def print_summary(nlp, pretty=True, no_print=False):
"""Print a formatted summary for the current nlp object's pipeline. Shows
a table with the pipeline components and why they assign and require, as
well as any problems if available.
nlp (Language): The nlp object.
pretty (bool): Pretty-print the results (color etc).
no_print (bool): Don't print anything, just return the data.
RETURNS (dict): A dict with "overview" and "problems".
"""
msg = Printer(pretty=pretty, no_print=no_print)
overview = []
problems = {}
for i, (name, pipe) in enumerate(nlp.pipeline):
requires = getattr(pipe, "requires", [])
assigns = getattr(pipe, "assigns", [])
retok = getattr(pipe, "retokenizes", False)
overview.append((i, name, requires, assigns, retok))
problems[name] = analyze_pipes(nlp.pipeline, name, pipe, i, warn=False)
msg.divider("Pipeline Overview")
header = ("#", "Component", "Requires", "Assigns", "Retokenizes")
msg.table(overview, header=header, divider=True, multiline=True)
n_problems = sum(len(p) for p in problems.values())
if any(p for p in problems.values()):
msg.divider("Problems ({})".format(n_problems))
for name, problem in problems.items():
if problem:
problem = ", ".join(problem)
msg.warn("'{}' requirements not met: {}".format(name, problem))
else:
msg.good("No problems found.")
if no_print:
return {"overview": overview, "problems": problems}
def validate_config_for_pretrain(config: Config, msg: Printer) -> None:
if "tok2vec" not in config["nlp"]["pipeline"]:
msg.warn(
"No tok2vec component found in the pipeline. If your tok2vec "
"component has a different name, you may need to adjust the "
"tok2vec_model reference in the [pretraining] block. If you don't "
"have a tok2vec component, make sure to add it to your [components] "
"and the pipeline specified in the [nlp] block, so you can pretrain "
"weights for it.")
def fill_config(
output_file: Path,
base_path: Path,
*,
pretraining: bool = False,
diff: bool = False,
silent: bool = False,
) -> Tuple[Config, Config]:
is_stdout = str(output_file) == "-"
no_print = is_stdout or silent
msg = Printer(no_print=no_print)
with show_validation_error(hint_fill=False):
config = util.load_config(base_path)
nlp = util.load_model_from_config(config,
auto_fill=True,
validate=False)
# Load a second time with validation to be extra sure that the produced
# config result is a valid config
nlp = util.load_model_from_config(nlp.config)
filled = nlp.config
# If we have sourced components in the base config, those will have been
# replaced with their actual config after loading, so we have to re-add them
sourced = util.get_sourced_components(config)
filled["components"].update(sourced)
if pretraining:
validate_config_for_pretrain(filled, msg)
pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
filled = pretrain_config.merge(filled)
before = config.to_str()
after = filled.to_str()
if before == after:
msg.warn("Nothing to auto-fill: base config is already complete")
else:
msg.good("Auto-filled config with all values")
if diff and not no_print:
if before == after:
msg.warn("No diff to show: nothing was auto-filled")
else:
msg.divider("START CONFIG DIFF")
print("")
print(diff_strings(before, after))
msg.divider("END CONFIG DIFF")
print("")
save_config(filled, output_file, is_stdout=is_stdout, silent=silent)
return config, filled
def debug_diff(
config_path: Path,
compare_to: Optional[Path],
gpu: bool,
optimize: Optimizations,
pretraining: bool,
markdown: bool,
):
msg = Printer()
with show_validation_error(hint_fill=False):
user_config = load_config(config_path)
if compare_to:
other_config = load_config(compare_to)
else:
# Recreate a default config based from user's config
lang = user_config["nlp"]["lang"]
pipeline = list(user_config["nlp"]["pipeline"])
msg.info(f"Found user-defined language: '{lang}'")
msg.info(f"Found user-defined pipelines: {pipeline}")
other_config = init_config(
lang=lang,
pipeline=pipeline,
optimize=optimize.value,
gpu=gpu,
pretraining=pretraining,
silent=True,
)
user = user_config.to_str()
other = other_config.to_str()
if user == other:
msg.warn("No diff to show: configs are identical")
else:
diff_text = diff_strings(other, user, add_symbols=markdown)
if markdown:
md = MarkdownRenderer()
md.add(md.code_block(diff_text, "diff"))
print(md.text)
else:
print(diff_text)
def debug_data(
lang,
train_path,
dev_path,
base_model=None,
pipeline="tagger,parser,ner",
ignore_warnings=False,
ignore_validation=False,
verbose=False,
no_format=False,
):
msg = Printer(pretty=not no_format, ignore_warnings=ignore_warnings)
# Make sure all files and paths exists if they are needed
if not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
# Initialize the model and pipeline
pipeline = [p.strip() for p in pipeline.split(",")]
if base_model:
nlp = load_model(base_model)
else:
lang_cls = get_lang_class(lang)
nlp = lang_cls()
msg.divider("Data format validation")
# Validate data format using the JSON schema
# TODO: update once the new format is ready
# TODO: move validation to GoldCorpus in order to be able to load from dir
train_data_errors = [] # TODO: validate_json
dev_data_errors = [] # TODO: validate_json
if not train_data_errors:
msg.good("Training data JSON format is valid")
if not dev_data_errors:
msg.good("Development data JSON format is valid")
for error in train_data_errors:
msg.fail("Training data: {}".format(error))
for error in dev_data_errors:
msg.fail("Develoment data: {}".format(error))
if (train_data_errors or dev_data_errors) and not ignore_validation:
sys.exit(1)
# Create the gold corpus to be able to better analyze data
loading_train_error_message = ""
loading_dev_error_message = ""
with msg.loading("Loading corpus..."):
corpus = GoldCorpus(train_path, dev_path)
try:
train_docs = list(corpus.train_docs(nlp))
train_docs_unpreprocessed = list(
corpus.train_docs_without_preprocessing(nlp))
except ValueError as e:
loading_train_error_message = "Training data cannot be loaded: {}".format(
str(e))
try:
dev_docs = list(corpus.dev_docs(nlp))
except ValueError as e:
loading_dev_error_message = "Development data cannot be loaded: {}".format(
str(e))
if loading_train_error_message or loading_dev_error_message:
if loading_train_error_message:
msg.fail(loading_train_error_message)
if loading_dev_error_message:
msg.fail(loading_dev_error_message)
sys.exit(1)
msg.good("Corpus is loadable")
# Create all gold data here to avoid iterating over the train_docs constantly
gold_train_data = _compile_gold(train_docs, pipeline)
gold_train_unpreprocessed_data = _compile_gold(train_docs_unpreprocessed,
pipeline)
gold_dev_data = _compile_gold(dev_docs, pipeline)
train_texts = gold_train_data["texts"]
dev_texts = gold_dev_data["texts"]
msg.divider("Training stats")
msg.text("Training pipeline: {}".format(", ".join(pipeline)))
for pipe in [p for p in pipeline if p not in nlp.factories]:
msg.fail(
"Pipeline component '{}' not available in factories".format(pipe))
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
else:
msg.text("Starting with blank model '{}'".format(lang))
msg.text("{} training docs".format(len(train_docs)))
msg.text("{} evaluation docs".format(len(dev_docs)))
overlap = len(train_texts.intersection(dev_texts))
if overlap:
msg.warn(
"{} training examples also in evaluation data".format(overlap))
else:
msg.good("No overlap between training and evaluation data")
if not base_model and len(train_docs) < BLANK_MODEL_THRESHOLD:
text = "Low number of examples to train from a blank model ({})".format(
len(train_docs))
if len(train_docs) < BLANK_MODEL_MIN_THRESHOLD:
msg.fail(text)
else:
msg.warn(text)
msg.text(
"It's recommended to use at least {} examples (minimum {})".format(
BLANK_MODEL_THRESHOLD, BLANK_MODEL_MIN_THRESHOLD),
show=verbose,
)
msg.divider("Vocab & Vectors")
n_words = gold_train_data["n_words"]
msg.info("{} total {} in the data ({} unique)".format(
n_words, "word" if n_words == 1 else "words",
len(gold_train_data["words"])))
if gold_train_data["n_misaligned_words"] > 0:
msg.warn("{} misaligned tokens in the training data".format(
gold_train_data["n_misaligned_words"]))
if gold_dev_data["n_misaligned_words"] > 0:
msg.warn("{} misaligned tokens in the dev data".format(
gold_dev_data["n_misaligned_words"]))
most_common_words = gold_train_data["words"].most_common(10)
msg.text(
"10 most common words: {}".format(
_format_labels(most_common_words, counts=True)),
show=verbose,
)
if len(nlp.vocab.vectors):
msg.info("{} vectors ({} unique keys, {} dimensions)".format(
len(nlp.vocab.vectors),
nlp.vocab.vectors.n_keys,
nlp.vocab.vectors_length,
))
else:
msg.info("No word vectors present in the model")
if "ner" in pipeline:
# Get all unique NER labels present in the data
labels = set(label for label in gold_train_data["ner"]
if label not in ("O", "-"))
label_counts = gold_train_data["ner"]
model_labels = _get_labels_from_model(nlp, "ner")
new_labels = [l for l in labels if l not in model_labels]
existing_labels = [l for l in labels if l in model_labels]
has_low_data_warning = False
has_no_neg_warning = False
has_ws_ents_error = False
msg.divider("Named Entity Recognition")
msg.info("{} new {}, {} existing {}".format(
len(new_labels),
"label" if len(new_labels) == 1 else "labels",
len(existing_labels),
"label" if len(existing_labels) == 1 else "labels",
))
missing_values = label_counts["-"]
msg.text("{} missing {} (tokens with '-' label)".format(
missing_values, "value" if missing_values == 1 else "values"))
if new_labels:
labels_with_counts = [
(label, count) for label, count in label_counts.most_common()
if label != "-"
]
labels_with_counts = _format_labels(labels_with_counts,
counts=True)
msg.text("New: {}".format(labels_with_counts), show=verbose)
if existing_labels:
msg.text("Existing: {}".format(_format_labels(existing_labels)),
show=verbose)
if gold_train_data["ws_ents"]:
msg.fail("{} invalid whitespace entity spans".format(
gold_train_data["ws_ents"]))
has_ws_ents_error = True
for label in new_labels:
if label_counts[label] <= NEW_LABEL_THRESHOLD:
msg.warn(
"Low number of examples for new label '{}' ({})".format(
label, label_counts[label]))
has_low_data_warning = True
with msg.loading("Analyzing label distribution..."):
neg_docs = _get_examples_without_label(train_docs, label)
if neg_docs == 0:
msg.warn(
"No examples for texts WITHOUT new label '{}'".format(
label))
has_no_neg_warning = True
if not has_low_data_warning:
msg.good("Good amount of examples for all labels")
if not has_no_neg_warning:
msg.good("Examples without occurrences available for all labels")
if not has_ws_ents_error:
msg.good(
"No entities consisting of or starting/ending with whitespace")
if has_low_data_warning:
msg.text(
"To train a new entity type, your data should include at "
"least {} instances of the new label".format(
NEW_LABEL_THRESHOLD),
show=verbose,
)
if has_no_neg_warning:
msg.text(
"Training data should always include examples of entities "
"in context, as well as examples without a given entity "
"type.",
show=verbose,
)
if has_ws_ents_error:
msg.text(
"As of spaCy v2.1.0, entity spans consisting of or starting/ending "
"with whitespace characters are considered invalid.")
if "textcat" in pipeline:
msg.divider("Text Classification")
labels = [label for label in gold_train_data["textcat"]]
model_labels = _get_labels_from_model(nlp, "textcat")
new_labels = [l for l in labels if l not in model_labels]
existing_labels = [l for l in labels if l in model_labels]
msg.info("Text Classification: {} new label(s), {} existing label(s)".
format(len(new_labels), len(existing_labels)))
if new_labels:
labels_with_counts = _format_labels(
gold_train_data["textcat"].most_common(), counts=True)
msg.text("New: {}".format(labels_with_counts), show=verbose)
if existing_labels:
msg.text("Existing: {}".format(_format_labels(existing_labels)),
show=verbose)
if "tagger" in pipeline:
msg.divider("Part-of-speech Tagging")
labels = [label for label in gold_train_data["tags"]]
tag_map = nlp.Defaults.tag_map
msg.info("{} {} in data ({} {} in tag map)".format(
len(labels),
"label" if len(labels) == 1 else "labels",
len(tag_map),
"label" if len(tag_map) == 1 else "labels",
))
labels_with_counts = _format_labels(
gold_train_data["tags"].most_common(), counts=True)
msg.text(labels_with_counts, show=verbose)
non_tagmap = [l for l in labels if l not in tag_map]
if not non_tagmap:
msg.good("All labels present in tag map for language '{}'".format(
nlp.lang))
for label in non_tagmap:
msg.fail(
"Label '{}' not found in tag map for language '{}'".format(
label, nlp.lang))
if "parser" in pipeline:
msg.divider("Dependency Parsing")
# profile sentence length
msg.info("Found {} sentence{} with an average length of {:.1f} words.".
