def comparisonExperiments(): data_dir = sys.argv[1] res_dir = sys.argv[2] complete_classifiers = [ "ShapeletForestClassifier", ] small_datasets = [ "Beef", "Car", "Coffee", "CricketX", "CricketY", "CricketZ", "DiatomSizeReduction", "Fish", "GunPoint", "ItalyPowerDemand", "MoteStrain", "OliveOil", "Plane", "SonyAIBORobotSurface1", "SonyAIBORobotSurface2", "SyntheticControl", "Trace", "TwoLeadECG", ] small_datasets = [ "Beef", "Coffee", ] num_folds = 2 import sktime.contrib.experiments as exp for f in range(num_folds): for d in small_datasets: for c in complete_classifiers: print(c, d, f) try: exp.run_experiment(data_dir, res_dir, c, d, f) except: print('\n\n FAILED: ', sys.exc_info()[0], '\n\n')
def comparisonExperiments(): data_dir = '/home/carl/Downloads/Univariate2018_ts/Univariate_ts/' # sys.argv[1] res_dir = '/home/carl/temp/' # sys.argv[2] complete_classifiers = [ "catch22ForestClassifier", ] small_datasets = [ "Beef", "Car", "Coffee", "CricketX", "CricketY", "CricketZ", "DiatomSizeReduction", "Fish", "GunPoint", "ItalyPowerDemand", "MoteStrain", "OliveOil", "Plane", "SonyAIBORobotSurface1", "SonyAIBORobotSurface2", "SyntheticControl", "Trace", "TwoLeadECG", ] small_datasets = [ "Beef", "Coffee", ] num_folds = 2 import sktime.contrib.experiments as exp for f in range(num_folds): for d in small_datasets: for c in complete_classifiers: print(c, d, f) # try: exp.run_experiment(data_dir, res_dir, c, d, f)
def tsf_benchmarking(): for i in range(len(benchmark_datasets)): dataset = benchmark_datasets[i] print(str(i) + " problem = " + dataset) tsf = ib.TimeSeriesForest(n_estimators=100) exp.run_experiment(overwrite=False, problem_path=data_dir, results_path=results_dir, cls_name="PythonTSF", classifier=tsf, dataset=dataset, train_file=False) steps = [ ('segment', RandomIntervalSegmenter(n_intervals='sqrt')), ('transform', FeatureUnion([('mean', RowTransformer( FunctionTransformer(func=np.mean, validate=False))), ('std', RowTransformer( FunctionTransformer(func=np.std, validate=False))), ('slope', RowTransformer( FunctionTransformer(func=time_series_slope, validate=False)))])), ('clf', DecisionTreeClassifier()) ] base_estimator = Pipeline(steps) tsf = TimeSeriesForestClassifier(estimator=base_estimator, n_estimators=100) exp.run_experiment(overwrite=False, problem_path=data_dir, results_path=results_dir, cls_name="PythonTSFComposite", classifier=tsf, dataset=dataset, train_file=False)
def elastic_distance_benchmarking(): for i in range(int(len(distance_test))): dataset = distance_test[i] print( str(i) + " problem = " + dataset + " writing to " + results_dir + "/DTW/") dtw = dist.KNeighborsTimeSeriesClassifier(metric="dtw") exp.run_experiment(overwrite=False, problem_path=data_dir, results_path=results_dir + "/DTW/", cls_name="PythonDTW", classifier=dtw, dataset=dataset, train_file=False) twe = dist.KNeighborsTimeSeriesClassifier(metric="dtw") exp.run_experiment(overwrite=False, problem_path=data_dir, results_path=results_dir + "/DTW/", cls_name="PythonTWE", classifier=twe, dataset=dataset, train_file=False)
def dlExperiment(data_dir, res_dir, classifier_name, dset, fold, classifier=None): if classifier is None: classifier = setNetwork(classifier_name, fold) exp.run_experiment(data_dir, res_dir, classifier_name, dset, classifier=classifier, resampleID=fold)
if __name__ == "__main__": """ Example simple usage, with arguments input via script or hard coded for testing """ print(" Local Run") results_dir = "C:/Temp/sktime-dl/" classifier = "resnet" resample = 0 # for i in range(0, len(univariate_datasets)): # dataset = univariate_datasets[i] # # print(i) # # print(" problem = "+dataset) problem = "ItalyPowerDemand" print("Loading ", problem) trX, trY = load_UCR_UEA_dataset(problem, split="train", return_X_y=True) teX, teY = load_UCR_UEA_dataset(problem, split="test", return_X_y=True) tf = False run_experiment( overwrite=True, trainX=trX, trainY=trY, testX=trX, testY=trY, results_path=results_dir, cls_name=classifier, dataset=problem, resampleID=resample, train_file=tf, )