Merge pull request #60 from jGaboardi/GH56_swap_for_ruff

code health -- tighten up linting & formatting

.pre-commit-config.yaml

@@ -1,14 +1,10 @@
 files: "spglm\/"
 repos:
-  - repo: https://github.com/psf/black
-    rev: "24.3.0"
-    hooks:
-      - id: black
-        language_version: python3
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.3.5"
+    rev: "v0.5.1"
     hooks:
       - id: ruff
+      - id: ruff-format
 
 ci:
   autofix_prs: false

pyproject.toml

@@ -38,7 +38,6 @@ Repository = "https://github.com/pysal/spglm"
 
 [project.optional-dependencies]
 dev = [
-    "black",
     "ruff",
     "pre-commit",
 ]
@@ -63,33 +62,22 @@ include = [
     "spglm.*",
 ]
 
-[tool.black]
-line-length = 88
-extend-exclude = '''
-(
-  docs/conf.py
-)
-#'''
-
 [tool.ruff]
 line-length = 88
-select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"]
-target-version = "py39"
-ignore = [
-    "B006",
-    "B008",
-    "B009",
-    "B010",
-    "C408",
-    "E731",
+lint.select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"]
+lint.ignore = [
+    "ARG002", # Unused method argument
+    "B008",   # Do not perform function call in argument defaults
+    "N801",   # Class name should use CapWords convention
+    "N802",   # Function name should be lowercase
+    "N806",   # Variable in function should be lowercase
+]
+
+[tool.ruff.lint.per-file-ignores]
+"*__init__.py" = [
     "F401",
     "F403",
-    "N803",
-    "N806",
-    "N999",
-    "UP007"
 ]
-exclude = ["spglm/tests/*", "docs/*"]
 
 [tool.coverage.run]
 source = ["./spglm"]
@@ -103,4 +91,4 @@ exclude_lines = [
     "except ImportError",
 ]
 ignore_errors = true
-omit = ["spglm/tests/*", "spglm/conf.py"]
+omit = ["spglm/tests/*", "spglm/docs/conf.py"]

spglm/__init__.py

@@ -2,10 +2,10 @@
 from importlib.metadata import PackageNotFoundError, version
 
 from . import (
-    family,  # noqa F401
-    glm,  # noqa F401
-    iwls,  # noqa F401
-    utils,  # noqa F401
+    family,
+    glm,
+    iwls,
+    utils,
 )
 
 with contextlib.suppress(PackageNotFoundError):

spglm/base.py

@@ -20,7 +20,7 @@ def __init__(self, model, params, **kwd):
         self.initialize(model, params, **kwd)
         self._data_attr = []
 
-    def initialize(self, model, params, **kwd):  # noqa ARG002
+    def initialize(self, model, params, **kwd):
         self.params = params
         self.model = model
         if hasattr(model, "k_constant"):
@@ -306,7 +306,7 @@ def cov_params(
         ]:
             ######################################################################
             # TODO - remove GH#38
-            dot_fun = nan_dot  # noqa F821 - `nan_dot` not defined - should remove
+            dot_fun = nan_dot  # noqa: F821 - `nan_dot` not defined - should remove
             ######################################################################
         else:
             dot_fun = np.dot
@@ -350,7 +350,7 @@ def cov_params(
         else:  # if r_matrix is None and column is None:
             return cov_p
 
-    def conf_int(self, alpha=0.05, cols=None, method="default"):  # noqa ARG002
+    def conf_int(self, alpha=0.05, cols=None, method="default"):
         """
         Returns the confidence interval of the fitted parameters.
 

spglm/family.py

@@ -9,8 +9,8 @@
 import numpy as np
 from scipy import special
 
-from . import links as L  # noqa N812
-from . import varfuncs as V  # noqa N812
+from . import links as L  # noqa: N812 - Lowercase imported as non-lowercase
+from . import varfuncs as V  # noqa: N812
 
 FLOAT_EPS = np.finfo(float).eps
 
@@ -455,7 +455,7 @@ def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):
         endog_mu = self._clean(endog / mu)
         return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale
 
-    def loglike(self, endog, mu, freq_weights=1.0, scale=1.0):  # noqa ARG002
+    def loglike(self, endog, mu, freq_weights=1.0, scale=1.0):
         r"""
         The log-likelihood function in terms of the fitted mean response.
 
