309 lines
7.6 KiB
Python
309 lines
7.6 KiB
Python
# Copyright 2019 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""test cases for cat distribution"""
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import numpy as np
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import pytest
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from scipy import stats
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.distribution as msd
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from mindspore import Tensor
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from mindspore import dtype
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Prob(nn.Cell):
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"""
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Test class: probability of categorical distribution.
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"""
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def __init__(self):
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super(Prob, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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return self.c.prob(x_)
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def test_pmf():
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"""
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Test pmf.
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"""
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expect_pmf = [0.7, 0.3, 0.7, 0.3, 0.3]
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pmf = Prob()
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x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(
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np.int32), dtype=dtype.float32)
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output = pmf(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_pmf) < tol).all()
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class LogProb(nn.Cell):
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"""
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Test class: log probability of categorical distribution.
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"""
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def __init__(self):
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super(LogProb, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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return self.c.log_prob(x_)
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def test_log_likelihood():
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"""
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Test log_pmf.
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"""
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expect_logpmf = np.log([0.7, 0.3, 0.7, 0.3, 0.3])
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logprob = LogProb()
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x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(
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np.int32), dtype=dtype.float32)
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output = logprob(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_logpmf) < tol).all()
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class KL(nn.Cell):
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"""
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Test class: kl_loss between categorical distributions.
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"""
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def __init__(self):
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super(KL, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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return self.c.kl_loss('Categorical', x_)
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def test_kl_loss():
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"""
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Test kl_loss.
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"""
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kl_loss = KL()
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output = kl_loss(Tensor([0.7, 0.3], dtype=dtype.float32))
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tol = 1e-6
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assert (np.abs(output.asnumpy()) < tol).all()
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class Sampling(nn.Cell):
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"""
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Test class: sampling of categorical distribution.
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"""
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def __init__(self):
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super(Sampling, self).__init__()
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self.c = msd.Categorical([0.2, 0.1, 0.7], dtype=dtype.int32)
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self.shape = (2, 3)
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def construct(self):
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return self.c.sample(self.shape)
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def test_sample():
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"""
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Test sample.
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"""
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with pytest.raises(NotImplementedError):
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sample = Sampling()
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sample()
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class Basics(nn.Cell):
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"""
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Test class: mean/var/mode of categorical distribution.
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"""
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def __init__(self):
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super(Basics, self).__init__()
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self.c = msd.Categorical([0.2, 0.1, 0.7], dtype=dtype.int32)
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def construct(self):
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return self.c.mean(), self.c.var(), self.c.mode()
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def test_basics():
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"""
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Test mean/variance/mode.
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"""
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basics = Basics()
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mean, var, mode = basics()
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expect_mean = 0 * 0.2 + 1 * 0.1 + 2 * 0.7
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expect_var = 0 * 0.2 + 1 * 0.1 + 4 * 0.7 - (expect_mean * expect_mean)
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expect_mode = 2
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tol = 1e-6
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assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
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assert (np.abs(var.asnumpy() - expect_var) < tol).all()
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assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
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class CDF(nn.Cell):
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"""
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Test class: cdf of categorical distributions.
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"""
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def __init__(self):
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super(CDF, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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return self.c.cdf(x_)
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def test_cdf():
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"""
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Test cdf.
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"""
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expect_cdf = [0.7, 0.7, 1, 0.7, 1]
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x_ = Tensor(np.array([0, 0, 1, 0, 1]).astype(
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np.int32), dtype=dtype.float32)
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cdf = CDF()
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output = cdf(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
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class LogCDF(nn.Cell):
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"""
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Test class: log cdf of categorical distributions.
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"""
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def __init__(self):
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super(LogCDF, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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return self.c.log_cdf(x_)
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def test_logcdf():
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"""
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Test log_cdf.
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"""
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expect_logcdf = np.log([0.7, 0.7, 1, 0.7, 1])
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x_ = Tensor(np.array([0, 0, 1, 0, 1]).astype(
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np.int32), dtype=dtype.float32)
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logcdf = LogCDF()
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output = logcdf(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
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class SF(nn.Cell):
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"""
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Test class: survival function of categorical distributions.
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"""
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def __init__(self):
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super(SF, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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return self.c.survival_function(x_)
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def test_survival():
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"""
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Test survival function.
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"""
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expect_survival = [0.3, 0., 0., 0.3, 0.3]
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x_ = Tensor(np.array([0, 1, 1, 0, 0]).astype(
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np.int32), dtype=dtype.float32)
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sf = SF()
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output = sf(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
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class LogSF(nn.Cell):
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"""
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Test class: log survival function of categorical distributions.
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"""
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def __init__(self):
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super(LogSF, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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return self.c.log_survival(x_)
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def test_log_survival():
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"""
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Test log survival function.
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"""
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expect_logsurvival = np.log([1., 0.3, 0.3, 0.3, 0.3])
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x_ = Tensor(np.array([-2, 0, 0, 0.5, 0.5]
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).astype(np.float32), dtype=dtype.float32)
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log_sf = LogSF()
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output = log_sf(x_)
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()
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class EntropyH(nn.Cell):
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"""
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Test class: entropy of categorical distributions.
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"""
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def __init__(self):
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super(EntropyH, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self):
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return self.c.entropy()
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def test_entropy():
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"""
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Test entropy.
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"""
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cat_benchmark = stats.multinomial(n=1, p=[0.7, 0.3])
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expect_entropy = cat_benchmark.entropy().astype(np.float32)
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entropy = EntropyH()
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output = entropy()
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
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class CrossEntropy(nn.Cell):
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"""
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Test class: cross entropy between categorical distributions.
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"""
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def __init__(self):
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super(CrossEntropy, self).__init__()
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self.c = msd.Categorical([0.7, 0.3], dtype=dtype.int32)
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def construct(self, x_):
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entropy = self.c.entropy()
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kl_loss = self.c.kl_loss('Categorical', x_)
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h_sum_kl = entropy + kl_loss
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cross_entropy = self.c.cross_entropy('Categorical', x_)
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return h_sum_kl - cross_entropy
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def test_cross_entropy():
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"""
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Test cross_entropy.
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"""
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cross_entropy = CrossEntropy()
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prob = Tensor([0.7, 0.3], dtype=dtype.float32)
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diff = cross_entropy(prob)
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tol = 1e-6
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assert (np.abs(diff.asnumpy()) < tol).all()
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