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')
default_val='500') manifest_genenerator.add_ait_parameters( name='mnist_type', type_='str', description= 'train = Training_data, test = test_data, validation = validation_data', default_val='train') manifest_genenerator.add_ait_measures( name='average', type_='float', description='Average number of NG output', structure='single', min='0', max='1') manifest_genenerator.add_ait_resources(name='result', type_='table', description='number of NG output') manifest_genenerator.add_ait_downloads(name='Log', description='AIT_log') manifest_genenerator.add_ait_downloads(name='DeepLog', description='deep_saucer_log') manifest_path = manifest_genenerator.write() # In[9]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch: from ait_sdk.common.files.ait_input_generator import AITInputGenerator input_generator = AITInputGenerator(manifest_path)
description='protected attributee', default_val='age') manifest_genenerator.add_ait_parameters(name='privileged_classes', type_='float', description='privileged classes', default_val='25.0') manifest_genenerator.add_ait_measures( name='mean_difference', type_='float', description='mean difference of metric fairness', structure='single', min='-1', max='1') manifest_genenerator.add_ait_resources( name='metric_fairness_plot', type_='picture', description= 'base rates polt of privileged_groups and unprivileged_groups') manifest_genenerator.add_ait_downloads(name='Log', description='AITLog') manifest_path = manifest_genenerator.write() # In[9]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch: from ait_sdk.common.files.ait_input_generator import AITInputGenerator input_generator = AITInputGenerator(manifest_path) input_generator.add_ait_inventories(name='Data',
manifest_genenerator.add_ait_parameters(name='target_variable', type_='str', description='target variable', default_val='') manifest_genenerator.add_ait_measures(name='RMSE', type_='float', description='The closer to 0, the smaller prediction error.', structure='single', min='0') manifest_genenerator.add_ait_measures(name='MAE', type_='float', description='The closer to 0, the smaller prediction error.', structure='single', min='0') manifest_genenerator.add_ait_resources(name='evaluation_index_matrix', type_='table', description='Table of evaluation indicators summary.') manifest_genenerator.add_ait_resources(name='observed_predicted_plot', type_='picture', description='Plot of actual values on the horizontal axis and predictions on the vertical axis. The more plots exist near the diagonal line, the better the prediction.') manifest_genenerator.add_ait_downloads(name='Log', description='AIT_log') manifest_path = manifest_genenerator.write() # In[9]: ######################################### # area:create input # should edit
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', description='mean of select column', structure='single', min='0') manifest_genenerator.add_ait_resources(name='pairplot', type_='picture', description='pairplot') manifest_genenerator.add_ait_downloads(name='Log', description='AIT実行ログ') manifest_path = manifest_genenerator.write() # In[9]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch: from ait_sdk.common.files.ait_input_generator import AITInputGenerator input_generator = AITInputGenerator(manifest_path) input_generator.add_ait_inventories( name='iris_data', value='iris_data/tableconvert_csv_4nryby.csv')
format_=['txt'], schema='https://github.com/Microsoft/pict/blob/master/doc/pict.md') manifest_genenerator.add_ait_parameters( name='order_combination', type_='int', description='Order of combinations.', default_val='2') manifest_genenerator.add_ait_parameters(name='seed', type_='int', description=''' Randomize generation, N - seed. if you fix seed, please set it to 1 or more. ''', default_val='-1') manifest_genenerator.add_ait_resources( name='generated_paie_wise', type_='table', description='PICT generate pair-wise.') manifest_genenerator.add_ait_downloads(name='Log', description='AIT execute log') manifest_path = manifest_genenerator.write() # In[6]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch: from ait_sdk.common.files.ait_input_generator import AITInputGenerator input_generator = AITInputGenerator(manifest_path) input_generator.add_ait_inventories('pair_wise_model',
