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test_quantile.py
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test_quantile.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle
class TestQuantile(unittest.TestCase):
"""
This class is used for numerical precision testing. If there is
a corresponding numpy API, the precision comparison can be performed directly.
Otherwise, it needs to be verified by numpy implementated function.
"""
def setUp(self):
np.random.seed(678)
self.input_data = np.random.rand(6, 7, 8, 9, 10)
# Test correctness when q and axis are set.
def test_quantile_single_q(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=0.5, axis=2)
np_res = np.quantile(self.input_data, q=0.5, axis=2)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
# Test correctness for default axis.
def test_quantile_with_no_axis(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=0.35)
np_res = np.quantile(self.input_data, q=0.35)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
# Test correctness for multiple axis.
def test_quantile_with_multi_axis(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=0.75, axis=[0, 2, 3])
np_res = np.quantile(self.input_data, q=0.75, axis=[0, 2, 3])
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
# Test correctness when keepdim is set.
def test_quantile_with_keepdim(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=0.35, axis=4, keepdim=True)
np_res = np.quantile(self.input_data, q=0.35, axis=4, keepdims=True)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
# Test correctness when all parameters are set.
def test_quantile_with_keepdim_and_multiple_axis(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=0.1, axis=[1, 4], keepdim=True)
np_res = np.quantile(self.input_data, q=0.1, axis=[1, 4], keepdims=True)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
# Test correctness when q = 0.
def test_quantile_with_boundary_q(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=0, axis=3)
np_res = np.quantile(self.input_data, q=0, axis=3)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
# Test correctness when input includes NaN.
def test_quantile_include_NaN(self):
input_data = np.random.randn(2, 3, 4)
input_data[0, 1, 1] = np.nan
x = paddle.to_tensor(input_data)
paddle_res = paddle.quantile(x, q=0.35, axis=0)
self.assertTrue(paddle.isnan(paddle_res[1, 1]))
class TestQuantileMuitlpleQ(unittest.TestCase):
"""
This class is used to test multiple input of q.
"""
def setUp(self):
np.random.seed(678)
self.input_data = np.random.rand(10, 3, 4, 5, 4)
def test_quantile(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=[0.3, 0.44], axis=-2)
np_res = np.quantile(self.input_data, q=[0.3, 0.44], axis=-2)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
def test_quantile_multiple_axis(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(x, q=[0.2, 0.67], axis=[1, -1])
np_res = np.quantile(self.input_data, q=[0.2, 0.67], axis=[1, -1])
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
def test_quantile_multiple_axis_keepdim(self):
x = paddle.to_tensor(self.input_data)
paddle_res = paddle.quantile(
x, q=[0.1, 0.2, 0.3], axis=[1, 2], keepdim=True)
np_res = np.quantile(
self.input_data, q=[0.1, 0.2, 0.3], axis=[1, 2], keepdims=True)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
class TestQuantileError(unittest.TestCase):
"""
This class is used to test that exceptions are thrown correctly.
Validity of all parameter values and types should be considered.
"""
def setUp(self):
self.x = paddle.randn((2, 3, 4))
def test_errors(self):
# Test error when q > 1
def test_q_range_error_1():
paddle_res = paddle.quantile(self.x, q=1.5)
self.assertRaises(ValueError, test_q_range_error_1)
# Test error when q < 0
def test_q_range_error_2():
paddle_res = paddle.quantile(self.x, q=[0.2, -0.3])
self.assertRaises(ValueError, test_q_range_error_2)
# Test error with no valid q
def test_q_range_error_3():
paddle_res = paddle.quantile(self.x, q=[])
self.assertRaises(ValueError, test_q_range_error_3)
# Test error when x is not Tensor
def test_x_type_error():
x = [1, 3, 4]
paddle_res = paddle.quantile(x, q=0.9)
self.assertRaises(TypeError, test_x_type_error)
# Test error when scalar axis is not int
def test_axis_type_error_1():
paddle_res = paddle.quantile(self.x, q=0.4, axis=0.4)
self.assertRaises(ValueError, test_axis_type_error_1)
# Test error when axis in List is not int
def test_axis_type_error_2():
paddle_res = paddle.quantile(self.x, q=0.4, axis=[1, 0.4])
self.assertRaises(ValueError, test_axis_type_error_2)
# Test error when axis not in [-D, D)
def test_axis_value_error_1():
paddle_res = paddle.quantile(self.x, q=0.4, axis=10)
self.assertRaises(ValueError, test_axis_value_error_1)
# Test error when axis not in [-D, D)
def test_axis_value_error_2():
paddle_res = paddle.quantile(self.x, q=0.4, axis=[1, -10])
self.assertRaises(ValueError, test_axis_value_error_2)
# Test error with no valid axis
def test_axis_value_error_3():
paddle_res = paddle.quantile(self.x, q=0.4, axis=[])
self.assertRaises(ValueError, test_axis_value_error_3)
class TestQuantileRuntime(unittest.TestCase):
"""
This class is used to test the API could run correctly with
different devices, different data types, and dygraph/static mode.
"""
def setUp(self):
np.random.seed(678)
self.input_data = np.random.rand(6, 7, 8, 9, 10)
self.dtypes = ['float32', 'float64']
self.devices = ['cpu']
if paddle.device.is_compiled_with_cuda():
self.devices.append('gpu')
def test_dygraph(self):
paddle.disable_static()
for device in self.devices:
# Check different devices
paddle.set_device(device)
for dtype in self.dtypes:
# Check different dtypes
np_input_data = self.input_data.astype(dtype)
x = paddle.to_tensor(np_input_data, dtype=dtype)
paddle_res = paddle.quantile(x, q=0.5, axis=2)
np_res = np.quantile(np_input_data, q=0.5, axis=2)
self.assertTrue(np.allclose(paddle_res.numpy(), np_res))
def test_static(self):
paddle.enable_static()
for device in self.devices:
x = paddle.static.data(
name="x", shape=self.input_data.shape, dtype=paddle.float32)
x_fp64 = paddle.static.data(
name="x_fp64",
shape=self.input_data.shape,
dtype=paddle.float64)
results = paddle.quantile(x, q=0.5, axis=2)
np_input_data = self.input_data.astype('float32')
results_fp64 = paddle.quantile(x_fp64, q=0.5, axis=2)
np_input_data_fp64 = self.input_data.astype('float64')
exe = paddle.static.Executor(device)
paddle_res, paddle_res_fp64 = exe.run(
paddle.static.default_main_program(),
feed={"x": np_input_data,
"x_fp64": np_input_data_fp64},
fetch_list=[results, results_fp64])
np_res = np.quantile(np_input_data, q=0.5, axis=2)
np_res_fp64 = np.quantile(np_input_data_fp64, q=0.5, axis=2)
self.assertTrue(
np.allclose(paddle_res, np_res) and np.allclose(paddle_res_fp64,
np_res_fp64))
if __name__ == '__main__':
unittest.main()