@@ -654,7 +654,7 @@ def _clean(self, x):
         """
         return np.clip(x, FLOAT_EPS, np.inf)
 
-    def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):  # noqa ARG002
+    def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):
         r"""
         Gamma deviance function
 
@@ -678,7 +678,7 @@ def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):  # noqa ARG002
         endog_mu = self._clean(endog / mu)
         return 2 * np.sum(freq_weights * ((endog - mu) / mu - np.log(endog_mu)))
 
-    def resid_dev(self, endog, mu, scale=1.0):  # noqa ARG002
+    def resid_dev(self, endog, mu, scale=1.0):
         r"""
         Gamma deviance residuals
 
@@ -805,7 +805,7 @@ def starting_mu(self, y):
         """
         return (y + 0.5) / 2
 
-    def initialize(self, endog, freq_weights):  # noqa ARG002
+    def initialize(self, endog, freq_weights):
         """
         Initialize the response variable.
 
@@ -833,7 +833,7 @@ def initialize(self, endog, freq_weights):  # noqa ARG002
         else:
             return endog, np.ones(endog.shape[0])
 
-    def deviance(self, endog, mu, freq_weights=1, scale=1.0, axis=None):  # noqa ARG002
+    def deviance(self, endog, mu, freq_weights=1, scale=1.0, axis=None):
         r"""
         Deviance function for either Bernoulli or Binomial data.
 
@@ -982,7 +982,7 @@ def resid_anscombe(self, endog, mu):
             Journal of the Royal Statistical Society B. 30, 248-75.
 
         """
-        cox_snell = lambda x: (  # noqa E731 - skip "don't use lambda"
+        cox_snell = lambda x: (  # noqa: E731 - skip "don't use lambda"
             special.betainc(2 / 3.0, 2 / 3.0, x) * special.beta(2 / 3.0, 2 / 3.0)
         )
         return np.sqrt(self.n) * (

spglm/glm.py

@@ -82,7 +82,13 @@ class GLM(RegressionPropsY):
     """
 
     def __init__(
-        self, y, X, family=family.Gaussian(), offset=None, y_fix=None, constant=True
+        self,
+        y,
+        X,  # noqa: N803 - Argument name should be lowercase
+        family=family.Gaussian(),
+        offset=None,
+        y_fix=None,
+        constant=True,
     ):
         """
         Initialize class
@@ -340,7 +346,7 @@ def pearson_chi2(self):
     @cache_readonly
     def null(self):
         y = np.reshape(self.y, (-1, 1))
-        model = self.model  # noqa F841 - `model` never used
+        model = self.model  # noqa: F841 - `model` never used
         X = np.ones((len(y), 1))
         null_mod = GLM(y, X, family=self.family, offset=self.offset, constant=False)
         return null_mod.fit().mu
@@ -382,25 +388,25 @@ def bic(self):
         return self.deviance - (self.model.n - self.df_model - 1) * np.log(self.model.n)
 
     @cache_readonly
-    def D2(self):  # noqa N802
+    def D2(self):
         return 1 - (self.deviance / self.null_deviance)
 
     @cache_readonly
-    def adj_D2(self):  # noqa N802
+    def adj_D2(self):
         return 1.0 - (float(self.n) - 1.0) / (float(self.n) - float(self.k)) * (
             1.0 - self.D2
         )
 
     @cache_readonly
-    def pseudoR2(self):  # noqa N802
+    def pseudoR2(self):
         return 1 - (self.llf / self.llnull)
 
