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test func represent flow basic function
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Remi Ehounou
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References/FLACCO REFERENCE.pdf
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References/FLACCO REFERENCE.pdf
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References/FOR THE REPORT
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References/FOR THE REPORT
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For each of the bi-objective problem instances, two feature objects (one per objective) were created via createFeatureObject, resulting in a total of 24 single-objective problems. Afterwards all 15 feature sets contained in FLACCO, with the exception of the general cell mapping and barrier tree features, are computed per scenario. Those features were excluded as preliminary studies did not reveal a substantial information gain in case of consideration. Note that not all discretized features have been discarded in general- the regular cell mapping features are part of the 15 considered feature sets.
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- https://ieeexplore.ieee.org/document/7748359 [fLACCO PAPER: The R-Package FLACCO for exploratory landscape analysis with applications to multi-objective optimization problems]
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@ -111,7 +111,7 @@ def writerepresentation(funcpath, charas):
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# create a string format of the representation variables
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representation = ''
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for line in list(charas):
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representation += '\n\t#_# ' + line + ': ' + str(charas[line])
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representation += '\n\t#_# ' + line + ': ' + str(charas[line]).replace('\n', ',')
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representation+='\n'
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# Creating the new docstring to be inserted into the file
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@ -159,18 +159,23 @@ def representfunc(funcpath):
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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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X = flacco.createInitialSample(n_obs = 500, dim = n, 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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# these are the retained features. Note that some features are being excluded for being problematic and to avoid overcomplicating the neural network
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# the excluded feature sets are: 'bt', 'ela_level'
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# feature sets that require special attention: 'cm_angle', 'cm_grad', 'limo', 'gcm' (soo big with some nans),
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featureset = ['cm_angle','cm_conv','cm_grad','ela_conv','ela_curv','ela_distr','ela_local','ela_meta','basic','disp','limo','nbc','pca','gcm','ic']
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pyfeats = dict()
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for feature in featureset:
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rawfeats = flacco.calculateFeatureSet(testfuncobj, set=feature)
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pyfeats[feature] = asarray(rawfeats)
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writerepresentation(funcpath, results)
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writerepresentation(funcpath, pyfeats)
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return results
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@ -7,25 +7,21 @@ def main(args):
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#_# dimmensions: 2
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# dimmensions: 2.0
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#_# cm_angle: [[1.33897501e+01], [ nan], [1.40464922e+01], [ nan], [3.10339907e+00], [ nan], [1.00000000e+00], [ nan], [0.00000000e+00], [1.40000000e-02]]
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#_# cm_conv: [[0.0297619 ], [0.00595238], [0.22619048], [0.33333333], [0. ], [0.062 ]]
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#_# cm_grad: [[0.64254617], [ nan], [0. ], [0.01 ]]
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#_# ela_conv: [[0.00000000e+00], [0.00000000e+00], [2.77028225e-02], [2.77028225e-02], [1.00000000e+03], [1.31000000e-01]]
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#_# ela_curv: [[1.00000000e+02], [1.00000355e+02], [1.00005040e+02], [1.00002931e+02], [1.00009346e+02], [1.00015828e+02], [5.10494927e-03], [0.00000000e+00], [5.62028278e+01], [7.31406032e+01], [5.13544460e+02], [1.30604608e+02], [3.75618981e+02], [1.05254867e+04], [1.23941806e+03], [0.00000000e+00], [3.02592268e+00], [9.02638335e+02], [1.56148314e+29], [3.81682127e+03], [3.25219248e+04], [2.89737144e+31], [2.05852895e+30], [5.00000000e-03], [8.40000000e+03], [1.13300000e+00]]
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#_# ela_distr: [[-0.03671804], [-1.24272349], [ 5. ], [ 0. ], [ 0.023 ]]
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#_# ela_local: [[3.00000000e+00], [3.00000000e-02], [1.00267380e+00], [6.66666667e-01], [1.10000000e-01], [4.45000000e-01], [4.45000000e-01], [2.00000000e+01], [2.00000000e+01], [2.26000000e+01], [2.00000000e+01], [2.50000000e+01], [3.00000000e+01], [3.51619629e+00], [2.26300000e+03], [2.35000000e-01]]
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#_# ela_meta: [[9.99993630e-01], [1.01176187e+02], [8.08905581e-01], [9.99825941e+01], [1.23602305e+02], [9.99993660e-01], [1.00000000e+00], [3.95467951e+13], [1.00000000e+00], [0.00000000e+00], [1.00000000e-02]]
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#_# basic: [[ 2. ], [500. ], [-10. ], [-10. ], [ 10. ], [ 10. ], [100.74931228], [199.87416126], [ 10. ], [ 10. ], [100. ], [ 1. ], [ 1. ], [ 0. ], [ 0. ]]
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#_# disp: [[ 0.75879825], [ 0.61920947], [ 0.61336996], [ 0.68491606], [ 0.76149282], [ 0.5882951 ], [ 0.56069976], [ 0.60774698], [-0.12954581], [-0.20451683], [-0.20765314], [-0.16922681], [-0.12717398], [-0.21952442], [-0.23423848], [-0.20915252], [ 0. ], [ 0.011 ]]
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#_# limo: [[9.99858662e+01], [1.00000000e+00], [9.99858662e+01], [ nan], [ nan], [ nan], [1.23602305e+02], [ nan], [ nan], [ nan], [ nan], [ nan], [0.00000000e+00], [3.00000000e-03]]
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#_# nbc: [[ 0.30653068], [ 0.64759411], [ 0.26842005], [ 0.37025091], [-0.10065679], [ 0. ], [ 0.031 ]]
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#_# pca: [[1. ], [1. ], [0.33333333], [0.66666667], [0.51467093], [0.50754981], [0.9998948 ], [0.66673408], [0. ], [0.002 ]]
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#_# gcm: [[1.00000000e+00], [1.00000000e-02], [0.00000000e+00], [0.00000000e+00], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [ nan], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [ nan], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [ nan], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [0.00000000e+00], [6.00000000e-03], [1.00000000e+00], [1.00000000e-02], [0.00000000e+00], [0.00000000e+00], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [ nan], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [ nan], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [ nan], [1.00000000e-02], [1.00000000e-02], [1.00000000e-02], [0.00000000e+00], [5.00000000e-03], [5.00000000e+00], [5.00000000e-02], [9.50000000e-01], [1.00000000e+00], [1.53846154e-01], [2.00000000e-01], [2.30769231e-01], [2.30769231e-01], [4.21325044e-02], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [2.00000000e-01], [1.00000000e-02], [6.40000000e-01], [2.88183969e-01], [1.00000000e+00], [1.00000000e+00], [5.00000000e-02], [0.00000000e+00], [1.02000000e-01]]
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#_# ic: [[ 0.71881882], [ 2.00700701], [64.09244019], [ 1.82682683], [ 0.29116466], [ 0. ], [ 0.203 ]]
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'''
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return 100*(args[1]-0.01*args[0]**2+1)+0.01*(args[0]+10)**2
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