load_and_run_clustering_experiment#

load_and_run_clustering_experiment(problem_path, results_path, dataset, clusterer, resample_id=0, cls_name=None, overwrite=False, format='.ts', train_file=False)[source]#

Run a clustering experiment.

Method to run a basic experiment and write the results to files called testFold<resampleID>.csv and, if required, trainFold<resampleID>.csv. This version loads the data from file based on a path. The clusterer is always trained on the

Parameters:
problem_pathstr

Location of problem files, full path.

results_pathstr

Location of where to write results. Any required directories will be created

datasetstr

Name of problem. Files must be <problem_path>/<dataset>/<dataset>+ “_TRAIN”+format, same for “_TEST”

clustererthe clusterer
cls_namestr, default =None

determines what to call the write directory. If None, it is set to type(clusterer).__name__

resample_idint, default = 0

Seed for resampling. If set to 0, the default train/test split from file is used. Also used in output file name.

overwriteboolean, default = False

if False, this will only build results if there is not a result file already present. If True, it will overwrite anything already there.

format: string, default = “.ts”

Valid formats are “.ts”, “.arff”, “.tsv” and “.long”. For more info on format, see examples/loading_data.ipynb

train_file: boolean, default = False

whether to generate train files or not. If true, it performs a 10xCV on the train and saves