Lint with ruff

README.md

@@ -4,6 +4,7 @@
 [![Continuous Integration](https://github.com/pysal/spglm/actions/workflows/testing.yml/badge.svg)](https://github.com/pysal/spglm/actions/workflows/testing.yml)
 [![Documentation Status](https://readthedocs.org/projects/spglm/badge/?version=latest)](https://spglm.readthedocs.io/en/latest/?badge=latest)
 [![PyPI version](https://badge.fury.io/py/spglm.svg)](https://badge.fury.io/py/spglm)
+[![codecov](https://codecov.io/gh/pysal/spglm/branch/main/graph/badge.svg)](https://codecov.io/gh/pysal/spglm)
 
 
 This module is an adaptation of a portion of [GLM functionality from the

spglm/__init__.py

@@ -1,6 +1,6 @@
 __version__ = "1.0.8"
 
-from . import glm
-from . import family
-from . import utils
-from . import iwls
+from . import glm  # noqa F401
+from . import family  # noqa F401
+from . import utils  # noqa F401
+from . import iwls  # noqa F401

spglm/base.py

@@ -306,7 +306,10 @@
             "l1",
             "l1_cvxopt_cp",
         ]:
-            dot_fun = nan_dot
+            ######################################################################
+            # TODO - remove GH#38
+            dot_fun = nan_dot  # noqa F821 - `nan_dot` not defined - should remove
+            ######################################################################
         else:
             dot_fun = np.dot
 
@@ -397,9 +400,9 @@
         >>> model = GLM(y, X)
         >>> results = model.fit()
         >>> results.conf_int()
-        array([[ 20.57281401,  72.28355135],
-               [ -0.42138121,   1.67934915],
-               [ -0.84292086,  -0.12685622]])
+        array([[20.57281401, 72.28355135],
+               [-0.42138121,  1.67934915],
+               [-0.84292086, -0.12685622]])
 
         Notes
         -----

spglm/family.py

@@ -797,7 +797,7 @@ def __init__(self, link=L.logit):  # , n=1.):
         self.link = link()
 
     def starting_mu(self, y):
-        """
+        r"""
         The starting values for the IRLS algorithm for the Binomial family.
         A good choice for the binomial family is :math:`\mu_0 = (Y_i + 0.5)/2`
         """
@@ -980,7 +980,7 @@ def resid_anscombe(self, endog, mu):
             Journal of the Royal Statistical Society B. 30, 248-75.
 
         """
-        cox_snell = lambda x: (
+        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

@@ -234,26 +234,26 @@ class GLMResults(LikelihoodModelResults):
         tr_S          : trace of the hat matrix S
         resid_response          : array
                                   response residuals; defined as y-mu
-        resid_pearson           : array
-                                  Pearson residuals; defined as (y-mu)/sqrt(VAR(mu))
-                                  where VAR is the distribution specific variance
-                                  function; see family.py and varfuncs.py for more information.
-        resid_working           : array
-                                  Working residuals; the working residuals are defined as
-                                  resid_response/link'(mu); see links.py for the
-                                  derivatives of the link functions.
-
-        resid_anscombe          : array
-                                  Anscombe residuals; see family.py for
-                                  distribution-specific Anscombe residuals.
-
-        resid_deviance          : array
-                                  deviance residuals; see family.py for
-                                  distribution-specific deviance residuals.
-
-        pearson_chi2            : float
-                                  chi-Squared statistic is defined as the sum
-                                  of the squares of the Pearson residuals
+        resid_pearson : array
+                        Pearson residuals; defined as (y-mu)/sqrt(VAR(mu))
+                        where VAR is the distribution specific variance
+                        function; see family.py and varfuncs.py for more information.
+        resid_working  : array
+                         Working residuals; the working residuals are defined as
+                         resid_response/link'(mu); see links.py for the
+                         derivatives of the link functions.
+
+        resid_anscombe : array
+                         Anscombe residuals; see family.py for
+                         distribution-specific Anscombe residuals.
+
+        resid_deviance : array
+                         deviance residuals; see family.py for
+                         distribution-specific deviance residuals.
+
+        pearson_chi2   : float
+                         chi-Squared statistic is defined as the sum
+                         of the squares of the Pearson residuals
 
         normalized_cov_params   : array
                                 k*k, approximates [X.T*X]-1
@@ -339,7 +339,7 @@ def pearson_chi2(self):
     @cache_readonly
     def null(self):
         y = np.reshape(self.y, (-1, 1))
-        model = self.model
+        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

spglm/iwls.py

@@ -2,7 +2,6 @@
 from scipy import linalg
 import numpy.linalg as la
 from scipy import sparse as sp
-from scipy.sparse import linalg as spla
 from spreg.utils import spdot, spmultiply
 from .family import Binomial, Poisson
 

spglm/utils.py

@@ -17,7 +17,10 @@
 except ImportError:
     import re
 
-    string_types = basestring
+    ######################################################################
+    # TODO - remove GH#39
+    string_types = basestring  # noqa F821 - `basestring` not defined - should remove
+    ######################################################################
 
     class NumpyVersion:
         """Parse and compare numpy version strings.
@@ -365,7 +368,7 @@
                 setattr(_cache, name, _cachedval)
             # Update the reset list if needed (and possible)
             resetlist = self.resetlist
-            if resetlist is not ():
+            if resetlist != ():
                 try:
                     _cache._resetdict[name] = self.resetlist
                 except AttributeError:

spglm/varfuncs.py

@@ -114,9 +114,16 @@
         """
         Derivative of the variance function v'(mu)
         """
-        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+
+        ########################################################################
+        # `approx_fprime_cs` is imported by unused
+        from statsmodels.tools.numdiff import approx_fprime_cs  # noqa F401
 
         # return approx_fprime_cs(mu, self)  # TODO fix breaks in `fabs
+        ########################################################################
+
+        from statsmodels.tools.numdiff import approx_fprime
+
         # TODO: diag is workaround problem with numdiff for 1d
         return np.diag(approx_fprime(mu, self))
 
@@ -205,7 +212,7 @@
         """
         Derivative of the variance function v'(mu)
         """
-        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+        from statsmodels.tools.numdiff import approx_fprime_cs
 
         # TODO: diag workaround proplem with numdiff for 1d
         return np.diag(approx_fprime_cs(mu, self))