base_dir = '/media/lukas/data/SharedStates/RESULTS/MVPA/Validation/Condition_average_mvpa' ### ARGUMENTS ### n_folds = 10 out_path = op.join(base_dir, 'other>>self') clf = SVC(kernel='linear', C=1, class_weight='balanced') mvp = joblib.load(op.join(base_dir, 'mvp_other.jl')) mvp_cross = joblib.load(op.join(base_dir, 'mvp_self.jl')) pipe = Pipeline([('scaler', StandardScaler()), ('ufs', MeanEuclidean(cutoff=2.3)), ('svm', clf)]) mvp_results = MvpResultsClassification(mvp_cross, n_folds, out_path=out_path, feature_scoring='coef', verbose=True) folds = StratifiedKFold(mvp.y, n_folds=n_folds) for train_idx, test_idx in folds: train, test = mvp.X[train_idx, :], mvp.X[test_idx, :] y_train, y_test = mvp.y[train_idx], mvp.y[test_idx] pipe.fit(train, y_train) pred = pipe.predict(mvp_cross.X) mvp_results.update(range(mvp_cross.y.size), pred, pipeline=pipe) mvp_results.compute_scores() mvp_results.write() mvp_results.save_model(pipe)