Put summary line on only one line (D205)

Author: Armavica

benchmarks/benchmarks/benchmarks.py

@@ -77,10 +77,7 @@ def mixture_model(random_seed=1234):
 
 
 class OverheadSuite:
-    """
-    Just tests how long sampling from a normal distribution takes for various
-    samplers.
-    """
+    """Test how long sampling from a normal distribution takes for various samplers."""
 
     params = [pm.NUTS, pm.HamiltonianMC, pm.Metropolis, pm.Slice]
     timer = timeit.default_timer

pymc/backends/base.py

@@ -102,8 +102,12 @@ def _slice(self, idx: slice) -> "IBaseTrace":
         raise NotImplementedError()
 
     def point(self, idx: int) -> dict[str, np.ndarray]:
-        """Return dictionary of point values at `idx` for current chain
-        with variables names as keys.
+        """Return point values at `idx` for current chain.
+
+        Returns
+        -------
+        values : dict[str, np.ndarray]
+            Dictionary of values with variable names as keys.
         """
         raise NotImplementedError()
 
@@ -568,9 +572,7 @@ def points(self, chains=None):
 
 
 def _squeeze_cat(results, combine: bool, squeeze: bool):
-    """Squeeze and concatenate the results depending on values of
-    `combine` and `squeeze`.
-    """
+    """Squeeze and/or concatenate the results."""
     if combine:
         results = np.concatenate(results)
         if not squeeze:

pymc/backends/ndarray.py

@@ -184,8 +184,12 @@ def _slice(self, idx: slice):
         return sliced
 
     def point(self, idx) -> dict[str, Any]:
-        """Return dictionary of point values at `idx` for current chain
-        with variable names as keys.
+        """Return point values at `idx` for current chain.
+
+        Returns
+        -------
+        values : dict[str, Any]
+            Dictionary of values with variable names as keys.
         """
         idx = int(idx)
         return {varname: values[idx] for varname, values in self.samples.items()}

pymc/data.py

@@ -87,9 +87,9 @@ def clone(self):
 
 
 class GeneratorAdapter:
-    """
-    Helper class that helps to infer data type of generator with looking
-    at the first item, preserving the order of the resulting generator.
+    """Class that helps infer data type of generator.
+
+    It looks at the first item, preserving the order of the resulting generator.
     """
 
     def make_variable(self, gop, name=None):

pymc/distributions/continuous.py

@@ -14,10 +14,7 @@
 
 # Contains code from AePPL, Copyright (c) 2021-2022, Aesara Developers.
 
-"""
-A collection of common probability distributions for stochastic
-nodes in PyMC.
-"""
+"""A collection of common probability distributions for stochastic nodes in PyMC."""
 
 import warnings
 
@@ -371,10 +368,7 @@ def rng_fn(cls, rng, size):
 
 
 class Flat(Continuous):
-    """
-    Uninformative log-likelihood that returns 0 regardless of
-    the passed value.
-    """
+    """Uninformative log-likelihood that returns 0 regardless of the passed value."""
 
     rv_op = flat
 
@@ -3767,8 +3761,7 @@ def rng_fn(cls, rng, x, pdf, cdf, size=None) -> np.ndarray:
 
 class Interpolated(BoundedContinuous):
     r"""
-    Univariate probability distribution defined as a linear interpolation
-    of probability density function evaluated on some lattice of points.
+    Univariate linear interpolation of pdf evaluated on some lattice of points.
 
     The lattice can be uneven, so the steps between different points can have
     different size and it is possible to vary the precision between regions

pymc/distributions/dist_math.py

@@ -172,16 +172,16 @@ def log_diff_normal_cdf(mu, sigma, x, y):
 
 
 def sigma2rho(sigma):
-    """
-    `sigma -> rho` PyTensor converter
+    """Convert `sigma` into `rho` with PyTensor.
+
     :math:`mu + sigma*e = mu + log(1+exp(rho))*e`.
     """
     return pt.log(pt.exp(pt.abs(sigma)) - 1.0)
 
 
 def rho2sigma(rho):
-    """
-    `rho -> sigma` PyTensor converter
+    """Convert `rho` to `sigma` with PyTensor.
+
     :math:`mu + sigma*e = mu + log(1+exp(rho))*e`.
     """
     return pt.softplus(rho)
@@ -193,8 +193,7 @@ def rho2sigma(rho):
 
 def log_normal(x, mean, **kwargs):
     """
-    Calculate logarithm of normal distribution at point `x`
-    with given `mean` and `std`.
+    Calculate logarithm of normal distribution at point `x` with given `mean` and `std`.
 
