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[MRG] Fix missing assert and parametrize some k-means tests #12368

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Merged
merged 5 commits into from
Oct 13, 2018
Merged

[MRG] Fix missing assert and parametrize some k-means tests #12368

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8301391
fix missing assert
jeremiedbb Oct 12, 2018
da049f2
refactor kmeans init tests
jeremiedbb Oct 12, 2018
da1fd5a
parametrize more tests
jeremiedbb Oct 12, 2018
06dc5fd
typo
jeremiedbb Oct 12, 2018
a81dbb6
ids in parametrize
jeremiedbb Oct 13, 2018
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153 changes: 33 additions & 120 deletions sklearn/cluster/tests/test_k_means.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,12 +181,6 @@ def _check_fitted_model(km):
% km.n_clusters, km.fit, [[0., 1.]])


def test_k_means_plus_plus_init():
km = KMeans(init="k-means++", n_clusters=n_clusters,
random_state=42).fit(X)
_check_fitted_model(km)


def test_k_means_new_centers():
# Explore the part of the code where a new center is reassigned
X = np.array([[0, 0, 1, 1],
Expand Down Expand Up @@ -229,24 +223,6 @@ def test_k_means_precompute_distances_flag():
assert_raises(ValueError, km.fit, X)


def test_k_means_plus_plus_init_sparse():
km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42)
km.fit(X_csr)
_check_fitted_model(km)


def test_k_means_random_init():
km = KMeans(init="random", n_clusters=n_clusters, random_state=42)
km.fit(X)
_check_fitted_model(km)


def test_k_means_random_init_sparse():
km = KMeans(init="random", n_clusters=n_clusters, random_state=42)
km.fit(X_csr)
_check_fitted_model(km)


def test_k_means_plus_plus_init_not_precomputed():
km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42,
precompute_distances=False).fit(X)
Expand All @@ -259,10 +235,11 @@ def test_k_means_random_init_not_precomputed():
_check_fitted_model(km)


def test_k_means_perfect_init():
km = KMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42,
n_init=1)
km.fit(X)
@pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse'])
@pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()])
def test_k_means_init(data, init):
km = KMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=1)
km.fit(data)
_check_fitted_model(km)


Expand Down Expand Up @@ -315,13 +292,6 @@ def test_k_means_fortran_aligned_data():
assert_array_equal(km.labels_, labels)


def test_mb_k_means_plus_plus_init_dense_array():
mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters,
random_state=42)
mb_k_means.fit(X)
_check_fitted_model(mb_k_means)


def test_mb_kmeans_verbose():
mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters,
random_state=42, verbose=1)
Expand All @@ -333,49 +303,25 @@ def test_mb_kmeans_verbose():
sys.stdout = old_stdout


def test_mb_k_means_plus_plus_init_sparse_matrix():
mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters,
random_state=42)
mb_k_means.fit(X_csr)
_check_fitted_model(mb_k_means)


def test_minibatch_init_with_large_k():
mb_k_means = MiniBatchKMeans(init='k-means++', init_size=10, n_clusters=20)
# Check that a warning is raised, as the number clusters is larger
# than the init_size
assert_warns(RuntimeWarning, mb_k_means.fit, X)


def test_minibatch_k_means_random_init_dense_array():
# increase n_init to make random init stable enough
mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters,
random_state=42, n_init=10).fit(X)
_check_fitted_model(mb_k_means)


def test_minibatch_k_means_random_init_sparse_csr():
# increase n_init to make random init stable enough
mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters,
random_state=42, n_init=10).fit(X_csr)
_check_fitted_model(mb_k_means)


def test_minibatch_k_means_perfect_init_dense_array():
mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters,
random_state=42, n_init=1).fit(X)
_check_fitted_model(mb_k_means)


def test_minibatch_k_means_init_multiple_runs_with_explicit_centers():
mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters,
random_state=42, n_init=10)
assert_warns(RuntimeWarning, mb_k_means.fit, X)


def test_minibatch_k_means_perfect_init_sparse_csr():
mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters,
random_state=42, n_init=1).fit(X_csr)
@pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse'])
@pytest.mark.parametrize('init', ["random", 'k-means++', centers.copy()])
def test_minibatch_k_means_init(data, init):
mb_k_means = MiniBatchKMeans(init=init, n_clusters=n_clusters,
random_state=42, n_init=10)
mb_k_means.fit(data)
_check_fitted_model(mb_k_means)