     @cache_readonly
-    def adj_pseudoR2(self):  # noqa N802
+    def adj_pseudoR2(self):
         return 1 - ((self.llf - self.k) / self.llnull)
 
     @cache_readonly
-    def tr_S(self):  # noqa N802
+    def tr_S(self):
         xtx_inv = np.linalg.inv(np.dot(self.X.T, self.X))
         xtx_inv_xt = np.dot(xtx_inv, self.X.T)
         S = np.dot(self.X, xtx_inv_xt)

spglm/iwls.py

@@ -44,7 +44,7 @@ def iwls(
     x,
     family,
     offset,
-    y_fix,  # noqa ARG001
+    y_fix,  # noqa: ARG001 - Unused function argument
     ini_betas=None,
     tol=1.0e-8,
     max_iter=200,

spglm/links.py

@@ -15,7 +15,7 @@ class Link:
     `Link` does nothing, but lays out the methods expected of any subclass.
     """
 
-    def __call__(self, p):  # noqa ARG002
+    def __call__(self, p):
         """
         Return the value of the link function.  This is just a placeholder.
 
@@ -31,7 +31,7 @@ def __call__(self, p):  # noqa ARG002
         """
         return NotImplementedError
 
-    def inverse(self, z):  # noqa ARG002
+    def inverse(self, z):
         """
         Inverse of the link function.  Just a placeholder.
 
@@ -50,7 +50,7 @@ def inverse(self, z):  # noqa ARG002
         """
         return NotImplementedError
 
-    def deriv(self, p):  # noqa ARG002
+    def deriv(self, p):
         """
         Derivative of the link function g'(p).  Just a placeholder.
 
@@ -230,7 +230,7 @@ def deriv2(self, p):
         return (2 * p - 1) / v**2
 
 
-class logit(Logit):  # noqa N801
+class logit(Logit):
     pass
 
 
@@ -357,7 +357,7 @@ def inverse_deriv(self, z):
         return np.power(z, (1 - self.power) / self.power) / self.power
 
 
-class inverse_power(Power):  # noqa N801
+class inverse_power(Power):
     """
     The inverse transform
 
@@ -372,7 +372,7 @@ def __init__(self):
         super().__init__(power=-1.0)
 
 
-class sqrt(Power):  # noqa N801
+class sqrt(Power):
     """
     The square-root transform
 
@@ -387,7 +387,7 @@ def __init__(self):
         super().__init__(power=0.5)
 
 
-class inverse_squared(Power):  # noqa N801
+class inverse_squared(Power):
     r"""
     The inverse squared transform
 
@@ -402,7 +402,7 @@ def __init__(self):
         super().__init__(power=-2.0)
 
 
-class identity(Power):  # noqa N801
+class identity(Power):
     """
     The identity transform
 
@@ -531,7 +531,7 @@ def inverse_deriv(self, z):
         return np.exp(z)
 
 
-class log(Log):  # noqa N801
+class log(Log):
     """
     The log transform
 
@@ -655,7 +655,7 @@ def inverse_deriv(self, z):
         return 1 / self.deriv(self.inverse(z))
 
 
-class probit(CDFLink):  # noqa N801
+class probit(CDFLink):
     """
     The probit (standard normal CDF) transform
 
@@ -669,7 +669,7 @@ class probit(CDFLink):  # noqa N801
     pass
 
 
-class cauchy(CDFLink):  # noqa N801
+class cauchy(CDFLink):
     """
     The Cauchy (standard Cauchy CDF) transform
 
@@ -814,7 +814,7 @@ def inverse_deriv(self, z):
         return np.exp(z - np.exp(z))
 
 
-class cloglog(CLogLog):  # noqa N801
+class cloglog(CLogLog):
     """
     The CLogLog transform link function.
 
@@ -950,7 +950,7 @@ def inverse_deriv(self, z):
         return t / (self.alpha * (1 - t) ** 2)
 
 
-class nbinom(NegativeBinomial):  # noqa N801
+class nbinom(NegativeBinomial):
     """
     The negative binomial link function.