parse_args¶

tsml_eval.utils.arguments.parse_args(args)[source]¶

Parse the command line arguments for tsml_eval.

The following is the –help output for tsml_eval:

usage: tsml_eval [-h] [–version] [-ow] [-pr] [-rs RANDOM_SEED] [-nj N_JOBS]

[-tr] [-te] [-fc FIT_CONTRACT] [-ch] [-rn] [-nc N_CLUSTERS] [-kw KEY VALUE TYPE] data_path results_path estimator_name dataset_name resample_id

positional arguments:

data_path the path to the directory storing dataset files. results_path the path to the directory where results files are

written to.

estimator_name the name of the estimator to run. See the: set_{task}.py file for each task learning task for available options.
dataset_name the name of the dataset to load.: {data_dir}/{dataset_name}/{dataset_name}_TRAIN.ts and {data_dir}/{dataset_name}/{dataset_name}_TEST.ts will be loaded.
resample_id the resample ID to use when randomly resampling the: data, as a random seed for estimators and the suffix when writing results files. An ID of 0 will use the default TRAIN/TEST split.

options:

-h, --help: show this help message and exit
--version: show program’s version number and exit
-ow, --overwrite: overwrite existing results files. If False, existing results files will be skipped (default: False).
-pr, --predefined_resample: load a dataset file with a predefined resample. The dataset file must follow the naming format ‘{dataset_name}_{resample_id}.ts’ (default: False).

-rs RANDOM_SEED, –random_seed RANDOM_SEED: use a different random seed than the resample ID. If None use the {resample_id} (default: None).
-nj N_JOBS, –n_jobs N_JOBS: the number of jobs to run in parallel. Only used if the experiments file and selected estimator allows threading (default: 1).

-tr, --train_fold: write a results file for the training data in the classification and regression task (default: False).
-te, --test_fold: write a results file for the test data in the clustering task (default: False).

-fc FIT_CONTRACT, –fit_contract FIT_CONTRACT: a time limit for estimator fit in minutes. Only used if the estimator can contract fit (default: 0).

-ch, --checkpoint: save the estimator fit to file periodically while building. Only used if the estimator can checkpoint (default: False).

-dtn DATA_TRANSFORM_NAME, –data_transform_name DATA_TRANSFORM_NAME: str to pass to get_data_transform_by_name to apply a transformation to the data prior to running the experiment. By default no transform is applied. Can be used multiple times (default: None).

-tto, --transform_train_only: if set, transformations will be applied only to the training dataset leaving the test dataset unchanged (default: False).
-rn, --row_normalise: normalise the data rows prior to fitting and predicting. effectively the same as passing Normalizer to –data_transform_name (default: False).

-nc N_CLUSTERS, –n_clusters N_CLUSTERS: the number of clusters to find for clusterers which have an {n_clusters} parameter. If {-1}, use the number of classes in the dataset. The {n_clusters} parameter for attributes will also be set. Please ensure that the argument input itself has the {n_clusters} parameters and is not a default such as None. (default: -1).

-ctts, --combine_test_train_split: whether to use a train/test split or not. If True, the train and test sets are combined and used the fit the estimator. Only available for clustering (default: False).
-bt, --benchmark_time: run a benchmark function and save the time spent in the results file (default: False).
-wa, --write_attributes: write the estimator attributes to file when running experiments. Will recursively write the attributes of sub-estimators if present. (default: False).

-ams ATT_MAX_SHAPE, –att_max_shape ATT_MAX_SHAPE: The max estimator collections shape allowed when writing attributes, at 0 no estimators in collections will be written, at 1 estimators in one-dimensional lists will be written etc. (default: 0).
-kw KEY VALUE TYPE, –kwargs KEY VALUE TYPE, –kwarg KEY VALUE TYPE: additional keyword arguments to pass to the estimator. Should contain the parameter to set, the parameter value, and the type of the value i.e. {–kwargs n_estimators 200 int} to change the size of an ensemble. Valid types are {int, float, bool, str}. Any other type will be passed as a str. Can be used multiple times (default: None).

Parameters:

Returns: