remove pylint and isort mentions

Author: juanitorduz

docs/source/conf.py

@@ -1,6 +1,5 @@
 """ Sphinx configuration file.
 
-   isort:skip_file
 """
 #!/usr/bin/env python3
 #
@@ -19,7 +18,7 @@
 import os
 from pathlib import Path
 
-import pymc  # isort:skip
+import pymc
 
 # -- General configuration ------------------------------------------------
 

pymc/__init__.py

@@ -12,7 +12,6 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-# pylint: disable=wildcard-import
 
 import logging
 

pymc/backends/arviz.py

@@ -15,7 +15,7 @@
 import logging
 import warnings
 
-from typing import (  # pylint: disable=unused-import
+from typing import (
     TYPE_CHECKING,
     Any,
     Dict,
@@ -41,14 +41,14 @@
 from pymc.util import get_default_varnames
 
 if TYPE_CHECKING:
-    from pymc.backends.base import MultiTrace  # pylint: disable=invalid-name
+    from pymc.backends.base import MultiTrace
 
 ___all__ = [""]
 
 _log = logging.getLogger(__name__)
 
 # random variable object ...
-Var = Any  # pylint: disable=invalid-name
+Var = Any
 
 
 def find_observations(model: "Model") -> Dict[str, Var]:
@@ -163,7 +163,7 @@ def insert(self, k: str, v, idx: int):
             self.trace_dict[k][idx, :] = v
 
 
-class InferenceDataConverter:  # pylint: disable=too-many-instance-attributes
+class InferenceDataConverter:
     """Encapsulate InferenceData specific logic."""
 
     model: Optional[Model] = None

pymc/backends/base.py

@@ -197,7 +197,6 @@ def _set_sampler_vars(self, sampler_vars):
 
         self.sampler_vars = sampler_vars
 
-    # pylint: disable=unused-argument
     def setup(self, draws, chain, sampler_vars=None) -> None:
         """Perform chain-specific setup.
 

pymc/gp/util.py

@@ -27,7 +27,7 @@
 from pymc.model import modelcontext
 from pymc.pytensorf import compile_pymc
 
-_ = Distribution  # keep both pylint and black happy
+_ = Distribution
 
 JITTER_DEFAULT = 1e-6
 

pymc/logprob/__init__.py

@@ -42,7 +42,6 @@
     transformed_conditional_logp,
 )
 
-# isort: off
 # Add rewrites to the DBs
 import pymc.logprob.binary
 import pymc.logprob.censoring
@@ -54,7 +53,6 @@
 import pymc.logprob.tensor
 import pymc.logprob.transforms
 
-# isort: on
 
 __all__ = (
     "logp",

pymc/logprob/rewriting.py

@@ -145,7 +145,7 @@ def apply(self, fgraph):
                     continue
                 if not any(out in rv_map_feature.needs_measuring for out in node.outputs):
                     continue
-                for node_rewriter in self.node_tracker.get_trackers(node.op):  # noqa: F402
+                for node_rewriter in self.node_tracker.get_trackers(node.op):  # noqa F402
                     node_rewriter_change = self.process_node(fgraph, node, node_rewriter)
                     if not node_rewriter_change:
                         continue

pymc/math.py

@@ -21,14 +21,13 @@
 import pytensor
 import pytensor.sparse
 import pytensor.tensor as pt
-import pytensor.tensor.slinalg  # pylint: disable=unused-import
+import pytensor.tensor.slinalg
 import scipy as sp
-import scipy.sparse  # pylint: disable=unused-import
+import scipy.sparse
 
 from pytensor.graph.basic import Apply
 from pytensor.graph.op import Op
 
-# pylint: disable=unused-import
 from pytensor.tensor import (
     abs,
     and_,
@@ -99,7 +98,6 @@
 
 from pymc.pytensorf import floatX, ix_, largest_common_dtype
 
-# pylint: enable=unused-import
 
 __all__ = [
     "abs",

pymc/model/core.py

@@ -109,7 +109,7 @@ class ContextMeta(type):
     the `with` statement.
     """
 
-    def __new__(cls, name, bases, dct, **kwargs):  # pylint: disable=unused-argument
+    def __new__(cls, name, bases, dct, **kwargs):
         """Add __enter__ and __exit__ methods to the class."""
 
         def __enter__(self):
@@ -121,7 +121,7 @@ def __enter__(self):
                 self._config_context.__enter__()
             return self
 
-        def __exit__(self, typ, value, traceback):  # pylint: disable=unused-argument
+        def __exit__(self, typ, value, traceback):
             self.__class__.context_class.get_contexts().pop()
             # self._pytensor_config is set in Model.__new__
             if self._config_context:
@@ -138,7 +138,7 @@ def __exit__(self, typ, value, traceback):  # pylint: disable=unused-argument
 
     # FIXME: is there a more elegant way to automatically add methods to the class that
     # are instance methods instead of class methods?
-    def __init__(cls, name, bases, nmspc, context_class: Optional[Type] = None, **kwargs):  # pylint: disable=unused-argument
+    def __init__(cls, name, bases, nmspc, context_class: Optional[Type] = None, **kwargs):
         """Add ``__enter__`` and ``__exit__`` methods to the new class automatically."""
         if context_class is not None:
             cls._context_class = context_class

pymc/sampling_jax.py

@@ -14,8 +14,6 @@
 # This file exists only for backward-compatibility with imports like
 # `import pymc.sampling_jax` or `from pymc import sampling_jax`.
 
-# pylint: disable=wildcard-import
-# pylint: disable=unused-wildcard-import
 import warnings
 
 warnings.warn("This module is deprecated, use pymc.sampling.jax", DeprecationWarning)

pymc/stats/log_likelihood.py

@@ -69,7 +69,6 @@ def compute_log_likelihood(
             raise ValueError(f"var_names must refer to observed_RVs in the model. Got: {var_names}")
 
     # We need to temporarily disable transforms, because the InferenceData only keeps the untransformed values
-    # pylint: disable=used-before-assignment
     try:
         original_rvs_to_values = model.rvs_to_values
         original_rvs_to_transforms = model.rvs_to_transforms
@@ -89,7 +88,6 @@ def compute_log_likelihood(
     finally:
         model.rvs_to_values = original_rvs_to_values
         model.rvs_to_transforms = original_rvs_to_transforms
-    # pylint: enable=used-before-assignment
 
     # Ignore Deterministics
     posterior_values = posterior[[rv.name for rv in model.free_RVs]]

tests/backends/test_arviz.py

@@ -11,7 +11,6 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
-# pylint: disable=no-member, invalid-name, redefined-outer-name, protected-access, too-many-public-methods
 import warnings
 
 from typing import Dict, Tuple
@@ -359,9 +358,7 @@ def test_mv_missing_data_model(self):
         with model:
             mu = pm.Normal("mu", 0, 1, size=2)
             sd_dist = pm.HalfNormal.dist(1.0, size=2)
-            # pylint: disable=unpacking-non-sequence
             chol, *_ = pm.LKJCholeskyCov("chol_cov", n=2, eta=1, sd_dist=sd_dist)
-            # pylint: enable=unpacking-non-sequence
             with pytest.warns(ImputationWarning):
                 y = pm.MvNormal("y", mu=mu, chol=chol, observed=data)
             inference_data = pm.sample(
@@ -443,7 +440,7 @@ def test_constant_data(self, use_context):
             y = pm.MutableData("y", [1.0, 2.0, 3.0])
             beta_sigma = pm.MutableData("beta_sigma", 1)
             beta = pm.Normal("beta", 0, beta_sigma)
-            obs = pm.Normal("obs", x * beta, 1, observed=y)  # pylint: disable=unused-variable
+            obs = pm.Normal("obs", x * beta, 1, observed=y)
             trace = pm.sample(100, chains=2, tune=100, return_inferencedata=False)
             if use_context:
                 inference_data = to_inference_data(trace=trace, log_likelihood=True)
@@ -466,7 +463,7 @@ def test_predictions_constant_data(self):
             x = pm.ConstantData("x", [1.0, 2.0, 3.0])
             y = pm.MutableData("y", [1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 1)
-            obs = pm.Normal("obs", x * beta, 1, observed=y)  # pylint: disable=unused-variable
+            obs = pm.Normal("obs", x * beta, 1, observed=y)
             trace = pm.sample(100, tune=100, return_inferencedata=False)
             inference_data = to_inference_data(trace)
 
@@ -478,7 +475,7 @@ def test_predictions_constant_data(self):
             x = pm.MutableData("x", [1.0, 2.0])
             y = pm.ConstantData("y", [1.0, 2.0])
             beta = pm.Normal("beta", 0, 1)
-            obs = pm.Normal("obs", x * beta, 1, observed=y)  # pylint: disable=unused-variable
+            obs = pm.Normal("obs", x * beta, 1, observed=y)
             predictive_trace = pm.sample_posterior_predictive(
                 inference_data, return_inferencedata=False
             )
@@ -506,7 +503,7 @@ def test_no_trace(self):
             x = pm.ConstantData("x", [1.0, 2.0, 3.0])
             y = pm.MutableData("y", [1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 1)
-            obs = pm.Normal("obs", x * beta, 1, observed=y)  # pylint: disable=unused-variable
+            obs = pm.Normal("obs", x * beta, 1, observed=y)
             idata = pm.sample(100, tune=100)
             prior = pm.sample_prior_predictive(return_inferencedata=False)
             posterior_predictive = pm.sample_posterior_predictive(idata, return_inferencedata=False)
@@ -540,7 +537,7 @@ def test_priors_separation(self, use_context):
             x = pm.MutableData("x", [1.0, 2.0, 3.0])
             y = pm.ConstantData("y", [1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 1)
-            obs = pm.Normal("obs", x * beta, 1, observed=y)  # pylint: disable=unused-variable
+            obs = pm.Normal("obs", x * beta, 1, observed=y)
             prior = pm.sample_prior_predictive(return_inferencedata=False)
 
         test_dict = {

tests/distributions/test_continuous.py

@@ -888,7 +888,6 @@ def test_moyal_icdf(self):
     def test_interpolated(self):
         for mu in R.vals:
             for sigma in Rplus.vals:
-                # pylint: disable=cell-var-from-loop
                 xmin = mu - 5 * sigma
                 xmax = mu + 5 * sigma
 
@@ -2371,7 +2370,6 @@ def interpolated_rng_fn(self, size, mu, sigma, rng):
     def check_draws(self):
         for mu in R.vals:
             for sigma in Rplus.vals:
-                # pylint: disable=cell-var-from-loop
                 rng = self.get_random_state()
 
                 def ref_rand(size):

tests/distributions/test_multivariate.py

@@ -1443,11 +1443,9 @@ def test_with_chol_rv(self):
         with pm.Model() as model:
             mu = pm.Normal("mu", 0.0, 1.0, size=3)
             sd_dist = pm.Exponential.dist(1.0, size=3)
-            # pylint: disable=unpacking-non-sequence
             chol, _, _ = pm.LKJCholeskyCov(
                 "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
-            # pylint: enable=unpacking-non-sequence
             mv = pm.MvNormal("mv", mu, chol=chol, size=4)
             prior = pm.sample_prior_predictive(samples=10, return_inferencedata=False)
 
@@ -1459,11 +1457,9 @@ def test_with_cov_rv(
         with pm.Model() as model:
             mu = pm.Normal("mu", 0.0, 1.0, shape=3)
             sd_dist = pm.Exponential.dist(1.0, shape=3)
-            # pylint: disable=unpacking-non-sequence
             chol, corr, stds = pm.LKJCholeskyCov(
                 "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
-            # pylint: enable=unpacking-non-sequence
             mv = pm.MvNormal("mv", mu, cov=pm.math.dot(chol, chol.T), size=4)
             prior = pm.sample_prior_predictive(samples=10, return_inferencedata=False)
 

tests/distributions/test_timeseries.py

@@ -429,11 +429,9 @@ def test_mvgaussian_with_chol_cov_rv(self, param):
         with pm.Model() as model:
             mu = Normal("mu", 0.0, 1.0, shape=3)
             sd_dist = Exponential.dist(1.0, shape=3)
-            # pylint: disable=unpacking-non-sequence
             chol, corr, stds = LKJCholeskyCov(
                 "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
-            # pylint: enable=unpacking-non-sequence
             with pytest.warns(UserWarning, match="Initial distribution not specified"):
                 if param == "chol":
                     mv = MvGaussianRandomWalk("mv", mu, chol=chol, shape=(10, 7, 3))

tests/logprob/test_transform_value.py

@@ -257,7 +257,7 @@ def test_default_value_transform_logprob(pt_dist, dist_params, sp_dist, size):
         if a_val.ndim > 0:
 
             def jacobian_estimate_novec(value):
-                dim_diff = a_val.ndim - value.ndim  # pylint: disable=cell-var-from-loop
+                dim_diff = a_val.ndim - value.ndim
                 if dim_diff > 0:
                     # Make sure the dimensions match the expected input
                     # dimensions for the compiled backward transform function

tests/model/test_core.py

@@ -416,7 +416,7 @@ def test_multiple_observed_rv():
     y2_data = np.random.randn(100)
     with pm.Model() as model:
         mu = pm.Normal("mu")
-        x = pm.CustomDist(  # pylint: disable=unused-variable
+        x = pm.CustomDist(
             "x", mu, logp=lambda value, mu: pm.Normal.logp(value, mu, 1.0), observed=0.1
         )
     assert not model["x"] == model["mu"]

tests/sampling/test_forward.py

@@ -294,7 +294,7 @@ def test_lkj_cholesky_cov(self):
         with pm.Model() as model:
             mu = np.zeros(3)
             sd_dist = pm.Exponential.dist(1.0, size=3)
-            chol, corr, stds = pm.LKJCholeskyCov(  # pylint: disable=unpacking-non-sequence
+            chol, corr, stds = pm.LKJCholeskyCov(
                 "chol_packed", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
             chol_packed = model["chol_packed"]

versioneer.py

@@ -273,11 +273,6 @@
 [travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
 
 """
-# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring
-# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements
-# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error
-# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with
-# pylint:disable=attribute-defined-outside-init,too-many-arguments
 
 import configparser
 import errno