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)