format(
gold_train_data["n_sents"],
"s" if len(train_docs) > 1 else "",
gold_train_data["n_words"] / gold_train_data["n_sents"]))
# profile labels
labels_train = [label for label in gold_train_data["deps"]]
labels_train_unpreprocessed = [
label for label in gold_train_unpreprocessed_data["deps"]
]
labels_dev = [label for label in gold_dev_data["deps"]]
if gold_train_unpreprocessed_data["n_nonproj"] > 0:
msg.info("Found {} nonprojective train sentence{}".format(
gold_train_unpreprocessed_data["n_nonproj"], "s"
if gold_train_unpreprocessed_data["n_nonproj"] > 1 else ""))
if gold_dev_data["n_nonproj"] > 0:
msg.info("Found {} nonprojective dev sentence{}".format(
gold_dev_data["n_nonproj"],
"s" if gold_dev_data["n_nonproj"] > 1 else ""))
msg.info("{} {} in train data".format(
len(labels_train_unpreprocessed),
"label" if len(labels_train) == 1 else "labels"))
msg.info("{} {} in projectivized train data".format(
len(labels_train),
"label" if len(labels_train) == 1 else "labels"))
labels_with_counts = _format_labels(
gold_train_unpreprocessed_data["deps"].most_common(), counts=True)
msg.text(labels_with_counts, show=verbose)
# rare labels in train
for label in gold_train_unpreprocessed_data["deps"]:
if gold_train_unpreprocessed_data["deps"][
label] <= DEP_LABEL_THRESHOLD:
msg.warn("Low number of examples for label '{}' ({})".format(
label, gold_train_unpreprocessed_data["deps"][label]))
has_low_data_warning = True
# rare labels in projectivized train
rare_projectivized_labels = []
for label in gold_train_data["deps"]:
if gold_train_data["deps"][
label] <= DEP_LABEL_THRESHOLD and "||" in label:
rare_projectivized_labels.append("{}: {}".format(
label, str(gold_train_data["deps"][label])))
if len(rare_projectivized_labels) > 0:
msg.warn(
"Low number of examples for {} label{} in the "
"projectivized dependency trees used for training. You may "
"want to projectivize labels such as punct before "
"training in order to improve parser performance.".format(
len(rare_projectivized_labels),
"s" if len(rare_projectivized_labels) > 1 else ""))
msg.warn("Projectivized labels with low numbers of examples: "
"{}".format("\n".join(rare_projectivized_labels)),
show=verbose)
has_low_data_warning = True
# labels only in train
if set(labels_train) - set(labels_dev):
msg.warn("The following labels were found only in the train data: "
"{}".format(
", ".join(set(labels_train) - set(labels_dev))),
show=verbose)
# labels only in dev
if set(labels_dev) - set(labels_train):
msg.warn("The following labels were found only in the dev data: " +
", ".join(set(labels_dev) - set(labels_train)),
show=verbose)
if has_low_data_warning:
msg.text(
"To train a parser, your data should include at "
"least {} instances of each label.".format(
DEP_LABEL_THRESHOLD),
show=verbose,
)
# multiple root labels
if len(gold_train_unpreprocessed_data["roots"]) > 1:
msg.warn(
"Multiple root labels ({}) ".format(", ".join(
gold_train_unpreprocessed_data["roots"])) +
"found in training data. spaCy's parser uses a single root "
"label ROOT so this distinction will not be available.")
# these should not happen, but just in case
if gold_train_data["n_nonproj"] > 0:
msg.fail(
"Found {} nonprojective projectivized train sentence{}".format(
gold_train_data["n_nonproj"],
"s" if gold_train_data["n_nonproj"] > 1 else ""))
if gold_train_data["n_cycles"] > 0:
msg.fail(
"Found {} projectivized train sentence{} with cycles".format(
gold_train_data["n_cycles"],
"s" if gold_train_data["n_cycles"] > 1 else ""))
msg.divider("Summary")
good_counts = msg.counts[MESSAGES.GOOD]
warn_counts = msg.counts[MESSAGES.WARN]
fail_counts = msg.counts[MESSAGES.FAIL]
if good_counts:
msg.good("{} {} passed".format(
good_counts, "check" if good_counts == 1 else "checks"))
if warn_counts:
msg.warn("{} {}".format(warn_counts,
"warning" if warn_counts == 1 else "warnings"))
if fail_counts:
msg.fail("{} {}".format(fail_counts,
"error" if fail_counts == 1 else "errors"))
if fail_counts:
sys.exit(1)
def conll_ner2json(input_data,
n_sents=10,
seg_sents=False,
model=None,
no_print=False,
**kwargs):
"""
Convert files in the CoNLL-2003 NER format and similar
whitespace-separated columns into JSON format for use with train cli.
The first column is the tokens, the final column is the IOB tags. If an
additional second column is present, the second column is the tags.
Sentences are separated with whitespace and documents can be separated
using the line "-DOCSTART- -X- O O".
Sample format:
-DOCSTART- -X- O O
I O
like O
London B-GPE
and O
New B-GPE
York I-GPE
City I-GPE
. O
"""
msg = Printer(no_print=no_print)
doc_delimiter = "-DOCSTART- -X- O O"
# check for existing delimiters, which should be preserved
if "\n\n" in input_data and seg_sents:
msg.warn(
"Sentence boundaries found, automatic sentence segmentation with "
"`-s` disabled.")
seg_sents = False
if doc_delimiter in input_data and n_sents:
msg.warn(
"Document delimiters found, automatic document segmentation with "
"`-n` disabled.")
n_sents = 0
# do document segmentation with existing sentences
if "\n\n" in input_data and doc_delimiter not in input_data and n_sents:
n_sents_info(msg, n_sents)
input_data = segment_docs(input_data, n_sents, doc_delimiter)
# do sentence segmentation with existing documents
if "\n\n" not in input_data and doc_delimiter in input_data and seg_sents:
input_data = segment_sents_and_docs(input_data,
0,
"",
model=model,
msg=msg)
# do both sentence segmentation and document segmentation according
# to options
if "\n\n" not in input_data and doc_delimiter not in input_data:
# sentence segmentation required for document segmentation
if n_sents > 0 and not seg_sents:
msg.warn(
"No sentence boundaries found to use with option `-n {}`. "
"Use `-s` to automatically segment sentences or `-n 0` "
"to disable.".format(n_sents))
else:
n_sents_info(msg, n_sents)
input_data = segment_sents_and_docs(input_data,
n_sents,
doc_delimiter,
model=model,
msg=msg)
# provide warnings for problematic data
if "\n\n" not in input_data:
msg.warn(
"No sentence boundaries found. Use `-s` to automatically segment "
"sentences.")
if doc_delimiter not in input_data:
msg.warn(
"No document delimiters found. Use `-n` to automatically group "
"sentences into documents.")
output_docs = []
for doc in input_data.strip().split(doc_delimiter):
doc = doc.strip()
if not doc:
continue
output_doc = []
for sent in doc.split("\n\n"):
sent = sent.strip()
if not sent:
continue
lines = [line.strip() for line in sent.split("\n") if line.strip()]
cols = list(zip(*[line.split() for line in lines]))
if len(cols) < 2:
raise ValueError(
"The token-per-line NER file is not formatted correctly. "
"Try checking whitespace and delimiters. See "
"https://spacy.io/api/cli#convert")
words = cols[0]
iob_ents = cols[-1]
if len(cols) > 2:
tags = cols[1]
else:
tags = ["-"] * len(words)
biluo_ents = iob_to_biluo(iob_ents)
output_doc.append({
"tokens": [{
"orth": w,
"tag": tag,
"ner": ent
} for (w, tag, ent) in zip(words, tags, biluo_ents)]
})
output_docs.append({
"id": len(output_docs),
"paragraphs": [{
"sentences": output_doc
}]
})
output_doc = []
return output_docs
def init_config(
*,
lang: str,
pipeline: List[str],
optimize: str,
gpu: bool,
pretraining: bool = False,
silent: bool = True,
) -> Config:
msg = Printer(no_print=silent)
with TEMPLATE_PATH.open("r") as f:
template = Template(f.read())
# Filter out duplicates since tok2vec and transformer are added by template
pipeline = [
pipe for pipe in pipeline if pipe not in ("tok2vec", "transformer")
]
defaults = RECOMMENDATIONS["__default__"]
reco = RecommendationSchema(**RECOMMENDATIONS.get(lang, defaults)).dict()
variables = {
"lang": lang,
"components": pipeline,
"optimize": optimize,
"hardware": "gpu" if gpu else "cpu",
"transformer_data": reco["transformer"],
"word_vectors": reco["word_vectors"],
"has_letters": reco["has_letters"],
}
if variables["transformer_data"] and not has_spacy_transformers():
msg.warn(
"To generate a more effective transformer-based config (GPU-only), "
"install the spacy-transformers package and re-run this command. "
"The config generated now does not use transformers.")
variables["transformer_data"] = None
base_template = template.render(variables).strip()
# Giving up on getting the newlines right in jinja for now
base_template = re.sub(r"\n\n\n+", "\n\n", base_template)
# Access variables declared in templates
template_vars = template.make_module(variables)
use_case = {
"Language":
lang,
"Pipeline":
", ".join(pipeline),
"Optimize for":
optimize,
"Hardware":
variables["hardware"].upper(),
"Transformer":
template_vars.transformer.get("name") # type: ignore[attr-defined]
if template_vars.use_transformer # type: ignore[attr-defined]
else None,
}
msg.info("Generated config template specific for your use case")
for label, value in use_case.items():
msg.text(f"- {label}: {value}")
with show_validation_error(hint_fill=False):
config = util.load_config_from_str(base_template)
nlp = util.load_model_from_config(config, auto_fill=True)
config = nlp.config
if pretraining:
validate_config_for_pretrain(config, msg)
pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
config = pretrain_config.merge(config)
msg.good("Auto-filled config with all values")
return config
def package(
input_dir: Path,
output_dir: Path,
meta_path: Optional[Path] = None,
code_paths: List[Path] = [],
name: Optional[str] = None,
version: Optional[str] = None,
create_meta: bool = False,
create_sdist: bool = True,
create_wheel: bool = False,
force: bool = False,
silent: bool = True,
) -> None:
msg = Printer(no_print=silent, pretty=not silent)
input_path = util.ensure_path(input_dir)
output_path = util.ensure_path(output_dir)
meta_path = util.ensure_path(meta_path)
if create_wheel and not has_wheel():
err = "Generating a binary .whl file requires wheel to be installed"
msg.fail(err, "pip install wheel", exits=1)
if not input_path or not input_path.exists():
msg.fail("Can't locate pipeline data", input_path, exits=1)
if not output_path or not output_path.exists():
msg.fail("Output directory not found", output_path, exits=1)
if create_sdist or create_wheel:
opts = ["sdist" if create_sdist else "", "wheel" if create_wheel else ""]
msg.info(f"Building package artifacts: {', '.join(opt for opt in opts if opt)}")
for code_path in code_paths:
if not code_path.exists():
msg.fail("Can't find code file", code_path, exits=1)
# Import the code here so it's available when model is loaded (via
# get_meta helper). Also verifies that everything works
util.import_file(code_path.stem, code_path)
if code_paths:
msg.good(f"Including {len(code_paths)} Python module(s) with custom code")
if meta_path and not meta_path.exists():
msg.fail("Can't find pipeline meta.json", meta_path, exits=1)
meta_path = meta_path or input_dir / "meta.json"
if not meta_path.exists() or not meta_path.is_file():
msg.fail("Can't load pipeline meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path)
meta = get_meta(input_dir, meta)
if meta["requirements"]:
msg.good(
f"Including {len(meta['requirements'])} package requirement(s) from "
f"meta and config",
", ".join(meta["requirements"]),
)
if name is not None:
if not name.isidentifier():
msg.fail(
f"Model name ('{name}') is not a valid module name. "
"This is required so it can be imported as a module.",
"We recommend names that use ASCII A-Z, a-z, _ (underscore), "
"and 0-9. "
"For specific details see: https://docs.python.org/3/reference/lexical_analysis.html#identifiers",
exits=1,
)
if not _is_permitted_package_name(name):
msg.fail(
f"Model name ('{name}') is not a permitted package name. "
"This is required to correctly load the model with spacy.load.",
"We recommend names that use ASCII A-Z, a-z, _ (underscore), "
"and 0-9. "
"For specific details see: https://www.python.org/dev/peps/pep-0426/#name",
exits=1,
)
meta["name"] = name
if version is not None:
meta["version"] = version
if not create_meta: # only print if user doesn't want to overwrite
msg.good("Loaded meta.json from file", meta_path)
else:
meta = generate_meta(meta, msg)
errors = validate(ModelMetaSchema, meta)
if errors:
msg.fail("Invalid pipeline meta.json")
print("\n".join(errors))
sys.exit(1)
model_name = meta["name"]
if not model_name.startswith(meta["lang"] + "_"):
model_name = f"{meta['lang']}_{model_name}"
model_name_v = model_name + "-" + meta["version"]
main_path = output_dir / model_name_v
package_path = main_path / model_name
if package_path.exists():
if force:
shutil.rmtree(str(package_path))
else:
msg.fail(
"Package directory already exists",
"Please delete the directory and try again, or use the "
"`--force` flag to overwrite existing directories.",
exits=1,
)
Path.mkdir(package_path, parents=True)
shutil.copytree(str(input_dir), str(package_path / model_name_v))
for file_name in FILENAMES_DOCS:
file_path = package_path / model_name_v / file_name
if file_path.exists():
shutil.copy(str(file_path), str(main_path))
readme_path = main_path / "README.md"
if not readme_path.exists():
readme = generate_readme(meta)
create_file(readme_path, readme)
create_file(package_path / model_name_v / "README.md", readme)
msg.good("Generated README.md from meta.json")
else:
msg.info("Using existing README.md from pipeline directory")
imports = []
for code_path in code_paths:
imports.append(code_path.stem)
shutil.copy(str(code_path), str(package_path))
create_file(main_path / "meta.json", srsly.json_dumps(meta, indent=2))
create_file(main_path / "setup.py", TEMPLATE_SETUP)
create_file(main_path / "MANIFEST.in", TEMPLATE_MANIFEST)
init_py = TEMPLATE_INIT.format(
imports="\n".join(f"from . import {m}" for m in imports)
)
create_file(package_path / "__init__.py", init_py)
msg.good(f"Successfully created package directory '{model_name_v}'", main_path)
if create_sdist:
with util.working_dir(main_path):
util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
zip_file = main_path / "dist" / f"{model_name_v}{SDIST_SUFFIX}"
msg.good(f"Successfully created zipped Python package", zip_file)
if create_wheel:
with util.working_dir(main_path):
util.run_command([sys.executable, "setup.py", "bdist_wheel"], capture=False)
wheel_name_squashed = re.sub("_+", "_", model_name_v)
wheel = main_path / "dist" / f"{wheel_name_squashed}{WHEEL_SUFFIX}"
msg.good(f"Successfully created binary wheel", wheel)
if "__" in model_name:
msg.warn(
f"Model name ('{model_name}') contains a run of underscores. "
"Runs of underscores are not significant in installed package names.",
)
def pretrain(
texts_loc,
vectors_model,
output_dir,
width=96,
depth=4,
bilstm_depth=2,
embed_rows=2000,
loss_func="cosine",
use_vectors=False,
dropout=0.2,
n_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
n_save_every=None,
init_tok2vec=None,
epoch_start=None,
):
"""
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
using an approximate language-modelling objective. Specifically, we load
pretrained vectors, and train a component like a CNN, BiLSTM, etc to predict
vectors which match the pretrained ones. The weights are saved to a directory
after each epoch. You can then pass a path to one of these pretrained weights
files to the 'spacy train' command.
