def test_domain_basic_stats(self):
domain = self.zoo.domain
attr_stats = [BasicStats(self.zoo, a) for a in domain.attributes]
class_var_stats = [BasicStats(self.zoo, a) for a in domain.class_vars]
meta_stats = [BasicStats(self.zoo, a) for a in domain.metas]
domain_stats = DomainBasicStats(self.zoo)
self.assertStatsEqual(domain_stats.stats, attr_stats + class_var_stats)
domain_stats = DomainBasicStats(self.zoo, include_metas=True)
self.assertStatsEqual(domain_stats.stats,
attr_stats + class_var_stats + meta_stats)
def test_speed(self):
n, m = 10, 10000
data = Table.from_numpy(None, np.random.rand(n, m))
start = time.time()
for i in range(m):
BasicStats(data, i)
elapsed = time.time() - start
self.assertLess(elapsed, 10.0)
def __call__(self, data, attribute, fixed=None):
if fixed:
min, max = fixed[attribute.name]
points = self._split_eq_width(min, max)
else:
if type(data) == SqlTable:
stats = BasicStats(data, attribute)
points = self._split_eq_width(stats.min, stats.max)
else:
values = data[:, attribute]
values = values.X if values.X.size else values.Y
min, max = ut.nanmin(values), ut.nanmax(values)
points = self._split_eq_width(min, max)
return Discretizer.create_discretized_var(
data.domain[attribute], points)
def __call__(self, data: Table, attribute, fixed=None):
if fixed:
mn, mx = fixed[attribute.name]
points = self._split_eq_width(mn, mx)
else:
if type(data) == SqlTable:
stats = BasicStats(data, attribute)
points = self._split_eq_width(stats.min, stats.max)
else:
values, _ = data.get_column_view(attribute)
if values.size:
mn, mx = ut.nanmin(values), ut.nanmax(values)
points = self._split_eq_width(mn, mx)
else:
points = []
return Discretizer.create_discretized_var(data.domain[attribute],
points)
def test_basic_stats(self):
iris = SqlTable(self.conn, self.iris, inspect_values=True)
stats = BasicStats(iris, iris.domain['sepal length'])
self.assertAlmostEqual(stats.min, 4.3)
self.assertAlmostEqual(stats.max, 7.9)
self.assertAlmostEqual(stats.mean, 5.8, 1)
self.assertEqual(stats.nans, 0)
self.assertEqual(stats.non_nans, 150)
domain_stats = DomainBasicStats(iris, include_metas=True)
self.assertEqual(len(domain_stats.stats),
len(iris.domain) + len(iris.domain.metas))
stats = domain_stats['sepal length']
self.assertAlmostEqual(stats.min, 4.3)
self.assertAlmostEqual(stats.max, 7.9)
self.assertAlmostEqual(stats.mean, 5.8, 1)
self.assertEqual(stats.nans, 0)
self.assertEqual(stats.non_nans, 150)
def test_basic_stats_on_large_data(self):
# By setting LARGE_TABLE to 100, iris will be treated as
# a large table and sampling will be used. As the table
# is actually small, time base sampling should return
# all rows, so the same assertions can be used.
iris = SqlTable(self.conn, self.iris, inspect_values=True)
stats = BasicStats(iris, iris.domain['sepal length'])
self.assertAlmostEqual(stats.min, 4.3)
self.assertAlmostEqual(stats.max, 7.9)
self.assertAlmostEqual(stats.mean, 5.8, 1)
self.assertEqual(stats.nans, 0)
self.assertEqual(stats.non_nans, 150)
domain_stats = DomainBasicStats(iris, include_metas=True)
self.assertEqual(len(domain_stats.stats),
len(iris.domain) + len(iris.domain.metas))
stats = domain_stats['sepal length']
self.assertAlmostEqual(stats.min, 4.3)
self.assertAlmostEqual(stats.max, 7.9)
self.assertAlmostEqual(stats.mean, 5.8, 1)
self.assertEqual(stats.nans, 0)
self.assertEqual(stats.non_nans, 150)