<filename>foreshadow/utils/default_estimator_factory.py
"""Estimator factory and supported estimators."""

from typing import Dict

from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.neural_network import MLPClassifier, MLPRegressor
from sklearn.svm import LinearSVC, LinearSVR

from .constants import EstimatorFamily, ProblemType


class EstimatorFamilyMixin:
    """Mixin class that allows an instance to retrieve an estimator.

    Retrieve is based on the type of the problem (classification or
    regression).
    """

    def initialize_estimators(self) -> Dict:  # noqa
        pass

    def get_estimator_by_type(self, problem_type):
        """Retrieve an estimator based on the problem type.

        Args:
            problem_type: either classification or regression

        Returns:
            an estimator of a particular model family

        """
        return self.initialize_estimators()[problem_type]


class LinearEstimatorFamily(EstimatorFamilyMixin):
    """Linear model estimator family.

    Estimators include LinearRegression and LogisticRegression.
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: LogisticRegression(),
            ProblemType.REGRESSION: LinearRegression(),
        }


class SVMEstimatorFamily(EstimatorFamilyMixin):
    """SVM model estimator family.

    Estimators include LinearSVC and LinearSVR.
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: LinearSVC(),
            ProblemType.REGRESSION: LinearSVR(),
        }


class RFEstimatorFamily(EstimatorFamilyMixin):
    """RandomForest model estimator family.

    Estimators include RandomForestClassifier and RandomForestRegressor.
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: RandomForestClassifier(),
            ProblemType.REGRESSION: RandomForestRegressor(),
        }


class NNEstimatorFamily(EstimatorFamilyMixin):
    """Neural Network model estimator family.

    Estimators include MLPClassifier and MLPRegressor
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: MLPClassifier(),
            ProblemType.REGRESSION: MLPRegressor(),
        }


class EstimatorFactory:
    """Factory class that retrieves an estimator.

    Retrieval is based on the model family and the problem type.

    TODO we can add a register method if we decide to add more model
    algorithm family here.
    """

    def __init__(self):
        self.registered_estimator_families = {
            EstimatorFamily.LINEAR: LinearEstimatorFamily(),
            EstimatorFamily.SVM: SVMEstimatorFamily(),
            EstimatorFamily.RF: RFEstimatorFamily(),
            EstimatorFamily.NN: NNEstimatorFamily(),
        }

    def get_estimator(self, family, problem_type):
        """Retrieve the estimator based on the model family and problem type.

        Args:
            family: model family type
            problem_type: either classification or regression

        Returns:
            an estimator

        Raises:
            KeyError: unknow family type or problem type

        """
        if family not in self.registered_estimator_families:
            raise KeyError("Unknown model family type: {}".format(family))
        if problem_type not in [
            ProblemType.CLASSIFICATION,
            ProblemType.REGRESSION,
        ]:
            raise KeyError(
                "Only classification and regression are "
                "supported, unknown operation type: {}".format(problem_type)
            )
        return self.registered_estimator_families[
            family
        ].get_estimator_by_type(problem_type)

<filename>foreshadow/utils/default_estimator_factory.py
"""Estimator factory and supported estimators."""

from typing import Dict

from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.neural_network import MLPClassifier, MLPRegressor
from sklearn.svm import LinearSVC, LinearSVR

from .constants import EstimatorFamily, ProblemType


class EstimatorFamilyMixin:
    """Mixin class that allows an instance to retrieve an estimator.

    Retrieve is based on the type of the problem (classification or
    regression).
    """

    def initialize_estimators(self) -> Dict:  # noqa
        pass

    def get_estimator_by_type(self, problem_type):
        """Retrieve an estimator based on the problem type.

        Args:
            problem_type: either classification or regression

        Returns:
            an estimator of a particular model family

        """
        return self.initialize_estimators()[problem_type]


class LinearEstimatorFamily(EstimatorFamilyMixin):
    """Linear model estimator family.

    Estimators include LinearRegression and LogisticRegression.
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: LogisticRegression(),
            ProblemType.REGRESSION: LinearRegression(),
        }


class SVMEstimatorFamily(EstimatorFamilyMixin):
    """SVM model estimator family.

    Estimators include LinearSVC and LinearSVR.
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: LinearSVC(),
            ProblemType.REGRESSION: LinearSVR(),
        }


class RFEstimatorFamily(EstimatorFamilyMixin):
    """RandomForest model estimator family.

    Estimators include RandomForestClassifier and RandomForestRegressor.
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: RandomForestClassifier(),
            ProblemType.REGRESSION: RandomForestRegressor(),
        }


class NNEstimatorFamily(EstimatorFamilyMixin):
    """Neural Network model estimator family.

    Estimators include MLPClassifier and MLPRegressor
    """

    def initialize_estimators(self) -> Dict:
        """Initialize estimators for all estimator types and return as a dict.

        Returns:
            A dictionary of problem type to estimator

        """
        return {
            ProblemType.CLASSIFICATION: MLPClassifier(),
            ProblemType.REGRESSION: MLPRegressor(),
        }


class EstimatorFactory:
    """Factory class that retrieves an estimator.

    Retrieval is based on the model family and the problem type.

    TODO we can add a register method if we decide to add more model
    algorithm family here.
    """

    def __init__(self):
        self.registered_estimator_families = {
            EstimatorFamily.LINEAR: LinearEstimatorFamily(),
            EstimatorFamily.SVM: SVMEstimatorFamily(),
            EstimatorFamily.RF: RFEstimatorFamily(),
            EstimatorFamily.NN: NNEstimatorFamily(),
        }

    def get_estimator(self, family, problem_type):
        """Retrieve the estimator based on the model family and problem type.

        Args:
            family: model family type
            problem_type: either classification or regression

        Returns:
            an estimator

        Raises:
            KeyError: unknow family type or problem type

        """
        if family not in self.registered_estimator_families:
            raise KeyError("Unknown model family type: {}".format(family))
        if problem_type not in [
            ProblemType.CLASSIFICATION,
            ProblemType.REGRESSION,
        ]:
            raise KeyError(
                "Only classification and regression are "
                "supported, unknown operation type: {}".format(problem_type)
            )
        return self.registered_estimator_families[
            family
        ].get_estimator_by_type(problem_type)

Estimator factory and supported estimators. Mixin class that allows an instance to retrieve an estimator.      Retrieve is based on the type of the problem (classification or     regression). # noqa Retrieve an estimator based on the problem type.          Args:             problem_type: either classification or regression          Returns:             an estimator of a particular model family Linear model estimator family.      Estimators include LinearRegression and LogisticRegression. Initialize estimators for all estimator types and return as a dict.          Returns:             A dictionary of problem type to estimator SVM model estimator family.      Estimators include LinearSVC and LinearSVR. Initialize estimators for all estimator types and return as a dict.          Returns:             A dictionary of problem type to estimator RandomForest model estimator family.      Estimators include RandomForestClassifier and RandomForestRegressor. Initialize estimators for all estimator types and return as a dict.          Returns:             A dictionary of problem type to estimator Neural Network model estimator family.      Estimators include MLPClassifier and MLPRegressor Initialize estimators for all estimator types and return as a dict.          Returns:             A dictionary of problem type to estimator Factory class that retrieves an estimator.      Retrieval is based on the model family and the problem type.      TODO we can add a register method if we decide to add more model     algorithm family here. Retrieve the estimator based on the model family and problem type.          Args:             family: model family type             problem_type: either classification or regression          Returns:             an estimator          Raises:             KeyError: unknow family type or problem type