def main():
"""
"""
#mine = MINE()
#hsic_lasso = HSICLasso()
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
root = 0
collist = []
for colname in glob.glob("./run*/COL*"):
for line in open(colname):
if "#" in line:
continue
line = line.split()
t = float(line[0])
if t < 10000.0:
continue
elif 50000.0 < t:
break
collist.append(line)
#break
#MINEs, TICs = pstats(collist)
#print(TICs)
for i in range(len(collist[0])-1):
for j in range(i+1, len(collist[0])-1):
#for j in range(i+1, i + 5):
miclist = []
ticlist = []
for _ in range(10):
#if True:
colpart = random.sample(collist, 10000)
#colpart = collist
x = np.array([a[i+1] for a in colpart], dtype=float)
y = np.array([a[j+1] for a in colpart], dtype=float)
#xy = np.array([x,y])
#mine = MINE()
mine = MINE(est="mic_e")
mine.compute_score(x,y)
miclist.append(mine.mic())
ticlist.append(mine.tic())
#miclist = comm.gather(mine.mic(), root=0)
#ticlist = comm.gather(mine.tic(), root=0)
#hsic_lasso.input(xy, np.array([0,1]))
#hsic_lasso.input(np.array([[1, 1, 1], [2, 2, 2]]), np.array([0, 1]))
#hsic_lasso.regression(5)
#hsic_lasso.classification(10)
#print(hsic_lasso.dump())
if rank == root:
print("%s,%s, %s, %s"%(i,j,np.mean(miclist),np.mean(ticlist)), flush = True)
with open("./minedata.csv", "a") as wf:
wf.write("%s, %s, %s, %s\n"%(i,j,np.mean(miclist),np.mean(ticlist)))
def Maximal_Compute(df, col, tic=False):
#http://web.mit.edu/dnreshef/www/websiteFiles/Papers/AOAS-2018.pdf
mine = MINE(alpha=0.6, c=15)
col_dict = dict()
col_dict['index'] = col
if tic:
tic_dict = dict()
tic_dict['index'] = col
for column in df.columns:
mine.compute_score(df[col], df[column])
col_dict[column] = mine.mic()
if tic:
tic_dict[column] = mine.tic()
if tic:
return (col_dict, tic_dict)
else:
return col_dict
def mine(pair):
assert len(pair) == 4 and isinstance(pair, tuple)
try:
x, y, x_name, y_name = pair
mine_ = MINE()
mine_.compute_score(x, y)
result = {
(x_name, y_name):
(mine_.mic(), mine_.mas(), mine_.mcn(), mine_.mev(), mine_.tic())
}
return result
except:
return None
# null hypothesis
for k in range(1, n_null+1):
x = np.random.rand(n)
r = np.random.randn(n)
y = f()
# resimulate x for the null scenario
x = np.random.rand(n)
mine_approx.compute_score(x, y)
mine_e.compute_score(x, y)
mic_approx_null.append(mine_approx.mic())
mic_e_null.append(mine_e.mic())
tic_e_null.append(mine_e.tic())
r2_null.append(np.corrcoef(x, y)[0][1]**2)
# alternative hypothesis
for k in range(1, n_alt+1):
x = np.random.rand(n)
r = np.random.randn(n)
y = f()
mine_approx.compute_score(x, y)
mine_e.compute_score(x, y)
mic_approx_alt.append(mine_approx.mic())
mic_e_alt.append(mine_e.mic())
tic_e_alt.append(mine_e.tic())
r2_alt.append(np.corrcoef(x, y)[0][1]**2)
