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How to use total_count method in avocado

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negative_binomial_test.py

Source:negative_binomial_test.py

1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15from __future__ import absolute_import16from __future__ import division17from __future__ import print_function18import numpy as np19from scipy import stats20from tensorflow.contrib.distributions.python.ops import negative_binomial21from tensorflow.python.framework import constant_op22from tensorflow.python.framework import dtypes23from tensorflow.python.framework import tensor_shape24from tensorflow.python.ops import array_ops25from tensorflow.python.ops import math_ops26from tensorflow.python.platform import test27# In all tests that follow, we use scipy.stats.nbinom, which28# represents a Negative Binomial distribution, with success and failure29# probabilities flipped.30class NegativeBinomialTest(test.TestCase):31  def testNegativeBinomialShape(self):32    with self.cached_session():33      probs = [.1] * 534      total_count = [2.0] * 535      negbinom = negative_binomial.NegativeBinomial(36          total_count=total_count, probs=probs)37      self.assertEqual([5], negbinom.batch_shape_tensor().eval())38      self.assertEqual(tensor_shape.TensorShape([5]), negbinom.batch_shape)39      self.assertAllEqual([], negbinom.event_shape_tensor().eval())40      self.assertEqual(tensor_shape.TensorShape([]), negbinom.event_shape)41  def testNegativeBinomialShapeBroadcast(self):42    with self.cached_session():43      probs = [[.1, .2, .3]] * 544      total_count = [[2.]] * 545      negbinom = negative_binomial.NegativeBinomial(46          total_count=total_count, probs=probs)47      self.assertAllEqual([5, 3], negbinom.batch_shape_tensor().eval())48      self.assertAllEqual(49          tensor_shape.TensorShape([5, 3]), negbinom.batch_shape)50      self.assertAllEqual([], negbinom.event_shape_tensor().eval())51      self.assertAllEqual(tensor_shape.TensorShape([]), negbinom.event_shape)52  def testLogits(self):53    logits = [[0., 9., -0.5]]54    with self.cached_session():55      negbinom = negative_binomial.NegativeBinomial(56          total_count=3., logits=logits)57      self.assertEqual([1, 3], negbinom.probs.get_shape())58      self.assertEqual([1, 3], negbinom.logits.get_shape())59      self.assertAllClose(logits, negbinom.logits.eval())60  def testInvalidP(self):61    invalid_ps = [-.01, 0., -2.,]62    with self.cached_session():63      with self.assertRaisesOpError("Condition x >= 0"):64        negbinom = negative_binomial.NegativeBinomial(65            5., probs=invalid_ps, validate_args=True)66        negbinom.probs.eval()67    invalid_ps = [1.01, 2., 1.001,]68    with self.cached_session():69      with self.assertRaisesOpError("probs has components greater than 1."):70        negbinom = negative_binomial.NegativeBinomial(71            5., probs=invalid_ps, validate_args=True)72        negbinom.probs.eval()73  def testInvalidNegativeCount(self):74    invalid_rs = [-.01, 0., -2.,]75    with self.cached_session():76      with self.assertRaisesOpError("Condition x > 0"):77        negbinom = negative_binomial.NegativeBinomial(78            total_count=invalid_rs, probs=0.1, validate_args=True)79        negbinom.total_count.eval()80  def testNegativeBinomialLogCdf(self):81    with self.cached_session():82      batch_size = 683      probs = [.2] * batch_size84      probs_v = .285      total_count = 5.86      x = np.array([2., 3., 4., 5., 6., 7.], dtype=np.float32)87      negbinom = negative_binomial.NegativeBinomial(88          total_count=total_count, probs=probs)89      expected_log_cdf = stats.nbinom.logcdf(x, n=total_count, p=1 - probs_v)90      log_cdf = negbinom.log_cdf(x)91      self.assertEqual([6], log_cdf.get_shape())92      self.assertAllClose(expected_log_cdf, log_cdf.eval())93      cdf = negbinom.cdf(x)94      self.assertEqual([6], cdf.get_shape())95      self.assertAllClose(np.exp(expected_log_cdf), cdf.eval())96  def testNegativeBinomialLogCdfValidateArgs(self):97    with self.cached_session():98      batch_size = 699      probs = [.9] * batch_size100      total_count = 5.101      with self.assertRaisesOpError("Condition x >= 0"):102        negbinom = negative_binomial.NegativeBinomial(103            total_count=total_count, probs=probs, validate_args=True)104        negbinom.log_cdf(-1.).eval()105  def testNegativeBinomialLogPmf(self):106    with self.cached_session():107      batch_size = 6108      probs = [.2] * batch_size109      probs_v = .2110      total_count = 5.111      x = np.array([2., 3., 4., 5., 6., 7.], dtype=np.float32)112      negbinom = negative_binomial.NegativeBinomial(113          total_count=total_count, probs=probs)114      expected_log_pmf = stats.nbinom.logpmf(x, n=total_count, p=1 - probs_v)115      log_pmf = negbinom.log_prob(x)116      self.assertEqual([6], log_pmf.get_shape())117      self.assertAllClose(expected_log_pmf, log_pmf.eval())118      pmf = negbinom.prob(x)119      self.assertEqual([6], pmf.get_shape())120      self.assertAllClose(np.exp(expected_log_pmf), pmf.eval())121  def testNegativeBinomialLogPmfValidateArgs(self):122    with self.cached_session():123      batch_size = 6124      probs = [.9] * batch_size125      total_count = 5.126      x = array_ops.placeholder(dtypes.float32, shape=[6])127      feed_dict = {x: [2.5, 3.2, 4.3, 5.1, 6., 7.]