def test_weighted_elm():
"""Check class weight impact on AUC.
Re-weighting classes is expected to improve model performance
for imbalanced classification problems.
"""
rng = np.random.RandomState(random_state)
n_samples_1 = 500
n_samples_2 = 10
X = np.r_[1.5 * rng.randn(n_samples_1, 20),
1.2 * rng.randn(n_samples_2, 20) + [2] * 20]
y = [0] * (n_samples_1) + [1] * (n_samples_2)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, y, test_size=0.8, random_state=random_state)
n_hidden = 20
for activation in ACTIVATION_TYPES:
elm_weightless = ELMClassifier(n_hidden=n_hidden,
class_weight=None,
random_state=random_state)
elm_weightless.fit(X_train, y_train)
elm_weight_auto = ELMClassifier(n_hidden=n_hidden,
class_weight='auto',
random_state=random_state)
elm_weight_auto.fit(X_train, y_train)
y_pred_weightless = elm_weightless.predict_proba(X_test)[:, 1]
score_weightless = roc_auc_score(y_test, y_pred_weightless)
y_pred_weighted = elm_weight_auto.predict_proba(X_test)[:, 1]
score_weighted = roc_auc_score(y_test, y_pred_weighted)
assert_greater(score_weighted, score_weightless)
def test_multilabel_classification():
"""Test that multi-label classification works as expected."""
# test fit method
X, y = make_multilabel_classification(n_samples=50, random_state=0,
return_indicator=True)
elm = ELMClassifier(weight_scale=100)
elm.fit(X, y)
assert_greater(elm.score(X, y), 0.95)
def test_predict_proba_multi():
"""Test whether predict_proba works as expected for multi class."""
X = Xdigits_multi[:10]
y = ydigits_multi[:10]
clf = ELMClassifier(n_hidden=5)
clf.fit(X, y)
y_proba = clf.predict_proba(X)
(n_samples, n_classes) = y.shape[0], np.unique(y).size
assert_equal(y_proba.shape, (n_samples, n_classes))
def test_predict_proba_binary():
"""Test whether predict_proba works as expected for binary class."""
X = Xdigits_binary[:50]
y = ydigits_binary[:50]
clf = ELMClassifier(n_hidden=10)
clf.fit(X, y)
y_proba = clf.predict_proba(X)
(n_samples, n_classes) = y.shape[0], 2
assert_equal(y_proba.shape, (n_samples, n_classes))
assert_greater(roc_auc_score(y, y_proba[:, 1]), 0.95)
def test_partial_fit_classes_error():
"""Test that passing different classes to partial_fit raises an error."""
X = [3, 2]
y = [0]
elm = ELMClassifier()
# different classes passed
assert_raises(ValueError, elm.partial_fit, X, y, classes=[1, 2])
def test_overfitting():
"""Larger number of hidden neurons should increase training score."""
X, y = classification_datasets['multi-class']
for activation in ACTIVATION_TYPES:
elm = ELMClassifier(n_hidden=5, activation=activation,
random_state=random_state)
elm.fit(X, y)
score_5_n_hidden = elm.score(X, y)
elm = ELMClassifier(n_hidden=15, activation=activation,
random_state=random_state)
elm.fit(X, y)
score_15_n_hidden = elm.score(X, y)
assert_greater(score_15_n_hidden, score_5_n_hidden)
def test_sample_weight_elm():
"""Smoke test - AdaBoostClassifier should work with ELMClassifer."""
X = Xdigits_binary[:50]
y = ydigits_binary[:50]
elm = ELMClassifier(n_hidden=20)
clf = AdaBoostClassifier(n_estimators=3, base_estimator=elm)
clf.fit(X, y)
assert_greater(clf.score(X, y), 0.9)
def test_non_uniform_weights_toy_edge():
X = [[1, 0], [1, 0], [1, 0], [0, 1]]
y = [0, 0, 1, 0]
# ignore the first 2 training samples by setting their weight to 0
sample_weight = [0, 0, 1, 1]
for activation in ACTIVATION_TYPES:
for elm in [ELMClassifier(), ELMRegressor(C=100)]:
elm.fit(X, y, sample_weight=sample_weight)
assert_greater(elm.predict([[1, 0]])[0], 0.5)
def test_classification():
"""Test ELMClassifier.
