def learn_metal_gem(parentLabels, parent, childLabels, child,
symmetric, squared, parameters=None):
"""learn optimal parameters for gem on metal data"""
if parameters == None:
ST0 = [1, 2]
ST1 = [1, 2]
TAU = [0,0.00390625,0.0078125,0.015625,0.03125,0.0625,0.125,0.25,0.5]
else:
ST0, ST1, TAU = parameters
errors = []
for St0 in ST0:
for St1 in ST1:
for E in TAU :
func = lambda query, subject: \
dist.gem(query, subject, St0, St1, E, symmetric, squared)
e, l = c.obtain_1NN_error(parentLabels, parent,
childLabels, child, func)
errors.append((e, (St0, St1, E, symmetric, squared)))
print errors[-1]
errs = list(sorted(errors, key=lambda x: x[0][0]))
# pick only the best and take parameter in the "middle"
best = filter(lambda (x, y): x[0]==errs[0][0][0], errs)
best.sort(key=lambda (x, y): y[2])
best = best[len(best)/2]
return best, errs
def loocv_gem(loocvLabels, loocvSet, St0, St1, E, symmetric, squared):
"""calculate LOOCV error rate for given parameters on a given set"""
error = 0
# 1NN with Leave One Out Cross Validation
for index, (queryLabel, query) in enumerate(zip(loocvLabels, loocvSet)):
search = np.vstack((loocvSet[:index], loocvSet[index+1:]))
labels = np.hstack((loocvLabels[:index], loocvLabels[index+1:]))
vals =[dist.gem(query, subject, St0, St1, E, symmetric, squared)
for subject in search]
best = np.array(vals).argmin()
if queryLabel != labels[best]:
error +=1
return error, len(loocvSet), float(error)/len(loocvSet)
def loocv_gem(loocvLabels, loocvSet, St0, St1, E, symmetric, squared):
"""calculate LOOCV error rate for given parameters on a given set"""
error = 0
# 1NN with Leave One Out Cross Validation
for index, (queryLabel, query) in enumerate(zip(loocvLabels, loocvSet)):
search = np.vstack((loocvSet[:index], loocvSet[index + 1:]))
labels = np.hstack((loocvLabels[:index], loocvLabels[index + 1:]))
vals = [
dist.gem(query, subject, St0, St1, E, symmetric, squared)
for subject in search
]
best = np.array(vals).argmin()
if queryLabel != labels[best]:
error += 1
return error, len(loocvSet), float(error) / len(loocvSet)
def learn_metal_gem(parentLabels,
parent,
childLabels,
child,
symmetric,
squared,
parameters=None):
"""learn optimal parameters for gem on metal data"""
if parameters == None:
ST0 = [1, 2]
ST1 = [1, 2]
TAU = [
0, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25,
0.5
]
else:
ST0, ST1, TAU = parameters
errors = []
for St0 in ST0:
for St1 in ST1:
for E in TAU:
func = lambda query, subject: \
dist.gem(query, subject, St0, St1, E, symmetric, squared)
e, l = c.obtain_1NN_error(parentLabels, parent, childLabels,
child, func)
errors.append((e, (St0, St1, E, symmetric, squared)))
print errors[-1]
errs = list(sorted(errors, key=lambda x: x[0][0]))
# pick only the best and take parameter in the "middle"
best = filter(lambda (x, y): x[0] == errs[0][0][0], errs)
best.sort(key=lambda (x, y): y[2])
best = best[len(best) / 2]
return best, errs
