def testQuantile(self):
raw = np.random.rand(100)
q = np.random.rand(10)
for dtype in [np.float32, np.int64, np.complex128]:
raw2 = raw.astype(dtype)
a = tensor(raw2, chunk_size=100)
b = quantile(a, q, overwrite_input=True)
self.assertEqual(b.shape, (10, ))
self.assertEqual(b.dtype, np.quantile(raw2, q).dtype)
b = b.tiles()
self.assertEqual(len(b.chunks), 1)
raw = np.random.rand(20, 10)
q = np.random.rand(10)
for dtype in [np.float32, np.int64, np.complex128]:
for axis in (None, 0, 1, [0, 1]):
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
raw2 = raw.astype(dtype)
a = tensor(raw2, chunk_size=(4, 3))
b = quantile(a,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
expected = np.quantile(raw2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
self.assertEqual(b.shape, expected.shape)
self.assertEqual(b.dtype, expected.dtype)
b = b.tiles()
self.assertEqual(b.shape, expected.shape)
self.assertEqual(b.dtype, expected.dtype)
a = tensor(raw, chunk_size=10)
b = quantile(a, q)
b = b.tiles()
self.assertEqual(b.shape, (10, ))
b = quantile(a, 0.3)
self.assertEqual(b.ndim, 0)
raw2 = np.random.rand(3, 4, 5)
a2 = tensor(raw2, chunk_size=3)
b2 = quantile(a2, q, axis=(0, 2))
expected = np.quantile(raw2, q, axis=(0, 2))
self.assertEqual(b2.shape, expected.shape)
b2 = b2.tiles()
self.assertEqual(b2.shape, expected.shape)
# q has to be 1-d
with self.assertRaises(ValueError):
quantile(a, q.reshape(5, 2))
# wrong out type
with self.assertRaises(TypeError):
quantile(a, q, out=2)
# wrong q
with self.assertRaises(ValueError):
q2 = q.copy()
q2[0] = 1.1
quantile(a, q2)
# wrong q, with size < 10
with self.assertRaises(ValueError):
q2 = np.random.rand(5)
q2[0] = 1.1
quantile(a, q2)
# wrong interpolation
with self.assertRaises(ValueError):
quantile(a, q, interpolation='unknown')
def test_quantile_execution(setup):
# test 1 chunk, 1-d
raw = np.random.rand(20)
a = tensor(raw, chunk_size=20)
raw2 = raw.copy()
raw2[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=20)
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
r = quantile(a,
q,
interpolation=interpolation,
keepdims=keepdims)
result = r.execute().fetch()
expected = np.quantile(raw,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
r2 = quantile(a2,
q,
interpolation=interpolation,
keepdims=keepdims)
result = r2.execute().fetch()
expected = np.quantile(raw2,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
# test 1 chunk, 2-d
raw = np.random.rand(20, 10)
a = tensor(raw, chunk_size=20)
raw2 = raw.copy()
raw2.flat[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=20)
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
for axis in [None, 0, 1]:
r = quantile(a,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = r.execute().fetch()
expected = np.quantile(raw,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
r2 = quantile(a2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = r2.execute().fetch()
expected = np.quantile(raw2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
# test multi chunks, 1-d
raw = np.random.rand(20)
a = tensor(raw, chunk_size=6)
raw2 = raw.copy()
raw2[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=20)
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
r = quantile(a,
q,
interpolation=interpolation,
keepdims=keepdims)
result = r.execute().fetch()
expected = np.quantile(raw,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
r2 = quantile(a2,
q,
interpolation=interpolation,
keepdims=keepdims)
result = r2.execute().fetch()
expected = np.quantile(raw2,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
# test multi chunk, 2-d
raw = np.random.rand(20, 10)
a = tensor(raw, chunk_size=(12, 6))
raw2 = raw.copy()
raw2.flat[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=(12, 6))
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
for axis in [None, 0, 1]:
r = quantile(a,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = r.execute().fetch()
expected = np.quantile(raw,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
r2 = quantile(a2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = r2.execute().fetch()
expected = np.quantile(raw2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
# test out, 1 chunk
raw = np.random.rand(20)
q = np.random.rand(11)
a = tensor(raw, chunk_size=20)
out = empty((5, 11))
quantile(a, q, out=out)
result = out.execute().fetch()
expected = np.quantile(raw, q, out=np.empty((5, 11)))
np.testing.assert_array_equal(result, expected)
# test out, multi chunks
raw = np.random.rand(20)
q = np.random.rand(11)
a = tensor(raw, chunk_size=6)
out = empty((5, 11))
quantile(a, q, out=out)
result = out.execute().fetch()
expected = np.quantile(raw, q, out=np.empty((5, 11)))
np.testing.assert_almost_equal(result, expected)
# test q which is a tensor
q_raw = np.random.RandomState(0).rand(5)
q = tensor(q_raw, chunk_size=6)
r = quantile(a, q, axis=None)
result = r.execute().fetch()
expected = np.quantile(raw, q_raw, axis=None)
np.testing.assert_almost_equal(result, expected)
with pytest.raises(ValueError):
q[0] = 1.1
r = quantile(a, q, axis=None)
_ = r.execute()
def testQuantileExecution(self):
# test 1 chunk, 1-d
raw = np.random.rand(20)
a = tensor(raw, chunk_size=20)
