def test_clone_2():
"""Tests that clone doesn't copy everything.
We first create an estimator, give it an own attribute, and
make a copy of its original state. Then we check that the copy doesn't have
the specific attribute we manually added to the initial estimator.
"""
from scikits.learn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
selector.own_attribute = "test"
new_selector = clone(selector)
assert_false(hasattr(new_selector, "own_attribute"))
def test_clone():
"""Tests that clone creates a correct deep copy.
We create an estimator, make a copy of its original state
(which, in this case, is the current state of the setimator),
and check that the obtained copy is a correct deep copy.
"""
from scikits.learn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
new_selector = clone(selector)
assert_true(selector is not new_selector)
assert_equal(selector._get_params(), new_selector._get_params())
def test_clone_2():
"""Tests that clone doesn't copy everything.
We first create an estimator, give it an own attribute, and
make a copy of its original state. Then we check that the copy doesn't have
the specific attribute we manually added to the initial estimator.
"""
from scikits.learn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
selector.own_attribute = "test"
new_selector = clone(selector)
assert_false(hasattr(new_selector, "own_attribute"))
def test_clone():
"""Tests that clone creates a correct deep copy.
We create an estimator, make a copy of its original state
(which, in this case, is the current state of the setimator),
and check that the obtained copy is a correct deep copy.
"""
from scikits.learn.feature_selection import SelectFpr, f_classif
selector = SelectFpr(f_classif, alpha=0.1)
new_selector = clone(selector)
assert_true(selector is not new_selector)
assert_equal(selector._get_params(), new_selector._get_params())
y = iris.target
################################################################################
pl.figure(1)
pl.clf()
x_indices = np.arange(x.shape[-1])
################################################################################
# Univariate feature selection
from scikits.learn.feature_selection import SelectFpr, f_classif
# As a scoring function, we use a F test for classification
# We use the default selection function: the 10% most significant
# features
selector = SelectFpr(f_classif, alpha=0.1)
selector.fit(x, y)
scores = -np.log10(selector._pvalues)
scores /= scores.max()
pl.bar(x_indices-.45, scores, width=.3,
label=r'Univariate score ($-Log(p_{value})$)',
color='g')
################################################################################
# Compare to the weights of an SVM
clf = svm.SVC(kernel='linear')
clf.fit(x, y)
svm_weights = (clf.coef_**2).sum(axis=0)
svm_weights /= svm_weights.max()
pl.bar(x_indices-.15, svm_weights, width=.3, label='SVM weight',
x_indices = np.arange(x.shape[-1])
################################################################################
# Univariate feature selection
from scikits.learn.feature_selection import SelectFpr, f_classif
# As a scoring function, we use a F test for classification
# We use the default selection function: the 10% most significant
# features
<<<<<<< HEAD
selector = us.SelectFpr(us.f_classif, alpha=0.1)
selector.fit(x, y)
scores = -np.log(selector._scores)
=======
selector = SelectFpr(f_classif, alpha=0.1)
selector.fit(x, y)
scores = -np.log10(selector._pvalues)
>>>>>>> remote
scores /= scores.max()
pl.bar(x_indices-.45, scores, width=.3,
label=r'Univariate score ($-Log(p_{value})$)',
color='g')
################################################################################
# Compare to the weights of an SVM
clf = svm.SVC(kernel='linear')
clf.fit(x, y)
svm_weights = (clf.coef_**2).sum(axis=0)
svm_weights /= svm_weights.max()