Expand Down Expand Up @@ -585,64 +531,39 @@ def test_predict():
assert_array_equal(pred, km.labels_)


def test_score():

km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42, n_init=1)
s1 = km1.fit(X).score(X)
km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42, n_init=1)
s2 = km2.fit(X).score(X)
assert_greater(s2, s1)

@pytest.mark.parametrize('algo', ['full', 'elkan'])
def test_score(algo):
# Check that fitting k-means with multiple inits gives better score
km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42, n_init=1,
algorithm='elkan')
algorithm=algo)
s1 = km1.fit(X).score(X)
km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42, n_init=1,
algorithm='elkan')
algorithm=algo)
s2 = km2.fit(X).score(X)
assert_greater(s2, s1)


def test_predict_minibatch_dense_input():
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, random_state=40).fit(X)

# sanity check: predict centroid labels
pred = mb_k_means.predict(mb_k_means.cluster_centers_)
assert_array_equal(pred, np.arange(n_clusters))

# sanity check: re-predict labeling for training set samples
pred = mb_k_means.predict(X)
assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_)


def test_predict_minibatch_kmeanspp_init_sparse_input():
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='k-means++',
n_init=10).fit(X_csr)
@pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse'])
@pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()])
def test_predict_minibatch(data, init):
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init=init,
n_init=10, random_state=0).fit(data)

# sanity check: re-predict labeling for training set samples
assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_)
assert_array_equal(mb_k_means.predict(data), mb_k_means.labels_)

# sanity check: predict centroid labels
pred = mb_k_means.predict(mb_k_means.cluster_centers_)
assert_array_equal(pred, np.arange(n_clusters))

# check that models trained on sparse input also works for dense input at
# predict time
assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_)
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Should we still keep this line?

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I moved it in a new function : test_predict_minibatch_dense_sparse.



def test_predict_minibatch_random_init_sparse_input():
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='random',
n_init=10).fit(X_csr)

# sanity check: re-predict labeling for training set samples
assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_)

# sanity check: predict centroid labels
pred = mb_k_means.predict(mb_k_means.cluster_centers_)
assert_array_equal(pred, np.arange(n_clusters))
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Should we keep these 2 lines as well?

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I did keep them :)

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in test_predict_minibatch, line 559-560


@pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()])
def test_predict_minibatch_dense_sparse(init):
# check that models trained on sparse input also works for dense input at
# predict time
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init=init,
n_init=10, random_state=0).fit(X_csr)

assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_)


Expand Down Expand Up @@ -694,27 +615,19 @@ def test_fit_transform():
assert_array_almost_equal(X1, X2)


def test_predict_equal_labels():
km = KMeans(random_state=13, n_jobs=1, n_init=1, max_iter=1,
algorithm='full')
km.fit(X)
assert_array_equal(km.predict(X), km.labels_)

@pytest.mark.parametrize('algo', ['full', 'elkan'])
def test_predict_equal_labels(algo):
km = KMeans(random_state=13, n_jobs=1, n_init=1, max_iter=1,
algorithm='elkan')
algorithm=algo)
km.fit(X)
assert_array_equal(km.predict(X), km.labels_)


def test_full_vs_elkan():
km1 = KMeans(algorithm='full', random_state=13).fit(X)
km2 = KMeans(algorithm='elkan', random_state=13).fit(X)

km1 = KMeans(algorithm='full', random_state=13)
km2 = KMeans(algorithm='elkan', random_state=13)

km1.fit(X)
km2.fit(X)

homogeneity_score(km1.predict(X), km2.predict(X)) == 1.0
assert homogeneity_score(km1.predict(X), km2.predict(X)) == 1.0


def test_n_init():
Expand Down