我已经使用PyTorch神经网络一段时间了。我决定添加一个排列特征重要性评分器，这开始引起一些问题。
我得到了">TypeError：如果未指定评分，则传递的估计器应具有"评分"方法。估计器<类'skarch.net.NeuralNet'>[未初始化](模块=<class'main.run..MultiLayerPredictor'>，)没有"-错误消息。这是我的代码：
class MultiLayerPredictor(torch.nn.Module):
def __init__(self, input_shape=9152, output_shape=1, hidden_dim=1024, **kwargs):
super().__init__()
self.fc1 = torch.nn.Linear(in_features=input_shape, out_features=hidden_dim)
self.fc2 = torch.nn.Linear(in_features=hidden_dim, out_features=hidden_dim)
self.fc3 = torch.nn.Linear(in_features=hidden_dim, out_features=output_shape)
def forward(self, x):
l1 = torch.relu(self.fc1(x))
l2 = torch.relu(self.fc2(l1))
return torch.sigmoid(self.fc3(l2)).reshape(-1)
print("Moving to wrapping the neural net")
net = NeuralNet(
MultiLayerPredictor,
criterion=nn.MSELoss,
max_epochs=10,
optimizer=optim.Adam,
lr=0.1,
iterator_train__shuffle=True
)
print("Moving to finding optimal hyperparameters")
lr = (10**np.random.uniform(-5,-2.5,1000)).tolist()
params = {
'optimizer__lr': lr,
'max_epochs':[300,400,500],
'module__num_units': [14,20,28,36,42],
'module__drop' : [0,.1,.2,.3,.4]
}
gs = RandomizedSearchCV(net,params,refit=True,cv=3,scoring='neg_mean_squared_error',n_iter=100)
gs.fit(X_train_scaled,y_train);
def report(results, n_top=3):
for i in range(1, n_top + 1):
candidates = np.flatnonzero(results['rank_test_score'] == i)
for candidate in candidates:
print("Model with rank: {0}".format(i))
print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
results['mean_test_score'][candidate],
results['std_test_score'][candidate]))
print("Parameters: {0}".format(results['params'][candidate]))
print("")
print(report(gs.cv_results_,10))
epochs = [i for i in range(len(gs.best_estimator_.history))]
train_loss = gs.best_estimator_.history[:,'train_loss']
valid_loss = gs.best_estimator_.history[:,'valid_loss']
plt.plot(epochs,train_loss,'g-');
plt.plot(epochs,valid_loss,'r-');
plt.title('Training Loss Curves');
plt.xlabel('Epochs');
plt.ylabel('Mean Squared Error');
plt.legend(['Train','Validation']);
plt.show()
r = permutation_importance(net, X_test, y_test, n_repeats=30,random_state=0)
for i in r.importances_mean.argsort()[::-1]:
if r.importances_mean[i] - 2 * r.importances_std[i] > 0:
print(f"{metabolites.feature_names[i]:<8}"
f"{r.importances_mean[i]:.3f}"
f" +/- {r.importances_std[i]:.3f}")
y_pred_acc = gs.predict(X_test)
print('Accuracy : ' + str(accuracy_score(y_test,y_pred_acc)))
Stacktrace会指出，错误源于我设置排列重要性的那一行。我该怎么解决这个问题？
完整堆叠：
*Traceback (most recent call last):
File "//ad..fi/home/h//Desktop/neuralnet/neuralnet_wrapped.py", line 141, in <module>
run()
File "//ad..fi/home/h//Desktop/neuralnet/neuralnet_wrapped.py", line 119, in run
r = permutation_importance(net, X_test, y_test,
File "C:Users\AppDataRoamingPythonPython38site-packagessklearnutilsvalidation.py", line 73, in inner_f
return f(**kwargs)
File "C:Users\AppDataRoamingPythonPython38site-packagessklearninspection_permutation_importance.py", line 132, in permutation_importance
scorer = check_scoring(estimator, scoring=scoring)
File "C:Users\AppDataRoamingPythonPython38site-packagessklearnutilsvalidation.py", line 73, in inner_f
return f(**kwargs)
File "C:Users\AppDataRoamingPythonPython38site-packagessklearnmetrics_scorer.py", line 425, in check_scoring
raise TypeError(
TypeError: If no scoring is specified, the estimator passed should have a 'score' method. The estimator <class 'skorch.net.NeuralNet'>[uninitialized](
module=<class '__main__.run.<locals>.MultiLayerPredictor'>,
) does not.*
来自文档：
NeuralNet仍然没有评分方法。如果你需要它，你必须自己实施它。
这就是问题所在。正如错误所说，NeuralNet没有score方法。文件中说"；你必须自己实施它"；。你也可以通过查看源代码来检查。
正如Berriel所说，这是失败的，因为您的神经网络实例没有实现score()方法。这是默认值，因为不清楚任意学习任务应返回的分数。
这种情况也发生在sklearn网格搜索中，您通过传递scoring='neg_mean_squared_error'来避免这种情况。你也可以在这里这样做：
r = permutation_importance(net, X_test, y_test,
scoring='neg_mean_squared_error', n_repeats=30, random_state=0)
或者，因为你也需要在其他地方得分，你可以自己实现score方法：
class MyNet(NeuralNetwork):
def score(self, X, y):
y = self.predict(X)
return sklearn.metrics.mean_squared_error(y, y_pred)