def test_two_dim_odd_input_axis_0(self):
x = np.array([[-1., 50., -3.5, 2., -1], [0., 0., 3., 2., 4.]]).T
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation, axis=0)
with self.cached_session():
# Get dim 1 with negative and positive indices.
pct_neg_index = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
pct_pos_index = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
self.assertAllEqual((2,), pct_neg_index.get_shape())
self.assertAllEqual((2,), pct_pos_index.get_shape())
self.assertAllClose(expected_percentile, pct_neg_index.eval())
self.assertAllClose(expected_percentile, pct_pos_index.eval())
def test_two_dim_odd_input_axis_0(self):
x = np.array([[-1., 50., -3.5, 2., -1], [0., 0., 3., 2., 4.]]).T
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation, axis=0)
with self.cached_session():
# Get dim 1 with negative and positive indices.
pct_neg_index = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
pct_pos_index = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
self.assertAllEqual((2, ), pct_neg_index.get_shape())
self.assertAllEqual((2, ), pct_pos_index.get_shape())
self.assertAllClose(expected_percentile, pct_neg_index.eval())
self.assertAllClose(expected_percentile, pct_pos_index.eval())
def test_vector_q_raises_dynamic(self):
x = [1., 5., 3., 2., 4.]
q_ph = array_ops.placeholder(dtypes.float32)
pct = sample_stats.percentile(x, q=q_ph, validate_args=True)
with self.cached_session():
with self.assertRaisesOpError("rank"):
pct.eval(feed_dict={q_ph: [0.5]})
def test_vector_q_raises_dynamic(self):
x = [1., 5., 3., 2., 4.]
q_ph = array_ops.placeholder(dtypes.float32)
pct = sample_stats.percentile(x, q=q_ph, validate_args=True)
with self.cached_session():
with self.assertRaisesOpError("rank"):
pct.eval(feed_dict={q_ph: [0.5]})
def test_finds_max_of_long_array(self):
# d - 1 == d in float32 and d = 3e7.
# So this test only passes if we use double for the percentile indices.
# If float is used, it fails with InvalidArgumentError about an index out of
# bounds.
x = math_ops.linspace(0., 3e7, num=int(3e7))
with self.cached_session():
minval = sample_stats.percentile(x, q=0, validate_args=True)
self.assertAllEqual(0, minval.eval())
def test_finds_max_of_long_array(self):
# d - 1 == d in float32 and d = 3e7.
# So this test only passes if we use double for the percentile indices.
# If float is used, it fails with InvalidArgumentError about an index out of
# bounds.
x = math_ops.linspace(0., 3e7, num=int(3e7))
with self.cached_session():
minval = sample_stats.percentile(x, q=0, validate_args=True)
self.assertAllEqual(0, minval.eval())
def test_one_dim_even_input(self):
x = [1., 5., 3., 2., 4., 5.]
for q in [0, 10.1, 25.1, 49.9, 50.1, 50.01, 89, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation)
with self.cached_session():
pct = sample_stats.percentile(x, q=q, interpolation=self._interpolation)
self.assertAllEqual((), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_one_dim_even_input(self):
x = [1., 5., 3., 2., 4., 5.]
for q in [0, 10.1, 25.1, 49.9, 50.1, 50.01, 89, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation)
with self.test_session():
pct = sample_stats.percentile(x, q=q, interpolation=self._interpolation)
self.assertAllEqual((), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_with_integer_dtype(self):
x = [1, 5, 3, 2, 4]
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation)
with self.cached_session():
pct = sample_stats.percentile(x, q=q, interpolation=self._interpolation)
self.assertEqual(dtypes.int32, pct.dtype)
self.assertAllEqual((), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_one_dim_odd_input(self):
x = [1., 5., 3., 2., 4.]
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation, axis=0)
with self.cached_session():
pct = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
self.assertAllEqual((), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_with_integer_dtype(self):
x = [1, 5, 3, 2, 4]
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation)
with self.test_session():
pct = sample_stats.percentile(x, q=q, interpolation=self._interpolation)
self.assertEqual(dtypes.int32, pct.dtype)
self.assertAllEqual((), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_one_dim_odd_input(self):
x = [1., 5., 3., 2., 4.]
