From 4598bfad1f4a1011e70512de1a26f633ddce2e38 Mon Sep 17 00:00:00 2001 From: lhenry15 Date: Sat, 13 Feb 2021 10:19:05 -0600 Subject: [PATCH] merge primitive_tests from Junjie branch, modified metadata for data_processing and timeseries_processing module --- datasets/anomaly/transform_yahoo.py | 2 +- .../SCORE/dataset_TEST/datasetDoc.json | 30 +- .../SCORE/dataset_TEST/tables/learningData.csv | 141 + .../yahoo_sub_5/SCORE/problem_TEST/dataSplits.csv | 1261 ++++ .../SCORE/problem_TEST}/problemDoc.json | 20 +- .../SCORE/targets.csv | 0 .../TEST/dataset_TEST/datasetDoc.json | 30 +- .../TEST/dataset_TEST/tables/learningData.csv | 141 + .../yahoo_sub_5/TEST/problem_TEST/dataSplits.csv | 1261 ++++ .../TEST/problem_TEST}/problemDoc.json | 20 +- .../TRAIN/dataset_TRAIN/datasetDoc.json | 32 +- .../TRAIN/dataset_TRAIN/tables/learningData.csv | 1261 ++++ .../yahoo_sub_5/TRAIN/problem_TRAIN/dataSplits.csv | 1261 ++++ .../TRAIN/problem_TRAIN}/problemDoc.json | 20 +- .../yahoo_sub_5_dataset}/datasetDoc.json | 34 +- .../yahoo_sub_5_dataset/tables/learningData.csv | 1401 ++++ .../yahoo_sub_5/yahoo_sub_5_problem/dataSplits.csv | 1261 ++++ .../yahoo_sub_5_problem}/problemDoc.json | 20 +- .../SCORE/dataset_TEST/tables/learningData.csv | 1401 ---- .../SCORE/problem_TEST/dataSplits.csv | 5601 ---------------- .../TEST/dataset_TEST/tables/learningData.csv | 1401 ---- .../TEST/problem_TEST/dataSplits.csv | 5601 ---------------- .../TRAIN/dataset_TRAIN/tables/learningData.csv | 5601 ---------------- .../TRAIN/problem_TRAIN/dataSplits.csv | 5601 ---------------- .../tables/learningData.csv | 7001 -------------------- .../yahoo_system_sub_5_problem/dataSplits.csv | 5601 ---------------- primitive_tests/build_ABOD_pipline.py | 70 - primitive_tests/build_CBLOF_pipline.py | 51 - primitive_tests/build_DeepLog_pipeline.py | 49 - primitive_tests/build_HoltSmoothing_pipline.py | 76 - ...uild_HoltWintersExponentialSmoothing_pipline.py | 76 - primitive_tests/build_KDiscord_pipeline.py | 71 - primitive_tests/build_KNN_pipline.py | 51 - primitive_tests/build_LODA_pipline.py | 51 - primitive_tests/build_LOF_pipline.py | 51 - primitive_tests/build_MatrixProfile_pipeline.py | 49 - .../build_MeanAverageTransform_pipline.py | 77 - primitive_tests/build_OCSVM_pipline.py | 51 - primitive_tests/build_PyodCOF.py | 51 - primitive_tests/build_QuantileTransform_pipline.py | 49 - primitive_tests/build_SOD_pipeline.py | 49 - .../build_SimpleExponentialSmoothing_pipline.py | 76 - primitive_tests/build_Standardize_pipline.py | 49 - .../build_SubsequenceClustering_pipline.py | 80 - primitive_tests/build_Telemanom.py | 48 - .../build_TimeIntervalTransform_pipeline.py | 86 - primitive_tests/build_WaveletTransform_pipline.py | 64 - .../build_test_detection_algorithm_PyodMoGaal.py | 50 - .../build_test_detection_algorithm_PyodSoGaal.py | 50 - ...nalysis_spectral_residual_transform_pipeline.py | 61 - ...test_feature_analysis_statistical_abs_energy.py | 62 - ...ld_test_feature_analysis_statistical_abs_sum.py | 62 - ...uild_test_feature_analysis_statistical_gmean.py | 62 - ...uild_test_feature_analysis_statistical_hmean.py | 62 - ...d_test_feature_analysis_statistical_kurtosis.py | 62 - ...ld_test_feature_analysis_statistical_maximum.py | 62 - ...build_test_feature_analysis_statistical_mean.py | 62 - ...d_test_feature_analysis_statistical_mean_abs.py | 62 - ...sis_statistical_mean_abs_temporal_derivative.py | 62 - ...nalysis_statistical_mean_temporal_derivative.py | 62 - ...ild_test_feature_analysis_statistical_median.py | 62 - ...alysis_statistical_median_absolute_deviation.py | 63 - ...ld_test_feature_analysis_statistical_minimum.py | 62 - ...build_test_feature_analysis_statistical_skew.py | 62 - ..._test_feature_analysis_statistical_variation.py | 62 - ...ld_test_feature_analysis_statistical_vec_sum.py | 62 - ...ture_analysis_statistical_willison_amplitude.py | 62 - ..._time_series_seasonality_trend_decomposition.py | 61 - .../CategoricalToBinary_pipeline.py} | 36 +- .../ColumnFilter_pipeline.py} | 25 +- .../ContinuityValidation_pipline.py} | 16 +- .../DuplicationValidation_pipeline.py} | 15 +- .../TimeIntervalTransform_pipeline.py} | 27 +- .../detection_algorithm/ABOD_pipeline.py | 53 + .../AutoEncoder_pipeline.py} | 42 +- .../detection_algorithm/AutoRegODetect_pipeline.py | 54 + .../detection_algorithm/CBLOF_pipline.py | 57 + .../detection_algorithm/DeepLog_pipeline.py | 54 + .../HBOS_pipline.py} | 44 +- .../HBOS_score_pipeline.py} | 51 +- .../IsolationForest_pipline.py} | 31 +- .../detection_algorithm/KDiscord_pipeline.py | 54 + .../detection_algorithm/KNN_pipeline.py | 55 + .../detection_algorithm/LODA_pipeline.py | 55 + .../detection_algorithm/LOF_pipeline.py | 55 + .../detection_algorithm/LSTMOD_pipeline.py | 55 + .../MatrixProfile_pipeline.py} | 43 +- .../detection_algorithm/OCSVM_pipline.py | 55 + .../detection_algorithm/PCAODetect_pipeline.py | 53 + primitive_tests/detection_algorithm/PyodCOF.py | 55 + .../detection_algorithm/PyodMoGaal_pipeline.py | 54 + .../detection_algorithm/PyodSoGaal_pipeline.py | 54 + .../SOD_pipeline.py} | 40 +- .../detection_algorithm/Telemanom_pipeline.py | 54 + .../VariationalAutoEncoder_pipeline.py} | 42 +- .../BKFilter_pipeline.py} | 34 +- .../DiscreteCosineTransform_pipeline.py} | 35 +- .../FastFourierTransform_pipeline.py} | 37 +- .../HPFilter_pipeline.py} | 38 +- .../NonNegativeMatrixFactorization_pipeline.py} | 38 +- .../SpectralResidualTransform_pipeline.py | 47 + .../StatisticalAbsEnergy_pipeline.py | 48 + .../feature_analysis/StatisticalAbsSum.py | 48 + .../feature_analysis/StatisticalGmean_pipeline.py | 50 + .../StatisticalHmean_pipeline.py} | 38 +- .../StatisticalKurtosis_pipeline.py | 50 + .../StatisticalMaximum_pipeline.py | 49 + .../StatisticalMeanAbs_pipeline.py | 49 + .../StatisticalMeanTemporalDerivative.py | 49 + .../feature_analysis/StatisticalMean_pipeline.py | 49 + .../StatisticalMedianAbsoluteDeviation.py | 49 + .../StatisticalMedian_pipeline.py} | 37 +- .../StatisticalMinimum_pipeline.py | 49 + .../feature_analysis/StatisticalSkew_pipeline.py | 49 + .../feature_analysis/StatisticalStd_pipeline.py | 49 + .../feature_analysis/StatisticalVar_pipeline.py | 49 + .../StatisticalVariation_pipeline.py | 49 + .../feature_analysis/StatisticalVecSum_pipeline.py | 49 + .../StatisticalWillisonAmplitude_pipeline.py | 49 + .../StatisticalZeroCrossing_pipeline.py} | 40 +- ...tistical_mean_absTemporalDerivative_pipeline.py | 49 + primitive_tests/feature_analysis/TRMF_pipeline.py | 44 + .../TruncatedSVD_pipeline.py} | 37 +- .../WaveletTransform_pipeline.py} | 46 +- .../RuleBasedFilter_pipline.py} | 19 +- primitive_tests/test.sh | 47 + .../AxiswiseScale_pipeline.py} | 38 +- .../HoltSmoothing_pipeline.py | 47 + .../HoltWintersExponentialSmoothing_pipeline.py | 47 + .../MeanAverageTransform_pipeline.py | 47 + .../PowerTransform_pipeline.py | 49 + .../QuantileTransform_pipeline.py | 49 + .../SeasonalityTrendDecomposition_pipeline.py | 49 + .../SimpleExponentialSmoothing_pipeline.py | 46 + .../Standardize_pipeline.py} | 39 +- test.sh | 40 - tested_file.txt | 216 - tods/data_processing/CategoricalToBinary.py | 11 +- tods/data_processing/ColumnFilter.py | 12 +- tods/data_processing/ColumnParser.py | 28 +- tods/data_processing/ConstructPredictions.py | 26 +- tods/data_processing/ContinuityValidation.py | 13 +- tods/data_processing/DatasetToDataframe.py | 26 +- tods/data_processing/DuplicationValidation.py | 13 +- .../ExtractColumnsBySemanticTypes.py | 18 +- tods/data_processing/SKImputer.py | 20 +- tods/data_processing/TimeIntervalTransform.py | 10 +- tods/data_processing/TimeStampValidation.py | 22 +- tods/timeseries_processing/HoltSmoothing.py | 16 +- .../HoltWintersExponentialSmoothing.py | 17 +- .../MovingAverageTransformer.py | 16 +- tods/timeseries_processing/SKAxiswiseScaler.py | 16 +- tods/timeseries_processing/SKPowerTransformer.py | 27 +- .../timeseries_processing/SKQuantileTransformer.py | 16 +- tods/timeseries_processing/SKStandardScaler.py | 14 +- .../SimpleExponentialSmoothing.py | 24 +- ...nceClustering.py => SubsequenceSegmentation.py} | 30 +- .../TimeSeriesSeasonalityTrendDecomposition.py | 44 +- 158 files changed, 10825 insertions(+), 41416 deletions(-) rename datasets/anomaly/{yahoo_system_sub_5 => yahoo_sub_5}/SCORE/dataset_TEST/datasetDoc.json (74%) create mode 100644 datasets/anomaly/yahoo_sub_5/SCORE/dataset_TEST/tables/learningData.csv create mode 100644 datasets/anomaly/yahoo_sub_5/SCORE/problem_TEST/dataSplits.csv rename datasets/anomaly/{yahoo_system_sub_5/TRAIN/problem_TRAIN => yahoo_sub_5/SCORE/problem_TEST}/problemDoc.json (69%) rename datasets/anomaly/{yahoo_system_sub_5 => yahoo_sub_5}/SCORE/targets.csv (100%) rename datasets/anomaly/{yahoo_system_sub_5 => yahoo_sub_5}/TEST/dataset_TEST/datasetDoc.json (74%) create mode 100644 datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/tables/learningData.csv create mode 100644 datasets/anomaly/yahoo_sub_5/TEST/problem_TEST/dataSplits.csv rename datasets/anomaly/{yahoo_system_sub_5/yahoo_system_sub_5_problem => yahoo_sub_5/TEST/problem_TEST}/problemDoc.json (69%) rename datasets/anomaly/{yahoo_system_sub_5 => yahoo_sub_5}/TRAIN/dataset_TRAIN/datasetDoc.json (73%) create mode 100644 datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/tables/learningData.csv create mode 100644 datasets/anomaly/yahoo_sub_5/TRAIN/problem_TRAIN/dataSplits.csv rename datasets/anomaly/{yahoo_system_sub_5/TEST/problem_TEST => yahoo_sub_5/TRAIN/problem_TRAIN}/problemDoc.json (69%) rename datasets/anomaly/{yahoo_system_sub_5/yahoo_system_sub_5_dataset => yahoo_sub_5/yahoo_sub_5_dataset}/datasetDoc.json (73%) create mode 100644 datasets/anomaly/yahoo_sub_5/yahoo_sub_5_dataset/tables/learningData.csv create mode 100644 datasets/anomaly/yahoo_sub_5/yahoo_sub_5_problem/dataSplits.csv rename datasets/anomaly/{yahoo_system_sub_5/SCORE/problem_TEST => yahoo_sub_5/yahoo_sub_5_problem}/problemDoc.json (69%) delete mode 100644 datasets/anomaly/yahoo_system_sub_5/SCORE/dataset_TEST/tables/learningData.csv delete mode 100644 datasets/anomaly/yahoo_system_sub_5/SCORE/problem_TEST/dataSplits.csv delete mode 100644 datasets/anomaly/yahoo_system_sub_5/TEST/dataset_TEST/tables/learningData.csv delete mode 100644 datasets/anomaly/yahoo_system_sub_5/TEST/problem_TEST/dataSplits.csv delete mode 100644 datasets/anomaly/yahoo_system_sub_5/TRAIN/dataset_TRAIN/tables/learningData.csv delete mode 100644 datasets/anomaly/yahoo_system_sub_5/TRAIN/problem_TRAIN/dataSplits.csv delete mode 100644 datasets/anomaly/yahoo_system_sub_5/yahoo_system_sub_5_dataset/tables/learningData.csv delete mode 100644 datasets/anomaly/yahoo_system_sub_5/yahoo_system_sub_5_problem/dataSplits.csv delete mode 100644 primitive_tests/build_ABOD_pipline.py delete mode 100644 primitive_tests/build_CBLOF_pipline.py delete mode 100644 primitive_tests/build_DeepLog_pipeline.py delete mode 100644 primitive_tests/build_HoltSmoothing_pipline.py delete mode 100644 primitive_tests/build_HoltWintersExponentialSmoothing_pipline.py delete mode 100644 primitive_tests/build_KDiscord_pipeline.py delete mode 100644 primitive_tests/build_KNN_pipline.py delete mode 100644 primitive_tests/build_LODA_pipline.py delete mode 100644 primitive_tests/build_LOF_pipline.py delete mode 100644 primitive_tests/build_MatrixProfile_pipeline.py delete mode 100644 primitive_tests/build_MeanAverageTransform_pipline.py delete mode 100644 primitive_tests/build_OCSVM_pipline.py delete mode 100644 primitive_tests/build_PyodCOF.py delete mode 100644 primitive_tests/build_QuantileTransform_pipline.py delete mode 100644 primitive_tests/build_SOD_pipeline.py delete mode 100644 primitive_tests/build_SimpleExponentialSmoothing_pipline.py delete mode 100644 primitive_tests/build_Standardize_pipline.py delete mode 100644 primitive_tests/build_SubsequenceClustering_pipline.py delete mode 100644 primitive_tests/build_Telemanom.py delete mode 100644 primitive_tests/build_TimeIntervalTransform_pipeline.py delete mode 100644 primitive_tests/build_WaveletTransform_pipline.py delete mode 100644 primitive_tests/build_test_detection_algorithm_PyodMoGaal.py delete mode 100644 primitive_tests/build_test_detection_algorithm_PyodSoGaal.py delete mode 100644 primitive_tests/build_test_feature_analysis_spectral_residual_transform_pipeline.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_abs_energy.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_abs_sum.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_gmean.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_hmean.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_kurtosis.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_maximum.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_mean.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_mean_abs.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_mean_temporal_derivative.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_median.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_median_absolute_deviation.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_minimum.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_skew.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_variation.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_vec_sum.py delete mode 100644 primitive_tests/build_test_feature_analysis_statistical_willison_amplitude.py delete mode 100644 primitive_tests/build_test_time_series_seasonality_trend_decomposition.py rename primitive_tests/{build_CategoricalToBinary.py => data_processing/CategoricalToBinary_pipeline.py} (54%) rename primitive_tests/{build_ColumnFilter_pipeline.py => data_processing/ColumnFilter_pipeline.py} (70%) rename primitive_tests/{build_ContinuityValidation_pipline.py => data_processing/ContinuityValidation_pipline.py} (87%) rename primitive_tests/{build_DuplicationValidation_pipline.py => data_processing/DuplicationValidation_pipeline.py} (88%) rename primitive_tests/{build_TRMF_pipline.py => data_processing/TimeIntervalTransform_pipeline.py} (62%) create mode 100644 primitive_tests/detection_algorithm/ABOD_pipeline.py rename primitive_tests/{build_AutoEncoder.py => detection_algorithm/AutoEncoder_pipeline.py} (61%) create mode 100644 primitive_tests/detection_algorithm/AutoRegODetect_pipeline.py create mode 100644 primitive_tests/detection_algorithm/CBLOF_pipline.py create mode 100644 primitive_tests/detection_algorithm/DeepLog_pipeline.py rename primitive_tests/{build_HBOS_pipline.py => detection_algorithm/HBOS_pipline.py} (62%) rename primitive_tests/{build_HBOS_score_pipline.py => detection_algorithm/HBOS_score_pipeline.py} (51%) rename primitive_tests/{build_IsolationForest_pipline.py => detection_algorithm/IsolationForest_pipline.py} (66%) create mode 100644 primitive_tests/detection_algorithm/KDiscord_pipeline.py create mode 100644 primitive_tests/detection_algorithm/KNN_pipeline.py create mode 100644 primitive_tests/detection_algorithm/LODA_pipeline.py create mode 100644 primitive_tests/detection_algorithm/LOF_pipeline.py create mode 100644 primitive_tests/detection_algorithm/LSTMOD_pipeline.py rename primitive_tests/{build_AutoRegODetect_pipeline.py => detection_algorithm/MatrixProfile_pipeline.py} (54%) create mode 100644 primitive_tests/detection_algorithm/OCSVM_pipline.py create mode 100644 primitive_tests/detection_algorithm/PCAODetect_pipeline.py create mode 100644 primitive_tests/detection_algorithm/PyodCOF.py create mode 100644 primitive_tests/detection_algorithm/PyodMoGaal_pipeline.py create mode 100644 primitive_tests/detection_algorithm/PyodSoGaal_pipeline.py rename primitive_tests/{build_PCAODetect_pipeline.py => detection_algorithm/SOD_pipeline.py} (56%) create mode 100644 primitive_tests/detection_algorithm/Telemanom_pipeline.py rename primitive_tests/{build_VariationalAutoEncoder.py => detection_algorithm/VariationalAutoEncoder_pipeline.py} (61%) rename primitive_tests/{build_BKFilter_pipline.py => feature_analysis/BKFilter_pipeline.py} (59%) rename primitive_tests/{build_FastFourierTransform.py => feature_analysis/DiscreteCosineTransform_pipeline.py} (54%) rename primitive_tests/{build_DiscreteCosineTransform.py => feature_analysis/FastFourierTransform_pipeline.py} (54%) rename primitive_tests/{build_HPFilter_pipline.py => feature_analysis/HPFilter_pipeline.py} (57%) rename primitive_tests/{build_NonNegativeMatrixFactorization.py => feature_analysis/NonNegativeMatrixFactorization_pipeline.py} (52%) create mode 100644 primitive_tests/feature_analysis/SpectralResidualTransform_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalAbsEnergy_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalAbsSum.py create mode 100644 primitive_tests/feature_analysis/StatisticalGmean_pipeline.py rename primitive_tests/{build_test_feature_analysis_statistical_var.py => feature_analysis/StatisticalHmean_pipeline.py} (54%) create mode 100644 primitive_tests/feature_analysis/StatisticalKurtosis_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalMaximum_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalMeanAbs_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalMeanTemporalDerivative.py create mode 100644 primitive_tests/feature_analysis/StatisticalMean_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalMedianAbsoluteDeviation.py rename primitive_tests/{build_test_feature_analysis_statistical_std.py => feature_analysis/StatisticalMedian_pipeline.py} (55%) create mode 100644 primitive_tests/feature_analysis/StatisticalMinimum_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalSkew_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalStd_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalVar_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalVariation_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalVecSum_pipeline.py create mode 100644 primitive_tests/feature_analysis/StatisticalWillisonAmplitude_pipeline.py rename primitive_tests/{build_test_feature_analysis_statistical_zero_crossing.py => feature_analysis/StatisticalZeroCrossing_pipeline.py} (53%) create mode 100644 primitive_tests/feature_analysis/Statistical_mean_absTemporalDerivative_pipeline.py create mode 100644 primitive_tests/feature_analysis/TRMF_pipeline.py rename primitive_tests/{build_TruncatedSVD_pipline.py => feature_analysis/TruncatedSVD_pipeline.py} (56%) rename primitive_tests/{build_LSTMOD_pipline.py => feature_analysis/WaveletTransform_pipeline.py} (58%) rename primitive_tests/{build_RuleBasedFilter_pipline.py => reinforcement/RuleBasedFilter_pipline.py} (91%) create mode 100644 primitive_tests/test.sh rename primitive_tests/{build_PowerTransform_pipline.py => timeseries_processing/AxiswiseScale_pipeline.py} (54%) create mode 100644 primitive_tests/timeseries_processing/HoltSmoothing_pipeline.py create mode 100644 primitive_tests/timeseries_processing/HoltWintersExponentialSmoothing_pipeline.py create mode 100644 primitive_tests/timeseries_processing/MeanAverageTransform_pipeline.py create mode 100644 primitive_tests/timeseries_processing/PowerTransform_pipeline.py create mode 100644 primitive_tests/timeseries_processing/QuantileTransform_pipeline.py create mode 100644 primitive_tests/timeseries_processing/SeasonalityTrendDecomposition_pipeline.py create mode 100644 primitive_tests/timeseries_processing/SimpleExponentialSmoothing_pipeline.py rename primitive_tests/{build_AxiswiseScale_pipline.py => timeseries_processing/Standardize_pipeline.py} (53%) delete mode 100644 test.sh delete mode 100644 tested_file.txt rename tods/timeseries_processing/{SubsequenceClustering.py => SubsequenceSegmentation.py} (95%) diff --git a/datasets/anomaly/transform_yahoo.py b/datasets/anomaly/transform_yahoo.py index 3f4a7ab..4dbc890 100644 --- a/datasets/anomaly/transform_yahoo.py +++ b/datasets/anomaly/transform_yahoo.py @@ -10,7 +10,7 @@ import json # Designed for time series data name = 'yahoo_sub_5' src_path = './raw_data/yahoo_sub_5.csv' -label_name = 'is_anomaly' +label_name = 'anomaly' timestamp_name = 'timestamp' value_names = ['value_{}'.format(i) for i in range(5)] ratio = 0.9 # Ratio of training data, the rest is for testing diff --git a/datasets/anomaly/yahoo_system_sub_5/SCORE/dataset_TEST/datasetDoc.json b/datasets/anomaly/yahoo_sub_5/SCORE/dataset_TEST/datasetDoc.json similarity index 74% rename from datasets/anomaly/yahoo_system_sub_5/SCORE/dataset_TEST/datasetDoc.json rename to datasets/anomaly/yahoo_sub_5/SCORE/dataset_TEST/datasetDoc.json index a4b8ddc..ff5dec4 100644 --- a/datasets/anomaly/yahoo_system_sub_5/SCORE/dataset_TEST/datasetDoc.json +++ b/datasets/anomaly/yahoo_sub_5/SCORE/dataset_TEST/datasetDoc.json @@ -1,6 +1,6 @@ { "about": { - "datasetID": "yahoo_system_sub_5_dataset_TEST", + "datasetID": "yahoo_sub_5_dataset_TEST", "datasetName": "NULL", "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", @@ -50,7 +50,7 @@ }, { "colIndex": 3, - "colName": "system_id", + "colName": "value_1", "colType": "real", "role": [ "attribute" @@ -58,6 +58,30 @@ }, { "colIndex": 4, + "colName": "value_2", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 5, + "colName": "value_3", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 6, + "colName": "value_4", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 7, "colName": "ground_truth", "colType": "integer", "role": [ @@ -65,7 +89,7 @@ ] } ], - "columnsCount": 5 + "columnsCount": 8 } ] } \ No newline at end of file diff --git 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"problemName": "yahoo_sub_5_problem", "problemDescription": "Anomaly detection", "problemVersion": "4.0.0", "problemSchemaVersion": "4.0.0", @@ -14,12 +14,12 @@ "inputs": { "data": [ { - "datasetID": "yahoo_system_sub_5_dataset", + "datasetID": "yahoo_sub_5_dataset", "targets": [ { "targetIndex": 0, "resID": "learningData", - "colIndex": 4, + "colIndex": 7, "colName": "ground_truth" } ] @@ -35,20 +35,20 @@ "datasetViewMaps": { "train": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TRAIN" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TRAIN" } ], "test": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TEST" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TEST" } ], "score": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_SCORE" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_SCORE" } ] } diff --git a/datasets/anomaly/yahoo_system_sub_5/SCORE/targets.csv b/datasets/anomaly/yahoo_sub_5/SCORE/targets.csv similarity index 100% rename from datasets/anomaly/yahoo_system_sub_5/SCORE/targets.csv rename to datasets/anomaly/yahoo_sub_5/SCORE/targets.csv diff --git a/datasets/anomaly/yahoo_system_sub_5/TEST/dataset_TEST/datasetDoc.json b/datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json similarity index 74% rename from datasets/anomaly/yahoo_system_sub_5/TEST/dataset_TEST/datasetDoc.json rename to datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json index a4b8ddc..ff5dec4 100644 --- a/datasets/anomaly/yahoo_system_sub_5/TEST/dataset_TEST/datasetDoc.json +++ b/datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json @@ -1,6 +1,6 @@ { "about": { - "datasetID": "yahoo_system_sub_5_dataset_TEST", + "datasetID": "yahoo_sub_5_dataset_TEST", "datasetName": "NULL", "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", @@ -50,7 +50,7 @@ }, { "colIndex": 3, - "colName": "system_id", + "colName": "value_1", "colType": "real", "role": [ "attribute" @@ -58,6 +58,30 @@ }, { "colIndex": 4, + "colName": "value_2", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 5, + "colName": "value_3", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 6, + "colName": "value_4", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 7, "colName": "ground_truth", "colType": "integer", "role": [ @@ -65,7 +89,7 @@ ] } ], - "columnsCount": 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"problemName": "yahoo_system_sub_5_problem", + "problemID": "yahoo_sub_5_problem", + "problemName": "yahoo_sub_5_problem", "problemDescription": "Anomaly detection", "problemVersion": "4.0.0", "problemSchemaVersion": "4.0.0", @@ -14,12 +14,12 @@ "inputs": { "data": [ { - "datasetID": "yahoo_system_sub_5_dataset", + "datasetID": "yahoo_sub_5_dataset", "targets": [ { "targetIndex": 0, "resID": "learningData", - "colIndex": 4, + "colIndex": 7, "colName": "ground_truth" } ] @@ -35,20 +35,20 @@ "datasetViewMaps": { "train": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TRAIN" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TRAIN" } ], "test": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TEST" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TEST" } ], "score": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_SCORE" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_SCORE" } ] } diff --git a/datasets/anomaly/yahoo_system_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json b/datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json similarity index 73% rename from datasets/anomaly/yahoo_system_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json rename to datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json index 63554e0..be6f5c0 100644 --- a/datasets/anomaly/yahoo_system_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json +++ b/datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json @@ -1,6 +1,6 @@ { "about": { - "datasetID": "yahoo_system_sub_5_dataset_TRAIN", + "datasetID": "yahoo_sub_5_dataset_TRAIN", "datasetName": "NULL", "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", @@ -50,7 +50,7 @@ }, { "colIndex": 3, - "colName": "system_id", + "colName": "value_1", "colType": "real", "role": [ "attribute" @@ -58,6 +58,30 @@ }, { "colIndex": 4, + "colName": "value_2", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 5, + "colName": "value_3", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 6, + "colName": "value_4", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 7, "colName": "ground_truth", "colType": "integer", "role": [ @@ -65,7 +89,7 @@ ] } ], - "columnsCount": 5 + "columnsCount": 8 } ] -} +} \ No newline at end of file diff --git a/datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/tables/learningData.csv 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detection", "problemVersion": "4.0.0", "problemSchemaVersion": "4.0.0", @@ -14,12 +14,12 @@ "inputs": { "data": [ { - "datasetID": "yahoo_system_sub_5_dataset", + "datasetID": "yahoo_sub_5_dataset", "targets": [ { "targetIndex": 0, "resID": "learningData", - "colIndex": 4, + "colIndex": 7, "colName": "ground_truth" } ] @@ -35,20 +35,20 @@ "datasetViewMaps": { "train": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TRAIN" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TRAIN" } ], "test": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TEST" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TEST" } ], "score": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_SCORE" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_SCORE" } ] } diff --git a/datasets/anomaly/yahoo_system_sub_5/yahoo_system_sub_5_dataset/datasetDoc.json b/datasets/anomaly/yahoo_sub_5/yahoo_sub_5_dataset/datasetDoc.json similarity index 73% rename from datasets/anomaly/yahoo_system_sub_5/yahoo_system_sub_5_dataset/datasetDoc.json rename to datasets/anomaly/yahoo_sub_5/yahoo_sub_5_dataset/datasetDoc.json index 32ba070..08f39bf 100644 --- a/datasets/anomaly/yahoo_system_sub_5/yahoo_system_sub_5_dataset/datasetDoc.json +++ b/datasets/anomaly/yahoo_sub_5/yahoo_sub_5_dataset/datasetDoc.json @@ -1,7 +1,7 @@ { "about": { - "datasetID": "yahoo_system_sub_5_dataset", - "datasetName": "yahoo_system_sub_5", + "datasetID": "yahoo_sub_5_dataset", + "datasetName": "yahoo_sub_5", "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'", "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ", "license": " CC Public Domain Mark 1.0 ", @@ -50,7 +50,7 @@ }, { "colIndex": 3, - "colName": "system_id", + "colName": "value_1", "colType": "real", "role": [ "attribute" @@ -58,6 +58,30 @@ }, { "colIndex": 4, + "colName": "value_2", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 5, + "colName": "value_3", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 6, + "colName": "value_4", + "colType": "real", + "role": [ + "attribute" + ] + }, + { + "colIndex": 7, "colName": "ground_truth", "colType": "integer", "role": [ @@ -65,7 +89,7 @@ ] } ], - "columnsCount": 5 + "columnsCount": 8 } ] -} +} \ No newline at end of file diff --git a/datasets/anomaly/yahoo_sub_5/yahoo_sub_5_dataset/tables/learningData.csv 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"problemName": "yahoo_system_sub_5_problem", + "problemID": "yahoo_sub_5_problem", + "problemName": "yahoo_sub_5_problem", "problemDescription": "Anomaly detection", "problemVersion": "4.0.0", "problemSchemaVersion": "4.0.0", @@ -14,12 +14,12 @@ "inputs": { "data": [ { - "datasetID": "yahoo_system_sub_5_dataset", + "datasetID": "yahoo_sub_5_dataset", "targets": [ { "targetIndex": 0, "resID": "learningData", - "colIndex": 4, + "colIndex": 7, "colName": "ground_truth" } ] @@ -35,20 +35,20 @@ "datasetViewMaps": { "train": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TRAIN" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TRAIN" } ], "test": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_TEST" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_TEST" } ], "score": [ { - "from": "yahoo_system_sub_5_dataset", - "to": "yahoo_system_sub_5_dataset_SCORE" + "from": "yahoo_sub_5_dataset", + "to": "yahoo_sub_5_dataset_SCORE" } ] } diff --git a/datasets/anomaly/yahoo_system_sub_5/SCORE/dataset_TEST/tables/learningData.csv b/datasets/anomaly/yahoo_system_sub_5/SCORE/dataset_TEST/tables/learningData.csv deleted file mode 100644 index 4e92aa3..0000000 --- a/datasets/anomaly/yahoo_system_sub_5/SCORE/dataset_TEST/tables/learningData.csv +++ /dev/null @@ -1,1401 +0,0 @@ -d3mIndex,timestamp,value_0,system_id,ground_truth -5600,1,2109.0,4,0 -5601,2,3229.0,4,0 -5602,3,3637.0,4,1 -5603,4,1982.0,4,1 -5604,5,2751.0,4,1 -5605,6,2128.0,4,1 -5606,7,2109.0,4,1 -5607,8,2328.0,4,0 -5608,9,2453.0,4,1 -5609,10,2847.0,4,1 -5610,11,3659.0,4,1 -5611,12,5207.0,4,1 -5612,13,5146.0,4,0 -5613,14,4712.0,4,1 -5614,15,6363.0,4,0 -5615,16,5010.0,4,0 -5616,17,3956.0,4,0 -5617,18,4063.0,4,0 -5618,19,3748.0,4,0 -5619,20,3047.0,4,0 -5620,21,4099.0,4,1 -5621,22,2122.0,4,1 -5622,23,3387.0,4,0 -5623,24,1950.0,4,0 -5624,25,2927.0,4,1 -5625,26,1889.0,4,0 -5626,27,1910.0,4,0 -5627,28,3747.0,4,0 -5628,29,4994.0,4,1 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-5534,1335,74.0,3,1 -5535,1336,439.0,3,1 -5536,1337,79.0,3,0 -5537,1338,165.0,3,0 -5538,1339,139.0,3,0 -5539,1340,56.0,3,1 -5540,1341,102.0,3,0 -5541,1342,101.0,3,0 -5542,1343,41.0,3,1 -5543,1344,94.0,3,1 -5544,1345,116.0,3,1 -5545,1346,94.0,3,1 -5546,1347,89.0,3,0 -5547,1348,141.0,3,1 -5548,1349,167.0,3,0 -5549,1350,148.0,3,1 -5550,1351,105.0,3,0 -5551,1352,210.0,3,1 -5552,1353,145.0,3,1 -5553,1354,136.0,3,0 -5554,1355,126.0,3,1 -5555,1356,157.0,3,0 -5556,1357,105.0,3,0 -5557,1358,114.0,3,0 -5558,1359,104.0,3,0 -5559,1360,69.0,3,1 -5560,1361,75.0,3,0 -5561,1362,73.0,3,0 -5562,1363,74.0,3,1 -5563,1364,126.0,3,0 -5564,1365,67.0,3,0 -5565,1366,32.0,3,0 -5566,1367,38.0,3,0 -5567,1368,34.0,3,0 -5568,1369,36.0,3,1 -5569,1370,26.0,3,1 -5570,1371,74.0,3,1 -5571,1372,85.0,3,1 -5572,1373,67.0,3,1 -5573,1374,84.0,3,0 -5574,1375,1630.0,3,1 -5575,1376,1435.0,3,1 -5576,1377,857.0,3,1 -5577,1378,31.0,3,1 -5578,1379,500.0,3,1 -5579,1380,53.0,3,1 -5580,1381,61.0,3,1 -5581,1382,158.0,3,1 -5582,1383,184.0,3,0 -5583,1384,91.0,3,0 -5584,1385,60.0,3,0 -5585,1386,107.0,3,1 -5586,1387,5157.0,3,1 -5587,1388,28.0,3,0 -5588,1389,24.0,3,0 -5589,1390,21.0,3,1 -5590,1391,12.0,3,1 -5591,1392,24.0,3,1 -5592,1393,17.0,3,1 -5593,1394,48.0,3,0 -5594,1395,41.0,3,0 -5595,1396,1088.0,3,1 -5596,1397,68.0,3,1 -5597,1398,2575.0,3,0 -5598,1399,4688.0,3,1 -5599,1400,477.0,3,1 diff --git a/primitive_tests/build_ABOD_pipline.py b/primitive_tests/build_ABOD_pipline.py deleted file mode 100644 index 5faccc2..0000000 --- a/primitive_tests/build_ABOD_pipline.py +++ /dev/null @@ -1,70 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep - - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# Step 1: column_parser -step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# Step 2: extract_columns_by_semantic_types(attributes) -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -pipeline_description.add_step(step_2) - -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) -pipeline_description.add_step(step_3) - -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_4.add_output('produce') -pipeline_description.add_step(step_4) - -# Step 5: ABOD -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_abod')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce') - -step_5.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_5.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2, 4,)) -step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='replace') - -step_5.add_output('produce') -pipeline_description.add_step(step_5) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_CBLOF_pipline.py b/primitive_tests/build_CBLOF_pipline.py deleted file mode 100644 index 2180b6d..0000000 --- a/primitive_tests/build_CBLOF_pipline.py +++ /dev/null @@ -1,51 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_cblof') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_DeepLog_pipeline.py b/primitive_tests/build_DeepLog_pipeline.py deleted file mode 100644 index 0ab8fa3..0000000 --- a/primitive_tests/build_DeepLog_pipeline.py +++ /dev/null @@ -1,49 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.deeplog') - -step_2 = PrimitiveStep(primitive=primitive_2) -#step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# # Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/build_HoltSmoothing_pipline.py b/primitive_tests/build_HoltSmoothing_pipline.py deleted file mode 100644 index 8f8a31e..0000000 --- a/primitive_tests/build_HoltSmoothing_pipline.py +++ /dev/null @@ -1,76 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# Step 1: column_parser -step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# Step 2: extract_columns_by_semantic_types(attributes) -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -pipeline_description.add_step(step_2) - -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) -pipeline_description.add_step(step_3) - -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_4.add_output('produce') -pipeline_description.add_step(step_4) - -# Step 5: holt smoothing -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.holt_smoothing')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_5.add_hyperparameter(name="exclude_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) -step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) -step_5.add_output('produce') -pipeline_description.add_step(step_5) - -# Step 6: isolation forest -#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm')) -#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') -#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets) -#step_6.add_output('produce') -#pipeline_description.add_step(step_6) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_HoltWintersExponentialSmoothing_pipline.py b/primitive_tests/build_HoltWintersExponentialSmoothing_pipline.py deleted file mode 100644 index 6ede370..0000000 --- a/primitive_tests/build_HoltWintersExponentialSmoothing_pipline.py +++ /dev/null @@ -1,76 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# Step 1: column_parser -step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# Step 2: extract_columns_by_semantic_types(attributes) -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -pipeline_description.add_step(step_2) - -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) -pipeline_description.add_step(step_3) - -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_4.add_output('produce') -pipeline_description.add_step(step_4) - -# Step 5: holt winters exponential smoothing -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.holt_winters_exponential_smoothing')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_5.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) -step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) -step_5.add_output('produce') -pipeline_description.add_step(step_5) - -# Step 6: isolation forest -#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm')) -#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') -#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets) -#step_6.add_output('produce') -#pipeline_description.add_step(step_6) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_KDiscord_pipeline.py b/primitive_tests/build_KDiscord_pipeline.py deleted file mode 100644 index fc12db9..0000000 --- a/primitive_tests/build_KDiscord_pipeline.py +++ /dev/null @@ -1,71 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import numpy as np - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# Step 2: extract_columns_by_semantic_types(attributes) -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -pipeline_description.add_step(step_2) - -# # Step 3: Standardization -primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,)) -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - -# # Step 4: test primitive -primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.KDiscordODetector') -step_4 = PrimitiveStep(primitive=primitive_4) -step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_4.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=10) -# step_4.add_hyperparameter(name='weights', argument_type=ArgumentType.VALUE, data=weights_ndarray) -step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False) -# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional -step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') -step_4.add_output('produce') -step_4.add_output('produce_score') -pipeline_description.add_step(step_4) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_KNN_pipline.py b/primitive_tests/build_KNN_pipline.py deleted file mode 100644 index 8b31557..0000000 --- a/primitive_tests/build_KNN_pipline.py +++ /dev/null @@ -1,51 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_knn') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_LODA_pipline.py b/primitive_tests/build_LODA_pipline.py deleted file mode 100644 index 05b022d..0000000 --- a/primitive_tests/build_LODA_pipline.py +++ /dev/null @@ -1,51 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_loda') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_LOF_pipline.py b/primitive_tests/build_LOF_pipline.py deleted file mode 100644 index ec444cf..0000000 --- a/primitive_tests/build_LOF_pipline.py +++ /dev/null @@ -1,51 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_lof') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_MatrixProfile_pipeline.py b/primitive_tests/build_MatrixProfile_pipeline.py deleted file mode 100644 index f21821e..0000000 --- a/primitive_tests/build_MatrixProfile_pipeline.py +++ /dev/null @@ -1,49 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.matrix_profile') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=3) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# # Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/build_MeanAverageTransform_pipline.py b/primitive_tests/build_MeanAverageTransform_pipline.py deleted file mode 100644 index 43bf392..0000000 --- a/primitive_tests/build_MeanAverageTransform_pipline.py +++ /dev/null @@ -1,77 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# Step 1: column_parser -step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - - -# Step 2: extract_columns_by_semantic_types(attributes) -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -pipeline_description.add_step(step_2) - -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) -pipeline_description.add_step(step_3) - -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_4.add_output('produce') -pipeline_description.add_step(step_4) - -# Step 5: mean average transform -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.moving_average_transform')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_5.