How to use the tpot.operators_disable.regressors.base.Regressor function in TPOT

Free Software Foundation, either version 3 of the License, or (at your option)
any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from .base import Regressor
from sklearn.ensemble import ExtraTreesRegressor


class TPOTExtraTreesRegressor(Regressor):
    """Fits an Extra Trees Regressor

    Parameters
    ----------
    criterion: int
        Integer that is used to select from the list of valid criteria,
        either 'gini', or 'entropy'
    max_features: float
        The number of features to consider when looking for the best split

    """
    import_hash = {'sklearn.ensemble': ['ExtraTreesRegressor']}
    sklearn_class = ExtraTreesRegressor
    arg_types = (float, )

    def __init__(self):

Free Software Foundation, either version 3 of the License, or (at your option)
any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from .base import Regressor
from xgboost import XGBRegressor


class TPOTXGBRegressor(Regressor):
    """Fits an XGBoost Regressor

    Parameters
    ----------
    max_depth: int
        Maximum tree depth for base learners
    min_child_weight: int
        Minimum sum of instance weight(hessian) needed in a child
    learning_rate: float
        Shrinks the contribution of each tree by learning_rate
    subsample: float
        Subsample ratio of the training instance
    """
    import_hash = {'xgboost': ['XGBRegressor']}
    sklearn_class = XGBRegressor
    arg_types = (int, int, float, float)

any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from ...gp_types import Bool
from .base import Regressor
from sklearn.svm import LinearSVR


class TPOTLinearSVR(Regressor):
    """Fits a Linear Support Vector Regressor

    Parameters
    ----------
    C: float
        Penalty parameter C of the error term.
    dual: bool
        Select the algorithm to either solve the dual or primal optimization problem.

    """
    import_hash = {'sklearn.svm': ['LinearSVR']}
    sklearn_class = LinearSVR
    arg_types = (float, Bool)

    def __init__(self):
        pass

Free Software Foundation, either version 3 of the License, or (at your option)
any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from .base import Regressor
from sklearn.neighbors import KNeighborsRegressor


class TPOTKNeighborsRegressor(Regressor):
    """Fits a k-nearest neighbor Regressor

    Parameters
    ----------
    n_neighbors: int
        Number of neighbors to use by default for k_neighbors queries; must be a positive value
    weights: int
        Selects a value from the list: ['uniform', 'distance']

    """
    import_hash = {'sklearn.neighbors': ['KNeighborsRegressor']}
    sklearn_class = KNeighborsRegressor
    arg_types = (int, int)

    def __init__(self):
        pass

Free Software Foundation, either version 3 of the License, or (at your option)
any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from .base import Regressor
from sklearn.ensemble import RandomForestRegressor


class TPOTRandomForestClassifier(Regressor):

    """Fits a random forest Regressor.

    Parameters
    ----------
    None
    """

    import_hash = {'sklearn.ensemble': ['RandomForestRegressor']}
    sklearn_class = RandomForestRegressor
    arg_types = ()

    def __init__(self):
        pass

    def preprocess_args(self):

Free Software Foundation, either version 3 of the License, or (at your option)
any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from .base import Regressor
from sklearn.ensemble import GradientBoostingRegressor


class TPOTGradientBRegressor(Regressor):
    """Fits a Gradient Boosting Regressor

    Parameters
    ----------
    learning_rate: float
        Shrinks the contribution of each tree by learning_rate
    max_features: float
        Maximum number of features to use (proportion of total features)

    """
    import_hash = {'sklearn.ensemble': ['GradientBoostingRegressor']}
    sklearn_class = GradientBoostingRegressor
    arg_types = (float, float)

    def __init__(self):
        pass

any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from ...gp_types import Bool
from .base import Regressor
from sklearn.linear_model import LassoLarsCV


class TPOTLassoLarsCV(Regressor):
    """Fits a LassoLarsCV Regressor

    Parameters
    ----------
    normalize: bool
        If True, the regressors X will be normalized before regression.

    """
    import_hash = {'sklearn.linear_model': ['LassoLarsCV']}
    sklearn_class = LassoLarsCV
    arg_types = (Bool, )

    def __init__(self):
        pass

    def preprocess_args(self, normalize):

Free Software Foundation, either version 3 of the License, or (at your option)
any later version.

The TPOT library is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details. You should have received a copy of the GNU General Public License along
with the TPOT library. If not, see http://www.gnu.org/licenses/.

"""

from .base import Regressor
from sklearn.linear_model import ElasticNet


class TPOTElasticNet(Regressor):
    """Fits a Elastic Net Regressor

    Parameters
    ----------
    alpha: float
        Constant that multiplies the penalty terms.
    l1_ratio: int
        The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1

    """
    import_hash = {'sklearn.linear_model': ['ElasticNet']}
    sklearn_class = ElasticNet
    arg_types = (float, float)

    def __init__(self):
        pass