This technique may be especially helpful if you have little labelled data.
However, it's still quite experimental, so your mileage may vary.
To load the weights back in during 'spacy train', you need to ensure
all settings are the same between pretraining and training. The API and
errors around this need some improvement.
"""
config = dict(locals())
for key in config:
if isinstance(config[key], Path):
config[key] = str(config[key])
msg = Printer()
util.fix_random_seed(seed)
has_gpu = prefer_gpu()
if has_gpu:
import torch
torch.set_default_tensor_type("torch.cuda.FloatTensor")
msg.info("Using GPU" if has_gpu else "Not using GPU")
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
msg.good("Created output directory")
srsly.write_json(output_dir / "config.json", config)
msg.good("Saved settings to config.json")
# Load texts from file or stdin
if texts_loc != "-": # reading from a file
texts_loc = Path(texts_loc)
if not texts_loc.exists():
msg.fail("Input text file doesn't exist", texts_loc, exits=1)
with msg.loading("Loading input texts..."):
texts = list(srsly.read_jsonl(texts_loc))
if not texts:
msg.fail("Input file is empty", texts_loc, exits=1)
msg.good("Loaded input texts")
random.shuffle(texts)
else: # reading from stdin
msg.text("Reading input text from stdin...")
texts = srsly.read_jsonl("-")
with msg.loading("Loading model '{}'...".format(vectors_model)):
nlp = util.load_model(vectors_model)
msg.good("Loaded model '{}'".format(vectors_model))
pretrained_vectors = None if not use_vectors else nlp.vocab.vectors.name
model = create_pretraining_model(
nlp,
Tok2Vec(
width,
embed_rows,
conv_depth=depth,
pretrained_vectors=pretrained_vectors,
bilstm_depth=bilstm_depth, # Requires PyTorch. Experimental.
cnn_maxout_pieces=3, # You can try setting this higher
subword_features=True, # Set to False for Chinese etc
),
)
# Load in pretrained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# Parse the epoch number from the given weight file
model_name = re.search(r"model\d+\.bin", str(init_tok2vec))
if model_name:
# Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
epoch_start = int(model_name.group(0)[5:][:-4]) + 1
else:
if not epoch_start:
msg.fail(
"You have to use the '--epoch-start' argument when using a renamed weight file for "
"'--init-tok2vec'",
exits=True,
)
elif epoch_start < 0:
msg.fail(
"The argument '--epoch-start' has to be greater or equal to 0. '%d' is invalid"
% epoch_start,
exits=True,
)
else:
# Without '--init-tok2vec' the '--epoch-start' argument is ignored
epoch_start = 0
optimizer = create_default_optimizer(model.ops)
tracker = ProgressTracker(frequency=10000)
msg.divider("Pre-training tok2vec layer - starting at epoch %d" %
epoch_start)
row_settings = {
"widths": (3, 10, 10, 6, 4),
"aligns": ("r", "r", "r", "r", "r")
}
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
def _save_model(epoch, is_temp=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
with (output_dir / ("model%d%s.bin" %
(epoch, is_temp_str))).open("wb") as file_:
file_.write(model.tok2vec.to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
skip_counter = 0
for epoch in range(epoch_start, n_iter + epoch_start):
for batch_id, batch in enumerate(
util.minibatch_by_words(((text, None) for text in texts),
size=batch_size)):
docs, count = make_docs(
nlp,
[text for (text, _) in batch],
max_length=max_length,
min_length=min_length,
)
skip_counter += count
loss = make_update(model,
docs,
optimizer,
objective=loss_func,
drop=dropout)
progress = tracker.update(epoch, loss, docs)
if progress:
msg.row(progress, **row_settings)
if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10**7:
break
if n_save_every and (batch_id % n_save_every == 0):
_save_model(epoch, is_temp=True)
_save_model(epoch)
tracker.epoch_loss = 0.0
if texts_loc != "-":
# Reshuffle the texts if texts were loaded from a file
random.shuffle(texts)
if skip_counter > 0:
msg.warn(
"Skipped {count} empty values".format(count=str(skip_counter)))
msg.good("Successfully finished pretrain")
def train(
lang,
output_path,
train_path,
dev_path,
raw_text=None,
base_model=None,
pipeline="tagger,parser,ner",
vectors=None,
n_iter=30,
n_early_stopping=None,
n_examples=0,
use_gpu=-1,
version="0.0.0",
meta_path=None,
init_tok2vec=None,
parser_multitasks="",
entity_multitasks="",
noise_level=0.0,
orth_variant_level=0.0,
eval_beam_widths="",
gold_preproc=False,
learn_tokens=False,
textcat_multilabel=False,
textcat_arch="bow",
textcat_positive_label=None,
verbose=False,
debug=False,
):
"""
Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
command.
"""
# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
import tqdm
msg = Printer()
util.fix_random_seed()
util.set_env_log(verbose)
# Make sure all files and paths exists if they are needed
train_path = util.ensure_path(train_path)
dev_path = util.ensure_path(dev_path)
meta_path = util.ensure_path(meta_path)
output_path = util.ensure_path(output_path)
if raw_text is not None:
raw_text = list(srsly.read_jsonl(raw_text))
if not train_path or not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path or not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
if meta_path is not None and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path) if meta_path else {}
if output_path.exists() and [
p for p in output_path.iterdir() if p.is_dir()
]:
msg.warn(
"Output directory is not empty",
"This can lead to unintended side effects when saving the model. "
"Please use an empty directory or a different path instead. If "
"the specified output path doesn't exist, the directory will be "
"created for you.",
)
if not output_path.exists():
output_path.mkdir()
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.2),
util.env_opt("dropout_to", 0.2),
util.env_opt("dropout_decay", 0.0),
)
batch_sizes = util.compounding(
util.env_opt("batch_from", 100.0),
util.env_opt("batch_to", 1000.0),
util.env_opt("batch_compound", 1.001),
)
if not eval_beam_widths:
eval_beam_widths = [1]
else:
eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")]
if 1 not in eval_beam_widths:
eval_beam_widths.append(1)
eval_beam_widths.sort()
has_beam_widths = eval_beam_widths != [1]
# Set up the base model and pipeline. If a base model is specified, load
# the model and make sure the pipeline matches the pipeline setting. If
# training starts from a blank model, intitalize the language class.
pipeline = [p.strip() for p in pipeline.split(",")]
msg.text("Training pipeline: {}".format(pipeline))
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
nlp = util.load_model(base_model)
if nlp.lang != lang:
msg.fail(
"Model language ('{}') doesn't match language specified as "
"`lang` argument ('{}') ".format(nlp.lang, lang),
exits=1,
)
nlp.disable_pipes([p for p in nlp.pipe_names if p not in pipeline])
for pipe in pipeline:
if pipe not in nlp.pipe_names:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
else:
if pipe == "textcat":
textcat_cfg = nlp.get_pipe("textcat").cfg
base_cfg = {
"exclusive_classes": textcat_cfg["exclusive_classes"],
"architecture": textcat_cfg["architecture"],
"positive_label": textcat_cfg["positive_label"],
}
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
if base_cfg != pipe_cfg:
msg.fail(
"The base textcat model configuration does"
"not match the provided training options. "
"Existing cfg: {}, provided cfg: {}".format(
base_cfg, pipe_cfg),
exits=1,
)
else:
msg.text("Starting with blank model '{}'".format(lang))
lang_cls = util.get_lang_class(lang)
nlp = lang_cls()
for pipe in pipeline:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
if vectors:
msg.text("Loading vector from model '{}'".format(vectors))
_load_vectors(nlp, vectors)
# Multitask objectives
multitask_options = [("parser", parser_multitasks),
("ner", entity_multitasks)]
for pipe_name, multitasks in multitask_options:
if multitasks:
if pipe_name not in pipeline:
msg.fail("Can't use multitask objective without '{}' in the "
"pipeline".format(pipe_name))
pipe = nlp.get_pipe(pipe_name)
for objective in multitasks.split(","):
pipe.add_multitask_objective(objective)
# Prepare training corpus
msg.text("Counting training words (limit={})".format(n_examples))
corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
n_train_words = corpus.count_train()
if base_model:
# Start with an existing model, use default optimizer
optimizer = create_default_optimizer(Model.ops)
else:
# Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_tuples,
device=use_gpu)
nlp._optimizer = None
# Load in pretrained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# Verify textcat config
if "textcat" in pipeline:
textcat_labels = nlp.get_pipe("textcat").cfg["labels"]
if textcat_positive_label and textcat_positive_label not in textcat_labels:
msg.fail(
"The textcat_positive_label (tpl) '{}' does not match any "
"label in the training data.".format(textcat_positive_label),
exits=1,
)
if textcat_positive_label and len(textcat_labels) != 2:
msg.fail(
"A textcat_positive_label (tpl) '{}' was provided for training "
"data that does not appear to be a binary classification "
"problem with two labels.".format(textcat_positive_label),
exits=1,
)
train_docs = corpus.train_docs(nlp,
noise_level=noise_level,
gold_preproc=gold_preproc,
max_length=0)
train_labels = set()
if textcat_multilabel:
multilabel_found = False
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1:
multilabel_found = True
if not multilabel_found and not base_model:
msg.warn("The textcat training instances look like they have "
"mutually-exclusive classes. Remove the flag "
"'--textcat-multilabel' to train a classifier with "
"mutually-exclusive classes.")
if not textcat_multilabel:
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1 and not base_model:
msg.warn(
"Some textcat training instances do not have exactly "
"one positive label. Modifying training options to "
"include the flag '--textcat-multilabel' for classes "
"that are not mutually exclusive.")
nlp.get_pipe("textcat").cfg["exclusive_classes"] = False
textcat_multilabel = True
break
if base_model and set(textcat_labels) != train_labels:
msg.fail(
"Cannot extend textcat model using data with different "
"labels. Base model labels: {}, training data labels: "
"{}.".format(textcat_labels, list(train_labels)),
exits=1,
)
if textcat_multilabel:
msg.text(
"Textcat evaluation score: ROC AUC score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels)))
elif textcat_positive_label and len(textcat_labels) == 2:
msg.text("Textcat evaluation score: F1-score for the "
"label '{}'".format(textcat_positive_label))
elif len(textcat_labels) > 1:
if len(textcat_labels) == 2:
msg.warn(
"If the textcat component is a binary classifier with "
"exclusive classes, provide '--textcat_positive_label' for "
"an evaluation on the positive class.")
msg.text(
"Textcat evaluation score: F1-score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels)))
else:
msg.fail(
"Unsupported textcat configuration. Use `spacy debug-data` "
"for more information.")
