Function evaluations

pattern/vector/svm/libsvmutil.py:51–78

evaluations(ty, pv) -> (ACC, MSE, SCC) Calculate accuracy, mean squared error and squared correlation coefficient using the true values (ty) and predicted values (pv).

(ty, pv)

	libsvm.svm_save_model(model_file_name.encode(), model)

def evaluations(ty, pv):
	"""
	evaluations(ty, pv) -> (ACC, MSE, SCC)

	Calculate accuracy, mean squared error and squared correlation coefficient
	using the true values (ty) and predicted values (pv).
	"""
	if len(ty) != len(pv):
		raise ValueError("len(ty) must equal to len(pv)")
	total_correct = total_error = 0
	sumv = sumy = sumvv = sumyy = sumvy = 0
	for v, y in zip(pv, ty):
		if y == v: 
			total_correct += 1
		total_error += (v-y)*(v-y)
		sumv += v
		sumy += y
		sumvv += v*v
		sumyy += y*y
		sumvy += v*y 
	l = len(ty)
	ACC = 100.0*total_correct/l
	MSE = total_error/l
	try:
		SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
	except:
		SCC = float('nan')
	return (ACC, MSE, SCC)

def svm_train(arg1, arg2=None, arg3=None):
	"""