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import numpy as np

import pandas as pd

#_______________________ File selection box

filename = 'breast10D.csv' # show an "Open" dialog box and return the path to the selected file

#Cfilename ='breast10.csv'

#_______________________ Converting csv file to list

df = pd.read_csv(filename)#,index_col=True)

U = df.values.tolist()

U = [[index] + value for index , value in enumerate(U) ]

#________________________ Equivalence partition function

def equivalence_partition( iterable , index ):

    classes = 

    dclasses = {}

    for o in iterable: # for each object

        # find the class it is in

        found = False

        for c in classes:

            indice_ele = next(iter(c))

            element = [iterable[indice_ele][ind] == o[ind] for ind in index]

            if all(element): # is it equivalent to this class?

                c.add( o[0])

                dclasses[o[0]] = c

                found = True

                break

        if not found: # it is in a new class

            classes.append( set([o[0]]))

            dclasses[o[0]] = classes[-1]

    return classes,dclasses

#_________________________ Finding lower approximation and positif region

def lower_appr(B):

    ind_B = equivalence_partition( U , B )[1]

    ind_d = equivalence_partition( U , D )[1]

    lower_appr_set = set()

    for x,ele in enumerate(U):

        if ind_B[x].issubset(ind_d[x]):

            lower_appr_set.add(x)

    return lower_appr_set

#________________________ Finding dependencey of features

def gamma(B):

    return float(len(lower_appr(B)))/float(len(U))

#_________________________ Rough set feature selection quickreduct algorithm

def qreduct(C):

    R = set()

    while True:

        T = R

        for x in C-R:

             if gamma(R.union(set([x]))) > gamma(T):

                T = R.union(set([x]))



        R = T

        if gamma(R) == gamma(C):

                break

    return R

#_________________________ Main fuction

decision=len(df.columns)#_________ defining le decision index

D = [decision]           

B = set([ i for i in range(1,decision)]) #__________ defining condition index

Features= qreduct(B)

import numpy as np

import pandas as pd

#_______________________ File selection box

filename = 'breast10D.csv' # show an "Open" dialog box and return the path to the selected file

#Cfilename ='breast10.csv'

#_______________________ Converting csv file to list

df = pd.read_csv(filename)#,index_col=True)

U = df.values.tolist()

U = [[index] + value for index , value in enumerate(U) ]

#________________________ Equivalence partition function

def equivalence_partition( iterable , index ):

    classes = 

    dclasses = {}

    for o in iterable: # for each object

        # find the class it is in

        found = False

        for c in classes:

            indice_ele = next(iter(c))

            element = [iterable[indice_ele][ind] == o[ind] for ind in index]

            if all(element): # is it equivalent to this class?

                c.add( o[0])

                dclasses[o[0]] = c

                found = True

                break

        if not found: # it is in a new class

            classes.append( set([o[0]]))

            dclasses[o[0]] = classes[-1]

    return classes,dclasses

#_________________________ Finding lower approximation and positif region

def lower_appr(B):

    ind_B = equivalence_partition( U , B )[1]

    ind_d = equivalence_partition( U , D )[1]

    lower_appr_set = set()

    for x,ele in enumerate(U):

        if ind_B[x].issubset(ind_d[x]):

            lower_appr_set.add(x)

    return lower_appr_set

#________________________ Finding dependencey of features

def gamma(B):

    return float(len(lower_appr(B)))/float(len(U))

#_________________________ Rough set feature selection quickreduct algorithm

def qreduct(C):

    R = set()

    while True:

        T = R

        for x in C-R:

             if gamma(R.union(set([x]))) > gamma(T):

                T = R.union(set([x]))



        R = T

        if gamma(R) == gamma(C):

                break

    return R

#_________________________ Main fuction

decision=len(df.columns)#_________ defining le decision index

D = [decision]           

