.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_algorithms/plot_algorithm_mestatic_IO.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_algorithms_plot_algorithm_mestatic_IO.py: Algorithm: MEStatic_IO ============================== .. GENERATED FROM PYTHON SOURCE LINES 5-19 .. code-block:: default # Windows users have to encapsulate the code into a main function to avoid multiprocessing errors. # def main(): import pygpc import numpy as np from scipy.integrate import odeint import matplotlib import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from collections import OrderedDict fn_results = 'tmp/mestatic_IO' # filename of output save_session_format = ".pkl" # file format of saved gpc session ".hdf5" (slow) or ".pkl" (fast) np.random.seed(1) .. GENERATED FROM PYTHON SOURCE LINES 20-22 Setup input and output data ---------------------------------------------------------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 22-52 .. code-block:: default # We artificially generate some coordinates for the input data the user has to provide where the model was sampled n_grid = 400 rho_0 = np.random.rand(n_grid) beta = np.random.rand(n_grid) * 20. alpha = 1. # define the properties of the random variables parameters = OrderedDict() parameters["rho_0"] = pygpc.Beta(pdf_shape=[1, 1], pdf_limits=[0, 1]) parameters["beta"] = pygpc.Beta(pdf_shape=[1, 1], pdf_limits=[0, 20]) # generate a grid object from the input data grid = pygpc.RandomGrid(parameters_random=parameters, coords=np.vstack((rho_0,beta)).T) # get output data (here: SurfaceCoverageSpecies function) def deq(rho, t, alpha, beta, gamma): return alpha * (1. - rho) - gamma * rho - beta * (rho - 1) ** 2 * rho # Constants gamma = 0.01 # Simulation parameters dt = 0.01 t_end = 1. t = np.arange(0, t_end, dt) # Solve results = odeint(deq, rho_0, t, args=(alpha, beta, gamma))[-1][:, np.newaxis] .. GENERATED FROM PYTHON SOURCE LINES 53-55 Setting up the algorithm ------------------------ .. GENERATED FROM PYTHON SOURCE LINES 55-79 .. code-block:: default # gPC options options = dict() options["solver"] = "LarsLasso" options["settings"] = None options["order"] = [9, 9] options["order_max"] = 9 options["interaction_order"] = 2 options["matrix_ratio"] = None options["n_cpu"] = 0 options["error_type"] = "loocv" options["qoi"] = "all" options["classifier"] = "learning" options["classifier_options"] = {"clusterer": "KMeans", "n_clusters": 2, "classifier": "MLPClassifier", "classifier_solver": "lbfgs"} options["fn_results"] = fn_results options["save_session_format"] = save_session_format options["verbose"] = True # define algorithm algorithm = pygpc.MEStatic_IO(parameters=parameters, options=options, grid=grid, results=results) .. GENERATED FROM PYTHON SOURCE LINES 80-82 Running the gpc --------------- .. GENERATED FROM PYTHON SOURCE LINES 82-89 .. code-block:: default # initialize gPC Session session = pygpc.Session(algorithm=algorithm) # run gPC algorithm session, coeffs, results = session.run() .. rst-class:: sphx-glr-script-out .. code-block:: none Determining gPC approximation for QOI #0: ========================================= Determine gPC coefficients using 'LarsLasso' solver ... Determine gPC coefficients using 'LarsLasso' solver ... LOOCV 01 from 25 [= ] 4.0% LOOCV 02 from 25 [=== ] 8.0% LOOCV 03 from 25 [==== ] 12.0% LOOCV 04 from 25 [====== ] 16.0% LOOCV 05 from 25 [======== ] 20.0% LOOCV 06 from 25 [========= ] 24.0% LOOCV 07 from 25 [=========== ] 28.0% LOOCV 08 from 25 [============ ] 32.0% LOOCV 09 from 25 [============== ] 36.0% LOOCV 10 from 25 [================ ] 40.0% LOOCV 11 from 25 [================= ] 44.0% LOOCV 12 from 25 [=================== ] 48.0% LOOCV 13 from 25 [==================== ] 52.0% LOOCV 14 from 25 [====================== ] 56.0% LOOCV 15 from 25 [======================== ] 60.0% LOOCV 16 from 25 [========================= ] 64.0% LOOCV 17 from 25 [=========================== ] 68.0% LOOCV 18 from 25 [============================ ] 72.0% LOOCV 19 from 25 [============================== ] 76.0% LOOCV 20 from 25 [================================ ] 80.0% LOOCV 21 from 25 [================================= ] 84.0% LOOCV 22 from 25 [=================================== ] 88.0% LOOCV 23 from 25 [==================================== ] 