exception):
|
||||
with pytest.raises(TypeError):
|
||||
nlp.evaluate([(doc, annots)])
|
||||
with pytest.raises(TypeError):
|
||||
nlp.evaluate([text, annots])
|
||||
|
||||
|
||||
|
|
@ -56,8 +76,9 @@ def test_evaluate_no_pipe(nlp):
|
|||
text = "hello world"
|
||||
annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
|
||||
nlp = Language(Vocab())
|
||||
doc = nlp(text)
|
||||
nlp.add_pipe(pipe)
|
||||
nlp.evaluate([(text, annots)])
|
||||
nlp.evaluate([Example.from_dict(doc, annots)])
|
||||
|
||||
|
||||
def vector_modification_pipe(doc):
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user