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Require arrays to be equal…, & xfail equality of instances of stochastic classes #203

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Require arrays to be equal…, & xfail equality of instances of stochastic classes #203

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ee13d6a
Require arrays to be equal, not just either both all truthy or both n…
JamesParrott Dec 14, 2023
ad808cf
Reformatted xfail marks with Black
JamesParrott Dec 15, 2023
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30 changes: 27 additions & 3 deletions mapclassify/tests/test_classify.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,9 @@

def _assertions(a, b):
assert a.k == b.k
assert a.yb.all() == b.yb.all()
assert a.bins.all() == b.bins.all()
assert a.counts.all() == b.counts.all()
assert (a.yb == b.yb).all()
assert (a.bins == b.bins).all()
assert (a.counts == b.counts).all()


class TestClassify:
Expand All @@ -33,6 +33,12 @@ def test_fisher_jenks(self):
b = mapclassify.FisherJenks(self.x, k=3)
_assertions(a, b)

# mapclassify\classifiers.py:FisherJenksSampled.__init__
# 2028 ids = np.random.randint(0, n, int(n * pct))
@pytest.mark.xfail(
reason="Stochastic. Passing a.s. requires random samples "
"to be the same in both instances. "
)
def test_fisher_jenks_sampled(self):
a = mapclassify.classify(
self.x, "FisherJenksSampled", k=3, pct_sampled=0.5, truncate=False
Expand Down Expand Up @@ -69,16 +75,34 @@ def test_jenks_caspall_forced(self):
b = mapclassify.JenksCaspallForced(self.x, k=3)
_assertions(a, b)

# mapclassify\classifiers.py:JenksCaspallSampled.__init__
# 2224 ids = np.random.randint(0, n, int(n * pct))
@pytest.mark.xfail(
reason="Stochastic. Passing a.s. requires random samples "
"to be the same in both instances. "
)
def test_jenks_caspall_sampled(self):
a = mapclassify.classify(self.x, "JenksCaspallSampled", pct_sampled=0.5)
b = mapclassify.JenksCaspallSampled(self.x, pct=0.5)
_assertions(a, b)

# KMeans iterates starting from a randomly generated centroids
@pytest.mark.xfail(
reason="Stochastic. Passing a.s. requires random centroids "
"to be the same in both instances. "
)
def test_natural_breaks(self):
a = mapclassify.classify(self.x, "natural_breaks")
b = mapclassify.NaturalBreaks(self.x)
_assertions(a, b)

# mapclassify\classifiers.py:MaxP._set_bins
# 2656 rseeds = np.random.permutation(list(range(k))).tolist()
# 2701 rseeds = np.random.permutation(list(range(k))).tolist()
@pytest.mark.xfail(
reason="Stochastic. Passing a.s. requires random selections "
"to be the same in both instances. "
)
def test_max_p_classifier(self):
a = mapclassify.classify(self.x, "max_p", k=3, initial=50)
b = mapclassify.MaxP(self.x, k=3, initial=50)
Expand Down