Fix docstrings.

Added raw string qualifiers for docstrings that were causing the deprecation warnings in issue #3878.
In passing, fixed some other minor docstring formatting issues.

Author: rpgoldman

pymc3/distributions/continuous.py

@@ -103,7 +103,7 @@ def assert_negative_support(var, label, distname, value=-1e-6):
 
 
 def get_tau_sigma(tau=None, sigma=None):
-    """
+    r"""
     Find precision and standard deviation. The link between the two
     parameterizations is given by the inverse relationship:
 
@@ -770,7 +770,7 @@ def _repr_latex_(self, name=None, dist=None):
         name = r'\text{%s}' % name
         return (
             r'${} \sim \text{{TruncatedNormal}}('
-            '\mathit{{mu}}={},~\mathit{{sigma}}={},a={},b={})$'
+            r'\mathit{{mu}}={},~\mathit{{sigma}}={},a={},b={})$'
             .format(
                 name,
                 get_variable_name(self.mu),
@@ -2968,7 +2968,7 @@ def _repr_latex_(self, name=None, dist=None):
                                                                 get_variable_name(beta))
 
     def logcdf(self, value):
-        """
+        r"""
         Compute the log of the cumulative distribution function for Weibull distribution
         at the specified value.
 
@@ -4130,7 +4130,7 @@ def _repr_latex_(self, name=None, dist=None):
                                                                 get_variable_name(s))
 
     def logcdf(self, value):
-        """
+        r"""
         Compute the log of the cumulative distribution function for Logistic distribution
         at the specified value.
 

pymc3/distributions/discrete.py

@@ -80,7 +80,7 @@ def __init__(self, n, p, *args, **kwargs):
         self.mode = tt.cast(tround(n * p), self.dtype)
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from Binomial distribution.
 
         Parameters
@@ -102,7 +102,7 @@ def random(self, point=None, size=None):
                                 size=size)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of Binomial distribution at specified value.
 
         Parameters
@@ -215,7 +215,7 @@ def _random(self, alpha, beta, n, size=None):
         return samples
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from BetaBinomial distribution.
 
         Parameters
@@ -238,7 +238,7 @@ def random(self, point=None, size=None):
                                 size=size)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of BetaBinomial distribution at specified value.
 
         Parameters
@@ -326,7 +326,7 @@ def __init__(self, p=None, logit_p=None, *args, **kwargs):
         self.mode = tt.cast(tround(self.p), 'int8')
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from Bernoulli distribution.
 
         Parameters
@@ -348,7 +348,7 @@ def random(self, point=None, size=None):
                                 size=size)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of Bernoulli distribution at specified value.
 
         Parameters
@@ -427,7 +427,7 @@ def __init__(self, q, beta, *args, **kwargs):
         self.median = self._ppf(0.5)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of DiscreteWeibull distribution at specified value.
 
         Parameters
@@ -449,7 +449,7 @@ def logp(self, value):
                      0 < beta)
 
     def _ppf(self, p):
-        """
+        r"""
         The percentile point function (the inverse of the cumulative
         distribution function) of the discrete Weibull distribution.
         """
@@ -464,7 +464,7 @@ def _random(self, q, beta, size=None):
         return np.ceil(np.power(np.log(1 - p) / np.log(q), 1. / beta)) - 1
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from DiscreteWeibull distribution.
 
         Parameters
@@ -547,7 +547,7 @@ def __init__(self, mu, *args, **kwargs):
         self.mode = intX(tt.floor(mu))
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from Poisson distribution.
 
         Parameters
@@ -569,7 +569,7 @@ def random(self, point=None, size=None):
                                 size=size)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of Poisson distribution at specified value.
 
         Parameters
@@ -656,7 +656,7 @@ def __init__(self, mu, alpha, *args, **kwargs):
         self.mode = intX(tt.floor(mu))
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from NegativeBinomial distribution.
 
         Parameters
@@ -680,7 +680,7 @@ def random(self, point=None, size=None):
         return np.asarray(stats.poisson.rvs(g)).reshape(g.shape)
 
     def _random(self, mu, alpha, size):
-        """ Wrapper around stats.gamma.rvs that converts NegativeBinomial's
+        r""" Wrapper around stats.gamma.rvs that converts NegativeBinomial's
         parametrization to scipy.gamma. All parameter arrays should have
         been broadcasted properly by generate_samples at this point and size is
         the scipy.rvs representation.
@@ -692,7 +692,7 @@ def _random(self, mu, alpha, size):
         )
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of NegativeBinomial distribution at specified value.
 
         Parameters
@@ -771,7 +771,7 @@ def __init__(self, p, *args, **kwargs):
         self.mode = 1
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from Geometric distribution.
 
         Parameters
@@ -793,7 +793,7 @@ def random(self, point=None, size=None):
                                 size=size)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of Geometric distribution at specified value.
 
         Parameters
@@ -872,7 +872,7 @@ def _random(self, lower, upper, size=None):
         return samples
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from DiscreteUniform distribution.
 
         Parameters
@@ -895,7 +895,7 @@ def random(self, point=None, size=None):
                                 size=size)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of DiscreteUniform distribution at specified value.
 
         Parameters
@@ -975,7 +975,7 @@ def __init__(self, p, *args, **kwargs):
             self.mode = tt.squeeze(self.mode)
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from Categorical distribution.
 
