def test_LinearSVC():
"""
Test basic routines using LinearSVC
"""
clf = svm.LinearSVC().fit(X, Y)
# by default should have intercept
assert clf.fit_intercept
assert_array_equal(clf.predict(T), true_result)
assert_array_almost_equal(clf.intercept_, [0], decimal=3)
# the same with l1 penalty
clf = svm.LinearSVC(penalty='l1', dual=False).fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# l2 penalty with dual formulation
clf = svm.LinearSVC(penalty='l2', dual=True).fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# l2 penalty, l1 loss
clf = svm.LinearSVC(penalty='l2', loss='l1', dual=True).fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# test also decision function
dec = clf.decision_function(T).ravel()
res = (dec > 0).astype(np.int) + 1
assert_array_equal(res, true_result)
def test_liblinear_predict():
"""
Test liblinear predict
Sanity check, test that predict implemented in python
returns the same as the one in libliblinear
"""
# multi-class case
clf = svm.LinearSVC().fit(iris.data, iris.target)
weights = clf.coef_.T
bias = clf.intercept_
H = np.dot(iris.data, weights) + bias
assert_array_equal(clf.predict(iris.data), H.argmax(axis=1))
# binary-class case
X = [[2, 1],
[3, 1],
[1, 3],
[2, 3]]
y = [0, 0, 1, 1]
clf = svm.LinearSVC().fit(X, y)
weights = np.ravel(clf.coef_)
bias = clf.intercept_
H = np.dot(X, weights) + bias
assert_array_equal(clf.predict(X), (H > 0).astype(int))
def train_liblinear_classifier_core(trainXy,
classifier_type="liblinear",
trace_normalize=False,
**kwargs):
""" Classifier training using SVMs
Input:
train_features = training features (both positive and negative)
train_labels = corresponding label vector
svm_eps = eps of svm
svm_C = C parameter of svm
classifier_type = liblinear or libsvm"""
#do normalization
(train_features, train_labels), train_mean, train_std, trace = normalize(
[trainXy], trace_normalize=trace_normalize)
if classifier_type == 'liblinear':
clf = sklearn_svm.LinearSVC(**kwargs)
if classifier_type == 'libSVM':
clf = sklearn_svm.SVC(**kwargs)
elif classifier_type == 'LRL':
clf = LogisticRegression(**kwargs)
elif classifier_type == 'MCC':
clf = CorrelationClassifier(**kwargs)
elif classifier_type.startswith('svm.'):
ct = classifier_type.split('.')[-1]
clf = getattr(sklearn_svm, ct)(**kwargs)
elif classifier_type.startswith('linear_model.'):
ct = classifier_type.split('.')[-1]
clf = getattr(sklearn_linear_model, ct)(**kwargs)
clf.fit(train_features, train_labels)
return clf, train_mean, train_std, trace
def test_coef_and_intercept_SVC_vs_LinearSVC():
"""
Test that SVC and LinearSVC return the same coef_ and intercept_
"""
svc = svm.SVC(kernel='linear', C=1).fit(X, Y)
linsvc = svm.LinearSVC(C=1, penalty='l2', loss='l1', dual=True).fit(X, Y)
assert_array_equal(linsvc.coef_.shape, svc.coef_.shape)
assert_array_almost_equal(linsvc.coef_, svc.coef_, decimal=5)
assert_array_almost_equal(linsvc.intercept_, svc.intercept_, decimal=5)
def test_LinearSVC_iris():
"""
Test that LinearSVC gives plausible predictions on the iris dataset
"""
clf = svm.LinearSVC().fit(iris.data, iris.target)
assert np.mean(clf.predict(iris.data) == iris.target) > 0.8
dec = clf.decision_function(iris.data)
pred = np.argmax(dec, 1)
assert_array_equal(pred, clf.predict(iris.data))
def test_LinearSVC():
"""
Test basic routines using LinearSVC
"""
clf = svm.LinearSVC().fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
assert_array_almost_equal(clf.intercept_, [0], decimal=5)
# the same with l1 penalty
clf = svm.LinearSVC(penalty='l1', dual=False).fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
# l2 penalty with dual formulation
clf = svm.LinearSVC(penalty='l2', dual=True).fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
#
clf = svm.LinearSVC(penalty='l2', loss='l1', dual=True).fit(X, Y)
assert_array_equal(clf.predict(T), true_result)
def svm_method(entries, lin_or_poly):
feature_vectors = []
classes = []
for e in entries:
feature_vectors.append(e.v)
classes.append(e.c)
if lin_or_poly:
curr_svm = svm.LinearSVC()
else:
curr_svm = svm.SVC(kernel='poly')
curr_svm.fit(feature_vectors, classes)
return curr_svm
def test_LinearSVC_iris():
"""Test the sparse LinearSVC with the iris dataset"""
sp_clf = svm.sparse.LinearSVC().fit(iris.data, iris.target)
clf = svm.LinearSVC().fit(iris.data.todense(), iris.target)
assert_array_almost_equal(clf.label_, sp_clf.label_)
assert_equal(clf.fit_intercept, sp_clf.fit_intercept)
assert_array_almost_equal(clf.raw_coef_, sp_clf.raw_coef_, decimal=1)