description='検証用データセット\n目的変数と説明変数のセットでラベルは必要', format_=['csv'], schema='uncreated') manifest_genenerator.add_ait_parameters(name='target_variable', type_='str', description='目的変数', default_val='') manifest_genenerator.add_ait_measures( name='degree_of_freedom_adjusted_coefficient_determination', type_='float', description='0~1の値をとり、1に近いほど精度の高い予測ができているといえる。', structure='single', min='0', max='1') manifest_genenerator.add_ait_resources( name='coefficient_of_determination_matrix', type_='table', description='決定係数の結果をまとめた表') manifest_genenerator.add_ait_resources( name='correlation_coefficient_1to1', type_='picture', description='説明変数と目的変数の相関グラフ\nファイル名は{目的変数}-{説明変数}.png') manifest_genenerator.add_ait_resources( name='correlation_coefficient_1to2', type_='picture', description='説明変数2つと目的変数の3次元相関グラフ\nファイル名は{目的変数}-{説明変数1}-{説明変数2}.png') manifest_genenerator.add_ait_downloads(name='Log', description='AIT実行ログ') manifest_genenerator.add_ait_downloads(name='predictive_value', description='予測値') manifest_path = manifest_genenerator.write() # In[ ]:
manifest_genenerator.add_ait_measures( name='coverage_rate_each_layer', type_='float', description='coverage of each layer in the model.', structure='sequence', min='0', max='1') manifest_genenerator.add_ait_measures( name='coverage_rate_combination', type_='float', description='coverage of select combination.', structure='single', min='0', max='1') manifest_genenerator.add_ait_resources( name='test_case_generator', type_='table', description='generate coverage increase data.') manifest_genenerator.add_ait_downloads( name='heatmap', description='the heat map of the coverage as an HTML file.') manifest_genenerator.add_ait_downloads( name='abs_dataset', description='the created input data by the manipulation as h5 file.') manifest_genenerator.add_ait_downloads(name='Log', description='AITLog') manifest_path = manifest_genenerator.write() # In[9]: ######################################### # area:create input # should edit
manifest_genenerator.add_ait_measures( name='bike_accuracy', type_='float', description='accuracy predicted of bike', structure='single', min='0', max='1') manifest_genenerator.add_ait_measures( name='train_accuracy', type_='float', description='accuracy predicted of train', structure='single', min='0', max='1') manifest_genenerator.add_ait_resources(name='all_label_accuracy_csv', type_='table', description='accuracy of all label') manifest_genenerator.add_ait_resources(name='all_label_accuracy_png', type_='picture', description='accuracy of all label') manifest_genenerator.add_ait_downloads(name='Log', description='AIT_log') manifest_genenerator.add_ait_downloads( name='each_label_accuracy', description='accuracy of each label') manifest_genenerator.add_ait_downloads( name='each_picture', description='predict of each picture') manifest_path = manifest_genenerator.write() # In[9]: ######################################### # area:create input
max='1') manifest_genenerator.add_ait_measures(name='RecallByClass', type_='float', description='Recall for each class.', structure='sequence', min='0', max='1') manifest_genenerator.add_ait_measures(name='F−measureByClass', type_='float', description='F−measure for each class.', structure='sequence', min='0', max='1') manifest_genenerator.add_ait_resources(name='ConfusionMatrixHeatmap', type_='picture', description='混同行列(ヒートマップ)') manifest_genenerator.add_ait_resources(name='ROC-curve', type_='picture', description='ROC曲線') manifest_genenerator.add_ait_resources(name='NGPredictImages', type_='picture', description='推論NGとなった画像の一覧を、正解ラベルの枚数分だけ出力する') manifest_genenerator.add_ait_downloads(name='Log', description='AIT実行ログ') manifest_genenerator.add_ait_downloads(name='ConfusionMatrixCSV', description='混同行列') manifest_genenerator.add_ait_downloads(name='PredictionResult', description='ID,正解ラベル,推論結果確率(ラベル毎)')
format_=['csv'], schema='User given data') manifest_genenerator.add_ait_measures( name='SingleCount', type_='int', description='Number of high risk cases in simple combinations', structure='single', min='0') manifest_genenerator.add_ait_measures( name='CombinedCount', type_='int', description='Number of high risk cases in combine combinations', structure='single', min='0') manifest_genenerator.add_ait_resources( name='CountResult', type_='table', description='Count of number of data in each high risk case') manifest_genenerator.add_ait_resources( name='CombinedCountResult', type_='table', description= 'Count of number of data in various high risk case combinations') manifest_genenerator.add_ait_resources( name='DistributionPlot', type_='picture', description='Plot of percentage of various high risk cases in data') manifest_genenerator.add_ait_downloads(name='Log', description='AITLog') manifest_path = manifest_genenerator.write() # In[6]:
