| """ |
| Note [Randomized statistical tests] |
| ----------------------------------- |
| |
| This note describes how to maintain tests in this file as random sources |
| change. This file contains two types of randomized tests: |
| |
| 1. The easier type of randomized test are tests that should always pass but are |
| initialized with random data. If these fail something is wrong, but it's |
| fine to use a fixed seed by inheriting from common.TestCase. |
| |
| 2. The trickier tests are statistical tests. These tests explicitly call |
| set_rng_seed(n) and are marked "see Note [Randomized statistical tests]". |
| These statistical tests have a known positive failure rate |
| (we set failure_rate=1e-3 by default). We need to balance strength of these |
| tests with annoyance of false alarms. One way that works is to specifically |
| set seeds in each of the randomized tests. When a random generator |
| occasionally changes (as in #4312 vectorizing the Box-Muller sampler), some |
| of these statistical tests may (rarely) fail. If one fails in this case, |
| it's fine to increment the seed of the failing test (but you shouldn't need |
| to increment it more than once; otherwise something is probably actually |
| wrong). |
| """ |
| |
| import math |
| import unittest |
| from collections import namedtuple |
| from itertools import product |
| |
| import torch |
| from common import TestCase, run_tests, set_rng_seed |
| from torch.autograd import Variable, grad, gradcheck |
| from torch.distributions import (Bernoulli, Beta, Categorical, Cauchy, Chi2, |
| Dirichlet, Exponential, Gamma, Gumbel, |
| Laplace, Normal, OneHotCategorical, Pareto, |
| StudentT, Uniform, kl_divergence) |
| from torch.distributions.dirichlet import _Dirichlet_backward |
| from torch.distributions.constraints import Constraint, is_dependent |
| from torch.distributions.utils import _finfo |
| |
| TEST_NUMPY = True |
| try: |
| import numpy as np |
| import scipy.stats |
| import scipy.special |
| except ImportError: |
| TEST_NUMPY = False |
| |
| |
| # Register all distributions for generic tests. |
| Example = namedtuple('Example', ['Dist', 'params']) |
| EXAMPLES = [ |
| Example(Bernoulli, [ |
| {'probs': Variable(torch.Tensor([0.7, 0.2, 0.4]), requires_grad=True)}, |
| {'probs': Variable(torch.Tensor([0.3]), requires_grad=True)}, |
| {'probs': 0.3}, |
| ]), |
| Example(Beta, [ |
| { |
| 'alpha': Variable(torch.exp(torch.randn(2, 3)), requires_grad=True), |
| 'beta': Variable(torch.exp(torch.randn(2, 3)), requires_grad=True), |
| }, |
| { |
| 'alpha': Variable(torch.exp(torch.randn(4)), requires_grad=True), |
| 'beta': Variable(torch.exp(torch.randn(4)), requires_grad=True), |
| }, |
| ]), |
| Example(Categorical, [ |
| {'probs': Variable(torch.Tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]]), requires_grad=True)}, |
| {'probs': Variable(torch.Tensor([[1.0, 0.0], [0.0, 1.0]]), requires_grad=True)}, |
| ]), |
| Example(Cauchy, [ |
| {'loc': 0.0, 'scale': 1.0}, |
| {'loc': Variable(torch.Tensor([0.0])), 'scale': 1.0}, |
| {'loc': Variable(torch.Tensor([[0.0], [0.0]])), |
| 'scale': Variable(torch.Tensor([[1.0], [1.0]]))} |
| ]), |
| Example(Chi2, [ |
| {'df': Variable(torch.exp(torch.randn(2, 3)), requires_grad=True)}, |
| {'df': Variable(torch.exp(torch.randn(1)), requires_grad=True)}, |
| ]), |
| Example(StudentT, [ |
| {'df': Variable(torch.exp(torch.randn(2, 3)), requires_grad=True)}, |
| {'df': Variable(torch.exp(torch.randn(1)), requires_grad=True)}, |
| ]), |
| Example(Dirichlet, [ |
| {'alpha': Variable(torch.exp(torch.randn(2, 3)), requires_grad=True)}, |
| {'alpha': Variable(torch.exp(torch.randn(4)), requires_grad=True)}, |
| ]), |
| Example(Exponential, [ |
| {'rate': Variable(torch.randn(5, 5).abs(), requires_grad=True)}, |
| {'rate': Variable(torch.randn(1).abs(), requires_grad=True)}, |
| ]), |
| Example(Gamma, [ |
| { |
| 'alpha': Variable(torch.exp(torch.randn(2, 3)), requires_grad=True), |
| 'beta': Variable(torch.exp(torch.randn(2, 3)), requires_grad=True), |
| }, |
| { |
| 'alpha': Variable(torch.exp(torch.randn(1)), requires_grad=True), |
| 'beta': Variable(torch.exp(torch.randn(1)), requires_grad=True), |
| }, |
| ]), |
| Example(Gumbel, [ |
| { |
| 'loc': Variable(torch.randn(5, 5), requires_grad=True), |
| 'scale': Variable(torch.randn(5, 5).abs(), requires_grad=True), |
| }, |
| { |
| 'loc': Variable(torch.randn(1), requires_grad=True), |
| 'scale': Variable(torch.randn(1).abs(), requires_grad=True), |
| }, |
| ]), |
| Example(Laplace, [ |
| { |
| 'loc': Variable(torch.randn(5, 5), requires_grad=True), |
| 'scale': Variable(torch.randn(5, 5).abs(), requires_grad=True), |
| }, |
| { |
| 'loc': Variable(torch.randn(1), requires_grad=True), |
| 'scale': Variable(torch.randn(1).abs(), requires_grad=True), |
| }, |
| { |
| 'loc': torch.Tensor([1.0, 0.0]), |
| 'scale': torch.Tensor([1e-5, 1e-5]), |
| }, |
| ]), |
| Example(Normal, [ |
| { |
| 'mean': Variable(torch.randn(5, 5), requires_grad=True), |
| 'std': Variable(torch.randn(5, 5).abs(), requires_grad=True), |
| }, |
| { |
| 'mean': Variable(torch.randn(1), requires_grad=True), |
| 'std': Variable(torch.randn(1).abs(), requires_grad=True), |
| }, |
| { |
| 'mean': torch.Tensor([1.0, 0.0]), |
| 'std': torch.Tensor([1e-5, 1e-5]), |
| }, |
| ]), |
| Example(OneHotCategorical, [ |
| {'probs': Variable(torch.Tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]]), requires_grad=True)}, |
| {'probs': Variable(torch.Tensor([[1.0, 0.0], [0.0, 1.0]]), requires_grad=True)}, |
| ]), |
| Example(Pareto, [ |
| { |
| 'scale': 1.0, |
| 'alpha': 1.0 |
| }, |
| { |
| 'scale': Variable(torch.randn(5, 5).abs(), requires_grad=True), |
| 'alpha': Variable(torch.randn(5, 5).abs(), requires_grad=True) |
| }, |
| { |
| 'scale': torch.Tensor([1.0]), |
| 'alpha': 1.0 |
| } |
| ]), |
| Example(Uniform, [ |
| { |
| 'low': Variable(torch.zeros(5, 5), requires_grad=True), |
| 'high': Variable(torch.ones(5, 5), requires_grad=True), |
| }, |
| { |
| 'low': Variable(torch.zeros(1), requires_grad=True), |
| 'high': Variable(torch.ones(1), requires_grad=True), |
| }, |
| { |
| 'low': torch.Tensor([1.0, 1.0]), |
| 'high': torch.Tensor([2.0, 3.0]), |
| }, |
| ]), |
| ] |
| |
| |
| def unwrap(value): |
| if isinstance(value, Variable): |
| return value.data |
| return value |
| |
| |
| class TestDistributions(TestCase): |
| def _gradcheck_log_prob(self, dist_ctor, ctor_params): |
| # performs gradient checks on log_prob |
| distribution = dist_ctor(*ctor_params) |
| s = distribution.sample() |
| |
| expected_shape = distribution.batch_shape + distribution.event_shape |
| if not expected_shape: |
| expected_shape = torch.Size((1,)) # Work around lack of scalars. |
| self.assertEqual(s.size(), expected_shape) |
| |
| def apply_fn(*params): |
| return dist_ctor(*params).log_prob(s) |
| |
| gradcheck(apply_fn, ctor_params, raise_exception=True) |
| |
| def _check_log_prob(self, dist, asset_fn): |
| # checks that the log_prob matches a reference function |
| s = dist.sample() |
| log_probs = dist.log_prob(s) |
| for i, (val, log_prob) in enumerate(zip(s.data.view(-1), log_probs.data.view(-1))): |
| asset_fn(i, val, log_prob) |
| |
| def _check_sampler_sampler(self, torch_dist, ref_dist, message, multivariate=False, |
| num_samples=10000, failure_rate=1e-3): |
| # Checks that the .sample() method matches a reference function. |
| torch_samples = torch_dist.sample_n(num_samples).squeeze() |
| if isinstance(torch_samples, Variable): |
| torch_samples = torch_samples.data |
| torch_samples = torch_samples.cpu().numpy() |
| ref_samples = ref_dist.rvs(num_samples) |
| if multivariate: |
| # Project onto a random axis. |
| axis = np.random.normal(size=torch_samples.shape[-1]) |
| axis /= np.linalg.norm(axis) |
| torch_samples = np.dot(torch_samples, axis) |
| ref_samples = np.dot(ref_samples, axis) |
| samples = [(x, +1) for x in torch_samples] + [(x, -1) for x in ref_samples] |
| samples.sort() |
| samples = np.array(samples)[:, 1] |
