Cache results for subject/pipeline

.github/workflows/testing.yml

@@ -35,6 +35,8 @@ jobs:
       run: |
         flake8 examples tests pyriemann_qiskit
     - name: Test with pytest
+      env:
+        FIREBASE_CERTIFICATE: ${{ secrets.FIREBASE_CERTIFICATE }}
       run: |
         pytest
 

doc/api.rst

@@ -83,6 +83,17 @@ Math
 
     cov_to_corr_matrix
 
+Firebase
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. _firebase_api:
+.. currentmodule:: pyriemann_qiskit.utils.firebase_connector
+
+.. autosummary::
+    :toctree: generated/
+
+    FirebaseConnector
+    Cache
+
 Datasets
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. _datasets_api:
@@ -95,4 +106,5 @@ Datasets
     get_linearly_separable_dataset
     get_qiskit_dataset
     get_feature_dimension
+    MockDataset
 

doc/requirements.txt

@@ -15,3 +15,4 @@ scipy==1.7.3
 moabb>=0.4.6
 git+https://github.com/pyRiemann/pyRiemann#egg=pyriemann
 docplex>=2.21.207
+firebase_admin==6.0.1

pyriemann_qiskit/classification.py

@@ -513,8 +513,9 @@ class QuantumClassifierWithDefaultRiemannianPipeline(BaseEstimator,
         If None, respective default values for Pegasos and (Q)SVC
         are used. The default value for Pegasos is 1000.
         For (Q)SVC it is -1 (that is not limit).
-    shots : int (default:1024)
+    shots : int | None (default: 1024)
         Number of repetitions of each circuit, for sampling.
+        If None, classical computation will be performed.
     feature_entanglement : str | list[list[list[int]]] | \
                    Callable[int, list[list[list[int]]]]
         Specifies the entanglement structure for the ZZFeatureMap.

pyriemann_qiskit/datasets/__init__.py

@@ -1,10 +1,12 @@
 from .utils import (get_mne_sample,
                     get_linearly_separable_dataset,
                     get_qiskit_dataset,
-                    get_feature_dimension)
+                    get_feature_dimension,
+                    MockDataset)
 
 
 __all__ = ["get_mne_sample",
            "get_linearly_separable_dataset",
            "get_qiskit_dataset",
-           "get_feature_dimension"]
+           "get_feature_dimension",
+           "MockDataset"]

pyriemann_qiskit/datasets/utils.py

@@ -1,6 +1,10 @@
+from warnings import warn
 import numpy as np
-from mne import io, read_events, pick_types, Epochs
-from mne.datasets import sample
+try:
+    from mne import io, read_events, pick_types, Epochs
+    from mne.datasets import sample
+except Exception:
+    warn("mne not available. get_mne_sample will fail.")
 from qiskit_machine_learning.datasets import ad_hoc_data
 from sklearn.datasets import make_classification
 
@@ -34,6 +38,10 @@ def get_mne_sample(n_trials=10):
     y : ndarray, shape (n_trials,)
         Predicted target vector relative to X.
 
+    Notes
+    -----
+    .. versionadded:: 0.0.1
+
     References
     ----------
     .. [1] Available from: \
@@ -81,6 +89,10 @@ def get_mne_sample(n_trials=10):
 def get_qiskit_dataset():
     """Return qiskit dataset.
 
+    Notes
+    -----
+    .. versionadded:: 0.0.1
+
     Returns
     -------
     X : ndarray, shape (n_samples, n_features)
@@ -108,6 +120,10 @@ def get_qiskit_dataset():
 def get_linearly_separable_dataset():
     """Return a linearly separable dataset.
 
+    Notes
+    -----
+    .. versionadded:: 0.0.1
+
     Returns
     -------
     X : ndarray, shape (n_samples, n_features)
@@ -151,6 +167,10 @@ def get_feature_dimension(dataset):
         n_features : int
             The feature dimension, -1 denotes no data in the dataset.
 
+    Notes
+    -----
+    .. versionadded:: 0.0.2
+
     Raises
     -------
     TypeError
@@ -166,3 +186,70 @@ def get_feature_dimension(dataset):
         return v.shape[1]
 
     return -1
+
+
+class MockDataset():
+    """A dataset with mock data.
+
+    Parameters
+    ----------
+    dataset_gen : Callable[[], (List, List)]
+        A function to generate datasets.
+        The function accepts no parameters and returns a pair of lists.
+        The first list contains the samples, the second one the labels.
+    n_subjects : int
+        The number of subjects in the dataset.
+        A dataset will be generated for all subjects using the handler
+        `dataset_gen`.
+
+    Attributes
+    ----------
+    code_ : str
+        The code of the dataset.
+        The code of the dataset is also the string representation
+        of the dataset.
+    data_ : Dict
+        A dictionnary representing the dataset. Ex:
+        ```
+        {
+            subject1: (samples1, labels1),
+            subject2: (samples2, labels2),
+            ...
+        }
+        ```
+    subjects_ : List[int]
+        The subjects of the dataset.
+
+    Notes
+    -----
+    .. versionadded:: 0.0.3
+
+    """
+    def __init__(self, dataset_gen, n_subjects: int):
+        self.code_ = "MockDataset"
+        self.subjects_ = range(n_subjects)
+        self.data_ = {}
+        for subject in self.subjects_:
+            self.data_[subject] = dataset_gen()
+
+    def get_data(self, subject):
+        """
+        Returns the data of a subject.
+
+        Parameters
+        ----------
+        subject : int
+            A subject in the list of subjects `subjects_`.
+
+        Returns
+        -------
+        data : the subject's data.
+
+        Notes
+        -----
+        .. versionadded:: 0.0.3
+        """
+        return self.data_[subject]
+
+    def __str__(self) -> str:
+        return self.code_

pyriemann_qiskit/utils/__init__.py

@@ -5,6 +5,7 @@
                       square_bin_mat_var,
                       ClassicalOptimizer,
                       NaiveQAOAOptimizer)
+from .firebase_connector import FirebaseConnector, Cache
 
 __all__ = [
     'hyper_params_factory',
@@ -14,5 +15,7 @@
     'square_int_mat_var',
     'square_bin_mat_var',
     'ClassicalOptimizer',
-    'NaiveQAOAOptimizer'
+    'NaiveQAOAOptimizer',
+    'FirebaseConnector',
+    'Cache'
 ]

pyriemann_qiskit/utils/firebase_cert.py

@@ -0,0 +1 @@
+certificate = {}