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation, axis=0)
with self.cached_session():
pct = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
self.assertAllEqual((), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_four_dimensional_input(self):
x = rng.rand(2, 3, 4, 5)
for axis in [None, 0, 1, -2, (0, ), (-1, ), (-1, 1), (3, 1), (-3, 0)]:
expected_percentile = np.percentile(
x, q=0.77, interpolation=self._interpolation, axis=axis)
with self.cached_session():
pct = sample_stats.percentile(
x, q=0.77, interpolation=self._interpolation, axis=axis)
self.assertAllEqual(expected_percentile.shape, pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_two_dim_even_axis_0(self):
x = np.array([[1., 2., 4., 50.], [1., 2., -4., 5.]]).T
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation, axis=0)
with self.cached_session():
pct = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
self.assertAllEqual((2, ), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_two_dim_even_axis_0(self):
x = np.array([[1., 2., 4., 50.], [1., 2., -4., 5.]]).T
for q in [0, 10, 25, 49.9, 50, 50.01, 90, 95, 100]:
expected_percentile = np.percentile(
x, q=q, interpolation=self._interpolation, axis=0)
with self.cached_session():
pct = sample_stats.percentile(
x, q=q, interpolation=self._interpolation, axis=[0])
self.assertAllEqual((2,), pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_four_dimensional_input_x_static_ndims_but_dynamic_sizes(self):
x = rng.rand(2, 3, 4, 5)
x_ph = array_ops.placeholder(dtypes.float64,
shape=[None, None, None, None])
for axis in [None, 0, 1, -2, (0, ), (-1, ), (-1, 1), (3, 1), (-3, 0)]:
expected_percentile = np.percentile(
x, q=0.77, interpolation=self._interpolation, axis=axis)
with self.cached_session():
pct = sample_stats.percentile(
x_ph, q=0.77, interpolation=self._interpolation, axis=axis)
self.assertAllClose(expected_percentile,
pct.eval(feed_dict={x_ph: x}))
def test_four_dimensional_input(self):
x = rng.rand(2, 3, 4, 5)
for axis in [None, 0, 1, -2, (0,), (-1,), (-1, 1), (3, 1), (-3, 0)]:
expected_percentile = np.percentile(
x, q=0.77, interpolation=self._interpolation, axis=axis)
with self.cached_session():
pct = sample_stats.percentile(
x,
q=0.77,
interpolation=self._interpolation,
axis=axis)
self.assertAllEqual(expected_percentile.shape, pct.get_shape())
self.assertAllClose(expected_percentile, pct.eval())
def test_four_dimensional_input_x_static_ndims_but_dynamic_sizes(self):
x = rng.rand(2, 3, 4, 5)
x_ph = array_ops.placeholder(dtypes.float64, shape=[None, None, None, None])
for axis in [None, 0, 1, -2, (0,), (-1,), (-1, 1), (3, 1), (-3, 0)]:
expected_percentile = np.percentile(
x, q=0.77, interpolation=self._interpolation, axis=axis)
with self.cached_session():
pct = sample_stats.percentile(
x_ph,
q=0.77,
interpolation=self._interpolation,
axis=axis)
self.assertAllClose(expected_percentile, pct.eval(feed_dict={x_ph: x}))
def test_vector_q_raises_static(self):
x = [1., 5., 3., 2., 4.]
with self.assertRaisesRegexp(ValueError, "Expected.*ndims"):
sample_stats.percentile(x, q=[0.5])
def test_invalid_interpolation_raises(self):
x = [1., 5., 3., 2., 4.]
with self.assertRaisesRegexp(ValueError, "interpolation"):
sample_stats.percentile(x, q=0.5, interpolation="bad")
def test_invalid_interpolation_raises(self):
x = [1., 5., 3., 2., 4.]
with self.assertRaisesRegexp(ValueError, "interpolation"):
sample_stats.percentile(x, q=0.5, interpolation="bad")
def test_vector_q_raises_static(self):
x = [1., 5., 3., 2., 4.]
with self.assertRaisesRegexp(ValueError, "Expected.*ndims"):
sample_stats.percentile(x, q=[0.5])