| |
| # Aggragate into bins filled with roughly zero-mean unit-variance RVs. |
| num_bins = 10 |
| samples_per_bin = len(samples) // num_bins |
| bins = samples.reshape((num_bins, samples_per_bin)).mean(axis=1) |
| stddev = samples_per_bin ** -0.5 |
| threshold = stddev * scipy.special.erfinv(1 - 2 * failure_rate / num_bins) |
| message = '{}.sample() is biased:\n{}'.format(message, bins) |
| for bias in bins: |
| self.assertLess(-threshold, bias, message) |
| self.assertLess(bias, threshold, message) |
| |
| def _check_enumerate_support(self, dist, examples): |
| for param, expected in examples: |
| param = torch.Tensor(param) |
| expected = torch.Tensor(expected) |
| actual = dist(param).enumerate_support() |
| self.assertEqual(actual, expected) |
| param = Variable(param) |
| expected = Variable(expected) |
| actual = dist(param).enumerate_support() |
| self.assertEqual(actual, expected) |
| |
| def test_enumerate_support_type(self): |
| for Dist, params in EXAMPLES: |
| for i, param in enumerate(params): |
| dist = Dist(**param) |
| try: |
| self.assertTrue(type(unwrap(dist.sample())) is type(unwrap(dist.enumerate_support())), |
| msg=('{} example {}/{}, return type mismatch between ' + |
| 'sample and enumerate_support.').format(Dist.__name__, i, len(params))) |
| except NotImplementedError: |
| pass |
| |
| def test_bernoulli(self): |
| p = Variable(torch.Tensor([0.7, 0.2, 0.4]), requires_grad=True) |
| r = Variable(torch.Tensor([0.3]), requires_grad=True) |
| s = 0.3 |
| self.assertEqual(Bernoulli(p).sample_n(8).size(), (8, 3)) |
| self.assertTrue(isinstance(Bernoulli(p).sample().data, torch.Tensor)) |
| self.assertEqual(Bernoulli(r).sample_n(8).size(), (8, 1)) |
| self.assertEqual(Bernoulli(r).sample().size(), (1,)) |
| self.assertEqual(Bernoulli(r).sample((3, 2)).size(), (3, 2, 1)) |
| self.assertEqual(Bernoulli(s).sample().size(), (1,)) |
| self._gradcheck_log_prob(Bernoulli, (p,)) |
| |
| def ref_log_prob(idx, val, log_prob): |
| prob = p.data[idx] |
| self.assertEqual(log_prob, math.log(prob if val else 1 - prob)) |
| |
| self._check_log_prob(Bernoulli(p), ref_log_prob) |
| self._check_log_prob(Bernoulli(logits=p.log() - (-p).log1p()), ref_log_prob) |
| self.assertRaises(NotImplementedError, Bernoulli(r).rsample) |
| |
| # check entropy computation |
| self.assertEqual(Bernoulli(p).entropy().data, torch.Tensor([0.6108, 0.5004, 0.6730]), prec=1e-4) |
| self.assertEqual(Bernoulli(torch.Tensor([0.0])).entropy(), torch.Tensor([0.0])) |
| self.assertEqual(Bernoulli(s).entropy(), torch.Tensor([0.6108]), prec=1e-4) |
| |
| def test_bernoulli_enumerate_support(self): |
| examples = [ |
| ([0.1], [[0], [1]]), |
| ([0.1, 0.9], [[0, 0], [1, 1]]), |
| ([[0.1, 0.2], [0.3, 0.4]], [[[0, 0], [0, 0]], [[1, 1], [1, 1]]]), |
| ] |
| self._check_enumerate_support(Bernoulli, examples) |
| |
| def test_bernoulli_3d(self): |
| p = Variable(torch.Tensor(2, 3, 5).fill_(0.5), requires_grad=True) |
| self.assertEqual(Bernoulli(p).sample().size(), (2, 3, 5)) |
| self.assertEqual(Bernoulli(p).sample(sample_shape=(2, 5)).size(), |
| (2, 5, 2, 3, 5)) |
| self.assertEqual(Bernoulli(p).sample_n(2).size(), (2, 2, 3, 5)) |
| |
| def test_categorical_1d(self): |
| p = Variable(torch.Tensor([0.1, 0.2, 0.3]), requires_grad=True) |
| # TODO: this should return a 0-dim tensor once we have Scalar support |
| self.assertEqual(Categorical(p).sample().size(), (1,)) |
| self.assertTrue(isinstance(Categorical(p).sample().data, torch.LongTensor)) |
| self.assertEqual(Categorical(p).sample((2, 2)).size(), (2, 2)) |
| self.assertEqual(Categorical(p).sample_n(1).size(), (1,)) |
| self._gradcheck_log_prob(Categorical, (p,)) |
| self.assertRaises(NotImplementedError, Categorical(p).rsample) |
| |
| def test_categorical_2d(self): |
| probabilities = [[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]] |
| probabilities_1 = [[1.0, 0.0], [0.0, 1.0]] |
| p = Variable(torch.Tensor(probabilities), requires_grad=True) |
| s = Variable(torch.Tensor(probabilities_1), requires_grad=True) |
| self.assertEqual(Categorical(p).sample().size(), (2,)) |
| self.assertEqual(Categorical(p).sample(sample_shape=(3, 4)).size(), (3, 4, 2)) |
| self.assertEqual(Categorical(p).sample_n(6).size(), (6, 2)) |
| self._gradcheck_log_prob(Categorical, (p,)) |
| |
| # sample check for extreme value of probs |
| set_rng_seed(0) |
| self.assertEqual(Categorical(s).sample(sample_shape=(2,)).data, |
| torch.Tensor([[0, 1], [0, 1]])) |
| |
| def ref_log_prob(idx, val, log_prob): |
| sample_prob = p.data[idx][val] / p.data[idx].sum() |
| self.assertEqual(log_prob, math.log(sample_prob)) |
| |
| self._check_log_prob(Categorical(p), ref_log_prob) |
| self._check_log_prob(Categorical(logits=p.log()), ref_log_prob) |
| |
| # check entropy computation |
| self.assertEqual(Categorical(p).entropy().data, torch.Tensor([1.0114, 1.0297]), prec=1e-4) |
| self.assertEqual(Categorical(s).entropy().data, torch.Tensor([0.0, 0.0])) |
| |
| def test_categorical_enumerate_support(self): |
| examples = [ |
| ([0.1, 0.2, 0.7], [0, 1, 2]), |
| ([[0.1, 0.9], [0.3, 0.7]], [[0, 0], [1, 1]]), |
| ] |
| self._check_enumerate_support(Categorical, examples) |
| |
| def test_one_hot_categorical_1d(self): |
| p = Variable(torch.Tensor([0.1, 0.2, 0.3]), requires_grad=True) |
| self.assertEqual(OneHotCategorical(p).sample().size(), (3,)) |
| self.assertTrue(isinstance(OneHotCategorical(p).sample().data, torch.Tensor)) |
| self.assertEqual(OneHotCategorical(p).sample((2, 2)).size(), (2, 2, 3)) |
| self.assertEqual(OneHotCategorical(p).sample_n(1).size(), (1, 3)) |
| self._gradcheck_log_prob(OneHotCategorical, (p,)) |
| self.assertRaises(NotImplementedError, OneHotCategorical(p).rsample) |
| |
| def test_one_hot_categorical_2d(self): |
| probabilities = [[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]] |
| probabilities_1 = [[1.0, 0.0], [0.0, 1.0]] |
| p = Variable(torch.Tensor(probabilities), requires_grad=True) |
| s = Variable(torch.Tensor(probabilities_1), requires_grad=True) |
| self.assertEqual(OneHotCategorical(p).sample().size(), (2, 3)) |
| self.assertEqual(OneHotCategorical(p).sample(sample_shape=(3, 4)).size(), (3, 4, 2, 3)) |
| self.assertEqual(OneHotCategorical(p).sample_n(6).size(), (6, 2, 3)) |
| self._gradcheck_log_prob(OneHotCategorical, (p,)) |
| |
| dist = OneHotCategorical(p) |
| x = dist.sample() |
| self.assertEqual(dist.log_prob(x), Categorical(p).log_prob(x.max(-1)[1])) |
| |
| def test_one_hot_categorical_enumerate_support(self): |
| examples = [ |
| ([0.1, 0.2, 0.7], [[1, 0, 0], [0, 1, 0], [0, 0, 1]]), |
| ([[0.1, 0.9], [0.3, 0.7]], [[[1, 0], [1, 0]], [[0, 1], [0, 1]]]), |
| ] |
| self._check_enumerate_support(OneHotCategorical, examples) |
| |
| def test_uniform(self): |
| low = Variable(torch.zeros(5, 5), requires_grad=True) |
| high = Variable(torch.ones(5, 5) * 3, requires_grad=True) |
| low_1d = Variable(torch.zeros(1), requires_grad=True) |
| high_1d = Variable(torch.ones(1) * 3, requires_grad=True) |
| self.assertEqual(Uniform(low, high).sample().size(), (5, 5)) |
| self.assertEqual(Uniform(low, high).sample_n(7).size(), (7, 5, 5)) |
| self.assertEqual(Uniform(low_1d, high_1d).sample().size(), (1,)) |
| self.assertEqual(Uniform(low_1d, high_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Uniform(0.0, 1.0).sample_n(1).size(), (1,)) |
| |
| # Check log_prob computation when value outside range |
| uniform = Uniform(low_1d, high_1d) |
| above_high = Variable(torch.Tensor([4.0])) |
| below_low = Variable(torch.Tensor([-1.0])) |
| self.assertEqual(uniform.log_prob(above_high).data[0], -float('inf'), allow_inf=True) |
| self.assertEqual(uniform.log_prob(below_low).data[0], -float('inf'), allow_inf=True) |
| |
| set_rng_seed(1) |
| self._gradcheck_log_prob(Uniform, (low, high)) |
| self._gradcheck_log_prob(Uniform, (low, 1.0)) |
| self._gradcheck_log_prob(Uniform, (0.0, high)) |
| |
| state = torch.get_rng_state() |
| rand = low.new(low.size()).uniform_() |
| torch.set_rng_state(state) |
