def test_varying_between_combined(self):
X = np.array([[0, 0, 0, 0, 0, 1,],
[0, 0, 1, 1, 0, 1,],
[0, 0, 0, 2, np.nan, np.nan,],
[0, 1, 0, 0, 0, 0,],
[0, 1, 0, 2, 0, 0,],
[0, 1, 0, 0, np.nan, 0,]])
M = np.array([["A", 0, 0, 0, 0, 0, 1,],
["A", 0, 0, 1, 1, 0, 1,],
["A", 0, 0, 0, 2, np.nan, np.nan,],
["B", 0, 1, 0, 0, 0, 0,],
["B", 0, 1, 0, 2, 0, 0,],
["B", 0, 1, 0, 0, np.nan, 0,]], dtype=str)
variables = [ContinuousVariable(name="F%d" % j) for j in range(X.shape[1])]
metas = [StringVariable(name="M%d" % j) for j in range(M.shape[1])]
domain = Domain(attributes=variables, metas=metas)
data = Table.from_numpy(X=X, domain=domain, metas=M)
self.assertEqual(varying_between(data, idvar=data.domain.metas[0]),
[variables[2], variables[3], metas[3], metas[4], metas[5], metas[6]])
# scipy.sparse uses matrix; this filter can be removed when it's fixed
warnings.filterwarnings(
"ignore", ".*the matrix subclass.*", PendingDeprecationWarning)
data = Table.from_numpy(X=sp.csr_matrix(X), domain=domain, metas=M)
self.assertEqual(varying_between(data, idvar=data.domain.metas[0]),
[variables[2], variables[3], metas[3], metas[4], metas[5], metas[6]])
def test_varying_between_combined(self):
X = np.array([[0, 0, 0, 0, 0, 1,],
[0, 0, 1, 1, 0, 1,],
[0, 0, 0, 2, np.nan, np.nan,],
[0, 1, 0, 0, 0, 0,],
[0, 1, 0, 2, 0, 0,],
[0, 1, 0, 0, np.nan, 0,]])
M = np.array([["A", 0, 0, 0, 0, 0, 1,],
["A", 0, 0, 1, 1, 0, 1,],
["A", 0, 0, 0, 2, np.nan, np.nan,],
["B", 0, 1, 0, 0, 0, 0,],
["B", 0, 1, 0, 2, 0, 0,],
["B", 0, 1, 0, 0, np.nan, 0,]], dtype=str)
variables = [ContinuousVariable(name="F%d" % j) for j in range(X.shape[1])]
metas = [StringVariable(name="M%d" % j) for j in range(M.shape[1])]
domain = Domain(attributes=variables, metas=metas)
data = Table.from_numpy(X=X, domain=domain, metas=M)
self.assertEqual(varying_between(data, idvar=data.domain.metas[0]),
[variables[2], variables[3], metas[3], metas[4], metas[5], metas[6]])
data = Table.from_numpy(X=sp.csr_matrix(X), domain=domain, metas=M)
self.assertEqual(varying_between(data, idvar=data.domain.metas[0]),
[variables[2], variables[3], metas[3], metas[4], metas[5], metas[6]])
def test_varying_between_combined(self):
X = np.array([[0, 0, 0, 0, 0, 1,],
[0, 0, 1, 1, 0, 1,],
[0, 0, 0, 2, np.nan, np.nan,],
[0, 1, 0, 0, 0, 0,],
[0, 1, 0, 2, 0, 0,],
[0, 1, 0, 0, np.nan, 0,]])
M = np.array([["A", 0, 0, 0, 0, 0, 1,],
["A", 0, 0, 1, 1, 0, 1,],
["A", 0, 0, 0, 2, np.nan, np.nan,],
["B", 0, 1, 0, 0, 0, 0,],
["B", 0, 1, 0, 2, 0, 0,],
["B", 0, 1, 0, 0, np.nan, 0,]], dtype=str)
variables = [ContinuousVariable(name="F%d" % j) for j in range(X.shape[1])]
metas = [StringVariable(name="M%d" % j) for j in range(M.shape[1])]
domain = Domain(attributes=variables, metas=metas)
data = Table.from_numpy(X=X, domain=domain, metas=M)
self.assertEqual(varying_between(data, idvar=data.domain.metas[0]),
[variables[2], variables[3], metas[3], metas[4], metas[5], metas[6]])
data = Table.from_numpy(X=sp.csr_matrix(X), domain=domain, metas=M)
self.assertEqual(varying_between(data, idvar=data.domain.metas[0]),
[variables[2], variables[3], metas[3], metas[4], metas[5], metas[6]])
def test_venn_diagram(self):
sources = ["SVM Learner", "Naive Bayes", "Random Forest"]
item_id_var = StringVariable("item_id")
source_var = StringVariable("source")
table = Table("zoo")
class_var = table.domain.class_var
cv = np.random.randint(len(class_var.values), size=(3, len(sources)))
