hyperparams object?

pyriemann_qiskit/classification.py

@@ -1,10 +1,12 @@
 """Module for classification function."""
 import numpy as np
 
+from .utils.hyperparams import QuanticHyperParams
+
 from sklearn.base import BaseEstimator, ClassifierMixin
 
 from qiskit import BasicAer, IBMQ
-from qiskit.circuit.library import ZZFeatureMap, TwoLocal
+from qiskit.circuit.library import TwoLocal
 from qiskit.aqua import QuantumInstance, aqua_globals
 from qiskit.aqua.quantum_instance import logger
 from qiskit.aqua.algorithms import QSVM, SklearnSVM, VQC
@@ -45,6 +47,8 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
         the classification task will be running on a IBM quantum backend
     verbose : bool (default:True)
         If true will output all intermediate results and logs
+    hyper_params : QuanticHyperParams (default:QuanticHyperParams())
+        The hyper parameters for quantum classifiers
 
     Notes
     -----
@@ -59,6 +63,7 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
     --------
     QuanticSVM
     QuanticVQC
+    QuanticHyperParams
 
     References
     ----------
@@ -68,11 +73,12 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
 
     """
 
-    def __init__(self, quantum=True, q_account_token=None, verbose=True):
+    def __init__(self, quantum=True, q_account_token=None, verbose=True, hyper_params=QuanticHyperParams()):
         self.verbose = verbose
         self._log("Initializing Quantum Classifier")
         self.q_account_token = q_account_token
         self.quantum = quantum
+        self.hyper_params = hyper_params
         # protected field for child classes
         self._training_input = {}
 
@@ -148,8 +154,7 @@ def fit(self, X, y):
 
         feature_dim = get_feature_dimension(self._training_input)
         self._log("Feature dimension = ", feature_dim)
-        self._feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2,
-                                         entanglement='linear')
+        self._feature_map = self.hyper_params.feature_map(feature_dim)
         if self.quantum:
             if not hasattr(self, "_backend"):
                 def filters(device):
@@ -247,7 +252,7 @@ def _init_algo(self, feature_dim):
         if self.quantum:
             classifier = QSVM(self._feature_map, self._training_input)
         else:
-            classifier = SklearnSVM(self._training_input)
+            classifier = SklearnSVM(self._training_input, gamma=self.hyper_params.gamma)
         return classifier
 
     def predict_proba(self, X):

pyriemann_qiskit/utils/__init__.py

@@ -0,0 +1,5 @@
+from . import hyperparams
+
+__all__ = [
+    'hyperparams',
+]

pyriemann_qiskit/utils/hyperparams.py

@@ -0,0 +1,45 @@
+from qiskit.circuit.library import ZZFeatureMap
+
+default = {
+    "gamma": None, #quantum=False
+    "lambda2":0.001, #QSVC
+    "feature_map":
+        lambda dim: 
+            ZZFeatureMap(feature_dimension=dim, reps=2, entanglement='linear'),
+    "nshots":None,
+    "optimizer":None, #VQC
+    "var_form":None, #VQC
+    "enforce_spd":None,
+    "output_norm":None, #quantum=Fale
+    "l2reg":None
+}
+
+
+class QuanticHyperParams():
+    """
+    This class is a wrapper around all hyper parameters for quantum classifier.
+
+    Parameters
+    ----------
+    gamma: TODO
+    lambda2 : L2 norm regularization factor (QSVM)
+    feature_map : (feature_dim)->(Union[QuantumCircuit, FeatureMap])
+        Feature map module, used to transform data (QSVM and VQC)
+
+    Notes
+    -----
+    .. versionadded:: 0.0.1
+
+
+    See Also
+    --------
+    QuanticClassifierBase
+    QuanticSVM
+    QuanticVQC
+
+    """
+    def __init__(self, gamma=None, lambda2=default["lambda2"], feature_map=default["feature_map"]):
+        self.gamma = gamma
+        self.lambda2 = lambda2
+        self.feature_map = feature_map
+