     Parameters
     ----------

pymc/distributions/distribution.py

@@ -193,8 +193,9 @@ def support_point(op, rv, *dist_params):
 
 
 class _class_or_instancemethod(classmethod):
-    """Allow a method to be called both as a classmethod and an instancemethod,
-    giving priority to the instancemethod.
+    """Allow a method to be called both as a classmethod and an instancemethod.
+
+    Priority is given to the instancemethod.
 
     This is used to allow extracting information from the signature of a SymbolicRandomVariable
     which may be provided either as a class attribute or as an instance attribute.
@@ -580,10 +581,7 @@ def dist(
 
 @node_rewriter([SymbolicRandomVariable])
 def inline_symbolic_random_variable(fgraph, node):
-    """
-    Optimization that expands the internal graph of a SymbolicRV when obtaining the logp
-    graph, if the flag `inline_logprob` is True.
-    """
+    """Expand a SymbolicRV when obtaining the logp graph if `inline_logprob` is True."""
     op = node.op
     if op.inline_logprob:
         return clone_replace(op.inner_outputs, dict(zip(op.inner_inputs, node.inputs)))

pymc/distributions/mixture.py

@@ -702,6 +702,7 @@ def dist(cls, psi, n, p, **kwargs):
 class ZeroInflatedNegativeBinomial:
     R"""
     Zero-Inflated Negative binomial log-likelihood.
+
     The Zero-inflated version of the Negative Binomial (NB).
     The NB distribution describes a Poisson random variable
     whose rate parameter is gamma distributed.

pymc/distributions/multivariate.py

@@ -279,8 +279,7 @@ def support_point(rv, size, mu, cov):
 
     def logp(value, mu, cov):
         """
-        Calculate log-probability of Multivariate Normal distribution
-        at specified value.
+        Calculate logp of Multivariate Normal distribution at specified value.
 
         Parameters
         ----------
@@ -469,8 +468,7 @@ def support_point(rv, size, nu, mu, scale):
 
     def logp(value, nu, mu, scale):
         """
-        Calculate log-probability of Multivariate Student's T distribution
-        at specified value.
+        Calculate logp of Multivariate Student's T distribution at specified value.
 
         Parameters
         ----------
@@ -535,8 +533,7 @@ def support_point(rv, size, a):
 
     def logp(value, a):
         """
-        Calculate log-probability of Dirichlet distribution
-        at specified value.
+        Calculate logp of Dirichlet distribution at specified value.
 
         Parameters
         ----------
@@ -642,8 +639,7 @@ def support_point(rv, size, n, p):
 
     def logp(value, n, p):
         """
-        Calculate log-probability of Multinomial distribution
-        at specified value.
+        Calculate logp of Multinomial distribution at specified value.
 
         Parameters
         ----------
@@ -736,8 +732,7 @@ def support_point(rv, size, n, a):
 
     def logp(value, n, a):
         """
-        Calculate log-probability of DirichletMultinomial distribution
-        at specified value.
+        Calculate logp of DirichletMultinomial distribution at specified value.
 
         Parameters
         ----------
@@ -773,6 +768,7 @@ def logp(value, n, a):
 class _OrderedMultinomial(Multinomial):
     r"""
     Underlying class for ordered multinomial distributions.
+
     See docs for the OrderedMultinomial wrapper class for more details on how to use it in models.
     """
 
@@ -1013,8 +1009,7 @@ def dist(cls, nu, V, *args, **kwargs):
 
     def logp(X, nu, V):
         """
-        Calculate log-probability of Wishart distribution
-        at specified value.
+        Calculate logp of Wishart distribution at specified value.
 
         Parameters
         ----------
@@ -1047,9 +1042,10 @@ def logp(X, nu, V):
 
 def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, initval=None):
     r"""
-    Bartlett decomposition of the Wishart distribution. As the Wishart
-    distribution requires the matrix to be symmetric positive semi-definite
-    it is impossible for MCMC to ever propose acceptable matrices.
+    Bartlett decomposition of the Wishart distribution.
+
+    As the Wishart distribution requires the matrix to be symmetric positive
+    semi-definite, it is impossible for MCMC to ever propose acceptable matrices.
 
     Instead, we can use the Barlett decomposition which samples a lower
     diagonal matrix. Specifically:
@@ -1248,6 +1244,7 @@ def update(self, node):
 
 class _LKJCholeskyCov(Distribution):
     r"""Underlying class for covariance matrix with LKJ distributed correlations.
+
     See docs for LKJCholeskyCov function for more details on how to use it in models.
     """
 
@@ -1599,8 +1596,7 @@ def support_point(rv, *args):
 
     def logp(value, n, eta):
         """
-        Calculate log-probability of LKJ distribution at specified
-        value.
+        Calculate logp of LKJ distribution at specified value.
 
