'''spec: OE, MN, FK, FM''' # students = win.getStudents(shuffle=False) # cl = students[0].class_ # sp = students[0].spec # anno = [] # anno2 = [] # for ii, st in enumerate(students): # if st.class_ != cl: # anno.append((ii - 1, students[ii - 1].class_)) # cl = st.class_ # if st.spec != sp: # anno2.append((ii - 1, students[ii - 1].spec)) # sp = st.spec # anno.append(((len(students) - 1), students[-1].class_)) # anno2.append(((len(students) - 1), students[-1].spec)) # anno = anno + anno2 students = win.getData(shuffle=False, spec='FK') # win.shuffleIt(students, 2) # win.shuffleIt(students, 1) # st_corr = pairwise_distances(students, students, 'jaccard') co_corr = pairwise_distances(students.T, students.T, 'jaccard') heatmap.plotheat(co_corr, xlabels=win.getCoursesNames(), ylabels=win.getCoursesNames(), mode='special') # heatmap.plotheat(st_corr, changeTicks=False, annotation=anno, mode='special')
matplotlib.rcParams["text.usetex"] = True
# win.shuffleIt(students, 2)
# win.shuffleIt(students, 1)
# st_corr = np.corrcoef(students)
# shuffle C
students = win.getData()
win.shuffleIt(students, 2)
co_corr = np.corrcoef(students, rowvar=0)
heatmap.plotheat(
co_corr,
xlabels=[pl_en[x][1] for x in win.getCoursesNames()],
ylabels=["" for x in win.getCoursesNames()],
rotation=90,
num_levels_=6,
mode="special",
vmin=vmin,
vmax=vmax,
showplot=False,
removebar=True,
)
savePic(name="c_heat_shufC.eps", path="/home/luke/Documents/APPA/img/fifth/")
# real
students = win.getData()
co_corr = np.corrcoef(students, rowvar=0)
heatmap.plotheat(
co_corr,
xlabels=[pl_en[x][1] for x in win.getCoursesNames()],
ylabels=[pl_en[x][1] for x in win.getCoursesNames()],
rotation=90,
num_levels_=6,
anno2 = []
for ii, st in enumerate(students):
if st.class_ != cl:
anno.append((ii - 1, students[ii - 1].class_))
cl = st.class_
if st.spec != sp:
anno2.append((ii - 1, students[ii - 1].spec))
sp = st.spec
anno.append(((len(students) - 1), students[-1].class_))
anno2.append(((len(students) - 1), students[-1].spec))
anno = anno + anno2
courses = win.getCourses()
'''correlation course-course'''
co_corr = np.empty([len(courses), len(courses)])
for ii, c1 in enumerate(courses):
for jj, c2 in enumerate(courses):
v1 = []
v2 = []
for st in students:
v1.append(st.grades[ii])
v2.append(st.grades[jj])
random.shuffle(v1)
random.shuffle(v2)
co_corr[ii, jj] = mutual_info_score(v1, v2)[0]
# print(len(students))
heatmap.plotheat(co_corr, xlabels=win.getCoursesNames(), ylabels=win.getCoursesNames())
heatmap.plotheat(st_corr, changeTicks=False, annotation=anno)
# heatmap.plotheat(st_corr, changeTicks=False)