Allow for AlgorithmType in estimation functions (#535)

src/estimagic/estimate_ml.py

@@ -95,8 +95,8 @@ def estimate_ml(
             optimize_options to False. Pytrees can be a numpy array, a pandas Series, a
             DataFrame with "value" column, a float and any kind of (nested) dictionary
             or list containing these elements. See :ref:`params` for examples.
-        optimize_options (dict, str or False): Keyword arguments that govern the
-            numerical optimization. Valid entries are all arguments of
+        optimize_options (dict, Algorithm, str or False): Keyword arguments that govern
+            the numerical optimization. Valid entries are all arguments of
             :func:`~estimagic.optimization.optimize.minimize` except for those that are
             passed explicilty to ``estimate_ml``. If you pass False as optimize_options
             you signal that ``params`` are already the optimal parameters and no
@@ -199,7 +199,10 @@ def estimate_ml(
     is_optimized = optimize_options is False
 
     if not is_optimized:
-        if isinstance(optimize_options, str):
+        # If optimize_options is not a dictionary and not False, we assume it represents
+        # an algorithm. The actual testing of whether it is a valid algorithm is done
+        # when `maximize` is called.
+        if not isinstance(optimize_options, dict):
             optimize_options = {"algorithm": optimize_options}
 
         check_optimization_options(

src/estimagic/estimate_msm.py

@@ -107,8 +107,8 @@ def estimate_msm(
             optimize_options to False. Pytrees can be a numpy array, a pandas Series, a
             DataFrame with "value" column, a float and any kind of (nested) dictionary
             or list containing these elements. See :ref:`params` for examples.
-        optimize_options (dict, str or False): Keyword arguments that govern the
-            numerical optimization. Valid entries are all arguments of
+        optimize_options (dict, Algorithm, str or False): Keyword arguments that govern
+            the numerical optimization. Valid entries are all arguments of
             :func:`~estimagic.optimization.optimize.minimize` except for those that can
             be passed explicitly to ``estimate_msm``.  If you pass False as
             ``optimize_options`` you signal that ``params`` are already
@@ -199,7 +199,10 @@ def estimate_msm(
     is_optimized = optimize_options is False
 
     if not is_optimized:
-        if isinstance(optimize_options, str):
+        # If optimize_options is not a dictionary and not False, we assume it represents
+        # an algorithm. The actual testing of whether it is a valid algorithm is done
+        # when `minimize` is called.
+        if not isinstance(optimize_options, dict):
             optimize_options = {"algorithm": optimize_options}
 
         check_optimization_options(

src/optimagic/shared/check_option_dicts.py

@@ -41,6 +41,6 @@ def check_optimization_options(options, usage, algorithm_mandatory=True):
         msg = (
             "The following are not valid entries of optimize_options because they are "
             "not only relevant for minimization but also for inference: "
-            "{invalid_general}"
+            f"{invalid_general}"
         )
         raise ValueError(msg)

tests/estimagic/test_estimate_ml.py

@@ -18,6 +18,7 @@
     scalar_logit_fun_and_jac,
 )
 from optimagic import mark
+from optimagic.optimizers import scipy_optimizers
 from optimagic.parameters.bounds import Bounds
 
 
@@ -349,6 +350,34 @@ def test_estimate_ml_optimize_options_false(fitted_logit_model, logit_np_inputs)
     aaae(got.cov(method="jacobian"), fitted_logit_model.covjac, decimal=4)
 
 
+def test_estimate_ml_algorithm_type(logit_np_inputs):
+    """Test that estimate_ml computes correct covariances given correct params."""
+    kwargs = {"y": logit_np_inputs["y"], "x": logit_np_inputs["x"]}
+
+    params = pd.DataFrame({"value": logit_np_inputs["params"]})
+
+    estimate_ml(
+        loglike=logit_loglike,
+        params=params,
+        loglike_kwargs=kwargs,
+        optimize_options=scipy_optimizers.ScipyLBFGSB,
+    )
+
+
+def test_estimate_ml_algorithm(logit_np_inputs):
+    """Test that estimate_ml computes correct covariances given correct params."""
+    kwargs = {"y": logit_np_inputs["y"], "x": logit_np_inputs["x"]}
+
+    params = pd.DataFrame({"value": logit_np_inputs["params"]})
+
+    estimate_ml(
+        loglike=logit_loglike,
+        params=params,
+        loglike_kwargs=kwargs,
+        optimize_options=scipy_optimizers.ScipyLBFGSB(stopping_maxfun=10),
+    )
+
+
 # ======================================================================================
 # Univariate normal case using dict params
 # ======================================================================================

tests/estimagic/test_estimate_msm.py

@@ -10,6 +10,7 @@
 
 from estimagic.estimate_msm import estimate_msm
 from optimagic.optimization.optimize_result import OptimizeResult
+from optimagic.optimizers import scipy_optimizers
 from optimagic.shared.check_option_dicts import (
     check_optimization_options,
 )
@@ -161,6 +162,40 @@ def test_estimate_msm_with_jacobian():
     aaae(calculated.cov(), cov_np)
 
 
+def test_estimate_msm_with_algorithm_type():
+    start_params = np.array([3, 2, 1])
+    expected_params = np.zeros(3)
+    empirical_moments = _sim_np(expected_params)
+    if isinstance(empirical_moments, dict):
+        empirical_moments = empirical_moments["simulated_moments"]
+
+    estimate_msm(
+        simulate_moments=_sim_np,
+        empirical_moments=empirical_moments,
+        moments_cov=cov_np,
+        params=start_params,
+        optimize_options=scipy_optimizers.ScipyLBFGSB,
+        jacobian=lambda x: np.eye(len(x)),
+    )
+
+
+def test_estimate_msm_with_algorithm():
+    start_params = np.array([3, 2, 1])
+    expected_params = np.zeros(3)
+    empirical_moments = _sim_np(expected_params)
+    if isinstance(empirical_moments, dict):
+        empirical_moments = empirical_moments["simulated_moments"]
+
+    estimate_msm(
+        simulate_moments=_sim_np,
+        empirical_moments=empirical_moments,
+        moments_cov=cov_np,
+        params=start_params,
+        optimize_options=scipy_optimizers.ScipyLBFGSB(stopping_maxfun=10),
+        jacobian=lambda x: np.eye(len(x)),
+    )
+
+
 def test_to_pickle(tmp_path):
     start_params = np.array([3, 2, 1])