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apply_fca_simulation.py
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apply_fca_simulation.py
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"""from concepts import Context
c = Context.fromstring('''
|human|knight|king |mysterious|
King Arthur| X | X | X | |
Sir Robin | X | X | | |
holy grail | | | | X |
''')
print(c.intension(['King Arthur', 'Sir Robin']))
"""
"""
import os,glob
from nltk.stem import WordNetLemmatizer
from xlwt import Workbook
lemmatizer = WordNetLemmatizer()
wb = Workbook()
sheet1 = wb.add_sheet("Sheet 1",cell_overwrite_ok=True)
path = r'C:\\Users\AMIT\Downloads\whatsapp\tags\tags'
skills = set()
for filename in glob.glob(os.path.join(path, '*.txt')):
temp_skills = open(filename,"r",encoding="utf-8").read().split(",")
for skill in temp_skills:
temp_skill = lemmatizer.lemmatize(skill.strip().lower())
skills.add(temp_skill)
print(len(skills))
for row,skill in enumerate(skills):
sheet1.write(row, 0, skill)
print(row,skill)
wb.save("tech_skills_2.xls")
"""
"""
from concepts import Context
# c = Context.fromfile("test_files\\temp_formal_context.csv",frmat="csv",encoding="utf-8")
c = Context.fromfile("test_files\\water_bodies_formal_context.csv",frmat="csv",encoding="utf-8")
# print(c.intension(['king arthur', 'sir robin']))
for extent, intent in c.lattice:
print("{} > {}".format(extent, intent))
c.lattice.graphviz(view=True)
"""
import pickle
import csv
import random
from concepts import Context
# # reading tech skills
# pick_file = open("pickled_data/tech_skills.pickle","rb")
# skills = pickle.load(pick_file)
# pick_file.close()
# # print(len(skills))
#
# # taking first 10 skills an write it to csv
# csvfile = open("test_files/tech_formal_context.csv","w",newline="")
# csvwriter = csv.writer(csvfile)
#
# skills = list(skills)
# skills.insert(0,"")
# # print(skills)
# csvwriter.writerow(skills)
#
# for i in range(10):
# row = []
# row.append("project "+str(i+1))
# for j in range(10):
# row.append("X" if random.randint(0,1)>0 else "")
# csvwriter.writerow(row)
#
# csvfile.close()
def generate_concept_matrix(filename,skill_list=None):
# applying fca
c = Context.fromfile(filename, frmat="csv")
# reading csv headers
csvfile = open(filename)
csvreader = csv.reader(csvfile)
# reading skills
if skill_list is None:
skill_list = csvreader.__next__()
skill_list.pop(0)
else:
csvreader.__next__()
# reading abstract names
row_header = list()
for row in csvreader:
row_header.append(row[0])
csvfile.close()
# matrix to return
mat = list()
for extent, intent in c.lattice:
print("{} > {}".format(extent, intent))
row = list()
for skill in skill_list:
if skill in intent:
row.append(1)
else:
row.append(0)
for header in row_header:
if header in extent:
row.append(1)
else:
row.append(0)
mat.append(row)
return mat, row_header, skill_list
def refine_concept_matrix(mat,skills_len):
i = 0
while i < len(mat):
conc = mat[i]
flag = False
for j in range(skills_len):
if conc[j] == 1:
flag = True
break
if not flag:
mat.pop(i)
continue
flag = False
for j in range(skills_len,len(conc)):
if conc[j] == 1:
flag = True
break
if not flag:
mat.pop(i)
continue
i += 1
# concept matrix, row, column
tasc, abstracts, skills = generate_concept_matrix("test_files/tech_formal_context.csv")
# tasc.pop(-1)
refine_concept_matrix(tasc,len(skills))
print("abstract concept matrix")
for row in tasc:
print(row)
tesc, students, skills = generate_concept_matrix("test_files/student_formal_context.csv",skills)
# tesc.pop(-1)
refine_concept_matrix(tesc,len(skills))
print("student concept matrix")
