⬆️ UPGRADE: Autoupdate pre-commit config

Author: twiecki

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 exclude: ^(docs/logos|pymc3/tests/data)/
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v3.4.0
+  rev: v4.0.1
   hooks:
     -   id: check-merge-conflict
     -   id: check-toml
@@ -19,16 +19,16 @@ repos:
     - id: isort
       name: isort
 - repo: https://github.com/asottile/pyupgrade
-  rev: v2.11.0
+  rev: v2.19.4
   hooks:
     - id: pyupgrade
       args: [--py37-plus]
 - repo: https://github.com/psf/black
-  rev: 20.8b1
+  rev: 21.6b0
   hooks:
     - id: black
 - repo: https://github.com/PyCQA/pylint
-  rev: pylint-2.7.4
+  rev: v2.8.3
   hooks:
     - id: pylint
       args: [--rcfile=.pylintrc]

docs/source/sphinxext/gallery_generator.py

@@ -187,7 +187,7 @@ def build_gallery(srcdir, gallery):
     with open(table_of_contents_file) as toc:
         table_of_contents = toc.read()
 
-    js_contents = "Gallery.examples = {}\n{}".format(json.dumps(data), table_of_contents)
+    js_contents = f"Gallery.examples = {json.dumps(data)}\n{table_of_contents}"
 
     with open(js_file, "w") as js:
         js.write(js_contents)

pymc3/aesaraf.py

@@ -543,7 +543,7 @@ def grad(self, inp, grads):
         return grads
 
     def c_code(self, node, name, inp, out, sub):
-        return "{z} = {x};".format(x=inp[0], z=out[0])
+        return f"{out[0]} = {inp[0]};"
 
     def __eq__(self, other):
         return isinstance(self, type(other))

pymc3/backends/base.py

@@ -365,7 +365,7 @@ def __getattr__(self, name):
             return self.get_values(name)
         if name in self.stat_names:
             return self.get_sampler_stats(name)
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, name))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
 
     def __len__(self):
         chain = self.chains[-1]

pymc3/distributions/bart.py

@@ -164,7 +164,7 @@ def get_residuals_loo(self, tree):
         return R_j
 
     def draw_leaf_value(self, idx_data_points):
-        """ Draw the residual mean."""
+        """Draw the residual mean."""
         R_j = self.get_residuals()[idx_data_points]
         draw = self.mean(R_j)
         return draw

pymc3/distributions/mixture.py

@@ -187,7 +187,7 @@ def comp_dists(self, comp_dists):
             # All component distributions must broadcast with each other
             try:
                 self._broadcast_shape = np.broadcast(
-                    *[np.empty(shape) for shape in self._comp_dist_shapes]
+                    *(np.empty(shape) for shape in self._comp_dist_shapes)
                 ).shape
             except Exception:
                 raise TypeError(

pymc3/distributions/shape_utils.py

@@ -426,7 +426,7 @@ def broadcast_dist_samples_to(to_shape, samples, size=None):
 
 
 def convert_dims(dims: Dims) -> Optional[WeakDims]:
-    """ Process a user-provided dims variable into None or a valid dims tuple. """
+    """Process a user-provided dims variable into None or a valid dims tuple."""
     if dims is None:
         return None
 
@@ -444,7 +444,7 @@ def convert_dims(dims: Dims) -> Optional[WeakDims]:
 
 
 def convert_shape(shape: Shape) -> Optional[WeakShape]:
-    """ Process a user-provided shape variable into None or a valid shape object. """
+    """Process a user-provided shape variable into None or a valid shape object."""
     if shape is None:
         return None
 
@@ -466,7 +466,7 @@ def convert_shape(shape: Shape) -> Optional[WeakShape]:
 
 
 def convert_size(size: Size) -> Optional[StrongSize]:
-    """ Process a user-provided size variable into None or a valid size object. """
+    """Process a user-provided size variable into None or a valid size object."""
     if size is None:
         return None
 

pymc3/distributions/timeseries.py

@@ -158,15 +158,15 @@ def logp(self, value):
         """
         if self.constant:
             x = at.add(
-                *[self.rho[i + 1] * value[self.p - (i + 1) : -(i + 1)] for i in range(self.p)]
+                *(self.rho[i + 1] * value[self.p - (i + 1) : -(i + 1)] for i in range(self.p))
             )
             eps = value[self.p :] - self.rho[0] - x
         else:
             if self.p == 1:
                 x = self.rho * value[:-1]
             else:
                 x = at.add(
-                    *[self.rho[i] * value[self.p - (i + 1) : -(i + 1)] for i in range(self.p)]
+                    *(self.rho[i] * value[self.p - (i + 1) : -(i + 1)] for i in range(self.p))
                 )
             eps = value[self.p :] - x
 

pymc3/exceptions.py

@@ -43,7 +43,7 @@ class ImputationWarning(UserWarning):
 
 
 class ShapeWarning(UserWarning):
-    """ Something that could lead to shape problems down the line. """
+    """Something that could lead to shape problems down the line."""
 
