Qutrit classifier

src/qibocal/fitting/classifier/data.py

@@ -17,11 +17,10 @@ def generate_models(data, qubit, test_size=0.25):
         - x_test: Test inputs.
         - y_test: Test outputs.
     """
-    data0 = data.data[qubit, 0].tolist()
-    data1 = data.data[qubit, 1].tolist()
+    qubit_data = data.data[qubit]
     return train_test_split(
-        np.array(np.concatenate((data0, data1))),
-        np.array([0] * len(data0) + [1] * len(data1)),
+        np.array(qubit_data[["i", "q"]].tolist())[:, :],
+        np.array(qubit_data[["state"]].tolist())[:, 0],
         test_size=test_size,
         random_state=0,
         shuffle=True,

src/qibocal/fitting/classifier/decision_tree.py

@@ -0,0 +1,40 @@
+from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold
+from sklearn.tree import DecisionTreeClassifier
+
+from . import scikit_utils
+
+
+def constructor(hyperpars):
+    r"""Return the model class.
+
+    Args:
+        hyperparams: Model hyperparameters.
+    """
+    return DecisionTreeClassifier().set_params(**hyperpars)
+
+
+def hyperopt(x_train, y_train, _path):
+    r"""Perform an hyperparameter optimization and return the hyperparameters.
+
+    Args:
+        x_train: Training inputs.
+        y_train: Training outputs.
+        _path (path): Model save path.
+
+    Returns:
+        Dictionary with model's hyperparameters.
+    """
+    clf = DecisionTreeClassifier()
+    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
+    space = {}
+    space["criterion"] = ["gini", "entropy", "log_loss"]
+    space["splitter"] = ["best", "random"]
+    search = GridSearchCV(clf, space, scoring="accuracy", n_jobs=-1, cv=cv)
+    _ = search.fit(x_train, y_train)
+
+    return search.best_params_
+
+
+normalize = scikit_utils.scikit_normalize
+dump = scikit_utils.scikit_dump
+predict_from_file = scikit_utils.scikit_predict

src/qibocal/fitting/classifier/run.py

@@ -23,6 +23,7 @@
     "random_forest",
     "rbf_svm",
     "qblox_fit",
+    "decision_tree",
 ]
 
 PRETTY_NAME = [
@@ -35,6 +36,7 @@
     "Random Forest",
     "RBF SVM",
     "Qblox Fit",
+    "Decision Tree",
 ]
 
 

src/qibocal/fitting/classifier/scikit_utils.py

@@ -30,7 +30,7 @@ def scikit_normalize(constructor):
 
 def scikit_dump(model, path: Path):
     r"""Dumps scikit `model` in `path`"""
-    initial_type = [("float_input", FloatTensorType([1, 2]))]
+    initial_type = [("float_input", FloatTensorType([None, 2]))]
     onx = to_onnx(model, initial_types=initial_type)
     with open(path.with_suffix(".onnx"), "wb") as f:
         f.write(onx.SerializeToString())

src/qibocal/protocols/characterization/__init__.py

@@ -24,6 +24,7 @@
 )
 from .qubit_spectroscopy import qubit_spectroscopy
 from .qubit_spectroscopy_ef import qubit_spectroscopy_ef
+from .qutrit_classification import qutrit_classification
 from .rabi.amplitude import rabi_amplitude
 from .rabi.ef import rabi_amplitude_ef
 from .rabi.length import rabi_length
@@ -92,4 +93,5 @@ class Operation(Enum):
     twpa_power = twpa_power
     rabi_amplitude_ef = rabi_amplitude_ef
     qubit_spectroscopy_ef = qubit_spectroscopy_ef
+    qutrit_classification = qutrit_classification
     resonator_amplitude = resonator_amplitude