'https://airc.aist.go.jp/aiqm/quality/internal/Distribution_of_training_data' ) manifest_genenerator.set_ait_reference('') manifest_genenerator.add_ait_inventories( 'Data', 'dataset', 'Classification of different attributes related to autonomous driving scenarios', ['csv'], 'https://bdd-data.berkeley.edu/') manifest_genenerator.add_ait_parameters( 'attribute_no', 'int', 'Total number of attribute for distibution analysis', '6') manifest_genenerator.add_ait_parameters( 'dimension', 'int', 'Dimensions of how many attributes to combine for distibution analysis', '2') manifest_genenerator.add_ait_resources( 'distibution_csv', 'table', 'Table of distribution for each combination') manifest_genenerator.add_ait_resources( 'distibution_plot', 'picture', 'Plot of distribution for each combination') manifest_genenerator.add_ait_downloads('Log', 'AITLog') manifest_path = manifest_genenerator.write() # In[6]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch: from ait_sdk.common.files.ait_input_generator import AITInputGenerator
manifest_genenerator.add_ait_measures(name='RecallByClass', type_='float', description='Recall for each class.', structure='sequence', min='0', max='1') manifest_genenerator.add_ait_measures( name='F−measureByClass', type_='float', description='F−measure for each class.', structure='sequence', min='0', max='1') manifest_genenerator.add_ait_resources(name='ConfusionMatrixHeatmap', type_='picture', description='混同行列(ヒートマップ)') manifest_genenerator.add_ait_resources(name='ROC-curve', type_='picture', description='ROC曲線') manifest_genenerator.add_ait_downloads(name='Log', description='AIT実行ログ') manifest_genenerator.add_ait_downloads(name='ConfusionMatrixCSV', description='混同行列') manifest_genenerator.add_ait_downloads(name='PredictionResult', description='ID,正解ラベル,推論結果確率(ラベル毎)') manifest_path = manifest_genenerator.write() # In[ ]:
manifest_genenerator.add_ait_measures( name='coverage_total_measures', type_='float', description='Overall coverage within the expected range.', structure='single', min='0', max='1') manifest_genenerator.add_ait_measures( name='coverage_each_measures', type_='float', description='Each class coverage within the expected range.', structure='sequence', min='0', max='1') manifest_genenerator.add_ait_resources( name='count_total_class', type_='picture', description='Total coverage for all classes.') manifest_genenerator.add_ait_resources( name='count_each_class', type_='picture', description='Total coverage for each classes.') manifest_genenerator.add_ait_downloads(name='Log', description='AITLog') manifest_path = manifest_genenerator.write() # In[ ]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch:
default_val='5') manifest_genenerator.add_ait_measures( name='count', type_='int', description='Quantity of unsound case', structure='single', min='0') manifest_genenerator.add_ait_measures( name='percentage', type_='float', description='Percentage of unsound case in dataset', structure='single', min='0', max='100') manifest_genenerator.add_ait_resources( name='CountPlot', type_='picture', description='Plot of number of data in each unsound case') manifest_genenerator.add_ait_resources( name='PercentagePlot', type_='picture', description='Plot of percentage of unsound cases in data') manifest_genenerator.add_ait_downloads(name='Log', description='AITLog') manifest_path = manifest_genenerator.write() # In[6]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch:
description='Pairwise_list.csv', format_=['csv'], schema='https://www.sciencedirect.com/topics/computer-science/pairwise-comparison') manifest_genenerator.add_ait_inventories(name='target', type_='dataset', description='target.csv', format_=['csv'], schema='https://www.sciencedirect.com/topics/computer-science/pairwise-comparison') manifest_genenerator.add_ait_measures(name='coverage', type_='float', description='coverage of all patterns are matched', structure='single', min='0', max='1') manifest_genenerator.add_ait_resources(name='matching_result', type_='table', description='pairwise_matching_result') manifest_genenerator.add_ait_downloads(name='Log', description='AIT_run_log') manifest_path = manifest_genenerator.write() # In[6]: ######################################### # area:create input # should edit ######################################### if not is_ait_launch: from ait_sdk.common.files.ait_input_generator import AITInputGenerator