Mixture of experts with PCA on Burgers dataset

In this demo, we apply a mixture of experts regression model to the Burgers dataset. In order to reduce the output dimension, we apply a PCA to the outputs.


Import from standard libraries and GEMSEO.

from __future__ import absolute_import, division, print_function, unicode_literals

from builtins import int

import matplotlib.pyplot as plt
from future import standard_library
from matplotlib.lines import Line2D
from numpy import nonzero

from gemseo.api import configure_logger, load_dataset
from gemseo.mlearning.api import create_regression_model
from gemseo.mlearning.transform.dimension_reduction.klsvd import KLSVD
from gemseo.mlearning.transform.dimension_reduction.kpca import KPCA
from gemseo.mlearning.transform.dimension_reduction.pca import PCA
from gemseo.mlearning.transform.sensor.jameson import JamesonSensor



Load dataset (Burgers)

n_samples = 50
dataset = load_dataset("BurgersDataset", n_samples=n_samples)
inputs = dataset.get_data_by_group(dataset.INPUT_GROUP)
outputs = dataset.get_data_by_group(dataset.OUTPUT_GROUP)

Mixture of experts (MoE)

In this section we load a mixture of experts regression model through the machine learning API, using clustering, classification and regression models.

Mixture of experts model

We construct the MoE model using the predefined parameters, and fit the model to the dataset through the learn() method.

klsvd = {dataset.OUTPUT_GROUP: KLSVD(mesh=dataset.metadata["x"], n_components=10)}
pca = {dataset.OUTPUT_GROUP: PCA(n_components=10)}
kpca = {dataset.OUTPUT_GROUP: KPCA(n_components=10, kernel="poly")}
jameson = {dataset.OUTPUT_GROUP: JamesonSensor()}

model = create_regression_model("MixtureOfExperts", dataset)
model.set_clusterer("KMeans", n_clusters=2, transformer=jameson)
model.set_classifier("KNNClassifier", n_neighbors=3)
model.set_regressor("GaussianProcessRegression")  # , transformer=pca)



/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/gaussian_process/_gpr.py:504: ConvergenceWarning: lbfgs failed to converge (status=2):

Increase the number of iterations (max_iter) or scale the data as shown in:
  _check_optimize_result("lbfgs", opt_res)

Make predictions

predictions = model.predict(inputs)
local_pred_0 = model.predict_local_model(inputs, 0)
local_pred_1 = model.predict_local_model(inputs, 1)

Plot clusters

for i in nonzero(model.clusterer.labels == 0)[0]:
    plt.plot(outputs[i], color="r")
for i in nonzero(model.clusterer.labels == 1)[0]:
    plt.plot(outputs[i], color="b")
    [Line2D([0], [0], color="r"), Line2D([0], [0], color="b")],
    ["Cluster 0", "Cluster 1"],
plot moe burgers

Plot predictions

def lines(i):
    return (0, (i + 3, 1, 1, 1))

for i, pred in enumerate(predictions):
    color = "b"
    if model.labels[i] == 0:
        color = "r"
    plt.plot(pred, color=color, linestyle=lines(i))
plot moe burgers

Plot local models

for i, pred in enumerate(local_pred_0):
    plt.plot(pred, color="r", linestyle=lines(i))
for i, pred in enumerate(local_pred_1):
    plt.plot(pred, color="b", linestyle=lines(i))
plot moe burgers

Plot selected predictions and exact curves

for i in [
    int(dataset.n_samples / 4),
    int(dataset.n_samples * 2 / 4),
    int(dataset.n_samples * 3 / 4),
    plt.plot(outputs[i], color="r")
    plt.plot(predictions[i], color="b", linestyle=":")
plot moe burgers

Plot components

if not isinstance(model.regress_models[0].transformer[“outputs”], KPCA):

plt.subplot(121) plt.plot(model.regress_models[0].transformer[“outputs”].components) plt.title(“1st local model”) plt.subplot(122) plt.plot(model.regress_models[1].transformer[“outputs”].components) plt.title(“2nd local model”) plt.show()

Total running time of the script: ( 0 minutes 2.926 seconds)

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