def test(data_file):
''' effectiveness test wrt ROC '''
data = np.genfromtxt(data_file, delimiter=',')
for coef in xrange(99999,99900, -1):
model = FFIDCAD(ff_lambda=0.99, p_value=coef*0.00001)
tp,fp,tn,fn = 0,0,0,0
for idx, exmp in enumerate(data[:,:2]):
pred,_ = model.predict(exmp)
tp = tp + (1 if pred == data[idx][2] and pred == 1 else 0)
fp = fp + (1 if (pred - data[idx][2]) == 1 else 0)
tn = tn + (1 if pred == data[idx][2] and pred == 0 else 0)
fn = fn + (1 if (data[idx][2] - pred) == 1 else 0)
print np.float(tp)/(tp+fn), np.float(fp)/(fp+tn)
def eff_test():
''' efficiency test wrt predition time cost '''
size = 1000
mu = np.array([5,5])
cov_matrix = np.array([[1,0.1],[0.1,1]])
for coef in xrange(1,11):
mus, cov_matrices = create_params(mu,
cov_matrix,
0.1,
[[0.,0.],[0.,0.]],
coef*size)
samples = generate(mus, cov_matrices, 1)
model = FFIDCAD(ff_lambda=0.99, p_value = 0.9999)
start = time.time()
for sample in samples:
model.predict(sample)
cost = time.time() - start
print coef*size, cost, ';'
