migrate internal code from CustomLMOptimizer to SimplerLMOptimizer an…

…d custom_leastsq to simplish_leastsq

pygsti/algorithms/core.py

@@ -31,8 +31,7 @@
 from pygsti.modelmembers import states as _state
 from pygsti.circuits.circuitlist import CircuitList as _CircuitList
 from pygsti.baseobjs.resourceallocation import ResourceAllocation as _ResourceAllocation
-from pygsti.optimize.customlm import CustomLMOptimizer as _CustomLMOptimizer
-from pygsti.optimize.customlm import Optimizer as _Optimizer
+from pygsti.optimize.simplerlm import Optimizer as _Optimizer, SimplerLMOptimizer as _SimplerLMOptimizer
 from pygsti import forwardsims as _fwdsims
 from pygsti import layouts as _layouts
 
@@ -619,7 +618,7 @@ def run_gst_fit_simple(dataset, start_model, circuits, optimizer, objective_func
     model : Model
         the best-fit model.
     """
-    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _CustomLMOptimizer.cast(optimizer)
+    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _SimplerLMOptimizer.cast(optimizer)
     objective_function_builder = _objfns.ObjectiveFunctionBuilder.cast(objective_function_builder)
     array_types = optimizer.array_types + \
         objective_function_builder.compute_array_types(optimizer.called_objective_methods, start_model.sim)
@@ -666,7 +665,7 @@ def run_gst_fit(mdc_store, optimizer, objective_function_builder, verbosity=0):
     objfn_store : MDCObjectiveFunction
         the objective function and store containing the best-fit model evaluated at the best-fit point.
     """
-    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _CustomLMOptimizer.cast(optimizer)
+    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _SimplerLMOptimizer.cast(optimizer)
     comm = mdc_store.resource_alloc.comm
     profiler = mdc_store.resource_alloc.profiler
     printer = VerbosityPrinter.create_printer(verbosity, comm)
@@ -843,7 +842,7 @@ def iterative_gst_generator(dataset, start_model, circuit_lists,
           (an "evaluated" model-dataset-circuits store).
     """
     resource_alloc = _ResourceAllocation.cast(resource_alloc)
-    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _CustomLMOptimizer.cast(optimizer)
+    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _SimplerLMOptimizer.cast(optimizer)
     comm = resource_alloc.comm
     profiler = resource_alloc.profiler
     printer = VerbosityPrinter.create_printer(verbosity, comm)

pygsti/algorithms/gaugeopt.py

@@ -290,7 +290,7 @@ def gaugeopt_custom(model, objective_fn, gauge_group=None,
     gaugeGroupEl = gauge_group.compute_element(x0)  # re-used element for evals
 
     def _call_objective_fn(gauge_group_el_vec, oob_check=False):
-        # Note: oob_check can be True if oob_check_interval>=1 is given to the custom_leastsq below
+        # Note: oob_check can be True if oob_check_interval>=1 is given to the simplish_leastsq below
         gaugeGroupEl.from_vector(gauge_group_el_vec)
         return objective_fn(gaugeGroupEl, oob_check)
 
@@ -309,7 +309,7 @@ def _call_jacobian_fn(gauge_group_el_vec):
         assert(_call_jacobian_fn is not None), "Cannot use 'ls' method unless jacobian is available"
         ralloc = _baseobjs.ResourceAllocation(comm)  # FUTURE: plumb up a resource alloc object?
         test_f = _call_objective_fn(x0)
-        solnX, converged, msg, _, _, _, _, _ = _opt.custom_leastsq(
+        solnX, converged, msg, _, _, _, _, _ = _opt.simplish_leastsq(
             _call_objective_fn, _call_jacobian_fn, x0, f_norm2_tol=tol,
             jac_norm_tol=tol, rel_ftol=tol, rel_xtol=tol,
             max_iter=maxiter, resource_alloc=ralloc,

pygsti/optimize/simplerlm.py

@@ -183,6 +183,19 @@ class SimplerLMOptimizer(Optimizer):
         "per-circuit quantities" computed by the objective function's `.percircuit()` and
         `.lsvec_percircuit()` methods (`'percircuit'` mode).
     """
+
+    @classmethod
+    def cast(cls, obj):
+        if isinstance(obj, cls):
+            return obj
+        if obj:
+            try:
+                return cls(**obj)
+            except:
+                from pygsti.optimize.customlm import CustomLMOptimizer
+                return CustomLMOptimizer(**obj)
+        return cls()
+
     def __init__(self, maxiter=100, maxfev=100, tol=1e-6, fditer=0, first_fditer=0,
                  uphill_step_threshold=0.0, init_munu="auto", oob_check_interval=0,
                  oob_action="reject", oob_check_mode=0, serial_solve_proc_threshold=100, lsvec_mode="normal"):
@@ -624,7 +637,7 @@ def simplish_leastsq(
                 if _np.allclose(x, best_x):
                     rawJTJ_scratch[:, :] = JTJ[:, :]  # use pre-allocated memory
                     rawJTJ_scratch[idiag] = undamped_JTJ_diag  # no damping; the "raw" JTJ
-                    best_x_state = best_x_state[0:5] + (rawJTJ_scratch,)  # update mu,nu,JTJ of initial "best state"
+                    best_x_state = best_x_state[0:4] + (rawJTJ_scratch,)  # update mu,nu,JTJ of initial "best state"
 
