Merge pull request #588 from qiboteam/fix_hpars

Fix hpars
qiboteam · Oct 27, 2023 · 7098692 · 7098692
2 parents 6fa349d + aaf4360
commit 7098692
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Showing 4 changed files with 36 additions and 38 deletions.
diff --git a/src/qibocal/fitting/classifier/run.py b/src/qibocal/fitting/classifier/run.py
@@ -251,12 +251,9 @@ def train_qubit(
         classifier = Classifier(mod, qubit_dir)
         classifier.savedir.mkdir(exist_ok=True)
         logging.info(f"Training quibt with classifier: {pretty_name(classifier.name)}")
-        if classifier.name not in cls_data.classifiers_hpars[qubit]:
-            hyperpars = classifier.hyperopt(
-                x_train, y_train.astype(np.int64), classifier.savedir
-            )
-        else:
-            hyperpars = cls_data.classifiers_hpars[qubit][classifier.name]
+        hyperpars = classifier.hyperopt(
+            x_train, y_train.astype(np.int64), classifier.savedir
+        )
         hpars_list.append(hyperpars)
         classifier.dump_hyper(hyperpars)
         model = classifier.create_model(hyperpars)

diff --git a/src/qibocal/protocols/characterization/classification.py b/src/qibocal/protocols/characterization/classification.py
@@ -60,8 +60,6 @@ class SingleShotClassificationParameters(Parameters):
 class SingleShotClassificationData(Data):
     nshots: int
     """Number of shots."""
-    classifiers_hpars: dict[QubitId, dict]
-    """Models' hyperparameters"""
     savedir: str
     """Dumping folder of the classification results"""
     data: dict[QubitId, npt.NDArray] = field(default_factory=dict)
@@ -173,7 +171,6 @@ def _acquisition(
 
     RX_pulses = {}
     ro_pulses = {}
-    hpars = {}
     for qubit in qubits:
         RX_pulses[qubit] = platform.create_RX_pulse(qubit, start=0)
         ro_pulses[qubit] = platform.create_qubit_readout_pulse(
@@ -183,12 +180,10 @@ def _acquisition(
         state0_sequence.add(ro_pulses[qubit])
         state1_sequence.add(RX_pulses[qubit])
         state1_sequence.add(ro_pulses[qubit])
-        hpars[qubit] = qubits[qubit].classifiers_hpars
-    # create a DataUnits object to store the results
+
     data = SingleShotClassificationData(
         nshots=params.nshots,
         classifiers_list=params.classifiers_list,
-        classifiers_hpars=hpars,
         savedir=params.savedir,
     )
 
@@ -303,7 +298,7 @@ def _fit(data: SingleShotClassificationData) -> SingleShotClassificationResults:
 def _plot(
     data: SingleShotClassificationData, qubit, fit: SingleShotClassificationResults
 ):
-    fitting_report = None
+    fitting_report = ""
     models_name = data.classifiers_list
     figures = plot_results(data, qubit, 2, fit)
     if fit is not None:
@@ -346,28 +341,28 @@ def _plot(
             )
             figures.append(fig_roc)
 
-            if models_name[i] == "qubit_fit":
-                fitting_report = table_html(
-                    table_dict(
-                        qubit,
-                        [
-                            "Average State 0",
-                            "Average State 1",
-                            "Rotational Angle",
-                            "Threshold",
-                            "Readout Fidelity",
-                            "Assignment Fidelity",
-                        ],
-                        [
-                            np.round(fit.mean_gnd_states[qubit], 3),
-                            np.round(fit.mean_exc_states[qubit], 3),
-                            np.round(fit.rotation_angle[qubit], 3),
-                            np.round(fit.threshold[qubit], 6),
-                            np.round(fit.fidelity[qubit], 3),
-                            np.round(fit.assignment_fidelity[qubit], 3),
-                        ],
-                    )
+        if "qubit_fit" in models_name:
+            fitting_report = table_html(
+                table_dict(
+                    qubit,
+                    [
+                        "Average State 0",
+                        "Average State 1",
+                        "Rotational Angle",
+                        "Threshold",
+                        "Readout Fidelity",
+                        "Assignment Fidelity",
+                    ],
+                    [
+                        np.round(fit.mean_gnd_states[qubit], 3),
+                        np.round(fit.mean_exc_states[qubit], 3),
+                        np.round(fit.rotation_angle[qubit], 3),
+                        np.round(fit.threshold[qubit], 6),
+                        np.round(fit.fidelity[qubit], 3),
+                        np.round(fit.assignment_fidelity[qubit], 3),
+                    ],
                 )
+            )
 
     return figures, fitting_report
 

diff --git a/src/qibocal/protocols/characterization/qutrit_classification.py b/src/qibocal/protocols/characterization/qutrit_classification.py
@@ -70,12 +70,10 @@ def _acquisition(
     # taking advantage of multiplexing, apply the same set of gates to all qubits in parallel
     states_sequences = [PulseSequence() for _ in range(3)]
     ro_pulses = {}
-    hpars = {}
     for qubit in qubits:
         rx_pulse = platform.create_RX_pulse(qubit, start=0)
         rx12_pulse = platform.create_RX12_pulse(qubit, start=rx_pulse.finish)
         drive_pulses = [rx_pulse, rx12_pulse]
-        hpars[qubit] = qubits[qubit].classifiers_hpars
         ro_pulses[qubit] = []
         for i, sequence in enumerate(states_sequences):
             sequence.add(*drive_pulses[:i])
@@ -88,7 +86,6 @@ def _acquisition(
     data = QutritClassificationData(
         nshots=params.nshots,
         classifiers_list=params.classifiers_list,
-        classifiers_hpars=hpars,
         savedir=params.savedir,
     )
     states_results = []

diff --git a/src/qibocal/protocols/characterization/utils.py b/src/qibocal/protocols/characterization/utils.py
@@ -426,7 +426,16 @@ def evaluate_grid(
     return np.vstack([i_values.ravel(), q_values.ravel()]).T
 
 
-def plot_results(data: Data, qubit, qubit_states, fit: Results):
+def plot_results(data: Data, qubit: QubitId, qubit_states: list, fit: Results):
+    """
+    Plots for the qubit and qutrit classification.
+
+    Args:
+        data (Data): acquisition data
+        qubit (QubitID): qubit
+        qubit_states (list): list of qubit states available.
+        fit (Results): fit results
+    """
     figures = []
     models_name = data.classifiers_list
     qubit_data = data.data[qubit]