def on_submit(_):
url = url_field.value.strip()
html = download(url)
path = storage.add_result(html, url, add_empty=False)
if path is None:
print("No forms at ", url)
else:
print("Added:", path, url)
url_field.value = ""
def on_submit(_):
url = url_field.value.strip()
html = download(url)
path = storage.add_result(html, url, add_empty=False)
if path is None:
print(("No forms at ", url))
else:
print(("Added:", path, url))
url_field.value = ""
def main():
args = docopt.docopt(__doc__, version=formasaurus.__version__)
data_folder = args['--data-folder']
if data_folder is None:
data_folder = DEFAULT_DATA_PATH
storage = Storage(data_folder)
if args['check-data']:
errors = storage.check()
storage.print_form_type_counts(simplify=False)
storage.print_form_type_counts(simplify=True)
print("Errors:", errors)
if errors:
sys.exit(1)
elif args['train']:
ex = formasaurus.FormFieldClassifier.trained_on(data_folder)
ex.save(args["<modelfile>"])
elif args['init']:
formasaurus.FormFieldClassifier.load()
elif args['run']:
threshold = float(args['--threshold'])
print("Loading the extractor..")
ex = formasaurus.FormFieldClassifier.load(args["<modelfile>"])
print("Downloading {}...".format(args["<url>"]))
data = download(args["<url>"])
tree = load_html(data, args['<url>'])
result = ex.extract_forms(tree, proba=True, threshold=threshold)
if not result:
print("No forms found.")
return
for form, info in result:
print("\n")
print("=" * 60)
print(get_cleaned_form_html(form))
print("-" * 60)
print("Form type: ", end="")
for form_tp, prob in Counter(info['form']).most_common():
print("%s %0.1f%%" % (form_tp, prob * 100), end=' ')
print("\n\nField types:")
for field_name, probs in info['fields'].items():
print(field_name, end=': ')
for field_tp, prob in Counter(probs).most_common():
print("%s %0.1f%%" % (field_tp, prob * 100), end=' ')
print("")
print("")
elif args['evaluate']:
n_splits = int(args["--cv"])
annotations = list(
storage.iter_annotations(verbose=True,
leave=True,
simplify_form_types=True,
simplify_field_types=True))
if args['forms'] or args['all']:
print("Evaluating form classifier...\n")
formtype_model.print_classification_report(annotations,
n_splits=n_splits)
print("")
if args['fields'] or args['all']:
print("Evaluating form field classifier...\n")
fieldtype_model.print_classification_report(annotations,
n_splits=n_splits)
def main():
args = docopt.docopt(__doc__, version=formasaurus.__version__)
data_folder = args['--data-folder']
if data_folder is None:
data_folder = DEFAULT_DATA_PATH
storage = Storage(data_folder)
if args['check-data']:
errors = storage.check()
storage.print_form_type_counts(simplify=False)
storage.print_form_type_counts(simplify=True)
print("Errors:", errors)
if errors:
sys.exit(1)
elif args['train']:
ex = formasaurus.FormFieldClassifier.trained_on(data_folder)
ex.save(args["<modelfile>"])
elif args['run']:
threshold = float(args['--threshold'])
print("Loading the extractor..")
ex = formasaurus.FormFieldClassifier.load(args["<modelfile>"])
print("Downloading data...")
data = download(args["<url>"])
tree = load_html(data, args['<url>'])
result = ex.extract_forms(tree, proba=True, threshold=threshold)
if not result:
print("No forms found.")
return
for form, info in result:
print("\n")
print("="*60)
print(get_cleaned_form_html(form))
print("-"*60)
print("Form type: ", end="")
for form_tp, prob in Counter(info['form']).most_common():
print("%s %0.1f%%" % (form_tp, prob * 100), end=' ')
print("\n\nField types:")
for field_name, probs in info['fields'].items():
print(field_name, end=': ')
for field_tp, prob in Counter(probs).most_common():
print("%s %0.1f%%" % (field_tp, prob * 100), end=' ')
print("")
print("")
elif args['evaluate']:
n_folds = int(args["--cv"])
annotations = list(
storage.iter_annotations(verbose=True, leave=True,
simplify_form_types=True,
simplify_field_types=True)
)
if args['forms'] or args['all']:
print("Evaluating form classifier...\n")
formtype_model.print_classification_report(annotations,
n_folds=n_folds)
print("")
if args['fields'] or args['all']:
print("Evaluating form field classifier...\n")
fieldtype_model.print_classification_report(annotations,
n_folds=n_folds)