raw2 = raw.copy()
raw2[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=20)
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
r = quantile(a,
q,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r, concat=True)[0]
expected = np.quantile(raw,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
r2 = quantile(a2,
q,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r2, concat=True)[0]
expected = np.quantile(raw2,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
# test 1 chunk, 2-d
raw = np.random.rand(20, 10)
a = tensor(raw, chunk_size=20)
raw2 = raw.copy()
raw2.flat[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=20)
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
for axis in [None, 0, 1]:
r = quantile(a,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r,
concat=True)[0]
expected = np.quantile(raw,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
r2 = quantile(a2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r2,
concat=True)[0]
expected = np.quantile(raw2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_array_equal(result, expected)
# test multi chunks, 1-d
raw = np.random.rand(20)
a = tensor(raw, chunk_size=3)
raw2 = raw.copy()
raw2[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=20)
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
r = quantile(a,
q,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r, concat=True)[0]
expected = np.quantile(raw,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
r2 = quantile(a2,
q,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r2, concat=True)[0]
expected = np.quantile(raw2,
q,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
# test multi chunk, 2-d
raw = np.random.rand(20, 10)
a = tensor(raw, chunk_size=(12, 6))
raw2 = raw.copy()
raw2.flat[np.random.RandomState(0).randint(raw.size, size=3)] = np.nan
a2 = tensor(raw2, chunk_size=(12, 6))
for q in [
np.random.RandomState(0).rand(),
np.random.RandomState(0).rand(5)
]:
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
for axis in [None, 0, 1]:
r = quantile(a,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r,
concat=True)[0]
expected = np.quantile(raw,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
r2 = quantile(a2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
result = self.executor.execute_tensor(r2,
concat=True)[0]
expected = np.quantile(raw2,
q,
axis=axis,
interpolation=interpolation,
keepdims=keepdims)
np.testing.assert_almost_equal(result, expected)
# test out, 1 chunk
raw = np.random.rand(20)
q = np.random.rand(11)
a = tensor(raw, chunk_size=20)
out = empty((5, 11))
quantile(a, q, out=out)
result = self.executor.execute_tensor(out, concat=True)[0]
expected = np.quantile(raw, q, out=np.empty((5, 11)))
np.testing.assert_array_equal(result, expected)
# test out, multi chunks
raw = np.random.rand(20)
q = np.random.rand(11)
a = tensor(raw, chunk_size=3)
out = empty((5, 11))
quantile(a, q, out=out)
result = self.executor.execute_tensor(out, concat=True)[0]
expected = np.quantile(raw, q, out=np.empty((5, 11)))
np.testing.assert_almost_equal(result, expected)
# test q which is a tensor
q_raw = np.random.RandomState(0).rand(5)
q = tensor(q_raw, chunk_size=3)
ctx, executor = self._create_test_context(self.executor)
with ctx:
r = quantile(a, q, axis=None)
result = executor.execute_tensors([r])[0]
expected = np.quantile(raw, q_raw, axis=None)
np.testing.assert_almost_equal(result, expected)
with self.assertRaises(ValueError):
q[0] = 1.1
r = quantile(a, q, axis=None)
_ = executor.execute_tensors(r)[0]
def test_quantile():
raw = np.random.rand(100)
q = np.random.rand(10)
for dtype in [np.float32, np.int64, np.complex128]:
raw2 = raw.astype(dtype)
a = tensor(raw2, chunk_size=100)
b = quantile(a, q, overwrite_input=True)
assert b.shape == (10,)
assert b.dtype == np.quantile(raw2, q).dtype
b = tile(b)
assert len(b.chunks) == 1
raw = np.random.rand(20, 10)
q = np.random.rand(10)
for dtype in [np.float32, np.int64, np.complex128]:
for axis in (None, 0, 1, [0, 1]):
for interpolation in INTERPOLATION_TYPES:
for keepdims in [True, False]:
raw2 = raw.astype(dtype)
a = tensor(raw2, chunk_size=(8, 6))
b = quantile(a, q, axis=axis,
interpolation=interpolation, keepdims=keepdims)
expected = np.quantile(raw2, q, axis=axis,
interpolation=interpolation,
keepdims=keepdims)
assert b.shape == expected.shape
assert b.dtype == expected.dtype
a = tensor(raw, chunk_size=10)
b = quantile(a, q)
b = tile(b)
assert b.shape == (10,)
b = quantile(a, 0.3)
assert b.ndim == 0
raw2 = np.random.rand(3, 4, 5)
a2 = tensor(raw2, chunk_size=3)
b2 = quantile(a2, q, axis=(0, 2))
expected = np.quantile(raw2, q, axis=(0, 2))
assert b2.shape == expected.shape
b2 = tile(b2)
assert b2.shape == expected.shape
# q has to be 1-d
with pytest.raises(ValueError):
quantile(a, q.reshape(5, 2))
# wrong out type
with pytest.raises(TypeError):
quantile(a, q, out=2)
# wrong q
with pytest.raises(ValueError):
q2 = q.copy()
q2[0] = 1.1
quantile(a, q2)
# wrong q, with size < 10
with pytest.raises(ValueError):
q2 = np.random.rand(5)
q2[0] = 1.1
quantile(a, q2)
# wrong interpolation
with pytest.raises(ValueError):
quantile(a, q, interpolation='unknown')