| u = Uniform(low, high).rsample() |
| u.backward(torch.ones_like(u)) |
| self.assertEqual(low.grad, 1 - rand) |
| self.assertEqual(high.grad, rand) |
| low.grad.zero_() |
| high.grad.zero_() |
| |
| def test_cauchy(self): |
| loc = Variable(torch.zeros(5, 5), requires_grad=True) |
| scale = Variable(torch.ones(5, 5), requires_grad=True) |
| loc_1d = Variable(torch.zeros(1), requires_grad=True) |
| scale_1d = Variable(torch.ones(1), requires_grad=True) |
| self.assertEqual(Cauchy(loc, scale).sample().size(), (5, 5)) |
| self.assertEqual(Cauchy(loc, scale).sample_n(7).size(), (7, 5, 5)) |
| self.assertEqual(Cauchy(loc_1d, scale_1d).sample().size(), (1,)) |
| self.assertEqual(Cauchy(loc_1d, scale_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Cauchy(0.0, 1.0).sample_n(1).size(), (1,)) |
| |
| set_rng_seed(1) |
| self._gradcheck_log_prob(Uniform, (loc, scale)) |
| self._gradcheck_log_prob(Uniform, (loc, 1.0)) |
| self._gradcheck_log_prob(Uniform, (0.0, scale)) |
| |
| state = torch.get_rng_state() |
| eps = loc.new(loc.size()).cauchy_() |
| torch.set_rng_state(state) |
| c = Cauchy(loc, scale).rsample() |
| c.backward(torch.ones_like(c)) |
| self.assertEqual(loc.grad, torch.ones_like(scale)) |
| self.assertEqual(scale.grad, eps) |
| loc.grad.zero_() |
| scale.grad.zero_() |
| |
| def test_normal(self): |
| mean = Variable(torch.randn(5, 5), requires_grad=True) |
| std = Variable(torch.randn(5, 5).abs(), requires_grad=True) |
| mean_1d = Variable(torch.randn(1), requires_grad=True) |
| std_1d = Variable(torch.randn(1), requires_grad=True) |
| mean_delta = torch.Tensor([1.0, 0.0]) |
| std_delta = torch.Tensor([1e-5, 1e-5]) |
| self.assertEqual(Normal(mean, std).sample().size(), (5, 5)) |
| self.assertEqual(Normal(mean, std).sample_n(7).size(), (7, 5, 5)) |
| self.assertEqual(Normal(mean_1d, std_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Normal(mean_1d, std_1d).sample().size(), (1,)) |
| self.assertEqual(Normal(0.2, .6).sample_n(1).size(), (1,)) |
| self.assertEqual(Normal(-0.7, 50.0).sample_n(1).size(), (1,)) |
| |
| # sample check for extreme value of mean, std |
| set_rng_seed(1) |
| self.assertEqual(Normal(mean_delta, std_delta).sample(sample_shape=(1, 2)), |
| torch.Tensor([[[1.0, 0.0], [1.0, 0.0]]]), |
| prec=1e-4) |
| |
| self._gradcheck_log_prob(Normal, (mean, std)) |
| self._gradcheck_log_prob(Normal, (mean, 1.0)) |
| self._gradcheck_log_prob(Normal, (0.0, std)) |
| |
| state = torch.get_rng_state() |
| eps = torch.normal(torch.zeros_like(mean), torch.ones_like(std)) |
| torch.set_rng_state(state) |
| z = Normal(mean, std).rsample() |
| z.backward(torch.ones_like(z)) |
| self.assertEqual(mean.grad, torch.ones_like(mean)) |
| self.assertEqual(std.grad, eps) |
| mean.grad.zero_() |
| std.grad.zero_() |
| self.assertEqual(z.size(), (5, 5)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| m = mean.data.view(-1)[idx] |
| s = std.data.view(-1)[idx] |
| expected = (math.exp(-(x - m) ** 2 / (2 * s ** 2)) / |
| math.sqrt(2 * math.pi * s ** 2)) |
| self.assertAlmostEqual(log_prob, math.log(expected), places=3) |
| |
| self._check_log_prob(Normal(mean, std), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_normal_sample(self): |
| set_rng_seed(0) # see Note [Randomized statistical tests] |
| for mean, std in product([-1.0, 0.0, 1.0], [0.1, 1.0, 10.0]): |
| self._check_sampler_sampler(Normal(mean, std), |
| scipy.stats.norm(loc=mean, scale=std), |
| 'Normal(mean={}, std={})'.format(mean, std)) |
| |
| def test_exponential(self): |
| rate = Variable(torch.randn(5, 5).abs(), requires_grad=True) |
| rate_1d = Variable(torch.randn(1).abs(), requires_grad=True) |
| self.assertEqual(Exponential(rate).sample().size(), (5, 5)) |
| self.assertEqual(Exponential(rate).sample((7,)).size(), (7, 5, 5)) |
| self.assertEqual(Exponential(rate_1d).sample((1,)).size(), (1, 1)) |
| self.assertEqual(Exponential(rate_1d).sample().size(), (1,)) |
| self.assertEqual(Exponential(0.2).sample((1,)).size(), (1,)) |
| self.assertEqual(Exponential(50.0).sample((1,)).size(), (1,)) |
| |
| self._gradcheck_log_prob(Exponential, (rate,)) |
| state = torch.get_rng_state() |
| eps = rate.new(rate.size()).exponential_() |
| torch.set_rng_state(state) |
| z = Exponential(rate).rsample() |
| z.backward(torch.ones_like(z)) |
| self.assertEqual(rate.grad, -eps / rate**2) |
| rate.grad.zero_() |
| self.assertEqual(z.size(), (5, 5)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| m = rate.data.view(-1)[idx] |
| expected = math.log(m) - m * x |
| self.assertAlmostEqual(log_prob, expected, places=3) |
| |
| self._check_log_prob(Exponential(rate), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_exponential_sample(self): |
| set_rng_seed(1) # see Note [Randomized statistical tests] |
| for rate in [1e-5, 1.0, 10.]: |
| self._check_sampler_sampler(Exponential(rate), |
| scipy.stats.expon(scale=1. / rate), |
| 'Exponential(rate={})'.format(rate)) |
| |
| def test_laplace(self): |
| loc = Variable(torch.randn(5, 5), requires_grad=True) |
| scale = Variable(torch.randn(5, 5).abs(), requires_grad=True) |
| loc_1d = Variable(torch.randn(1), requires_grad=True) |
| scale_1d = Variable(torch.randn(1), requires_grad=True) |
| loc_delta = torch.Tensor([1.0, 0.0]) |
| scale_delta = torch.Tensor([1e-5, 1e-5]) |
| self.assertEqual(Laplace(loc, scale).sample().size(), (5, 5)) |
| self.assertEqual(Laplace(loc, scale).sample_n(7).size(), (7, 5, 5)) |
| self.assertEqual(Laplace(loc_1d, scale_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Laplace(loc_1d, scale_1d).sample().size(), (1,)) |
| self.assertEqual(Laplace(0.2, .6).sample_n(1).size(), (1,)) |
| self.assertEqual(Laplace(-0.7, 50.0).sample_n(1).size(), (1,)) |
| |
| # sample check for extreme value of mean, std |
| set_rng_seed(0) |
| self.assertEqual(Laplace(loc_delta, scale_delta).sample(sample_shape=(1, 2)), |
| torch.Tensor([[[1.0, 0.0], [1.0, 0.0]]]), |
| prec=1e-4) |
| |
| self._gradcheck_log_prob(Laplace, (loc, scale)) |
| self._gradcheck_log_prob(Laplace, (loc, 1.0)) |
| self._gradcheck_log_prob(Laplace, (0.0, scale)) |
| |
| state = torch.get_rng_state() |
| eps = torch.ones_like(loc).uniform_(-.5, .5) |
| torch.set_rng_state(state) |
| z = Laplace(loc, scale).rsample() |
| z.backward(torch.ones_like(z)) |
| self.assertEqual(loc.grad, torch.ones_like(loc)) |
| self.assertEqual(scale.grad, -eps.sign() * torch.log1p(-2 * eps.abs())) |
| loc.grad.zero_() |
| scale.grad.zero_() |
| self.assertEqual(z.size(), (5, 5)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| m = loc.data.view(-1)[idx] |
| s = scale.data.view(-1)[idx] |
| expected = (-math.log(2 * s) - abs(x - m) / s) |
| self.assertAlmostEqual(log_prob, expected, places=3) |
| |
| self._check_log_prob(Laplace(loc, scale), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_laplace_sample(self): |
| set_rng_seed(1) # see Note [Randomized statistical tests] |
| for loc, scale in product([-1.0, 0.0, 1.0], [0.1, 1.0, 10.0]): |
| self._check_sampler_sampler(Laplace(loc, scale), |
| scipy.stats.laplace(loc=loc, scale=scale), |
| 'Laplace(loc={}, scale={})'.format(loc, scale)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_gamma_shape(self): |
| alpha = Variable(torch.exp(torch.randn(2, 3)), requires_grad=True) |
| beta = Variable(torch.exp(torch.randn(2, 3)), requires_grad=True) |
| alpha_1d = Variable(torch.exp(torch.randn(1)), requires_grad=True) |
| beta_1d = Variable(torch.exp(torch.randn(1)), requires_grad=True) |
| self.assertEqual(Gamma(alpha, beta).sample().size(), (2, 3)) |
| self.assertEqual(Gamma(alpha, beta).sample_n(5).size(), (5, 2, 3)) |
| self.assertEqual(Gamma(alpha_1d, beta_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Gamma(alpha_1d, beta_1d).sample().size(), (1,)) |
| self.assertEqual(Gamma(0.5, 0.5).sample().size(), (1,)) |
| self.assertEqual(Gamma(0.5, 0.5).sample_n(1).size(), (1,)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| a = alpha.data.view(-1)[idx] |
| b = beta.data.view(-1)[idx] |
| expected = scipy.stats.gamma.logpdf(x, a, scale=1 / b) |
