def test_write_output(self):
print('++ テスト開始')
aaa = AITManifestGenerator('./')
aaa.set_ait_name("set_ait_name")
aaa.set_ait_description("set_ait_description")
aaa.set_ait_author("set_ait_author")
aaa.set_ait_email("set_ait_email")
aaa.set_ait_version("0.1")
aaa.set_ait_quality("set_ait_quality")
aaa.set_ait_reference("set_ait_reference")
aaa.add_ait_inventories('name1', 'type1', 'description1', ['csv'],
'schema1')
aaa.add_ait_inventories('name2', 'type2', 'description2',
['gz', 'zip'], 'schema')
aaa.add_ait_parameters('name1', 'type1', 'description1',
'default_val1')
aaa.add_ait_parameters('name2', 'type2', 'description2')
aaa.add_ait_measures('name1', 'type1', 'description1', 'structure1')
aaa.add_ait_measures('name2', 'type2', 'description2', 'structure2')
aaa.add_ait_resources('name1', 'type1', 'description1')
aaa.add_ait_resources('name2', 'type2', 'description2')
aaa.add_ait_downloads('name1', 'description1')
aaa.add_ait_downloads('name2', 'description2')
aaa.write()
print('++ テスト終了')
manifest_genenerator.set_ait_description('''
detecting bias on credit decisions, data set is german_credit.
Available protected_attribute and privileged_classes example (See german.doc for details)
month(>=24.0),
credit_amount(>=3000.0),
investment_as_income_percentage(>=3.0),
residence_since(>=3.0),
age(>=25.0, defult),
number_of_credits(>=2.0),
people_liable_for(>=2.0)
''')
manifest_genenerator.set_ait_author('AIST')
manifest_genenerator.set_ait_email('')
manifest_genenerator.set_ait_version('0.1')
manifest_genenerator.set_ait_quality(
'https://airc.aist.go.jp/aiqm/quality/internal/Distribution_of_training_data'
)
manifest_genenerator.set_ait_reference('')
manifest_genenerator.add_ait_inventories(
name='Data',
type_='dataset',
description='german credit data',
format_=['csv'],
schema='https://archive.ics.uci.edu/ml/datasets/')
manifest_genenerator.add_ait_parameters(name='protected_attribute',
type_='str',
description='protected attributee',
default_val='age')
manifest_genenerator.add_ait_parameters(name='privileged_classes',
type_='float',
description='privileged classes',
#########################################
# area:create manifest
# should edit
#########################################
if not is_ait_launch:
from ait_sdk.common.files.ait_manifest_generator import AITManifestGenerator
manifest_genenerator = AITManifestGenerator(current_dir)
manifest_genenerator.set_ait_name('dev_template_local_docker')
manifest_genenerator.set_ait_description(
'AIT template (docker image regist to local)')
manifest_genenerator.set_ait_author('AIST')
manifest_genenerator.set_ait_email('')
manifest_genenerator.set_ait_version('0.1')
manifest_genenerator.set_ait_quality(
'https://airc.aist.go.jp/aiqm/quality/internal/Coverage_for_distinguished_problem_cases'
)
manifest_genenerator.set_ait_reference('')
manifest_genenerator.add_ait_inventories(
name='iris_data',
type_='dataset',
description='アヤメの分類データです',
format_=['csv'],
schema='https://archive.ics.uci.edu/ml/datasets/iris')
manifest_genenerator.add_ait_parameters(
name='mean_column_name',
type_='str',
description='sepal.width\nsepal.length\npetal.width\npetal.length',
default_val='sepal.width')
manifest_genenerator.add_ait_measures(name='mean',
type_='float',
# area:create manifest
# should edit
#########################################
if not is_ait_launch:
from ait_sdk.common.files.ait_manifest_generator import AITManifestGenerator
manifest_genenerator = AITManifestGenerator(current_dir)
manifest_genenerator.set_ait_name('eval_metamorphic_test_tf1.13')
manifest_genenerator.set_ait_description('''Metamorphic test.
Make sure can be classified in the same result as the original class be added a little processing to the original data.'''
)
manifest_genenerator.set_ait_author('AIST')
manifest_genenerator.set_ait_email('')
manifest_genenerator.set_ait_version('0.1')
manifest_genenerator.set_ait_quality(
'https://airc.aist.go.jp/aiqm/quality/internal/Robustness_of_trained_model'
)
manifest_genenerator.set_ait_reference('')
manifest_genenerator.add_ait_inventories(
name='mnist_dataset',
type_='dataset',
description=
'MNIST_dataset are train image, train label, test image, test label',
format_=['zip'],
schema='http://yann.lecun.com/exdb/mnist/')
manifest_genenerator.add_ait_inventories(
name='mnist_model',
type_='model',
description='MNIST_model',
format_=['zip'],
schema='https://github.com/hitachi-rd-yokohama/deep_saucer')
#########################################
# area:create manifest
# should edit
#########################################
if not is_ait_launch:
from ait_sdk.common.files.ait_manifest_generator import AITManifestGenerator
manifest_genenerator = AITManifestGenerator(current_dir)
manifest_genenerator.set_ait_name('eval_find_ca_high_risk')
manifest_genenerator.set_ait_description(
'Evaluating quantity of high risk CA combination cases in BDD dataset)'
)
manifest_genenerator.set_ait_author('AIST')
manifest_genenerator.set_ait_email('')
manifest_genenerator.set_ait_version('0.1')
manifest_genenerator.set_ait_quality(
'https://airc.aist.go.jp/aiqm/quality/internal/Diversity_of_test_data')
manifest_genenerator.set_ait_reference('')
manifest_genenerator.add_ait_inventories(
name='Data',
type_='dataset',
description=
'Classification of different attributes related to autonomous driving scenarios',
format_=['csv'],
schema='https://bdd-data.berkeley.edu/')
manifest_genenerator.add_ait_inventories(
name='High_risk_CA_combinations',
type_='attribute set',
description=
'Combinations of different attribute values that are high risk situations in real life',
format_=['csv'],
schema='User given data')