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Improve doc for classifiers #197

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Oct 23, 2023
Merged

Improve doc for classifiers #197

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ad57579
complete doc and sort imports
qbarthelemy Oct 23, 2023
b5d3328
Update Dockerfile
gcattan Oct 23, 2023
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6 changes: 3 additions & 3 deletions Dockerfile
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ RUN apt-get -y install build-essential

RUN pip install urllib3==1.26.12
RUN pip install "numpy<1.24"
RUN pip install qiskit-terra==0.25.2.1
RUN pip install qiskit-terra==0.45.0rc1
RUN python setup.py develop
RUN pip install .[docs]
RUN pip install .[tests]
Expand All @@ -24,10 +24,10 @@ RUN mkdir /root/mne_data
RUN mkdir /home/mne_data

## Workaround for firestore
RUN pip install protobuf==4.24.4
RUN pip install protobuf==4.25.0rc2
RUN pip install google_cloud_firestore==2.12.0
### Missing __init__ file in protobuf
RUN touch /usr/local/lib/python3.9/site-packages/protobuf-4.24.4-py3.9.egg/google/__init__.py
RUN touch /usr/local/lib/python3.9/site-packages/protobuf-4.25.0rc2-py3.9-linux-x86_64.egg/google/__init__.py
## google.cloud.location is never used in these files, and is missing in path.
RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.12.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/client.py'
RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.12.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/base.py'
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58 changes: 32 additions & 26 deletions pyriemann_qiskit/classification.py
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Expand Up @@ -4,32 +4,34 @@
in several modes quantum/classical and simulated/real
quantum computer.
"""
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.svm import SVC
from qiskit_ibm_provider import IBMProvider, least_busy
from qiskit.utils import QuantumInstance, algorithm_globals
from qiskit.utils.quantum_instance import logger
from qiskit_machine_learning.algorithms import QSVC, VQC, PegasosQSVC
from qiskit_machine_learning.kernels.quantum_kernel import QuantumKernel
from datetime import datetime
import logging
from .utils.hyper_params_factory import gen_zz_feature_map, gen_two_local, get_spsa
from .utils import get_provider, get_devices, get_simulator
import numpy as np

from pyriemann.classification import MDM
from pyriemann_qiskit.datasets import get_feature_dimension
from pyriemann_qiskit.utils import (
ClassicalOptimizer,
NaiveQAOAOptimizer,
set_global_optimizer,
)
from qiskit.utils import QuantumInstance, algorithm_globals
from qiskit.utils.quantum_instance import logger
from qiskit_ibm_provider import IBMProvider, least_busy
from qiskit_machine_learning.algorithms import QSVC, VQC, PegasosQSVC
from qiskit_machine_learning.kernels.quantum_kernel import QuantumKernel
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.svm import SVC

from .utils.hyper_params_factory import gen_zz_feature_map, gen_two_local, get_spsa
from .utils import get_provider, get_devices, get_simulator

logger.level = logging.INFO


class QuanticClassifierBase(BaseEstimator, ClassifierMixin):

"""Quantum classification.
"""Quantum classifier

This class implements a scikit-learn wrapper around Qiskit library [1]_.
It provides a mean to run classification tasks on a local and
Expand All @@ -49,12 +51,12 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
(depending on q_account_token value),
- If false, will perform classical computing instead.
q_account_token : string (default:None)
If quantum==True and q_account_token provided,
If `quantum` is True and `q_account_token` provided,
the classification task will be running on a IBM quantum backend.
If `load_account` is provided, the classifier will use the previous
token saved with `IBMProvider.save_account()`.
verbose : bool (default:True)
If true will output all intermediate results and logs.
If true, will output all intermediate results and logs.
shots : int (default:1024)
Number of repetitions of each circuit, for sampling.
gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
Expand All @@ -76,6 +78,7 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
--------
QuanticSVM
QuanticVQC
QuanticMDM

References
----------
Expand Down Expand Up @@ -234,9 +237,10 @@ def _predict(self, X):

class QuanticSVM(QuanticClassifierBase):

"""Quantum-enhanced SVM classification.
"""Quantum-enhanced SVM classifier

This class implements SVC [1]_ on a quantum machine [2]_.
This class implements a support-vector machine (SVM) classifier [1]_,
called SVC, on a quantum machine [2]_.
Note that if `quantum` parameter is set to `False`
then a classical SVC will be perfomed instead.

Expand Down Expand Up @@ -270,12 +274,12 @@ class QuanticSVM(QuanticClassifierBase):
(depending on q_account_token value),
- If false, will perform classical computing instead.
q_account_token : string (default:None)
If quantum==True and q_account_token provided,
If `quantum` is True and `q_account_token` provided,
the classification task will be running on a IBM quantum backend.
If `load_account` is provided, the classifier will use the previous
token saved with `IBMProvider.save_account()`.
verbose : bool (default:True)
If true will output all intermediate results and logs.
If true, will output all intermediate results and logs.
shots : int (default:1024)
Number of repetitions of each circuit, for sampling.
gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
Expand Down Expand Up @@ -401,8 +405,9 @@ def predict(self, X):

class QuanticVQC(QuanticClassifierBase):

"""Variational Quantum Classifier
"""Variational quantum classifier

This class implements a variational quantum classifier (VQC).
Note that there is no classical version of this algorithm.
This will always run on a quantum computer (simulated or not).

Expand All @@ -419,12 +424,12 @@ class QuanticVQC(QuanticClassifierBase):
(depending on q_account_token value).
- If false, will perform classical computing instead.
q_account_token : string (default:None)
If quantum==True and q_account_token provided,
If `quantum` is True and `q_account_token` provided,
the classification task will be running on a IBM quantum backend.
If `load_account` is provided, the classifier will use the previous
token saved with `IBMProvider.save_account()`.
verbose : bool (default:True)
If true will output all intermediate results and logs
If true, will output all intermediate results and logs
shots : int (default:1024)
Number of repetitions of each circuit, for sampling
gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
Expand Down Expand Up @@ -552,11 +557,11 @@ def parameter_count(self):

class QuanticMDM(QuanticClassifierBase):

"""Quantum-enhanced MDM
"""Quantum-enhanced MDM classifier

This class is a convex implementation of the MDM [1]_,
that can runs with quantum optimization.
Only log-euclidian distance between trial and class prototypes is supported
This class is a convex implementation of the Minimum Distance to Mean (MDM)
[1]_, which can run with quantum optimization.
Only log-Euclidean distance between trial and class prototypes is supported
at the moment, but any type of metric can be used for centroid estimation.

Notes
Expand All @@ -577,16 +582,17 @@ class QuanticMDM(QuanticClassifierBase):
distance in order to keep the good sensitivity for the classification.
quantum : bool (default: True)
Only applies if `metric` contains a convex distance or mean.

- If true will run on local or remote backend
(depending on q_account_token value),
- If false, will perform classical computing instead.
q_account_token : string (default:None)
If quantum==True and q_account_token provided,
If `quantum` is True and `q_account_token` provided,
the classification task will be running on a IBM quantum backend.
If `load_account` is provided, the classifier will use the previous
token saved with `IBMProvider.save_account()`.
verbose : bool (default:True)
If true will output all intermediate results and logs.
If true, will output all intermediate results and logs.
shots : int (default:1024)
Number of repetitions of each circuit, for sampling.
gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
Expand Down