wetOrDry, month, region, randomSeed, multiThreadApplyModels)) threads.append(t) else: # Run each pipeline in sequence print('Running pipeline for %s, %s' % (region, month.capitalize())) thesisFunctions.runKFoldPipeline(baseDirectoryPath, myFeaturesIndex, myLabelIndex, selectedFeatureList, kFolds, wetOrDry, month, region, randomSeed, multiThreadApplyModels) print() if multiThreading: # Start all threads for t in threads: t.start() # Wait for all threads to finish before continuing for t in threads: t.join()
myFeaturesIndex = 6 myLabelIndex = 5 kFolds = 5 modelApproach = 'sacramento' randomSeed = constants.randomSeed selectedFeatureList = ['p0', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10', 'p11', 'p12', 't0', 't1', 't2', 't3', 't4', 't5', 't6', 't7', 't8', 't9', 't10', 't11', 't12', 'p2sum', 'p3sum', 'p6sum', 'PERMAVE', 'RFACT', 'DRAIN_SQKM', 'ELEV_MEAN_M_BASIN_30M', 'WD_BASIN', 'IntMnt', 'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov'] multiThreadApplyModels = True # Run the flow model pipeline for five folds thesisFunctions.runKFoldPipeline(baseDirectoryPath, myFeaturesIndex, myLabelIndex, selectedFeatureList, kFolds, modelApproach, randomSeed=randomSeed, multiThreadApplyModels=multiThreadApplyModels) endSecond = time.time() endTime = time.strftime('%a, %d %b %Y %X') totalSeconds = endSecond - startSecond print() print('Start time:', startTime) print('End time:', endTime) print('Total: {} minutes and {} seconds'.format(int(totalSeconds // 60), round(totalSeconds % 60)))