}128      negbinom = negative_binomial.NegativeBinomial(129          total_count=total_count, probs=probs, validate_args=True)130      with self.assertRaisesOpError("Condition x == y"):131        log_pmf = negbinom.log_prob(x)132        log_pmf.eval(feed_dict=feed_dict)133      with self.assertRaisesOpError("Condition x >= 0"):134        log_pmf = negbinom.log_prob([-1.])135        log_pmf.eval(feed_dict=feed_dict)136      negbinom = negative_binomial.NegativeBinomial(137          total_count=total_count, probs=probs, validate_args=False)138      log_pmf = negbinom.log_prob(x)139      self.assertEqual([6], log_pmf.get_shape())140      pmf = negbinom.prob(x)141      self.assertEqual([6], pmf.get_shape())142  def testNegativeBinomialLogPmfMultidimensional(self):143    with self.cached_session():144      batch_size = 6145      probs = constant_op.constant([[.2, .3, .5]] * batch_size)146      probs_v = np.array([.2, .3, .5])147      total_count = 5.148      x = np.array([[2., 3., 4., 5., 6., 7.]], dtype=np.float32).T149      negbinom = negative_binomial.NegativeBinomial(150          total_count=total_count, probs=probs)151      expected_log_pmf = stats.nbinom.logpmf(152          x, n=total_count, p=1 - probs_v)153      log_pmf = negbinom.log_prob(x)154      log_pmf_values = log_pmf.eval()155      self.assertEqual([6, 3], log_pmf.get_shape())156      self.assertAllClose(expected_log_pmf, log_pmf_values)157      pmf = negbinom.prob(x)158      pmf_values = pmf.eval()159      self.assertEqual([6, 3], pmf.get_shape())160      self.assertAllClose(np.exp(expected_log_pmf), pmf_values)161  def testNegativeBinomialMean(self):162    with self.cached_session():163      total_count = 5.164      probs = np.array([.1, .3, .25], dtype=np.float32)165      negbinom = negative_binomial.NegativeBinomial(166          total_count=total_count, probs=probs)167      expected_means = stats.nbinom.mean(n=total_count, p=1 - probs)168      self.assertEqual([3], negbinom.mean().get_shape())169      self.assertAllClose(expected_means, negbinom.mean().eval())170  def testNegativeBinomialVariance(self):171    with self.cached_session():172      total_count = 5.173      probs = np.array([.1, .3, .25], dtype=np.float32)174      negbinom = negative_binomial.NegativeBinomial(175          total_count=total_count, probs=probs)176      expected_vars = stats.nbinom.var(n=total_count, p=1 - probs)177      self.assertEqual([3], negbinom.variance().get_shape())178      self.assertAllClose(expected_vars, negbinom.variance().eval())179  def testNegativeBinomialStddev(self):180    with self.cached_session():181      total_count = 5.182      probs = np.array([.1, .3, .25], dtype=np.float32)183      negbinom = negative_binomial.NegativeBinomial(184          total_count=total_count, probs=probs)185      expected_stds = stats.nbinom.std(n=total_count, p=1 - probs)186      self.assertEqual([3], negbinom.stddev().get_shape())187      self.assertAllClose(expected_stds, negbinom.stddev().eval())188  def testNegativeBinomialSample(self):189    with self.cached_session() as sess:190      probs = [.3, .9]191      total_count = [4., 11.]192      n = int(100e3)193      negbinom = negative_binomial.NegativeBinomial(194          total_count=total_count, probs=probs)195      samples = negbinom.sample(n, seed=12345)196      self.assertEqual([n, 2], samples.get_shape())197      sample_mean = math_ops.reduce_mean(samples, axis=0)198      sample_var = math_ops.reduce_mean(199          (samples - sample_mean[array_ops.newaxis, ...])**2., axis=0)200      sample_min = math_ops.reduce_min(samples)201      [sample_mean_, sample_var_, sample_min_] = sess.run([202          sample_mean, sample_var, sample_min])203      self.assertAllEqual(np.ones(sample_min_.shape, dtype=np.bool),204                          sample_min_ >= 0.0)205      for i in range(2):206        self.assertAllClose(sample_mean_[i],207                            stats.nbinom.mean(total_count[i], 1 - probs[i]),208                            atol=0.,209                            rtol=.02)210        self.assertAllClose(sample_var_[i],211                            stats.nbinom.var(total_count[i], 1 - probs[i]),212                            atol=0.,213                            rtol=.02)214  def testLogProbOverflow(self):215    with self.cached_session() as sess:216      logits = np.float32([20., 30., 40.])217      total_count = np.float32(1.)218      x = np.float32(0.)219      nb = negative_binomial.NegativeBinomial(220          total_count=total_count, logits=logits)221      log_prob_ = sess.run(nb.log_prob(x))222      self.assertAllEqual(np.ones_like(log_prob_, dtype=np.bool),223                          np.isfinite(log_prob_))224  def testLogProbUnderflow(self):225    with self.cached_session() as sess:226      logits = np.float32([-90, -100, -110])227      total_count = np.float32(1.)228      x = np.float32(0.)229      nb = negative_binomial.NegativeBinomial(230          total_count=total_count, logits=logits)231      log_prob_ = sess.run(nb.log_prob(x))232      self.assertAllEqual(np.ones_like(log_prob_, dtype=np.bool),233                          np.isfinite(log_prob_))234if __name__ == "__main__":...

