Remove use in scheduling names

qiboteam · Feb 28, 2024 · 54ced5b · 54ced5b
1 parent bfec996
commit 54ced5b
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Showing 3 changed files with 36 additions and 25 deletions.
diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb
@@ -87,7 +87,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The default scheduling strategy is grid search: `DoubleBracketScheduling.use_grid_serach`. This strategy specifies a list of step durations to test one by one and finds the one that maximizes the cost function (off-digonal norm of Hamiltonian)"
+    "The default scheduling strategy is grid search: `DoubleBracketScheduling.\n",
+    "grid_serach`. This strategy specifies a list of step durations to test one by one and finds the one that maximizes the cost function (off-digonal norm of Hamiltonian)"
    ]
   },
   {
@@ -97,16 +98,26 @@
    "outputs": [],
    "source": [
     "# grid_search\n",
-    "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search)\n",
+    "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search)\n",
     "print('grid_search step:', step_grid)\n",
     "# hyperopt\n",
-    "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, max_evals=100, step_max=0.6)\n",
+    "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, max_evals=100, step_max=0.6)\n",
     "print('hyperopt_search step:', step_hyperopt)\n",
     "# polynomial expansion\n",
-    "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, n=5)\n",
+    "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, n=5)\n",
     "print('polynomial_approximation step:', step_poly)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step_poly = dbi.polynomial_step(n=5)\n",
+    "print(step_poly)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -169,15 +180,15 @@
    "outputs": [],
    "source": [
     "# grid_search\n",
-    "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_max=0.6, d=d)\n",
+    "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_max=0.6, d=d)\n",
     "grid_min = dbi.loss(step=step_grid, d=d)-dbi.off_diagonal_norm\n",
     "print('grid_search step:', step_grid, 'loss', grid_min)\n",
     "# hyperopt\n",
-    "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, d=d, max_evals=100, step_max=0.6)\n",
+    "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, d=d, max_evals=100, step_max=0.6)\n",
     "hyperopt_min = dbi.loss(step=step_hyperopt, d=d)-dbi.off_diagonal_norm\n",
     "print('hyperopt_search step:', step_hyperopt, 'loss', hyperopt_min)\n",
     "# polynomial expansion\n",
-    "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, d=d, n=5)\n",
+    "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.polynomial_approximation, d=d, n=5)\n",
     "poly_min = dbi.loss(step=step_poly, d=d)-dbi.off_diagonal_norm\n",
     "print('polynomial_approximation step:', step_poly, 'loss', poly_min)"
    ]
@@ -230,10 +241,10 @@
     "    step_min = step_poly - search_range/2\n",
     "    step_max = step_poly + search_range/2\n",
     "# grid_search\n",
-    "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_min=step_min, step_max=step_max, d=d)\n",
+    "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.grid_search, step_min=step_min, step_max=step_max, d=d)\n",
     "print('grid_search step:', step_grid)\n",
     "# hyperopt\n",
-    "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, step_min=step_min, step_max=step_max, max_evals=100, d=d,)\n",
+    "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.hyperopt, step_min=step_min, step_max=step_max, max_evals=100, d=d,)\n",
     "print('hyperopt_search step:', step_hyperopt)"
    ]
   },
@@ -313,9 +324,9 @@
    "outputs": [],
    "source": [
     "NSTEPS = 8\n",
-    "scheduling_list = [DoubleBracketScheduling.use_grid_search,\n",
-    "                   DoubleBracketScheduling.use_hyperopt,\n",
-    "                   DoubleBracketScheduling.use_polynomial_approximation,]\n",
+    "scheduling_list = [DoubleBracketScheduling.grid_search,\n",
+    "                   DoubleBracketScheduling.hyperopt,\n",
+    "                   DoubleBracketScheduling.polynomial_approximation,]\n",
     "scheduling_labels = ['grid search',\n",
     "                     'hyperopt',\n",
     "                     'polynomial',]\n",

diff --git a/src/qibo/models/dbi/double_bracket.py b/src/qibo/models/dbi/double_bracket.py
@@ -25,11 +25,11 @@ class DoubleBracketGeneratorType(Enum):
 class DoubleBracketScheduling(Enum):
     """Define the DBI scheduling strategies."""
 
