run_classification_experiment#

run_classification_experiment(X_train, y_train, X_test, y_test, classifier, results_path, cls_name='', dataset='', resample_id=0, train_file=False, test_file=True)[source]#

Run a classification experiment and save the results to file.

Method to run a basic experiment and write the results to files called testFold<resampleID>.csv and, if required, trainFold<resampleID>.csv.

Parameters:
X_trainpd.DataFrame or np.array

The data to train the classifier.

y_trainnp.array, default = None

Training data class labels.

X_testpd.DataFrame or np.array, default = None

The data used to test the trained classifier.

y_testnp.array, default = None

Testing data class labels.

classifierBaseClassifier

Classifier to be used in the experiment.

results_pathstr

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

cls_namestr, default=””

Name of the classifier.

datasetstr, default=””

Name of problem.

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.

train_filebool, default=False

Whether to generate train files or not. If true, it performs a 10-fold cross-validation on the train data and saves. If the classifier can produce its own estimates, those are used instead.

test_filebool, default=True:

Whether to generate test files or not. If the classifier can generate its own train probabilities, the classifier will be built but no file will be output.