Merge pull request statsmodels#8826 from bashtage/pre-deprecations

MAINT/DOC: Fix more deprecations and restore doc build

Author: bashtage

docs/source/contingency_tables.rst

@@ -50,6 +50,7 @@ contingency table cell counts:
     import statsmodels.api as sm
 
     df = sm.datasets.get_rdataset("Arthritis", "vcd").data
+    df.fillna({"Improved":"None"}, inplace=True)
 
     tab = pd.crosstab(df['Treatment'], df['Improved'])
     tab = tab.loc[:, ["None", "Some", "Marked"]]

docs/source/plots/stl_plot.py

@@ -1,3 +1,4 @@
+import numpy as np
 import matplotlib.pyplot as plt
 from pandas.plotting import register_matplotlib_converters
 
@@ -8,6 +9,6 @@
 data = co2.load().data
 data = data.resample('M').mean().ffill()
 
-res = STL(data).fit()
+res = STL(np.squeeze(data)).fit()
 res.plot()
 plt.show()

docs/source/treatment.rst

@@ -1,8 +1,10 @@
 
 .. module:: statsmodels.treatment
-.. currentmodule:: statsmodels.treatment
    :synopsis: Treatment Effect
 
+.. currentmodule:: statsmodels.treatment
+
+
 
 .. _othermod:
 

requirements-doc.txt

@@ -1,5 +1,7 @@
 # Requirements for doc build, in addition to requirements.txt
 sphinx>=5,<6
+# Avoid warnings which break Sphinx by pinning pandas
+pandas>=2.0,<2.1
 sphinx-material
 jupyter
 notebook
@@ -14,3 +16,4 @@ theano-pymc; os_name != "nt"
 pymc3; os_name != "nt"
 arviz; os_name != "nt"
 jinja2==3.0.3
+

setup.cfg

@@ -77,7 +77,7 @@ filterwarnings =
     error:The probit link alias is deprecated:FutureWarning:
     error:Parsing dates in:UserWarning
     error:A value is trying to be set on a copy::
-    error:Conversion of an array with ndim:DeprecationWarning:statsmodels
+    error:Conversion of an array with ndim:DeprecationWarning:
 markers =
     example: mark a test that runs example code
     matplotlib: mark a test that requires matplotlib

statsmodels/datasets/danish_data/data.py

@@ -60,6 +60,11 @@ def load():
 
 def _get_data():
     data = du.load_csv(__file__, "data.csv")
+    for i, val in enumerate(data.period):
+        parts = val.split("Q")
+        month = (int(parts[1]) - 1) * 3 + 1
+
+        data.loc[data.index[i], "period"] = f"{parts[0]}-{month:02d}-01"
     data["period"] = pd.to_datetime(data.period)
     return data.set_index("period").astype(float)
 

statsmodels/distributions/copula/archimedean.py

@@ -21,9 +21,9 @@ def _debye(alpha):
     EPSILON = np.finfo(np.float64).eps * 100
 
     def integrand(t):
-        return t / (np.exp(t) - 1)
-
-    debye_value = integrate.quad(integrand, EPSILON, alpha)[0] / alpha
+        return np.squeeze(t / (np.exp(t) - 1))
+    _alpha = np.squeeze(alpha)
+    debye_value = integrate.quad(integrand, EPSILON, _alpha)[0] / _alpha
     return debye_value
 
 

statsmodels/emplike/originregress.py

@@ -250,6 +250,7 @@ def conf_int_el(self, param_num, upper_bound=None,
         f = lambda b0:  self.el_test(np.array([b0]), param_num,
                                      method=method,
                                  stochastic_exog=stochastic_exog)[0] - r0
-        lowerl = optimize.brentq(f, lower_bound, self.params[param_num])
-        upperl = optimize.brentq(f, self.params[param_num], upper_bound)
+        _param = np.squeeze(self.params[param_num])
+        lowerl = optimize.brentq(f, np.squeeze(lower_bound), _param)
+        upperl = optimize.brentq(f, _param, np.squeeze(upper_bound))
         return (lowerl, upperl)

statsmodels/genmod/families/tests/test_family.py

@@ -100,8 +100,10 @@ def test_tweedie_loglike_obs(power):
     scale = 2.9
 
     def pdf(y):
-        return np.exp(tweedie.loglike_obs(
-                    endog=y, mu=mu, scale=scale
-        ))
+        return np.squeeze(
+            np.exp(
+                tweedie.loglike_obs(endog=y, mu=mu, scale=scale)
+            )
+        )
 
     assert_allclose(pdf(0) + integrate.quad(pdf, 0, 1e2)[0], 1, atol=1e-4)

statsmodels/sandbox/distributions/extras.py

@@ -817,6 +817,7 @@ def _sf(self, x, *args, **kwargs):
             return 1.0 - self._cdf(x, *args, **kwargs)
 
     def _munp(self, n, *args, **kwargs):
+        args = [np.squeeze(arg) for arg in args]
         out = np.squeeze(self._mom0_sc(n, *args))
         if np.isscalar(out):
             return float(out)