Go to the source code of this file.
Namespaces | |
| namespace | data_parser |
Functions | |
| def | data_parser.create_ROC |
| def | data_parser.extract_data |
| def | data_parser.process |
Variables | |
| list | data_parser.ACT_LIST = ['WINCE', 'NOD', 'SHAKE', 'JOY', "FEAR", "SUPRISE", "ANGER", "DISGUST", "SADNESS"] |
| dictionary | data_parser.ACTIONS |
| list | data_parser.all_tpr = [] |
| tuple | data_parser.args = parser.parse_args() |
| tuple | data_parser.cv = StratifiedKFold(y, k=9) |
| Code below modified from http://scikit-learn.org/stable/auto_examples/plot_roc_crossval.html#example-plot-roc-crossval-py. | |
| dictionary | data_parser.DEGREES |
| string | data_parser.help = "One or more training data files to process" |
| string | data_parser.label = 'Mean ROC (area = %0.2f)' |
| tuple | data_parser.mean_auc = auc(mean_fpr, mean_tpr) |
| tuple | data_parser.mean_fpr = np.linspace(0, 1, n_samples) |
| float | data_parser.mean_tpr = 0.0 |
| tuple | data_parser.parser |
| tuple | data_parser.probas_ = classifier.fit(X[train], y[train]) |
| tuple | data_parser.roc_auc = auc(fpr, tpr) |