Allow for AlgorithmType in estimation functions

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 `maximize` 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])