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_preproc(self):
"""
As a domain expert, I have a particular pre-processing that I believe
reveals important patterns in my data. I would like to know how good
a classifier can be built on top of my preprocessing algorithm.
"""
# -- for testing purpose, suppose that the RBM is our "domain-specific
# pre-processing"
algo = SklearnClassifier(
partial(
hyperopt_estimator,
preprocessing=hp.choice('pp',
[
# -- VQ (alone)
[
hpc.colkmeans('vq0',
n_init=1),
],
# -- VQ -> RBM
[
hpc.colkmeans('vq1',
n_clusters=scope.int(
hp.quniform(
'vq1.n_clusters', 1, 5, q=1)),
n_init=1),
hpc.rbm(name='rbm:alone',
verbose=0)
],
# -- VQ -> RBM -> PCA
[
hpc.colkmeans('vq2',
n_clusters=scope.int(
hp.quniform(
'vq2.n_clusters', 1, 5, q=1)),
n_init=1),
hpc.rbm(name='rbm:pre-pca',
verbose=0),
hpc.pca('pca')
],
]),
classifier=hpc.any_classifier('classif'),
algo=tpe.suggest,
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)
def test_preproc(self):
"""
As a domain expert, I have a particular pre-processing that I believe
reveals important patterns in my data. I would like to know how good
a classifier can be built on top of my preprocessing algorithm.
"""
# -- for testing purpose, suppose that the RBM is our "domain-specific
# pre-processing"
algo = LearningAlgo(
partial(
hyperopt_estimator,
preprocessing=hp.choice(
'pp',
[
# -- VQ (alone)
[
hpc.colkmeans(
'vq0',
n_clusters=scope.int(
hp.quniform(
'vq0.n_clusters', 1.5, 5.5, q=1)),
n_init=1,
max_iter=100),
],
# -- VQ -> RBM
[
hpc.colkmeans(
'vq1',
n_clusters=scope.int(
hp.quniform(
'vq1.n_clusters', 1.5, 5.5, q=1)),
n_init=1,
max_iter=100),
hpc.rbm(name='rbm:alone',
n_components=scope.int(
hp.qloguniform('rbm1.n_components',
np.log(4.5),
np.log(20.5), 1)),
n_iter=100,
verbose=0)
],
# -- VQ -> RBM -> PCA
[
hpc.colkmeans('vq2',
n_clusters=scope.int(
hp.quniform('vq2.n_clusters',
1.5,
5.5,
q=1)),
n_init=1,
max_iter=100),
hpc.rbm(name='rbm:pre-pca',
n_components=scope.int(
hp.qloguniform('rbm2.n_components',
np.log(4.5),
np.log(20.5), 1)),
n_iter=100,
verbose=0),
hpc.pca('pca')
],
]),
classifier=hpc.any_classifier('classif'),
algo=tpe.suggest,
#trial_timeout=5.0, # seconds
verbose=1,
max_evals=10,
))
mean_test_error = self.view.protocol(algo)
print('\n====Iris: VQ + RBM + PCA + any classifier====',
file=sys.stderr)
print('mean test error:', mean_test_error, file=sys.stderr)
print('====End optimization====')
HYPERTREE_EXISTS = False
import time
import argparse
# remove headers, footers, and citations from 20 newsgroups data
REMOVE_HEADERS = False
# use the default settings up TfidfVectorizer before doing optimization
PRE_VECTORIZE = False
# Record the test score for every evaluation point
#TEST_ALL_EVALS=True
suppress_output = False
optional_pca = hp.pchoice('preproc', [(0.8, [pca('pca')]),
(0.1, [min_max_scaler('mms')]),
(0.1, [])])
def score(y1, y2):
length = len(y1)
correct = 0.0
for i in xrange(length):
if y1[i] == y2[i]:
correct += 1.0
return correct / length
# TODO: currently does not use seed for anything
def sklearn_newsgroups(classifier,
import time
import argparse
# remove headers, footers, and citations from 20 newsgroups data
REMOVE_HEADERS=False
# use the default settings up TfidfVectorizer before doing optimization
PRE_VECTORIZE=False
# Record the test score for every evaluation point
#TEST_ALL_EVALS=True
suppress_output = False
optional_pca = hp.pchoice('preproc', [
( 0.8, [pca('pca')]),
( 0.1, [min_max_scaler('mms')]),
( 0.1, [] ) ])
def score( y1, y2 ):
length = len( y1 )
correct = 0.0
for i in xrange(length):
if y1[i] == y2[i]:
correct += 1.0
return correct / length
# TODO: currently does not use seed for anything
def sklearn_newsgroups( classifier, algorithm, max_evals=100, seed=1,
filename='none', preproc=[], loss=None ):