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) -step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) -step_5.add_output('produce') -pipeline_description.add_step(step_5) - -# Step 6: isolation forest -#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm')) -#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') -#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets) -#step_6.add_output('produce') -#pipeline_description.add_step(step_6) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_OCSVM_pipline.py b/primitive_tests/build_OCSVM_pipline.py deleted file mode 100644 index d8cd8c9..0000000 --- a/primitive_tests/build_OCSVM_pipline.py +++ /dev/null @@ -1,51 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ocsvm') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_PyodCOF.py b/primitive_tests/build_PyodCOF.py deleted file mode 100644 index fcd0d2b..0000000 --- a/primitive_tests/build_PyodCOF.py +++ /dev/null @@ -1,51 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_cof') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_QuantileTransform_pipline.py b/primitive_tests/build_QuantileTransform_pipline.py deleted file mode 100644 index f6c4868..0000000 --- a/primitive_tests/build_QuantileTransform_pipline.py +++ /dev/null @@ -1,49 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.quantile_transformer') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_SOD_pipeline.py b/primitive_tests/build_SOD_pipeline.py deleted file mode 100644 index e4ed1b3..0000000 --- a/primitive_tests/build_SOD_pipeline.py +++ /dev/null @@ -1,49 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_sod') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# # Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/build_SimpleExponentialSmoothing_pipline.py b/primitive_tests/build_SimpleExponentialSmoothing_pipline.py deleted file mode 100644 index b33db22..0000000 --- a/primitive_tests/build_SimpleExponentialSmoothing_pipline.py +++ /dev/null @@ -1,76 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# Step 1: column_parser -step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# Step 2: extract_columns_by_semantic_types(attributes) -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -pipeline_description.add_step(step_2) - -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) -pipeline_description.add_step(step_3) - -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_4.add_output('produce') -pipeline_description.add_step(step_4) - -# Step 5: simple exponential smoothing -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.simple_exponential_smoothing')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_5.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (1,)) -step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) -step_5.add_output('produce') -pipeline_description.add_step(step_5) - -# Step 6: isolation forest -#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm')) -#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') -#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets) -#step_6.add_output('produce') -#pipeline_description.add_step(step_6) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_Standardize_pipline.py b/primitive_tests/build_Standardize_pipline.py deleted file mode 100644 index 8300d7c..0000000 --- a/primitive_tests/build_Standardize_pipline.py +++ /dev/null @@ -1,49 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_SubsequenceClustering_pipline.py b/primitive_tests/build_SubsequenceClustering_pipline.py deleted file mode 100644 index d42515f..0000000 --- a/primitive_tests/build_SubsequenceClustering_pipline.py +++ /dev/null @@ -1,80 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - - -# Step 2: extract_columns_by_semantic_types(attributes) -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common')) -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -pipeline_description.add_step(step_2) - - -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) -pipeline_description.add_step(step_3) - -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: test primitive -primitive_4 = index.get_primitive('d3m.primitives.tods.timeseries_processing.subsequence_clustering') -step_4 = PrimitiveStep(primitive=primitive_4) - -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_4.add_output('produce') -pipeline_description.add_step(step_4) - -# Step 5: test primitive -primitive_5 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_loda') -step_5 = PrimitiveStep(primitive=primitive_5) -step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce') -step_5.add_output('produce') -pipeline_description.add_step(step_5) - -# Step 6: Predictions -step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.construct_predictions.Common')) -step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') -step_6.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_6.add_output('produce') -pipeline_description.add_step(step_6) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.6.produce') - -# Output to json -data = pipeline_description.to_json() -with open('example_pipeline.json', 'w') as f: - f.write(data) - print(data) - diff --git a/primitive_tests/build_Telemanom.py b/primitive_tests/build_Telemanom.py deleted file mode 100644 index 06a192c..0000000 --- a/primitive_tests/build_Telemanom.py +++ /dev/null @@ -1,48 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# Step 1: Column Parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# Step 2: Fast Fourier Transform -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.telemanom') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/build_TimeIntervalTransform_pipeline.py b/primitive_tests/build_TimeIntervalTransform_pipeline.py deleted file mode 100644 index be7990f..0000000 --- a/primitive_tests/build_TimeIntervalTransform_pipeline.py +++ /dev/null @@ -1,86 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: dataframe transformation -# primitive_1 = index.get_primitive('d3m.primitives.data_transformation.SKPowerTransformer') -# primitive_1 = index.get_primitive('d3m.primitives.data_transformation.SKStandardization') -# primitive_1 = index.get_primitive('d3m.primitives.data_transformation.SKQuantileTransformer') - -#Step 1: column_parser -step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -primitive_2 = index.get_primitive('d3m.primitives.tods.data_processing.time_interval_transform') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name="time_interval", argument_type=ArgumentType.VALUE, data = '5T') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) -# -# # Step 2: column_parser -# step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) -# step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -# step_2.add_output('produce') -# pipeline_description.add_step(step_2) -# -# -# # Step 3: extract_columns_by_semantic_types(attributes) -# step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -# step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -# step_3.add_output('produce') -# step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, -# data=['https://metadata.datadrivendiscovery.org/types/Attribute']) -# pipeline_description.add_step(step_3) -# -# # Step 4: extract_columns_by_semantic_types(targets) -# step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -# step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -# step_4.add_output('produce') -# step_4.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, -# data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) -# pipeline_description.add_step(step_4) -# -# attributes = 'steps.3.produce' -# targets = 'steps.4.produce' -# -# # Step 5: imputer -# step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_cleaning.imputer.SKlearn')) -# step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -# step_5.add_output('produce') -# pipeline_description.add_step(step_5) -# -# # Step 6: random_forest -# step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.regression.random_forest.SKlearn')) -# step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') -# step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets) -# step_6.add_output('produce') -# pipeline_description.add_step(step_6) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.1.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/build_WaveletTransform_pipline.py b/primitive_tests/build_WaveletTransform_pipline.py deleted file mode 100644 index ee6c766..0000000 --- a/primitive_tests/build_WaveletTransform_pipline.py +++ /dev/null @@ -1,64 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test WaveletTransform -primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.wavelet_transform') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='wavelet', argument_type=ArgumentType.VALUE, data='db8') -step_2.add_hyperparameter(name='level', argument_type=ArgumentType.VALUE, data=2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 2: test inverse WaveletTransform -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.wavelet_transform') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='wavelet', argument_type=ArgumentType.VALUE, data='db8') -step_3.add_hyperparameter(name='level', argument_type=ArgumentType.VALUE, data=2) -step_3.add_hyperparameter(name='inverse', argument_type=ArgumentType.VALUE, data=1) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False) -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_detection_algorithm_PyodMoGaal.py b/primitive_tests/build_test_detection_algorithm_PyodMoGaal.py deleted file mode 100644 index 713a2cd..0000000 --- a/primitive_tests/build_test_detection_algorithm_PyodMoGaal.py +++ /dev/null @@ -1,50 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_mogaal') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/build_test_detection_algorithm_PyodSoGaal.py b/primitive_tests/build_test_detection_algorithm_PyodSoGaal.py deleted file mode 100644 index 4caa752..0000000 --- a/primitive_tests/build_test_detection_algorithm_PyodSoGaal.py +++ /dev/null @@ -1,50 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_sogaal') - -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/build_test_feature_analysis_spectral_residual_transform_pipeline.py b/primitive_tests/build_test_feature_analysis_spectral_residual_transform_pipeline.py deleted file mode 100644 index 6278460..0000000 --- a/primitive_tests/build_test_feature_analysis_spectral_residual_transform_pipeline.py +++ /dev/null @@ -1,61 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.spectral_residual_transform') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='avg_filter_dimension', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_abs_energy.py b/primitive_tests/build_test_feature_analysis_statistical_abs_energy.py deleted file mode 100644 index cb28366..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_abs_energy.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_abs_energy') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_abs_sum.py b/primitive_tests/build_test_feature_analysis_statistical_abs_sum.py deleted file mode 100644 index 91b3d42..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_abs_sum.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_abs_sum') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_gmean.py b/primitive_tests/build_test_feature_analysis_statistical_gmean.py deleted file mode 100644 index 5d54b3c..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_gmean.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_g_mean') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_hmean.py b/primitive_tests/build_test_feature_analysis_statistical_hmean.py deleted file mode 100644 index 19fa5b2..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_hmean.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_h_mean') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_kurtosis.py b/primitive_tests/build_test_feature_analysis_statistical_kurtosis.py deleted file mode 100644 index b6b01a7..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_kurtosis.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_kurtosis') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_maximum.py b/primitive_tests/build_test_feature_analysis_statistical_maximum.py deleted file mode 100644 index 900a5c1..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_maximum.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_maximum') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_mean.py b/primitive_tests/build_test_feature_analysis_statistical_mean.py deleted file mode 100644 index 29c7bb0..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_mean.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_mean_abs.py b/primitive_tests/build_test_feature_analysis_statistical_mean_abs.py deleted file mode 100644 index 6be3c45..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_mean_abs.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_abs') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py b/primitive_tests/build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py deleted file mode 100644 index 15c12aa..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_abs_temporal_derivative') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_mean_temporal_derivative.py b/primitive_tests/build_test_feature_analysis_statistical_mean_temporal_derivative.py deleted file mode 100644 index d63dddb..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_mean_temporal_derivative.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_temporal_derivative') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_median.py b/primitive_tests/build_test_feature_analysis_statistical_median.py deleted file mode 100644 index cefe002..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_median.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_median') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_median_absolute_deviation.py b/primitive_tests/build_test_feature_analysis_statistical_median_absolute_deviation.py deleted file mode 100644 index 499a877..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_median_absolute_deviation.py +++ /dev/null @@ -1,63 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_median_abs_deviation') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_minimum.py b/primitive_tests/build_test_feature_analysis_statistical_minimum.py deleted file mode 100644 index 01c918d..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_minimum.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_minimum') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_skew.py b/primitive_tests/build_test_feature_analysis_statistical_skew.py deleted file mode 100644 index 7ca113c..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_skew.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_skew') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_variation.py b/primitive_tests/build_test_feature_analysis_statistical_variation.py deleted file mode 100644 index 5292e03..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_variation.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_variation') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_vec_sum.py b/primitive_tests/build_test_feature_analysis_statistical_vec_sum.py deleted file mode 100644 index fa8f99b..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_vec_sum.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_vec_sum') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_feature_analysis_statistical_willison_amplitude.py b/primitive_tests/build_test_feature_analysis_statistical_willison_amplitude.py deleted file mode 100644 index f750dad..0000000 --- a/primitive_tests/build_test_feature_analysis_statistical_willison_amplitude.py +++ /dev/null @@ -1,62 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_willison_amplitude') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_test_time_series_seasonality_trend_decomposition.py b/primitive_tests/build_test_time_series_seasonality_trend_decomposition.py deleted file mode 100644 index ab172bf..0000000 --- a/primitive_tests/build_test_time_series_seasonality_trend_decomposition.py +++ /dev/null @@ -1,61 +0,0 @@ -from d3m import index -from d3m.metadata.base import ArgumentType -from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy - -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ - -# Creating pipeline -pipeline_description = Pipeline() -pipeline_description.add_input(name='inputs') - -# Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) -step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') -step_0.add_output('produce') -pipeline_description.add_step(step_0) - -# # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) -step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') -step_1.add_output('produce') -pipeline_description.add_step(step_1) - -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') -step_2.add_output('produce') -pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.decomposition.time_series_seasonality_trend_decomposition') -step_3 = PrimitiveStep(primitive=primitive_3) -step_3.add_hyperparameter(name='period', argument_type=ArgumentType.VALUE, data=5) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce') -pipeline_description.add_step(step_3) - - - -# Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - -# Or you can output json -#data = pipline_description.to_json() - diff --git a/primitive_tests/build_CategoricalToBinary.py b/primitive_tests/data_processing/CategoricalToBinary_pipeline.py similarity index 54% rename from primitive_tests/build_CategoricalToBinary.py rename to primitive_tests/data_processing/CategoricalToBinary_pipeline.py index 9f9782e..ea6ccea 100644 --- a/primitive_tests/build_CategoricalToBinary.py +++ b/primitive_tests/data_processing/CategoricalToBinary_pipeline.py @@ -2,14 +2,11 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() pipeline_description.add_input(name='inputs') - # Step 0: dataset_to_dataframe primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') step_0 = PrimitiveStep(primitive=primitive_0) @@ -24,25 +21,28 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -# Step 2: Categorical to Binary -primitive_2 = index.get_primitive('d3m.primitives.tods.data_processing.categorical_to_binary') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(3,)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) +# Step 3: Categorical to Binary +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.categorical_to_binary')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(3,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_ColumnFilter_pipeline.py b/primitive_tests/data_processing/ColumnFilter_pipeline.py similarity index 70% rename from primitive_tests/build_ColumnFilter_pipeline.py rename to primitive_tests/data_processing/ColumnFilter_pipeline.py index 7c1aa55..83f0087 100644 --- a/primitive_tests/build_ColumnFilter_pipeline.py +++ b/primitive_tests/data_processing/ColumnFilter_pipeline.py @@ -22,16 +22,16 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.auto_correlation') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) -step_2.