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[MRG+1] add Convergence warning in LabelPropagation #5893

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Merged
merged 13 commits into from
Aug 6, 2017
Merged

[MRG+1] add Convergence warning in LabelPropagation #5893

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1efbd76
EHN: Show a Convergence warning if the max_iters were performed.
musically-ut Nov 20, 2015
693e7bd
DOC Indicate that max_iter is an integer.
musically-ut Jul 4, 2017
5ec86d6
FIX Change max_iter in Doctests to 1000.
musically-ut Jul 4, 2017
8d7cdda
Add test for ConvergenceWarning.
musically-ut Jul 5, 2017
8e313bd
Change defaults for LabelPropagation and LabelSpreading
musically-ut Jul 20, 2017
8dc12a1
Add seed for tests.
musically-ut Jul 21, 2017
9e04e3e
Add assert_no_warn on all tests which rely on convergance.
musically-ut Jul 22, 2017
0b23b24
Use a RandomState instead of setting seed globally
musically-ut Jul 23, 2017
54a5f26
Move assert_no_warnings to the dedicated test.
musically-ut Jul 23, 2017
d0bf21a
Fix bug with max_iter.
musically-ut Jul 28, 2017
cae57df
Add myself to file authors.
musically-ut Jul 28, 2017
5e2e301
Fix flake8 errors.
musically-ut Jul 29, 2017
3dfe110
While loop -> for loop.
musically-ut Aug 3, 2017
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43 changes: 22 additions & 21 deletions sklearn/semi_supervised/label_propagation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,8 +34,8 @@
>>> from sklearn.semi_supervised import LabelPropagation
>>> label_prop_model = LabelPropagation()
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = np.where(np.random.randint(0, 2,
... size=len(iris.target)))
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
Expand All @@ -53,6 +53,7 @@
"""

# Authors: Clay Woolam <[email protected]>
# Utkarsh Upadhyay <[email protected]>
# License: BSD
from abc import ABCMeta, abstractmethod

Expand All @@ -67,13 +68,7 @@
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_X_y, check_is_fitted, check_array


# Helper functions

def _not_converged(y_truth, y_prediction, tol=1e-3):
"""basic convergence check"""
return np.abs(y_truth - y_prediction).sum() > tol
from ..exceptions import ConvergenceWarning


class BaseLabelPropagation(six.with_metaclass(ABCMeta, BaseEstimator,
Expand All @@ -97,7 +92,7 @@ class BaseLabelPropagation(six.with_metaclass(ABCMeta, BaseEstimator,
alpha : float
Clamping factor

max_iter : float
max_iter : integer
Change maximum number of iterations allowed

tol : float
Expand Down Expand Up @@ -264,12 +259,14 @@ def fit(self, X, y):

l_previous = np.zeros((self.X_.shape[0], n_classes))

remaining_iter = self.max_iter
unlabeled = unlabeled[:, np.newaxis]
if sparse.isspmatrix(graph_matrix):
graph_matrix = graph_matrix.tocsr()
while (_not_converged(self.label_distributions_, l_previous, self.tol)
and remaining_iter > 1):

for self.n_iter_ in range(self.max_iter):
if np.abs(self.label_distributions_ - l_previous).sum() < self.tol:
break

l_previous = self.label_distributions_
self.label_distributions_ = safe_sparse_dot(
graph_matrix, self.label_distributions_)
Expand All @@ -285,7 +282,12 @@ def fit(self, X, y):
# clamp
self.label_distributions_ = np.multiply(
alpha, self.label_distributions_) + y_static
remaining_iter -= 1
else:
warnings.warn(
'max_iter=%d was reached without convergence.' % self.max_iter,
category=ConvergenceWarning
)
self.n_iter_ += 1

normalizer = np.sum(self.label_distributions_, axis=1)[:, np.newaxis]
self.label_distributions_ /= normalizer
Expand All @@ -294,7 +296,6 @@ def fit(self, X, y):
transduction = self.classes_[np.argmax(self.label_distributions_,
axis=1)]
self.transduction_ = transduction.ravel()
self.n_iter_ = self.max_iter - remaining_iter
return self


