Improve shape handling in generate_samples

.travis.yml

@@ -22,14 +22,14 @@ install:
   - conda list && pip freeze
 
 env:
-  - FLOATX='float32' TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py --ignore=pymc3/tests/test_parallel_sampling.py --ignore=pymc3/tests/test_dist_math.py --ignore=pymc3/tests/test_distribution_defaults.py --ignore=pymc3/tests/test_distributions_timeseries.py --ignore=pymc3/tests/test_random.py --ignore=pymc3/tests/test_gp.py"
+  - FLOATX='float32' TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py --ignore=pymc3/tests/test_parallel_sampling.py --ignore=pymc3/tests/test_dist_math.py --ignore=pymc3/tests/test_distribution_defaults.py --ignore=pymc3/tests/test_distributions_timeseries.py --ignore=pymc3/tests/test_random.py --ignore=pymc3/tests/test_gp.py --ignore=pymc3/tests/test_shape_handling.py"
   - FLOATX='float32' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py pymc3/tests/test_parallel_sampling.py pymc3/tests/test_dist_math.py pymc3/tests/test_distribution_defaults.py pymc3/tests/test_distributions_timeseries.py pymc3/tests/test_random.py"
   - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py pymc3/tests/test_gp.py"
-  - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
-  - FLOATX='float64' TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py --ignore=pymc3/tests/test_parallel_sampling.py --ignore=pymc3/tests/test_dist_math.py --ignore=pymc3/tests/test_distribution_defaults.py --ignore=pymc3/tests/test_distributions_timeseries.py --ignore=pymc3/tests/test_random.py --ignore=pymc3/tests/test_gp.py"
+  - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py pymc3/tests/test_shape_handling.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py --ignore=pymc3/tests/test_parallel_sampling.py --ignore=pymc3/tests/test_dist_math.py --ignore=pymc3/tests/test_distribution_defaults.py --ignore=pymc3/tests/test_distributions_timeseries.py --ignore=pymc3/tests/test_random.py --ignore=pymc3/tests/test_gp.py --ignore=pymc3/tests/test_shape_handling.py"
   - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py pymc3/tests/test_parallel_sampling.py pymc3/tests/test_dist_math.py pymc3/tests/test_distribution_defaults.py pymc3/tests/test_distributions_timeseries.py pymc3/tests/test_random.py"
   - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py pymc3/tests/test_gp.py"
-  - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py pymc3/tests/test_shape_handling.py"
 
 script:
   - . ./scripts/test.sh $TESTCMD

RELEASE-NOTES.md

@@ -8,6 +8,8 @@
 - Add function `set_data` to update variables defined as `Data`.
 - `Mixture` now supports mixtures of multidimensional probability distributions, not just lists of 1D distributions.
 - `GLM.from_formula` and `LinearComponent.from_formula` can extract variables from the calling scope. Customizable via the new `eval_env` argument. Fixing #3382.
+- Added the `distributions.shape_utils` module with functions used to help broadcast samples drawn from distributions using the `size` keyword argument.
+- Used `numpy.vectorize` in `distributions.distribution._compile_theano_function`. This enables `sample_prior_predictive` and `sample_posterior_predictive` to ask for tuples of samples instead of just integers. This fixes issue #3422.
 
 ### Maintenance
 - All occurances of `sd` as a parameter name have been renamed to `sigma`. `sd` will continue to function for backwards compatibility.
@@ -31,6 +33,10 @@
 - Add `sigma`, `tau`, and `sd` to signature of `NormalMixture`.
 - Resolved issue #3248. Set default lower and upper values of -inf and inf for pm.distributions.continuous.TruncatedNormal. This avoids errors caused by their previous values of None.
 - Resolved issue #3399. Converted all calls to `pm.distributions.bound._ContinuousBounded` and `pm.distributions.bound._DiscreteBounded` to use only and all positional arguments.
+- Restructured `distributions.distribution.generate_samples` to use the `shape_utils` module. This solves issues #3421 and #3147 by using the `size` aware broadcating functions in `shape_utils`.
+- Fixed the `Multinomial.random` and `Multinomial.random_` methods to make them compatible with the new `generate_samples` function. In the process, a bug of the `Multinomial.random_` shape handling was discovered and fixed.
+- Fixed a defect found in `Bound.random` where the `point` dictionary was passed to `generate_samples` as an `arg` instead of in `not_broadcast_kwargs`.
+- Fixed a defect found in `Bound.random_` where `total_size` could end up as a `float64` instead of being an integer if given `size=tuple()`.
 
 ### Deprecations
 

pymc3/distributions/bound.py

@@ -59,7 +59,7 @@ def _random(self, lower, upper, point=None, size=None):
                 "Drawing samples from distributions with "
                 "array-valued bounds is not supported."
             )
-        total_size = np.prod(size)
+        total_size = np.prod(size).astype(np.int)
         samples = []
         s = 0
         while s < total_size:
@@ -81,17 +81,32 @@ def random(self, point=None, size=None):
         elif self.lower is not None and self.upper is not None:
             lower, upper = draw_values([self.lower, self.upper], point=point, size=size)
             return generate_samples(
-                self._random, lower, upper, point, dist_shape=self.shape, size=size
+                self._random,
+                lower,
+                upper,
+                dist_shape=self.shape,
+                size=size,
+                not_broadcast_kwargs={'point': point},
             )
         elif self.lower is not None:
             lower = draw_values([self.lower], point=point, size=size)
             return generate_samples(
-                self._random, lower, np.inf, point, dist_shape=self.shape, size=size
+                self._random,
+                lower,
+                np.inf,
+                dist_shape=self.shape,
+                size=size,
+                not_broadcast_kwargs={'point': point},
             )
         else:
             upper = draw_values([self.upper], point=point, size=size)
             return generate_samples(
-                self._random, -np.inf, upper, point, dist_shape=self.shape, size=size
+                self._random,
+                -np.inf,
+                upper,
+                dist_shape=self.shape,
+                size=size,
+                not_broadcast_kwargs={'point': point},
             )
 