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -primitive_3 = index.get_primitive('d3m.primitives.tods.data_processing.column_filter') -step_3 = PrimitiveStep(primitive=primitive_3) +# Step 3: column_filter +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_filter')) step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') pipeline_description.add_step(step_3) @@ -39,11 +39,8 @@ pipeline_description.add_step(step_3) # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_ContinuityValidation_pipline.py b/primitive_tests/data_processing/ContinuityValidation_pipline.py similarity index 87% rename from primitive_tests/build_ContinuityValidation_pipline.py rename to primitive_tests/data_processing/ContinuityValidation_pipline.py index 3b76d84..bf6f772 100644 --- a/primitive_tests/build_ContinuityValidation_pipline.py +++ b/primitive_tests/data_processing/ContinuityValidation_pipline.py @@ -18,8 +18,7 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) - -# Step 2: ContinuityValidation +# Step 3: ContinuityValidation step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.continuity_validation')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') @@ -32,12 +31,9 @@ pipeline_description.add_step(step_2) # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_DuplicationValidation_pipline.py b/primitive_tests/data_processing/DuplicationValidation_pipeline.py similarity index 88% rename from primitive_tests/build_DuplicationValidation_pipline.py rename to primitive_tests/data_processing/DuplicationValidation_pipeline.py index 57673d2..15788d6 100644 --- a/primitive_tests/build_DuplicationValidation_pipline.py +++ b/primitive_tests/data_processing/DuplicationValidation_pipeline.py @@ -13,14 +13,12 @@ step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_0.add_output('produce') pipeline_description.add_step(step_0) - # Step 1: column_parser step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_1.add_output('produce') pipeline_description.add_step(step_1) - # Step 2: DuplicationValidation step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.duplication_validation')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') @@ -31,12 +29,9 @@ pipeline_description.add_step(step_2) # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_TRMF_pipline.py b/primitive_tests/data_processing/TimeIntervalTransform_pipeline.py similarity index 62% rename from primitive_tests/build_TRMF_pipline.py rename to primitive_tests/data_processing/TimeIntervalTransform_pipeline.py index 7d7c407..fa26892 100644 --- a/primitive_tests/build_TRMF_pipline.py +++ b/primitive_tests/data_processing/TimeIntervalTransform_pipeline.py @@ -8,7 +8,8 @@ pipeline_description = Pipeline() pipeline_description.add_input(name='inputs') # Step 0: dataset_to_dataframe -step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')) +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') step_0.add_output('produce') pipeline_description.add_step(step_0) @@ -19,26 +20,18 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) - -# Step 2: TRMF -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.trmf')) +# Step 2: time_interval_transform +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.time_interval_transform')) +step_2.add_hyperparameter(name="time_interval", argument_type=ArgumentType.VALUE, data = 'T') step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') - -step_2.add_hyperparameter(name = 'lags', argument_type=ArgumentType.VALUE, data = [1,2,10,100]) -# step_2.add_hyperparameter(name = 'K', argument_type=ArgumentType.VALUE, data = 3) -# step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2, 3, 4, 5, 6)) - pipeline_description.add_step(step_2) # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/detection_algorithm/ABOD_pipeline.py b/primitive_tests/detection_algorithm/ABOD_pipeline.py new file mode 100644 index 0000000..7d5f89a --- /dev/null +++ b/primitive_tests/detection_algorithm/ABOD_pipeline.py @@ -0,0 +1,53 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: ABOD +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_abod')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + + diff --git a/primitive_tests/build_AutoEncoder.py b/primitive_tests/detection_algorithm/AutoEncoder_pipeline.py similarity index 61% rename from primitive_tests/build_AutoEncoder.py rename to primitive_tests/detection_algorithm/AutoEncoder_pipeline.py index 7482be5..fe10f68 100644 --- a/primitive_tests/build_AutoEncoder.py +++ b/primitive_tests/detection_algorithm/AutoEncoder_pipeline.py @@ -2,8 +2,6 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -29,39 +27,25 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +# Step 3: auto encoder +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ae')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) pipeline_description.add_step(step_3) -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_4.add_output('produce') pipeline_description.add_step(step_4) -# Step 5: auto encoder -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ae')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_5.add_output('produce') -pipeline_description.add_step(step_5) - - # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/detection_algorithm/AutoRegODetect_pipeline.py b/primitive_tests/detection_algorithm/AutoRegODetect_pipeline.py new file mode 100644 index 0000000..ca9644c --- /dev/null +++ b/primitive_tests/detection_algorithm/AutoRegODetect_pipeline.py @@ -0,0 +1,54 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep +from d3m.metadata import hyperparams +import numpy as np + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: AutoRegODetector +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.AutoRegODetector')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/CBLOF_pipline.py b/primitive_tests/detection_algorithm/CBLOF_pipline.py new file mode 100644 index 0000000..0a993ff --- /dev/null +++ b/primitive_tests/detection_algorithm/CBLOF_pipline.py @@ -0,0 +1,57 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep +from d3m.metadata import hyperparams +import copy + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: CBLOF +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_cblof')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/DeepLog_pipeline.py b/primitive_tests/detection_algorithm/DeepLog_pipeline.py new file mode 100644 index 0000000..106337b --- /dev/null +++ b/primitive_tests/detection_algorithm/DeepLog_pipeline.py @@ -0,0 +1,54 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep +from d3m.metadata import hyperparams + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: deeplog +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.deeplog')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_HBOS_pipline.py b/primitive_tests/detection_algorithm/HBOS_pipline.py similarity index 62% rename from primitive_tests/build_HBOS_pipline.py rename to primitive_tests/detection_algorithm/HBOS_pipline.py index b281ba0..5735598 100644 --- a/primitive_tests/build_HBOS_pipline.py +++ b/primitive_tests/detection_algorithm/HBOS_pipline.py @@ -27,42 +27,26 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +# Step 3: HBOS +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_hbos')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) pipeline_description.add_step(step_3) -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_4.add_output('produce') pipeline_description.add_step(step_4) -# Step 5: HBOS -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_hbos')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce') - -step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -# step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') - -step_5.add_output('produce') -pipeline_description.add_step(step_5) - # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_HBOS_score_pipline.py b/primitive_tests/detection_algorithm/HBOS_score_pipeline.py similarity index 51% rename from primitive_tests/build_HBOS_score_pipline.py rename to primitive_tests/detection_algorithm/HBOS_score_pipeline.py index b389a1e..84fee00 100644 --- a/primitive_tests/build_HBOS_score_pipline.py +++ b/primitive_tests/detection_algorithm/HBOS_score_pipeline.py @@ -27,45 +27,22 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +# Step 3: HBOS +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_hbos')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True) +step_3.add_output('produce_score') step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) pipeline_description.add_step(step_3) -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_4.add_output('produce') -pipeline_description.add_step(step_4) - -# Step 5: HBOS -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_hbos')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce') - -step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_5.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True) -# step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') - -step_5.add_output('produce_score') -step_5.add_output('produce') -pipeline_description.add_step(step_5) - # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') -# pipeline_description.add_output(name='output score', data_reference='steps.5.produce_score') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') +pipeline_description.add_output(name='output score', data_reference='steps.3.produce_score') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_IsolationForest_pipline.py b/primitive_tests/detection_algorithm/IsolationForest_pipline.py similarity index 66% rename from primitive_tests/build_IsolationForest_pipline.py rename to primitive_tests/detection_algorithm/IsolationForest_pipline.py index 80923c9..d1ca478 100644 --- a/primitive_tests/build_IsolationForest_pipline.py +++ b/primitive_tests/detection_algorithm/IsolationForest_pipline.py @@ -1,11 +1,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -36,24 +32,23 @@ pipeline_description.add_step(step_2) primitive_3 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_iforest') step_3 = PrimitiveStep(primitive=primitive_3) step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -# step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -# step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_3.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True) step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') -step_3.add_output('produce_score') step_3.add_output('produce') pipeline_description.add_step(step_3) -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce_score') +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/detection_algorithm/KDiscord_pipeline.py b/primitive_tests/detection_algorithm/KDiscord_pipeline.py new file mode 100644 index 0000000..1d13683 --- /dev/null +++ b/primitive_tests/detection_algorithm/KDiscord_pipeline.py @@ -0,0 +1,54 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: KDiscordODetector +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.KDiscordODetector')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=10) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/KNN_pipeline.py b/primitive_tests/detection_algorithm/KNN_pipeline.py new file mode 100644 index 0000000..0431cb5 --- /dev/null +++ b/primitive_tests/detection_algorithm/KNN_pipeline.py @@ -0,0 +1,55 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: KNN +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_knn')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/LODA_pipeline.py b/primitive_tests/detection_algorithm/LODA_pipeline.py new file mode 100644 index 0000000..96d1062 --- /dev/null +++ b/primitive_tests/detection_algorithm/LODA_pipeline.py @@ -0,0 +1,55 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: LODA +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_loda')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/LOF_pipeline.py b/primitive_tests/detection_algorithm/LOF_pipeline.py new file mode 100644 index 0000000..b5eb0c7 --- /dev/null +++ b/primitive_tests/detection_algorithm/LOF_pipeline.py @@ -0,0 +1,55 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: LOF +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_lof')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/LSTMOD_pipeline.py b/primitive_tests/detection_algorithm/LSTMOD_pipeline.py new file mode 100644 index 0000000..2aef056 --- /dev/null +++ b/primitive_tests/detection_algorithm/LSTMOD_pipeline.py @@ -0,0 +1,55 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: LSTMODetector +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.LSTMODetector')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='diff_group_method', argument_type=ArgumentType.VALUE, data='average') +step_3.add_hyperparameter(name='feature_dim', argument_type=ArgumentType.VALUE, data=6) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/build_AutoRegODetect_pipeline.py b/primitive_tests/detection_algorithm/MatrixProfile_pipeline.py similarity index 54% rename from primitive_tests/build_AutoRegODetect_pipeline.py rename to primitive_tests/detection_algorithm/MatrixProfile_pipeline.py index e6debfa..64efdeb 100644 --- a/primitive_tests/build_AutoRegODetect_pipeline.py +++ b/primitive_tests/detection_algorithm/MatrixProfile_pipeline.py @@ -2,10 +2,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep from d3m.metadata import hyperparams -import numpy as np -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -18,7 +15,7 @@ step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_0.add_output('produce') pipeline_description.add_step(step_0) -# # Step 1: column_parser +# Step 1: column_parser primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') step_1 = PrimitiveStep(primitive=primitive_1) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') @@ -32,40 +29,28 @@ step_2.add_output('produce') step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# # Step 3: Standardization -primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_3 = PrimitiveStep(primitive=primitive_3) +# Step 3: matrix_profile +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.matrix_profile')) step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,)) -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,)) # There is sth wrong with multi-dimensional +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=3) # There is sth wrong with multi-dimensional +# step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') pipeline_description.add_step(step_3) -# # Step 4: test primitive -primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.AutoRegODetector') -step_4 = PrimitiveStep(primitive=primitive_4) -step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_4.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=10) -# step_4.add_hyperparameter(name='weights', argument_type=ArgumentType.VALUE, data=weights_ndarray) -step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False) -# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional -step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True) +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_4.add_output('produce') -step_4.add_output('produce_score') pipeline_description.add_step(step_4) # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/detection_algorithm/OCSVM_pipline.py b/primitive_tests/detection_algorithm/OCSVM_pipline.py new file mode 100644 index 0000000..860e89c --- /dev/null +++ b/primitive_tests/detection_algorithm/OCSVM_pipline.py @@ -0,0 +1,55 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: OCSVM +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ocsvm')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/PCAODetect_pipeline.py b/primitive_tests/detection_algorithm/PCAODetect_pipeline.py new file mode 100644 index 0000000..9471fab --- /dev/null +++ b/primitive_tests/detection_algorithm/PCAODetect_pipeline.py @@ -0,0 +1,53 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: PCAODetector +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.PCAODetector')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/PyodCOF.py b/primitive_tests/detection_algorithm/PyodCOF.py new file mode 100644 index 0000000..e7df294 --- /dev/null +++ b/primitive_tests/detection_algorithm/PyodCOF.py @@ -0,0 +1,55 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: COF +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_cof')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/PyodMoGaal_pipeline.py b/primitive_tests/detection_algorithm/PyodMoGaal_pipeline.py new file mode 100644 index 0000000..81ba1b8 --- /dev/null +++ b/primitive_tests/detection_algorithm/PyodMoGaal_pipeline.py @@ -0,0 +1,54 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: MoGaal +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_mogaal')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/detection_algorithm/PyodSoGaal_pipeline.py b/primitive_tests/detection_algorithm/PyodSoGaal_pipeline.py new file mode 100644 index 0000000..c80cb96 --- /dev/null +++ b/primitive_tests/detection_algorithm/PyodSoGaal_pipeline.py @@ -0,0 +1,54 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: SoGaal +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_sogaal')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_PCAODetect_pipeline.py b/primitive_tests/detection_algorithm/SOD_pipeline.py similarity index 56% rename from primitive_tests/build_PCAODetect_pipeline.py rename to primitive_tests/detection_algorithm/SOD_pipeline.py index 1c7da0e..6c3ac72 100644 --- a/primitive_tests/build_PCAODetect_pipeline.py +++ b/primitive_tests/detection_algorithm/SOD_pipeline.py @@ -2,10 +2,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep from d3m.metadata import hyperparams -import numpy as np -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -32,40 +29,27 @@ step_2.add_output('produce') step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# # Step 3: Standardization -primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_3 = PrimitiveStep(primitive=primitive_3) +# Step 3: SOD +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_sod')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,)) -step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') pipeline_description.add_step(step_3) -# # Step 4: test primitive -primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.PCAODetector') -step_4 = PrimitiveStep(primitive=primitive_4) -step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_4.