B = set([ i for i in range(1,decision)]) #__________ defining condition index

Features= qreduct(B)

import numpy as np

import pandas as pd

#_______________________ File selection box

filename = 'breast10D.csv' # show an "Open" dialog box and return the path to the selected file

#Cfilename ='breast10.csv'

#_______________________ Converting csv file to list

df = pd.read_csv(filename)#,index_col=True)

U = df.values.tolist()

U = [[index] + value for index , value in enumerate(U) ]

#________________________ Equivalence partition function

def equivalence_partition( iterable , index ):

    classes = 

    dclasses = {}

    for o in iterable: # for each object

        # find the class it is in

        found = False

        for c in classes:

            indice_ele = next(iter(c))

            element = [iterable[indice_ele][ind] == o[ind] for ind in index]

            if all(element): # is it equivalent to this class?

                c.add( o[0])

                dclasses[o[0]] = c

                found = True

                break

        if not found: # it is in a new class

            classes.append( set([o[0]]))

            dclasses[o[0]] = classes[-1]

    return classes,dclasses

#_________________________ Finding lower approximation and positif region

def lower_appr(B):

    ind_B = equivalence_partition( U , B )[1]

    ind_d = equivalence_partition( U , D )[1]

    lower_appr_set = set()

    for x,ele in enumerate(U):

        if ind_B[x].issubset(ind_d[x]):

            lower_appr_set.add(x)

    return lower_appr_set

#________________________ Finding dependencey of features

def gamma(B):

    return float(len(lower_appr(B)))/float(len(U))

#_________________________ Rough set feature selection quickreduct algorithm

def qreduct(C):

    R = set()

    while True:

        T = R

        for x in C-R:

             if gamma(R.union(set([x]))) > gamma(T):

                T = R.union(set([x]))



        R = T

        if gamma(R) == gamma(C):

                break

    return R

#_________________________ Main fuction

decision=len(df.columns)#_________ defining le decision index

D = [decision]           

B = set([ i for i in range(1,decision)]) #__________ defining condition index

Features= qreduct(B)

import numpy as np

import pandas as pd

#_______________________ File selection box

filename = 'breast10D.csv' # show an "Open" dialog box and return the path to the selected file

#Cfilename ='breast10.csv'

#_______________________ Converting csv file to list

df = pd.read_csv(filename)#,index_col=True)

U = df.values.tolist()

U = [[index] + value for index , value in enumerate(U) ]

#________________________ Equivalence partition function

def equivalence_partition( iterable , index ):

    classes = 

    dclasses = {}

    for o in iterable: # for each object

        # find the class it is in

        found = False

        for c in classes:

            indice_ele = next(iter(c))

            element = [iterable[indice_ele][ind] == o[ind] for ind in index]

            if all(element): # is it equivalent to this class?

                c.add( o[0])

                dclasses[o[0]] = c

                found = True

                break

        if not found: # it is in a new class

            classes.append( set([o[0]]))

            dclasses[o[0]] = classes[-1]

    return classes,dclasses

#_________________________ Finding lower approximation and positif region

def lower_appr(B):

    ind_B = equivalence_partition( U , B )[1]

    ind_d = equivalence_partition( U , D )[1]

    lower_appr_set = set()

    for x,ele in enumerate(U):

        if ind_B[x].issubset(ind_d[x]):

            lower_appr_set.add(x)

    return lower_appr_set

#________________________ Finding dependencey of features

def gamma(B):

    return float(len(lower_appr(B)))/float(len(U))

#_________________________ Rough set feature selection quickreduct algorithm

def qreduct(C):

    R = set()

    while True:

        T = R

        for x in C-R:

             if gamma(R.union(set([x]))) > gamma(T):

                T = R.union(set([x]))



        R = T

        if gamma(R) == gamma(C):

                break

    return R

#_________________________ Main fuction

decision=len(df.columns)#_________ defining le decision index

D = [decision]           

B = set([ i for i in range(1,decision)]) #__________ defining condition index

Features= qreduct(B)

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