def test_eigenpro_classification_conflict_data():
"""Make sure that the classifier doesn't crash
when given conflicting input data"""
X, y = make_classification(random_state=1)
X, y = np.concatenate([X, X]), np.concatenate([y, 1 - y])
# Make sure we don't throw an error when fitting or predicting
EigenProClassifier(kernel="linear", n_epoch=5,
random_state=1).fit(X, y).predict(X)
def test_eigenpro_classification_duplicate_data():
"""
Make sure that the classifier correctly handles cases
where some data is repeated.
"""
X, y = make_classification(n_features=200, n_repeated=50, random_state=1)
prediction = (EigenProClassifier(kernel="rbf",
n_epoch=60,
gamma=0.002,
random_state=1).fit(X, y).predict(X))
assert_allclose(prediction, y, rtol=5e-3)
eig_err = []
svc_fit_times = []
svc_pred_times = []
svc_err = []
train_sizes = [2000, 5000, 10000, 20000, 50000]
gamma = 0.005
for train_size in train_sizes:
for name, estimator in [
(
"EigenPro",
EigenProClassifier(
n_epoch=3,
gamma=gamma,
n_components=30,
subsample_size=1000,
random_state=rng,
),
),
("SupportVector", SVC(C=5, gamma=gamma)),
]:
stime = time()
estimator.fit(x_train[:train_size], y_train[:train_size])
fit_t = time() - stime
stime = time()
y_pred_test = estimator.predict(x_test)
pred_t = time() - stime
err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test)
# Tests for FastKernelClassification
@pytest.mark.parametrize(
"data, estimator",
[
# Test rbf kernel
(
gen_classification({
"n_samples": 10,
"hypercube": False
}),
EigenProClassifier(
batch_size=9,
kernel="rbf",
gamma=0.08,
n_epoch=100,
random_state=1,
),
),
# Test laplacian kernel
(
gen_classification({}),
EigenProClassifier(
kernel="laplace", n_epoch=100, gamma=0.003, random_state=1),
),
# Test cauchy kernel
(
gen_classification({}),
EigenProClassifier(
kernel="cauchy", n_epoch=100, gamma=0.005, random_state=1),
for n_features in feature_counts:
x, y = make_classification(
n_samples=train_size + test_size,
n_features=n_features,
random_state=rng,
)
x_train = x[:train_size]
y_train = y[:train_size]
x_test = x[train_size:]
y_test = y[train_size:]
for name, estimator in [
(
"EigenPro",
EigenProClassifier(n_epoch=2,
gamma=gamma,
n_components=400,
random_state=rng),
),
("SupportVector", SVC(gamma=gamma, random_state=rng)),
]:
stime = time()
estimator.fit(x_train, y_train)
fit_t = time() - stime
stime = time()
y_pred_test = estimator.predict(x_test)
pred_t = time() - stime
err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test)
if name == "EigenPro":
eig_fit_times.append(fit_t)
svc_pred_times = []
svc_err = []
train_sizes = [500, 1000, 2000]
print("Train Sizes: " + str(train_sizes))
bandwidth = 5.0
# Fit models to data
for train_size in train_sizes:
for name, estimator in [
(
"EigenPro",
EigenProClassifier(
n_epoch=2, bandwidth=bandwidth, random_state=rng
),
),
(
"SupportVector",
SVC(
C=5, gamma=1.0 / (2 * bandwidth * bandwidth), random_state=rng
),
),
]:
stime = time()
estimator.fit(x_train[:train_size], y_train[:train_size])
fit_t = time() - stime
stime = time()
y_pred_test = estimator.predict(x_test)
x, y = make_classification(
n_samples=train_size + test_size,
n_features=n_features,
random_state=rng,
)
x_train = x[:train_size]
y_train = y[:train_size]
x_test = x[train_size:]
y_test = y[train_size:]
for name, estimator in [
(
"EigenPro",
EigenProClassifier(
n_epoch=2,
bandwidth=bandwidth,
n_components=400,
random_state=rng,
),
),
(
"SupportVector",
SVC(gamma=1.0 / (2 * bandwidth * bandwidth), random_state=rng),
),
]:
stime = time()
estimator.fit(x_train, y_train)
fit_t = time() - stime
stime = time()
y_pred_test = estimator.predict(x_test)
pred_t = time() - stime
eig_fit_times = []
eig_pred_times = []
eig_err = []
svc_fit_times = []
svc_pred_times = []
svc_err = []
train_sizes = [500, 1000, 2000, 5000, 10000, 20000, 40000, 60000]
gamma = 0.02
# Fit models to data
for train_size in train_sizes:
for name, estimator in [
(
"EigenPro",
EigenProClassifier(n_epoch=2, gamma=gamma, random_state=rng),
),
("SupportVector", SVC(C=5, gamma=gamma, random_state=rng)),
]:
stime = time()
estimator.fit(x_train[:train_size], y_train[:train_size])
fit_t = time() - stime
stime = time()
y_pred_test = estimator.predict(x_test)
pred_t = time() - stime
err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test)
if name == "EigenPro":
eig_fit_times.append(fit_t)
eig_pred_times.append(pred_t)
).fit(X, y).predict(X)
# Tests for FastKernelClassification
@pytest.mark.parametrize(
"data, estimator",
