Skip to content

Complexity

This file is part of the TPOT library.

The current version of TPOT was developed at Cedars-Sinai by: - Pedro Henrique Ribeiro (https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/) - Anil Saini (anil.saini@cshs.org) - Jose Hernandez (jgh9094@gmail.com) - Jay Moran (jay.moran@cshs.org) - Nicholas Matsumoto (nicholas.matsumoto@cshs.org) - Hyunjun Choi (hyunjun.choi@cshs.org) - Gabriel Ketron (gabriel.ketron@cshs.org) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

The original version of TPOT was primarily developed at the University of Pennsylvania by: - Randal S. Olson (rso@randalolson.com) - Weixuan Fu (weixuanf@upenn.edu) - Daniel Angell (dpa34@drexel.edu) - Jason Moore (moorejh28@gmail.com) - and many more generous open-source contributors

TPOT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

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 http://www.gnu.org/licenses/.

complexity_scorer(est, X=None, y=None)

Estimates the number of learned parameters across all classifiers and regressors in the pipelines. Additionally, currently transformers add 1 point and selectors add 0 points (since they don't affect the complexity of the "final" predictive pipeline.

Parameters:

Name Type Description Default
est

The estimator or pipeline to compute the complexity for

 required
X

The input samples (unused)

 None
y

The target values (unused)

 None
Source code in tpot/objectives/complexity.py 
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
def complexity_scorer(est, X=None, y=None):
    """
    Estimates the number of learned parameters across all classifiers and regressors in the pipelines. 
    Additionally, currently transformers add 1 point and selectors add 0 points (since they don't affect the complexity of the "final" predictive pipeline.

    Parameters
    ----------
    est: sklearn.base.BaseEstimator
        The estimator or pipeline to compute the complexity for
    X: array-like
        The input samples (unused)
    y: array-like
        The target values (unused)

    """
    return calculate_model_complexity(est)