| self.assertAlmostEqual(log_prob, expected, places=3) |
| |
| self._check_log_prob(Gamma(alpha, beta), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_gamma_sample(self): |
| set_rng_seed(0) # see Note [Randomized statistical tests] |
| for alpha, beta in product([0.1, 1.0, 5.0], [0.1, 1.0, 10.0]): |
| self._check_sampler_sampler(Gamma(alpha, beta), |
| scipy.stats.gamma(alpha, scale=1.0 / beta), |
| 'Gamma(alpha={}, beta={})'.format(alpha, beta)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_pareto_shape(self): |
| scale = Variable(torch.randn(2, 3).abs(), requires_grad=True) |
| alpha = Variable(torch.randn(2, 3).abs(), requires_grad=True) |
| scale_1d = torch.randn(1).abs() |
| alpha_1d = torch.randn(1).abs() |
| self.assertEqual(Pareto(scale, alpha).sample().size(), (2, 3)) |
| self.assertEqual(Pareto(scale, alpha).sample_n(5).size(), (5, 2, 3)) |
| self.assertEqual(Pareto(scale_1d, alpha_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Pareto(scale_1d, alpha_1d).sample().size(), (1,)) |
| self.assertEqual(Pareto(1.0, 1.0).sample().size(), (1,)) |
| self.assertEqual(Pareto(1.0, 1.0).sample_n(1).size(), (1,)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| s = scale.data.view(-1)[idx] |
| a = alpha.data.view(-1)[idx] |
| expected = scipy.stats.pareto.logpdf(x, a, scale=s) |
| self.assertAlmostEqual(log_prob, expected, places=3) |
| |
| self._check_log_prob(Pareto(scale, alpha), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_pareto_sample(self): |
| set_rng_seed(1) # see Note [Randomized statistical tests] |
| for scale, alpha in product([0.1, 1.0, 5.0], [0.1, 1.0, 10.0]): |
| self._check_sampler_sampler(Pareto(scale, alpha), |
| scipy.stats.pareto(alpha, scale=scale), |
| 'Pareto(scale={}, alpha={})'.format(scale, alpha)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_gumbel_shape(self): |
| loc = Variable(torch.randn(2, 3), requires_grad=True) |
| scale = Variable(torch.randn(2, 3).abs(), requires_grad=True) |
| loc_1d = torch.randn(1) |
| scale_1d = torch.randn(1).abs() |
| self.assertEqual(Gumbel(loc, scale).sample().size(), (2, 3)) |
| self.assertEqual(Gumbel(loc, scale).sample_n(5).size(), (5, 2, 3)) |
| self.assertEqual(Gumbel(loc_1d, scale_1d).sample().size(), (1,)) |
| self.assertEqual(Gumbel(loc_1d, scale_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Gumbel(1.0, 1.0).sample().size(), (1,)) |
| self.assertEqual(Gumbel(1.0, 1.0).sample_n(1).size(), (1,)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| l = loc.data.view(-1)[idx] |
| s = scale.data.view(-1)[idx] |
| expected = scipy.stats.gumbel_r.logpdf(x, loc=l, scale=s) |
| self.assertAlmostEqual(log_prob, expected, places=3) |
| |
| self._check_log_prob(Gumbel(loc, scale), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_gumbel_sample(self): |
| set_rng_seed(1) # see note [Randomized statistical tests] |
| for loc, scale in product([-5.0, -1.0, -0.1, 0.1, 1.0, 5.0], [0.1, 1.0, 10.0]): |
| self._check_sampler_sampler(Gumbel(loc, scale), |
| scipy.stats.gumbel_r(loc=loc, scale=scale), |
| 'Gumbel(loc={}, scale={})'.format(loc, scale)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_chi2_shape(self): |
| df = Variable(torch.exp(torch.randn(2, 3)), requires_grad=True) |
| df_1d = Variable(torch.exp(torch.randn(1)), requires_grad=True) |
| self.assertEqual(Chi2(df).sample().size(), (2, 3)) |
| self.assertEqual(Chi2(df).sample_n(5).size(), (5, 2, 3)) |
| self.assertEqual(Chi2(df_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(Chi2(df_1d).sample().size(), (1,)) |
| self.assertEqual(Chi2(0.5).sample().size(), (1,)) |
| self.assertEqual(Chi2(0.5).sample_n(1).size(), (1,)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| d = df.data.view(-1)[idx] |
| expected = scipy.stats.chi2.logpdf(x, d) |
| self.assertAlmostEqual(log_prob, expected, places=3) |
| |
| self._check_log_prob(Chi2(df), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_chi2_sample(self): |
| set_rng_seed(0) # see Note [Randomized statistical tests] |
| for df in [0.1, 1.0, 5.0]: |
| self._check_sampler_sampler(Chi2(df), |
| scipy.stats.chi2(df), |
| 'Chi2(df={})'.format(df)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| def test_studentT_shape(self): |
| df = Variable(torch.exp(torch.randn(2, 3)), requires_grad=True) |
| df_1d = Variable(torch.exp(torch.randn(1)), requires_grad=True) |
| self.assertEqual(StudentT(df).sample().size(), (2, 3)) |
| self.assertEqual(StudentT(df).sample_n(5).size(), (5, 2, 3)) |
| self.assertEqual(StudentT(df_1d).sample_n(1).size(), (1, 1)) |
| self.assertEqual(StudentT(df_1d).sample().size(), (1,)) |
| self.assertEqual(StudentT(0.5).sample().size(), (1,)) |
| self.assertEqual(StudentT(0.5).sample_n(1).size(), (1,)) |
| |
| def ref_log_prob(idx, x, log_prob): |
| d = df.data.view(-1)[idx] |
| expected = scipy.stats.t.logpdf(x, d) |
| self.assertAlmostEqual(log_prob, expected, places=3) |
| |
| self._check_log_prob(StudentT(df), ref_log_prob) |
| |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| def test_studentT_sample(self): |
| set_rng_seed(11) # see Note [Randomized statistical tests] |
| for df, loc, scale in product([0.1, 1.0, 5.0, 10.0], [-1.0, 0.0, 1.0], [0.1, 1.0, 10.0]): |
| self._check_sampler_sampler(StudentT(df=df, loc=loc, scale=scale), |
| scipy.stats.t(df=df, loc=loc, scale=scale), |
| 'StudentT(df={}, loc={}, scale={})'.format(df, loc, scale)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| def test_studentT_log_prob(self): |
| set_rng_seed(0) # see Note [Randomized statistical tests] |
| num_samples = 10 |
| for df, loc, scale in product([0.1, 1.0, 5.0, 10.0], [-1.0, 0.0, 1.0], [0.1, 1.0, 10.0]): |
| dist = StudentT(df=df, loc=loc, scale=scale) |
| x = dist.sample((num_samples,)) |
| actual_log_prob = dist.log_prob(x) |
| for i in range(num_samples): |
| expected_log_prob = scipy.stats.t.logpdf(x[i], df=df, loc=loc, scale=scale) |
| self.assertAlmostEqual(actual_log_prob[i], expected_log_prob, places=3) |
| |
| def test_dirichlet_shape(self): |
| alpha = Variable(torch.exp(torch.randn(2, 3)), requires_grad=True) |
| alpha_1d = Variable(torch.exp(torch.randn(4)), requires_grad=True) |
| self.assertEqual(Dirichlet(alpha).sample().size(), (2, 3)) |
| self.assertEqual(Dirichlet(alpha).sample((5,)).size(), (5, 2, 3)) |
| self.assertEqual(Dirichlet(alpha_1d).sample().size(), (4,)) |
| self.assertEqual(Dirichlet(alpha_1d).sample((1,)).size(), (1, 4)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_dirichlet_log_prob(self): |
| num_samples = 10 |
| alpha = torch.exp(torch.randn(5)) |
| dist = Dirichlet(alpha) |
| x = dist.sample((num_samples,)) |
| actual_log_prob = dist.log_prob(x) |
| for i in range(num_samples): |
| expected_log_prob = scipy.stats.dirichlet.logpdf(x[i].numpy(), alpha.numpy()) |
| self.assertAlmostEqual(actual_log_prob[i], expected_log_prob, places=3) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_dirichlet_sample(self): |
| set_rng_seed(0) # see Note [Randomized statistical tests] |
| alpha = torch.exp(torch.randn(3)) |
| self._check_sampler_sampler(Dirichlet(alpha), |
| scipy.stats.dirichlet(alpha.numpy()), |
| 'Dirichlet(alpha={})'.format(list(alpha)), |
| multivariate=True) |
| |
| def test_beta_shape(self): |
| alpha = Variable(torch.exp(torch.randn(2, 3)), requires_grad=True) |
| beta = Variable(torch.exp(torch.randn(2, 3)), requires_grad=True) |
| alpha_1d = Variable(torch.exp(torch.randn(4)), requires_grad=True) |
| beta_1d = Variable(torch.exp(torch.randn(4)), requires_grad=True) |
| self.assertEqual(Beta(alpha, beta).sample().size(), (2, 3)) |
| self.assertEqual(Beta(alpha, beta).sample((5,)).size(), (5, 2, 3)) |
| self.assertEqual(Beta(alpha_1d, beta_1d).sample().size(), (4,)) |
| self.assertEqual(Beta(alpha_1d, beta_1d).sample((1,)).size(), (1, 4)) |
| self.assertEqual(Beta(0.1, 0.3).sample().size(), (1,)) |
| self.assertEqual(Beta(0.1, 0.3).sample((5,)).size(), (5,)) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_beta_log_prob(self): |