         Parameters
         ----------
@@ -1900,8 +1896,7 @@ def support_point(rv, size, mu, rowchol, colchol):
 
     def logp(value, mu, rowchol, colchol):
         """
-        Calculate log-probability of Matrix-valued Normal distribution
-        at specified value.
+        Calculate logp of Matrix-valued Normal distribution at specified value.
 
         Parameters
         ----------
@@ -2083,8 +2078,7 @@ def support_point(rv, rng, size, mu, sigma, *covs):
 
     def logp(value, rng, size, mu, sigma, *covs):
         """
-        Calculate log-probability of Multivariate Normal distribution
-        with Kronecker-structured covariance at specified value.
+        Calculate logp of Multivariate Normal distribution with Kronecker-structured covariance at specified value.
 
         Parameters
         ----------
@@ -2209,8 +2203,10 @@ def rng_fn(cls, rng: np.random.RandomState, mu, W, alpha, tau, W_is_valid, size)
 
 class CAR(Continuous):
     r"""
-    Likelihood for a conditional autoregression. This is a special case of the
-    multivariate normal with an adjacency-structured covariance matrix.
+    Likelihood for a conditional autoregression.
+
+    This is a special case of the multivariate normal with an
+    adjacency-structured covariance matrix.
 
     .. math::
 
@@ -2271,8 +2267,9 @@ def support_point(rv, size, mu, W, alpha, tau, W_is_valid):
 
     def logp(value, mu, W, alpha, tau, W_is_valid):
         """
-        Calculate log-probability of a CAR-distributed vector
-        at specified value. This log probability function differs from
+        Calculate logp of a CAR-distributed vector at specified value.
+
+        This log probability function differs from
         the true CAR log density (AKA a multivariate normal with CAR-structured
         covariance matrix) by an additive constant.
 
@@ -2356,9 +2353,10 @@ def rng_fn(cls, rng, size, W, sigma, zero_sum_stdev):
 
 class ICAR(Continuous):
     r"""
-    The intrinsic conditional autoregressive prior. It is primarily used to model
-    covariance between neighboring areas. It is a special case
-    of the :class:`~pymc.CAR` distribution where alpha is set to 1.
+    The intrinsic conditional autoregressive prior.
+
+    It is primarily used to model covariance between neighboring areas. It is a
+    special case of the :class:`~pymc.CAR` distribution where alpha is set to 1.
 
     The log probability density function is
 
@@ -2541,7 +2539,9 @@ def rng_fn(cls, rng, alpha, K, size):
 
 class StickBreakingWeights(SimplexContinuous):
     r"""
-    Likelihood of truncated stick-breaking weights. The weights are generated from a
+    Likelihood of truncated stick-breaking weights.
+
+    The weights are generated from a
     stick-breaking proceduce where :math:`x_k = v_k \prod_{\ell < k} (1 - v_\ell)` for
     :math:`k \in \{1, \ldots, K\}` and :math:`x_K = \prod_{\ell = 1}^{K} (1 - v_\ell) = 1 - \sum_{\ell=1}^K x_\ell`
     with :math:`v_k \stackrel{\text{i.i.d.}}{\sim} \text{Beta}(1, \alpha)`.
@@ -2605,8 +2605,7 @@ def support_point(rv, size, alpha, K):
 
     def logp(value, alpha, K):
         """
-        Calculate log-probability of the distribution induced from the stick-breaking process
-        at specified value.
+        Calculate logp of the distribution induced from the stick-breaking process at specified value.
 
         Parameters
         ----------
@@ -2688,8 +2687,8 @@ def rv_op(cls, sigma, support_shape, *, size=None, rng=None):
 
 class ZeroSumNormal(Distribution):
     r"""
-    ZeroSumNormal distribution, i.e Normal distribution where one or
-    several axes are constrained to sum to zero.
+    Normal distribution where one or several axes are constrained to sum to zero.
+
     By default, the last axis is constrained to sum to zero.
     See `n_zerosum_axes` kwarg for more details.
 

pymc/distributions/shape_utils.py

@@ -12,10 +12,7 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-"""
-A collection of common shape operations needed for broadcasting
-samples from probability distributions for stochastic nodes in PyMC.
-"""
+"""Common shape operations to broadcast samples from probability distributions for stochastic nodes in PyMC."""
 
 import warnings
 

pymc/distributions/simulator.py

@@ -63,8 +63,7 @@ def sum_stat(cls, *args, **kwargs):
 
 class Simulator(Distribution):
     r"""
-    Simulator distribution, used for Approximate Bayesian Inference (ABC)
-    with Sequential Monte Carlo (SMC) sampling via :func:`~pymc.sample_smc`.
+    Used for Approximate Bayesian Inference with SMC sampling via :func:`~pymc.sample_smc`.
 
     Simulator distributions have a stochastic pseudo-loglikelihood defined by
     a distance metric between the observed and simulated data, and tweaked