It should score higher than 0.95 for binary and multi-class
classification digits datasets.
"""
for name, activation in product(classification_datasets, ACTIVATION_TYPES):
elm = ELMClassifier(n_hidden=50, activation=activation,
weight_scale=10, random_state=random_state)
check_elm(elm, 'classification', name, 150, 0.95)
from sklearn.datasets import load_digits, load_boston from sklearn.preprocessing import StandardScaler, MinMaxScaler from extreme_learning_machines import ELMClassifier from extreme_learning_machines import ELMRegressor boston = load_boston() X = StandardScaler().fit_transform(boston.data)[:200] y = boston.target[:200] elm = ELMRegressor(n_hidden=50, weight_scale=10) elm.fit(X, y) print "Regression score %f" % elm.score(X, y) digits_dataset_binary = load_digits(n_class=2) X = MinMaxScaler().fit_transform(digits_dataset_binary.data[:200]) y = digits_dataset_binary.target[:200] elm = ELMClassifier(n_hidden=50, weight_scale=10) elm.fit(X, y) print "Classification score %f" % elm.score(X, y)
def test_sparse_matrices():
"""Test that sparse and dense input matrices yield equal output."""
X = Xdigits_binary[:50]
y = ydigits_binary[:50]
X = csr_matrix(X)
n_hidden = 15
batch_size = 10
# Standard ELM
elm = ELMClassifier(random_state=1, n_hidden=n_hidden)
# Batch based
elm_batch_based = ELMClassifier(random_state=1, n_hidden=n_hidden,
batch_size=10)
# ELM for partial fitting
elm_parital = ELMClassifier(random_state=1, n_hidden=n_hidden)
# Train classifiers
elm.fit(X, y)
elm_batch_based.fit(X, y)
for batch_slice in gen_batches(X.shape[0], batch_size):
elm_parital.partial_fit(X[batch_slice], y[batch_slice])
# Get decision scores
y_pred = elm.decision_function(X)
y_pred_batch_based = elm_batch_based.decision_function(X)
y_pred_partial = elm_parital.decision_function(X)
# The prediction values should be the same
assert_almost_equal(y_pred, y_pred_batch_based)
assert_almost_equal(y_pred_batch_based, y_pred_partial)
def test_recursive_and_standard():
"""Test that recursive lsqr return the same result as standard lsqr."""
batch_size = 50
for dataset, class_weight in product(classification_datasets.values(),
[None, 'auto']):
X, y = dataset
elm_standard = ELMClassifier(class_weight=class_weight,
random_state=random_state)
elm_recursive = ELMClassifier(class_weight=class_weight,
random_state=random_state,
batch_size=batch_size)
elm_standard.fit(X, y)
elm_recursive.fit(X, y)
pred1 = elm_standard.predict(X)
pred2 = elm_recursive.predict(X)
assert_array_equal(pred1, pred2)
assert_greater(elm_standard.score(X, y), 0.95)
def test_partial_fit_classification():
"""Test partial_fit for classification.
It should output the same results as 'fit' for binary and
multi-class classification.
"""
for X, y in classification_datasets.values():
batch_size = 100
n_samples = X.shape[0]
elm_fit = ELMClassifier(random_state=random_state,
batch_size=batch_size)
elm_partial_fit = ELMClassifier(random_state=random_state)
elm_fit.fit(X, y)
for batch_slice in gen_batches(n_samples, batch_size):
elm_partial_fit.partial_fit(X[batch_slice], y[batch_slice],
classes=np.unique(y))
pred1 = elm_fit.predict(X)
pred2 = elm_partial_fit.predict(X)
assert_array_equal(pred1, pred2)
assert_greater(elm_fit.score(X, y), 0.95)
assert_greater(elm_partial_fit.score(X, y), 0.95)