tables = []
for i in range(len(sources)):
temp_table = Table.from_table(table.domain, table,
[0 + i, 1 + i, 2 + i])
temp_d = (
DiscreteVariable("%s(%s)" % (class_var.name, sources[0 + i]),
class_var.values),
source_var,
item_id_var,
)
temp_m = np.array(
[
[cv[0, i], sources[i], table.metas[0 + i, 0]],
[cv[1, i], sources[i], table.metas[1 + i, 0]],
[cv[2, i], sources[i], table.metas[2 + i, 0]],
],
dtype=object,
)
temp_table = self.add_metas(temp_table, temp_d, temp_m)
tables.append(temp_table)
data = table_concat(tables)
varying = varying_between(data, item_id_var)
if source_var in varying:
varying.remove(source_var)
data = reshape_wide(data, varying, [item_id_var], [source_var])
data = drop_columns(data, [item_id_var])
result = np.array(
[
[table.metas[0, 0], cv[0, 0], np.nan, np.nan],
[table.metas[1, 0], cv[1, 0], cv[0, 1], np.nan],
[table.metas[2, 0], cv[2, 0], cv[1, 1], cv[0, 2]],
[table.metas[3, 0], np.nan, cv[2, 1], cv[1, 2]],
[table.metas[4, 0], np.nan, np.nan, cv[2, 2]],
],
dtype=object,
)
for i in range(len(result)):
for j in range(len(result[0])):
val = result[i][j]
if isinstance(val, float) and np.isnan(val):
self.assertTrue(np.isnan(data.metas[i][j]))
else:
np.testing.assert_equal(data.metas[i][j], result[i][j])
def test_venn_diagram(self):
sources = ["SVM Learner", "Naive Bayes", "Random Forest"]
item_id_var = StringVariable("item_id")
source_var = StringVariable("source")
table = Table("zoo")
class_var = table.domain.class_var
cv = np.random.randint(len(class_var.values), size=(3, len(sources)))
tables = []
# pylint: disable=consider-using-enumerate
for i in range(len(sources)):
temp_table = Table.from_table(table.domain, table,
[0 + i, 1 + i, 2 + i])
temp_d = (DiscreteVariable("%s(%s)" % (class_var.name,
sources[0 + i]),
class_var.values),
source_var, item_id_var)
temp_m = np.array([[cv[0, i], sources[i], table.metas[0 + i, 0]],
[cv[1, i], sources[i], table.metas[1 + i, 0]],
[cv[2, i], sources[i], table.metas[2 + i, 0]]],
dtype=object)
temp_table = self.add_metas(temp_table, temp_d, temp_m)
tables.append(temp_table)
data = table_concat(tables)
varying = varying_between(data, item_id_var)
if source_var in varying:
varying.remove(source_var)
data = reshape_wide(data, varying, [item_id_var], [source_var])
data = drop_columns(data, [item_id_var])
result = np.array([[table.metas[0, 0], cv[0, 0], np.nan, np.nan],
[table.metas[1, 0], cv[1, 0], cv[0, 1], np.nan],
[table.metas[2, 0], cv[2, 0], cv[1, 1], cv[0, 2]],
[table.metas[3, 0], np.nan, cv[2, 1], cv[1, 2]],
[table.metas[4, 0], np.nan, np.nan, cv[2, 2]]],
dtype=object)
for i in range(len(result)):
for j in range(len(result[0])):
val = result[i][j]
if isinstance(val, float) and np.isnan(val):
self.assertTrue(np.isnan(data.metas[i][j]))
else:
np.testing.assert_equal(data.metas[i][j], result[i][j])
def test_varying_between_missing_vals(self):
data = Table(test_filename("test9.tab"))
self.assertEqual(6, len(varying_between(data, data.domain[0])))
def test_varying_between_missing_vals(self):
data = Table(test_filename("test9.tab"))
self.assertEqual(6, len(varying_between(data, [data.domain[0]])))
def test_varying_between_missing_vals(self):
data = Table("../../../tests/test9.tab")
self.assertEqual(6, len(varying_between(data, [data.domain[0]])))
def test_varying_between_missing_vals(self):
data = Table("../../../tests/test9.tab")
self.assertEqual(6, len(varying_between(data, [data.domain[0]])))