# fmt: off
row_head, output_stats = _configure_training_output(
pipeline, use_gpu, has_beam_widths)
row_widths = [len(w) for w in row_head]
row_settings = {
"widths": row_widths,
"aligns": tuple(["r" for i in row_head]),
"spacing": 2
}
# fmt: on
print("")
msg.row(row_head, **row_settings)
msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
try:
iter_since_best = 0
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp,
noise_level=noise_level,
orth_variant_level=orth_variant_level,
gold_preproc=gold_preproc,
max_length=0,
)
if raw_text:
random.shuffle(raw_text)
raw_batches = util.minibatch(
(nlp.make_doc(rt["text"]) for rt in raw_text), size=8)
words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in util.minibatch_by_words(train_docs,
size=batch_sizes):
if not batch:
continue
docs, golds = zip(*batch)
nlp.update(
docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
if not int(os.environ.get("LOG_FRIENDLY", 0)):
pbar.update(sum(len(doc) for doc in docs))
words_seen += sum(len(doc) for doc in docs)
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ("model%d" % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
for beam_width in eval_beam_widths:
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc))
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords / (end_time - start_time)
else:
gpu_wps = nwords / (end_time - start_time)
with Model.use_device("cpu"):
nlp_loaded = util.load_model_from_path(
epoch_model_path)
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(nlp_loaded,
gold_preproc=gold_preproc))
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs,
verbose=verbose)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = output_path / ("model%d" % i) / "accuracy.json"
srsly.write_json(acc_loc, scorer.scores)
# Update model meta.json
meta["lang"] = nlp.lang
meta["pipeline"] = nlp.pipe_names
meta["spacy_version"] = ">=%s" % about.__version__
if beam_width == 1:
meta["speed"] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["accuracy"] = scorer.scores
else:
meta.setdefault("beam_accuracy", {})
meta.setdefault("beam_speed", {})
meta["beam_accuracy"][beam_width] = scorer.scores
meta["beam_speed"][beam_width] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
"name": nlp.vocab.vectors.name,
}
meta.setdefault("name", "model%d" % i)
meta.setdefault("version", version)
meta["labels"] = nlp.meta["labels"]
meta_loc = output_path / ("model%d" % i) / "meta.json"
srsly.write_json(meta_loc, meta)
util.set_env_log(verbose)
progress = _get_progress(
i,
losses,
scorer.scores,
output_stats,
beam_width=beam_width if has_beam_widths else None,
cpu_wps=cpu_wps,
gpu_wps=gpu_wps,
)
if i == 0 and "textcat" in pipeline:
textcats_per_cat = scorer.scores.get(
"textcats_per_cat", {})
for cat, cat_score in textcats_per_cat.items():
if cat_score.get("roc_auc_score", 0) < 0:
msg.warn(
"Textcat ROC AUC score is undefined due to "
"only one value in label '{}'.".format(
cat))
msg.row(progress, **row_settings)
# Early stopping
if n_early_stopping is not None:
current_score = _score_for_model(meta)
if current_score < best_score:
iter_since_best += 1
else:
iter_since_best = 0
best_score = current_score
if iter_since_best >= n_early_stopping:
msg.text("Early stopping, best iteration "
"is: {}".format(i - iter_since_best))
msg.text("Best score = {}; Final iteration "
"score = {}".format(best_score,
current_score))
break
finally:
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final"
nlp.to_disk(final_model_path)
msg.good("Saved model to output directory", final_model_path)
with msg.loading("Creating best model..."):
best_model_path = _collate_best_model(meta, output_path,
nlp.pipe_names)
msg.good("Created best model", best_model_path)
def train(
lang,
output_path,
train_path,
dev_path,
raw_text=None,
base_model=None,
pipeline="tagger,parser,ner",
vectors=None,
n_iter=30,
n_early_stopping=None,
n_examples=0,
use_gpu=-1,
version="0.0.0",
meta_path=None,
init_tok2vec=None,
parser_multitasks="",
entity_multitasks="",
noise_level=0.0,
eval_beam_widths="",
gold_preproc=False,
learn_tokens=False,
verbose=False,
debug=False,
):
"""
Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
command.
"""
msg = Printer()
util.fix_random_seed()
util.set_env_log(verbose)
# Make sure all files and paths exists if they are needed
train_path = util.ensure_path(train_path)
dev_path = util.ensure_path(dev_path)
meta_path = util.ensure_path(meta_path)
output_path = util.ensure_path(output_path)
if raw_text is not None:
raw_text = list(srsly.read_jsonl(raw_text))
if not train_path or not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path or not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
if meta_path is not None and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path) if meta_path else {}
if output_path.exists() and [
p for p in output_path.iterdir() if p.is_dir()
]:
msg.warn(
"Output directory is not empty",
"This can lead to unintended side effects when saving the model. "
"Please use an empty directory or a different path instead. If "
"the specified output path doesn't exist, the directory will be "
"created for you.",
)
if not output_path.exists():
output_path.mkdir()
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.2),
util.env_opt("dropout_to", 0.2),
util.env_opt("dropout_decay", 0.0),
)
batch_sizes = util.compounding(
util.env_opt("batch_from", 100.0),
util.env_opt("batch_to", 1000.0),
util.env_opt("batch_compound", 1.001),
)
if not eval_beam_widths:
eval_beam_widths = [1]
else:
eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")]
if 1 not in eval_beam_widths:
eval_beam_widths.append(1)
eval_beam_widths.sort()
has_beam_widths = eval_beam_widths != [1]
# Set up the base model and pipeline. If a base model is specified, load
# the model and make sure the pipeline matches the pipeline setting. If
# training starts from a blank model, intitalize the language class.
pipeline = [p.strip() for p in pipeline.split(",")]
msg.text("Training pipeline: {}".format(pipeline))
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
nlp = util.load_model(base_model)
if nlp.lang != lang:
msg.fail(
"Model language ('{}') doesn't match language specified as "
"`lang` argument ('{}') ".format(nlp.lang, lang),
exits=1,
)
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipeline]
nlp.disable_pipes(*other_pipes)
for pipe in pipeline:
if pipe not in nlp.pipe_names:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
else:
msg.text("Starting with blank model '{}'".format(lang))
lang_cls = util.get_lang_class(lang)
nlp = lang_cls()
for pipe in pipeline:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
if vectors:
msg.text("Loading vector from model '{}'".format(vectors))
_load_vectors(nlp, vectors)
# Multitask objectives
multitask_options = [("parser", parser_multitasks),
("ner", entity_multitasks)]
for pipe_name, multitasks in multitask_options:
if multitasks:
if pipe_name not in pipeline:
msg.fail("Can't use multitask objective without '{}' in the "
"pipeline".format(pipe_name))
pipe = nlp.get_pipe(pipe_name)
for objective in multitasks.split(","):
pipe.add_multitask_objective(objective)
# Prepare training corpus
msg.text("Counting training words (limit={})".format(n_examples))
corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
n_train_words = corpus.count_train()
if base_model:
# Start with an existing model, use default optimizer
optimizer = create_default_optimizer(Model.ops)
else:
# Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_tuples,
device=use_gpu)
nlp._optimizer = None
# Load in pre-trained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# fmt: off
row_head = [
"Itn", "Dep Loss", "NER Loss", "UAS", "NER P", "NER R", "NER F",
"Tag %", "Token %", "CPU WPS", "GPU WPS"
]
row_widths = [3, 10, 10, 7, 7, 7, 7, 7, 7, 7, 7]
if has_beam_widths:
row_head.insert(1, "Beam W.")
row_widths.insert(1, 7)
row_settings = {
"widths": row_widths,
"aligns": tuple(["r" for i in row_head]),
"spacing": 2
}
# fmt: on
print("")
msg.row(row_head, **row_settings)
msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
try:
iter_since_best = 0
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(nlp,
noise_level=noise_level,
gold_preproc=gold_preproc,
max_length=0)
if raw_text:
random.shuffle(raw_text)
raw_batches = util.minibatch(
(nlp.make_doc(rt["text"]) for rt in raw_text), size=8)
words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in util.minibatch_by_words(train_docs,
size=batch_sizes):
if not batch:
continue
docs, golds = zip(*batch)
nlp.update(
docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
if not int(os.environ.get("LOG_FRIENDLY", 0)):
pbar.update(sum(len(doc) for doc in docs))
words_seen += sum(len(doc) for doc in docs)
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ("model%d" % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
for beam_width in eval_beam_widths:
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc))
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, debug)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords / (end_time - start_time)
else:
gpu_wps = nwords / (end_time - start_time)
with Model.use_device("cpu"):
nlp_loaded = util.load_model_from_path(
epoch_model_path)
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(nlp_loaded,
gold_preproc=gold_preproc))
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = output_path / ("model%d" % i) / "accuracy.json"
srsly.write_json(acc_loc, scorer.scores)
# Update model meta.json
meta["lang"] = nlp.lang
meta["pipeline"] = nlp.pipe_names
meta["spacy_version"] = ">=%s" % about.__version__
if beam_width == 1:
meta["speed"] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["accuracy"] = scorer.scores
else:
meta.setdefault("beam_accuracy", {})
meta.setdefault("beam_speed", {})
meta["beam_accuracy"][beam_width] = scorer.scores
meta["beam_speed"][beam_width] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
"name": nlp.vocab.vectors.name,
}
meta.setdefault("name", "model%d" % i)
meta.setdefault("version", version)
meta_loc = output_path / ("model%d" % i) / "meta.json"
srsly.write_json(meta_loc, meta)
util.set_env_log(verbose)
progress = _get_progress(
i,
losses,
scorer.scores,
beam_width=beam_width if has_beam_widths else None,
cpu_wps=cpu_wps,
gpu_wps=gpu_wps,
)
msg.row(progress, **row_settings)
# Early stopping
if n_early_stopping is not None:
current_score = _score_for_model(meta)
if current_score < best_score:
iter_since_best += 1
else:
iter_since_best = 0
best_score = current_score
if iter_since_best >= n_early_stopping:
msg.text("Early stopping, best iteration "
"is: {}".format(i - iter_since_best))
msg.text("Best score = {}; Final iteration "
"score = {}".format(best_score,
current_score))
break
finally:
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final"
nlp.to_disk(final_model_path)
msg.good("Saved model to output directory", final_model_path)
with msg.loading("Creating best model..."):
best_model_path = _collate_best_model(meta, output_path,
nlp.pipe_names)
msg.good("Created best model", best_model_path)
def convert(
input_file,
output_dir="-",
file_type="json",
n_sents=1,
seg_sents=False,
model=None,
morphology=False,
converter="auto",
lang=None,
):
"""
Convert files into JSON format for use with train command and other
experiment management functions. If no output_dir is specified, the data
is written to stdout, so you can pipe them forward to a JSON file:
$ spacy convert some_file.conllu > some_file.json
"""
no_print = output_dir == "-"
msg = Printer(no_print=no_print)
input_path = Path(input_file)
if file_type not in FILE_TYPES:
msg.fail(
"Unknown file type: '{}'".format(file_type),
"Supported file types: '{}'".format(", ".join(FILE_TYPES)),
exits=1,
)
if file_type not in FILE_TYPES_STDOUT and output_dir == "-":
# TODO: support msgpack via stdout in srsly?
msg.fail(
"Can't write .{} data to stdout.".format(file_type),
"Please specify an output directory.",
exits=1,
)
if not input_path.exists():
msg.fail("Input file not found", input_path, exits=1)
if output_dir != "-" and not Path(output_dir).exists():
msg.fail("Output directory not found", output_dir, exits=1)
input_data = input_path.open("r", encoding="utf-8").read()
if converter == "auto":
converter = input_path.suffix[1:]
if converter == "ner" or converter == "iob":
converter_autodetect = autodetect_ner_format(input_data)
if converter_autodetect == "ner":
msg.info("Auto-detected token-per-line NER format")
converter = converter_autodetect
elif converter_autodetect == "iob":
msg.info("Auto-detected sentence-per-line NER format")
converter = converter_autodetect
else:
msg.warn(
"Can't automatically detect NER format. Conversion may not succeed. See https://spacy.io/api/cli#convert"
)
if converter not in CONVERTERS:
msg.fail("Can't find converter for {}".format(converter), exits=1)
# Use converter function to convert data
func = CONVERTERS[converter]
data = func(
input_data,
n_sents=n_sents,
seg_sents=seg_sents,
use_morphology=morphology,
lang=lang,
model=model,
no_print=no_print,
)
if output_dir != "-":
# Export data to a file
suffix = ".{}".format(file_type)
output_file = Path(output_dir) / Path(
input_path.parts[-1]).with_suffix(suffix)
if file_type == "json":
srsly.write_json(output_file, data)
elif file_type == "jsonl":
srsly.write_jsonl(output_file, data)
elif file_type == "msg":
srsly.write_msgpack(output_file, data)
msg.good("Generated output file ({} documents): {}".format(
len(data), output_file))
else:
# Print to stdout
if file_type == "json":
srsly.write_json("-", data)
elif file_type == "jsonl":
srsly.write_jsonl("-", data)
def conll_ner_to_docs(
input_data, n_sents=10, seg_sents=False, model=None, no_print=False, **kwargs
):
"""
Convert files in the CoNLL-2003 NER format and similar
whitespace-separated columns into Doc objects.
The first column is the tokens, the final column is the IOB tags. If an
additional second column is present, the second column is the tags.
Sentences are separated with whitespace and documents can be separated
using the line "-DOCSTART- -X- O O".
Sample format:
-DOCSTART- -X- O O
I O
like O
London B-GPE
and O
New B-GPE
York I-GPE
City I-GPE
. O
"""
msg = Printer(no_print=no_print)
doc_delimiter = "-DOCSTART- -X- O O"
# check for existing delimiters, which should be preserved
if "\n\n" in input_data and seg_sents:
msg.warn(
"Sentence boundaries found, automatic sentence segmentation with "
"`-s` disabled."
)
seg_sents = False
if doc_delimiter in input_data and n_sents:
msg.warn(
"Document delimiters found, automatic document segmentation with "
"`-n` disabled."
)
n_sents = 0
# do document segmentation with existing sentences
if "\n\n" in input_data and doc_delimiter not in input_data and n_sents:
n_sents_info(msg, n_sents)
input_data = segment_docs(input_data, n_sents, doc_delimiter)
# do sentence segmentation with existing documents
if "\n\n" not in input_data and doc_delimiter in input_data and seg_sents:
input_data = segment_sents_and_docs(input_data, 0, "", model=model, msg=msg)
# do both sentence segmentation and document segmentation according
# to options
if "\n\n" not in input_data and doc_delimiter not in input_data:
# sentence segmentation required for document segmentation
if n_sents > 0 and not seg_sents:
msg.warn(
f"No sentence boundaries found to use with option `-n {n_sents}`. "
f"Use `-s` to automatically segment sentences or `-n 0` "
f"to disable."
)
else:
n_sents_info(msg, n_sents)
input_data = segment_sents_and_docs(
input_data, n_sents, doc_delimiter, model=model, msg=msg
)
# provide warnings for problematic data
if "\n\n" not in input_data:
msg.warn(
"No sentence boundaries found. Use `-s` to automatically segment "
"sentences."
)
if doc_delimiter not in input_data:
msg.warn(
"No document delimiters found. Use `-n` to automatically group "
"sentences into documents."