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=10) -# step_4.add_hyperparameter(name='weights', argument_type=ArgumentType.VALUE, data=weights_ndarray) -step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False) -# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional -step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True) +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_4.add_output('produce') -step_4.add_output('produce_score') pipeline_description.add_step(step_4) # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() - +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/detection_algorithm/Telemanom_pipeline.py b/primitive_tests/detection_algorithm/Telemanom_pipeline.py new file mode 100644 index 0000000..0978b34 --- /dev/null +++ b/primitive_tests/detection_algorithm/Telemanom_pipeline.py @@ -0,0 +1,54 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: Column Parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: telemanom +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.telemanom')) +# step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +# step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_VariationalAutoEncoder.py b/primitive_tests/detection_algorithm/VariationalAutoEncoder_pipeline.py similarity index 61% rename from primitive_tests/build_VariationalAutoEncoder.py rename to primitive_tests/detection_algorithm/VariationalAutoEncoder_pipeline.py index e585a0a..c800cf4 100644 --- a/primitive_tests/build_VariationalAutoEncoder.py +++ b/primitive_tests/detection_algorithm/VariationalAutoEncoder_pipeline.py @@ -2,8 +2,6 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -29,39 +27,25 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Step 3: extract_columns_by_semantic_types(targets) -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +# Step 3: variatinal auto encoder +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_vae')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') -step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, - data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) pipeline_description.add_step(step_3) -attributes = 'steps.2.produce' -targets = 'steps.3.produce' - -# Step 4: imputer -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_4.add_output('produce') pipeline_description.add_step(step_4) -# Step 5: variatinal auto encoder -step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_vae')) -step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) -step_5.add_output('produce') -pipeline_description.add_step(step_5) - - # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_BKFilter_pipline.py b/primitive_tests/feature_analysis/BKFilter_pipeline.py similarity index 59% rename from primitive_tests/build_BKFilter_pipline.py rename to primitive_tests/feature_analysis/BKFilter_pipeline.py index c2b306f..006e414 100644 --- a/primitive_tests/build_BKFilter_pipline.py +++ b/primitive_tests/feature_analysis/BKFilter_pipeline.py @@ -13,32 +13,34 @@ step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_0.add_output('produce') pipeline_description.add_step(step_0) - # Step 1: column_parser step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_1.add_output('produce') pipeline_description.add_step(step_1) - -# Step 2: BKFilter -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.bk_filter')) -# step_2.add_hyperparameter(name = 'columns_using_method', argument_type=ArgumentType.VALUE, data = 'name') -step_2.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True) -step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2,3)) +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') +# Step 3: BKFilter +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.bk_filter')) +step_3.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True) +step_3.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2,3)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_FastFourierTransform.py b/primitive_tests/feature_analysis/DiscreteCosineTransform_pipeline.py similarity index 54% rename from primitive_tests/build_FastFourierTransform.py rename to primitive_tests/feature_analysis/DiscreteCosineTransform_pipeline.py index 5c7f083..f6ef9e5 100644 --- a/primitive_tests/build_FastFourierTransform.py +++ b/primitive_tests/feature_analysis/DiscreteCosineTransform_pipeline.py @@ -2,8 +2,6 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -24,25 +22,28 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -# Step 2: Fast Fourier Transform -primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.fast_fourier_transform') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) +# Step 3: discrete_cosine_transform +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.discrete_cosine_transform')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) \ No newline at end of file diff --git a/primitive_tests/build_DiscreteCosineTransform.py b/primitive_tests/feature_analysis/FastFourierTransform_pipeline.py similarity index 54% rename from primitive_tests/build_DiscreteCosineTransform.py rename to primitive_tests/feature_analysis/FastFourierTransform_pipeline.py index c052207..6c353cd 100644 --- a/primitive_tests/build_DiscreteCosineTransform.py +++ b/primitive_tests/feature_analysis/FastFourierTransform_pipeline.py @@ -2,8 +2,6 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -24,27 +22,28 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) - -# Step 2: Discrete Cosine Transform -primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.discrete_cosine_transform') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) +# Step 3: Fast Fourier Transform +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.fast_fourier_transform')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_HPFilter_pipline.py b/primitive_tests/feature_analysis/HPFilter_pipeline.py similarity index 57% rename from primitive_tests/build_HPFilter_pipline.py rename to primitive_tests/feature_analysis/HPFilter_pipeline.py index fbf6941..4113719 100644 --- a/primitive_tests/build_HPFilter_pipline.py +++ b/primitive_tests/feature_analysis/HPFilter_pipeline.py @@ -13,34 +13,34 @@ step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_0.add_output('produce') pipeline_description.add_step(step_0) - # Step 1: column_parser step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_1.add_output('produce') pipeline_description.add_step(step_1) - -# Step 2: HPFilter -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.hp_filter')) +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') - -step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = [2,3,6]) - -step_2.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True) -step_2.add_hyperparameter(name = 'return_result', argument_type=ArgumentType.VALUE, data = 'append') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +# Step 3: HPFilter +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.hp_filter')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2,3)) +step_3.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True) +step_3.add_hyperparameter(name = 'return_result', argument_type=ArgumentType.VALUE, data = 'append') +step_3.add_output('produce') +pipeline_description.add_step(step_3) -# Or you can output json -#data = pipline_description.to_json() +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_NonNegativeMatrixFactorization.py b/primitive_tests/feature_analysis/NonNegativeMatrixFactorization_pipeline.py similarity index 52% rename from primitive_tests/build_NonNegativeMatrixFactorization.py rename to primitive_tests/feature_analysis/NonNegativeMatrixFactorization_pipeline.py index 787013c..2f40a9b 100644 --- a/primitive_tests/build_NonNegativeMatrixFactorization.py +++ b/primitive_tests/feature_analysis/NonNegativeMatrixFactorization_pipeline.py @@ -2,8 +2,6 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -24,27 +22,29 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -# Step 2: Non Negative Matrix Factorization -primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.non_negative_matrix_factorization') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_2.add_hyperparameter(name='rank', argument_type=ArgumentType.VALUE, data=5) +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) +# Step 3: Non Negative Matrix Factorization +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.non_negative_matrix_factorization')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_hyperparameter(name='rank', argument_type=ArgumentType.VALUE, data=5) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/SpectralResidualTransform_pipeline.py b/primitive_tests/feature_analysis/SpectralResidualTransform_pipeline.py new file mode 100644 index 0000000..c6424e1 --- /dev/null +++ b/primitive_tests/feature_analysis/SpectralResidualTransform_pipeline.py @@ -0,0 +1,47 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: spectral_residual_transform +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.spectral_residual_transform')) +step_3.add_hyperparameter(name='avg_filter_dimension', argument_type=ArgumentType.VALUE, data=4) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/feature_analysis/StatisticalAbsEnergy_pipeline.py b/primitive_tests/feature_analysis/StatisticalAbsEnergy_pipeline.py new file mode 100644 index 0000000..20c9e65 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalAbsEnergy_pipeline.py @@ -0,0 +1,48 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: statistical_abs_energy +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_abs_energy')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/feature_analysis/StatisticalAbsSum.py b/primitive_tests/feature_analysis/StatisticalAbsSum.py new file mode 100644 index 0000000..b4e089a --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalAbsSum.py @@ -0,0 +1,48 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_abs_sum +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_abs_sum')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/feature_analysis/StatisticalGmean_pipeline.py b/primitive_tests/feature_analysis/StatisticalGmean_pipeline.py new file mode 100644 index 0000000..69c3a63 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalGmean_pipeline.py @@ -0,0 +1,50 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: statistical_g_mean +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_g_mean')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/build_test_feature_analysis_statistical_var.py b/primitive_tests/feature_analysis/StatisticalHmean_pipeline.py similarity index 54% rename from primitive_tests/build_test_feature_analysis_statistical_var.py rename to primitive_tests/feature_analysis/StatisticalHmean_pipeline.py index bd13e96..e712be5 100644 --- a/primitive_tests/build_test_feature_analysis_statistical_var.py +++ b/primitive_tests/feature_analysis/StatisticalHmean_pipeline.py @@ -1,10 +1,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -24,39 +21,30 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_var') -step_3 = PrimitiveStep(primitive=primitive_3) +# Step 3: statistical_h_mean +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_h_mean')) step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') pipeline_description.add_step(step_3) - - # Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalKurtosis_pipeline.py b/primitive_tests/feature_analysis/StatisticalKurtosis_pipeline.py new file mode 100644 index 0000000..2bba408 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalKurtosis_pipeline.py @@ -0,0 +1,50 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: statistical_kurtosis +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_kurtosis')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/feature_analysis/StatisticalMaximum_pipeline.py b/primitive_tests/feature_analysis/StatisticalMaximum_pipeline.py new file mode 100644 index 0000000..b75bc23 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalMaximum_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_maximum +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_maximum')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalMeanAbs_pipeline.py b/primitive_tests/feature_analysis/StatisticalMeanAbs_pipeline.py new file mode 100644 index 0000000..94ce9cf --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalMeanAbs_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_mean_abs +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_abs')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalMeanTemporalDerivative.py b/primitive_tests/feature_analysis/StatisticalMeanTemporalDerivative.py new file mode 100644 index 0000000..803d926 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalMeanTemporalDerivative.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_mean_temporal_derivative +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_temporal_derivative')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalMean_pipeline.py b/primitive_tests/feature_analysis/StatisticalMean_pipeline.py new file mode 100644 index 0000000..0ebfbe6 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalMean_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_mean +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalMedianAbsoluteDeviation.py b/primitive_tests/feature_analysis/StatisticalMedianAbsoluteDeviation.py new file mode 100644 index 0000000..81f03a8 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalMedianAbsoluteDeviation.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_median_abs_deviation +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_median_abs_deviation')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_test_feature_analysis_statistical_std.py b/primitive_tests/feature_analysis/StatisticalMedian_pipeline.py similarity index 55% rename from primitive_tests/build_test_feature_analysis_statistical_std.py rename to primitive_tests/feature_analysis/StatisticalMedian_pipeline.py index 66d3180..72b400c 100644 --- a/primitive_tests/build_test_feature_analysis_statistical_std.py +++ b/primitive_tests/feature_analysis/StatisticalMedian_pipeline.py @@ -1,10 +1,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -24,39 +21,31 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_std') -step_3 = PrimitiveStep(primitive=primitive_3) +# # Step 3: statistical_median +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_median')) step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') pipeline_description.add_step(step_3) - - # Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) -# Or you can output json -#data = pipline_description.to_json() diff --git a/primitive_tests/feature_analysis/StatisticalMinimum_pipeline.py b/primitive_tests/feature_analysis/StatisticalMinimum_pipeline.py new file mode 100644 index 0000000..7ff83a6 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalMinimum_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_minimum +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_minimum')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) \ No newline at end of file diff --git a/primitive_tests/feature_analysis/StatisticalSkew_pipeline.py b/primitive_tests/feature_analysis/StatisticalSkew_pipeline.py new file mode 100644 index 0000000..bd0c78b --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalSkew_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_skew +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_skew')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalStd_pipeline.py b/primitive_tests/feature_analysis/StatisticalStd_pipeline.py new file mode 100644 index 0000000..b5a1af5 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalStd_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_std +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_std')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalVar_pipeline.py b/primitive_tests/feature_analysis/StatisticalVar_pipeline.py new file mode 100644 index 0000000..2356a73 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalVar_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_var +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_var')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalVariation_pipeline.py b/primitive_tests/feature_analysis/StatisticalVariation_pipeline.py new file mode 100644 index 0000000..719cdbd --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalVariation_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_variation +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_variation')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalVecSum_pipeline.py b/primitive_tests/feature_analysis/StatisticalVecSum_pipeline.py new file mode 100644 index 0000000..037705e --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalVecSum_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_vec_sum +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_vec_sum')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/StatisticalWillisonAmplitude_pipeline.py b/primitive_tests/feature_analysis/StatisticalWillisonAmplitude_pipeline.py new file mode 100644 index 0000000..c830128 --- /dev/null +++ b/primitive_tests/feature_analysis/StatisticalWillisonAmplitude_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_willison_amplitude +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_willison_amplitude')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_test_feature_analysis_statistical_zero_crossing.py b/primitive_tests/feature_analysis/StatisticalZeroCrossing_pipeline.py similarity index 53% rename from primitive_tests/build_test_feature_analysis_statistical_zero_crossing.py rename to primitive_tests/feature_analysis/StatisticalZeroCrossing_pipeline.py index 1c4efa1..d39a2bb 100644 --- a/primitive_tests/build_test_feature_analysis_statistical_zero_crossing.py +++ b/primitive_tests/feature_analysis/StatisticalZeroCrossing_pipeline.py @@ -1,10 +1,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -24,39 +21,28 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -# # Step 2: Standardization -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) - -# # Step 3: test primitive -# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive') -primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_zero_crossing') -step_3 = PrimitiveStep(primitive=primitive_3) +# Step 3: statistical_zero_crossing +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_zero_crossing')) step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(9,10)) # There is sth wrong with multi-dimensional +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') pipeline_description.add_step(step_3) - - # Final Output -pipeline_description.add_output(name='output', data_reference='steps.3.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) - - -# Or you can output json -#data = pipline_description.to_json() +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/Statistical_mean_absTemporalDerivative_pipeline.py b/primitive_tests/feature_analysis/Statistical_mean_absTemporalDerivative_pipeline.py new file mode 100644 index 0000000..b1247de --- /dev/null +++ b/primitive_tests/feature_analysis/Statistical_mean_absTemporalDerivative_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# # Step 3: statistical_mean_abs_temporal_derivative +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_abs_temporal_derivative')) +step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/feature_analysis/TRMF_pipeline.py b/primitive_tests/feature_analysis/TRMF_pipeline.py new file mode 100644 index 0000000..35fee6c --- /dev/null +++ b/primitive_tests/feature_analysis/TRMF_pipeline.py @@ -0,0 +1,44 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: TRMF +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.trmf')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name = 'lags', argument_type=ArgumentType.VALUE, data = [1,2,10,100]) +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_TruncatedSVD_pipline.py b/primitive_tests/feature_analysis/TruncatedSVD_pipeline.py similarity index 56% rename from primitive_tests/build_TruncatedSVD_pipline.py rename to primitive_tests/feature_analysis/TruncatedSVD_pipeline.py index 290f181..1ebb1bc 100644 --- a/primitive_tests/build_TruncatedSVD_pipline.py +++ b/primitive_tests/feature_analysis/TruncatedSVD_pipeline.py @@ -19,26 +19,29 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) - -# Step 2: TruncatedSVD -step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.truncated_svd')) +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') -step_2.add_hyperparameter(name = 'n_components', argument_type=ArgumentType.VALUE, data = 3) -step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2, 3, 4, 5, 6)) -step_2.add_hyperparameter(name = 'return_result', argument_type=ArgumentType.VALUE, data = 'append') -step_2.