for row in tesc:
print(row)
def generate_affinity_matrix(team_concept, task_concept, a=1, b=1, c=1, d=1):
mat = [ [ 0 for j in range(len(task_concept)) ] for i in range(len(team_concept)) ]
for i in range(len(team_concept)):
for j in range(len(task_concept)):
for k in range(len(skills)):
if team_concept[i][k] == 1 and task_concept[j][k] == 1:
mat[i][j] = mat[i][j] + a*team_concept[i][k]
elif team_concept[i][k] == 0 and task_concept[j][k] == 0:
mat[i][j] = mat[i][j] + b
elif team_concept[i][k] == 1 and task_concept[j][k] == 0:
mat[i][j] = mat[i][j] - c*team_concept[i][k]
else:
mat[i][j] = mat[i][j] - d
return mat
def generate_pref(mat, r_c_val, isRow = True):
tup_list = list()
for i in range( len(mat[0]) if isRow else len(mat) ):
if isRow:
tup_list.append((mat[r_c_val][i], i))
else:
tup_list.append((mat[i][r_c_val], i))
def sort_by_val(elem):
return elem[0]
sorted_list = sorted(tup_list,key=sort_by_val,reverse=True)
pref_list = list()
for val,i in sorted_list:
pref_list.append(i)
return pref_list
# hospital/resident problem
# pref_1 = hospital pref. pref_2 = resident_pref.
def extended_sma(pref_1, pref_2):
pairs = list()
res_partner = dict()
res_count_in_pref = len(pref_1.keys())*len(pref_2.keys())
while res_count_in_pref > 0:
# for each hospital
for h, pref in pref_1.items():
# print("for h=",h)
# for each resident in pref.
for r in pref:
# if already paired with ith hospital
# print("for r=",r)
if (h, r) in pairs:
# print("already found!")
continue
elif r in res_partner:
# print("pair found n breaking")
pairs.remove((res_partner[r], r))
res_partner.pop(r)
res_count_in_pref += 1
# print("paired",(h,r))
pairs.append((h, r))
res_partner[r] = h
res_count_in_pref -= 1
# for each successor h_ of h in r's pref. remove h_ and r from each other
# using index from h+1 to end of r's pref
# print("h found in r's pref. at ",pref_2[r].index(h),"len of r's pref. is",len(pref_2[r]))
hpos = pref_2[r].index(h)
for h_i in range(hpos+1, len(pref_2[r])):
# print("removing h_(s) n r h_=",pref_2[r][hpos+1],"h_i=",h_i,"pref.=",pref_2[r])
# removing r from h_'s pref
pref_1[ pref_2[r][hpos+1] ].remove(r)
res_count_in_pref -= 1
# removing h_ from r's pref
pref_2[r].pop(hpos+1)
break
# check if any h's pref. still left with unallocated resident
# if so then break
# edit this //////////////////////////////////////////////////////////////////////////here////////
# if len(res_partner.keys()) == len(pref_2.keys()):
# print("done quiting sma")
# break
return pairs
peak_per = 0
avg_peak_per = 0
best_a = 0
best_b = 0
best_c = 0
best_d = 0
for a in range(0, 11):
# a = a/10
for b in range(0, 11):
# b = b/10
for c in range(0, 11):
# c = c/10
for d in range(0, 11):
# d = d/10
print("---------------------------------------------------------------------------")
print("---------------------------------------------------------------------------")
print("for a=",a,"b=",b,"c=",c,"d=",d)
print("---------------------------------------------------------------------------")
print("---------------------------------------------------------------------------")
aff_mat = generate_affinity_matrix(tesc, tasc, a,b,c,d)
# print("---------------------------------------------------------------------------")
# print("aff mat")
# for row in aff_mat:
# for val in row:
# print("{:3}".format(val),end="|")
# print()
# task concept preference
task_c_pref = dict()
# student concept preference
stu_c_pref = dict()
# print(generate_pref(aff_mat,0,False))
# generating pref order for student concepts
for stu_c_i in range(len(aff_mat)):
stu_c_pref[stu_c_i] = generate_pref(aff_mat,stu_c_i,True)
