REF: raise instead of warn if k_constant > 0

statsmodels · Sep 21, 2020 · 01c95e7 · 01c95e7
1 parent eeb1eb2
commit 01c95e7
Show file tree

Hide file tree

Showing 2 changed files with 22 additions and 19 deletions.
diff --git a/statsmodels/miscmodels/ordinal_model.py b/statsmodels/miscmodels/ordinal_model.py
@@ -107,6 +107,9 @@ def __init__(self, endog, exog, offset=None, distr='probit', **kwds):
                 # Note: Doing the following here would break from_formula
                 # self.endog = self.endog.argmax(1)
 
+        if self.k_constant > 0:
+            raise ValueError("there should not be a constant in the model")
+
         self._initialize_labels(labels)
 
         self.results_class = OrderedResults
@@ -178,11 +181,11 @@ def from_formula(cls, formula, data, subset=None, drop_cols=None,
         terms = re.split(r"[+\-~]", formula)
         for term in terms:
             term = term.strip()
-            if term in ("0", "1"):
-                import warnings
-                msg = ("OrderedModel formulas should not include any '0' or "
-                       "'1' terms if those create an implicit constant.")
-                warnings.warn(msg, SpecificationWarning)
+#             if term in ("0", "1"):
+#                 import warnings
+#                 msg = ("OrderedModel formulas should not include any '0' or "
+#                        "'1' terms if those create an implicit constant.")
+#                 warnings.warn(msg, SpecificationWarning)
 
         endog_name = formula.split("~")[0].strip()
         original_endog = data[endog_name]

diff --git a/statsmodels/miscmodels/tests/test_ordinal_model.py b/statsmodels/miscmodels/tests/test_ordinal_model.py
@@ -304,15 +304,15 @@ def test_setup(self):
         # warns but doesn't raise
         formula = "apply ~ 0 + pared + public + gpa + C(dummy)"
 
-        with pytest.warns(SpecificationWarning):
+        with pytest.raises(ValueError):
             modf2 = OrderedModel.from_formula(formula, data, distr='logit')
 
-        with pytest.warns(HessianInversionWarning):
-            resf2 = modf2.fit(method='bfgs')
-        assert resf2.converged is False
-        # assert np.isnan(resf2.bse).all()
+#         with pytest.warns(HessianInversionWarning):
+#        resf2 = modf2.fit(method='bfgs')
+#         assert resf2.mle_retvals["converged"] is False
+#         # assert np.isnan(resf2.bse).all()
 
-        assert_allclose(resf2.predict(data[:5]), fittedvalues[:5], rtol=1e-4)
+#        assert_allclose(resf2.predict(data[:5]), fittedvalues[:5], rtol=1e-4)
 
 
 class TestCLogLogModel(CheckOrdinalModelMixin):
@@ -342,14 +342,14 @@ def _cdf(self, x):
                             distr=cloglog)
         resp = modp.fit(method='bfgs', disp=False)
 
-        with pytest.warns(UserWarning):
-            modf = OrderedModel.from_formula(
-                "apply ~ pared + public + gpa - 1",
-                data={"apply": data['apply'].values.codes,
-                      "pared": data['pared'],
-                      "public": data['public'],
-                      "gpa": data['gpa']},
-                distr=cloglog)
+        # with pytest.warns(UserWarning):
+        modf = OrderedModel.from_formula(
+            "apply ~ pared + public + gpa - 1",
+            data={"apply": data['apply'].values.codes,
+                  "pared": data['pared'],
+                  "public": data['public'],
+                  "gpa": data['gpa']},
+            distr=cloglog)
         resf = modf.fit(method='bfgs', disp=False)
 
         modu = OrderedModel(