#!/bin/bash #modules="data_processing timeseries_processing feature_analysis detection_algorithms reinforcement" #modules="data_processing timeseries_processing" modules="feature_analysis" #test_scripts=$(ls primitive_tests | grep -v -f tested_file.txt) for module in $modules do test_scripts=$(ls $module | 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 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