)
if model:
nlp = load_model(model)
else:
nlp = get_lang_class("xx")()
for conll_doc in input_data.strip().split(doc_delimiter):
conll_doc = conll_doc.strip()
if not conll_doc:
continue
words = []
sent_starts = []
pos_tags = []
biluo_tags = []
for conll_sent in conll_doc.split("\n\n"):
conll_sent = conll_sent.strip()
if not conll_sent:
continue
lines = [line.strip() for line in conll_sent.split("\n") if line.strip()]
cols = list(zip(*[line.split() for line in lines]))
if len(cols) < 2:
raise ValueError(Errors.E903)
length = len(cols[0])
words.extend(cols[0])
sent_starts.extend([True] + [False] * (length - 1))
biluo_tags.extend(iob_to_biluo(cols[-1]))
pos_tags.extend(cols[1] if len(cols) > 2 else ["-"] * length)
doc = Doc(nlp.vocab, words=words)
for i, token in enumerate(doc):
token.tag_ = pos_tags[i]
token.is_sent_start = sent_starts[i]
entities = tags_to_entities(biluo_tags)
doc.ents = [Span(doc, start=s, end=e + 1, label=L) for L, s, e in entities]
yield doc
def train(
lang,
output_path,
train_path,
dev_path,
raw_text=None,
base_model=None,
pipeline="tagger,parser,ner",
vectors=None,
n_iter=30,
n_early_stopping=None,
n_examples=0,
use_gpu=-1,
version="0.0.0",
meta_path=None,
init_tok2vec=None,
parser_multitasks="",
entity_multitasks="",
noise_level=0.0,
eval_beam_widths="",
gold_preproc=False,
learn_tokens=False,
verbose=False,
debug=False,
):
"""
Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
command.
"""
msg = Printer()
util.fix_random_seed()
util.set_env_log(verbose)
# Make sure all files and paths exists if they are needed
train_path = util.ensure_path(train_path)
dev_path = util.ensure_path(dev_path)
meta_path = util.ensure_path(meta_path)
output_path = util.ensure_path(output_path)
if raw_text is not None:
raw_text = list(srsly.read_jsonl(raw_text))
if not train_path or not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path or not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
if meta_path is not None and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path) if meta_path else {}
if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:
msg.warn(
"Output directory is not empty",
"This can lead to unintended side effects when saving the model. "
"Please use an empty directory or a different path instead. If "
"the specified output path doesn't exist, the directory will be "
"created for you.",
)
if not output_path.exists():
output_path.mkdir()
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.2),
util.env_opt("dropout_to", 0.2),
util.env_opt("dropout_decay", 0.0),
)
batch_sizes = util.compounding(
util.env_opt("batch_from", 100.0),
util.env_opt("batch_to", 1000.0),
util.env_opt("batch_compound", 1.001),
)
if not eval_beam_widths:
eval_beam_widths = [1]
else:
eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")]
if 1 not in eval_beam_widths:
eval_beam_widths.append(1)
eval_beam_widths.sort()
has_beam_widths = eval_beam_widths != [1]
# Set up the base model and pipeline. If a base model is specified, load
# the model and make sure the pipeline matches the pipeline setting. If
# training starts from a blank model, intitalize the language class.
pipeline = [p.strip() for p in pipeline.split(",")]
msg.text("Training pipeline: {}".format(pipeline))
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
nlp = util.load_model(base_model)
if nlp.lang != lang:
msg.fail(
"Model language ('{}') doesn't match language specified as "
"`lang` argument ('{}') ".format(nlp.lang, lang),
exits=1,
)
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipeline]
nlp.disable_pipes(*other_pipes)
for pipe in pipeline:
if pipe not in nlp.pipe_names:
nlp.add_pipe(nlp.create_pipe(pipe))
else:
msg.text("Starting with blank model '{}'".format(lang))
lang_cls = util.get_lang_class(lang)
nlp = lang_cls()
for pipe in pipeline:
nlp.add_pipe(nlp.create_pipe(pipe))
if learn_tokens:
nlp.add_pipe(nlp.create_pipe("merge_subtokens"))
if vectors:
msg.text("Loading vector from model '{}'".format(vectors))
_load_vectors(nlp, vectors)
# Multitask objectives
multitask_options = [("parser", parser_multitasks), ("ner", entity_multitasks)]
for pipe_name, multitasks in multitask_options:
if multitasks:
if pipe_name not in pipeline:
msg.fail(
"Can't use multitask objective without '{}' in the "
"pipeline".format(pipe_name)
)
pipe = nlp.get_pipe(pipe_name)
for objective in multitasks.split(","):
pipe.add_multitask_objective(objective)
# Prepare training corpus
msg.text("Counting training words (limit={})".format(n_examples))
corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
n_train_words = corpus.count_train()
if base_model:
# Start with an existing model, use default optimizer
optimizer = create_default_optimizer(Model.ops)
else:
# Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
nlp._optimizer = None
# Load in pre-trained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# fmt: off
row_head = ["Itn", "Dep Loss", "NER Loss", "UAS", "NER P", "NER R", "NER F", "Tag %", "Token %", "CPU WPS", "GPU WPS"]
row_widths = [3, 10, 10, 7, 7, 7, 7, 7, 7, 7, 7]
if has_beam_widths:
row_head.insert(1, "Beam W.")
row_widths.insert(1, 7)
row_settings = {"widths": row_widths, "aligns": tuple(["r" for i in row_head]), "spacing": 2}
# fmt: on
print("")
msg.row(row_head, **row_settings)
msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
try:
iter_since_best = 0
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
)
if raw_text:
random.shuffle(raw_text)
raw_batches = util.minibatch(
(nlp.make_doc(rt["text"]) for rt in raw_text), size=8
)
words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in util.minibatch_by_words(train_docs, size=batch_sizes):
if not batch:
continue
docs, golds = zip(*batch)
nlp.update(
docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
if not int(os.environ.get("LOG_FRIENDLY", 0)):
pbar.update(sum(len(doc) for doc in docs))
words_seen += sum(len(doc) for doc in docs)
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ("model%d" % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
for beam_width in eval_beam_widths:
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc)
)
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, debug)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords / (end_time - start_time)
else:
gpu_wps = nwords / (end_time - start_time)
with Model.use_device("cpu"):
nlp_loaded = util.load_model_from_path(epoch_model_path)
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc)
)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = output_path / ("model%d" % i) / "accuracy.json"
srsly.write_json(acc_loc, scorer.scores)
# Update model meta.json
meta["lang"] = nlp.lang
meta["pipeline"] = nlp.pipe_names
meta["spacy_version"] = ">=%s" % about.__version__
if beam_width == 1:
meta["speed"] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["accuracy"] = scorer.scores
else:
meta.setdefault("beam_accuracy", {})
meta.setdefault("beam_speed", {})
meta["beam_accuracy"][beam_width] = scorer.scores
meta["beam_speed"][beam_width] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
"name": nlp.vocab.vectors.name,
}
meta.setdefault("name", "model%d" % i)
meta.setdefault("version", version)
meta_loc = output_path / ("model%d" % i) / "meta.json"
srsly.write_json(meta_loc, meta)
util.set_env_log(verbose)
progress = _get_progress(
i,
losses,
scorer.scores,
beam_width=beam_width if has_beam_widths else None,
cpu_wps=cpu_wps,
gpu_wps=gpu_wps,
)
msg.row(progress, **row_settings)
# Early stopping
if n_early_stopping is not None:
current_score = _score_for_model(meta)
if current_score < best_score:
iter_since_best += 1
else:
iter_since_best = 0
best_score = current_score
if iter_since_best >= n_early_stopping:
msg.text(
"Early stopping, best iteration "
"is: {}".format(i - iter_since_best)
)
msg.text(
"Best score = {}; Final iteration "
"score = {}".format(best_score, current_score)
)
break
finally:
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final"
nlp.to_disk(final_model_path)
msg.good("Saved model to output directory", final_model_path)
with msg.loading("Creating best model..."):
best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
msg.good("Created best model", best_model_path)
def train(
nlp: "Language",
output_path: Optional[Path] = None,
*,
use_gpu: int = -1,
stdout: IO = sys.stdout,
stderr: IO = sys.stderr,
) -> Tuple["Language", Optional[Path]]:
"""Train a pipeline.
nlp (Language): The initialized nlp object with the full config.
output_path (Path): Optional output path to save trained model to.
use_gpu (int): Whether to train on GPU. Make sure to call require_gpu
before calling this function.
stdout (file): A file-like object to write output messages. To disable
printing, set to io.StringIO.
stderr (file): A second file-like object to write output messages. To disable
printing, set to io.StringIO.
RETURNS (tuple): The final nlp object and the path to the exported model.
"""
# We use no_print here so we can respect the stdout/stderr options.
msg = Printer(no_print=True)
# Create iterator, which yields out info after each optimization step.
config = nlp.config.interpolate()
if config["training"]["seed"] is not None:
fix_random_seed(config["training"]["seed"])
allocator = config["training"]["gpu_allocator"]
if use_gpu >= 0 and allocator:
set_gpu_allocator(allocator)
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
dot_names = [T["train_corpus"], T["dev_corpus"]]
train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
optimizer = T["optimizer"]
score_weights = T["score_weights"]
batcher = T["batcher"]
train_logger = T["logger"]
before_to_disk = create_before_to_disk_callback(T["before_to_disk"])
# Helper function to save checkpoints. This is a closure for convenience,
# to avoid passing in all the args all the time.
def save_checkpoint(is_best):
with nlp.use_params(optimizer.averages):
before_to_disk(nlp).to_disk(output_path / DIR_MODEL_LAST)
if is_best:
# Avoid saving twice (saving will be more expensive than
# the dir copy)
if (output_path / DIR_MODEL_BEST).exists():
shutil.rmtree(output_path / DIR_MODEL_BEST)
shutil.copytree(output_path / DIR_MODEL_LAST,
output_path / DIR_MODEL_BEST)
# Components that shouldn't be updated during training
frozen_components = T["frozen_components"]
# Components that should set annotations on update
annotating_components = T["annotating_components"]