assert_array_almost_equal(clf.predict(iris.data.todense()),
sp_clf.predict(iris.data))
# check decision_function
pred = np.argmax(sp_clf.decision_function(iris.data), 1)
assert_array_almost_equal(pred, clf.predict(iris.data.todense()))
def test_weight():
"""
Test class weights
"""
clf = svm.SVC()
# we give a small weights to class 1
clf.fit(X, Y, {1: 0.1})
# so all predicted values belong to class 2
assert_array_almost_equal(clf.predict(X), [2] * 6)
X_, y_ = test_dataset_classif(n_samples=200, n_features=100, param=[5, 1],
seed=0)
for clf in (linear_model.LogisticRegression(), svm.LinearSVC(), svm.SVC()):
clf.fit(X_[: 180], y_[: 180], class_weight={0: 5})
y_pred = clf.predict(X_[180:])
assert np.sum(y_pred == y_[180:]) >= 11
def test_LinearSVC():
"""
Similar to test_SVC
"""
clf = svm.LinearSVC().fit(X, Y)
sp_clf = svm.sparse.LinearSVC().fit(X, Y)
assert sp_clf.fit_intercept
assert_array_almost_equal(clf.raw_coef_, sp_clf.raw_coef_, decimal=4)
assert_array_almost_equal(clf.predict(X), sp_clf.predict(X))
clf.fit(X2, Y2)
sp_clf.fit(X2, Y2)
assert_array_almost_equal(clf.raw_coef_, sp_clf.raw_coef_, decimal=4)
def test_auto_weight():
"""Test class weights for imbalanced data"""
from scikits.learn.linear_model import LogisticRegression
# we take as dataset a the two-dimensional projection of iris so
# that it is not separable and remove half of predictors from
# class 1
from scikits.learn.svm.base import _get_class_weight
X, y = iris.data[:, :2], iris.target
unbalanced = np.delete(np.arange(y.size), np.where(y > 1)[0][::2])
assert np.argmax(_get_class_weight('auto', y[unbalanced])[0]) == 2
for clf in (svm.SVC(kernel='linear'), svm.LinearSVC(), LogisticRegression()):
# check that score is better when class='auto' is set.
y_pred = clf.fit(X[unbalanced], y[unbalanced],
class_weight={}).predict(X)
y_pred_balanced = clf.fit(X[unbalanced], y[unbalanced],
class_weight='auto').predict(X)
assert metrics.f1_score(y, y_pred) <= metrics.f1_score(y, y_pred_balanced)
def test_bad_input():
"""
Test that it gives proper exception on deficient input
"""
# impossible value of C
assert_raises(ValueError, svm.SVC(C=-1).fit, X, Y)
# impossible value of nu
clf = svm.NuSVC(nu=0.0)
assert_raises(ValueError, clf.fit, X, Y)
Y2 = Y[:-1] # wrong dimensions for labels
assert_raises(ValueError, clf.fit, X, Y2)
# Test with arrays that are non-contiguous.
for clf in (svm.SVC(), svm.LinearSVC(), svm.sparse.SVC(),
svm.sparse.LinearSVC()):
Xf = np.asfortranarray(X)
assert Xf.flags['C_CONTIGUOUS'] == False
yf = np.ascontiguousarray(np.tile(Y, (2, 1)).T)
yf = yf[:, -1]
assert yf.flags['F_CONTIGUOUS'] == False
assert yf.flags['C_CONTIGUOUS'] == False
clf.fit(Xf, yf)
assert_array_equal(clf.predict(T), true_result)
# error for precomputed kernelsx
clf = svm.SVC(kernel='precomputed')
assert_raises(ValueError, clf.fit, X, Y)
Xt = np.array(X).T
clf = svm.SVC(kernel='precomputed')
clf.fit(np.dot(X, Xt), Y)
assert_raises(ValueError, clf.predict, X)
clf = svm.SVC()
clf.fit(X, Y)
assert_raises(ValueError, clf.predict, Xt)
from scikits.learn import svm, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
Y = iris.target
h=.02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
svc = svm.SVC(kernel='linear').fit(X, Y)
rbf_svc = svm.SVC(kernel='poly').fit(X, Y)
nu_svc = svm.NuSVC(kernel='linear').fit(X,Y)
lin_svc = svm.LinearSVC().fit(X, Y)
# create a mesh to plot in
x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
y_min, y_max = X[:,1].min()-1, X[:,1].max()+1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# title for the plots
titles = ['SVC with linear kernel',
'SVC with polynomial (degree 3) kernel',
'NuSVC with linear kernel',
'LinearSVC (linear kernel)']
pl.set_cmap(pl.cm.Paired)
def __init__(self, value):
if isinstance(value, type(svm.SVC())) or isinstance(
value, type(svm.LinearSVC())) or True:
self._value = value
else:
assert False, "Not a value scikits.learn.SVM type."
def validate(self, value):
if isinstance(value, type(svm.SVC())) or isinstance(
value, type(svm.LinearSVC())):
pass
else:
assert False, "SVMModelField: Improper type."
def _make_model(self):
from scikits.learn import svm
kw = dict(self._param)
self._m = svm.LinearSVC(**kw)
from scikits.learn import datasets, svm
import pylab as pl
digits = datasets.load_digits()
clf = svm.LinearSVC(fit_intercept=False)
clf.fit(digits.data, digits.target)
for i in range(4):
pl.subplot(2, 4, 1 + i)
pl.imshow(clf.coef_[i].reshape(8, 8),
cmap=pl.cm.gray_r,
interpolation='nearest')
pl.axis('off')
pl.show()
