def empty_storage(tmpdir):
storage = Storage(str(tmpdir))
config = {
"form_types": {
"types": [
{"short": "s", "full": "search"},
{"short": "l", "full": "login"},
{"short": "o", "full": "other"},
{"short": "X", "full": "NOT ANNOTATED"}
],
"simplify_map": {
"l": "o",
},
"NA_value": "X",
"skip_value": "-"
},
"field_types": {
"types": [
{"short": "us", "full": "username"},
{"short": "p1", "full": "password"},
{"short": "qq", "full": "search query"},
{"short": "XX", "full": "NOT ANNOTATED"}
],
"simplify_map": {},
"NA_value": "XX",
"skip_value": "--"
}
}
storage.initialize(config)
return storage
def main():
args = docopt.docopt(__doc__)
if args['--data-folder'] is None:
# by default, use 'data' folder relative to this file
args['--data-folder'] = os.path.join(os.path.dirname(__file__), 'data')
if args['add']:
annotate_forms(
data_folder=args['--data-folder'],
url_argument=args["<url>"],
)
elif args['check-data']:
check_annotated_data(args['--data-folder'])
elif args['train']:
ex = FormExtractor.trained_on(
data_folder=args["--data-folder"],
train_ratio=1.0,
)
ex.save(args["<modelfile>"])
elif args['run']:
threshold = float(args['--threshold'])
print("Loading the extractor..")
ex = FormExtractor.load(args["<modelfile>"])
print("Downloading data...")
data, url = load_data(args["<url>"])
tree = load_html(data, url)
result = ex.extract_forms_proba(tree, threshold)
if not result:
print("No forms found.")
return
for form, probs in result:
print("-"*40)
print_form_html(form)
print("")
for tp, prob in Counter(probs).most_common():
tp_full = FORM_TYPES_INV[tp]
print("%s %0.1f%%" % (tp_full, prob*100), end=' ')
print("")
elif args['evaluate']:
n_folds = int(args["--cv"])
ratio = float(args['--test-size'])
store = Storage(args["--data-folder"])
model = get_model()
X, y = store.get_Xy(drop_duplicates=True, verbose=True, leave=True)
test_size = int(len(y) * ratio)
train_size = len(y) - test_size
X_train, X_test, y_train, y_test = X[:train_size], X[train_size:], y[:train_size], y[train_size:]
evaluation.print_metrics(model, X, y, X_train, X_test, y_train, y_test,
ipython=False, cv=n_folds, short_matrix=True)
def check_annotated_data(data_folder):
"""
Check that annotated data is correct; exit with code 1 if it is not.
"""
storage = Storage(data_folder)
ok = storage.check()
storage.print_type_counts()
if not ok:
sys.exit(1)
def check_annotated_data(data_folder):
"""
Check that annotated data is correct; exit with code 1 if it is not.
"""
storage = Storage(data_folder)
ok = storage.check()
storage.print_type_counts()
if not ok:
sys.exit(1)
def check_annotated_data(data_folder):
"""
Check that annotated data is correct; exit with code 1 if it is not.
"""
storage = Storage(data_folder)
errors = storage.check()
storage.print_type_counts()
print("Errors:", errors)
if errors:
sys.exit(1)
def train(self, data_folder, train_ratio=1.0):
""" Train the model using data from ``data_folder``. """
store = Storage(data_folder)
X, y = store.get_Xy(drop_duplicates=True, verbose=True, leave=True)
train_size = int(len(y) * train_ratio)
X, y = X[:train_size], y[:train_size]
model = get_model()
print("Training on %d example(s)..." % len(y))
model.fit(X, y)
self.model = model
def train(self, data_folder, train_ratio=1.0):
""" Train the model using data from ``data_folder``. """
store = Storage(data_folder)
X, y = store.get_Xy(drop_duplicates=True, verbose=True, leave=True)
train_size = int(len(y) * train_ratio)
X, y = X[:train_size], y[:train_size]
model = get_model()
print("Training on %d example(s)..." % len(y))
model.fit(X, y)
self.model = model
def trained_on(cls, data_folder):
""" Return Formasaurus object trained on data from data_folder """
store = Storage(data_folder)
print("Loading training data...")
annotations = list(store.iter_annotations(
simplify_form_types=True,
simplify_field_types=True,
verbose=True,
leave=True,
))
ex = cls()
ex.train(annotations)
return ex
def annotate_forms(data_folder, url_argument):
"""
Run an interactive HTML form annotation tool.
The process is to download a web page, display all HTML forms and for
each form ask user about form type. The result is saved on disk:
web page is stored as a html file and the URL and the annotation
results are added to index.json file.
"""
storage = Storage(data_folder)
html, url = load_data(url_argument)
doc = load_html(html, url)
answers = _annotate_forms(storage, doc)
if answers:
storage.store_result(html, answers, url)
def annotate_forms(data_folder, url_argument):
"""
Run an interactive HTML form annotation tool.
The process is to download a web page, display all HTML forms and for
each form ask user about form type. The result is saved on disk:
web page is stored as a html file and the URL and the annotation
results are added to index.json file.
"""
storage = Storage(data_folder)
html, url = load_data(url_argument)
doc = load_html(html, url)
answers = _annotate_forms(storage, doc)
if answers:
storage.store_result(html, answers, url)
def main():
args = docopt.docopt(__doc__)
if args['--data-folder'] is None:
# by default, use 'data' folder relative to this file
args['--data-folder'] = os.path.join(os.path.dirname(__file__), 'data')
if args['add']:
annotate_forms(
data_folder=args['--data-folder'],
url_argument=args["<url>"],
)
elif args['check-data']:
check_annotated_data(args['--data-folder'])
elif args['train']:
ex = FormExtractor.trained_on(
data_folder=args["--data-folder"],
train_ratio=1.0,
)
ex.save(args["<modelfile>"])
elif args['run']:
threshold = float(args['--threshold'])
print("Loading the extractor..")
ex = FormExtractor.load(args["<modelfile>"])
print("Downloading data...")
data, url = load_data(args["<url>"])
tree = load_html(data, url)
result = ex.extract_forms_proba(tree, threshold)
if not result:
print("No forms found.")
return
for form, probs in result:
print("-" * 40)
print_form_html(form)
print("")
for tp, prob in Counter(probs).most_common():
tp_full = FORM_TYPES_INV[tp]
print("%s %0.1f%%" % (tp_full, prob * 100), end=' ')
print("")
elif args['evaluate']:
n_folds = int(args["--cv"])
ratio = float(args['--test-size'])
store = Storage(args["--data-folder"])
model = get_model()
X, y = store.get_Xy(drop_duplicates=True, verbose=True, leave=True)
test_size = int(len(y) * ratio)
train_size = len(y) - test_size
X_train, X_test, y_train, y_test = X[:train_size], X[
train_size:], y[:train_size], y[train_size:]
evaluation.print_metrics(model,
X,
y,
X_train,
X_test,
y_train,
y_test,
ipython=False,
cv=n_folds,
short_matrix=True)
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 storage():
return Storage(DEFAULT_DATA_PATH)
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)