# Create iterator, which yields out info after each optimization step.
training_step_iterator = train_while_improving(
nlp,
optimizer,
create_train_batches(nlp, train_corpus, batcher, T["max_epochs"]),
create_evaluation_callback(nlp, dev_corpus, score_weights),
dropout=T["dropout"],
accumulate_gradient=T["accumulate_gradient"],
patience=T["patience"],
max_steps=T["max_steps"],
eval_frequency=T["eval_frequency"],
exclude=frozen_components,
annotating_components=annotating_components,
)
clean_output_dir(output_path)
stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}") + "\n")
if frozen_components:
stdout.write(
msg.info(f"Frozen components: {frozen_components}") + "\n")
if annotating_components:
stdout.write(
msg.info(f"Set annotations on update for: {annotating_components}")
+ "\n")
stdout.write(
msg.info(f"Initial learn rate: {optimizer.learn_rate}") + "\n")
with nlp.select_pipes(disable=frozen_components):
log_step, finalize_logger = train_logger(nlp, stdout, stderr)
try:
for batch, info, is_best_checkpoint in training_step_iterator:
if is_best_checkpoint is not None:
with nlp.select_pipes(disable=frozen_components):
update_meta(T, nlp, info)
if output_path is not None:
save_checkpoint(is_best_checkpoint)
info["output_path"] = str(output_path / DIR_MODEL_LAST)
log_step(info if is_best_checkpoint is not None else None)
except Exception as e:
if output_path is not None:
stdout.write(
msg.warn(f"Aborting and saving the final best model. "
f"Encountered exception: {repr(e)}") + "\n")
raise e
finally:
finalize_logger()
if output_path is not None:
save_checkpoint(False)
# This will only run if we did't hit an error
if optimizer.averages:
nlp.use_params(optimizer.averages)
if output_path is not None:
stdout.write(
msg.good("Saved pipeline to output directory", output_path /
DIR_MODEL_LAST) + "\n")
return (nlp, output_path / DIR_MODEL_LAST)
else:
return (nlp, None)
def debug_data(
lang,
train_path,
dev_path,
base_model=None,
pipeline="tagger,parser,ner",
ignore_warnings=False,
ignore_validation=False,
verbose=False,
no_format=False,
):
msg = Printer(pretty=not no_format, ignore_warnings=ignore_warnings)
# Make sure all files and paths exists if they are needed
if not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
# Initialize the model and pipeline
pipeline = [p.strip() for p in pipeline.split(",")]
if base_model:
nlp = load_model(base_model)
else:
lang_cls = get_lang_class(lang)
nlp = lang_cls()
msg.divider("Data format validation")
# Load the data in one – might take a while but okay in this case
train_data = _load_file(train_path, msg)
dev_data = _load_file(dev_path, msg)
# Validate data format using the JSON schema
# TODO: update once the new format is ready
train_data_errors = [] # TODO: validate_json
dev_data_errors = [] # TODO: validate_json
if not train_data_errors:
msg.good("Training data JSON format is valid")
if not dev_data_errors:
msg.good("Development data JSON format is valid")
for error in train_data_errors:
msg.fail("Training data: {}".format(error))
for error in dev_data_errors:
msg.fail("Develoment data: {}".format(error))
if (train_data_errors or dev_data_errors) and not ignore_validation:
sys.exit(1)
# Create the gold corpus to be able to better analyze data
with msg.loading("Analyzing corpus..."):
train_data = read_json_object(train_data)
dev_data = read_json_object(dev_data)
corpus = GoldCorpus(train_data, dev_data)
train_docs = list(corpus.train_docs(nlp))
dev_docs = list(corpus.dev_docs(nlp))
msg.good("Corpus is loadable")
# Create all gold data here to avoid iterating over the train_docs constantly
gold_data = _compile_gold(train_docs, pipeline)
train_texts = gold_data["texts"]
dev_texts = set([doc.text for doc, gold in dev_docs])
msg.divider("Training stats")
msg.text("Training pipeline: {}".format(", ".join(pipeline)))
for pipe in [p for p in pipeline if p not in nlp.factories]:
msg.fail("Pipeline component '{}' not available in factories".format(pipe))
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
else:
msg.text("Starting with blank model '{}'".format(lang))
msg.text("{} training docs".format(len(train_docs)))
msg.text("{} evaluation docs".format(len(dev_docs)))
overlap = len(train_texts.intersection(dev_texts))
if overlap:
msg.warn("{} training examples also in evaluation data".format(overlap))
else:
msg.good("No overlap between training and evaluation data")
if not base_model and len(train_docs) < BLANK_MODEL_THRESHOLD:
text = "Low number of examples to train from a blank model ({})".format(
len(train_docs)
)
if len(train_docs) < BLANK_MODEL_MIN_THRESHOLD:
msg.fail(text)
else:
msg.warn(text)
msg.text(
"It's recommended to use at least {} examples (minimum {})".format(
BLANK_MODEL_THRESHOLD, BLANK_MODEL_MIN_THRESHOLD
),
show=verbose,
)
msg.divider("Vocab & Vectors")
n_words = gold_data["n_words"]
msg.info(
"{} total {} in the data ({} unique)".format(
n_words, "word" if n_words == 1 else "words", len(gold_data["words"])
)
)
most_common_words = gold_data["words"].most_common(10)
msg.text(
"10 most common words: {}".format(
_format_labels(most_common_words, counts=True)
),
show=verbose,
)
if len(nlp.vocab.vectors):
msg.info(
"{} vectors ({} unique keys, {} dimensions)".format(
len(nlp.vocab.vectors),
nlp.vocab.vectors.n_keys,
nlp.vocab.vectors_length,
)
)
else:
msg.info("No word vectors present in the model")
if "ner" in pipeline:
# Get all unique NER labels present in the data
labels = set(label for label in gold_data["ner"] if label not in ("O", "-"))
label_counts = gold_data["ner"]
model_labels = _get_labels_from_model(nlp, "ner")
new_labels = [l for l in labels if l not in model_labels]
existing_labels = [l for l in labels if l in model_labels]
has_low_data_warning = False
has_no_neg_warning = False
has_ws_ents_error = False
msg.divider("Named Entity Recognition")
msg.info(
"{} new {}, {} existing {}".format(
len(new_labels),
"label" if len(new_labels) == 1 else "labels",
len(existing_labels),
"label" if len(existing_labels) == 1 else "labels",
)
)
missing_values = label_counts["-"]
msg.text(
"{} missing {} (tokens with '-' label)".format(
missing_values, "value" if missing_values == 1 else "values"
)
)
if new_labels:
labels_with_counts = [
(label, count)
for label, count in label_counts.most_common()
if label != "-"
]
labels_with_counts = _format_labels(labels_with_counts, counts=True)
msg.text("New: {}".format(labels_with_counts), show=verbose)
if existing_labels:
msg.text(
"Existing: {}".format(_format_labels(existing_labels)), show=verbose
)
if gold_data["ws_ents"]:
msg.fail("{} invalid whitespace entity spans".format(gold_data["ws_ents"]))
has_ws_ents_error = True
for label in new_labels:
if label_counts[label] <= NEW_LABEL_THRESHOLD:
msg.warn(
"Low number of examples for new label '{}' ({})".format(
label, label_counts[label]
)
)
has_low_data_warning = True
with msg.loading("Analyzing label distribution..."):
neg_docs = _get_examples_without_label(train_docs, label)
if neg_docs == 0:
msg.warn(
"No examples for texts WITHOUT new label '{}'".format(label)
)
has_no_neg_warning = True
if not has_low_data_warning:
msg.good("Good amount of examples for all labels")
if not has_no_neg_warning:
msg.good("Examples without occurences available for all labels")
if not has_ws_ents_error:
msg.good("No entities consisting of or starting/ending with whitespace")
if has_low_data_warning:
msg.text(
"To train a new entity type, your data should include at "
"least {} insteances of the new label".format(NEW_LABEL_THRESHOLD),
show=verbose,
)
if has_no_neg_warning:
msg.text(
"Training data should always include examples of entities "
"in context, as well as examples without a given entity "
"type.",
show=verbose,
)
if has_ws_ents_error:
msg.text(
"As of spaCy v2.1.0, entity spans consisting of or starting/ending "
"with whitespace characters are considered invalid."
)
if "textcat" in pipeline:
msg.divider("Text Classification")
labels = [label for label in gold_data["textcat"]]
model_labels = _get_labels_from_model(nlp, "textcat")
new_labels = [l for l in labels if l not in model_labels]
existing_labels = [l for l in labels if l in model_labels]
msg.info(
"Text Classification: {} new label(s), {} existing label(s)".format(
len(new_labels), len(existing_labels)
)
)
if new_labels:
labels_with_counts = _format_labels(
gold_data["textcat"].most_common(), counts=True
)
msg.text("New: {}".format(labels_with_counts), show=verbose)
if existing_labels:
msg.text(
"Existing: {}".format(_format_labels(existing_labels)), show=verbose
)
if "tagger" in pipeline:
msg.divider("Part-of-speech Tagging")
labels = [label for label in gold_data["tags"]]
tag_map = nlp.Defaults.tag_map
msg.info(
"{} {} in data ({} {} in tag map)".format(
len(labels),
"label" if len(labels) == 1 else "labels",
len(tag_map),
"label" if len(tag_map) == 1 else "labels",
)
)
labels_with_counts = _format_labels(
gold_data["tags"].most_common(), counts=True
)
msg.text(labels_with_counts, show=verbose)
non_tagmap = [l for l in labels if l not in tag_map]
if not non_tagmap:
msg.good("All labels present in tag map for language '{}'".format(nlp.lang))
for label in non_tagmap:
msg.fail(
"Label '{}' not found in tag map for language '{}'".format(
label, nlp.lang
)
)
if "parser" in pipeline:
msg.divider("Dependency Parsing")
labels = [label for label in gold_data["deps"]]
msg.info(
"{} {} in data".format(
len(labels), "label" if len(labels) == 1 else "labels"
)
)
labels_with_counts = _format_labels(
gold_data["deps"].most_common(), counts=True
)
msg.text(labels_with_counts, show=verbose)
msg.divider("Summary")
good_counts = msg.counts[MESSAGES.GOOD]
warn_counts = msg.counts[MESSAGES.WARN]
fail_counts = msg.counts[MESSAGES.FAIL]
if good_counts:
msg.good(
"{} {} passed".format(
good_counts, "check" if good_counts == 1 else "checks"
)
)
if warn_counts:
msg.warn(
"{} {}".format(warn_counts, "warning" if warn_counts == 1 else "warnings")
)
if fail_counts:
msg.fail("{} {}".format(fail_counts, "error" if fail_counts == 1 else "errors"))
if fail_counts:
sys.exit(1)
import os
from pathlib import Path
import numpy as np
from tqdm import tqdm
from typer import Option, Typer
from wasabi import Printer
from src.elastic import ES
from src.lsh_encoder import LSHEncoder
msg = Printer()
es = ES()
msg.warn("loading features and ids...")
feature_vectors = np.load("/data/feature_vectors.npy")
document_ids = np.load("/data/ids.npy")
msg.good("data loaded successfully")
# populate exact index with dense vectors
msg.warn("indexing exact features...")
def gendata_exact(document_ids, feature_vectors):
loop = tqdm(
zip(document_ids, feature_vectors),
total=len(document_ids)
)
for (document_id, feature_vector) in loop:
features_1, features_2 = feature_vector.reshape(2, 2048)
yield {
class Engine(ClassNursery):
def __init__(
self,
model: nn.Module,
datasets_manager: DatasetsManager,
optimizer: optim,
batch_size: int,
save_dir: str,
num_epochs: int,
save_every: int,
log_train_metrics_every: int,
train_metric: BaseMetric,
validation_metric: BaseMetric,
test_metric: BaseMetric,
experiment_name: Optional[str] = None,
experiment_hyperparams: Optional[Dict[str, Any]] = None,
tensorboard_logdir: str = None,
track_for_best: str = "loss",
collate_fn=list,
device: Union[torch.device, str] = torch.device("cpu"),
gradient_norm_clip_value: Optional[float] = 5.0,
lr_scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
use_wandb: bool = False,
sample_proportion: float = 1.0,
seeds: Dict[str, int] = None,
):
""" Engine runs the models end to end. It iterates through the train dataset and passes
it through the model. During training it helps in tracking a lot of parameters for the run
and saving the parameters. It also reports validation and test parameters from time to time.
Many utilities required for end-end running of the model is here.
Parameters
----------
model : nn.Module
A pytorch module defining a model to be run
datasets_manager : DatasetsManager
A datasets manager that handles all the different datasets
optimizer : torch.optim
Any Optimizer object instantiated using ``torch.optim``
batch_size : int
Batch size for the dataset. The same batch size is used for ``train``, ``valid``
and ``test`` dataset
save_dir : int
The experiments are saved in ``save_dir``. We save checkpoints, the best model,
logs and other information into the save dir
num_epochs : int
The number of epochs to run the training
save_every : int
The model will be checkpointed every ``save_every`` number of iterations
log_train_metrics_every : int
The train metrics will be reported every ``log_train_metrics_every`` iterations
during training
train_metric : BaseMetric
Anything that is an instance of ``BaseMetric`` for calculating training metrics
validation_metric : BaseMetric
Anything that is an instance of ``BaseMetric`` for calculating validation metrics
test_metric : BaseMetric
Anything that is an instance of ``BaseMetric`` for calculating test metrics
experiment_name : str
The experiment should be given a name for ease of tracking. Instead experiment
name is not given, we generate a unique 10 digit sha for the experiment.
experiment_hyperparams : Dict[str, Any]
This is mostly used for tracking the different hyper-params of the experiment
being run. This may be used by ``wandb`` to save the hyper-params
tensorboard_logdir : str
The directory where all the tensorboard runs are stored. If ``None`` is passed
then it defaults to the tensorboard default of storing the log in the current directory.
track_for_best : str
Which metric should be tracked for deciding the best model?. Anything that
the metric emits and is a single value can be used for tracking. The defauly value
is ``loss``. If its loss, then the best value will be the lowest one. For some
other metrics like ``macro_fscore``, the best metric might be the one that has the highest
value
collate_fn : Callable[[List[Any]], List[Any]]
Collates the different examples into a single batch of examples.
This is the same terminology adopted from ``pytorch``. There is no different
device : torch.device
The device on which the model will be placed. If this is "cpu", then the model
and the tensors will all be on cpu. If this is "cuda:0", then the model and
the tensors will be placed on cuda device 0. You can mention any other cuda
device that is suitable for your environment
gradient_norm_clip_value : float
To avoid gradient explosion, the gradients of the norm will be clipped
if the gradient norm exceeds this value
lr_scheduler : torch.optim.lr_scheduler
Any pytorch ``lr_scheduler`` can be used for reducing the learning rate
if the performance on the validation set reduces.
use_wandb : bool
wandb or weights and biases is a tool that is used to track experiments
online. Sciwing comes with inbuilt functionality to track experiments
on weights and biases
seeds: Dict[str, int]
The dict of seeds to be set.
Set the random_seed, pytorch_seed and numpy_seed
Found in
https://github.com/allenai/allennlp/blob/master/allennlp/common/util.py
"""
if isinstance(device, str):
device = torch.device(device)
if seeds is None:
seeds = {}
self.seeds = seeds
self._set_seeds()
self.model = model
self.datasets_manager = datasets_manager
self.train_dataset = self.datasets_manager.train_dataset
self.validation_dataset = self.datasets_manager.dev_dataset
self.test_dataset = self.datasets_manager.test_dataset
self.optimizer = optimizer
self.batch_size = batch_size
self.save_dir = pathlib.Path(save_dir)
self.num_epochs = num_epochs
self.msg_printer = Printer()
self.save_every = save_every
self.log_train_metrics_every = log_train_metrics_every
self.tensorboard_logdir = tensorboard_logdir
self.train_metric_calc = train_metric
self.validation_metric_calc = validation_metric
self.test_metric_calc = test_metric
self.summaryWriter = SummaryWriter(log_dir=tensorboard_logdir)
self.track_for_best = track_for_best
self.collate_fn = collate_fn
self.device = device
self.best_track_value = None
self.set_best_track_value(self.best_track_value)
self.gradient_norm_clip_value = gradient_norm_clip_value
self.lr_scheduler = lr_scheduler
self.lr_scheduler_is_plateau = isinstance(
self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
self.use_wandb = wandb and use_wandb
self.sample_proportion = sample_proportion
self.label_namespaces = self.datasets_manager.label_namespaces
self.datasets_manager.print_stats()
if experiment_name is None:
hash_ = hashlib.sha1()
hash_.update(str(time.time()).encode("utf-8"))
digest = hash_.hexdigest()
experiment_name = digest[:10]
self.experiment_name = experiment_name
self.experiment_hyperparams = experiment_hyperparams or {}
if self.use_wandb:
wandb.init(
project="project-scwing",
name=self.experiment_name,
config=self.experiment_hyperparams,
)
if not self.save_dir.is_dir():
self.save_dir.mkdir(parents=True)
with open(self.save_dir.joinpath("hyperparams.json"), "w") as fp:
json.dump(self.experiment_hyperparams, fp)
self.num_workers = 1
self.model.to(self.device)
self.train_loader = self.get_loader(self.train_dataset)
self.validation_loader = self.get_loader(self.validation_dataset)
self.test_loader = self.get_loader(self.test_dataset)
# refresh the iters at the beginning of every epoch
self.train_iter = None
self.validation_iter = None
self.test_iter = None
# initializing loss meters
self.train_loss_meter = LossMeter()
self.validation_loss_meter = LossMeter()
self.msg_printer.divider("ENGINE STARTING")
time.sleep(3)
# get the loggers ready
self.train_log_filename = self.save_dir.joinpath("train.log")
self.validation_log_filename = self.save_dir.joinpath("validation.log")
self.test_log_filename = self.save_dir.joinpath("test.log")
self.train_logger = logzero.setup_logger(
name="train-logger",
logfile=self.train_log_filename,
level=logging.INFO)
self.validation_logger = logzero.setup_logger(
name="valid-logger",
logfile=self.validation_log_filename,
level=logging.INFO,
)
self.test_logger = logzero.setup_logger(name="test-logger",
logfile=self.test_log_filename,
level=logging.INFO)
if self.lr_scheduler_is_plateau:
if self.best_track_value == "loss" and self.lr_scheduler.mode == "max":
self.msg_printer.warn(
"You are optimizing loss and lr schedule mode is max instead of min"
)
if (self.best_track_value == "macro_fscore"
or self.best_track_value == "fscore"
and self.lr_scheduler.mode == "min"):
self.msg_printer.warn(
f"You are optimizing for macro_fscore and lr scheduler mode is min instead of max"
)
if (self.best_track_value == "micro_fscore"
and self.lr_scheduler.mode == "min"):
self.msg_printer.warn(
f"You are optimizing for micro_fscore and lr scheduler mode is min instead of max"
)
def get_loader(self, dataset: Dataset) -> DataLoader:
""" Returns the DataLoader for the Dataset
Parameters
----------
dataset : Dataset
Returns
-------
DataLoader
A pytorch DataLoader
"""
dataset_size = len(dataset)
sample_size = int(np.floor(dataset_size * self.sample_proportion))
indices = np.random.choice(range(dataset_size),
size=sample_size,
replace=False)
sampler = SubsetRandomSampler(indices=indices)
loader = DataLoader(
dataset=dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
collate_fn=self.collate_fn,
pin_memory=True,
sampler=sampler,
)
return loader
def is_best_lower(self, current_best=None):
""" Returns True if the current value of the metric is lower than the best metric.