| for _ in range(100): |
| alpha = np.exp(np.random.normal()) |
| beta = np.exp(np.random.normal()) |
| dist = Beta(alpha, beta) |
| x = dist.sample() |
| actual_log_prob = dist.log_prob(x).sum() |
| expected_log_prob = scipy.stats.beta.logpdf(x, alpha, beta)[0] |
| self.assertAlmostEqual(actual_log_prob, expected_log_prob, places=3, allow_inf=True) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_beta_sample(self): |
| set_rng_seed(1) # see Note [Randomized statistical tests] |
| for alpha, beta in product([0.1, 1.0, 10.0], [0.1, 1.0, 10.0]): |
| self._check_sampler_sampler(Beta(alpha, beta), |
| scipy.stats.beta(alpha, beta), |
| 'Beta(alpha={}, beta={})'.format(alpha, beta)) |
| # Check that small alphas do not cause NANs. |
| for Tensor in [torch.FloatTensor, torch.DoubleTensor]: |
| x = Beta(Tensor([1e-6]), Tensor([1e-6])).sample()[0] |
| self.assertTrue(np.isfinite(x) and x > 0, 'Invalid Beta.sample(): {}'.format(x)) |
| |
| def test_valid_parameter_broadcasting(self): |
| # Test correct broadcasting of parameter sizes for distributions that have multiple |
| # parameters. |
| # example type (distribution instance, expected sample shape) |
| valid_examples = [ |
| (Normal(mean=torch.Tensor([0, 0]), std=1), |
| (2,)), |
| (Normal(mean=0, std=torch.Tensor([1, 1])), |
| (2,)), |
| (Normal(mean=torch.Tensor([0, 0]), std=torch.Tensor([1])), |
| (2,)), |
| (Normal(mean=torch.Tensor([0, 0]), std=torch.Tensor([[1], [1]])), |
| (2, 2)), |
| (Normal(mean=torch.Tensor([0, 0]), std=torch.Tensor([[1]])), |
| (1, 2)), |
| (Normal(mean=torch.Tensor([0]), std=torch.Tensor([[1]])), |
| (1, 1)), |
| (Gamma(alpha=torch.Tensor([1, 1]), beta=1), |
| (2,)), |
| (Gamma(alpha=1, beta=torch.Tensor([1, 1])), |
| (2,)), |
| (Gamma(alpha=torch.Tensor([1, 1]), beta=torch.Tensor([[1], [1], [1]])), |
| (3, 2)), |
| (Gamma(alpha=torch.Tensor([1, 1]), beta=torch.Tensor([[1], [1]])), |
| (2, 2)), |
| (Gamma(alpha=torch.Tensor([1, 1]), beta=torch.Tensor([[1]])), |
| (1, 2)), |
| (Gamma(alpha=torch.Tensor([1]), beta=torch.Tensor([[1]])), |
| (1, 1)), |
| (Laplace(loc=torch.Tensor([0, 0]), scale=1), |
| (2,)), |
| (Laplace(loc=0, scale=torch.Tensor([1, 1])), |
| (2,)), |
| (Laplace(loc=torch.Tensor([0, 0]), scale=torch.Tensor([1])), |
| (2,)), |
| (Laplace(loc=torch.Tensor([0, 0]), scale=torch.Tensor([[1], [1]])), |
| (2, 2)), |
| (Laplace(loc=torch.Tensor([0, 0]), scale=torch.Tensor([[1]])), |
| (1, 2)), |
| (Laplace(loc=torch.Tensor([0]), scale=torch.Tensor([[1]])), |
| (1, 1)), |
| (StudentT(df=torch.Tensor([1, 1]), loc=1), |
| (2,)), |
| (StudentT(df=1, scale=torch.Tensor([1, 1])), |
| (2,)), |
| (StudentT(df=torch.Tensor([1, 1]), loc=torch.Tensor([1])), |
| (2,)), |
| (StudentT(df=torch.Tensor([1, 1]), scale=torch.Tensor([[1], [1]])), |
| (2, 2)), |
| (StudentT(df=torch.Tensor([1, 1]), loc=torch.Tensor([[1]])), |
| (1, 2)), |
| (StudentT(df=torch.Tensor([1]), scale=torch.Tensor([[1]])), |
| (1, 1)), |
| ] |
| |
| for dist, expected_size in valid_examples: |
| dist_sample_size = dist.sample().size() |
| self.assertEqual(dist_sample_size, expected_size, |
| 'actual size: {} != expected size: {}'.format(dist_sample_size, expected_size)) |
| |
| def test_invalid_parameter_broadcasting(self): |
| # invalid broadcasting cases; should throw error |
| # example type (distribution class, distribution params) |
| invalid_examples = [ |
| (Normal, { |
| 'mean': torch.Tensor([[0, 0]]), |
| 'std': torch.Tensor([1, 1, 1, 1]) |
| }), |
| (Normal, { |
| 'mean': torch.Tensor([[[0, 0, 0], [0, 0, 0]]]), |
| 'std': torch.Tensor([1, 1]) |
| }), |
| (Gamma, { |
| 'alpha': torch.Tensor([0, 0]), |
| 'beta': torch.Tensor([1, 1, 1]) |
| }), |
| (Laplace, { |
| 'loc': torch.Tensor([0, 0]), |
| 'scale': torch.Tensor([1, 1, 1]) |
| }), |
| (StudentT, { |
| 'df': torch.Tensor([1, 1]), |
| 'scale': torch.Tensor([1, 1, 1]) |
| }), |
| (StudentT, { |
| 'df': torch.Tensor([1, 1]), |
| 'loc': torch.Tensor([1, 1, 1]) |
| }) |
| ] |
| |
| for dist, kwargs in invalid_examples: |
| self.assertRaises(RuntimeError, dist, **kwargs) |
| |
| |
| # These tests are only needed for a few distributions that implement custom |
| # reparameterized gradients. Most .rsample() implementations simply rely on |
| # the reparameterization trick and do not need to be tested for accuracy. |
| class TestRsample(TestCase): |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_gamma(self): |
| num_samples = 100 |
| for alpha in [1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4]: |
| alphas = Variable(torch.FloatTensor([alpha] * num_samples), requires_grad=True) |
| betas = Variable(torch.ones(num_samples).type_as(alphas)) |
| x = Gamma(alphas, betas).rsample() |
| x.sum().backward() |
| x, ind = x.data.sort() |
| x = x.numpy() |
| actual_grad = alphas.grad.data[ind].numpy() |
| # Compare with expected gradient dx/dalpha along constant cdf(x,alpha). |
| cdf = scipy.stats.gamma.cdf |
| pdf = scipy.stats.gamma.pdf |
| eps = 0.01 * alpha / (1.0 + alpha ** 0.5) |
| cdf_alpha = (cdf(x, alpha + eps) - cdf(x, alpha - eps)) / (2 * eps) |
| cdf_x = pdf(x, alpha) |
| expected_grad = -cdf_alpha / cdf_x |
| rel_error = np.abs(actual_grad - expected_grad) / (expected_grad + 1e-30) |
| self.assertLess(np.max(rel_error), 0.0005, '\n'.join([ |
| 'Bad gradient dx/alpha for x ~ Gamma({}, 1)'.format(alpha), |
| 'x {}'.format(x), |
| 'expected {}'.format(expected_grad), |
| 'actual {}'.format(actual_grad), |
| 'rel error {}'.format(rel_error), |
| 'max error {}'.format(rel_error.max()), |
| 'at alpha={}, x={}'.format(alpha, x[rel_error.argmax()]), |
| ])) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_chi2(self): |
| num_samples = 100 |
| for df in [1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4]: |
| dfs = Variable(torch.FloatTensor([df] * num_samples), requires_grad=True) |
| x = Chi2(dfs).rsample() |
| x.sum().backward() |
| x, ind = x.data.sort() |
| x = x.numpy() |
| actual_grad = dfs.grad.data[ind].numpy() |
| # Compare with expected gradient dx/ddf along constant cdf(x,df). |
| cdf = scipy.stats.chi2.cdf |
| pdf = scipy.stats.chi2.pdf |
| eps = 0.01 * df / (1.0 + df ** 0.5) |
| cdf_df = (cdf(x, df + eps) - cdf(x, df - eps)) / (2 * eps) |
| cdf_x = pdf(x, df) |
| expected_grad = -cdf_df / cdf_x |
| rel_error = np.abs(actual_grad - expected_grad) / (expected_grad + 1e-30) |
| self.assertLess(np.max(rel_error), 0.001, '\n'.join([ |
| 'Bad gradient dx/ddf for x ~ Chi2({})'.format(df), |
| 'x {}'.format(x), |
| 'expected {}'.format(expected_grad), |
| 'actual {}'.format(actual_grad), |
| 'rel error {}'.format(rel_error), |
| 'max error {}'.format(rel_error.max()), |
| ])) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_dirichlet_on_diagonal(self): |
| num_samples = 20 |
| grid = [1e-1, 1e0, 1e1] |
| for a0, a1, a2 in product(grid, grid, grid): |
| alphas = Variable(torch.FloatTensor([[a0, a1, a2]] * num_samples), requires_grad=True) |
| x = Dirichlet(alphas).rsample()[:, 0] |
| x.sum().backward() |
| x, ind = x.data.sort() |
| x = x.numpy() |
| actual_grad = alphas.grad.data[ind].numpy()[:, 0] |
| # Compare with expected gradient dx/dalpha0 along constant cdf(x,alpha). |
| # This reduces to a distribution Beta(alpha[0], alpha[1] + alpha[2]). |
| cdf = scipy.stats.beta.cdf |
| pdf = scipy.stats.beta.pdf |
| alpha, beta = a0, a1 + a2 |
| eps = 0.01 * alpha / (1.0 + np.sqrt(alpha)) |
| cdf_alpha = (cdf(x, alpha + eps, beta) - cdf(x, alpha - eps, beta)) / (2 * eps) |
| cdf_x = pdf(x, alpha, beta) |
| expected_grad = -cdf_alpha / cdf_x |
| rel_error = np.abs(actual_grad - expected_grad) / (expected_grad + 1e-30) |
| self.assertLess(np.max(rel_error), 0.001, '\n'.join([ |
| 'Bad gradient dx[0]/dalpha[0] for Dirichlet([{}, {}, {}])'.format(a0, a1, a2), |
| 'x {}'.format(x), |
| 'expected {}'.format(expected_grad), |
| 'actual {}'.format(actual_grad), |
| 'rel error {}'.format(rel_error), |
| 'max error {}'.format(rel_error.max()), |
| 'at x={}'.format(x[rel_error.argmax()]), |