# print("---------------------------------------------------------------------------")
# print("student preferences")
# for k, v in stu_c_pref.items():
# print(k,v)
# generating pref order for abstract concepts
for task_c_i in range(len(aff_mat[0])):
task_c_pref[task_c_i] = generate_pref(aff_mat,task_c_i,False)
# print("abstract preferences")
# for k, v in task_c_pref.items():
# print(k,v)
# h_pref = {0:[0,2,4,1,3], 1:[2,3,4,0,1], 2:[3,0,2,1,4]}
# r_pref = {0:[0,2,1], 1:[2,0,1], 2:[1,2,0], 3:[0,2,1], 4:[2,1,0]}
# task as hospital, students as residents
print("sma")
# pairss = extended_sma(h_pref,r_pref)
concept_pairs = extended_sma(task_c_pref, stu_c_pref)
print("---------------------------------------------------------------------------")
print("task_concept-student_concept pairs")
print(concept_pairs)
# analysing the correctness according to common skills
avg_stable_percentage = 0
for abst_con_i, stu_cons_i in concept_pairs:
print("for pair (",abst_con_i,",",stu_cons_i,")")
total_skill_pres = 0
common_skill_pres = 0
for i in range(len(skills)):
if tasc[abst_con_i][i] == 1 and tesc[stu_cons_i][i] == 1:
total_skill_pres += 1
common_skill_pres += 1
elif tasc[abst_con_i][i] == 1 or tesc[stu_cons_i][i] == 1:
total_skill_pres += 1
print("stable percentage:",1 if total_skill_pres == 0 else common_skill_pres/total_skill_pres)
avg_stable_percentage += 1 if total_skill_pres == 0 else common_skill_pres/total_skill_pres
print("---------------------------------------------------------------------------")
print("average stable percentage:",avg_stable_percentage/len(concept_pairs))
print("---------------------------------------------------------------------------")
if avg_stable_percentage/len(concept_pairs) > peak_per:
peak_per = avg_stable_percentage/len(concept_pairs)
best_a = a
best_b = b
best_c = c
best_d = d
avg_peak_per += avg_stable_percentage/len(concept_pairs)
print("---------------------------------------------------------------------------")
print("peak_per=",peak_per,"where a=",best_a,"b=",best_b,"c=",best_c,"d=",best_d)
print("---------------------------------------------------------------------------")
# project_part = dict()
# student_part = dict()
#
# for abst_con_i, stu_cons_i in concept_pairs:
# tasc_row = tasc[abst_con_i]
# tesc_row = tesc[stu_cons_i]
#
# proj_list = []
# stu_list = []
#
# for i in range(len(tasc_row)):
# if tasc_row[i] == 1:
# if i < len(skills):
# print(skills[i],end=" | ")
# else:
# print(abstracts[i-len(skills)],end=" | ")
# proj_list.append(abstracts[i-len(skills)])
# print(" > ",end="")
# for i in range(len(tesc_row)):
# if tesc_row[i] == 1:
# if i < len(skills):
# print(skills[i], end=" | ")
# else:
# print(students[i - len(skills)], end=" | ")
# stu_list.append(students[i - len(skills)])
#
# for proj in proj_list:
# for stu in stu_list:
# if proj in project_part:
# project_part[proj].add(stu)
# else:
# project_part[proj] = {stu}
#
# if stu in student_part:
# student_part[stu].add(proj)
# else:
# student_part[stu] = {proj}
# print()
#
# print("---------------------------------------------------------------------------")
# print("project part ....")
# for proj, stus in project_part.items():
# print(proj,">",stus)
# print("---------------------------------------------------------------------------")
# print("student part ....")
# for stus, proj in student_part.items():
# print(stus,">",proj)
# tesc = Context.fromfile("test_files/temp_student_formal_context.csv", frmat="csv")
# c.lattice.graphviz(view=True,directory="outputs",filename="temp_tech_concept_lattice")