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https://github.com/ejeanboris/MDAF.git
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flacco has been integrated w rpy2 - basic function
This commit is contained in:
Remi Ehounou
@@ -1,5 +1,6 @@
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# directly running the DOE because existing surrogates can be explored with another workflow
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from os import path
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from os import sys
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import importlib.util
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import multiprocessing
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import time
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@@ -15,6 +16,11 @@ import itertools
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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# Test function representation
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from rpy2 import robjects as robjs
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from rpy2.robjects.packages import importr
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from rpy2 import rinterface
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# Test function characteristics
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import statistics as st
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from scipy import signal, misc, ndimage
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@@ -140,93 +146,30 @@ def representfunc(funcpath):
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# Automatically generate the representation if the docstrings did not return anything
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if not ('Represented' in results):
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print("Warning, the Representation of the Test Function has not specified\n===\n******Calculating the Characteristics******")
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print("Warning, the Representation of the Test Function has not been specified\n===\n******Calculating the Characteristics******")
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n = int(results['dimmensions'])
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# pickle these steps
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coords = arange(-10,10,0.5)
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samplemx = array([*itertools.product(coords, repeat=n)])
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funcmap = array([* map(funcmodule.main, samplemx)])
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# Arrays for plotting the test function
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X = array([tp[0] for tp in samplemx])
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Y = array([tp[1] for tp in samplemx])
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Z = array(funcmap)
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# reshaping the array into a 3D topology
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topology = reshape(Z,(coords.size,coords.size))
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ck = topology
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# Plotting the test function
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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ax.plot_trisurf(X, Y, Z)
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# plt.show()
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execpath = sys.executable
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# creating the r functions
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rlist = robjs.r['list']
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rapply = robjs.r['apply']
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rtestfunc = rinterface.rternalize(funcmodule.main)
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# Number of Modes filter the data for local optima: look for circle like shapes, or squares or rectangles of very low derivative (tip of modes)
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###
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lower =-10
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upper = 10
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X = flacco.createInitialSample(n_obs = 500, dim = 2, control = rlist(init_sample_type = 'lhs', init_sample_lower = lower, init_sample_upper = upper))
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y = rapply(X, 1, rtestfunc)
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testfuncobj = flacco.createFeatureObject(X = X, y = y, fun = rtestfunc, lower = lower, upper = upper, blocks = 10)
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rawfeats = flacco.calculateFeatureSet(testfuncobj, set='ela_meta')
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pyfeat = asarray(rawfeats)
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# Valleys and Bassins
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# Alternative filter used for calculating derivatives
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#derfilt = array([1.0, -2, 1.0], dtype=float32)
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#alpha = signal.sepfir2d(ck, derfilt, [1]) + signal.sepfir2d(ck, [1], derfilt)
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# Currently used filter for Valley detection
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hor = array([[0,1,1],[-1,0,1], [-1,-1,0]])
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vert = array([[-1,-1,0], [-1,0,1], [0,1,1]])
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for i in range(1): betaH = signal.convolve(ck,hor,mode='valid')
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for i in range(1): betaV = signal.convolve(ck,vert, mode='valid')
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beta = sqrt(betaH ** 2 + betaV ** 2)
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#beta = beta[5:-5][5:-5]
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norm = linalg.norm(beta)
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beta/= norm # normalized matrix
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# custom filter for detection should light up the locaton of pattern
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kernel = array([[1,1,1], [1,100,1], [1,1,1]])
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beta = beta < average(beta)
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beta = beta * 1
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for i in range(100):
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beta = ndimage.convolve(beta,kernel)
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beta = beta >= 101
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beta = beta * 1
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if any(beta): results['Valleys'] = True
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# Separability: calculate the derivatives in one dimension and see if independant from other dimension
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# Dimensionality: number of objectives, inputs: call function once and see what it gives | for number of inputs call until it works; try catch
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# Pareto fronts:
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# Noisyness: use the previously generated DOE and calculate a noisyness factor; average of derivative
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# Displaying the plots for development purposes
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#img1 = plt.figure()
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#ax2 = img1.add_subplot(111)
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#ax2.imshow(alpha)
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img2 = plt.figure()
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ax3 = img2.add_subplot(111)
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ax3.imshow(beta)
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plt.show()
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# Writing the calculated representation into the test function file
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# results['Represented'] = True
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writerepresentation(funcpath, results)
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@@ -285,14 +228,23 @@ if __name__ == '__main__':
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funcnames = ["Bukin2", "Bukin4", "Brown"]
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# testfunctionpaths = ["/home/remi/Documents/MDAF-GitLAB/SourceCode/TestFunctions/Bukin4.py"]
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# funcnames = ["Bukin4"]
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# Installing the packages needed for FLACCO
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utils = importr('utils')
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#utils.install_packages('flacco', repos='https://utstat.toronto.edu/cran/')
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#utils.install_packages('list', repos='https://utstat.toronto.edu/cran/')
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####utils.install_packages('reticulate', repos='https://utstat.toronto.edu/cran/')
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reticulate = importr('reticulate')
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flacco = importr('flacco')
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objs = 0
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args = {"high": 200, "low": -200, "t": 1000, "p": 0.95}
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scale = 1
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data = doe (heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale)
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print(data['Bukin2'][1][2])
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#representfunc("TestFunctions/Bukin6.py")
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# data = doe (heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale)
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# print([point[2] for point in data['Bukin2'][1]])
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representfunc("TestFunctions/Bukin2.py")
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# %%
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