             #determing increment using adaptive damping
             while True:  # inner loop

pygsti/protocols/gst.py

@@ -1249,15 +1249,16 @@ def __init__(self, initial_model=None, gaugeopt_suite='stdgaugeopt',
 
         if isinstance(optimizer, _opt.Optimizer):
             self.optimizer = optimizer
-            if isinstance(optimizer, _opt.CustomLMOptimizer) and optimizer.first_fditer is None:
-                #special behavior: can set optimizer's first_fditer to `None` to mean "fill with default"
+            if hasattr(optimizer,'first_fditer') and optimizer.first_fditer is None:
+                # special behavior: can set optimizer's first_fditer to `None` to mean "fill with default"
                 self.optimizer = _copy.deepcopy(optimizer)  # don't mess with caller's optimizer
                 self.optimizer.first_fditer = default_first_fditer
         else:
-            if optimizer is None: optimizer = {}
+            if optimizer is None:
+                optimizer = {}
             if 'first_fditer' not in optimizer:  # then add default first_fditer value
                 optimizer['first_fditer'] = default_first_fditer
-            self.optimizer = _opt.CustomLMOptimizer.cast(optimizer)
+            self.optimizer = _opt.SimplerLMOptimizer.cast(optimizer)
 
         self.objfn_builders = GSTObjFnBuilders.cast(objfn_builders)
 
@@ -1751,12 +1752,12 @@ def __init__(self, modes=('full TP','CPTPLND','Target'), gaugeopt_suite='stdgaug
         self.target_model = target_model
         self.gaugeopt_suite = GSTGaugeOptSuite.cast(gaugeopt_suite)
         self.objfn_builders = GSTObjFnBuilders.cast(objfn_builders) if (objfn_builders is not None) else None
-        self.optimizer = _opt.CustomLMOptimizer.cast(optimizer)
+        self.optimizer = _opt.SimplerLMOptimizer.cast(optimizer)
         self.badfit_options = GSTBadFitOptions.cast(badfit_options)
         self.verbosity = verbosity
 
         if not isinstance(optimizer, _opt.Optimizer) and isinstance(optimizer, dict) \
-           and 'first_fditer' not in optimizer:  # then a dict was cast into a CustomLMOptimizer above.
+           and 'first_fditer' not in optimizer:  # then a dict was cast into an Optimizer above.
             # by default, set special "first_fditer=auto" behavior (see logic in GateSetTomography.__init__)
             self.optimizer.first_fditer = None
 

test/unit/optimize/test_customlm.py → test/unit/optimize/test_simplerlm.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 from pygsti.optimize import arraysinterface as _ari
-from pygsti.optimize import customlm as lm
+from pygsti.optimize import simplerlm as lm
 from ..util import BaseCase
 
 
@@ -19,25 +19,25 @@ def gjac(x):
     return np.array([2 * x[0]],'d')
 
 
-class CustomLMTester(BaseCase):
-    def test_custom_leastsq_infinite_objective_fn_norm_at_x0(self):
+class LMTester(BaseCase):
+    def test_simplish_leastsq_infinite_objective_fn_norm_at_x0(self):
         x0 = np.ones(3, 'd')
         ari = _ari.UndistributedArraysInterface(2, 3)
-        xf, converged, msg, *_ = lm.custom_leastsq(f, jac, x0, arrays_interface=ari)
+        xf, converged, msg, *_ = lm.simplish_leastsq(f, jac, x0, arrays_interface=ari)
         self.assertEqual(msg, "Infinite norm of objective function at initial point!")
 
-    def test_custom_leastsq_max_iterations_exceeded(self):
+    def test_simplish_leastsq_max_iterations_exceeded(self):
         x0 = np.ones(3, 'd')
         ari = _ari.UndistributedArraysInterface(2, 3)
-        xf, converged, msg, *_ = lm.custom_leastsq(f, jac, x0, max_iter=0, arrays_interface=ari)
+        xf, converged, msg, *_ = lm.simplish_leastsq(f, jac, x0, max_iter=0, arrays_interface=ari)
         self.assertEqual(msg, "Maximum iterations (0) exceeded")
 
-    def test_custom_leastsq_x_limits(self):
+    def test_simplish_leastsq_x_limits(self):
         #perform optimization of g(x), which has minimum at 0, using limits so x must be 10 > x > 1
         # and check that the optimal x is near 1.0:
         x0 = np.array([6.0],'d')
         xlimits = np.array([[1.0, 10.0]], 'd')
         ari = _ari.UndistributedArraysInterface(1, 1)
-        xf, converged, msg, *_ = lm.custom_leastsq(g, gjac, x0, max_iter=100, arrays_interface=ari,
+        xf, converged, msg, *_ = lm.simplish_leastsq(g, gjac, x0, max_iter=100, arrays_interface=ari,
                                                    x_limits=xlimits)
         self.assertAlmostEqual(xf[0], 1.0)

test/unit/protocols/test_gst.py

@@ -5,7 +5,7 @@
 from pygsti.objectivefns.objectivefns import PoissonPicDeltaLogLFunction
 from pygsti.models.gaugegroup import TrivialGaugeGroup
 from pygsti.objectivefns import FreqWeightedChi2Function
-from pygsti.optimize.customlm import CustomLMOptimizer
+from pygsti.optimize.simplerlm import SimplerLMOptimizer
 from pygsti.protocols import gst
 from pygsti.protocols.estimate import Estimate
 from pygsti.protocols.protocol import ProtocolData, Protocol
@@ -65,7 +65,7 @@ def test_gaugeopt_suite_raises_on_bad_suite(self):
 
     def test_add_badfit_estimates(self):
         builder = PoissonPicDeltaLogLFunction.builder()
-        opt = CustomLMOptimizer()
+        opt = SimplerLMOptimizer()
         badfit_opts = gst.GSTBadFitOptions(threshold=-10, actions=("robust", "Robust", "robust+", "Robust+",
                                                                    "wildcard", "do nothing"))
         res = self.results.copy()