This is useful for tracking metrics like loss where, lower the value, the better it is
Parameters
----------
current_best : float
The current value for the metric that is being tracked
Returns
-------
bool
"""
return True if current_best < self.best_track_value else False
def is_best_higher(self, current_best=None):
""" Returns ``True`` if the current value of the metric is HIGHER than the best metric.
This is useful for tracking metrics like FSCORE where, higher the value, the better it is
Parameters
----------
current_best : float
The current value for the metric that is being tracked
Returns
-------
bool
"""
return True if current_best >= self.best_track_value else False
def set_best_track_value(self, current_best=None):
""" Set the best value of the value being tracked
Parameters
----------
current_best : float
The current value that is best
Returns
-------
"""
if self.track_for_best == "loss":
self.best_track_value = np.inf if current_best is None else current_best
elif self.track_for_best == "macro_fscore" or self.track_for_best == "fscore":
self.best_track_value = 0 if current_best is None else current_best
elif self.track_for_best == "micro_fscore":
self.best_track_value = 0 if current_best is None else current_best
def run(self):
"""
Run the engine
:return:
"""
for epoch_num in range(self.num_epochs):
self.train_epoch(epoch_num)
self.validation_epoch(epoch_num)
self.test_epoch(epoch_num)
def train_epoch(self, epoch_num: int):
"""
Run the training for one epoch
:param epoch_num: type: int
The current epoch number
"""
# refresh everything necessary before training begins
num_iterations = 0
train_iter = self.get_iter(self.train_loader)
self.train_loss_meter.reset()
self.train_metric_calc.reset()
self.model.train()
self.msg_printer.info(
f"Starting Training Epoch: {epoch_num+1}/{self.num_epochs}")
while True:
try:
# N*T, N * 1, N * 1
lines_labels = next(train_iter)
lines_labels = list(zip(*lines_labels))
lines = lines_labels[0]
labels = lines_labels[1]
batch_size = len(lines)
model_forward_out = self.model(
lines=lines,
labels=labels,
is_training=True,
is_validation=False,
is_test=False,
)
self.train_metric_calc.calc_metric(
lines=lines,
labels=labels,
model_forward_dict=model_forward_out)
try:
self.optimizer.zero_grad()
loss = model_forward_out["loss"]
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(),
max_norm=self.gradient_norm_clip_value)
self.optimizer.step()
self.train_loss_meter.add_loss(loss.item(), batch_size)
except KeyError:
self.msg_printer.fail(
"The model output dictionary does not have "
"a key called loss. Please check to have "
"loss in the model output")
num_iterations += 1
if (num_iterations + 1) % self.log_train_metrics_every == 0:
metrics = self.train_metric_calc.report_metrics()
for label_namespace, table in metrics.items():
self.msg_printer.divider(
text=f"Train Metrics for {label_namespace.upper()}"
)
print(table)
except StopIteration:
self.train_epoch_end(epoch_num)
break
def train_epoch_end(self, epoch_num: int):
""" Performs house-keeping at the end of a training epoch
At the end of the training epoch, it does some house-keeping. It reports the average loss, the
average metric and other information.
Parameters
----------
epoch_num : int
The current epoch number (0 based)
"""
self.msg_printer.divider(f"Training end @ Epoch {epoch_num + 1}")
average_loss = self.train_loss_meter.get_average()
self.msg_printer.text("Average Loss: {0}".format(average_loss))
self.train_logger.info(
f"Average loss @ Epoch {epoch_num+1} - {average_loss}")
metric = self.train_metric_calc.get_metric()
if self.use_wandb:
wandb.log({"train_loss": average_loss}, step=epoch_num + 1)
if self.track_for_best != "loss":
for label_namespace in self.label_namespaces:
wandb.log(
{
f"train_{self.track_for_best}_{label_namespace}":
metric[label_namespace][self.track_for_best]
},
step=epoch_num + 1,
)
# save the model after every `self.save_every` epochs
if (epoch_num + 1) % self.save_every == 0:
torch.save(
{
"epoch_num": epoch_num,
"optimizer_state": self.optimizer.state_dict(),
"model_state": self.model.state_dict(),
"loss": average_loss,
},
self.save_dir.joinpath(f"model_epoch_{epoch_num+1}.pt"),
)
# log loss to tensor board
self.summaryWriter.add_scalars(
"train_validation_loss",
{"train_loss": average_loss or np.inf},
epoch_num + 1,
)
def validation_epoch(self, epoch_num: int):
""" Runs one validation epoch on the validation dataset
Parameters
----------
epoch_num : int
0-based epoch number
"""
self.model.eval()
valid_iter = iter(self.validation_loader)
self.validation_loss_meter.reset()
self.validation_metric_calc.reset()
self.msg_printer.info(
f"Starting Validation Epoch: {epoch_num + 1}/{self.num_epochs}")
while True:
try:
lines_labels = next(valid_iter)
lines_labels = list(zip(*lines_labels))
lines = lines_labels[0]
labels = lines_labels[1]
batch_size = len(lines)
with torch.no_grad():
model_forward_out = self.model(
lines=lines,
labels=labels,
is_training=False,
is_validation=True,
is_test=False,
)
loss = model_forward_out["loss"]
self.validation_loss_meter.add_loss(loss, batch_size)
self.validation_metric_calc.calc_metric(
lines=lines,
labels=labels,
model_forward_dict=model_forward_out)
except StopIteration:
self.validation_epoch_end(epoch_num)
break
def validation_epoch_end(self, epoch_num: int):
"""Performs house-keeping at the end of validation epoch
Parameters
----------
epoch_num : int
The current epoch number
"""
self.msg_printer.divider(f"Validation @ Epoch {epoch_num+1}")
metric_report = self.validation_metric_calc.report_metrics()
average_loss = self.validation_loss_meter.get_average()
for label_namespace, table in metric_report.items():
self.msg_printer.divider(
text=f"Validation Metrics for {label_namespace.upper()}")
print(table)
self.msg_printer.text(f"Average Loss: {average_loss}")
self.validation_logger.info(
f"Validation Loss @ Epoch {epoch_num+1} - {average_loss}")
if self.use_wandb:
wandb.log({"validation_loss": average_loss}, step=epoch_num + 1)
metric = self.validation_metric_calc.get_metric()
if self.track_for_best != "loss":
for label_namespace in self.label_namespaces:
wandb.log(
{
f"validation_{self.track_for_best}_{label_namespace}":
metric[label_namespace][self.track_for_best]
},
step=epoch_num + 1,
)
self.summaryWriter.add_scalars(
"train_validation_loss",
{"validation_loss": average_loss or np.inf},
epoch_num + 1,
)
is_best: bool = None
value_tracked: str = None
if self.track_for_best == "loss":
value_tracked = average_loss
is_best = self.is_best_lower(average_loss)
elif (self.track_for_best == "micro_fscore"
or self.track_for_best == "macro_fscore"
or self.track_for_best == "fscore"):
# If there are multiple namespaces for the metric
# we decide the best model based on the average score
values_tracked = []
metrics = self.validation_metric_calc.get_metric()
for label_namespace in self.label_namespaces:
value_tracked = metrics[label_namespace][self.track_for_best]
values_tracked.append(value_tracked)
value_tracked = sum(values_tracked) / len(values_tracked)
is_best = self.is_best_higher(current_best=value_tracked)
if self.lr_scheduler is not None:
self.lr_scheduler.step(value_tracked)
if is_best:
self.set_best_track_value(current_best=value_tracked)
self.msg_printer.good(f"Found Best Model @ epoch {epoch_num + 1}")
torch.save(
{
"epoch_num": epoch_num,
"optimizer_state": self.optimizer.state_dict(),
"model_state": self.model.state_dict(),
"loss": average_loss,
},
self.save_dir.joinpath("best_model.pt"),
)
def test_epoch(self, epoch_num: int):
"""Runs the test epoch for ``epoch_num``
Loads the best model that is saved during the training
and runs the test dataset.
Parameters
----------
epoch_num : int
zero based epoch number for which the test dataset is run
This is after the last training epoch.
"""
self.msg_printer.divider("Running on Test Batch")
self.load_model_from_file(self.save_dir.joinpath("best_model.pt"))
self.model.eval()
test_iter = iter(self.test_loader)
while True:
try:
lines_labels = next(test_iter)
lines_labels = list(zip(*lines_labels))
lines = lines_labels[0]
labels = lines_labels[1]
with torch.no_grad():
model_forward_out = self.model(
lines=lines,
labels=labels,
is_training=False,
is_validation=False,
is_test=True,
)
self.test_metric_calc.calc_metric(
lines=lines,
labels=labels,
model_forward_dict=model_forward_out)
except StopIteration:
self.test_epoch_end(epoch_num)
break
def test_epoch_end(self, epoch_num: int):
""" Performs house-keeping at the end of the test epoch
It reports the metric that is being traced at the end
of the test epoch
Parameters
----------
epoch_num : int
Epoch num after which the test dataset is run
"""
metric_report = self.test_metric_calc.report_metrics()
for label_namespace, table in metric_report.items():
self.msg_printer.divider(
text=f"Test Metrics for {label_namespace.upper()}")
print(table)
precision_recall_fmeasure = self.test_metric_calc.get_metric()
self.msg_printer.divider(f"Test @ Epoch {epoch_num+1}")
self.test_logger.info(
f"Test Metrics @ Epoch {epoch_num+1} - {precision_recall_fmeasure}"
)
if self.use_wandb:
wandb.log({"test_metrics": str(precision_recall_fmeasure)})
self.summaryWriter.close()
def get_train_dataset(self):
""" Returns the train dataset of the experiment
Returns
-------
Dataset
Anything that conforms to the pytorch style dataset.
"""
return self.train_dataset
def get_validation_dataset(self):
""" Returns the validation dataset of the experiment
Returns
-------
Dataset
Anything that conforms to the pytorch style dataset.
"""
return self.validation_dataset
def get_test_dataset(self):
""" Returns the test dataset of the experiment
Returns
-------
Dataset
Anything that conforms to the pytorch style dataset.
"""
return self.test_dataset
@staticmethod
def get_iter(loader: DataLoader) -> Iterator:
""" Returns the iterator for a pytorch data loader.
The ``loader`` is a pytorch DataLoader that iterates
over the dataset in batches and employs many strategies to do
so. We want an iterator that returns the dataset in batches.
The end of the iterator would signify the end of an epoch
and then we can use that information to perform house-keeping.
Parameters
----------
loader : DataLoader
a pytorch data loader
Returns
-------
Iterator
An iterator over the data loader
"""
iterator = iter(loader)
return iterator
def load_model_from_file(self, filename: str):
self.msg_printer.divider("LOADING MODEL FROM FILE")
with self.msg_printer.loading(
f"Loading Pytorch Model from file {filename}"):
model_chkpoint = torch.load(filename)
self.msg_printer.good("Finished Loading the Model")
model_state = model_chkpoint["model_state"]
self.model.load_state_dict(model_state)
def _set_seeds(self):
seed = self.seeds.get("random_seed", 17290)
numpy_seed = self.seeds.get("numpy_seed", 1729)
torch_seed = self.seeds.get("pytorch_seed", 172)
if seed is not None:
random.seed(seed)
if numpy_seed is not None:
np.random.seed(numpy_seed)
if torch_seed is not None:
torch.manual_seed(torch_seed)
# Seed all GPUs with the same seed if available.
if torch.cuda.is_available():
torch.cuda.manual_seed_all(torch_seed)
class Vocab:
def __init__(
self,
instances: Optional[List[List[str]]] = None,
max_num_tokens: int = None,
min_count: int = 1,
unk_token: str = "<UNK>",
pad_token: str = "<PAD>",
start_token: str = "<SOS>",
end_token: str = "<EOS>",
special_token_freq: float = 1e10,
store_location: str = None,
max_instance_length: int = 100,
include_special_vocab: bool = True,
preprocessing_pipeline: List[Callable] = None,
):
"""
Parameters
----------
instances : Optional[List[List[str]]]
A list of tokenized instances
max_num_tokens : int
The maximum number of tokens to be used in the vocab
All the other tokens above this number will be replaced
by UNK.
