ezmodel - A Common Interface for Models and Model Selection

For more information about our toolbox, users are encouraged to read our documentation. https://anyoptimization.com/projects/ezmodel/

Installation

The official release is always available at PyPi:

pip install -U ezmodel

Usage

Benchmarking

importnumpyasnpimportpandasaspdpd.set_option('display.expand_frame_repr', False)
pd.set_option('max_colwidth', 1000)
fromezmodel.core.benchmarkimportBenchmarkfromezmodel.core.factoryimportmodels_from_clazzesfromezmodel.models.krigingimportKrigingfromezmodel.models.rbfimportRBFfromezmodel.util.partitioning.crossvalidationimportCrossvalidationPartitioningX=np.random.random((100, 3)) *2*np.piy=np.sin(X).sum(axis=1)
models=models_from_clazzes(RBF, Kriging)
# set up the benchmark and add the models to be usedbenchmark=Benchmark(models, n_threads=4, verbose=True, raise_exception=True)
# create partitions to validate the performance of each modelpartitions=CrossvalidationPartitioning(k_folds=5, seed=1).do(X)
# runs the experiment with the specified partitioningbenchmark.do(X, y, partitions=partitions)
# print out the benchmark resultsprint(benchmark.statistics("mae"))

 mae
mean std min max median
label
Kriging[regr=constant,corr=gauss,thetaU=100,ARD=False] 0.017159 0.007472 0.009658 0.025359 0.014855
Kriging[regr=constant,corr=gauss,thetaU=20,ARD=False] 0.017159 0.007472 0.009658 0.025359 0.014855
Kriging[regr=linear,corr=gauss,thetaU=100,ARD=False] 0.018064 0.008069 0.010350 0.027456 0.014246
Kriging[regr=linear,corr=gauss,thetaU=20,ARD=False] 0.018064 0.008069 0.010350 0.027456 0.014246
Kriging[regr=constant,corr=gauss,thetaU=100,ARD=True] 0.021755 0.007409 0.011955 0.028896 0.025163
Kriging[regr=constant,corr=gauss,thetaU=20,ARD=True] 0.021755 0.007409 0.011955 0.028896 0.025163
Kriging[regr=linear,corr=gauss,thetaU=20,ARD=True] 0.025018 0.011348 0.011576 0.040585 0.022124
Kriging[regr=linear,corr=gauss,thetaU=100,ARD=True] 0.025018 0.011348 0.011576 0.040585 0.022124
Kriging[regr=constant,corr=exp,thetaU=100,ARD=False] 0.034493 0.009328 0.025092 0.045610 0.030661
Kriging[regr=constant,corr=exp,thetaU=20,ARD=False] 0.034493 0.009328 0.025092 0.045610 0.030661
Kriging[regr=linear,corr=exp,thetaU=100,ARD=False] 0.035734 0.009922 0.025611 0.047926 0.031473
Kriging[regr=linear,corr=exp,thetaU=20,ARD=False] 0.035734 0.009922 0.025611 0.047926 0.031473
Kriging[regr=constant,corr=exp,thetaU=100,ARD=True] 0.051527 0.010941 0.037944 0.065866 0.047440
Kriging[regr=constant,corr=exp,thetaU=20,ARD=True] 0.051527 0.010941 0.037944 0.065866 0.047440
Kriging[regr=linear,corr=exp,thetaU=100,ARD=True] 0.065867 0.025312 0.039058 0.104449 0.059957
Kriging[regr=linear,corr=exp,thetaU=20,ARD=True] 0.065867 0.025312 0.039058 0.104449 0.059957
RBF[kernel=cubic,tail=quadratic,normalized=True] 0.121947 0.033552 0.077895 0.167120 0.127345
RBF[kernel=cubic,tail=constant,normalized=True] 0.125348 0.037982 0.072579 0.169413 0.140753
RBF[kernel=cubic,tail=linear,normalized=True] 0.125474 0.038609 0.071268 0.169843 0.137987
RBF[kernel=cubic,tail=linear+quadratic,normalized=True] 0.126070 0.039773 0.071279 0.171862 0.135489

RBF

importmatplotlib.pyplotaspltimportnumpyasnpfromezmodel.models.rbfimportRBFfromezmodel.util.sample_from_funcimportsine_functionrbf=RBF(kernel="gaussian")
# create some data to test this model onX, y, _X, _y=sine_function(20, 200)
# let the model fit the datarbf.fit(X, y)
# predict the data using the modely_hat=rbf.predict(_X)
# predict the data using the model_X=_X[np.argsort(_X[:, 0])]
y_hat=rbf.predict(_X)
plt.scatter(X, y, label="Data")
plt.plot(_X, y_hat, color="black", label="RBF")
plt.legend()
plt.show()

Kriging

importmatplotlib.pyplotaspltimportnumpyasnpfromezmodel.models.krigingimportKrigingfromezmodel.util.sample_from_funcimportsquare_functionmodel=Kriging(regr="linear",
corr="gauss",
ARD=False)
# create some data to test this model onX, y, _X, _y=square_function(100, 20)
# let the model fit the datamodel.fit(X, y)
# predict the data using the modely_hat=model.predict(_X)
# predict the data using the model_X=_X[np.argsort(_X[:, 0])]
y_hat=model.predict(_X)
plt.scatter(X, y, label="Data")
plt.plot(_X, y_hat, color="black", label="RBF")
plt.legend()
plt.show()

Contact

Feel free to contact us if you have any question:

Julian Blank (blankjul [at] msu.edu)
Michigan State University
Computational Optimization and Innovation Laboratory (COIN)
East Lansing, MI 48824, USA