     pass
 

pymc3/gp/cov.py

@@ -158,7 +158,7 @@ def __array_wrap__(self, result):
 class Combination(Covariance):
     def __init__(self, factor_list):
         input_dim = max(
-            [factor.input_dim for factor in factor_list if isinstance(factor, Covariance)]
+            factor.input_dim for factor in factor_list if isinstance(factor, Covariance)
         )
         super().__init__(input_dim=input_dim)
         self.factor_list = []

pymc3/gp/util.py

@@ -36,7 +36,7 @@ def infer_shape(X, n_points=None):
 
 
 def stabilize(K):
-    """ adds small diagonal to a covariance matrix """
+    """adds small diagonal to a covariance matrix"""
     return K + 1e-6 * at.identity_like(K)
 
 
@@ -62,7 +62,7 @@ def kmeans_inducing_points(n_inducing, X):
 
 
 def conditioned_vars(varnames):
-    """ Decorator for validating attrs that are conditioned on. """
+    """Decorator for validating attrs that are conditioned on."""
 
     def gp_wrapper(cls):
         def make_getter(name):

pymc3/model_graph.py

@@ -84,7 +84,7 @@ def _filter_parents(self, var, parents) -> Set[VarName]:
                 if self.transform_map[p] != var.name:
                     keep.add(self.transform_map[p])
             else:
-                raise AssertionError("Do not know what to do with {}".format(get_var_name(p)))
+                raise AssertionError(f"Do not know what to do with {get_var_name(p)}")
         return keep
 
     def get_parents(self, var: TensorVariable) -> Set[VarName]:

pymc3/ode/ode.py

@@ -137,7 +137,7 @@ def _simulate(self, y0, theta):
 
     def make_node(self, y0, theta):
         inputs = (y0, theta)
-        _log.debug("make_node for inputs {}".format(hash(inputs)))
+        _log.debug(f"make_node for inputs {hash(inputs)}")
         states = self._otypes[0]()
         sens = self._otypes[1]()
 
@@ -221,7 +221,7 @@ def infer_shape(self, fgraph, node, input_shapes):
         return output_shapes
 
     def grad(self, inputs, output_grads):
-        _log.debug("grad w.r.t. inputs {}".format(hash(tuple(inputs))))
+        _log.debug(f"grad w.r.t. inputs {hash(tuple(inputs))}")
 
         # fetch symbolic sensitivity output node from cache
         ihash = hash(tuple(inputs))

pymc3/sampling.py

@@ -2010,7 +2010,7 @@ def sample_prior_predictive(
         inputs, vars_to_sample, allow_input_downcast=True, accept_inplace=True, mode=mode
     )
 
-    values = zip(*[sampler_fn() for i in range(samples)])
+    values = zip(*(sampler_fn() for i in range(samples)))
 
     data = {k: np.stack(v) for k, v in zip(names, values)}
     if data is None:

pymc3/sampling_jax.py

@@ -5,7 +5,7 @@
 
 xla_flags = os.getenv("XLA_FLAGS", "").lstrip("--")
 xla_flags = re.sub(r"xla_force_host_platform_device_count=.+\s", "", xla_flags).split()
-os.environ["XLA_FLAGS"] = " ".join(["--xla_force_host_platform_device_count={}".format(100)])
+os.environ["XLA_FLAGS"] = " ".join([f"--xla_force_host_platform_device_count={100}"])
 
 import aesara.tensor as at
 import arviz as az
@@ -47,8 +47,8 @@ def __init__(
         self.seed = seed
 
         self.inputs, self.outputs = clone(inputs, outputs, copy_inputs=False)
-        self.inputs_type = tuple([input.type for input in inputs])
-        self.outputs_type = tuple([output.type for output in outputs])
+        self.inputs_type = tuple(input.type for input in inputs)
+        self.outputs_type = tuple(output.type for output in outputs)
         self.nin = len(inputs)
         self.nout = len(outputs)
         self.nshared = len([v for v in inputs if isinstance(v, SharedVariable)])
@@ -174,7 +174,7 @@ def sample_numpyro_nuts(
     init_state_batched_at = [at.as_tensor(v) for v in init_state_batched]
 
