def diff():
"""Git like diff tool to compare sentence generated by our tokenizer vs actual list of sentences."""
click.secho("Loading corpus...")
raw, sents = load_raw_corpus(False), load_sentence_corpus()
y_true = [doc['sentences'] for doc in sents]
y_pred = [Doc(doc) for doc in raw]
paths = file_paths()
for i in range(len(y_true)):
if y_true[i] != y_pred[i]:
click.secho(f"Document {paths[i]}")
for s_true in y_true[i]:
if s_true not in y_pred[i]:
click.secho(f"+ {s_true}", fg="green")
click.secho()
for s_pred in y_pred[i]:
if s_pred not in y_true[i]:
click.secho(f"- {s_pred}", fg="red")
click.secho()
click.secho()
def build():
"""Build a ML based SBD"""
raw_corpus = load_raw_corpus(False)
sent_corpus = load_sentence_corpus(False)
features = flatten([[span.span_features() for span in Doc(raw).spans]
for raw in raw_corpus])
y = flatten([[is_eos(span, sent['sentences']) for span in Doc(raw).spans]
for raw, sent in zip(raw_corpus, sent_corpus)])
if len(features) != len(y):
raise Exception(
f"Sanity check failed feature list length {len(features)} whereas target list length {len(y)}."
)
sbd_model = create_model()
scores = cross_val_score(sbd_model.pipeline, features, y, scoring="f1")
for i, score in enumerate(scores):
click.secho(f"Fold {i + 1}: {score:.4f}", fg="yellow")
sbd_model.fit(features, y)
click.secho("\nTop 10 Features")
feature_importance = sbd_model.pipeline.steps[1][1].feature_importances_
for idx in list(reversed(feature_importance.argsort()))[:20]:
click.secho(
f" {sbd_model.pipeline.steps[0][1].feature_names_[idx]}: {feature_importance[idx]:.4f}",
fg="yellow")
save_model(sbd_model)
def diff(verbose):
"""Git like diff tool to compare sentence generated by our tokenizer vs actual list of sentences."""
click.secho("Loading corpus...")
raw, sents = load_raw_corpus(False), load_sentence_corpus()
y_true = [doc['sentences'] for doc in sents]
y_pred = [[str(s) for s in Doc(doc)] for doc in raw]
paths = file_paths()
differ = Differ()
for t, p, f in zip(y_true, y_pred, paths):
table = Table(show_header=True,
header_style="bold magenta",
show_edge=False)
table.add_column("true", style="dim", width=100)
table.add_column("predict", style="dim", width=100)
table.columns[0].style = "green"
table.columns[1].style = "red"
ndiff = 0
match = 0
for sent in differ.compare(p, t):
if sent.startswith('+'):
if match > 0 and verbose > 0:
table.add_row(f"[blue]{match} sentences...[/blue]",
f"[blue]{match} sentences...[/blue]")
match = 0
table.add_row(sent[2:], "")
ndiff += 1
elif sent.startswith('-'):
if match > 0 and verbose > 0:
table.add_row(f"[blue]{match} sentences...[/blue]",
f"[blue]{match} sentences...[/blue]")
match = 0
table.add_row("", sent[2:])
ndiff += 1
else:
match += 1
if match > 0 and verbose > 0:
table.add_row(f"[blue]{match} sentences...[/blue]",
f"[blue]{match} sentences...[/blue]")
if ndiff > 0:
console.print(f)
console.print(table)
console.print(f"[blue]{len(t)} sentences...[/blue]")
console.print()
def evaluate(v):
"""Evaluate IoU metric for different SBD algorithms over our stock dataset."""
click.secho("Loading corpus...")
raw, sents = load_raw_corpus(False), load_sentence_corpus()
nltk = NLTKPunctTokenizer()
reg = RegexpSentenceTokenizer()
y_pred = [nltk(doc) for doc in raw]
y_true = [doc['sentences'] for doc in sents]
iou_eval("NLTKPunctTokenizer", y_true, y_pred, file_paths() if v > 0 else None)
y_pred = [reg(doc) for doc in raw]
iou_eval("RegexpSentenceTokenizer", y_true, y_pred, file_paths() if v > 0 else None)
y_pred = [[s.text for s in Doc(doc).sents] for doc in raw]
iou_eval("MLBasedTokenizer", y_true, y_pred, file_paths() if v > 0 else None)
def validate(v, base_path):
from sadedegel.dataset import load_raw_corpus, load_sentence_corpus, load_annotated_corpus, file_paths, \
CorpusTypeEnum
click.secho("Corpus loading...")
raw = load_raw_corpus(False, base_path)
sents = load_sentence_corpus(False, base_path)
anno = load_annotated_corpus(False, base_path)
click.secho(".done.", fg="green")
click.secho(f"Number of News Documents (raw): {len(raw)}".rjust(50))
click.secho(
f"Number of News Documents (sentences): {len(sents)}".rjust(50))
click.secho(f"Number of News Documents (annotated): {len(anno)}".rjust(50))
if len(anno) != len(sents):
anno_files = file_paths(CorpusTypeEnum.ANNOTATED, True, True,
base_path)
sent_files = file_paths(CorpusTypeEnum.SENTENCE, True, True, base_path)
click.secho(
"\nSymmetric Difference between sentences & annotated corpus.")
for diff in set(anno_files).symmetric_difference(set(sent_files)):
click.secho(f"{diff}".rjust(50))
click.secho(".warn", fg="yellow")
click.secho("\nPerforming span checks...")
for a, b, file in zip(raw, sents, file_paths()):
for i, sent in enumerate(b['sentences']):
if sent not in a:
logger.error(
f"""{sent}[{i}] \n\t\t is not a span in raw document \n {a} \n\n Corpus file: {file}
""")
sys.exit(1)
click.secho(".done", fg="green")
click.secho("\nPerforming span order checks...")
for a, b, file in zip(raw, sents, file_paths()):
start = 0
for i, sent in enumerate(b['sentences']):
idx = a.find(sent, start)
if idx == -1:
logger.error(
f"""{sent}[{i}] \n\t\t is potential our of order in "sentences" array of sentence corpus\n {a} \n\n Corpus file: {file}
""")
sys.exit(1)
else:
start = start + len(sent)
click.secho(".done", fg="green")
click.secho("\nComparing annotated corpus with sentences corpus...")
anno_names = file_paths(CorpusTypeEnum.ANNOTATED,
noext=True,
use_basename=True,
base_path=base_path)
sents_names = file_paths(CorpusTypeEnum.SENTENCE,
noext=True,
use_basename=True,
base_path=base_path)
anno_dict = dict((name, doc) for name, doc in zip(anno_names, anno))
sents_dict = dict((name, doc) for name, doc in zip(sents_names, sents))
match = 0
for _name, _anno in anno_dict.items():
sent = sents_dict[_name]
if sent['sentences'] != _anno['sentences']:
click.secho(
f"\nSentences in annotated corpus {_name} doesn't match with document in sentence corpus."
)
sys.exit(1)
else:
match += 1
click.secho(f".done ({match}/{len(anno_dict)})", fg="green")