Merge pull request #330 from bashtage/more-cov

TST: Improve edge case test coverage

Author: bashtage

linearmodels/iv/results.py

@@ -1493,7 +1493,7 @@ def c_stat(
             elif isinstance(variables, str):
                 variable_lst = [variables]
             else:
-                raise TypeError("variables must be a str of a list of str.")
+                raise TypeError("variables must be a str or a list of str.")
             exog_e = c_[exog.ndarray, endog.pandas[variable_lst].values]
             ex = [c for c in endog.pandas if c not in variable_lst]
             endog_e = endog.pandas[ex].values

linearmodels/system/_utility.py

@@ -119,8 +119,7 @@ def blocked_inner_prod(x: ArraySequence, s: NDArray) -> NDArray:
             s_ij = s[i, j]
             prod = s_ij * (xi.T @ xj)
             out[sel_i, sel_j] = prod
-            if i != j:
-                out[sel_j, sel_i] = prod.T
+            out[sel_j, sel_i] = prod.T
 
     return cast(np.ndarray, out)
 
@@ -242,7 +241,9 @@ def __init__(
             if require_pandas and not isinstance(q, pd.Series):
                 raise TypeError("q must be a Series")
             elif not isinstance(q, (pd.Series, np.ndarray)):
-                raise TypeError("q must be a Series")
+                raise TypeError("q must be a Series or an array")
+            if r.shape[0] != q.shape[0]:
+                raise ValueError("Constraint inputs are not shape compatible")
             q_pd = pd.Series(q, index=r_pd.index)
         else:
             q_pd = pd.Series(np.zeros(r_pd.shape[0]), index=r_pd.index)
@@ -261,8 +262,6 @@ def __str__(self) -> str:
     def _verify_constraints(self) -> None:
         r = self._ra
         q = self._qa
-        if r.shape[0] != q.shape[0]:
-            raise ValueError("Constraint inputs are not shape compatible")
         if self._num_params is not None:
             if r.shape[1] != self._num_params:
                 raise ValueError(

linearmodels/system/model.py

@@ -595,9 +595,14 @@ def predict(
             assert self.formula is not None
             parser = SystemFormulaParser(self.formula, data=data, eval_env=eval_env)
             equations = parser.data
-        params = np.asarray(params)
-        if params.ndim == 1:
-            params = params[:, None]
+        params = np.atleast_2d(np.asarray(params))
+        if params.shape[0] == 1:
+            params = params.T
+        nx = int(sum([_x.shape[1] for _x in self._x]))
+        if params.shape[0] != nx:
+            raise ValueError(
+                f"Parameters must have {nx} elements; found {params.shape[0]}."
+            )
         loc = 0
         out = AttrDict()
         for i, label in enumerate(self._eq_labels):
@@ -1437,23 +1442,6 @@ def multivariate_iv(
             )
         return cls(equations)
 
-    @classmethod
-    def multivariate_ls(
-        cls,
-        dependent: ArrayLike,
-        exog: OptionalArrayLike = None,
-        endog: OptionalArrayLike = None,
-        instruments: OptionalArrayLike = None,
-    ) -> "IV3SLS":
-        """
-        Deprecated. Use multivariate_iv.
-        """
-        warnings.warn(
-            "multivariate_ls is deprecated and will be removed " "after July 24, 2020.",
-            FutureWarning,
-        )
-        return cls.multivariate_iv(dependent, exog, endog, instruments)
-
     @classmethod
     def from_formula(
         cls,

linearmodels/tests/iv/test_formulas.py

@@ -8,6 +8,7 @@
 
 from linearmodels.formula import iv_2sls, iv_gmm, iv_gmm_cue, iv_liml
 from linearmodels.iv import IV2SLS, IVGMM, IVGMMCUE, IVLIML
+from linearmodels.iv._utility import IVFormulaParser
 
 
 @pytest.fixture(
@@ -298,3 +299,11 @@ def test_ols_formula(data):
     mod = IV2SLS.from_formula(fmla, data)
     res = mod.fit()
     assert "OLS Estimation Summary" in str(res)
+
+
+def test_iv_formula_parser(data, model_and_func, formula):
+    parser = IVFormulaParser(formula, data)
+    assert parser.eval_env == 2
+    parser.eval_env = 3
+    assert parser.eval_env == 3
+    assert isinstance(parser.exog, DataFrame)

linearmodels/tests/iv/test_postestimation.py

@@ -122,6 +122,15 @@ def test_c_stat_smoke(data):
     assert_allclose(c_stat.stat, c_stat2.stat)
 
 
+def test_c_stat_exception(data):
+    res = IVGMM(data.dep, data.exog, data.endog, data.instr).fit(cov_type="robust")
+    match = "variables must be a str or a list of str"
+    with pytest.raises(TypeError, match=match):
+        res.c_stat(variables=1)
+    with pytest.raises(TypeError, match=match):
+        res.c_stat(variables=("x1", "x2"))
+
+
 def test_linear_restriction(data):
     res = IV2SLS(data.dep, data.exog, data.endog, data.instr).fit(cov_type="robust")
     nvar = len(res.params)

linearmodels/tests/system/test_sur.py

@@ -375,6 +375,10 @@ def test_gls_without_mv_ols_equiv(mvreg_data):
 def test_ols_against_gls(data):
     mod = SUR(data)
     res = mod.fit(method="gls")
+    if isinstance(data[list(data.keys())[0]], dict):
+        predictions = mod.predict(res.params, equations=data)
+        predictions2 = mod.predict(np.asarray(res.params)[:, None], equations=data)
+        assert_allclose(predictions, predictions2)
     sigma = res.sigma
     sigma_m12 = inv_matrix_sqrt(np.asarray(sigma))
     key = list(data.keys())[0]
@@ -688,6 +692,9 @@ def test_fitted(data):
 def test_predict(missing_data):
     mod = SUR(missing_data)
     res = mod.fit()
+    pred_no_missing = res.predict(equations=missing_data, missing=False, dataframe=True)
+    pred_missing = res.predict(equations=missing_data, missing=True, dataframe=True)
+    assert pred_missing.shape[0] <= pred_no_missing.shape[0]
     pred = res.predict()
     for key in pred:
         assert_series_equal(

linearmodels/tests/system/test_utility.py

@@ -147,6 +147,12 @@ def test_linear_constraint_errors():
         LinearConstraint(r_df, q)
     with pytest.raises(TypeError):
         LinearConstraint(r_df, [0, 0])
+    with pytest.raises(TypeError, match="q must be a Series or"):
+        LinearConstraint(r=np.eye(2), q=[0, 0], require_pandas=False)
+    with pytest.raises(ValueError, match="r is incompatible"):
+        LinearConstraint(r=np.eye(4), q=np.zeros(4), require_pandas=False, num_params=5)
+    with pytest.raises(ValueError, match="Constraint inputs are"):
+        LinearConstraint(r=np.eye(4), q=np.zeros(2), require_pandas=False)
 
 
 def test_linear_constraint_repr():