def test_stats_sparse(self):
X = csr_matrix(np.identity(5))
np.testing.assert_equal(
stats(X),
[
[0, 1, 0.2, 0, 4, 1],
[0, 1, 0.2, 0, 4, 1],
[0, 1, 0.2, 0, 4, 1],
[0, 1, 0.2, 0, 4, 1],
[0, 1, 0.2, 0, 4, 1],
],
)
# assure last two columns have just zero elements
X = X[:3]
np.testing.assert_equal(
stats(X),
[
[0, 1, 1 / 3, 0, 2, 1],
[0, 1, 1 / 3, 0, 2, 1],
[0, 1, 1 / 3, 0, 2, 1],
[0, 0, 0, 0, 3, 0],
[0, 0, 0, 0, 3, 0],
],
)
def test_stats_weights(self):
X = np.arange(4).reshape(2, 2).astype(float)
weights = np.array([1, 3])
np.testing.assert_equal(stats(X, weights), [[0, 2, 1.5, 0, 0, 2],
[1, 3, 2.5, 0, 0, 2]])
X = np.arange(4).reshape(2, 2).astype(object)
np.testing.assert_equal(stats(X, weights), stats(X))
def test_stats_sparse(self):
"""
Stats should not fail when trying to calculate mean on sparse data.
GH-2357
"""
data = Table("iris")
sparse_x = sp.csr_matrix(data.X)
self.assertTrue(stats(data.X).all() == stats(sparse_x).all())
def test_stats_sparse(self):
"""
Stats should not fail when trying to calculate mean on sparse data.
GH-2357
"""
data = Table("iris")
sparse_x = sp.csr_matrix(data.X)
self.assertTrue(stats(data.X).all() == stats(sparse_x).all())
def test_stats_weights(self):
X = np.arange(4).reshape(2, 2).astype(float)
weights = np.array([1, 3])
np.testing.assert_equal(stats(X, weights),
[[0, 2, 1.5, 0, 0, 2], [1, 3, 2.5, 0, 0, 2]])
X = np.arange(4).reshape(2, 2).astype(object)
np.testing.assert_equal(stats(X, weights), stats(X))
def test_stats(self):
X = np.arange(4).reshape(2, 2).astype(float)
X[1, 1] = np.nan
np.testing.assert_equal(stats(X), [[0, 2, 1, 0, 0, 2],
[1, 1, 1, 0, 1, 1]])
# empty table should return ~like metas
X = X[:0]
np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 0, 0],
[np.inf, -np.inf, 0, 0, 0, 0]])
def test_stats(self):
X = np.arange(4).reshape(2, 2).astype(float)
X[1, 1] = np.nan
np.testing.assert_equal(stats(X), [[0, 2, 1, 0, 0, 2],
[1, 1, 1, 0, 1, 1]])
# empty table should return ~like metas
X = X[:0]
np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 0, 0],
[np.inf, -np.inf, 0, 0, 0, 0]])
def test_stats_sparse(self):
X = csr_matrix(np.identity(5))
np.testing.assert_equal(stats(X), [[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1]])
# assure last two columns have just zero elements
X = X[:3]
np.testing.assert_equal(stats(X), [[0, 1, 1/3, 0, 2, 1],
[0, 1, 1/3, 0, 2, 1],
[0, 1, 1/3, 0, 2, 1],
[0, 0, 0, 0, 3, 0],
[0, 0, 0, 0, 3, 0]])
def test_stats_sparse(self):
X = csr_matrix(np.identity(5))
np.testing.assert_equal(stats(X), [[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1],
[0, 1, .2, 0, 4, 1]])
def test_coef_table_multiple(self):
data = Table("zoo")
learn = LogisticRegressionLearner()
classifier = learn(data)
coef_table = create_coef_table(classifier)
self.assertEqual(1, len(stats(coef_table.metas, None)))
self.assertEqual(len(coef_table), len(classifier.domain.attributes) + 1)
self.assertEqual(len(coef_table[0]), len(classifier.domain.class_var.values))
def test_coef_table_single(self):
data = Table("titanic")
learn = LogisticRegressionLearner()
classifier = learn(data)