binomial.py

Source:binomial.py

1from numbers import Number2import torch3from torch.distributions import constraints4from torch.distributions.distribution import Distribution5from torch.distributions.utils import broadcast_all, probs_to_logits, lazy_property, logits_to_probs6class Binomial(Distribution):7    r"""8    Creates a Binomial distribution parameterized by `total_count` and9    either `probs` or `logits` (but not both). `total_count` must be10    broadcastable with `probs`/`logits`.11    Example::12        >>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))13        >>> x = m.sample()14        tensor([   0.,   22.,   71.,  100.])15        >>> m = Binomial(torch.tensor([[5.], [10.]]), torch.tensor([0.5, 0.8]))16        >>> x = m.sample()17        tensor([[ 4.,  5.],18                [ 7.,  6.]])19    Args:20        total_count (int or Tensor): number of Bernoulli trials21        probs (Tensor): Event probabilities22        logits (Tensor): Event log-odds23    """24    arg_constraints = {'total_count': constraints.nonnegative_integer,25                       'probs': constraints.unit_interval}26    has_enumerate_support = True27    def __init__(self, total_count=1, probs=None, logits=None, validate_args=None):28        if (probs is None) == (logits is None):29            raise ValueError("Either `probs` or `logits` must be specified, but not both.")30        if probs is not None:31            self.total_count, self.probs, = broadcast_all(total_count, probs)32            self.total_count = self.total_count.type_as(self.logits)33            is_scalar = isinstance(self.probs, Number)34        else:35            self.total_count, self.logits, = broadcast_all(total_count, logits)36            self.total_count = self.total_count.type_as(self.logits)37            is_scalar = isinstance(self.logits, Number)38        self._param = self.probs if probs is not None else self.logits39        if is_scalar:40            batch_shape = torch.Size()41        else:42            batch_shape = self._param.size()43        super(Binomial, self).__init__(batch_shape, validate_args=validate_args)44    def _new(self, *args, **kwargs):45        return self._param.new(*args, **kwargs)46    @constraints.dependent_property47    def support(self):48        return constraints.integer_interval(0, self.total_count)49    @property50    def mean(self):51        return self.total_count * self.probs52    @property53    def variance(self):54        return self.total_count * self.probs * (1 - self.probs)55    @lazy_property56    def logits(self):57        return probs_to_logits(self.probs, is_binary=True)58    @lazy_property59    def probs(self):60        return logits_to_probs(self.logits, is_binary=True)61    @property62    def param_shape(self):63        return self._param.size()64    def sample(self, sample_shape=torch.Size()):65        with torch.no_grad():66            max_count = max(int(self.total_count.max()), 1)67            shape = self._extended_shape(sample_shape) + (max_count,)68            bernoullis = torch.bernoulli(self.probs.unsqueeze(-1).expand(shape))69            if self.total_count.min() != max_count:70                arange = torch.arange(max_count, out=self.total_count.new_empty(max_count))71                mask = arange >= self.total_count.unsqueeze(-1)72                bernoullis.masked_fill_(mask, 0.)73            return bernoullis.sum(dim=-1)74    def log_prob(self, value):75        if self._validate_args:76            self._validate_sample(value)77        log_factorial_n = torch.lgamma(self.total_count + 1)78        log_factorial_k = torch.lgamma(value + 1)79        log_factorial_nmk = torch.lgamma(self.total_count - value + 1)80        max_val = (-self.logits).clamp(min=0.0)81        # Note that: torch.log1p(-self.probs)) = max_val - torch.log1p((self.logits + 2 * max_val).exp()))82        return (log_factorial_n - log_factorial_k - log_factorial_nmk +83                value * self.logits + self.total_count * max_val -84                self.total_count * torch.log1p((self.logits + 2 * max_val).exp()))85    def enumerate_support(self):86        total_count = int(self.total_count.max())87        if not self.total_count.min() == total_count:88            raise NotImplementedError("Inhomogeneous total count not supported by `enumerate_support`.")89        values = self._new(1 + total_count,)90        torch.arange(1 + total_count, out=values)91        values = values.view((-1,) + (1,) * len(self._batch_shape))92        values = values.expand((-1,) + self._batch_shape)...

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