         Parameters
@@ -1001,7 +1001,7 @@ def random(self, point=None, size=None):
                                 size=size)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of Categorical distribution at specified value.
 
         Parameters
@@ -1054,7 +1054,7 @@ def _repr_latex_(self, name=None, dist=None):
 
 
 class Constant(Discrete):
-    """
+    r"""
     Constant log-likelihood.
 
     Parameters
@@ -1070,7 +1070,7 @@ def __init__(self, c, *args, **kwargs):
         self.mean = self.median = self.mode = self.c = c = tt.as_tensor_variable(c)
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from Constant distribution.
 
         Parameters
@@ -1096,7 +1096,7 @@ def _random(c, dtype=dtype, size=None):
                                 size=size).astype(dtype)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of Constant distribution at specified value.
 
         Parameters
@@ -1180,7 +1180,7 @@ def __init__(self, psi, theta, *args, **kwargs):
         self.mode = self.pois.mode
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from ZeroInflatedPoisson distribution.
 
         Parameters
@@ -1204,7 +1204,7 @@ def random(self, point=None, size=None):
         return g * (np.random.random(g.shape) < psi)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of ZeroInflatedPoisson distribution at specified value.
 
         Parameters
@@ -1302,7 +1302,7 @@ def __init__(self, psi, n, p, *args, **kwargs):
         self.mode = self.bin.mode
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from ZeroInflatedBinomial distribution.
 
         Parameters
@@ -1326,7 +1326,7 @@ def random(self, point=None, size=None):
         return g * (np.random.random(g.shape) < psi)
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of ZeroInflatedBinomial distribution at specified value.
 
         Parameters
@@ -1448,7 +1448,7 @@ def __init__(self, psi, mu, alpha, *args, **kwargs):
         self.mode = self.nb.mode
 
     def random(self, point=None, size=None):
-        """
+        r"""
         Draw random values from ZeroInflatedNegativeBinomial distribution.
 
         Parameters
@@ -1478,7 +1478,7 @@ def random(self, point=None, size=None):
         return stats.poisson.rvs(g) * (np.random.random(g.shape) < psi)
 
     def _random(self, mu, alpha, size):
-        """ Wrapper around stats.gamma.rvs that converts NegativeBinomial's
+        r""" Wrapper around stats.gamma.rvs that converts NegativeBinomial's
         parametrization to scipy.gamma. All parameter arrays should have
         been broadcasted properly by generate_samples at this point and size is
         the scipy.rvs representation.
@@ -1490,7 +1490,7 @@ def _random(self, mu, alpha, size):
         )
 
     def logp(self, value):
-        """
+        r"""
         Calculate log-probability of ZeroInflatedNegativeBinomial distribution at specified value.
 
         Parameters

pymc3/gp/gp.py

@@ -224,7 +224,7 @@ def conditional(self, name, Xnew, given=None, **kwargs):
 
 @conditioned_vars(["X", "f", "nu"])
 class TP(Latent):
-    """
+    r"""
     Student's T process prior.
 
     The usage is nearly identical to that of `gp.Latent`.  The differences
@@ -239,11 +239,11 @@ class TP(Latent):
 
     Parameters
     ----------
-    cov_func: None, 2D array, or instance of Covariance
+    cov_func : None, 2D array, or instance of Covariance
         The covariance function.  Defaults to zero.
-    mean_func: None, instance of Mean
+    mean_func : None, instance of Mean
         The mean function.  Defaults to zero.
-    nu: float
+    nu : float
         The degrees of freedom
 
     References

pymc3/math.py

@@ -37,12 +37,18 @@
 
 
 def kronecker(*Ks):
-    """Return the Kronecker product of arguments:
+    r"""Return the Kronecker product of arguments:
           :math:`K_1 \otimes K_2 \otimes ... \otimes K_D`
 
     Parameters
     ----------
-    Ks: 2D array-like
+    Ks : Iterable of 2D array-like
+        Arrays of which to take the product.
+
+    Returns
+    -------
+    np.ndarray :
+        Block matrix Kroncker product of the argument matrices.
     """
     return reduce(tt.slinalg.kron, Ks)
 
@@ -60,16 +66,20 @@ def cartesian(*arrays):
 
 
 def kron_matrix_op(krons, m, op):
-    """Apply op to krons and m in a way that reproduces ``op(kronecker(*krons), m)``
+    r"""Apply op to krons and m in a way that reproduces ``op(kronecker(*krons), m)``
 
     Parameters
     -----------
-    krons: list of square 2D array-like objects
-            D square matrices :math:`[A_1, A_2, ..., A_D]` to be Kronecker'ed
-            :math:`A = A_1 \otimes A_2 \otimes ... \otimes A_D`
-            Product of column dimensions must be :math:`N`
-    m   : NxM array or 1D array (treated as Nx1)
-           Object that krons act upon
+    krons : list of square 2D array-like objects
+        D square matrices :math:`[A_1, A_2, ..., A_D]` to be Kronecker'ed
+        :math:`A = A_1 \otimes A_2 \otimes ... \otimes A_D`
+        Product of column dimensions must be :math:`N`
+    m : NxM array or 1D array (treated as Nx1)
+        Object that krons act upon
+
+    Returns
+    -------
+    numpy array
     """
     def flat_matrix_op(flat_mat, mat):
         Nmat = mat.shape[1]