| ])) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_beta_wrt_alpha(self): |
| num_samples = 20 |
| grid = [1e-2, 1e-1, 1e0, 1e1, 1e2] |
| for alpha, beta in product(grid, grid): |
| alphas = Variable(torch.FloatTensor([alpha] * num_samples), requires_grad=True) |
| betas = Variable(torch.FloatTensor([beta] * num_samples).type_as(alphas)) |
| x = Beta(alphas, betas).rsample() |
| x.sum().backward() |
| x, ind = x.data.sort() |
| x = x.numpy() |
| actual_grad = alphas.grad.data[ind].numpy() |
| # Compare with expected gradient dx/dalpha along constant cdf(x,alpha,beta). |
| cdf = scipy.stats.beta.cdf |
| pdf = scipy.stats.beta.pdf |
| eps = 0.01 * alpha / (1.0 + np.sqrt(alpha)) |
| cdf_alpha = (cdf(x, alpha + eps, beta) - cdf(x, alpha - eps, beta)) / (2 * eps) |
| cdf_x = pdf(x, alpha, beta) |
| expected_grad = -cdf_alpha / cdf_x |
| rel_error = np.abs(actual_grad - expected_grad) / (expected_grad + 1e-30) |
| self.assertLess(np.max(rel_error), 0.005, '\n'.join([ |
| 'Bad gradient dx/dalpha for x ~ Beta({}, {})'.format(alpha, beta), |
| 'x {}'.format(x), |
| 'expected {}'.format(expected_grad), |
| 'actual {}'.format(actual_grad), |
| 'rel error {}'.format(rel_error), |
| 'max error {}'.format(rel_error.max()), |
| 'at x = {}'.format(x[rel_error.argmax()]), |
| ])) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| def test_beta_wrt_beta(self): |
| num_samples = 20 |
| grid = [1e-2, 1e-1, 1e0, 1e1, 1e2] |
| for alpha, beta in product(grid, grid): |
| betas = Variable(torch.FloatTensor([beta] * num_samples), requires_grad=True) |
| alphas = Variable(torch.FloatTensor([alpha] * num_samples).type_as(betas)) |
| x = Beta(alphas, betas).rsample() |
| x.sum().backward() |
| x, ind = x.data.sort() |
| x = x.numpy() |
| actual_grad = betas.grad.data[ind].numpy() |
| # Compare with expected gradient dx/dbeta along constant cdf(x,alpha,beta). |
| cdf = scipy.stats.beta.cdf |
| pdf = scipy.stats.beta.pdf |
| eps = 0.01 * beta / (1.0 + np.sqrt(beta)) |
| cdf_beta = (cdf(x, alpha, beta + eps) - cdf(x, alpha, beta - eps)) / (2 * eps) |
| cdf_x = pdf(x, alpha, beta) |
| expected_grad = -cdf_beta / cdf_x |
| rel_error = np.abs(actual_grad - expected_grad) / (expected_grad + 1e-30) |
| self.assertLess(np.max(rel_error), 0.005, '\n'.join([ |
| 'Bad gradient dx/dbeta for x ~ Beta({}, {})'.format(alpha, beta), |
| 'x {}'.format(x), |
| 'expected {}'.format(expected_grad), |
| 'actual {}'.format(actual_grad), |
| 'rel error {}'.format(rel_error), |
| 'max error {}'.format(rel_error.max()), |
| 'at x = {!r}'.format(x[rel_error.argmax()]), |
| ])) |
| |
| def test_dirichlet_multivariate(self): |
| alpha_crit = 0.25 * (5.0 ** 0.5 - 1.0) |
| num_samples = 100000 |
| for shift in [-0.1, -0.05, -0.01, 0.0, 0.01, 0.05, 0.10]: |
| alpha = alpha_crit + shift |
| alpha = Variable(torch.FloatTensor([alpha]), requires_grad=True) |
| alpha_vec = torch.cat([alpha, alpha, alpha.new([1])]) |
| z = Dirichlet(alpha_vec.expand(num_samples, 3)).rsample() |
| mean_z3 = 1.0 / (2.0 * alpha + 1.0) |
| loss = torch.pow(z[:, 2] - mean_z3, 2.0).mean() |
| actual_grad = grad(loss, [alpha])[0].data |
| # Compute expected gradient by hand. |
| num = 1.0 - 2.0 * alpha - 4.0 * alpha**2 |
| den = (1.0 + alpha)**2 * (1.0 + 2.0 * alpha)**3 |
| expected_grad = (num / den).data |
| self.assertEqual(actual_grad, expected_grad, 0.002, '\n'.join([ |
| "alpha = alpha_c + %.2g" % shift, |
| "expected_grad: %.5g" % expected_grad, |
| "actual_grad: %.5g" % actual_grad, |
| "error = %.2g" % torch.abs(expected_grad - actual_grad).max(), |
| ])) |
| |
| def test_dirichlet_tangent_field(self): |
| num_samples = 20 |
| alpha_grid = [0.5, 1.0, 2.0] |
| |
| # v = dx/dalpha[0] is the reparameterized gradient aka tangent field. |
| def compute_v(x, alpha): |
| return torch.stack([ |
| _Dirichlet_backward(x, alpha, torch.eye(3, 3)[i].expand_as(x))[:, 0] |
| for i in range(3) |
| ], dim=-1) |
| |
| for a1, a2, a3 in product(alpha_grid, alpha_grid, alpha_grid): |
| alpha = Variable(torch.Tensor([a1, a2, a3]).expand(num_samples, 3), requires_grad=True) |
| x = Dirichlet(alpha).rsample() |
| dlogp_da = grad([Dirichlet(alpha).log_prob(x.detach()).sum()], |
| [alpha], retain_graph=True)[0].data[:, 0] |
| dlogp_dx = grad([Dirichlet(alpha.detach()).log_prob(x).sum()], |
| [x], retain_graph=True)[0].data |
| v = torch.stack([grad([x[:, i].sum()], [alpha], retain_graph=True)[0].data[:, 0] |
| for i in range(3)], dim=-1) |
| # Compute ramaining properties by finite difference. |
| x = x.data |
| alpha = alpha.data |
| self.assertEqual(compute_v(x, alpha), v, message='Bug in compute_v() helper') |
| # dx is an arbitrary orthonormal basis tangent to the simplex. |
| dx = torch.Tensor([[2, -1, -1], [0, 1, -1]]) |
| dx /= dx.norm(2, -1, True) |
| eps = 1e-2 * x.min(-1, True)[0] # avoid boundary |
| dv0 = (compute_v(x + eps * dx[0], alpha) - compute_v(x - eps * dx[0], alpha)) / (2 * eps) |
| dv1 = (compute_v(x + eps * dx[1], alpha) - compute_v(x - eps * dx[1], alpha)) / (2 * eps) |
| div_v = (dv0 * dx[0] + dv1 * dx[1]).sum(-1) |
| # This is a modification of the standard continuity equation, using the product rule to allow |
| # expression in terms of log_prob rather than the less numerically stable log_prob.exp(). |
| error = dlogp_da + (dlogp_dx * v).sum(-1) + div_v |
| self.assertLess(torch.abs(error).max(), 0.005, '\n'.join([ |
| 'Dirichlet([{}, {}, {}]) gradient violates continuity equation:'.format(a1, a2, a3), |
| 'error = {}'.format(error), |
| ])) |
| |
| |
| class TestDistributionShapes(TestCase): |
| def setUp(self): |
| super(TestCase, self).setUp() |
| self.scalar_sample = 1 |
| self.tensor_sample_1 = torch.ones(3, 2) |
| self.tensor_sample_2 = torch.ones(3, 2, 3) |
| |
| def test_entropy_shape(self): |
| for Dist, params in EXAMPLES: |
| for i, param in enumerate(params): |
| dist = Dist(**param) |
| actual_shape = dist.entropy().size() |
| expected_shape = dist._batch_shape |
| if not expected_shape: |
| expected_shape = torch.Size((1,)) # TODO Remove this once scalars are supported. |
| message = '{} example {}/{}, shape mismatch. expected {}, actual {}'.format( |
| Dist.__name__, i, len(params), expected_shape, actual_shape) |
| self.assertEqual(actual_shape, expected_shape, message=message) |
| |
| def test_bernoulli_shape_scalar_params(self): |
| bernoulli = Bernoulli(0.3) |
| self.assertEqual(bernoulli._batch_shape, torch.Size()) |
| self.assertEqual(bernoulli._event_shape, torch.Size()) |
| self.assertEqual(bernoulli.sample().size(), torch.Size((1,))) |
| self.assertEqual(bernoulli.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, bernoulli.log_prob, self.scalar_sample) |
| self.assertEqual(bernoulli.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(bernoulli.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_bernoulli_shape_tensor_params(self): |
| bernoulli = Bernoulli(torch.Tensor([[0.6, 0.3], [0.6, 0.3], [0.6, 0.3]])) |
| self.assertEqual(bernoulli._batch_shape, torch.Size((3, 2))) |
| self.assertEqual(bernoulli._event_shape, torch.Size(())) |
| self.assertEqual(bernoulli.sample().size(), torch.Size((3, 2))) |
| self.assertEqual(bernoulli.sample((3, 2)).size(), torch.Size((3, 2, 3, 2))) |
| self.assertEqual(bernoulli.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, bernoulli.log_prob, self.tensor_sample_2) |
| |
| def test_beta_shape_scalar_params(self): |
| dist = Beta(0.1, 0.1) |
| self.assertEqual(dist._batch_shape, torch.Size()) |
| self.assertEqual(dist._event_shape, torch.Size()) |
| self.assertEqual(dist.sample().size(), torch.Size((1,))) |
| self.assertEqual(dist.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, dist.log_prob, self.scalar_sample) |
| self.assertEqual(dist.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(dist.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_beta_shape_tensor_params(self): |
| dist = Beta(torch.Tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]), |
| torch.Tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])) |
| self.assertEqual(dist._batch_shape, torch.Size((3, 2))) |