92.0% LOOCV 24 from 25 [====================================== ] 96.0% LOOCV 25 from 25 [========================================] 100.0% LOOCV computation time: 1.563474416732788 sec -> relative loocv error = 0.07496878596307728 LOOCV 01 from 25 [= ] 4.0% LOOCV 02 from 25 [=== ] 8.0% LOOCV 03 from 25 [==== ] 12.0% LOOCV 04 from 25 [====== ] 16.0% LOOCV 05 from 25 [======== ] 20.0% LOOCV 06 from 25 [========= ] 24.0% LOOCV 07 from 25 [=========== ] 28.0% LOOCV 08 from 25 [============ ] 32.0% LOOCV 09 from 25 [============== ] 36.0% LOOCV 10 from 25 [================ ] 40.0% LOOCV 11 from 25 [================= ] 44.0% LOOCV 12 from 25 [=================== ] 48.0% LOOCV 13 from 25 [==================== ] 52.0% LOOCV 14 from 25 [====================== ] 56.0% LOOCV 15 from 25 [======================== ] 60.0% LOOCV 16 from 25 [========================= ] 64.0% LOOCV 17 from 25 [=========================== ] 68.0% LOOCV 18 from 25 [============================ ] 72.0% LOOCV 19 from 25 [============================== ] 76.0% LOOCV 20 from 25 [================================ ] 80.0% LOOCV 21 from 25 [================================= ] 84.0% LOOCV 22 from 25 [=================================== ] 88.0% LOOCV 23 from 25 [==================================== ] 92.0% LOOCV 24 from 25 [====================================== ] 96.0% LOOCV 25 from 25 [========================================] 100.0% LOOCV computation time: 1.715503215789795 sec LOOCV 01 from 25 [= ] 4.0% LOOCV 02 from 25 [=== ] 8.0% LOOCV 03 from 25 [==== ] 12.0% LOOCV 04 from 25 [====== ] 16.0% LOOCV 05 from 25 [======== ] 20.0% LOOCV 06 from 25 [========= ] 24.0% LOOCV 07 from 25 [=========== ] 28.0% LOOCV 08 from 25 [============ ] 32.0% LOOCV 09 from 25 [============== ] 36.0% LOOCV 10 from 25 [================ ] 40.0% LOOCV 11 from 25 [================= ] 44.0% LOOCV 12 from 25 [=================== ] 48.0% LOOCV 13 from 25 [==================== ] 52.0% LOOCV 14 from 25 [====================== ] 56.0% LOOCV 15 from 25 [======================== ] 60.0% LOOCV 16 from 25 [========================= ] 64.0% LOOCV 17 from 25 [=========================== ] 68.0% LOOCV 18 from 25 [============================ ] 72.0% LOOCV 19 from 25 [============================== ] 76.0% LOOCV 20 from 25 [================================ ] 80.0% LOOCV 21 from 25 [================================= ] 84.0% LOOCV 22 from 25 [=================================== ] 88.0% LOOCV 23 from 25 [==================================== ] 92.0% LOOCV 24 from 25 [====================================== ] 96.0% LOOCV 25 from 25 [========================================] 100.0% LOOCV computation time: 0.6258642673492432 sec .. GENERATED FROM PYTHON SOURCE LINES 90-92 Postprocessing -------------- .. GENERATED FROM PYTHON SOURCE LINES 92-121 .. code-block:: default # read session session = pygpc.read_session(fname=session.fn_session, folder=session.fn_session_folder) # Post-process gPC pygpc.get_sensitivities_hdf5(fn_gpc=options["fn_results"], output_idx=None, calc_sobol=True, calc_global_sens=True, calc_pdf=True, algorithm="standard", n_samples=int(1e4)) # plot gPC approximation and IO data pygpc.plot_gpc(session=session, coeffs=coeffs, random_vars=["rho_0", "beta"], output_idx=0, n_grid=[100, 100], coords=grid.coords, results=results, camera_pos=[45., 65]) # On Windows subprocesses will import (i.e. execute) the main module at start. # You need to insert an if __name__ == '__main__': guard in the main module to avoid # creating subprocesses recursively. # # if __name__ == '__main__': # main() .. image-sg:: /auto_algorithms/images/sphx_glr_plot_algorithm_mestatic_IO_001.png :alt: gPC approximation, Probability density :srcset: /auto_algorithms/images/sphx_glr_plot_algorithm_mestatic_IO_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none > Loading gpc session object: tmp/mestatic_IO.pkl > Loading gpc coeffs: tmp/mestatic_IO.hdf5 > Adding results to: tmp/mestatic_IO.hdf5 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.789 seconds) .. _sphx_glr_download_auto_algorithms_plot_algorithm_mestatic_IO.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_algorithm_mestatic_IO.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_algorithm_mestatic_IO.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_