MAINT: Remove deprecated options in SciPy

azure-pipelines.yml

@@ -21,9 +21,9 @@ jobs:
 - template: ci/azure_template_posix.yml
   parameters:
     name: Linux
-    vmImage: ubuntu-20.04
+    vmImage: ubuntu-latest
 
 - template: ci/azure_template_windows.yml
   parameters:
     name: Windows
-    vmImage: windows-2019
+    vmImage: windows-latest

linearmodels/asset_pricing/model.py

@@ -1002,7 +1002,7 @@ def fit(
         # 2. Step 1 using w = inv(s) from SV
         callback = callback_factory(self._j, args, disp=disp)
         _default_options: dict[str, Any] = {"callback": callback}
-        options = {"disp": bool(disp), "maxiter": max_iter}
+        options = {"maxiter": max_iter}
         opt_options = {} if opt_options is None else opt_options
         options.update(opt_options.get("options", {}))
         _default_options.update(opt_options)
@@ -1031,7 +1031,7 @@ def fit(
                     params,
                     args=args,
                     callback=callback,
-                    options={"disp": bool(disp), "maxiter": max_iter},
+                    options={"maxiter": max_iter},
                 )
                 params = opt_res.x
                 obj = opt_res.fun
@@ -1047,7 +1047,7 @@ def fit(
                 params,
                 args=cue_args,
                 callback=callback,
-                options={"disp": bool(disp), "maxiter": max_iter},
+                options={"maxiter": max_iter},
             )
             params = opt_res.x
 

linearmodels/iv/model.py

@@ -4,7 +4,7 @@
 
 from __future__ import annotations
 
-from typing import Any, TypeVar, Union, cast
+from typing import Any, Callable, TypeVar, Union, cast
 import warnings
 
 from numpy import (
@@ -16,11 +16,14 @@
     average,
     c_,
     column_stack,
+    dtype,
     eye,
+    float64,
     isscalar,
     logical_not,
     nan,
     nanmean,
+    ndarray,
     ones,
     sqrt,
     squeeze,
@@ -1511,7 +1514,7 @@ def estimate_parameters(
         x: linearmodels.typing.data.Float64Array,
         y: linearmodels.typing.data.Float64Array,
         z: linearmodels.typing.data.Float64Array,
-        display: bool = False,
+        display: int = 0,
         opt_options: dict[str, Any] | None = None,
     ) -> tuple[linearmodels.typing.data.Float64Array, int]:
         r"""
@@ -1525,8 +1528,8 @@ def estimate_parameters(
             Regressand matrix (nobs by 1)
         z : ndarray
             Instrument matrix (nobs by ninstr)
-        display : bool
-            Flag indicating whether to display iterative optimizer output
+        display : int
+            Number of iterations between displaying. Set to 0 to suppress output.
         opt_options : dict
             Dictionary containing additional keyword arguments to pass to
             scipy.optimize.minimize.
@@ -1549,12 +1552,34 @@ def estimate_parameters(
         if opt_options is None:
             opt_options = {}
         assert opt_options is not None
-        options = {"disp": display}
+        options = {}
         if "options" in opt_options:
             opt_options = opt_options.copy()
             options.update(opt_options.pop("options"))
 
-        res = minimize(self.j, starting, args=args, options=options, **opt_options)
+        def callback_factory(
+            _disp: int,
+        ) -> Callable[[ndarray[tuple[int], dtype[float64]]], None]:
+            d = {"iter": 0}
+
+            def _callback(params: ndarray[tuple[int], dtype[float64]]) -> None:
+                fval = self.j(params, *args)
+                if _disp > 0 and (d["iter"] % _disp == 0):
+                    print("Iteration: {}, Objective: {}".format(d["iter"], fval))
+                d["iter"] += 1
+
+            return _callback
+
+        callback = callback_factory(display)
+
+        res = minimize(
+            self.j,
+            starting,
+            args=args,
+            options=options,
+            callback=callback,
+            **opt_options,
+        )
 
         return res.x[:, None], res.nit
 

linearmodels/tests/iv/test_model.py

@@ -404,7 +404,7 @@ def test_gmm_cue_optimization_options(small_data):
     res_none = mod.fit(display=False)
     opt_options = dict(method="BFGS", options={"disp": False})
     res_bfgs = mod.fit(display=False, opt_options=opt_options)
-    opt_options = dict(method="L-BFGS-B", options={"disp": False})
+    opt_options = dict(method="L-BFGS-B")
     res_lbfgsb = mod.fit(display=False, opt_options=opt_options)
     assert res_none.iterations > 2
     assert res_bfgs.iterations > 2