Make scikit-learn an optional dependency (#524)

* Make scikit-learn an optional dependency

We previously had scikit-learn as a requirement that always needed to be
installed. However it was only ever used by the measurement
discriminators and not needed for ignis in general. Since this is a
fairly large package it is a bit heavy to require for every install
especially if the discriminators aren't being used. This commit removes
scikit-learn from the requirements list and adds it as an optional
dependency.

Co-authored-by: Christopher J. Wood <[email protected]>

README.md

@@ -31,8 +31,11 @@ for running tomogography you'll need to install cvxpy. You can do this with
 error mitigation using the CTMP method performance can be improved using
 just-in-time compiling if Numbda is installed. You can do this with
 `pip install numba` or when you install ignis with
-`pip install qiskit-ignis[jit]`. If you want to install all extra requirements
-when you install ignis you can run `pip install qiskit-ignis[visualization,cvx,jit]`.
+`pip install qiskit-ignis[jit]`. For using the discriminator classes in
+`qiskit.ignis.measurement` scikit-learn needs to be installed. You can do this with
+`pip install scikit-learn` or when you install ignis with
+`pip install qiskit-ignis[iq]`. If you want to install all extra requirements
+when you install ignis you can run `pip install qiskit-ignis[visualization,cvx,jit,iq]`.
 
 ## Creating your first quantum experiment with Qiskit Ignis
 Now that you have Qiskit Ignis installed, you can start creating experiments, to reveal information about the device quality. Here is a basic example:

qiskit/ignis/measurement/discriminator/iq_discriminators.py

@@ -21,8 +21,6 @@
 from typing import Union, List
 
 import numpy as np
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
 
 from qiskit.exceptions import QiskitError
 from qiskit.ignis.measurement.discriminator.discriminators import \
@@ -36,6 +34,13 @@
 except ImportError:
     HAS_MATPLOTLIB = False
 
+try:
+    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+    from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
+    HAS_SKLEARN = True
+except ImportError:
+    HAS_SKLEARN = False
+
 
 class IQDiscriminationFitter(BaseDiscriminationFitter):
     """
@@ -65,7 +70,6 @@ def __init__(self, cal_results: Union[Result, List[Result]],
                 instead of the schedules. If schedules is None, then all the
                 schedules in cal_results are used.
         """
-
         BaseDiscriminationFitter.__init__(self, cal_results, qubit_mask,
                                           expected_states, standardize,
                                           schedules)
@@ -423,6 +427,8 @@ def __init__(self, cal_results: Union[Result, List[Result]],
                 instead of the schedules. If schedules is None, then all the
                 schedules in cal_results are used.
             discriminator_parameters (dict): parameters for Sklearn's LDA.
+        Raises:
+            ImportError: If scikit-learn is not installed
         """
         if not discriminator_parameters:
             discriminator_parameters = {}
@@ -431,7 +437,10 @@ def __init__(self, cal_results: Union[Result, List[Result]],
         shrink = discriminator_parameters.get('shrinkage', None)
         store_cov = discriminator_parameters.get('store_covariance', False)
         tol = discriminator_parameters.get('tol', 1.0e-4)
-
+        if not HAS_SKLEARN:
+            raise ImportError("To use the LinearIQDiscriminator class "
+                              "scikit-learn needs to be installed. This can "
+                              "be done with 'pip install scikit-learn'")
         self._lda = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrink,
                                                store_covariance=store_cov,
                                                tol=tol)
@@ -492,12 +501,18 @@ def __init__(self, cal_results: Union[Result, List[Result]],
                 instead of the schedules. If schedules is None, then all the
                 schedules in cal_results are used.
             discriminator_parameters (dict): parameters for Sklearn's LDA.
+        Raises:
+            ImportError: If scikit-learn is not installed
         """
         if not discriminator_parameters:
             discriminator_parameters = {}
 
         store_cov = discriminator_parameters.get('store_covariance', False)
         tol = discriminator_parameters.get('tol', 1.0e-4)
+        if not HAS_SKLEARN:
+            raise ImportError("To use the QuadraticIQDiscriminator class "
+                              "scikit-learn needs to be installed. This can "
+                              "be done with 'pip install scikit-learn'")
 
         self._qda = QuadraticDiscriminantAnalysis(store_covariance=store_cov,
                                                   tol=tol)

releasenotes/notes/scikit-learn-is-optional-0540027be0003aad.yaml

@@ -0,0 +1,12 @@
+---
+upgrade:
+  - |
+    The ``scikit-learn`` dependency is no longer required and is now an optional
+    requirement. If you're using the IQ measurement discriminators
+    (:class:`~qiskit.ignis.measurement.IQDiscriminationFitter`,
+    :class:`~qiskit.ignis.measurement.LinearIQDiscriminationFitter`,
+    :class:`~qiskit.ignis.measurement.QuadraticIQDiscriminationFitter`,
+    or :class:`~qiskit.ignis.measurement.SklearnIQDiscriminator`) you will
+    now need to manually install scikit-learn, either by running
+    ``pip install scikit-learn`` or when you're also installing
+    qiskit-ignis with ``pip install qiskit-ignis[iq]``.

requirements-dev.txt

@@ -13,3 +13,4 @@ jupyter-sphinx;python_version<'3.9'
 reno
 matplotlib
 pyfakefs
+scikit-learn>=0.17

setup.py

@@ -24,7 +24,6 @@
     "retworkx>=0.8.0",
     "scipy>=0.19,!=0.19.1",
     "setuptools>=40.1.0",
-    "scikit-learn>=0.17",
 ]
 
 
@@ -81,6 +80,7 @@
     extras_require={
         'visualization': ['matplotlib>=2.1'],
         'cvx': ['cvxpy>=1.0.15'],
+        'iq': ["scikit-learn>=0.17"],
         'jit': ['numba'],
     },
     install_requires=requirements,

test/measurement/discriminator/test_filter.py

@@ -19,12 +19,19 @@
 import unittest
 from operator import getitem
 
+try:
+    import sklearn  # pylint: disable=unused-import
+    HAS_SKLEARN = True
+except ImportError:
+    HAS_SKLEARN = False
+
 from qiskit.ignis.measurement.discriminator.filters import DiscriminationFilter
 from qiskit.result import Result
 from qiskit.ignis.measurement.discriminator.iq_discriminators import \
     LinearIQDiscriminator
 
 
+@unittest.skipUnless(HAS_SKLEARN, 'scikit-learn is required for these tests')
 class TestDiscriminationFilter(unittest.TestCase):
     """
     Test methods of discrimination filters.

test/measurement/discriminator/test_iq_discriminator.py

@@ -22,7 +22,11 @@
 
 import test.utils as utils
 
-from sklearn.svm import SVC
+try:
+    from sklearn.svm import SVC
+    HAS_SKLEARN = True
+except ImportError:
+    HAS_SKLEARN = False
 
 import qiskit
 from qiskit import Aer
@@ -34,6 +38,7 @@
 from qiskit.result.models import ExperimentResultData
 
 
+@unittest.skipUnless(HAS_SKLEARN, 'scikit-learn is required to run these tests')
 class BaseTestIQDiscriminator(unittest.TestCase):
     """
     Base class for IQ discriminator test cases.