Advanced mixture of experts

from __future__ import absolute_import, division, print_function, unicode_literals

from gemseo.api import load_dataset
from gemseo.mlearning.api import create_regression_model
from gemseo.mlearning.qual_measure.f1_measure import F1Measure
from gemseo.mlearning.qual_measure.mse_measure import MSEMeasure
from gemseo.mlearning.qual_measure.silhouette import SilhouetteMeasure

In this example, we seek to estimate the Rosenbrock function from the RosenbrockDataset.

dataset = load_dataset("RosenbrockDataset", opt_naming=False)

For that purpose, we will use a MixtureOfExperts in an advanced way: we will not set the clustering, classification and regression algorithms but select them according to their performance from several candidates that we will provide. Moreover, for a given candidate, we will propose several settings, compare their performances and select the best one.


First, we initialize a MixtureOfExperts with soft classification by means of the machine learning API function create_regression_model().

model = create_regression_model("MixtureOfExperts", dataset, hard=False)


Then, we add two clustering algorithms with different numbers of clusters (called components for the Gaussian Mixture) and set the SilhouetteMeasure as clustering measure to be evaluated from the learning set. During the learning stage, the mixture of experts will select the clustering algorithm and the number of clusters minimizing this measure.

model.add_clusterer_candidate("KMeans", n_clusters=[2, 3, 4])
model.add_clusterer_candidate("GaussianMixture", n_components=[3, 4, 5])


We also add classification algorithms with different settings and set the F1Measure as classification measure to be evaluated from the learning set. During the learning stage, the mixture of experts will select the classification algorithm and the settings minimizing this measure.

model.add_classifier_candidate("KNNClassifier", n_neighbors=[3, 4, 5])
model.add_classifier_candidate("RandomForestClassifier", n_estimators=[100])


We also add regression algorithms and set the MSEMeasure as regression measure to be evaluated from the learning set. During the learning stage, for each cluster, the mixture of experts will select the regression algorithm minimizing this measure.



We could also add candidates for some learning stages, e.g. clustering and regression, and set the machine learning algorithms for the remaining ones, e.g. classification.


Lastly, we learn the data and select the best machine learning algorithm for both clustering, classification and regression steps.



We can get information on this model, on the sub-machine learning models selected among the candidates and on their selected settings. We can see that a KMeans with four clusters has been selected for the clustering stage, as well as a RandomForestClassifier for the classification stage and a RBFRegression for each cluster.



   built from 100 learning samples
         KMeans(n_clusters=4, random_state=0, var_names=None)
         Local model 0
            RBFRegression(epsilon=None, function='multiquadric')
         Local model 1
            RBFRegression(epsilon=None, function='multiquadric')
         Local model 2
            RBFRegression(epsilon=None, function='multiquadric')
         Local model 3
            RBFRegression(epsilon=None, function='multiquadric')


By adding candidates, and depending on the complexity of the function to be approximated, one could obtain different regression models according to the clusters. For example, one could use a PolynomialRegression with order 2 on a sub-part of the input space and a GaussianProcessRegression on another sub-part of the input space.

Once built, this mixture of experts can be used as any MLRegressionAlgo.

See also

Another example proposes a standard use of MixtureOfExperts.

Total running time of the script: ( 0 minutes 1.032 seconds)

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