8000 [MRG] Fixed warnings for DataDimensionalityWarning and decision function by amueller · Pull Request #5395 · scikit-learn/scikit-learn · GitHub
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[MRG] Fixed warnings for DataDimensionalityWarning and decision function #5395

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Merged
merged 1 commit into from
Oct 15, 2015
Merged

[MRG] Fixed warnings for DataDimensionalityWarning and decision function #5395

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2 changes: 1 addition & 1 deletion sklearn/linear_model/tests/test_least_angle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -370,7 +370,7 @@ def test_multitarget():
for estimator in (linear_model.LassoLars(), linear_model.Lars()):
estimator.fit(X, Y)
Y_pred = estimator.predict(X)
Y_dec = estimator.decision_function(X)
Y_dec = assert_warns(DeprecationWarning, estimator.decision_function, X)
assert_array_almost_equal(Y_pred, Y_dec)
alphas, active, coef, path = (estimator.alphas_, estimator.active_,
estimator.coef_, estimator.coef_path_)
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2 changes: 1 addition & 1 deletion sklearn/linear_model/tests/test_sgd.py
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Expand Up @@ -1040,7 +1040,7 @@ def test_partial_fit(self):
clf.partial_fit(X[:third], Y[:third])
assert_equal(clf.coef_.shape, (X.shape[1], ))
assert_equal(clf.intercept_.shape, (1,))
assert_equal(clf.decision_function([[0, 0]]).shape, (1, ))
assert_equal(clf.predict([[0, 0]]).shape, (1, ))
id1 = id(clf.coef_.data)

clf.partial_fit(X[third:], Y[third:])
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27 changes: 14 additions & 13 deletions sklearn/neighbors/tests/test_approximate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@ def test_neighbors_accuracy_with_n_candidates():

for i, n_candidates in enumerate(n_candidates_values):
lshf = LSHForest(n_candidates=n_candidates)
lshf.fit(X)
ignore_warnings(lshf.fit)(X)
for j in range(n_iter):
query = X[rng.randint(0, n_samples)].reshape(1, -1)

Expand Down Expand Up @@ -74,7 +74,7 @@ def test_neighbors_accuracy_with_n_estimators():

for i, t in enumerate(n_estimators):
lshf = LSHForest(n_candidates=500, n_estimators=t)
lshf.fit(X)
ignore_warnings(lshf.fit)(X)
for j in range(n_iter):
query = X[rng.randint(0, n_samples)].reshape(1, -1)
neighbors = lshf.kneighbors(query, n_neighbors=n_points,
Expand Down Expand Up @@ -111,7 +111,7 @@ def test_kneighbors():
# Test unfitted estimator
assert_raises(ValueError, lshf.kneighbors, X[0])

lshf.fit(X)
ignore_warnings(lshf.fit)(X)

for i in range(n_iter):
n_neighbors = rng.randint(0, n_samples)
Expand Down Expand Up @@ -162,7 +162,7 @@ def test_radius_neighbors():
# Test unfitted estimator
assert_raises(ValueError, lshf.radius_neighbors, X[0])

lshf.fit(X)
ignore_warnings(lshf.fit)(X)

for i in range(n_iter):
# Select a random point in the dataset as the query
Expand Down Expand Up @@ -218,6 +218,7 @@ def test_radius_neighbors():
sorted_dists_approx)))


@ignore_warnings
def test_radius_neighbors_boundary_handling():
X = [[0.999, 0.001], [0.5, 0.5], [0, 1.], [-1., 0.001]]
n_points = len(X)
Expand Down Expand Up @@ -286,7 +287,7 @@ def test_distances():
X = rng.rand(n_samples, n_features)

lshf = LSHForest()
lshf.fit(X)
ignore_warnings(lshf.fit)(X)

for i in range(n_iter):
n_neighbors = rng.randint(0, n_samples)
Expand All @@ -312,7 +313,7 @@ def test_fit():
X = rng.rand(n_samples, n_features)

lshf = LSHForest(n_estimators=n_estimators)
lshf.fit(X)
ignore_warnings(lshf.fit)(X)