[
# Test rbf kernel
(
gen_classification({"n_samples": 10, "hypercube": False}),
EigenProClassifier(
batch_size=9,
kernel="rbf",
bandwidth=2.5,
n_epoch=100,
random_state=1,
),
),
# Test laplacian kernel
(
gen_classification({}),
EigenProClassifier(
kernel="laplace", n_epoch=100, bandwidth=13, random_state=1
),
),
# Test cauchy kernel
(
gen_classification({}),
EigenProClassifier(
eig_err = []
svc_fit_times = []
svc_pred_times = []
svc_err = []
train_sizes = [2000, 5000, 10000, 20000, 50000]
bandwidth = 10.0
for train_size in train_sizes:
for name, estimator in [
(
"EigenPro",
EigenProClassifier(
n_epoch=3,
bandwidth=bandwidth,
n_components=30,
subsample_size=1000,
random_state=rng,
),
),
("SupportVector", SVC(C=5, gamma=1.0 / (2 * bandwidth * bandwidth))),
]:
stime = time()
estimator.fit(x_train[:train_size], y_train[:train_size])
fit_t = time() - stime
stime = time()
y_pred_test = estimator.predict(x_test)
pred_t = time() - stime
err = 100.0 * np.sum(y_pred_test != y_test) / len(y_test)
def instanciate_estimators(clf_type, classifiers, clf_seed, y=None, **kw):
score_metric, _ = get_score_metric(clf_type)
param_grid_LGBM = {
'learning_rate': [0.1, .05, .5],
'num_leaves': [7, 15, 31]
}
param_grid_XGB = {'learning_rate': [0.1, .05, .3], 'max_depth': [3, 6, 9]}
param_grid_MLP = {
'learning_rate_init': [.001, .0005, .005],
'hidden_layer_sizes': [(30, ), (50, ), (100, ), (30, 30), (50, 50),
(100, 100)]
}
param_grid_EigenProGaussian = {'bandwidth': [1, 5, 25]}
n_components_eigenpro = 160
param_grid_nystroem_ridgecv = {
'kernel_approx__n_components': [1000, 3000],
'kernel_approx__degree': [2, 3],
}
if clf_type == 'binary':
print(('Fraction by class: True: %0.2f; False: %0.2f' %
(list(y).count(True) / len(y), list(y).count(False) / len(y))))
cw = 'balanced'
clfs = {
'L2RegularizedLinearModel':
linear_model.LogisticRegressionCV(class_weight=cw,
max_iter=100,
solver='sag',
penalty='l2',
n_jobs=1,
cv=3,
multi_class='multinomial'),
'GradientBoosting':
ensemble.GradientBoostingClassifier(n_estimators=100),
'LGBM':
GridSearchCV(estimator=LGBMClassifier(n_estimators=100,
n_jobs=1,
is_unbalance=True),
param_grid=param_grid_LGBM,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'XGB':
GridSearchCV(estimator=XGBClassifier(n_estimators=100, n_jobs=1),
param_grid=param_grid_XGB,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'MLP':
MLPClassifier(hidden_layer_sizes=(30, 30),
activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='constant',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10),
'MLPGridSearchCV':
GridSearchCV(estimator=MLPClassifier(hidden_layer_sizes=(30, 30),
activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='adaptive',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10),
param_grid=param_grid_MLP,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'EigenProPolynomial':
EigenProClassifier(batch_size="auto",
n_epoch=10,
n_components=n_components_eigenpro,
subsample_size="auto",
kernel="polynomial",
bandwidth=5,
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
'EigenProGaussian160':
GridSearchCV(estimator=EigenProClassifier(
batch_size="auto",
n_epoch=10,
n_components=n_components_eigenpro,
subsample_size="auto",
kernel="gaussian",
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
param_grid=param_grid_EigenProGaussian,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'EigenProGaussian1000':
GridSearchCV(estimator=EigenProClassifier(batch_size="auto",
n_epoch=10,
n_components=1000,
subsample_size="auto",
kernel="gaussian",
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
param_grid=param_grid_EigenProGaussian,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'NystroemRidgeCV':
GridSearchCV(estimator=Pipeline([
('kernel_approx',
Nystroem(kernel="polynomial",
n_components=None,
random_state=clf_seed,
degree=2)),
('classifier',
linear_model.LogisticRegressionCV(class_weight=cw,
max_iter=100,
solver='sag',
penalty='l2',
n_jobs=1,
cv=3,
multi_class='multinomial'))
]),
param_grid=param_grid_nystroem_ridgecv,
cv=3,
scoring=metrics.make_scorer(score_metric)),
}
elif clf_type == 'multiclass':
print('fraction of the most frequent class:',
max([list(y).count(x) for x in set(list(y))]) / len(list(y)))
clfs = {
'L2RegularizedLinearModel':
linear_model.LogisticRegressionCV(penalty='l2',
n_jobs=1,
cv=3,
multi_class='multinomial',
solver='sag',
max_iter=100),
'GradientBoosting':
ensemble.GradientBoostingClassifier(n_estimators=100),
'LGBM':
