Delay scikit-learn import until first use

qiskit_experiments/data_processing/sklearn_discriminators.py

@@ -1,181 +1,165 @@
-# 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
-
-from qiskit_experiments.data_processing.discriminator import BaseDiscriminator
-from qiskit_experiments.data_processing.exceptions import DataProcessorError
-
-try:
-    from sklearn.discriminant_analysis import (
-        LinearDiscriminantAnalysis,
-        QuadraticDiscriminantAnalysis,
-    )
-
-    HAS_SKLEARN = True
-except ImportError:
-    HAS_SKLEARN = False
-
-
-class SkLDA(BaseDiscriminator):
-    """A wrapper for the SKlearn linear discriminant analysis."""
-
-    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.
-        """
-        if not HAS_SKLEARN:
-            raise DataProcessorError(
-                f"SKlearn is needed to initialize an {self.__class__.__name__}."
-            )
-
-        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
-
-    def is_trained(self) -> bool:
-        """Return True if the discriminator has been trained on data."""
-        return not getattr(self._lda, "classes_", None) is None
-
-    def predict(self, data: List):
-        """Wrap the predict method of the LDA."""
-        return self._lda.predict(data)
-
-    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)
-
-    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}
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "SkLDA":
-        """Deserialize from an object."""
-
-        if not HAS_SKLEARN:
-            raise DataProcessorError(f"SKlearn is needed to initialize an {cls.__name__}.")
-
-        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)
-
-
-class SkQDA(BaseDiscriminator):
-    """A wrapper for the SKlearn quadratic discriminant analysis."""
-
-    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.
-        """
-        if not HAS_SKLEARN:
-            raise DataProcessorError(
-                f"SKlearn is needed to initialize an {self.__class__.__name__}."
-            )
-
-        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
-
-    def is_trained(self) -> bool:
-        """Return True if the discriminator has been trained on data."""
-        return not getattr(self._qda, "classes_", None) is None
-
-    def predict(self, data: List):
-        """Wrap the predict method of the QDA."""
-        return self._qda.predict(data)
-
-    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)
-
-    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}
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "SkQDA":
-        """Deserialize from an object."""
-
-        if not HAS_SKLEARN:
-            raise DataProcessorError(f"SKlearn is needed to initialize an {cls.__name__}.")
-
-        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)
+# 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,
+    )
+
+
+class SkLDA(BaseDiscriminator):
+    """A wrapper for the SKlearn linear discriminant analysis."""
+
+    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
+
+    def is_trained(self) -> bool:
+        """Return True if the discriminator has been trained on data."""
+        return not getattr(self._lda, "classes_", None) is None
+
+    def predict(self, data: List):
+        """Wrap the predict method of the LDA."""
+        return self._lda.predict(data)
+
+    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)
+
+    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}
+
+    @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)
+
+
+class SkQDA(BaseDiscriminator):
+    """A wrapper for the SKlearn quadratic discriminant analysis."""
+
+    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
+
+    def is_trained(self) -> bool:
+        """Return True if the discriminator has been trained on data."""
+        return not getattr(self._qda, "classes_", None) is None
+
+    def predict(self, data: List):
+        """Wrap the predict method of the QDA."""
+        return self._qda.predict(data)
+
+    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)
+
+    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}
+
+    @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)