def test_image_predictor(fit_helper):
train_data, _, test_data = ImageDataset.from_folders(
'https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
feature_metadata = FeatureMetadata.from_df(train_data).add_special_types(
{'image': ['image_path']})
predictor = TabularPredictor(label='label').fit(
train_data=train_data,
hyperparameters={'AG_IMAGE_NN': {
'epochs': 2
}},
feature_metadata=feature_metadata)
leaderboard = predictor.leaderboard(test_data)
assert len(leaderboard) > 0
def test_feature_metadata(data_helper):
# Given
input_data = data_helper.generate_multi_feature_full()
expected_feature_metadata_full = {
('category', ()): ['cat'],
('datetime', ()): ['datetime'],
('float', ()): ['float'],
('int', ()): ['int'],
('object', ()): ['obj'],
('object', ('datetime_as_object', )): ['datetime_as_object'],
('object', ('text', )): ['text']
}
expected_feature_metadata_get_features = [
'int', 'float', 'obj', 'cat', 'datetime', 'text', 'datetime_as_object'
]
expected_type_map_raw = {
'cat': 'category',
'datetime': 'datetime',
'datetime_as_object': 'object',
'float': 'float',
'int': 'int',
'obj': 'object',
'text': 'object'
}
expected_type_group_map_special = {
'datetime_as_object': ['datetime_as_object'],
'text': ['text']
}
expected_feature_metadata_renamed_full = {
('category', ()): ['cat'],
('datetime', ()): ['datetime'],
('float', ()): ['obj'],
('int', ()): ['int_renamed'],
('object', ()): ['float'],
('object', ('datetime_as_object', )): ['datetime_as_object'],
('object', ('text', )): ['text_renamed']
}
expected_feature_metadata_recombined_full_full = {
('category', ()): ['cat'],
('custom_raw_type', ('custom_special_type', )): ['new_feature'],
('datetime', ()): ['datetime'],
('float', ()): ['float'],
('int', ('custom_special_type', )): ['int'],
('object', ()): ['obj'],
('object', ('datetime_as_object', )): ['datetime_as_object'],
('object', ('text', )): ['text']
}
# When
feature_metadata = FeatureMetadata.from_df(input_data)
feature_metadata_renamed = feature_metadata.rename_features(
rename_map={
'text': 'text_renamed',
'int': 'int_renamed',
'obj': 'float',
'float': 'obj'
})
feature_metadata_remove = feature_metadata.remove_features(
features=['text', 'obj', 'float'])
feature_metadata_keep = feature_metadata.keep_features(
features=['text', 'obj', 'float'])
feature_metadata_custom = FeatureMetadata(
type_map_raw={
'int': 'int',
'new_feature': 'custom_raw_type'
},
type_group_map_special={'custom_special_type': ['int', 'new_feature']})
feature_metadata_recombined = feature_metadata_keep.join_metadata(
feature_metadata_remove)
feature_metadata_recombined_alternate = FeatureMetadata.join_metadatas(
metadata_list=[feature_metadata_keep, feature_metadata_remove])
feature_metadata_recombined_full = FeatureMetadata.join_metadatas(
metadata_list=[
feature_metadata_keep, feature_metadata_remove,
feature_metadata_custom
],
shared_raw_features='error_if_diff')
# Therefore
with pytest.raises(AssertionError):
# Error because special contains feature not in raw
FeatureMetadata(type_map_raw={'int': 'int'},
type_group_map_special={
'custom_special_type': ['int', 'new_feature']
})
with pytest.raises(AssertionError):
# Error because renaming to another existing feature without also renaming that feature
feature_metadata.rename_features(rename_map={'text': 'obj'})
with pytest.raises(KeyError):
# Error if removing unknown feature
feature_metadata_remove.remove_features(features=['text'])
with pytest.raises(KeyError):
# Error if getting unknown feature type
feature_metadata_remove.get_feature_type_raw('text')
with pytest.raises(KeyError):
# Error if getting unknown feature type
feature_metadata_remove.get_feature_types_special('text')
with pytest.raises(AssertionError):
# Error because feature_metadata_remove and feature_metadata_custom share a raw feature
FeatureMetadata.join_metadatas(metadata_list=[
feature_metadata_keep, feature_metadata_remove,
feature_metadata_custom
])
assert feature_metadata.to_dict(
inverse=True) == expected_feature_metadata_full
assert feature_metadata.get_features(
) == expected_feature_metadata_get_features
assert feature_metadata.type_map_raw == expected_type_map_raw
assert dict(feature_metadata.type_group_map_special
) == expected_type_group_map_special
assert feature_metadata.get_feature_type_raw('text') == 'object'
assert feature_metadata.get_feature_types_special('text') == ['text']
assert feature_metadata.get_feature_type_raw('int') == 'int'
assert feature_metadata.get_feature_types_special('int') == []
assert feature_metadata_recombined_full.get_feature_types_special(
'int') == ['custom_special_type']
assert feature_metadata_recombined_full.get_feature_type_raw(
'new_feature') == 'custom_raw_type'
assert feature_metadata_renamed.to_dict(
inverse=True) == expected_feature_metadata_renamed_full
assert feature_metadata_recombined.to_dict() == feature_metadata.to_dict()
assert feature_metadata_recombined_alternate.to_dict(
) == feature_metadata.to_dict()
assert feature_metadata_recombined_full.to_dict(
inverse=True) == expected_feature_metadata_recombined_full_full