def test_pca_svm(self): """ As a ML researcher, I want to evaluate a certain parly-defined model class, in order to do model-family comparisons. For example, PCA followed by linear SVM. """ algo = SklearnClassifier( partial(hyperopt_estimator, preprocessing=[hpc.pca('pca')], classifier=hpc.svc_linear('classif'), max_evals=10)) mean_test_error = self.view.protocol(algo) print 'mean test error:', mean_test_error
def test_pca_svm(self): """ As a ML researcher, I want to evaluate a certain parly-defined model class, in order to do model-family comparisons. For example, PCA followed by linear SVM. """ algo = SklearnClassifier( partial( hyperopt_estimator, preprocessing=[hpc.pca('pca')], classifier=hpc.svc_linear('classif'), max_evals=10)) mean_test_error = self.view.protocol(algo) print('mean test error:', mean_test_error)
def test_pca_svm(self): """ As a ML researcher, I want to evaluate a certain parly-defined model class, in order to do model-family comparisons. For example, PCA followed by linear SVM. """ algo = LearningAlgo( partial( hyperopt_estimator, preprocessing=[hpc.pca('pca')], classifier=hpc.svc_linear('classif'), # trial_timeout=30.0, # seconds verbose=1, max_evals=10)) mean_test_error = self.view.protocol(algo) print('\n====Iris: PCA + SVM====', file=sys.stderr) print('mean test error:', mean_test_error, file=sys.stderr) print('====End optimization====', file=sys.stderr)