Exemple #1
0
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)
Exemple #3
0
    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,
        )