     nuts_inputs = sorted(
-        [v for v in graph_inputs([model.logpt]) if not isinstance(v, Constant)],
+        (v for v in graph_inputs([model.logpt]) if not isinstance(v, Constant)),
         key=lambda x: isinstance(x, SharedVariable),
     )
     map_seed = jax.random.split(seed, chains)

pymc3/step_methods/hmc/quadpotential.py

@@ -161,13 +161,9 @@ def __init__(
         if initial_mean.ndim != 1:
             raise ValueError("Initial mean must be one-dimensional.")
         if initial_diag is not None and len(initial_diag) != n:
-            raise ValueError(
-                "Wrong shape for initial_diag: expected {} got {}".format(n, len(initial_diag))
-            )
+            raise ValueError(f"Wrong shape for initial_diag: expected {n} got {len(initial_diag)}")
         if len(initial_mean) != n:
-            raise ValueError(
-                "Wrong shape for initial_mean: expected {} got {}".format(n, len(initial_mean))
-            )
+            raise ValueError(f"Wrong shape for initial_mean: expected {n} got {len(initial_mean)}")
 
         if dtype is None:
             dtype = aesara.config.floatX
@@ -512,9 +508,7 @@ def __init__(
         if initial_cov is not None and initial_cov.shape != (n, n):
             raise ValueError(f"Wrong shape for initial_cov: expected {n} got {initial_cov.shape}")
         if len(initial_mean) != n:
-            raise ValueError(
-                "Wrong shape for initial_mean: expected {} got {}".format(n, len(initial_mean))
-            )
+            raise ValueError(f"Wrong shape for initial_mean: expected {n} got {len(initial_mean)}")
 
         if dtype is None:
             dtype = aesara.config.floatX

pymc3/step_methods/mlda.py

@@ -896,24 +896,16 @@ def update_error_estimate(self, accepted, skipped_logp):
                 pm.set_data(
                     {
                         "mu_B": sum(
-                            [
-                                bias.get_mu()
-                                for bias in self.bias_all[
-                                    : len(self.bias_all) - self.num_levels + 2
-                                ]
-                            ]
+                            bias.get_mu()
+                            for bias in self.bias_all[: len(self.bias_all) - self.num_levels + 2]
                         )
                     }
                 )
                 pm.set_data(
                     {
                         "Sigma_B": sum(
-                            [
-                                bias.get_sigma()
-                                for bias in self.bias_all[
-                                    : len(self.bias_all) - self.num_levels + 2
-                                ]
-                            ]
+                            bias.get_sigma()
+                            for bias in self.bias_all[: len(self.bias_all) - self.num_levels + 2]
                         )
                     }
                 )
@@ -992,7 +984,7 @@ def extract_Q_estimate(trace, levels):
 
     Q_0_raw = trace.get_sampler_stats("Q_0")
     # total number of base level samples from all iterations
-    total_base_level_samples = sum([it.shape[0] for it in Q_0_raw])
+    total_base_level_samples = sum(it.shape[0] for it in Q_0_raw)
     Q_0 = np.concatenate(Q_0_raw).reshape((1, total_base_level_samples))
     ess_Q_0 = az.ess(np.array(Q_0, np.float64))
     Q_0_var = Q_0.var() / ess_Q_0
@@ -1002,7 +994,7 @@ def extract_Q_estimate(trace, levels):
     for l in range(1, levels):
         Q_diff_raw = trace.get_sampler_stats(f"Q_{l}_{l-1}")
         # total number of samples from all iterations
-        total_level_samples = sum([it.shape[0] for it in Q_diff_raw])
+        total_level_samples = sum(it.shape[0] for it in Q_diff_raw)
         Q_diff = np.concatenate(Q_diff_raw).reshape((1, total_level_samples))
         ess_diff = az.ess(np.array(Q_diff, np.float64))
 

pymc3/tests/test_distributions.py

@@ -191,7 +191,7 @@ def product(domains, n_samples=-1):
         names, domains = zip(*domains.items())
     except ValueError:  # domains.items() is empty
         return [{}]
-    all_vals = [zip(names, val) for val in itertools.product(*[d.vals for d in domains])]
+    all_vals = [zip(names, val) for val in itertools.product(*(d.vals for d in domains))]
     if n_samples > 0 and len(all_vals) > n_samples:
         return (all_vals[j] for j in nr.choice(len(all_vals), n_samples, replace=False))
     return all_vals
@@ -294,7 +294,7 @@ def multinomial_logpdf(value, n, p):
 