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True) +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') - -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +# Step 3: TruncatedSVD +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.truncated_svd')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_3.add_output('produce') +step_3.add_hyperparameter(name = 'n_components', argument_type=ArgumentType.VALUE, data = 3) +step_3.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2, 3, 4, 5)) +step_3.add_hyperparameter(name = 'return_result', argument_type=ArgumentType.VALUE, data = 'append') +step_3.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True) +pipeline_description.add_step(step_3) -# Or you can output json -#data = pipline_description.to_json() +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) \ No newline at end of file diff --git a/primitive_tests/build_LSTMOD_pipline.py b/primitive_tests/feature_analysis/WaveletTransform_pipeline.py similarity index 58% rename from primitive_tests/build_LSTMOD_pipline.py rename to primitive_tests/feature_analysis/WaveletTransform_pipeline.py index 3575904..65c2256 100644 --- a/primitive_tests/build_LSTMOD_pipline.py +++ b/primitive_tests/feature_analysis/WaveletTransform_pipeline.py @@ -1,11 +1,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import numpy as np -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -18,7 +14,7 @@ step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_0.add_output('produce') pipeline_description.add_step(step_0) -# # Step 1: column_parser +# Step 1: column_parser primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') step_1 = PrimitiveStep(primitive=primitive_1) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') @@ -29,29 +25,28 @@ pipeline_description.add_step(step_1) step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# # Step 2: Standardization -primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler') -step_3 = PrimitiveStep(primitive=primitive_3) +# Step 3: test WaveletTransform +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.wavelet_transform')) +step_3.add_hyperparameter(name='wavelet', argument_type=ArgumentType.VALUE, data='db8') +step_3.add_hyperparameter(name='level', argument_type=ArgumentType.VALUE, data=2) step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,)) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') pipeline_description.add_step(step_3) -# # Step 3: test primitive -primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.LSTMODetector') -step_4 = PrimitiveStep(primitive=primitive_4) -step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_4.add_hyperparameter(name='diff_group_method', argument_type=ArgumentType.VALUE, data='average') -step_4.add_hyperparameter(name='feature_dim', argument_type=ArgumentType.VALUE, data=5) +# Step 4: test inverse WaveletTransform +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.wavelet_transform')) +step_4.add_hyperparameter(name='wavelet', argument_type=ArgumentType.VALUE, data='db8') +step_4.add_hyperparameter(name='level', argument_type=ArgumentType.VALUE, data=2) +step_4.add_hyperparameter(name='inverse', argument_type=ArgumentType.VALUE, data=1) step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False) -# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional -step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') -step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True) +step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new') step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') step_4.add_output('produce') pipeline_description.add_step(step_4) @@ -59,12 +54,9 @@ pipeline_description.add_step(step_4) # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/build_RuleBasedFilter_pipline.py b/primitive_tests/reinforcement/RuleBasedFilter_pipline.py similarity index 91% rename from primitive_tests/build_RuleBasedFilter_pipline.py rename to primitive_tests/reinforcement/RuleBasedFilter_pipline.py index 87a74b9..3d040dd 100644 --- a/primitive_tests/build_RuleBasedFilter_pipline.py +++ b/primitive_tests/reinforcement/RuleBasedFilter_pipline.py @@ -26,29 +26,22 @@ step_2.add_output('produce') step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) - +# Step 3: Rule-based Filter step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.reinforcement.rule_filter')) step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') step_3.add_output('produce') - step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2, 4,)) step_3.add_hyperparameter(name='rule', argument_type=ArgumentType.VALUE, data='#4# % 2 == 0 and #2# <= 0.3') - step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') pipeline_description.add_step(step_3) - - # Final Output pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/test.sh b/primitive_tests/test.sh new file mode 100644 index 0000000..17ad223 --- /dev/null +++ b/primitive_tests/test.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +#modules="data_processing timeseries_processing feature_analysis detection_algorithms reinforcement" +modules="data_processing timeseries_processing" +#test_scripts=$(ls primitive_tests | grep -v -f tested_file.txt) + +for module in $modules +do + test_scripts=$(ls $module) + + for file in $test_scripts + do + for f in $tested_file + do + echo $f + done + echo $file + + # Test pipeline building + #python primitive_tests/$file > tmp.txt 2>>tmp.txt + python $module/$file > tmp.txt 2>>tmp.txt + error=$(cat tmp.txt | grep 'Error' | wc -l) + echo "\t#Pipeline Building Errors:" $error + if [ "$error" -gt "0" ] + then + cat tmp.txt + #rm tmp.txt + break + fi + # Test on KPI dataset + #python3 -m d3m runtime fit-produce -p pipeline.yml -r datasets/anomaly/kpi/TRAIN/problem_TRAIN/problemDoc.json -i datasets/anomaly/kpi/TRAIN/dataset_TRAIN/datasetDoc.json -t datasets/anomaly/kpi/TEST/dataset_TEST/datasetDoc.json -o results.csv -O pipeline_run.yml + #python3 -m d3m runtime fit-produce -p pipeline.yml -r datasets/anomaly/kpi/TRAIN/problem_TRAIN/problemDoc.json -i datasets/anomaly/kpi/TRAIN/dataset_TRAIN/datasetDoc.json -t datasets/anomaly/kpi/TEST/dataset_TEST/datasetDoc.json -o results.csv 2>>tmp.txt + + # Test on Yahoo dataset + #python3 -m d3m runtime fit-produce -p pipeline.yml -r datasets/anomaly/yahoo_sub_5/TRAIN/problem_TRAIN/problemDoc.json -i datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json -t datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json -o results.csv -O pipeline_run.yml + python3 -m d3m runtime fit-produce -p example_pipeline.json -r ../datasets/anomaly/yahoo_sub_5/TRAIN/problem_TRAIN/problemDoc.json -i ../datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json -t ../datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json -o results.csv 2> tmp.txt + error=$(cat tmp.txt | grep 'Error' | wc -l) + echo "\t#Pipeline Running Errors:" $error + if [ "$error" -gt "0" ] + then + cat tmp.txt + #rm tmp.txt + break + fi + echo $file >> tested_file.txt + done +done diff --git a/primitive_tests/build_PowerTransform_pipline.py b/primitive_tests/timeseries_processing/AxiswiseScale_pipeline.py similarity index 54% rename from primitive_tests/build_PowerTransform_pipline.py rename to primitive_tests/timeseries_processing/AxiswiseScale_pipeline.py index b855dc7..fe5dda3 100644 --- a/primitive_tests/build_PowerTransform_pipline.py +++ b/primitive_tests/timeseries_processing/AxiswiseScale_pipeline.py @@ -1,11 +1,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -25,25 +21,29 @@ step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_1.add_output('produce') pipeline_description.add_step(step_1) -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.power_transformer') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') +# Step 3: axiswise_scaler +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/primitive_tests/timeseries_processing/HoltSmoothing_pipeline.py b/primitive_tests/timeseries_processing/HoltSmoothing_pipeline.py new file mode 100644 index 0000000..129a310 --- /dev/null +++ b/primitive_tests/timeseries_processing/HoltSmoothing_pipeline.py @@ -0,0 +1,47 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: holt smoothing +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.holt_smoothing')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name="exclude_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) +step_3.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/timeseries_processing/HoltWintersExponentialSmoothing_pipeline.py b/primitive_tests/timeseries_processing/HoltWintersExponentialSmoothing_pipeline.py new file mode 100644 index 0000000..cabfca9 --- /dev/null +++ b/primitive_tests/timeseries_processing/HoltWintersExponentialSmoothing_pipeline.py @@ -0,0 +1,47 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: holt winters exponential smoothing +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.holt_winters_exponential_smoothing')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) +step_3.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/timeseries_processing/MeanAverageTransform_pipeline.py b/primitive_tests/timeseries_processing/MeanAverageTransform_pipeline.py new file mode 100644 index 0000000..3c46f00 --- /dev/null +++ b/primitive_tests/timeseries_processing/MeanAverageTransform_pipeline.py @@ -0,0 +1,47 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: mean average transform +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.moving_average_transform')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) +step_3.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/timeseries_processing/PowerTransform_pipeline.py b/primitive_tests/timeseries_processing/PowerTransform_pipeline.py new file mode 100644 index 0000000..fe16fe2 --- /dev/null +++ b/primitive_tests/timeseries_processing/PowerTransform_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: power_transformer +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.power_transformer')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/timeseries_processing/QuantileTransform_pipeline.py b/primitive_tests/timeseries_processing/QuantileTransform_pipeline.py new file mode 100644 index 0000000..a23edae --- /dev/null +++ b/primitive_tests/timeseries_processing/QuantileTransform_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: quantile_transformer +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.quantile_transformer')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/timeseries_processing/SeasonalityTrendDecomposition_pipeline.py b/primitive_tests/timeseries_processing/SeasonalityTrendDecomposition_pipeline.py new file mode 100644 index 0000000..9b82dd6 --- /dev/null +++ b/primitive_tests/timeseries_processing/SeasonalityTrendDecomposition_pipeline.py @@ -0,0 +1,49 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# # Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: time_series_seasonality_trend_decomposition +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.decomposition.time_series_seasonality_trend_decomposition')) +step_3.add_hyperparameter(name='period', argument_type=ArgumentType.VALUE, data=5) +# step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +# step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/timeseries_processing/SimpleExponentialSmoothing_pipeline.py b/primitive_tests/timeseries_processing/SimpleExponentialSmoothing_pipeline.py new file mode 100644 index 0000000..6311b72 --- /dev/null +++ b/primitive_tests/timeseries_processing/SimpleExponentialSmoothing_pipeline.py @@ -0,0 +1,46 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: simple exponential smoothing +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.simple_exponential_smoothing')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3)) +step_3.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True) +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) \ No newline at end of file diff --git a/primitive_tests/build_AxiswiseScale_pipline.py b/primitive_tests/timeseries_processing/Standardize_pipeline.py similarity index 53% rename from primitive_tests/build_AxiswiseScale_pipline.py rename to primitive_tests/timeseries_processing/Standardize_pipeline.py index 3352f48..bd52d2e 100644 --- a/primitive_tests/build_AxiswiseScale_pipline.py +++ b/primitive_tests/timeseries_processing/Standardize_pipeline.py @@ -1,11 +1,7 @@ from d3m import index from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep -from d3m.metadata import hyperparams -import copy -# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest -# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() @@ -18,33 +14,36 @@ step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_re step_0.add_output('produce') pipeline_description.add_step(step_0) -# # Step 1: column_parser +# Step 1: column_parser primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') step_1 = PrimitiveStep(primitive=primitive_1) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_1.add_output('produce') pipeline_description.add_step(step_1) -# # Step 2: test primitive -primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler') -step_2 = PrimitiveStep(primitive=primitive_2) -step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) -step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) +# Step 3: standard_scaler +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append') +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce') +pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce') -# Output to YAML -yaml = pipeline_description.to_yaml() -with open('pipeline.yml', 'w') as f: - f.write(yaml) -print(yaml) - -# Or you can output json -#data = pipline_description.to_json() +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/test.sh b/test.sh deleted file mode 100644 index bcd4448..0000000 --- a/test.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/bin/bash - -test_scripts=$(ls primitive_tests) -#test_scripts=$(ls primitive_tests | grep -v -f tested_file.txt) - -for file in $test_scripts -do - for f in $tested_file - do - echo $f - done - echo $file - - # Test pipeline building - python primitive_tests/$file > tmp.txt 2>>tmp.txt - error=$(cat tmp.txt | grep 'Error' | wc -l) - echo "\t#Pipeline Building Errors:" $error - if [ "$error" -gt "0" ] - then - cat tmp.txt - #rm tmp.txt - break - fi - # Test on KPI dataset - #python3 -m d3m runtime fit-produce -p pipeline.yml -r datasets/anomaly/kpi/TRAIN/problem_TRAIN/problemDoc.json -i datasets/anomaly/kpi/TRAIN/dataset_TRAIN/datasetDoc.json -t datasets/anomaly/kpi/TEST/dataset_TEST/datasetDoc.json -o results.csv -O pipeline_run.yml - #python3 -m d3m runtime fit-produce -p pipeline.yml -r datasets/anomaly/kpi/TRAIN/problem_TRAIN/problemDoc.json -i datasets/anomaly/kpi/TRAIN/dataset_TRAIN/datasetDoc.json -t datasets/anomaly/kpi/TEST/dataset_TEST/datasetDoc.json -o results.csv 2>>tmp.txt - - # Test on Yahoo dataset - #python3 -m d3m runtime fit-produce -p pipeline.yml -r datasets/anomaly/yahoo_sub_5/TRAIN/problem_TRAIN/problemDoc.json -i datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json -t datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json -o results.csv -O pipeline_run.yml - python3 -m d3m runtime fit-produce -p pipeline.yml -r datasets/anomaly/yahoo_sub_5/TRAIN/problem_TRAIN/problemDoc.json -i datasets/anomaly/yahoo_sub_5/TRAIN/dataset_TRAIN/datasetDoc.json -t datasets/anomaly/yahoo_sub_5/TEST/dataset_TEST/datasetDoc.json -o results.csv 2> tmp.txt - error=$(cat tmp.txt | grep 'Error' | wc -l) - echo "\t#Pipeline Running Errors:" $error - if [ "$error" -gt "0" ] - then - cat tmp.txt - #rm tmp.txt - break - fi - echo $file >> tested_file.txt -done diff --git a/tested_file.txt b/tested_file.txt deleted file mode 100644 index c521d58..0000000 --- a/tested_file.txt +++ /dev/null @@ -1,216 +0,0 @@ -build_ABOD_pipline.py -build_AutoEncoder.py -build_AutoRegODetect_pipeline.py -build_AxiswiseScale_pipline.py -build_BKFilter_pipline.py -build_CategoricalToBinary.py -build_CBLOF_pipline.py -build_ColumnFilter_pipeline.py -build_ContinuityValidation_pipline.py -build_DeepLog_pipeline.py -build_DiscreteCosineTransform.py -build_DuplicationValidation_pipline.py -build_FastFourierTransform.py -build_HBOS_pipline.py -build_HBOS_score_pipline.py -build_HoltSmoothing_pipline.py -build_HoltWintersExponentialSmoothing_pipline.py -build_HPFilter_pipline.py -build_IsolationForest_pipline.py -build_KDiscord_pipeline.py -build_KNN_pipline.py -build_LODA_pipline.py -build_LOF_pipline.py -build_LSTMOD_pipline.py -build_ABOD_pipline.py -build_AutoEncoder.py -build_AutoRegODetect_pipeline.py -build_AxiswiseScale_pipline.py -build_BKFilter_pipline.py -build_CategoricalToBinary.py -build_CBLOF_pipline.py -build_ColumnFilter_pipeline.py -build_ContinuityValidation_pipline.py -build_DeepLog_pipeline.py -build_DiscreteCosineTransform.py -build_DuplicationValidation_pipline.py -build_FastFourierTransform.py -build_HBOS_pipline.py -build_HBOS_score_pipline.py -build_HoltSmoothing_pipline.py -build_HoltWintersExponentialSmoothing_pipline.py -build_HPFilter_pipline.py -build_IsolationForest_pipline.py -build_KDiscord_pipeline.py -build_KNN_pipline.py -build_LODA_pipline.py -build_LOF_pipline.py -build_LSTMOD_pipline.py -build_MatrixProfile_pipeline.py -build_MeanAverageTransform_pipline.py -build_NonNegativeMatrixFactorization.py -build_OCSVM_pipline.py -build_PCAODetect_pipeline.py -build_PowerTransform_pipline.py -build_PyodCOF.py -build_QuantileTransform_pipline.py -build_RuleBasedFilter_pipline.py -build_SimpleExponentialSmoothing_pipline.py -build_SOD_pipeline.py -build_Standardize_pipline.py -build_Telemanom.py -build_test_detection_algorithm_PyodMoGaal.py -build_test_detection_algorithm_PyodSoGaal.py -build_test_feature_analysis_spectral_residual_transform_pipeline.py -build_test_feature_analysis_statistical_abs_energy.py -build_test_feature_analysis_statistical_abs_sum.py -build_test_feature_analysis_statistical_gmean.py -build_test_feature_analysis_statistical_hmean.py -build_test_feature_analysis_statistical_kurtosis.py -build_test_feature_analysis_statistical_maximum.py -build_test_feature_analysis_statistical_mean_abs.py -build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py -build_test_feature_analysis_statistical_mean.py -build_test_feature_analysis_statistical_mean_temporal_derivative.py -build_test_feature_analysis_statistical_median_absolute_deviation.py -build_test_feature_analysis_statistical_median.py -build_test_feature_analysis_statistical_minimum.py -build_test_feature_analysis_statistical_skew.py -build_test_feature_analysis_statistical_std.py -build_test_feature_analysis_statistical_variation.py -build_test_feature_analysis_statistical_var.py -build_test_feature_analysis_statistical_vec_sum.py -build_test_feature_analysis_statistical_willison_amplitude.py -build_test_feature_analysis_statistical_zero_crossing.py -build_test_time_series_seasonality_trend_decomposition.py -build_TimeIntervalTransform_pipeline.py -build_TRMF_pipline.py -build_TruncatedSVD_pipline.py -build_VariationalAutoEncoder.py -build_WaveletTransform_pipline.py -build_ABOD_pipline.py -build_AutoEncoder.py -build_AutoRegODetect_pipeline.py -build_AxiswiseScale_pipline.py -build_BKFilter_pipline.py -build_CategoricalToBinary.py -build_CBLOF_pipline.py -build_ColumnFilter_pipeline.py -build_ContinuityValidation_pipline.py -build_DeepLog_pipeline.py -build_DiscreteCosineTransform.py -build_DuplicationValidation_pipline.py -build_FastFourierTransform.py -build_HBOS_pipline.py -build_HBOS_score_pipline.py -build_HoltSmoothing_pipline.py -build_HoltWintersExponentialSmoothing_pipline.py -build_HPFilter_pipline.py -build_IsolationForest_pipline.py -build_KDiscord_pipeline.py -build_KNN_pipline.py -build_LODA_pipline.py -build_LOF_pipline.py -build_LSTMOD_pipline.py -build_MatrixProfile_pipeline.py -build_MeanAverageTransform_pipline.py -build_NonNegativeMatrixFactorization.py -build_OCSVM_pipline.py -build_PCAODetect_pipeline.py -build_PowerTransform_pipline.py -build_PyodCOF.py -build_QuantileTransform_pipline.py -build_RuleBasedFilter_pipline.py -build_SimpleExponentialSmoothing_pipline.py -build_SOD_pipeline.py -build_Standardize_pipline.py -build_Telemanom.py -build_test_detection_algorithm_PyodMoGaal.py -build_ABOD_pipline.py -build_AutoEncoder.py -build_AutoRegODetect_pipeline.py -build_AxiswiseScale_pipline.py -build_BKFilter_pipline.py -build_CategoricalToBinary.py -build_CBLOF_pipline.py -build_ColumnFilter_pipeline.py -build_ContinuityValidation_pipline.py -build_DeepLog_pipeline.py -build_DiscreteCosineTransform.py -build_DuplicationValidation_pipline.py -build_FastFourierTransform.py -build_HBOS_pipline.py -build_HBOS_score_pipline.py -build_HoltSmoothing_pipline.py -build_HoltWintersExponentialSmoothing_pipline.py -build_HPFilter_pipline.py -build_IsolationForest_pipline.py -build_KDiscord_pipeline.py -build_KNN_pipline.py -build_LODA_pipline.py -build_ABOD_pipline.py -build_AutoEncoder.py -build_AutoRegODetect_pipeline.py -build_AxiswiseScale_pipline.py -build_BKFilter_pipline.py -build_CategoricalToBinary.py -build_CBLOF_pipline.py -build_ColumnFilter_pipeline.py -build_ContinuityValidation_pipline.py -build_DeepLog_pipeline.py -build_DiscreteCosineTransform.py -build_DuplicationValidation_pipline.py -build_FastFourierTransform.py -build_HBOS_pipline.py -build_HBOS_score_pipline.py -build_HoltSmoothing_pipline.py -build_HoltWintersExponentialSmoothing_pipline.py -build_HPFilter_pipline.py -build_IsolationForest_pipline.py -build_KDiscord_pipeline.py -build_KNN_pipline.py -build_LODA_pipline.py -build_LOF_pipline.py -build_LSTMOD_pipline.py -build_MatrixProfile_pipeline.py -build_MeanAverageTransform_pipline.py -build_NonNegativeMatrixFactorization.py -build_OCSVM_pipline.py -build_PCAODetect_pipeline.py -build_PowerTransform_pipline.py -build_PyodCOF.py -build_QuantileTransform_pipline.py -build_RuleBasedFilter_pipline.py -build_SimpleExponentialSmoothing_pipline.py -build_SOD_pipeline.py -build_Standardize_pipline.py -build_Telemanom.py -build_test_detection_algorithm_PyodMoGaal.py -build_test_detection_algorithm_PyodSoGaal.py -build_test_feature_analysis_spectral_residual_transform_pipeline.py -build_test_feature_analysis_statistical_abs_energy.py -build_test_feature_analysis_statistical_abs_sum.py -build_test_feature_analysis_statistical_gmean.py -build_test_feature_analysis_statistical_hmean.py -build_test_feature_analysis_statistical_kurtosis.py -build_test_feature_analysis_statistical_maximum.py -build_test_feature_analysis_statistical_mean_abs.py -build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py -build_test_feature_analysis_statistical_mean.py -build_test_feature_analysis_statistical_mean_temporal_derivative.py -build_test_feature_analysis_statistical_median_absolute_deviation.py -build_test_feature_analysis_statistical_median.py -build_test_feature_analysis_statistical_minimum.py -build_test_feature_analysis_statistical_skew.py -build_test_feature_analysis_statistical_std.py -build_test_feature_analysis_statistical_variation.py -build_test_feature_analysis_statistical_var.py -build_test_feature_analysis_statistical_vec_sum.py -build_test_feature_analysis_statistical_willison_amplitude.py -build_test_feature_analysis_statistical_zero_crossing.py -build_test_time_series_seasonality_trend_decomposition.py -build_TimeIntervalTransform_pipeline.py -build_TRMF_pipline.py -build_TruncatedSVD_pipline.py -build_VariationalAutoEncoder.py -build_WaveletTransform_pipline.py diff --git a/tods/data_processing/CategoricalToBinary.py b/tods/data_processing/CategoricalToBinary.py index 1dad198..1e57d96 100644 --- a/tods/data_processing/CategoricalToBinary.py +++ b/tods/data_processing/CategoricalToBinary.py @@ -2,6 +2,7 @@ import os import typing import pandas as pd import numpy as np +import uuid from d3m import container, utils @@ -147,22 +148,18 @@ class CategoricalToBinaryPrimitive(transformer.TransformerPrimitiveBase[Inputs, __author__ = "DATA LAB" metadata = metadata_base.PrimitiveMetadata( { - "__author__ " : "DATA Lab at Texas A&M University", + "__author__ " : "DATALAB @ Texas A&M University", 'name': "Converting Categorical to Binary", 'python_path': 'd3m.primitives.tods.data_processing.categorical_to_binary', 'source': { 'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', - 'uris': [ - 'https://gitlab.com/lhenry15/tods.git', - 'https://gitlab.com/lhenry15/tods/-/blob/purav/anomaly-primitives/anomaly_primitives/CategoricalToBinaryDataframe.py', - ], }, 'algorithm_types': [ - metadata_base.PrimitiveAlgorithmType.CATEGORICAL_TO_BINARY, + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, ], 'primitive_family': metadata_base.PrimitiveFamily.DATA_PREPROCESSING, - 'id': 'bb6fb64d-cf20-45f0-8c4b-d7218f9c58c2', + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'CategoricalToBinaryPrimitive')), 'hyperparameters_to_tune':"None", 'version': '0.0.1', }, diff --git a/tods/data_processing/ColumnFilter.py b/tods/data_processing/ColumnFilter.py index f16d24f..a3d3c31 100644 --- a/tods/data_processing/ColumnFilter.py +++ b/tods/data_processing/ColumnFilter.py @@ -94,12 +94,16 @@ class ColumnFilterPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outputs """ metadata = metadata_base.PrimitiveMetadata({ - '__author__': "DATA Lab @Texas A&M University", + '__author__': "DATA Lab @ Texas A&M University", 'name': "Column Filter", 'python_path': 'd3m.primitives.tods.data_processing.column_filter', - 'source': {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods/-/blob/Yile/tods/tods/data_processing/column_filter.py']}, - 'algorithm_types': [metadata_base.PrimitiveAlgorithmType.COLUMN_FILTER,], + 'source': { + 'name': "DATA Lab @ Texas A&M University", + 'contact': 'mailto:khlai037@tamu.edu', + }, + 'algorithm_types': [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE + ], 'primitive_family': metadata_base.PrimitiveFamily.DATA_PREPROCESSING, 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'ColumnFilterPrimitive')), 'version': '0.0.1', diff --git a/tods/data_processing/ColumnParser.py b/tods/data_processing/ColumnParser.py index de02b36..40e9b2e 100644 --- a/tods/data_processing/ColumnParser.py +++ b/tods/data_processing/ColumnParser.py @@ -1,6 +1,7 @@ import hashlib import os import typing +import uuid import numpy # type: ignore @@ -90,32 +91,21 @@ class ColumnParserPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outputs What is returned is controlled by ``return_result`` and ``add_index_columns``. """ - metadata = metadata_base.PrimitiveMetadata( - { - 'id': 'd510cb7a-1782-4f51-b44c-58f0236e47c7', + metadata = metadata_base.PrimitiveMetadata({ + '__author__': "DATA Lab @Texas A&M University", 'version': '0.6.0', 'name': "Parses strings into their types", 'python_path': 'd3m.primitives.tods.data_processing.column_parser', 'source': { - 'name': "DataLab@Texas A&M University", - 'contact': 'mailto:mitar.commonprimitives@tnode.com', - 'uris': [ - 'https://gitlab.com/datadrivendiscovery/common-primitives/blob/master/common_primitives/column_parser.py', - 'https://gitlab.com/datadrivendiscovery/common-primitives.git', - ], + 'name': "DATA Lab @ Texas A&M University", + 'contact': 'mailto:khlai037@tamu.edu', }, - 'installation': [{ - 'type': metadata_base.PrimitiveInstallationType.PIP, - 'package_uri': 'git+https://gitlab.com/datadrivendiscovery/common-primitives.git@{git_commit}#egg=common_primitives'.format( - git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)), - ), - }], 'algorithm_types': [ - metadata_base.PrimitiveAlgorithmType.DATA_CONVERSION, - ], + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE + ], 'primitive_family': metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, - }, - ) + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'ColumnParserPrimitive')), + }) def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]: columns_to_use, output_columns = self._produce_columns(inputs) diff --git a/tods/data_processing/ConstructPredictions.py b/tods/data_processing/ConstructPredictions.py index d84d56a..7eda72f 100644 --- a/tods/data_processing/ConstructPredictions.py +++ b/tods/data_processing/ConstructPredictions.py @@ -1,5 +1,6 @@ import os import typing +import uuid from d3m import container, utils as d3m_utils from d3m.metadata import base as metadata_base, hyperparams @@ -43,32 +44,21 @@ class ConstructPredictionsPrimitive(transformer.TransformerPrimitiveBase[Inputs, assumes that all ``inputs`` columns are predicted targets, without confidence column(s). """ - metadata = metadata_base.PrimitiveMetadata( - { - 'id': '8d38b340-f83f-4877-baaa-162f8e551736', + metadata = metadata_base.PrimitiveMetadata({ + "__author__ " : "DATA Lab @ Texas A&M University", 'version': '0.3.0', 'name': "Construct pipeline predictions output", 'python_path': 'd3m.primitives.tods.data_processing.construct_predictions', 'source': { - 'name': "DataLab@Texas A&M University", - 'contact': 'mailto:mitar.commonprimitives@tnode.com', - 'uris': [ - 'https://gitlab.com/datadrivendiscovery/common-primitives/blob/master/common_primitives/construct_predictions.py', - 'https://gitlab.com/datadrivendiscovery/common-primitives.git', - ], + 'name': "DATA Lab @ Texas A&M University", + 'contact': 'mailto:khlai037@tamu.edu', }, - 'installation': [{ - 'type': metadata_base.PrimitiveInstallationType.PIP, - 'package_uri': 'git+https://gitlab.com/datadrivendiscovery/common-primitives.git@{git_commit}#egg=common_primitives'.format( - git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)), - ), - }], 'algorithm_types': [ - metadata_base.PrimitiveAlgorithmType.DATA_CONVERSION, + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, ], 'primitive_family': metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, - }, - ) + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'ConstructPredictionsPrimitive')), + }) def produce(self, *, inputs: Inputs, reference: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]: # type: ignore index_columns = inputs.metadata.get_index_columns() diff --git a/tods/data_processing/ContinuityValidation.py b/tods/data_processing/ContinuityValidation.py index 462cec1..388bb4e 100644 --- a/tods/data_processing/ContinuityValidation.py +++ b/tods/data_processing/ContinuityValidation.py @@ -1,6 +1,7 @@ from d3m import container, exceptions from d3m.primitive_interfaces import base, transformer from d3m.metadata import base as metadata_base, hyperparams +import uuid import os.path from d3m import utils @@ -48,13 +49,17 @@ class ContinuityValidationPrimitive(transformer.TransformerPrimitiveBase[Inputs, metadata = metadata_base.PrimitiveMetadata({ "name": "continuity validation primitive", "python_path": "d3m.primitives.tods.data_processing.continuity_validation", - "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/Junjie/anomaly-primitives/anomaly_primitives/ContinuityValidation.py']}, - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.CONTINUITY_VALIDATION, ], + "source": { + 'name': 'DATA Lab at Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, - "id": "ef8fb025-d157-476c-8e2e-f8fe56162195", "hyperparams_to_tune": ['continuity_option', 'interval'], "version": "0.0.1", + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'ContinuityValidationPrimitive')), }) def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]: diff --git a/tods/data_processing/DatasetToDataframe.py b/tods/data_processing/DatasetToDataframe.py index 8ba1dce..82b8a78 100644 --- a/tods/data_processing/DatasetToDataframe.py +++ b/tods/data_processing/DatasetToDataframe.py @@ -1,5 +1,6 @@ import os import typing +import uuid from d3m import container, utils as d3m_utils from d3m.base import utils as base_utils @@ -25,32 +26,21 @@ class DatasetToDataFramePrimitive(transformer.TransformerPrimitiveBase[Inputs, O A primitive which extracts a DataFrame out of a Dataset. """ - metadata = metadata_base.PrimitiveMetadata( - { - 'id': '4b42ce1e-9b98-4a25-b68e-fad13311eb65', + metadata = metadata_base.PrimitiveMetadata({ + "__author__ " : "DATA Lab @ Texas A&M University", 'version': '0.3.0', 'name': "Extract a DataFrame from a Dataset", 'python_path': 'd3m.primitives.tods.data_processing.dataset_to_dataframe', 'source': { - 'name': 'common-primitives', - 'contact': 'mailto:mitar.commonprimitives@tnode.com', - 'uris': [ - 'https://gitlab.com/datadrivendiscovery/common-primitives/blob/master/common_primitives/dataset_to_dataframe.py', - 'https://gitlab.com/datadrivendiscovery/common-primitives.git', - ], + 'name': "DATA Lab @ Texas A&M University", + 'contact': 'mailto:khlai037@tamu.edu', }, - 'installation': [{ - 'type': metadata_base.PrimitiveInstallationType.PIP, - 'package_uri': 'git+https://gitlab.com/datadrivendiscovery/common-primitives.git@{git_commit}#egg=common_primitives'.format( - git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)), - ), - }], 'algorithm_types': [ - metadata_base.PrimitiveAlgorithmType.DATA_CONVERSION, + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, ], 'primitive_family': metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, - }, - ) + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'DatasetToDataFramePrimitive')), + }) def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]: dataframe_resource_id, dataframe = base_utils.get_tabular_resource(inputs, self.hyperparams['dataframe_resource']) diff --git a/tods/data_processing/DuplicationValidation.py b/tods/data_processing/DuplicationValidation.py index d480670..fd4a08a 100644 --- a/tods/data_processing/DuplicationValidation.py +++ b/tods/data_processing/DuplicationValidation.py @@ -1,6 +1,7 @@ from d3m import container from d3m.primitive_interfaces import base, transformer from d3m.metadata import base as metadata_base, hyperparams +import uuid import os.path from d3m import utils @@ -39,11 +40,15 @@ class DuplicationValidationPrimitive(transformer.TransformerPrimitiveBase[Inputs metadata = metadata_base.PrimitiveMetadata({ "name": "duplication validation primitive", "python_path": "d3m.primitives.tods.data_processing.duplication_validation", - "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/Junjie/anomaly-primitives/anomaly_primitives/DuplicationValidation.py']}, - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.DUPLICATION_VALIDATION,], + "source": { + 'name': 'DATALAB @ Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, - "id": "cf6d8137-73d8-496e-a2e3-49f941ee716d", + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'DuplicationValidationPrimitive')), "hyperparams_to_tune": ['keep_option'], "version": "0.0.1", }) diff --git a/tods/data_processing/ExtractColumnsBySemanticTypes.py b/tods/data_processing/ExtractColumnsBySemanticTypes.py index bbd2e6c..db38fda 100644 --- a/tods/data_processing/ExtractColumnsBySemanticTypes.py +++ b/tods/data_processing/ExtractColumnsBySemanticTypes.py @@ -1,5 +1,6 @@ import os import typing +import uuid from d3m import container, exceptions, utils as d3m_utils from d3m.base import utils as base_utils @@ -67,28 +68,19 @@ class ExtractColumnsBySemanticTypesPrimitive(transformer.TransformerPrimitiveBas metadata = metadata_base.PrimitiveMetadata( { - 'id': '4503a4c6-42f7-45a1-a1d4-ed69699cf5e1', + "__author__ " : "DATA Lab @ Texas A&M University", 'version': '0.4.0', 'name': "Extracts columns by semantic type", 'python_path': 'd3m.primitives.tods.data_processing.extract_columns_by_semantic_types', 'source': { - 'name': "DataLab@Texas A&M University", + 'name': "DATA Lab @ Texas A&M University", 'contact': 'mailto:mitar.commonprimitives@tnode.com', - 'uris': [ - 'https://gitlab.com/datadrivendiscovery/common-primitives/blob/master/common_primitives/extract_columns_semantic_types.py', - 'https://gitlab.com/datadrivendiscovery/common-primitives.git', - ], }, - 'installation': [{ - 'type': metadata_base.PrimitiveInstallationType.PIP, - 'package_uri': 'git+https://gitlab.com/datadrivendiscovery/common-primitives.git@{git_commit}#egg=common_primitives'.format( - git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)), - ), - }], 'algorithm_types': [ - metadata_base.PrimitiveAlgorithmType.ARRAY_SLICING, + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, ], 'primitive_family': metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'ExtractColumnsBySemanticTypesPrimitive')), }, ) diff --git a/tods/data_processing/SKImputer.py b/tods/data_processing/SKImputer.py index 4e6c3b0..30f89db 100644 --- a/tods/data_processing/SKImputer.py +++ b/tods/data_processing/SKImputer.py @@ -6,6 +6,7 @@ import os import sklearn import numpy import typing +import uuid # Custom import commands if any from sklearn.impute import SimpleImputer @@ -129,22 +130,19 @@ class SKImputerPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Outputs, Param """ - __author__ = "DataLab @ Texas A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.IMPUTATION, ], + "__author__": "DATA Lab @ Texas A&M University", "name": "sklearn.impute.SimpleImputer", - "primitive_family": metadata_base.PrimitiveFamily.DATA_CLEANING, "python_path": "d3m.primitives.tods.data_processing.impute_missing", - "source": {'name': 'JPL', 'contact': 'mailto:shah@jpl.nasa.gov', 'uris': ['https://gitlab.com/datadrivendiscovery/sklearn-wrap/issues', 'https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html']}, + "source": { + 'name': 'DATA Lab @ Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, "version": "2019.11.13", - "id": "d016df89-de62-3c53-87ed-c06bb6a23cde", "hyperparams_to_tune": ['strategy'], - 'installation': [ - {'type': metadata_base.PrimitiveInstallationType.PIP, - 'package_uri': 'git+https://gitlab.com/datadrivendiscovery/sklearn-wrap.git@{git_commit}#egg=sklearn_wrap'.format( - git_commit=utils.current_git_commit(os.path.dirname(__file__)), - ), - }] + "algorithm_types": [metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_CLEANING, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SKImputerPrimitive')), }) def __init__(self, *, diff --git a/tods/data_processing/TimeIntervalTransform.py