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Test function represent flow operational
This commit is contained in:
Remi Ehounou
@ -26,6 +26,11 @@ import statistics as st
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from scipy import signal, misc, ndimage
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def installFalcoo(mirror = 'https://utstat.toronto.edu/cran/'):
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utils = importr('utils')
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utils.install_packages('flacco', repos=mirror)
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utils.install_packages('list', repos=mirror)
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class counter:
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#wraps a function, to keep a running count of how many
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#times it's been called
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@ -111,8 +116,8 @@ 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]).replace('\n', ',')
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representation+='\n'
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representation += '\n\t#_# ' + line + ': ' + repr(charas[line]).replace('\n','')
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representation+='\n\n\t#_# Represented: 1\n\n'
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# Creating the new docstring to be inserted into the file
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with open(funcpath, "r") as file:
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@ -121,7 +126,7 @@ def writerepresentation(funcpath, charas):
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docstrs += representation
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repl = "\\1"+docstrs+"\t\\2"
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# Create the new content of the file to replace the old. Overwriting the whole thing
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# Create the new content of the file to replace the old. Replacing the whole thing
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pattrn = re.compile("(def main\(.*?\):.*?''').*?('''.*?return\s+.*?\n|$)", flags=re.DOTALL)
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newContent = pattrn.sub(repl, content, count=1)
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# Overwrite the test function file
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@ -138,46 +143,45 @@ def representfunc(funcpath):
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# Finding the function characteristics inside the docstring
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if funcmodule.main.__doc__:
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regex = re.compile("#_#\s?(\w+):\s?([-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?)")
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regex = re.compile("#_#\s?(\w+):(.+)?\n") # this regular expression matches the characteristics already specified in the docstring section of the function -- old exp: "#_#\s?(\w+):\s?([-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?)"
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characs = re.findall(regex, funcmodule.main.__doc__)
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results = {}
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for charac in characs:
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results[charac[0]] = float(charac[1])
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results[charac[0]] = eval(charac[1])
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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 been specified\n===\n******Calculating the Characteristics******")
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n = int(results['dimmensions'])
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execpath = sys.executable
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# Importing FLACCO using rpy2
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flacco = importr('flacco')
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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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rvector = robjs.r['c']
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r_unlist = robjs.r['unlist']
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rtestfunc = rinterface.rternalize(funcmodule.main)
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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 = n, control = rlist(init_sample_type = 'lhs', init_sample_lower = lower, init_sample_upper = upper))
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lower = r_unlist(rvector(results['lower']))
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upper = r_unlist(rvector(results['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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# 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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# these are the retained features. Note that some features are being excluded for being problematic and to avoid overcomplicating the neural network.... the feature sets are redundant and the most relevant ones have been retained
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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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# feature sets that require special attention: 'cm_angle', 'cm_grad', 'limo', 'gcm' (large set 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, pyfeats)
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return results
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@ -234,14 +238,7 @@ if __name__ == '__main__':
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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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@ -1,28 +1,29 @@
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def main(args):
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'''
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:param args: list of floats
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:return: float
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#_# dimmensions: 2
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#_# upper: [-5, 3]
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#_# lower: [-15, -3]
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#_# minimum: [-10,0]
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#_# cm_angle: array([[3.49881571e-01], [1.07838645e-01], [3.22906733e-01], [1.09086923e-01], [1.36740093e+02], [3.72333248e+01], [6.24743683e-02], [1.45683932e-02], [0.00000000e+00], [7.70000000e-02]])
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#_# cm_conv: array([[0.08928571], [0.04166667], [0.51785714], [0.48214286], [0. ], [0.038 ]])
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#_# cm_grad: array([[0.80986036], [0.11715403], [0. ], [0.05 ]])
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#_# ela_conv: array([[0.00000000e+00], [0.00000000e+00], [2.74360332e+00], [2.74360332e+00], [1.00000000e+03], [1.39000000e-01]])
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#_# ela_curv: array([[1.00520667e+02], [1.01157352e+02], [1.02102548e+02], [1.01875092e+02], [1.03042277e+02], [1.04371521e+02], [1.09894163e+00], [0.00000000e+00], [3.34559633e+00], [4.02357156e+00], [5.48926800e+00], [5.13982675e+00], [6.55405463e+00], [9.78678891e+00], [1.70558030e+00], [0.00000000e+00], [3.29353234e+00], [1.48351404e+04], [2.19236653e+31], [4.03941866e+04], [3.14916313e+07], [4.17710288e+33], [2.95558281e+32], [0.00000000e+00], [8.40000000e+03], [1.06400000e+00]])
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#_# ela_distr: array([[ 0.01739786], [-1.01700978], [ 1. ], [ 0. ], [ 0.02 ]])
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#_# ela_local: array([[1.000e+00], [1.000e-02], [1.000e+00], [ nan], [1.000e+00], [0.000e+00], [1.000e+00], [1.000e+01], [1.000e+01], [1.000e+01], [1.000e+01], [1.000e+01], [1.000e+01], [0.000e+00], [1.001e+03], [9.300e-02]])
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#_# ela_meta: array([[9.98338849e-01], [1.91750766e+02], [1.99998995e+01], [1.00059864e+02], [5.00301834e+00], [9.98335620e-01], [1.00000000e+00], [3.55077954e+14], [1.00000000e+00], [0.00000000e+00], [9.00000000e-03]])
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#_# basic: array([[ 2. ], [ 500. ], [ -15. ], [ -3. ], [ -5. ], [ 3. ], [-388.17734244], [ 369.94308917], [ 10. ], [ 10. ], [ 100. ], [ 100. ], [ 1. ], [ 0. ], [ 0. ]])
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#_# disp: array([[ 0.26139886], [ 0.36358526], [ 0.5516879 ], [ 0.80572442], [ 0.25023966], [ 0.32900436], [ 0.49402763], [ 0.72135232], [-3.13648576], [-2.70254902], [-1.90376706], [-0.82499549], [-3.02850003], [-2.7103465 ], [-2.04376951], [-1.125539 ], [ 0. ], [ 0.01 ]])
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#_# limo: array([[ 1.02002699e+02], [ 9.98507972e-01], [ 1.02155253e+02], [ 1.13240181e+00], [-3.88783841e-02], [-9.92102532e-01], [ 5.48006296e+00], [ 1.74733189e+00], [ 3.39374032e+01], [ 5.00221530e+00], [ 2.94406289e+00], [ 3.22874553e-02], [ 0.00000000e+00], [ 9.50000000e-02]])
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#_# nbc: array([[ 0.5341454 ], [ 0.87561488], [ 0.47062778], [ 0.15902654], [-0.19513986], [ 0. ], [ 0.027 ]])
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#_# pca: array([[1. ], [1. ], [0.33333333], [0.66666667], [0.73549361], [0.51109865], [0.99976032], [0.66217335], [0. ], [0.002 ]])
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#_# gcm: array([[1. ], [0.01 ], [0.99 ], [0. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [0.01 ], [0. ], [0.085], [1. ], [0.01 ], [0.99 ], [0. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [0.01 ], [0. ], [0.084], [1. ], [0.01 ], [0.99 ], [0. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [0.01 ], [0. ], [0.088]])
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#_# ic: array([[ 0.67118887], [ 2.02702703], [70.28244264], [ 1.90690691], [ 0.23293173], [ 0. ], [ 0.204 ]])
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#_# Represented: 1
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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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return 0
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