coef_table = create_coef_table(classifier)
self.assertEqual(1, len(stats(coef_table.metas, None)))
self.assertEqual(len(coef_table), len(classifier.domain.attributes) + 1)
self.assertEqual(len(coef_table[0]), 1)
def test_coef_table_single(self):
data = Table("titanic")
learn = LogisticRegressionLearner()
classifier = learn(data)
coef_table = create_coef_table(classifier)
self.assertEqual(1, len(stats(coef_table.metas, None)))
self.assertEqual(len(coef_table), len(classifier.domain.attributes) + 1)
self.assertEqual(len(coef_table[0]), 1)
def test_stats_non_numeric(self):
X = np.array([
['', 'a', 'b'],
['a', '', 'b'],
['a', 'b', ''],
], dtype=object)
np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 1, 2]])
def test_coef_table_multiple(self):
data = Table("zoo")
learn = LogisticRegressionLearner()
classifier = learn(data)
coef_table = create_coef_table(classifier)
self.assertEqual(1, len(stats(coef_table.metas, None)))
self.assertEqual(len(coef_table), len(classifier.domain.attributes) + 1)
self.assertEqual(len(coef_table[0]),
len(classifier.domain.class_var.values))
def test_stats_non_numeric(self):
X = np.array([
['', 'a', 'b'],
['a', '', 'b'],
['a', 'b', ''],
], dtype=object)
np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 1, 2]])
def test_stats_non_numeric(self):
X = np.array([
["", "a", np.nan, 0],
["a", "", np.nan, 1],
["a", "b", 0, 0],
], dtype=object)
np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 2, 1],
[np.inf, -np.inf, 0, 0, 0, 3]])
def test_stats_non_numeric(self):
X = np.array([
["", "a", np.nan, 0],
["a", "", np.nan, 1],
["a", "b", 0, 0],
], dtype=object)
np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 1, 2],
[np.inf, -np.inf, 0, 0, 2, 1],
[np.inf, -np.inf, 0, 0, 0, 3]])
def test_stats_long_string_mem_use(self):
X = np.full((1000, 1000), "a", dtype=object)
t = time.time()
stats(X)
t_a = time.time() - t # time for an array with constant-len strings
# Add one very long string
X[0, 0] = "a" * 2000
# The implementation of stats() in Orange 3.30.2 used .astype("str")
# internally. X.astype("str") would take ~1000x the memory as X,
# because its type would be "<U1000" (the length of the longest string).
# That is about 7.5 GiB of memory on a 64-bit Linux system
# Because it is hard to measure CPU, we here measure time as
# memory allocation of such big tables takes time. On Marko's
# Linux system .astype("str") took ~3 seconds.
t = time.time()
stats(X)
t_b = time.time() - t
self.assertLess(t_b, 2 * t_a + 0.1) # some grace period
def test_stats_weights_sparse(self):
X = np.arange(4).reshape(2, 2).astype(float)
X = csr_matrix(X)
weights = np.array([1, 3])
np.testing.assert_equal(stats(X, weights),
[[0, 2, 1.5, 0, 1, 1], [1, 3, 2.5, 0, 0, 2]])
def test_stats(self):
X = np.arange(4).reshape(2, 2).astype(float)
X[1, 1] = np.nan
np.testing.assert_equal(stats(X), [[0, 2, 1, 0, 0, 2],
[1, 1, 1, 0, 1, 1]])
def test_stats_weights_sparse(self):
X = np.arange(4).reshape(2, 2).astype(float)
X = csr_matrix(X)
weights = np.array([1, 3])
np.testing.assert_equal(stats(X, weights), [[0, 2, 1.5, 0, 1, 1],
[1, 3, 2.5, 0, 0, 2]])