 

pymc3/distributions/continuous.py

@@ -19,8 +19,7 @@
     alltrue_elemwise, betaln, bound, gammaln, i0e, incomplete_beta, logpow,
     normal_lccdf, normal_lcdf, SplineWrapper, std_cdf, zvalue,
 )
-from .distribution import (Continuous, draw_values, generate_samples,
-                           broadcast_distribution_samples)
+from .distribution import (Continuous, draw_values, generate_samples)
 
 __all__ = ['Uniform', 'Flat', 'HalfFlat', 'Normal', 'TruncatedNormal', 'Beta',
            'Kumaraswamy', 'Exponential', 'Laplace', 'StudentT', 'Cauchy',
@@ -966,8 +965,6 @@ def random(self, point=None, size=None):
         """
         mu, lam, alpha = draw_values([self.mu, self.lam, self.alpha],
                                      point=point, size=size)
-        mu, lam, alpha = broadcast_distribution_samples([mu, lam, alpha],
-                                                        size=size)
         return generate_samples(self._random,
                                 mu, lam, alpha,
                                 dist_shape=self.shape,
@@ -1297,7 +1294,6 @@ def random(self, point=None, size=None):
         """
         a, b = draw_values([self.a, self.b],
                            point=point, size=size)
-        a, b = broadcast_distribution_samples([a, b], size=size)
         return generate_samples(self._random, a, b,
                                 dist_shape=self.shape,
                                 size=size)
@@ -1674,7 +1670,6 @@ def random(self, point=None, size=None):
         array
         """
         mu, tau = draw_values([self.mu, self.tau], point=point, size=size)
-        mu, tau = broadcast_distribution_samples([mu, tau], size=size)
         return generate_samples(self._random, mu, tau,
                                 dist_shape=self.shape,
                                 size=size)
@@ -1965,7 +1960,6 @@ def random(self, point=None, size=None):
         """
         alpha, m = draw_values([self.alpha, self.m],
                                point=point, size=size)
-        alpha, m = broadcast_distribution_samples([alpha, m], size=size)
         return generate_samples(self._random, alpha, m,
                                 dist_shape=self.shape,
                                 size=size)
@@ -2090,7 +2084,6 @@ def random(self, point=None, size=None):
         """
         alpha, beta = draw_values([self.alpha, self.beta],
                                   point=point, size=size)
-        alpha, beta = broadcast_distribution_samples([alpha, beta], size=size)
         return generate_samples(self._random, alpha, beta,
                                 dist_shape=self.shape,
                                 size=size)
@@ -2669,7 +2662,6 @@ def random(self, point=None, size=None):
         """
         alpha, beta = draw_values([self.alpha, self.beta],
                                   point=point, size=size)
-        alpha, beta = broadcast_distribution_samples([alpha, beta], size=size)
 
         def _random(a, b, size=None):
             return b * (-np.log(np.random.uniform(size=size)))**(1 / a)
@@ -2963,8 +2955,6 @@ def random(self, point=None, size=None):
         """
         mu, sigma, nu = draw_values([self.mu, self.sigma, self.nu],
                                     point=point, size=size)
-        mu, sigma, nu = broadcast_distribution_samples([mu, sigma, nu],
-                                                       size=size)
 
         def _random(mu, sigma, nu, size=None):
             return (np.random.normal(mu, sigma, size=size)
@@ -3369,7 +3359,7 @@ def random(self, point=None, size=None):
         scale = upper - lower
         c_ = (c - lower) / scale
         return generate_samples(stats.triang.rvs, c=c_, loc=lower, scale=scale,
-                                size=size, dist_shape=self.shape, random_state=None)
+                                size=size, dist_shape=self.shape)
 
     def logp(self, value):
         """

pymc3/distributions/discrete.py

@@ -5,8 +5,8 @@
 
 from pymc3.util import get_variable_name
 from .dist_math import bound, factln, binomln, betaln, logpow, random_choice
-from .distribution import (Discrete, draw_values, generate_samples,
-                           broadcast_distribution_samples)
+from .distribution import Discrete, draw_values, generate_samples
+from .shape_utils import broadcast_distribution_samples
 from pymc3.math import tround, sigmoid, logaddexp, logit, log1pexp
 from ..theanof import floatX, intX
 
@@ -351,7 +351,6 @@ def _random(self, q, beta, size=None):
 
     def random(self, point=None, size=None):
         q, beta = draw_values([self.q, self.beta], point=point, size=size)
-        q, beta = broadcast_distribution_samples([q, beta], size=size)
 
         return generate_samples(self._random, q, beta,
                                 dist_shape=self.shape,

pymc3/distributions/dist_math.py

@@ -11,6 +11,7 @@
 from theano.tensor.slinalg import Cholesky
 from theano.scan_module import until
 from theano import scan
+from .shape_utils import to_tuple
 
 from .special import gammaln
 from pymc3.theanof import floatX
@@ -20,17 +21,6 @@
 c = - .5 * np.log(2. * np.pi)
 
 
-def to_tuple(shape):
-    """Convert ints, arrays, and Nones to tuples"""
-    if shape is None:
-        return tuple()
-    temp = np.atleast_1d(shape)
-    if temp.size == 0:
-        return tuple()
-    else:
-        return tuple(temp)
-
-
 def bound(logp, *conditions, **kwargs):
     """
     Bounds a log probability density with several conditions.