If this is not passed then the maximum possible number
will be used
min_count : int
All words that do not have min count will be mapped to `unk_token`
unk_token : str
This token will be used for unknown words
pad_token : str
This token will be used for <PAD> words
start_token : str
This token will be used for start of line indicator
end_token : str
This token will be used for end of sentence indicator
special_token_freq : float
special tokens should have high frequency.
store_location : str
The users can provide a store location optionally.
The vocab will be stored in the location
If the file exists then, the vocab will be restored from the file, rather than building it.
max_instance_length : int
Every vocab is related to a namespace. Every instance
in that namespace will be clipped or padded to this
length
include_special_vocab : bool
Boolean value to indicate whether special vocab should be included or no
If this is false, you will have to set add_start_end_token to False
and you cannot pad your instances. This is mostly set for labels -
such as for classification that require no padding. For such
cases please make sure that min_count is always 1 and max_num_tokens
is always None. Otherwise some of the labels will be missed and it
might result in error
preprocessing_pipeline: List[Callable]
You can add a set of callables that take in a list of
str and return a list of str for pre-processing. For
example methods look at instance_preprocessing module in sciwing.preprocessing
"""
self.instances = instances
self.max_num_tokens = max_num_tokens
self.min_count = min_count
self.unk_token = unk_token
self.pad_token = pad_token
self.start_token = start_token
self.end_token = end_token
self.special_token_freq = special_token_freq
self.vocab = None
self.orig_vocab = None
self.idx2token = None
self.token2idx = None
self.store_location = store_location
self.max_instance_length = max_instance_length
self.include_special_vocab = include_special_vocab
self.preprocessing_pipeline = preprocessing_pipeline
self.msg_printer = Printer()
# store the special tokens
if self.include_special_vocab:
self.special_vocab = {
self.unk_token: (self.special_token_freq + 3, 0),
self.pad_token: (self.special_token_freq + 2, 1),
self.start_token: (self.special_token_freq + 1, 2),
self.end_token: (self.special_token_freq, 3),
}
else:
if self.min_count != 1:
self.msg_printer.warn(
"Warning: You are building vocab without special vocab. "
"Please make sure that min_count is 1")
if self.max_num_tokens is not None:
self.msg_printer.warn(
"You are building vocab without special vocab. Please make "
"sure that max_num_tokens is None")
self.special_vocab = {}
if instances is not None:
self.instances = list(
flatten(instances)) # just flatten the entire instance
if isinstance(self.instances[0], Token):
self.instances = [tok.text for tok in self.instances]
if self.preprocessing_pipeline:
self.instances = self.apply_preprocessing()
def apply_preprocessing(self):
instances = deepcopy(self.instances)
for preprocessor in self.preprocessing_pipeline:
instances = preprocessor(instances)
return instances
def map_tokens_to_freq_idx(self) -> Dict[str, Tuple[int, int]]:
"""
Build vocab from instances
return the word -> (freq, idx)
:return:
"""
all_tokens = deepcopy(self.instances)
# counter will map a list to Dict[str, count] values
counter = Counter(all_tokens)
# order the order in decreasing order of their frequencies
# List[Tuple]
counter = sorted(counter.items(), key=itemgetter(1), reverse=True)
vocab = {}
for idx, (token, freq) in enumerate(counter):
vocab[token] = (freq, len(self.special_vocab) + idx)
# merge the two dictionaries
# courtesy https://stackoverflow.com/questions/38987/how-to-merge-two-dictionaries-in-a-single-expression
vocab = {**vocab, **self.special_vocab}
# BUG: if vocab and special vocab share same token, then
# the index of the vocab will get overwritten by special vocab
# the only way now is to recalculate indices based on frequencies
vocab = sorted(vocab.items(), key=itemgetter(1), reverse=True)
new_vocab = {}
for idx, (token, (freq, _)) in enumerate(vocab):
new_vocab[token] = (freq, idx)
return new_vocab
def clip_on_mincount(
self, vocab: Dict[str, Tuple[int,
int]]) -> Dict[str, Tuple[int, int]]:
"""
Clip the vocab based on min count
We decide to keep the word and it count
We just change the idx of the token to idx of the unknown token
:return: vocab: type: Dict[str, Tuple[int, int]]
The new vocab
"""
for key, (freq, idx) in vocab.items():
if freq < self.min_count:
vocab[key] = (freq, vocab[self.unk_token][1])
return vocab
def clip_on_max_num(
self, vocab: Dict[str, Tuple[int,
int]]) -> Dict[str, Tuple[int, int]]:
"""
Clip the vocab based on the maximum number of words
We return `max_num_words + len(self.special_vocab)` words effectively
The rest of them will be mapped to `self.unk_token`
Parameters
----------
vocab : Dict[str, Tuple[int, int]]
The mapping from token to idx and frequency
Returns
-------
Dict[str, Tuple[int, int]]
The new vocab
"""
for key, (freq, idx) in vocab.items():
if idx >= len(self.special_vocab) + self.max_num_tokens:
vocab[key] = (freq, vocab[self.unk_token][1])
return vocab
def _add_token(self, token: str, save_vocab: bool = False):
"""
Add token to an already existing vocabulary
:param token: type str
:return:
"""
try:
vocab = self.vocab
except AttributeError:
self.msg_printer.fail("Please build vocab using build vocab")
tokens = vocab.keys()
indices = [idx for freq, idx in vocab.values()]
indices = sorted(indices, reverse=True)
highest_idx = indices[0]
if token not in tokens:
self.vocab[token] = (1, highest_idx + 1)
self.idx2token[highest_idx + 1] = token
self.token2idx[token] = highest_idx + 1
if save_vocab:
self.save_to_file(
self.store_location) # this can be expensive.
def add_tokens(self, tokens: List[str]):
try:
vocab = self.vocab
except AttributeError:
self.msg_printer.fail("Please build vocab first")
for token in tokens:
self._add_token(token, save_vocab=False)
if self.store_location:
self.save_to_file(self.store_location)
def build_vocab(self) -> Dict[str, Tuple[int, int]]:
if self.store_location and os.path.isfile(self.store_location):
vocab_object = self.load_from_file(self.store_location)
self.msg_printer.good("Loaded vocab from file {0}".format(
self.store_location))
self.vocab = vocab_object.vocab
self.orig_vocab = vocab_object.orig_vocab
self.idx2token = vocab_object.idx2token
self.token2idx = vocab_object.token2idx
vocab = vocab_object.vocab
else:
self.msg_printer.info("BUILDING VOCAB")
vocab = self.map_tokens_to_freq_idx()
# dictionary are passed by reference. Be careful
self.orig_vocab = deepcopy(vocab)
# set max num of tokens to maximum possible if it is not set
if self.max_num_tokens is None:
self.max_num_tokens = len(self.orig_vocab.keys())
vocab = self.clip_on_mincount(vocab)
vocab = self.clip_on_max_num(vocab)
self.vocab = vocab
self.idx2token = self.get_idx2token_mapping()
self.token2idx = self.get_token2idx_mapping()
if self.store_location:
self.msg_printer.info("SAVING VOCAB TO FILE")
self.save_to_file(self.store_location)
return vocab
def get_vocab_len(self) -> int:
if not self.vocab:
raise ValueError("Build vocab first by calling build_vocab()")
length = len(set(idx for freq, idx in self.vocab.values()))
return length
def get_orig_vocab_len(self) -> int:
if not self.orig_vocab:
raise ValueError("Build vocab first by calling build_vocab()")
length = len(set(idx for freq, idx in self.orig_vocab.values()))
return length
def get_token2idx_mapping(self) -> Dict[str, int]:
if not self.vocab:
raise ValueError("Build vocab first by calling build_vocab()")
token2idx = {}
for word, (freq, idx) in self.vocab.items():
token2idx[word] = idx
return token2idx
def get_idx2token_mapping(self) -> Dict[int, str]:
if not self.vocab:
raise ValueError("Build vocab first by calling build_vocab()")
idx2words = {}
for word, (freq, idx) in self.vocab.items():
idx2words[idx] = word
return idx2words
def save_to_file(self, filename: str):
"""
:param filename: str
The filename where the result to the file will be stored
The vocab will be stored in the json file name
Please make sure that this is a json filename
:return: None
The whole vocab object will be saved to the file
"""
if not self.vocab:
raise ValueError("Build vocab first by calling build_vocab()")
vocab_state = dict()
vocab_state["options"] = {
"max_num_words": self.max_num_tokens,
"min_count": self.min_count,
"unk_token": self.unk_token,
"pad_token": self.pad_token,
"start_token": self.start_token,
"end_token": self.end_token,
"special_token_freq": self.special_token_freq,
"special_vocab": self.special_vocab,
}
vocab_state["vocab"] = self.vocab
vocab_state["orig_vocab"] = self.orig_vocab
try:
with open(filename, "w") as fp:
json.dump(vocab_state, fp)
except FileNotFoundError:
print("You passed {0} for the filename. Please check whether "
"the path exists and try again".format(filename))
@classmethod
def load_from_file(cls, filename: str) -> "Vocab":
try:
with open(filename, "r") as fp:
vocab_state = json.load(fp)
vocab_options = vocab_state["options"]
vocab_dict = vocab_state["vocab"]
orig_vocab_dict = vocab_state["orig_vocab"]
# restore the object
# restore all the property values from the file
max_num_tokens = vocab_options["max_num_words"]
min_count = vocab_options["min_count"]
unk_token = vocab_options["unk_token"]
pad_token = vocab_options["pad_token"]
start_token = vocab_options["start_token"]
end_token = vocab_options["end_token"]
special_token_freq = vocab_options["special_token_freq"]
store_location = filename
vocab = cls(
max_num_tokens=max_num_tokens,
min_count=min_count,
unk_token=unk_token,
pad_token=pad_token,
start_token=start_token,
end_token=end_token,
instances=None,
special_token_freq=special_token_freq,
store_location=store_location,
)
# instead of building the vocab, set the vocab from vocab_dict
vocab.set_vocab(vocab=vocab_dict)
vocab.set_orig_vocab(orig_vocab_dict)
idx2token = vocab.get_idx2token_mapping()
token2idx = vocab.get_token2idx_mapping()
vocab.set_idx2token(idx2token)
vocab.set_token2idx(token2idx)
return vocab
except FileNotFoundError:
print("You passed {0} for the filename. Please check whether "
"the path exists and try again. Please pass "
"a json file".format(filename))
def get_token_from_idx(self, idx: int) -> str:
if not self.vocab:
raise ValueError("Please build the vocab first")
if not self.idx2token:
self.idx2token = self.get_idx2token_mapping()
vocab_len = self.get_vocab_len()
if idx > vocab_len - 1:
message = (
f"You tried to access idx {idx} of the vocab The length of the vocab is "
f"{vocab_len}. Please Provide Number between 0 and {vocab_len - 1}"
)
raise ValueError(message)
token = self.idx2token.get(idx)
return token
def get_idx_from_token(self, token: str) -> int:
if not self.vocab:
raise ValueError("Please build the vocab first")
if not self.token2idx:
self.token2idx = self.get_token2idx_mapping()
try:
return self.token2idx[token]
except KeyError:
return self.token2idx.get(self.unk_token, None)
def get_topn_frequent_words(self, n: int = 5) -> List[Tuple[str, int]]:
idx2token = self.idx2token
token_freqs = []
max_n = min(len(self.special_vocab) + n, self.get_vocab_len())
for idx in range(len(self.special_vocab), max_n):
token = idx2token[idx]
freq = self.orig_vocab[token][0]
token_freqs.append((token, freq))
return token_freqs
def print_stats(self) -> None:
orig_vocab_len = self.get_orig_vocab_len()
vocab_len = self.get_vocab_len()
N = 5
top_n = self.get_topn_frequent_words(n=N)
data = [
("Original vocab length", orig_vocab_len),
("Clipped vocab length", vocab_len),
("Top {0} words".format(N), top_n),
]
header = ("Stats Description", "#")
table_string = wasabi.table(data=data, header=header, divider=True)
self.msg_printer.divider("VOCAB STATS")
print(table_string)
def set_vocab(self, vocab: Dict[str, Tuple[int, int]]):
self.vocab = vocab
def set_orig_vocab(self, orig_vocab: Dict[str, Tuple[int, int]]):
self.orig_vocab = orig_vocab
def set_idx2token(self, idx2token: Dict[int, str]):
self.idx2token = idx2token
def set_token2idx(self, token2idx: Dict[str, int]):
self.token2idx = token2idx
def get_disp_sentence_from_indices(self, indices: List[int]) -> str:
""" Given a set of indices in vocab, it returns a sentence mapping the index to string
Parameters
----------
indices : List[int]
A list of indices where every index is between ``[0, vocab_len-1)``.
Returns
-------
str
A string representing the index
"""
if self.special_vocab:
pad_token_index = self.get_idx_from_token(self.pad_token)
start_token_index = self.get_idx_from_token(self.start_token)
end_token_index = self.get_idx_from_token(self.end_token)
special_indices = [
pad_token_index, start_token_index, end_token_index
]
else:
special_indices = []
token = [
self.get_token_from_idx(idx) for idx in indices
if idx not in special_indices
]
sentence = " ".join(token)
return sentence
@property
def token2idx(self):
return self._token2idx
@token2idx.setter
def token2idx(self, value):
self._token2idx = value
@property
def idx2token(self):
return self._idx2token
@idx2token.setter
def idx2token(self, value):
self._idx2token = value
