Source code for evalml.model_family.model_family

from enum import Enum


class ModelFamily(Enum):
    """Enum for family of machine learning models."""

    K_NEIGHBORS = 'k_neighbors'
    """K Nearest Neighbors model family."""

    RANDOM_FOREST = 'random_forest'
    """Random Forest model family."""

    SVM = 'svm'
    """SVM model family."""

    XGBOOST = 'xgboost'
    """XGBoost model family."""

    LIGHTGBM = 'lightgbm'
    """LightGBM model family."""

    LINEAR_MODEL = 'linear_model'
    """Linear model family."""

    CATBOOST = 'catboost'
    """CatBoost model family."""

    EXTRA_TREES = 'extra_trees'
    """Extra Trees model family."""

    ENSEMBLE = 'ensemble'
    """Ensemble model family."""

    DECISION_TREE = 'decision_tree'
    """Decision Tree model family."""

    BASELINE = 'baseline'
    """Baseline model family."""

    NONE = 'none'
    """None"""

    def __str__(self):
        model_family_dict = {ModelFamily.K_NEIGHBORS.name: "K Nearest Neighbors",
                             ModelFamily.RANDOM_FOREST.name: "Random Forest",
                             ModelFamily.SVM.name: "SVM",
                             ModelFamily.XGBOOST.name: "XGBoost",
                             ModelFamily.LIGHTGBM.name: "LightGBM",
                             ModelFamily.LINEAR_MODEL.name: "Linear",
                             ModelFamily.CATBOOST.name: "CatBoost",
                             ModelFamily.EXTRA_TREES.name: "Extra Trees",
                             ModelFamily.DECISION_TREE.name: "Decision Tree",
                             ModelFamily.BASELINE.name: "Baseline",
                             ModelFamily.ENSEMBLE.name: "Ensemble",
                             ModelFamily.NONE.name: "None"}
        return model_family_dict[self.name]

    def __repr__(self):
        return "ModelFamily." + self.name

    def is_tree_estimator(self):
        """Checks whether the estimator's model family uses trees."""
        tree_estimators = {self.CATBOOST, self.EXTRA_TREES, self.RANDOM_FOREST,
                           self.DECISION_TREE, self.XGBOOST, self.LIGHTGBM}
        return self in tree_estimators