Upgrade pre-commit from main and apply automated code style

Except the check against print statements.

Author: michaelosthege

.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
@@ -14,44 +14,43 @@ repos:
         exclude: ^requirements-dev\.txt$
     -   id: trailing-whitespace
 - repo: https://github.com/PyCQA/isort
-  rev: 5.7.0
+  rev: 5.10.1
   hooks:
     - id: isort
       name: isort
 - repo: https://github.com/asottile/pyupgrade
-  rev: v2.10.0
+  rev: v2.29.1
   hooks:
     - id: pyupgrade
       args: [--py37-plus]
 - repo: https://github.com/psf/black
-  rev: 20.8b1
+  rev: 21.12b0
   hooks:
     - id: black
 - repo: https://github.com/PyCQA/pylint
-  rev: pylint-2.6.0
+  rev: v2.12.2
   hooks:
     - id: pylint
       args: [--rcfile=.pylintrc]
-      files: ^pymc3/
+      files: ^pymc/
+- repo: https://github.com/MarcoGorelli/madforhooks
+  rev: 0.2.1
+  hooks:
+    - id: conda-env-sorter
+      files: ^conda-envs/environment-dev-py.+\.yml$
 - repo: local
   hooks:
     - id: check-no-tests-are-ignored
+      additional_dependencies: [pandas,pyyaml]
       entry: python scripts/check_all_tests_are_covered.py
       files: ^\.github/workflows/pytest\.yml$
       language: python
       name: Check no tests are ignored
       pass_filenames: false
-    - id: conda-env-sort
-      additional_dependencies: [pyyaml]
-      entry: python scripts/sort_conda_envs.py
-      files: ^conda-envs/environment-dev-py37\.yml$
-      language: python
-      name: Sort dependencies in conda envs
-      types: [yaml]
     - id: pip-from-conda
       additional_dependencies: [pyyaml]
       entry: python scripts/generate_pip_deps_from_conda.py
-      files: ^conda-envs/
+      files: ^conda-envs/environment-dev-py.+.yml$
       language: python
       name: Generate pip dependency from conda
     - id: no-relative-imports

docs/source/sphinxext/gallery_generator.py

@@ -194,7 +194,7 @@ 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/__init__.py

@@ -63,7 +63,7 @@ def __set_compiler_flags():
 
 
 def _hotfix_theano_printing():
-    """ This is a workaround for https://github.com/pymc-devs/aesara/issues/309 """
+    """This is a workaround for https://github.com/pymc-devs/aesara/issues/309"""
     try:
         import pydot
         import theano.printing

pymc3/backends/base.py

@@ -364,7 +364,7 @@ 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/distribution.py

@@ -95,7 +95,7 @@ def __new__(cls, name, *args, **kwargs):
         data = kwargs.pop("observed", None)
         cls.data = data
         if isinstance(data, ObservedRV) or isinstance(data, FreeRV):
-            raise TypeError("observed needs to be data but got: {}".format(type(data)))
+            raise TypeError(f"observed needs to be data but got: {type(data)}")
         total_size = kwargs.pop("total_size", None)
 
         dims = kwargs.pop("dims", None)

pymc3/distributions/posterior_predictive.py

@@ -342,7 +342,7 @@ def __init__(self, vars, trace: _TraceDict, samples, model: Model | None, size=N
         self.size = size
         self.logger = logging.getLogger("posterior_predictive")
 
-    def __enter__(self) -> "_PosteriorPredictiveSampler":
+    def __enter__(self) -> _PosteriorPredictiveSampler:
         self._tok = vectorized_ppc.set(posterior_predictive_draw_values)
         return self
 

pymc3/gp/cov.py

@@ -155,7 +155,7 @@ 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 * tt.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.py

@@ -723,7 +723,7 @@ def __call__(self, array, grad_out=None, extra_vars=None):
 
         if array.shape != (self.size,):
             raise ValueError(
-                "Invalid shape for array. Must be {} but is {}.".format((self.size,), array.shape)
+                f"Invalid shape for array. Must be {(self.size,)} but is {array.shape}."
             )
 
         if grad_out is None:

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: Tensor) -> Set[VarName]:

pymc3/ode/ode.py

@@ -136,7 +136,7 @@ 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]()
 
@@ -220,7 +220,7 @@ 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/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 = theano.config.floatX
@@ -520,9 +516,7 @@ 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 = theano.config.floatX

pymc3/step_methods/mlda.py

@@ -883,24 +883,16 @@ 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]
                         )
                     }
                 )
@@ -979,7 +971,7 @@ 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
@@ -989,7 +981,7 @@ 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

