def test_training(storage, capsys):
annotations = (a for a in storage.iter_annotations(
simplify_form_types=True,
simplify_field_types=True,
) if a.fields_annotated)
annotations = list(itertools.islice(annotations, 0, 300))
crf = train(annotations=annotations,
use_precise_form_types=False,
optimize_hyperparameters_iters=2,
optimize_hyperparameters_folds=2,
optimize_hyperparameters_jobs=-1,
full_form_type_names=False,
full_field_type_names=False)
out, err = capsys.readouterr()
assert 'Training on 300 forms' in out
assert 'realistic form types' in out
assert 'Best hyperparameters' in out
assert 0.0 < crf.c1 < 2.5
assert 0.0 < crf.c2 < 0.9
assert crf.c1, crf.c2 != _REALISTIC_C1_C2
assert crf.c1, crf.c2 != _PRECISE_C1_C2
form_types = np.asarray([a.type for a in annotations])
X, y = get_Xy(annotations, form_types, full_type_names=False)
y_pred = crf.predict(X)
score = flat_accuracy_score(y, y_pred)
assert 0.9 < score < 1.0 # overfitting FTW!
field_schema = storage.get_field_schema()
short_names = set(field_schema.types_inv.keys())
assert set(crf.classes_).issubset(short_names)
def test_training(storage, capsys):
annotations = list(
a for a in storage.iter_annotations(simplify_form_types=True, simplify_field_types=True) if a.fields_annotated
)[:300]
crf = train(
annotations=annotations,
use_precise_form_types=False,
optimize_hyperparameters_iters=10,
full_form_type_names=False,
full_field_type_names=False,
)
out, err = capsys.readouterr()
assert "Training on 300 forms" in out
assert "realistic form types" in out
assert "Best hyperparameters" in out
assert 0.0 < crf.c1 < 1.5
assert 0.0 < crf.c2 < 0.9
assert crf.c1, crf.c2 != _REALISTIC_C1_C2
assert crf.c1, crf.c2 != _PRECISE_C1_C2
form_types = np.asarray([a.type for a in annotations])
X, y = get_Xy(annotations, form_types, full_type_names=False)
y_pred = crf.predict(X)
score = flat_accuracy_score(y, y_pred)
assert 0.9 < score < 1.0 # overfitting FTW!
field_schema = storage.get_field_schema()
short_names = set(field_schema.types_inv.keys())
assert set(crf.classes_).issubset(short_names)
def train(self, annotations):
""" Train FormFieldExtractor on a list of FormAnnotation objects. """
print("Training form type detector on %d example(s)..." % len(annotations))
self.form_classifier = FormClassifier(full_type_names=True)
self.form_classifier.train(annotations)
print("Training field type detector...")
self._field_model = fieldtype_model.train(
annotations=annotations,
use_precise_form_types=True,
full_field_type_names=True,
full_form_type_names=self.form_classifier.full_type_names,
verbose=True,
)
