How to use the tpot.base.TPOTBase function in TPOT

def test_get_params():
    """Assert that get_params returns the exact dictionary of parameters used by TPOT."""
    kwargs = {
        'population_size': 500,
        'generations': 1000,
        'config_dict': 'TPOT light',
        'offspring_size': 2000,
        'verbosity': 1
    }

    tpot_obj = TPOTClassifier(**kwargs)
    # Get default parameters of TPOT and merge with our specified parameters
    initializer = inspect.getargspec(TPOTBase.__init__)
    default_kwargs = dict(zip(initializer.args[1:], initializer.defaults))
    default_kwargs.update(kwargs)

    assert tpot_obj.get_params()['config_dict'] == 'TPOT light'
    assert tpot_obj.get_params() == default_kwargs

def test_TPOTBase():
    """Assert that TPOTBase class raises RuntimeError when using it directly."""
    assert_raises(RuntimeError, TPOTBase)

from .base import TPOTBase
from .config_classifier import classifier_config_dict
from .config_regressor import regressor_config_dict


class TPOTClassifier(TPOTBase):
    """TPOT estimator for classification problems"""

    scoring_function = 'accuracy'  # Classification scoring
    default_config_dict = classifier_config_dict # Classification dictionary
    classification = True
    regression = False


class TPOTRegressor(TPOTBase):
    """TPOT estimator for regression problems"""

    scoring_function = 'neg_mean_squared_error'  # Regression scoring
    default_config_dict = regressor_config_dict # Regression dictionary
    classification = False
    regression = True

from .base import TPOTBase
from .config.classifier import classifier_config_dict
from .config.regressor import regressor_config_dict


class TPOTClassifier(TPOTBase):
    """TPOT estimator for classification problems."""

    scoring_function = 'accuracy'  # Classification scoring
    default_config_dict = classifier_config_dict  # Classification dictionary
    classification = True
    regression = False


class TPOTRegressor(TPOTBase):
    """TPOT estimator for regression problems."""

    scoring_function = 'neg_mean_squared_error'  # Regression scoring
    default_config_dict = regressor_config_dict  # Regression dictionary
    classification = False
    regression = True

TPOT 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 Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with TPOT. If not, see .

"""

from .base import TPOTBase
from .config.classifier import classifier_config_dict
from .config.regressor import regressor_config_dict


class TPOTClassifier(TPOTBase):
    """TPOT estimator for classification problems."""

    scoring_function = 'accuracy'  # Classification scoring
    default_config_dict = classifier_config_dict  # Classification dictionary
    classification = True
    regression = False


class TPOTRegressor(TPOTBase):
    """TPOT estimator for regression problems."""

    scoring_function = 'neg_mean_squared_error'  # Regression scoring
    default_config_dict = regressor_config_dict  # Regression dictionary
    classification = False
    regression = True

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 TPOTBase
from .config_classifier import classifier_config_dict
from .config_regressor import regressor_config_dict


class TPOTClassifier(TPOTBase):
    """TPOT estimator for classification problems"""

    scoring_function = 'accuracy'  # Classification scoring
    default_config_dict = classifier_config_dict # Classification dictionary
    classification = True
    regression = False


class TPOTRegressor(TPOTBase):
    """TPOT estimator for regression problems"""

    scoring_function = 'neg_mean_squared_error'  # Regression scoring
    default_config_dict = regressor_config_dict # Regression dictionary
    classification = False
    regression = True