Expand Down Expand Up @@ -324,7 +325,7 @@ class LabelPropagation(BaseLabelPropagation):
This parameter will be removed in 0.21.
'alpha' is fixed to zero in 'LabelPropagation'.

max_iter : float
max_iter : integer
Change maximum number of iterations allowed

tol : float
Expand Down Expand Up @@ -358,8 +359,8 @@ class LabelPropagation(BaseLabelPropagation):
>>> from sklearn.semi_supervised import LabelPropagation
>>> label_prop_model = LabelPropagation()
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = np.where(np.random.randint(0, 2,
... size=len(iris.target)))
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
Expand Down Expand Up @@ -441,7 +442,7 @@ class LabelSpreading(BaseLabelPropagation):
alpha=0 means keeping the initial label information; alpha=1 means
replacing all initial information.

max_iter : float
max_iter : integer
maximum number of iterations allowed

tol : float
Expand Down Expand Up @@ -475,8 +476,8 @@ class LabelSpreading(BaseLabelPropagation):
>>> from sklearn.semi_supervised import LabelSpreading
>>> label_prop_model = LabelSpreading()
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = np.where(np.random.randint(0, 2,
... size=len(iris.target)))
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
Expand Down
25 changes: 23 additions & 2 deletions sklearn/semi_supervised/tests/test_label_propagation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from sklearn.semi_supervised import label_propagation
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.datasets import make_classification
from sklearn.exceptions import ConvergenceWarning
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal

Expand Down Expand Up @@ -70,7 +71,7 @@ def test_alpha_deprecation():
y[::3] = -1

lp_default = label_propagation.LabelPropagation(kernel='rbf', gamma=0.1)
lp_default_y = assert_no_warnings(lp_default.fit, X, y).transduction_
lp_default_y = lp_default.fit(X, y).transduction_

lp_0 = label_propagation.LabelPropagation(alpha=0, kernel='rbf', gamma=0.1)
lp_0_y = assert_warns(DeprecationWarning, lp_0.fit, X, y).transduction_
Expand Down Expand Up @@ -108,7 +109,8 @@ def test_label_propagation_closed_form():
labelled_idx = (Y[:, (-1,)] == 0).nonzero()[0]

clf = label_propagation.LabelPropagation(max_iter=10000,
gamma=0.1).fit(X, y)
gamma=0.1)
clf.fit(X, y)
# adopting notation from Zhu et al 2002
T_bar = clf._build_graph()
Tuu = T_bar[np.meshgrid(unlabelled_idx, unlabelled_idx, indexing='ij')]
Expand Down Expand Up @@ -145,3 +147,22 @@ def test_convergence_speed():
# this should converge quickly:
assert mdl.n_iter_ < 10
assert_array_equal(mdl.predict(X), [0, 1, 1])


def test_convergence_warning():
# This is a non-regression test for #5774
X = np.array([[1., 0.], [0., 1.], [1., 2.5]])
y = np.array([0, 1, -1])
mdl = label_propagation.LabelSpreading(kernel='rbf', max_iter=1)
assert_warns(ConvergenceWarning, mdl.fit, X, y)
assert_equal(mdl.n_iter_, mdl.max_iter)

mdl = label_propagation.LabelPropagation(kernel='rbf', max_iter=1)
assert_warns(ConvergenceWarning, mdl.fit, X, y)
assert_equal(mdl.n_iter_, mdl.max_iter)

mdl = label_propagation.LabelSpreading(kernel='rbf', max_iter=500)
assert_no_warnings(mdl.fit, X, y)

mdl = label_propagation.LabelPropagation(kernel='rbf', max_iter=500)
assert_no_warnings(mdl.fit, X, y)