def validate(param_queue, mse_queue, train_data, train_r1, val_data, val_r1):
while True:
param = param_queue.get()
if param is None:
break
index = param[0]
g = param[1]
c = param[2]
svr = SVR(kernel='rbf', C = c, gamma = g)
svr.fit(train_data, train_r1)
val_predict = svr.predict(val_data)
mse = np.mean((val_predict - val_r1)**2)
mse_queue.put((index, mse))
process[idx].start()
index = 0
for g in gamma:
for c in C:
param_queue.put((index, g, c))
index += 1
for idx in xrange(n_process):
param_queue.put(None)
val_mse = [None] * len(gamma) * len(C)
for i in xrange(len(gamma) * len(C)):
mse_entry = mse_queue.get()
val_mse[mse_entry[0]] = mse_entry[1]
if i % 1 == 0:
print 'processed %d' %(i)
best_idx = val_mse.index(min(val_mse))
best_gamma = gamma[best_idx / len(C)]
best_c = C[best_idx % len(C)]
print best_idx, best_gamma, best_c
svr = SVR(kernel='rbf', C = best_c, gamma = best_gamma)
svr.fit(train_data, train_r1)
test_predict = svr.predict(test_data)
mse_r1 = np.mean((test_r1 - test_predict)**2)
mse_r2 = np.mean((test_r2 - test_predict)**2)
print pearsonr(test_r1,test_predict)
print 'mse_r1: %.3f mse_r2: %.3f' %(mse_r1, mse_r2)