b/tods/data_processing/TimeIntervalTransform.py index c202bf7..3686c83 100644 --- a/tods/data_processing/TimeIntervalTransform.py +++ b/tods/data_processing/TimeIntervalTransform.py @@ -90,12 +90,14 @@ class TimeIntervalTransformPrimitive(transformer.TransformerPrimitiveBase[Inputs '__author__': "DATA Lab @Texas A&M University", 'name': "Time Interval Transform", 'python_path': 'd3m.primitives.tods.data_processing.time_interval_transform', - 'source': {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods/-/blob/Yile/anomaly-primitives/anomaly_primitives/TimeIntervalTransform.py']}, - 'algorithm_types': [metadata_base.PrimitiveAlgorithmType.TIME_INTERVAL_TRANSFORM,], + 'source': { + 'name': "DATA Lab @Taxes A&M University", + 'contact': 'mailto:khlai037@tamu.edu', + }, + 'algorithm_types': [metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,], 'primitive_family': metadata_base.PrimitiveFamily.DATA_PREPROCESSING, - 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'TimeIntervalTransformPrimitive')), 'hyperparams_to_tune': ['time_interval'], + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'TimeIntervalTransformPrimitive')), 'version': '0.0.2' }) diff --git a/tods/data_processing/TimeStampValidation.py b/tods/data_processing/TimeStampValidation.py index 946e93f..f9f2271 100644 --- a/tods/data_processing/TimeStampValidation.py +++ b/tods/data_processing/TimeStampValidation.py @@ -2,6 +2,7 @@ import os import typing import numpy +import uuid from d3m import container, utils as d3m_utils from d3m.metadata import base as metadata_base @@ -22,34 +23,23 @@ class TimeStampValidationPrimitive(transformer.TransformerPrimitiveBase[Inputs, """ A primitive to check time series is sorted by time stamp , if not then return sorted time series """ - __author__ = "DATA Lab at Texas A&M University", - metadata = metadata_base.PrimitiveMetadata( - { - 'id': '5f791b09-e16f-42e1-bc53-39de308f5861', + metadata = metadata_base.PrimitiveMetadata({ + '__author__': "DATA Lab at Texas A&M University", 'version': '0.1.0', 'name': 'Time Stamp Validation', 'python_path': 'd3m.primitives.tods.data_processing.timestamp_validation', 'keywords': ['Time Stamp', 'Sort Order'], 'source': { 'name': 'DATA Lab at Texas A&M University', - 'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/data_processing/TimeStampValidation.py'], 'contact': 'mailto:khlai037@tamu.edu' }, - 'installation': [ - {'type': metadata_base.PrimitiveInstallationType.PIP, - 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format( - git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)), - ), - } - - ], 'algorithm_types': [ - metadata_base.PrimitiveAlgorithmType.DATA_PROFILING , + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, ], 'primitive_family': metadata_base.PrimitiveFamily.DATA_VALIDATION, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'TimeStampValidationPrimitive')), - } - ) + }) def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]: """ diff --git a/tods/timeseries_processing/HoltSmoothing.py b/tods/timeseries_processing/HoltSmoothing.py index 140ee9f..e48b62a 100644 --- a/tods/timeseries_processing/HoltSmoothing.py +++ b/tods/timeseries_processing/HoltSmoothing.py @@ -1,6 +1,7 @@ from typing import Any, Callable, List, Dict, Union, Optional, Sequence, Tuple from numpy import ndarray from collections import OrderedDict +import uuid from scipy import sparse import os import sklearn @@ -110,16 +111,21 @@ class HoltSmoothingPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Outputs, P """ - __author__ = "DATA Lab at Texas A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.HOLT_SMOOTHING, ], + "__author__": "DATA Lab @ Texas A&M University", "name": "statsmodels.preprocessing.HoltSmoothing", - "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, "python_path": "d3m.primitives.tods.timeseries_processing.transformation.holt_smoothing", - "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/mia/anomaly-primitives/anomaly_primitives/HoltSmoothing.py']}, + "source": { + 'name': 'DATA Lab @ Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, "version": "0.0.1", - "id": "3688b5b4-885c-40bb-9731-fe3969ea81b0", "hyperparams_to_tune": ['endog','use_columns'], + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'HoltSmoothingPrimitive')), }) def __init__(self, *, diff --git a/tods/timeseries_processing/HoltWintersExponentialSmoothing.py b/tods/timeseries_processing/HoltWintersExponentialSmoothing.py index 8a193da..df0e0c0 100644 --- a/tods/timeseries_processing/HoltWintersExponentialSmoothing.py +++ b/tods/timeseries_processing/HoltWintersExponentialSmoothing.py @@ -5,6 +5,7 @@ from scipy import sparse import os import sklearn import numpy +import uuid import typing import pandas as pd # Custom import commands if any @@ -109,16 +110,20 @@ class HoltWintersExponentialSmoothingPrimitive(UnsupervisedLearnerPrimitiveBase[ """ - __author__ = "DATA Lab at Texas A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.HOLT_WINTERS_EXPONENTIAL_SMOOTHING, ], + "__author__": "DATA Lab at Texas A&M University", "name": "statsmodels.preprocessing.data.HoltWintersExponentialSmoothing", - "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, - #3"python_path": "d3m.primitives.tods.timeseries_processing.transformation.holt_winters_exponential_smoothing.Preprocessing", "python_path": "d3m.primitives.tods.timeseries_processing.transformation.holt_winters_exponential_smoothing", - "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/mia/anomaly-primitives/anomaly_primitives/HoltWintersExponentialSmoothing.py']}, + "source": { + 'name': 'DATA Lab at Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, "version": "0.0.1", - "id": "b8c6647c-3787-4efd-bf01-b0ca11c643c6", + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'HoltWintersExponentialSmoothingPrimitive')), "hyperparams_to_tune": ['endog','use_columns'], }) diff --git a/tods/timeseries_processing/MovingAverageTransformer.py b/tods/timeseries_processing/MovingAverageTransformer.py index 5659043..20b0be5 100644 --- a/tods/timeseries_processing/MovingAverageTransformer.py +++ b/tods/timeseries_processing/MovingAverageTransformer.py @@ -7,6 +7,7 @@ import sklearn import numpy import typing import pandas as pd +import uuid # Custom import commands if any from sklearn.preprocessing.data import Normalizer @@ -107,16 +108,19 @@ class MovingAverageTransformerPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Columns for which moving average is calculated is passed as hyperparameter . Default is all values column """ - __author__ = "DATA Lab at Texas A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.MOVING_AVERAGE_TRANSFORM, ], + "__author__": "DATA Lab @ Texas A&M University", "name": "pandas.preprocessing.data.MovingAverageTransform", - "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, "python_path": "d3m.primitives.tods.timeseries_processing.transformation.moving_average_transform", - "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/mia/anomaly-primitives/anomaly_primitives/MovingAverageTransform.py']}, - "version": "0.0.1", - "id": "ab8c90a6-d10e-49f1-8c5a-38884defc570", + "source": { + 'name': 'DATA Lab @ Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, "hyperparams_to_tune": ['window_size'], + "algorithm_types": [metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'MovingAverageTransformerPrimitive')), + "version": "0.0.1", }) def __init__(self, *, diff --git a/tods/timeseries_processing/SKAxiswiseScaler.py b/tods/timeseries_processing/SKAxiswiseScaler.py index d34de79..30d80fd 100644 --- a/tods/timeseries_processing/SKAxiswiseScaler.py +++ b/tods/timeseries_processing/SKAxiswiseScaler.py @@ -138,17 +138,21 @@ class SKAxiswiseScalerPrimitive(transformer.TransformerPrimitiveBase[Inputs, Out If True, scale the data to unit variance (or equivalently, unit standard deviation). """ - __author__ = "DATALAB @Taxes A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.DATA_MAPPING, ], + "__author__": "DATA Lab @ Taxes A&M University", "name": "Axis_wise_scale", - "primitive_family": metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, "python_path": "d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler", "hyperparams_to_tune": ['with_mean', 'with_std', 'axis'], - "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods.git']}, - "version": "0.0.1", + "source": { + 'name': "DATA Lab @Taxes A&M University", + 'contact': 'mailto:khlai037@tamu.edu', + }, + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SKAxiswiseScaler')), + "version": "0.0.1", }) def __init__(self, *, hyperparams: Hyperparams) -> None: diff --git a/tods/timeseries_processing/SKPowerTransformer.py b/tods/timeseries_processing/SKPowerTransformer.py index 27a350a..fa1fb97 100644 --- a/tods/timeseries_processing/SKPowerTransformer.py +++ b/tods/timeseries_processing/SKPowerTransformer.py @@ -7,6 +7,7 @@ import sklearn import numpy import typing import copy +import uuid # Custom import commands if any from sklearn.preprocessing import PowerTransformer @@ -29,7 +30,6 @@ from d3m import exceptions import pandas from d3m import container, utils as d3m_utils -import uuid Inputs = d3m_dataframe # Inputs = container.Dataset @@ -132,18 +132,21 @@ class SKPowerTransformerPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Outpu """ - __author__ = "DATALAB @Taxes A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.DATA_MAPPING, ], - "name": "Power_transformation", - "primitive_family": metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, - "python_path": "d3m.primitives.tods.timeseries_processing.transformation.power_transformer", - "hyperparams_to_tune": ['method', 'standardize'], - "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods.git']}, - "version": "0.0.1", - "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SKPowerTransformer')), - }) + '__author__': "DATA Lab @Texas A&M University", + "name": "Power_transformation", + "python_path": "d3m.primitives.tods.timeseries_processing.transformation.power_transformer", + 'source': { + 'name': "DATA Lab @ Taxes A&M University", + 'contact': 'mailto:khlai037@tamu.edu', + }, + 'algorithm_types': [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE + ], + 'primitive_family': metadata_base.PrimitiveFamily.DATA_PREPROCESSING, + "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SKPowerTransformer')), + 'version': '0.0.1', + }) def __init__(self, *, hyperparams: Hyperparams, diff --git a/tods/timeseries_processing/SKQuantileTransformer.py b/tods/timeseries_processing/SKQuantileTransformer.py index 48427e3..184fadd 100644 --- a/tods/timeseries_processing/SKQuantileTransformer.py +++ b/tods/timeseries_processing/SKQuantileTransformer.py @@ -152,17 +152,21 @@ class SKQuantileTransformerPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Ou Quantiles of references. """ - __author__ = "DATALAB @Taxes A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.DATA_CONVERSION, ], + "__author__": "DATA Lab @ Taxes A&M University", "name": "Quantile_transformation", - "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, "python_path": "d3m.primitives.tods.timeseries_processing.transformation.quantile_transformer", + "source": { + 'name': "DATALAB @Taxes A&M University", + 'contact': 'mailto:khlai037@tamu.edu', + }, "hyperparams_to_tune": ['n_quantiles', 'output_distribution', 'ignore_implicit_zeros', 'subsample', 'random_state'], - "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods.git']}, - "version": "0.0.1", + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SKQuantileTransformer')), + "version": "0.0.1", }) def __init__(self, *, diff --git a/tods/timeseries_processing/SKStandardScaler.py b/tods/timeseries_processing/SKStandardScaler.py index 7d67f5d..1eb41ab 100644 --- a/tods/timeseries_processing/SKStandardScaler.py +++ b/tods/timeseries_processing/SKStandardScaler.py @@ -142,15 +142,19 @@ class SKStandardScalerPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Outputs The number of samples processed by the estimator for each feature. If there are not missing samples, the n_samples_seen will be an integer, otherwise it will be an array. Will be reset on new calls to fit, but increments across partial_fit calls. """ - __author__ = "DATALAB @Taxes A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.DATA_CONVERSION, ], + "__author__": "DATA Lab @Taxes A&M University", "name": "Standard_scaler", - "primitive_family": metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, "python_path": "d3m.primitives.tods.timeseries_processing.transformation.standard_scaler", + "source": { + 'name': "DATA Lab @ Taxes A&M University", + 'contact': 'mailto:khlai037@tamu.edu', + }, "hyperparams_to_tune": ['with_mean', 'with_std'], - "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods.git']}, + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_TRANSFORMATION, "version": "0.0.1", "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SKStandardScaler')), }) diff --git a/tods/timeseries_processing/SimpleExponentialSmoothing.py b/tods/timeseries_processing/SimpleExponentialSmoothing.py index 0deb741..e5da57b 100644 --- a/tods/timeseries_processing/SimpleExponentialSmoothing.py +++ b/tods/timeseries_processing/SimpleExponentialSmoothing.py @@ -10,6 +10,7 @@ import pandas as pd # Custom import commands if any from sklearn.preprocessing.data import Normalizer from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt +import uuid from d3m.container.numpy import ndarray as d3m_ndarray @@ -114,16 +115,21 @@ class SimpleExponentialSmoothingPrimitive(UnsupervisedLearnerPrimitiveBase[Input """ - __author__ = "DATA Lab at Texas A&M University" metadata = metadata_base.PrimitiveMetadata({ - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.SIMPLE_EXPONENTIAL_SMOOTHING,], - "name": "statsmodels.preprocessing.data.SimpleExponentialSmoothing", - "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, - "python_path": "d3m.primitives.tods.timeseries_processing.transformation.simple_exponential_smoothing", - "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/mia/anomaly-primitives/anomaly_primitives/SimpleExponentialSmoothing.py']}, - "version": "0.0.1", - "id": "3e92984e-b7d1-4de0-9203-3a6093ddb38e", - "hyperparams_to_tune": ['endog','use_columns'], + "__author__": "DATA Lab at Texas A&M University", + "name": "statsmodels.preprocessing.data.SimpleExponentialSmoothing", + "python_path": "d3m.primitives.tods.timeseries_processing.transformation.simple_exponential_smoothing", + "source": { + 'name': 'DATA Lab at Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SimpleExponentialSmoothingPrimitive')), + "hyperparams_to_tune": ['endog','use_columns'], + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, + "version": "0.0.1", }) def __init__(self, *, diff --git a/tods/timeseries_processing/SubsequenceClustering.py b/tods/timeseries_processing/SubsequenceSegmentation.py similarity index 95% rename from tods/timeseries_processing/SubsequenceClustering.py rename to tods/timeseries_processing/SubsequenceSegmentation.py index 354cd38..8a17678 100644 --- a/tods/timeseries_processing/SubsequenceClustering.py +++ b/tods/timeseries_processing/SubsequenceSegmentation.py @@ -21,7 +21,7 @@ from d3m.exceptions import PrimitiveNotFittedError from d3m.primitive_interfaces.base import CallResult, DockerContainer -__all__ = ('SubsequenceClustering',) +__all__ = ('SubsequenceSegmentationPrimitive',) Inputs = container.DataFrame Outputs = container.DataFrame @@ -118,9 +118,9 @@ class Hyperparams(hyperparams.Hyperparams): ) -class SubsequenceClustering(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hyperparams]): +class SubsequenceSegmentationPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hyperparams]): """ - Subsequence Time Seires Clustering. + Subsequence Time Seires Segmentation. Parameters ---------- @@ -166,17 +166,21 @@ class SubsequenceClustering(transformer.TransformerPrimitiveBase[Inputs, Outputs Decides what semantic type to attach to generated attributes' """ - __author__: "DATA Lab at Texas A&M University" metadata = metadata_base.PrimitiveMetadata({ - "name": "Subsequence Clustering Primitive", - "python_path": "d3m.primitives.tods.timeseries_processing.subsequence_clustering", - "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', - 'uris': ['https://gitlab.com/lhenry15/tods.git', ]}, - "algorithm_types": [metadata_base.PrimitiveAlgorithmType.BK_FILTER,], - "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, - "id": "cf0bd4c1-9e09-4471-a2a3-6956deed17ac", - "hyperparams_to_tune": ['window_size', 'step', 'flatten_order'], - "version": "0.0.1", + "__author__": "DATA Lab @ Texas A&M University", + "name": "Subsequence Segmentation Primitive", + "python_path": "d3m.primitives.tods.timeseries_processing.subsequence_segmentation", + "source": { + 'name': 'DATA Lab @ Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SubsequenceSegmentationPrimitive')), + "hyperparams_to_tune": ['window_size', 'step', 'flatten_order'], + "version": "0.0.1", }) diff --git a/tods/timeseries_processing/TimeSeriesSeasonalityTrendDecomposition.py b/tods/timeseries_processing/TimeSeriesSeasonalityTrendDecomposition.py index e347ebc..9e6f35b 100644 --- a/tods/timeseries_processing/TimeSeriesSeasonalityTrendDecomposition.py +++ b/tods/timeseries_processing/TimeSeriesSeasonalityTrendDecomposition.py @@ -1,8 +1,7 @@ - import os from typing import Any,Optional,List import statsmodels.api as sm - +import uuid from d3m import container, utils as d3m_utils from d3m import utils @@ -94,31 +93,22 @@ class TimeSeriesSeasonalityTrendDecompositionPrimitive(transformer.TransformerPr The columns for which decomposition is done is passed as hyperparameter .Default is all value columns """ - __author__ = "DATA Lab at Texas A&M University", - metadata = metadata_base.PrimitiveMetadata( - { - 'id': 'fe79c99b-7e9b-4b4c-bc70-6e0ec798acbc', - 'version': '0.1.0', - 'name': 'Time Series Decompostional', - 'python_path': 'd3m.primitives.tods.timeseries_processing.decomposition.time_series_seasonality_trend_decomposition', - 'keywords': ['Time Series', 'Trend', 'Seasonality','Residual'], - 'source': { - 'name': 'DATA Lab at Texas A&M University', - 'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/TimeSeriesSeasonalityTrendDecomposition.py'], - 'contact': 'mailto:khlai037@tamu.edu' - }, - 'installation': [ - {'type': metadata_base.PrimitiveInstallationType.PIP, - 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format( - git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)), - ), - } - - ], - 'algorithm_types': [ - metadata_base.PrimitiveAlgorithmType.DATA_PROFILING , - ], - 'primitive_family': metadata_base.PrimitiveFamily.DATA_VALIDATION, + metadata = metadata_base.PrimitiveMetadata({ + "__author__": "DATA Lab at Texas A&M University", + 'name': 'Time Series Decompostional', + 'python_path': 'd3m.primitives.tods.timeseries_processing.decomposition.time_series_seasonality_trend_decomposition', + 'keywords': ['Time Series', 'Trend', 'Seasonality','Residual'], + 'source': { + 'name': 'DATA Lab at Texas A&M University', + 'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/TimeSeriesSeasonalityTrendDecomposition.py'], + 'contact': 'mailto:khlai037@tamu.edu' + }, + 'algorithm_types': [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + 'primitive_family': metadata_base.PrimitiveFamily.DATA_PREPROCESSING, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'TimeSeriesSeasonalityTrendDecompositionPrimitive')), + 'version': '0.1.0', } )