# This code is part of Qiskit.
#
# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Discriminators that wrap SKLearn."""
from typing import Any, List, Dict, TYPE_CHECKING
from qiskit_experiments.data_processing.discriminator import BaseDiscriminator
from qiskit_experiments.warnings import HAS_SKLEARN
if TYPE_CHECKING:
from sklearn.discriminant_analysis import (
LinearDiscriminantAnalysis,
QuadraticDiscriminantAnalysis,
)
[docs]
class SkLDA(BaseDiscriminator):
"""A wrapper for the scikit-learn linear discriminant analysis.
.. note::
This class requires that scikit-learn is installed.
"""
def __init__(self, lda: "LinearDiscriminantAnalysis"):
"""
Args:
lda: The sklearn linear discriminant analysis. This may be a trained or an
untrained discriminator.
Raises:
DataProcessorError: If SKlearn could not be imported.
"""
self._lda = lda
self.attributes = [
"coef_",
"intercept_",
"covariance_",
"explained_variance_ratio_",
"means_",
"priors_",
"scalings_",
"xbar_",
"classes_",
"n_features_in_",
"feature_names_in_",
]
@property
def discriminator(self) -> Any:
"""Return then SKLearn object."""
return self._lda
[docs]
def is_trained(self) -> bool:
"""Return True if the discriminator has been trained on data."""
return not getattr(self._lda, "classes_", None) is None
[docs]
def predict(self, data: List):
"""Wrap the predict method of the LDA."""
return self._lda.predict(data)
[docs]
def fit(self, data: List, labels: List):
"""Fit the LDA.
Args:
data: The independent data.
labels: The labels corresponding to data.
"""
self._lda.fit(data, labels)
[docs]
def config(self) -> Dict[str, Any]:
"""Return the configuration of the LDA."""
attr_conf = {attr: getattr(self._lda, attr, None) for attr in self.attributes}
return {"params": self._lda.get_params(), "attributes": attr_conf}
[docs]
@classmethod
@HAS_SKLEARN.require_in_call
def from_config(cls, config: Dict[str, Any]) -> "SkLDA":
"""Deserialize from an object."""
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda = LinearDiscriminantAnalysis()
lda.set_params(**config["params"])
for name, value in config["attributes"].items():
if value is not None:
setattr(lda, name, value)
return SkLDA(lda)
[docs]
class SkQDA(BaseDiscriminator):
"""A wrapper for the SKlearn quadratic discriminant analysis.
.. note::
This class requires that scikit-learn is installed.
"""
def __init__(self, qda: "QuadraticDiscriminantAnalysis"):
"""
Args:
qda: The sklearn quadratic discriminant analysis. This may be a trained or an
untrained discriminator.
Raises:
DataProcessorError: If SKlearn could not be imported.
"""
self._qda = qda
self.attributes = [
"coef_",
"intercept_",
"covariance_",
"explained_variance_ratio_",
"means_",
"priors_",
"scalings_",
"xbar_",
"classes_",
"n_features_in_",
"feature_names_in_",
"rotations_",
]
@property
def discriminator(self) -> Any:
"""Return then SKLearn object."""
return self._qda
[docs]
def is_trained(self) -> bool:
"""Return True if the discriminator has been trained on data."""
return not getattr(self._qda, "classes_", None) is None
[docs]
def predict(self, data: List):
"""Wrap the predict method of the QDA."""
return self._qda.predict(data)
[docs]
def fit(self, data: List, labels: List):
"""Fit the QDA.
Args:
data: The independent data.
labels: The labels corresponding to data.
"""
self._qda.fit(data, labels)
[docs]
def config(self) -> Dict[str, Any]:
"""Return the configuration of the QDA."""
attr_conf = {attr: getattr(self._qda, attr, None) for attr in self.attributes}
return {"params": self._qda.get_params(), "attributes": attr_conf}
[docs]
@classmethod
@HAS_SKLEARN.require_in_call
def from_config(cls, config: Dict[str, Any]) -> "SkQDA":
"""Deserialize from an object."""
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
qda = QuadraticDiscriminantAnalysis()
qda.set_params(**config["params"])
for name, value in config["attributes"].items():
if value is not None:
setattr(qda, name, value)
return SkQDA(qda)