# _input_array = X
assert_array_equal(X, lshf._fit_X)
Expand Down Expand Up @@ -343,16 +344,16 @@ def test_partial_fit():
lshf = LSHForest()

# Test unfitted estimator
lshf.partial_fit(X)
ignore_warnings(lshf.partial_fit)(X)
assert_array_equal(X, lshf._fit_X)

lshf.fit(X)
ignore_warnings(lshf.fit)(X)

# Insert wrong dimension
assert_raises(ValueError, lshf.partial_fit,
np.random.randn(n_samples_partial_fit, n_features - 1))

lshf.partial_fit(X_partial_fit)
ignore_warnings(lshf.partial_fit)(X_partial_fit)

# size of _input_array = samples + 1 after insertion
assert_equal(lshf._fit_X.shape[0],
Expand All @@ -379,7 +380,7 @@ def test_hash_functions():

lshf = LSHForest(n_estimators=n_estimators,
random_state=rng.randint(0, np.iinfo(np.int32).max))
lshf.fit(X)
ignore_warnings(lshf.fit)(X)

hash_functions = []
for i in range(n_estimators):
Expand All @@ -405,7 +406,7 @@ def test_candidates():

# For zero candidates
lshf = LSHForest(min_hash_match=32)
lshf.fit(X_train)
ignore_warnings(lshf.fit)(X_train)

message = ("Number of candidates is not sufficient to retrieve"
" %i neighbors with"
Expand All @@ -419,7 +420,7 @@ def test_candidates():

# For candidates less than n_neighbors
lshf = LSHForest(min_hash_match=31)
lshf.fit(X_train)
ignore_warnings(lshf.fit)(X_train)
message = ("Number of candidates is not sufficient to retrieve"
" %i neighbors with"
Expand All @@ -441,7 +442,7 @@ def test_graphs():
for n_samples in n_samples_sizes:
X = rng.rand(n_samples, n_features)
lshf = LSHForest(min_hash_match=0)
lshf.fit(X)
ignore_warnings(lshf.fit)(X)

kneighbors_graph = lshf.kneighbors_graph(X)
radius_neighbors_graph = lshf.radius_neighbors_graph(X)
Expand Down
2 changes: 1 addition & 1 deletion sklearn/svm/tests/test_sparse.py
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Expand Up @@ -63,7 +63,7 @@ def check_svm_model_equal(dense_svm, sparse_svm, X_train, y_train, X_test):
msg = "cannot use sparse input in 'OneClassSVM' trained on dense data"
else:
assert_array_almost_equal(dense_svm.predict_proba(X_test_dense),
sparse_svm.predict_proba(X_test), 4)
sparse_svm.predict_proba(X_test), 4)
msg = "cannot use sparse input in 'SVC' trained on dense data"
if sparse.isspmatrix(X_test):
assert_raise_message(ValueError, msg, dense_svm.predict, X_test)
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9 changes: 4 additions & 5 deletions sklearn/svm/tests/test_svm.py
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Expand Up @@ -354,10 +354,9 @@ def test_decision_function_shape():
assert_equal(dec.shape, (len(X_train), 10))


@ignore_warnings
def test_svr_decision_function():
def test_svr_predict():
# Test SVR's decision_function
# Sanity check, test that decision_function implemented in python
# Sanity check, test that predict implemented in python
# returns the same as the one in libsvm

X = iris.data
Expand All @@ -367,14 +366,14 @@ def test_svr_decision_function():
reg = svm.SVR(kernel='linear', C=0.1).fit(X, y)

dec = np.dot(X, reg.coef_.T) + reg.intercept_
assert_array_almost_equal(dec.r 607E avel(), reg.decision_function(X).ravel())
assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())

# rbf kernel
reg = svm.SVR(kernel='rbf', gamma=1).fit(X, y)

rbfs = rbf_kernel(X, reg.support_vectors_, gamma=reg.gamma)
dec = np.dot(rbfs, reg.dual_coef_.T) + reg.intercept_
assert_array_almost_equal(dec.ravel(), reg.decision_function(X).ravel())
assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())


def test_weight():
Expand Down
0