def qwk_score(est, features, labels):
raw_pred = est.predict(features)
pred = preds_to_rank(raw_pred, np.min(labels), np.max(labels))
return quadratic_weighted_kappa(labels, pred)
if i % FLAGS.evaluation_interval == 0 or i == FLAGS.epochs:
# test on training data
train_preds = []
for start in range(0, n_train, test_batch_size):
end = min(n_train, start+test_batch_size)
#batched_memory = []
#for _ in range(end-start):
# batched_memory.append(memory)
batched_memory = [memory] * (end-start)
preds, _ = test_step(trainE[start:end], batched_memory)
for ite in preds:
train_preds.append(ite)
train_preds = np.add(train_preds, min_score)
#train_kappp_score = kappa(train_scores, train_preds, 'quadratic')
train_kappp_score = quadratic_weighted_kappa(
train_scores, train_preds, min_score, max_score)
# test on eval data
eval_preds = []
for start in range(0, n_eval, test_batch_size):
end = min(n_eval, start+test_batch_size)
#batched_memory = []
#for _ in range(end-start):
# batched_memory.append(memory)
batched_memory = [memory] * (end-start)
preds, _ = test_step(evalE[start:end], batched_memory)
for ite in preds:
eval_preds.append(ite)
eval_preds = np.add(eval_preds, min_score)
#eval_kappp_score = kappa(eval_scores, eval_preds, 'quadratic')
m.eval()
result = []
itrator = iter(test_dl)
while True:
try:
x, y = next(itrator)
#print (x)
m.eval()
out = m(V(x))
result.append(F.sigmoid(out).cpu().data.numpy())
#print (result)
except:
break
#print (result)
predictions = []
for ix in result:
for iy in ix:
predictions.append(10 * iy + 2)
predictions = np.rint(np.stack(predictions))
return np.reshape(predictions, (-1))
df_test = pd.read_csv(f'data/essay1/test.csv', header=None)
ds_test = pd.DataFrame({0: 0, 1: df_test[1]})
ans = predict(m, ds_test)
print(quadratic_weighted_kappa(df_test[0].values, ans))
#df_test[4]=ans
#df_test.to_csv('data/task1_results.csv',header=None,index=False)
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=200, batch_size=50, verbose=0)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print("Wider: %.2f (%.2f) MSE" % (results.mean(), results.std()))
#prediction
# X_new =scaler.fit_transform(X_new)
pipeline.fit(X,Y)
ynew = pipeline.predict(X_new)
# show the inputs and predicted outputs
ynew=np.around(ynew, decimals=0)
for i in range(len(ynew)):
print("X=%s, Predicted=%s" % (Y_new[i], ynew[i]))
# pred=pred.tolist()
# Y_new=Y_new.tolist()
qwk = quadratic_weighted_kappa(Y_new,ynew)
print(qwk)
end = time.time()
print(end - start)
def get_metric(preds,targs):
targs=targs.cpu().numpy()
preds=np.reshape(np.rint(F.sigmoid(V(preds)).data.cpu().numpy()*info[1] + info[0]),-1)
return quadratic_weighted_kappa(targs,preds)