 
 def dirichlet_multinomial_logpmf(value, n, a):
-    value, n, a = [np.asarray(x) for x in [value, n, a]]
+    value, n, a = (np.asarray(x) for x in [value, n, a])
     assert value.ndim == 1
     assert n.ndim == 0
     assert a.shape == value.shape
@@ -318,7 +318,7 @@ def beta_mu_sigma(value, mu, sigma):
 
 class ProductDomain:
     def __init__(self, domains):
-        self.vals = list(itertools.product(*[d.vals for d in domains]))
+        self.vals = list(itertools.product(*(d.vals for d in domains)))
         self.shape = (len(domains),) + domains[0].shape
         self.lower = [d.lower for d in domains]
         self.upper = [d.upper for d in domains]
@@ -2187,7 +2187,7 @@ def test_multinomial_vec(self):
         )
 
         assert_almost_equal(
-            sum([model_single.fastlogp({"m": val}) for val in vals]),
+            sum(model_single.fastlogp({"m": val}) for val in vals),
             model_many.fastlogp({"m": vals}),
             decimal=4,
         )
@@ -2201,7 +2201,7 @@ def test_multinomial_vec_1d_n(self):
             Multinomial("m", n=ns, p=p)
 
         assert_almost_equal(
-            sum([multinomial_logpdf(val, n, p) for val, n in zip(vals, ns)]),
+            sum(multinomial_logpdf(val, n, p) for val, n in zip(vals, ns)),
             model.fastlogp({"m": vals}),
             decimal=4,
         )
@@ -2215,7 +2215,7 @@ def test_multinomial_vec_1d_n_2d_p(self):
             Multinomial("m", n=ns, p=ps)
 
         assert_almost_equal(
-            sum([multinomial_logpdf(val, n, p) for val, n, p in zip(vals, ns, ps)]),
+            sum(multinomial_logpdf(val, n, p) for val, n, p in zip(vals, ns, ps)),
             model.fastlogp({"m": vals}),
             decimal=4,
         )
@@ -2229,7 +2229,7 @@ def test_multinomial_vec_2d_p(self):
             Multinomial("m", n=n, p=ps)
 
         assert_almost_equal(
-            sum([multinomial_logpdf(val, n, p) for val, p in zip(vals, ps)]),
+            sum(multinomial_logpdf(val, n, p) for val, p in zip(vals, ps)),
             model.fastlogp({"m": vals}),
             decimal=4,
         )
@@ -2309,7 +2309,7 @@ def test_dirichlet_multinomial_vec(self):
         )
 
         assert_almost_equal(
-            sum([model_single.fastlogp({"m": val}) for val in vals]),
+            sum(model_single.fastlogp({"m": val}) for val in vals),
             model_many.fastlogp({"m": vals}),
             decimal=4,
         )
@@ -2324,7 +2324,7 @@ def test_dirichlet_multinomial_vec_1d_n(self):
             DirichletMultinomial("m", n=ns, a=a, size=vals.shape)
 
         assert_almost_equal(
-            sum([dirichlet_multinomial_logpmf(val, n, a) for val, n in zip(vals, ns)]),
+            sum(dirichlet_multinomial_logpmf(val, n, a) for val, n in zip(vals, ns)),
             model.fastlogp({"m": vals}),
             decimal=4,
         )
@@ -2339,7 +2339,7 @@ def test_dirichlet_multinomial_vec_1d_n_2d_a(self):
             DirichletMultinomial("m", n=ns, a=as_, size=vals.shape)
 
         assert_almost_equal(
-            sum([dirichlet_multinomial_logpmf(val, n, a) for val, n, a in zip(vals, ns, as_)]),
+            sum(dirichlet_multinomial_logpmf(val, n, a) for val, n, a in zip(vals, ns, as_)),
             model.fastlogp({"m": vals}),
             decimal=4,
         )
@@ -2354,7 +2354,7 @@ def test_dirichlet_multinomial_vec_2d_a(self):
             DirichletMultinomial("m", n=n, a=as_, size=vals.shape)
 
         assert_almost_equal(
-            sum([dirichlet_multinomial_logpmf(val, n, a) for val, a in zip(vals, as_)]),
+            sum(dirichlet_multinomial_logpmf(val, n, a) for val, a in zip(vals, as_)),
             model.fastlogp({"m": vals}),
             decimal=4,
         )