| self.assertEqual(dist._event_shape, torch.Size(())) |
| self.assertEqual(dist.sample().size(), torch.Size((3, 2))) |
| self.assertEqual(dist.sample((3, 2)).size(), torch.Size((3, 2, 3, 2))) |
| self.assertEqual(dist.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, dist.log_prob, self.tensor_sample_2) |
| |
| def test_categorical_shape(self): |
| dist = Categorical(torch.Tensor([[0.6, 0.3], [0.6, 0.3], [0.6, 0.3]])) |
| self.assertEqual(dist._batch_shape, torch.Size((3,))) |
| self.assertEqual(dist._event_shape, torch.Size(())) |
| self.assertEqual(dist.sample().size(), torch.Size((3,))) |
| self.assertEqual(dist.sample((3, 2)).size(), torch.Size((3, 2, 3,))) |
| self.assertRaises(ValueError, dist.log_prob, self.tensor_sample_1) |
| self.assertEqual(dist.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_one_hot_categorical_shape(self): |
| dist = OneHotCategorical(torch.Tensor([[0.6, 0.3], [0.6, 0.3], [0.6, 0.3]])) |
| self.assertEqual(dist._batch_shape, torch.Size((3,))) |
| self.assertEqual(dist._event_shape, torch.Size((2,))) |
| self.assertEqual(dist.sample().size(), torch.Size((3, 2))) |
| self.assertEqual(dist.sample((3, 2)).size(), torch.Size((3, 2, 3, 2))) |
| self.assertEqual(dist.log_prob(self.tensor_sample_1).size(), torch.Size((3,))) |
| self.assertRaises(ValueError, dist.log_prob, self.tensor_sample_2) |
| self.assertEqual(dist.log_prob(dist.enumerate_support()).size(), torch.Size((2, 3))) |
| |
| def test_cauchy_shape_scalar_params(self): |
| cauchy = Cauchy(0, 1) |
| self.assertEqual(cauchy._batch_shape, torch.Size()) |
| self.assertEqual(cauchy._event_shape, torch.Size()) |
| self.assertEqual(cauchy.sample().size(), torch.Size((1,))) |
| self.assertEqual(cauchy.sample(torch.Size((3, 2))).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, cauchy.log_prob, self.scalar_sample) |
| self.assertEqual(cauchy.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(cauchy.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_cauchy_shape_tensor_params(self): |
| cauchy = Cauchy(torch.Tensor([0, 0]), torch.Tensor([1, 1])) |
| self.assertEqual(cauchy._batch_shape, torch.Size((2,))) |
| self.assertEqual(cauchy._event_shape, torch.Size(())) |
| self.assertEqual(cauchy.sample().size(), torch.Size((2,))) |
| self.assertEqual(cauchy.sample(torch.Size((3, 2))).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(cauchy.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, cauchy.log_prob, self.tensor_sample_2) |
| |
| def test_dirichlet_shape(self): |
| dist = Dirichlet(torch.Tensor([[0.6, 0.3], [1.6, 1.3], [2.6, 2.3]])) |
| self.assertEqual(dist._batch_shape, torch.Size((3,))) |
| self.assertEqual(dist._event_shape, torch.Size((2,))) |
| self.assertEqual(dist.sample().size(), torch.Size((3, 2))) |
| self.assertEqual(dist.sample((5, 4)).size(), torch.Size((5, 4, 3, 2))) |
| self.assertEqual(dist.log_prob(self.tensor_sample_1).size(), torch.Size((3,))) |
| self.assertRaises(ValueError, dist.log_prob, self.tensor_sample_2) |
| |
| def test_gamma_shape_scalar_params(self): |
| gamma = Gamma(1, 1) |
| self.assertEqual(gamma._batch_shape, torch.Size()) |
| self.assertEqual(gamma._event_shape, torch.Size()) |
| self.assertEqual(gamma.sample().size(), torch.Size((1,))) |
| self.assertEqual(gamma.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, gamma.log_prob, self.scalar_sample) |
| self.assertEqual(gamma.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(gamma.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_gamma_shape_tensor_params(self): |
| gamma = Gamma(torch.Tensor([1, 1]), torch.Tensor([1, 1])) |
| self.assertEqual(gamma._batch_shape, torch.Size((2,))) |
| self.assertEqual(gamma._event_shape, torch.Size(())) |
| self.assertEqual(gamma.sample().size(), torch.Size((2,))) |
| self.assertEqual(gamma.sample((3, 2)).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(gamma.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, gamma.log_prob, self.tensor_sample_2) |
| |
| def test_chi2_shape_scalar_params(self): |
| chi2 = Chi2(1) |
| self.assertEqual(chi2._batch_shape, torch.Size()) |
| self.assertEqual(chi2._event_shape, torch.Size()) |
| self.assertEqual(chi2.sample().size(), torch.Size((1,))) |
| self.assertEqual(chi2.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, chi2.log_prob, self.scalar_sample) |
| self.assertEqual(chi2.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(chi2.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_chi2_shape_tensor_params(self): |
| chi2 = Chi2(torch.Tensor([1, 1])) |
| self.assertEqual(chi2._batch_shape, torch.Size((2,))) |
| self.assertEqual(chi2._event_shape, torch.Size(())) |
| self.assertEqual(chi2.sample().size(), torch.Size((2,))) |
| self.assertEqual(chi2.sample((3, 2)).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(chi2.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, chi2.log_prob, self.tensor_sample_2) |
| |
| def test_studentT_shape_scalar_params(self): |
| st = StudentT(1) |
| self.assertEqual(st._batch_shape, torch.Size()) |
| self.assertEqual(st._event_shape, torch.Size()) |
| self.assertEqual(st.sample().size(), torch.Size((1,))) |
| self.assertEqual(st.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, st.log_prob, self.scalar_sample) |
| self.assertEqual(st.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(st.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_studentT_shape_tensor_params(self): |
| st = StudentT(torch.Tensor([1, 1])) |
| self.assertEqual(st._batch_shape, torch.Size((2,))) |
| self.assertEqual(st._event_shape, torch.Size(())) |
| self.assertEqual(st.sample().size(), torch.Size((2,))) |
| self.assertEqual(st.sample((3, 2)).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(st.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, st.log_prob, self.tensor_sample_2) |
| |
| def test_pareto_shape_scalar_params(self): |
| pareto = Pareto(1, 1) |
| self.assertEqual(pareto._batch_shape, torch.Size()) |
| self.assertEqual(pareto._event_shape, torch.Size()) |
| self.assertEqual(pareto.sample().size(), torch.Size((1,))) |
| self.assertEqual(pareto.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, pareto.log_prob, self.scalar_sample) |
| self.assertEqual(pareto.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(pareto.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_normal_shape_scalar_params(self): |
| normal = Normal(0, 1) |
| self.assertEqual(normal._batch_shape, torch.Size()) |
| self.assertEqual(normal._event_shape, torch.Size()) |
| self.assertEqual(normal.sample().size(), torch.Size((1,))) |
| self.assertEqual(normal.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, normal.log_prob, self.scalar_sample) |
| self.assertEqual(normal.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(normal.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_normal_shape_tensor_params(self): |
| normal = Normal(torch.Tensor([0, 0]), torch.Tensor([1, 1])) |
| self.assertEqual(normal._batch_shape, torch.Size((2,))) |
| self.assertEqual(normal._event_shape, torch.Size(())) |
| self.assertEqual(normal.sample().size(), torch.Size((2,))) |
| self.assertEqual(normal.sample((3, 2)).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(normal.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, normal.log_prob, self.tensor_sample_2) |
| |
| def test_uniform_shape_scalar_params(self): |
| uniform = Uniform(0, 1) |
| self.assertEqual(uniform._batch_shape, torch.Size()) |
| self.assertEqual(uniform._event_shape, torch.Size()) |
| self.assertEqual(uniform.sample().size(), torch.Size((1,))) |
| self.assertEqual(uniform.sample(torch.Size((3, 2))).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, uniform.log_prob, self.scalar_sample) |
| self.assertEqual(uniform.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(uniform.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_uniform_shape_tensor_params(self): |
| uniform = Uniform(torch.Tensor([0, 0]), torch.Tensor([1, 1])) |