-    use_hyperopt = auto()
+    hyperopt = auto()
     """Use hyperopt package."""
-    use_grid_search = auto()
+    grid_search = auto()
     """Use greedy grid search."""
-    use_polynomial_approximation = auto()
+    polynomial_approximation = auto()
     """Use polynomial expansion (analytical) of the loss function."""
 
 
@@ -61,7 +61,7 @@ def __init__(
         self,
         hamiltonian: Hamiltonian,
         mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.canonical,
-        scheduling: DoubleBracketScheduling = DoubleBracketScheduling.use_grid_search,
+        scheduling: DoubleBracketScheduling = DoubleBracketScheduling.grid_search,
     ):
         self.h = hamiltonian
         self.h0 = deepcopy(self.h)
@@ -223,7 +223,7 @@ def polynomial_step(
             d = self.diagonal_h_matrix
 
         if backup_scheduling is None:
-            backup_scheduling = DoubleBracketScheduling.use_grid_search
+            backup_scheduling = DoubleBracketScheduling.grid_search
 
         def sigma(h: np.array):
             return h - self.backend.cast(np.diag(np.diag(self.backend.to_numpy(h))))
@@ -270,7 +270,7 @@ def Gamma(k: int):
             return min(real_positive_roots)
         # solution does not exist, resort to backup scheduling
         elif (
-            backup_scheduling == DoubleBracketScheduling.use_polynomial_approximation
+            backup_scheduling == DoubleBracketScheduling.polynomial_approximation
             and n < n_max + 1
         ):
             return self.polynomial_step(
@@ -287,11 +287,11 @@ def choose_step(
     ):
         if scheduling is None:
             scheduling = self.scheduling
-        if scheduling is DoubleBracketScheduling.use_grid_search:
+        if scheduling is DoubleBracketScheduling.grid_search:
             return self.grid_search_step(d=d, **kwargs)
-        if scheduling is DoubleBracketScheduling.use_hyperopt:
+        if scheduling is DoubleBracketScheduling.hyperopt:
             return self.hyperopt_step(d=d, **kwargs)
-        if scheduling is DoubleBracketScheduling.use_polynomial_approximation:
+        if scheduling is DoubleBracketScheduling.polynomial_approximation:
             return self.polynomial_step(d=d, **kwargs)
 
     def loss(self, step: float, d: np.array = None, look_ahead: int = 1):

diff --git a/tests/test_models_dbi.py b/tests/test_models_dbi.py
@@ -112,7 +112,7 @@ def test_energy_fluctuations(backend):
 
 @pytest.mark.parametrize(
     "scheduling",
-    [DoubleBracketScheduling.use_grid_search, DoubleBracketScheduling.use_hyperopt],
+    [DoubleBracketScheduling.grid_search, DoubleBracketScheduling.hyperopt],
 )
 @pytest.mark.parametrize("nqubits", [3, 4, 5])
 def test_double_bracket_iteration_scheduling_grid_hyperopt(
@@ -136,7 +136,7 @@ def test_double_bracket_iteration_scheduling_grid_hyperopt(
 @pytest.mark.parametrize("nqubits", [3, 4, 6])
 @pytest.mark.parametrize("n", [2, 3])
 @pytest.mark.parametrize(
-    "backup_scheduling", [None, DoubleBracketScheduling.use_polynomial_approximation]
+    "backup_scheduling", [None, DoubleBracketScheduling.polynomial_approximation]
 )
 def test_double_bracket_iteration_scheduling_polynomial(
     backend, nqubits, n, backup_scheduling
@@ -146,14 +146,14 @@ def test_double_bracket_iteration_scheduling_polynomial(
     dbi = DoubleBracketIteration(
         Hamiltonian(nqubits, h0, backend=backend),
         mode=DoubleBracketGeneratorType.single_commutator,
-        scheduling=DoubleBracketScheduling.use_polynomial_approximation,
+        scheduling=DoubleBracketScheduling.polynomial_approximation,
     )
     initial_off_diagonal_norm = dbi.off_diagonal_norm
     for _ in range(NSTEPS):
         step1 = dbi.polynomial_step(n=n, d=d, backup_scheduling=backup_scheduling)
         dbi(d=d, step=step1)
     step2 = dbi.choose_step(
-        scheduling=DoubleBracketScheduling.use_polynomial_approximation, n=n
+        scheduling=DoubleBracketScheduling.polynomial_approximation, n=n
     )
     dbi(step=step2)
     assert initial_off_diagonal_norm > dbi.off_diagonal_norm