def testChiChiKL(self):
# We make sure a_df and b_df don't have any overlap. If this is not done,
# then the check for true_kl vs kl_sample_ ends up failing because the
# true_kl is zero and the sample is nonzero (though very small).
a_df = np.arange(1, 6, step=2, dtype=np.float64)
b_df = np.arange(2, 7, step=2, dtype=np.float64)
a_df = a_df.reshape((len(a_df), 1))
b_df = b_df.reshape((1, len(b_df)))
a = tfd.Chi(df=a_df)
b = tfd.Chi(df=b_df)
true_kl = (0.5 * special.digamma(0.5 * a_df) * (a_df - b_df) +
special.gammaln(0.5 * b_df) - special.gammaln(0.5 * a_df))
kl = tfd.kl_divergence(a, b)
x = a.sample(int(8e5), seed=test_util.test_seed())
kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0)
kl_, kl_sample_ = self.evaluate([kl, kl_sample])
self.assertAllClose(true_kl, kl_, atol=0., rtol=1e-14)
self.assertAllClose(true_kl, kl_sample_, atol=0., rtol=5e-2)
zero_kl = tfd.kl_divergence(a, a)
true_zero_kl_, zero_kl_ = self.evaluate(
[tf.zeros_like(zero_kl), zero_kl])
self.assertAllEqual(true_zero_kl_, zero_kl_)
def testAssertsPositiveDfAfterMutation(self):
df = tf.Variable([1., 2., 3.])
d = tfd.Chi(df, validate_args=True)
self.evaluate([v.initializer for v in d.variables])
with self.assertRaisesOpError('Argument `df` must be positive.'):
with tf.control_dependencies([df.assign([1., 2., -3.])]):
self.evaluate(d.sample())
def testLogPdfAssertsOnInvalidSample(self):
d = tfd.Chi(df=13.37, validate_args=True)
with self.assertRaisesOpError('All elements must be non-negative.'):
self.evaluate(d.log_prob([14.2, -5.3]))
with self.assertRaisesOpError('Sample must be positive.'):
print(self.evaluate(d.log_prob([0.0, 0.0])))
def testGradientThroughParams(self):
df = tf.Variable(19.43, dtype=tf.float64)
d = tfd.Chi(df, validate_args=True)
with tf.GradientTape() as tape:
loss = -d.log_prob([1., 2., 3.])
grad = tape.gradient(loss, d.trainable_variables)
self.assertLen(grad, 1)
self.assertAllNotNone(grad)
def testChiCDF(self):
df = np.arange(1, 6, dtype=np.float64)
x = np.arange(1, 6, dtype=np.float64)
df = df.reshape((len(df), 1))
x = x.reshape((1, len(x)))
chi = tfd.Chi(df=df)
expected_cdf = stats.chi.cdf(x, df)
cdf = chi.cdf(x)
self.assertEqual(cdf.shape, np.broadcast(df, x).shape)
self.assertAllClose(self.evaluate(cdf), expected_cdf)
def testChiLogPDF(self):
df = np.arange(1, 6, dtype=np.float64)
x = np.arange(1, 6, dtype=np.float64)
df = df.reshape((len(df), 1))
x = x.reshape((1, len(x)))
chi = tfd.Chi(df=df)
expected_log_pdf = stats.chi.logpdf(x, df)
log_pdf = chi.log_prob(x)
self.assertEqual(log_pdf.shape, np.broadcast(df, x).shape)
self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf)
pdf = chi.prob(x)
self.assertEqual(pdf.shape, np.broadcast(df, x).shape)
self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf))
def testChiEntropy(self):
df = np.arange(1, 6, dtype=np.float64)
expected_entropy = stats.chi.entropy(df)
chi = tfd.Chi(df=df)
self.assertEqual(chi.entropy().shape, df.shape)
self.assertAllClose(self.evaluate(chi.entropy()), expected_entropy)
def testChiVariance(self):
df = np.arange(1, 6, dtype=np.float64)
expected_variances = stats.chi.var(df)
chi = tfd.Chi(df=df)
self.assertEqual(chi.variance().shape, df.shape)
self.assertAllClose(self.evaluate(chi.variance()), expected_variances)
def testChiMean(self):
df = np.arange(1, 6, dtype=np.float64)
expected_mean = stats.chi.mean(df)
chi = tfd.Chi(df=df)
self.assertEqual(chi.mean().shape, df.shape)
self.assertAllClose(self.evaluate(chi.mean()), expected_mean)
def testAssertsPositiveDf(self):
df = tf.Variable([1., 2., -3.])
with self.assertRaisesOpError('Argument `df` must be positive.'):
d = tfd.Chi(df, validate_args=True)
self.evaluate([v.initializer for v in d.variables])
self.evaluate(d.entropy())
def testPdfOnBoundary(self):
d = tfd.Chi(df=[1., 3.], validate_args=True)
log_prob_boundary = self.evaluate(d.log_prob(0.))
self.assertAllNegativeInf(log_prob_boundary[1])
def testLogPdfAssertsOnInvalidSample(self):
d = tfd.Chi(df=13.37, validate_args=True)
with self.assertRaisesOpError('Condition x >= 0'):
self.evaluate(d.log_prob([14.2, -5.3]))