@@ -280,7 +280,7 @@ 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
@@ -1875,7 +1875,7 @@ 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,
         )
@@ -1889,7 +1889,7 @@ def test_multinomial_vec_1d_n(self):
             Multinomial("m", n=ns, p=p, shape=vals.shape)
 
         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,
         )
@@ -1903,7 +1903,7 @@ def test_multinomial_vec_1d_n_2d_p(self):
             Multinomial("m", n=ns, p=ps, shape=vals.shape)
 
         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,
         )
@@ -1917,7 +1917,7 @@ def test_multinomial_vec_2d_p(self):
             Multinomial("m", n=n, p=ps, shape=vals.shape)
 
         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,
         )
@@ -2009,7 +2009,7 @@ 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,
         )
@@ -2023,7 +2023,7 @@ def test_dirichlet_multinomial_vec_1d_n(self):
             DirichletMultinomial("m", n=ns, a=a, shape=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,
         )
@@ -2037,7 +2037,7 @@ def test_dirichlet_multinomial_vec_1d_n_2d_a(self):
             DirichletMultinomial("m", n=ns, a=as_, shape=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,
         )
@@ -2051,7 +2051,7 @@ def test_dirichlet_multinomial_vec_2d_a(self):
             DirichletMultinomial("m", n=n, a=as_, shape=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,
         )

pymc3/tests/test_distributions_random.py

@@ -216,7 +216,7 @@ def setup_method(self, *args, **kwargs):
             self.model = pm.Model()
 
         def get_random_variable(self, shape, with_vector_params=False, name=None):
-            """ Creates a RandomVariable of the parametrized distribution. """
+            """Creates a RandomVariable of the parametrized distribution."""
             if with_vector_params:
                 params = {
                     key: value * np.ones(self.shape, dtype=np.dtype(type(value)))
@@ -240,7 +240,7 @@ def get_random_variable(self, shape, with_vector_params=False, name=None):
 
         @staticmethod
         def sample_random_variable(random_variable, size):
-            """ Draws samples from a RandomVariable using its .random() method. """
+            """Draws samples from a RandomVariable using its .random() method."""
             try:
                 if size is None:
                     return random_variable.random()
@@ -255,7 +255,7 @@ def sample_random_variable(random_variable, size):
         @pytest.mark.parametrize("size", [None, (), 1, (1,), 5, (4, 5)], ids=str)
         @pytest.mark.parametrize("shape", [None, ()], ids=str)
         def test_scalar_distribution_shape(self, shape, size):
-            """ Draws samples of different [size] from a scalar [shape] RV. """
+            """Draws samples of different [size] from a scalar [shape] RV."""
             rv = self.get_random_variable(shape)
             exp_shape = self.default_shape if shape is None else tuple(np.atleast_1d(shape))
             exp_size = self.default_size if size is None else tuple(np.atleast_1d(size))
@@ -275,7 +275,7 @@ def test_scalar_distribution_shape(self, shape, size):
             "shape", [None, (), (1,), (1, 1), (1, 2), (10, 11, 1), (9, 10, 2)], ids=str
         )
         def test_scalar_sample_shape(self, shape, size):
-            """ Draws samples of scalar [size] from a [shape] RV. """
+            """Draws samples of scalar [size] from a [shape] RV."""
             rv = self.get_random_variable(shape)
             exp_shape = self.default_shape if shape is None else tuple(np.atleast_1d(shape))
             exp_size = self.default_size if size is None else tuple(np.atleast_1d(size))

pymc3/tests/test_modelcontext.py

@@ -34,7 +34,7 @@ def test_thread_safety(self):
         that thread A enters the context manager first, then B,
         then A attempts to declare a variable while B is still in the context manager.
         """
-        aInCtxt, bInCtxt, aDone = [threading.Event() for _ in range(3)]
+        aInCtxt, bInCtxt, aDone = (threading.Event() for _ in range(3))
         modelA = Model()
         modelB = Model()
 

pymc3/tests/test_random.py

@@ -44,8 +44,8 @@ def test_draw_value():
 
     assert _draw_value(5) == 5
     assert _draw_value(5.0) == 5
-    assert isinstance(_draw_value(5.0), type(5.0))
-    assert isinstance(_draw_value(5), type(5))
+    assert isinstance(_draw_value(5.0), float)
+    assert isinstance(_draw_value(5), int)
 
     with pm.Model():
         mu = 2 * tt.constant(np.array([5.0, 6.0])) + theano.shared(np.array(5))