| self.assertEqual(uniform._batch_shape, torch.Size((2,))) |
| self.assertEqual(uniform._event_shape, torch.Size(())) |
| self.assertEqual(uniform.sample().size(), torch.Size((2,))) |
| self.assertEqual(uniform.sample(torch.Size((3, 2))).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(uniform.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, uniform.log_prob, self.tensor_sample_2) |
| |
| def test_exponential_shape_scalar_param(self): |
| expon = Exponential(1.) |
| self.assertEqual(expon._batch_shape, torch.Size()) |
| self.assertEqual(expon._event_shape, torch.Size()) |
| self.assertEqual(expon.sample().size(), torch.Size((1,))) |
| self.assertEqual(expon.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, expon.log_prob, self.scalar_sample) |
| self.assertEqual(expon.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(expon.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_exponential_shape_tensor_param(self): |
| expon = Exponential(torch.Tensor([1, 1])) |
| self.assertEqual(expon._batch_shape, torch.Size((2,))) |
| self.assertEqual(expon._event_shape, torch.Size(())) |
| self.assertEqual(expon.sample().size(), torch.Size((2,))) |
| self.assertEqual(expon.sample((3, 2)).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(expon.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, expon.log_prob, self.tensor_sample_2) |
| |
| def test_laplace_shape_scalar_params(self): |
| laplace = Laplace(0, 1) |
| self.assertEqual(laplace._batch_shape, torch.Size()) |
| self.assertEqual(laplace._event_shape, torch.Size()) |
| self.assertEqual(laplace.sample().size(), torch.Size((1,))) |
| self.assertEqual(laplace.sample((3, 2)).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, laplace.log_prob, self.scalar_sample) |
| self.assertEqual(laplace.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertEqual(laplace.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3))) |
| |
| def test_laplace_shape_tensor_params(self): |
| laplace = Laplace(torch.Tensor([0, 0]), torch.Tensor([1, 1])) |
| self.assertEqual(laplace._batch_shape, torch.Size((2,))) |
| self.assertEqual(laplace._event_shape, torch.Size(())) |
| self.assertEqual(laplace.sample().size(), torch.Size((2,))) |
| self.assertEqual(laplace.sample((3, 2)).size(), torch.Size((3, 2, 2))) |
| self.assertEqual(laplace.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2))) |
| self.assertRaises(ValueError, laplace.log_prob, self.tensor_sample_2) |
| |
| |
| class TestKL(TestCase): |
| def setUp(self): |
| self.examples = [ |
| (Gamma(1, 2), Gamma(3, 4)), |
| (Chi2(2), Chi2(3)), |
| (Gamma(1, 2), Chi2(3)), |
| (Chi2(2), Gamma(3, 4)), |
| ] |
| |
| def test_kl_monte_carlo(self): |
| set_rng_seed(0) # see Note [Randomized statistical tests] |
| for p, q in self.examples: |
| x = p.sample(sample_shape=(10000,)) |
| expected = (p.log_prob(x) - q.log_prob(x)).mean(0) |
| actual = kl_divergence(p, q) |
| message = 'Incorrect KL({}, {}). expected {}, actual {}'.format( |
| type(p).__name__, type(q).__name__, expected, actual) |
| self.assertEqual(expected, actual, prec=0.1, message=message) |
| |
| |
| class TestConstraints(TestCase): |
| def test_params_contains(self): |
| for Dist, params in EXAMPLES: |
| for i, param in enumerate(params): |
| dist = Dist(**param) |
| for name, value in param.items(): |
| if not (torch.is_tensor(value) or isinstance(value, Variable)): |
| value = torch.Tensor([value]) |
| if Dist in (Categorical, OneHotCategorical) and name == 'probs': |
| # These distributions accept positive probs, but elsewhere we |
| # use a stricter constraint to the simplex. |
| value = value / value.sum(-1, True) |
| constraint = dist.params[name] |
| if is_dependent(constraint): |
| continue |
| message = '{} example {}/{} parameter {} = {}'.format( |
| Dist.__name__, i, len(params), name, value) |
| self.assertTrue(constraint.check(value).all(), msg=message) |
| |
| def test_support_contains(self): |
| for Dist, params in EXAMPLES: |
| self.assertIsInstance(Dist.support, Constraint) |
| for i, param in enumerate(params): |
| dist = Dist(**param) |
| value = dist.sample() |
| constraint = dist.support |
| message = '{} example {}/{} sample = {}'.format( |
| Dist.__name__, i, len(params), value) |
| self.assertTrue(constraint.check(value).all(), msg=message) |
| |
| |
| class TestNumericalStability(TestCase): |
| def _test_pdf_score(self, |
| dist_class, |
| x, |
| expected_value, |
| probs=None, |
| logits=None, |
| expected_gradient=None, |
| prec=1e-5): |
| if probs is not None: |
| p = Variable(probs, requires_grad=True) |
| dist = dist_class(p) |
| else: |
| p = Variable(logits, requires_grad=True) |
| dist = dist_class(logits=p) |
| log_pdf = dist.log_prob(Variable(x)) |
| log_pdf.sum().backward() |
| self.assertEqual(log_pdf.data, |
| expected_value, |
| prec=prec, |
| message='Incorrect value for tensor type: {}. Expected = {}, Actual = {}' |
| .format(type(x), expected_value, log_pdf.data)) |
| if expected_gradient is not None: |
| self.assertEqual(p.grad.data, |
| expected_gradient, |
| prec=prec, |
| message='Incorrect gradient for tensor type: {}. Expected = {}, Actual = {}' |
| .format(type(x), expected_gradient, p.grad.data)) |
| |
| def test_bernoulli_gradient(self): |
| for tensor_type in [torch.FloatTensor, torch.DoubleTensor]: |
| self._test_pdf_score(dist_class=Bernoulli, |
| probs=tensor_type([0]), |
| x=tensor_type([0]), |
| expected_value=tensor_type([0]), |
| expected_gradient=tensor_type([0])) |
| |
| self._test_pdf_score(dist_class=Bernoulli, |
| probs=tensor_type([0]), |
| x=tensor_type([1]), |
| expected_value=tensor_type([_finfo(tensor_type([])).eps]).log(), |
| expected_gradient=tensor_type([0])) |
| |
| self._test_pdf_score(dist_class=Bernoulli, |
| probs=tensor_type([1e-4]), |
| x=tensor_type([1]), |
| expected_value=tensor_type([math.log(1e-4)]), |
| expected_gradient=tensor_type([10000])) |
| |
| # Lower precision due to: |
| # >>> 1 / (1 - torch.FloatTensor([0.9999])) |
| # 9998.3408 |
| # [torch.FloatTensor of size 1] |
| self._test_pdf_score(dist_class=Bernoulli, |
| probs=tensor_type([1 - 1e-4]), |
| x=tensor_type([0]), |
| expected_value=tensor_type([math.log(1e-4)]), |
| expected_gradient=tensor_type([-10000]), |
| prec=2) |
| |
| self._test_pdf_score(dist_class=Bernoulli, |
| logits=tensor_type([math.log(9999)]), |
| x=tensor_type([0]), |
| expected_value=tensor_type([math.log(1e-4)]), |
| expected_gradient=tensor_type([-1]), |
| prec=1e-3) |
| |
| def test_bernoulli_with_logits_underflow(self): |
| for tensor_type, lim in ([(torch.FloatTensor, -1e38), |
| (torch.DoubleTensor, -1e308)]): |
| self._test_pdf_score(dist_class=Bernoulli, |
| logits=tensor_type([lim]), |
| x=tensor_type([0]), |
| expected_value=tensor_type([0]), |
| expected_gradient=tensor_type([0])) |
| |
| def test_bernoulli_with_logits_overflow(self): |
| for tensor_type, lim in ([(torch.FloatTensor, 1e38), |
| (torch.DoubleTensor, 1e308)]): |
| self._test_pdf_score(dist_class=Bernoulli, |
| logits=tensor_type([lim]), |
| x=tensor_type([1]), |
| expected_value=tensor_type([0]), |
| expected_gradient=tensor_type([0])) |
| |
| def test_categorical_log_prob(self): |
| for tensor_type in ([torch.FloatTensor, torch.DoubleTensor]): |
| p = Variable(tensor_type([0, 1]), requires_grad=True) |
| categorical = OneHotCategorical(p) |
| log_pdf = categorical.log_prob(Variable(tensor_type([0, 1]))) |
| self.assertEqual(log_pdf.data[0], 0) |
| |
| def test_categorical_log_prob_with_logits(self): |
| for tensor_type in ([torch.FloatTensor, torch.DoubleTensor]): |
| p = Variable(tensor_type([-float('inf'), 0]), requires_grad=True) |
| categorical = OneHotCategorical(logits=p) |
| log_pdf_prob_1 = categorical.log_prob(Variable(tensor_type([0, 1]))) |
| self.assertEqual(log_pdf_prob_1.data[0], 0) |
| log_pdf_prob_0 = categorical.log_prob(Variable(tensor_type([1, 0]))) |
| self.assertEqual(log_pdf_prob_0.data[0], -float('inf'), allow_inf=True) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