GridSearchCV(estimator=LGBMClassifier(n_estimators=100, n_jobs=1),
param_grid=param_grid_LGBM,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'XGB':
GridSearchCV(estimator=XGBClassifier(n_estimators=100,
n_jobs=1,
objective='multi:softmax',
num_class=len(np.unique(y))),
param_grid=param_grid_XGB,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'MLP':
MLPClassifier(hidden_layer_sizes=(30, 30),
activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='constant',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10),
'MLPGridSearchCV':
GridSearchCV(estimator=MLPClassifier(hidden_layer_sizes=(30, 30),
activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='adaptive',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10),
param_grid=param_grid_MLP,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'EigenProPolynomial':
EigenProClassifier(batch_size="auto",
n_epoch=10,
n_components=n_components_eigenpro,
subsample_size="auto",
kernel="polynomial",
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
'EigenProGaussian160':
GridSearchCV(estimator=EigenProClassifier(
batch_size="auto",
n_epoch=10,
n_components=n_components_eigenpro,
subsample_size="auto",
kernel="gaussian",
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
param_grid=param_grid_EigenProGaussian,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'EigenProGaussian1000':
GridSearchCV(estimator=EigenProClassifier(batch_size="auto",
n_epoch=10,
n_components=1000,
subsample_size="auto",
kernel="gaussian",
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
param_grid=param_grid_EigenProGaussian,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'NystroemRidgeCV':
GridSearchCV(estimator=Pipeline([
('kernel_approx',
Nystroem(kernel="polynomial",
n_components=None,
random_state=clf_seed,
degree=2)),
('classifier',
linear_model.LogisticRegressionCV(penalty='l2',
n_jobs=1,
cv=3,
multi_class='multinomial',
solver='sag',
max_iter=100))
]),
param_grid=param_grid_nystroem_ridgecv,
cv=3,
scoring=metrics.make_scorer(score_metric)),
}
elif clf_type == 'regression':
clfs = {
'L2RegularizedLinearModel':
linear_model.RidgeCV(cv=3),
'GradientBoosting':
ensemble.GradientBoostingRegressor(n_estimators=100),
'LGBM':
GridSearchCV(estimator=LGBMRegressor(n_estimators=100, n_jobs=1),
param_grid=param_grid_LGBM,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'XGB':
GridSearchCV(estimator=XGBRegressor(n_estimators=100, n_jobs=1),
param_grid=param_grid_XGB,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'MLP':
MLPRegressor(hidden_layer_sizes=(30, 30),
activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='constant',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10),
'MLPGridSearchCV':
GridSearchCV(estimator=MLPRegressor(hidden_layer_sizes=(30, 30),
activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='adaptive',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10),
param_grid=param_grid_MLP,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'EigenProPolynomial':
EigenProRegressor(batch_size="auto",
n_epoch=10,
n_components=n_components_eigenpro,
subsample_size="auto",
kernel="polynomial",
bandwidth=5,
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
'EigenProGaussian160':
GridSearchCV(estimator=EigenProRegressor(
batch_size="auto",
n_epoch=10,
n_components=n_components_eigenpro,
subsample_size="auto",
kernel="gaussian",
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
param_grid=param_grid_EigenProGaussian,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'EigenProGaussian1000':
GridSearchCV(estimator=EigenProRegressor(batch_size="auto",
n_epoch=10,
n_components=1000,
subsample_size="auto",
kernel="gaussian",
gamma=None,
degree=2,
coef0=1,
kernel_params=None,
random_state=None),
param_grid=param_grid_EigenProGaussian,
cv=3,
scoring=metrics.make_scorer(score_metric)),
'NystroemRidgeCV':
GridSearchCV(estimator=Pipeline([('kernel_approx',
Nystroem(kernel="polynomial",
n_components=None,
random_state=clf_seed,
degree=2)),
('classifier',
linear_model.RidgeCV(cv=3))]),
param_grid=param_grid_nystroem_ridgecv,
cv=3,
scoring=metrics.make_scorer(score_metric)),
}
else:
raise ValueError("{} not recognized".format(clf_type))
clfs = [clfs[clf] for clf in classifiers]
for clf in clfs:
try:
if 'random_state' in clf.estimator.get_params():
clf.estimator.set_params(random_state=clf_seed)
except AttributeError:
if 'random_state' in clf.get_params():
clf.set_params(random_state=clf_seed)
return clfs
