密钥错误： "None of [Int64Index([112, 113,..121,n .n 58, 559],n dtype='int64', length=448)] are i

Question

我使用极限学习机(ELM)模型进行预测。我使用K-fold来验证模型预测。但是在执行以下代码之后，我得到了这个消息错误:

KeyError: "None of [Int64Index([112, 113, 114, 115, 116, 117, 118, 119, 120, 121,n            ...n            550, 551, 552, 553, 554, 555, 556, 557, 558, 559],n           dtype='int64', length=448)] are in the [columns]"

我该如何解决这个问题?怎么了?代码:

dataset = pd.read_excel("un.xls")

X=dataset.iloc[:,:-1]
y=dataset.iloc[:,-1:]


#----------Scaler----------
scaler = MinMaxScaler()
scaler_X = MinMaxScaler()
X=scaler.fit_transform(X)

#---------------------- Divided the datset----------------------

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2)

# Splits dataset into k consecutive folds (without shuffling by default).

kfolds = KFold(n_splits=5, random_state=16, shuffle=False)   
for train_index, test_index in kfolds.split(X_train, y_train):
X_train_folds, X_test_folds = X_train[train_index], X_train[test_index]
y_train_folds, y_test_folds = y_train[train_index], y_train[test_index]

# put all code in the for loop so that for every set of (X_train_folds, y_train_folds), the model is fitted.
# call predict() for corresponding set of X_test_folds
# put all code in the for loop so that for every set of (X_train_folds, y_train_folds), the model is fitted.
# call predict() for corresponding set of X_test_folds

#----------------------------(input size)-------------
input_size = X_train.shape[1]
hidden_size = 23
#---------------------------(To fix the RESULT)-------
seed =22   # can be any number, and the exact value does not matter
np.random.seed(seed)
#---------------------------(weights & biases)------------
input_weights = np.random.normal(size=[input_size,hidden_size])
biases = np.random.normal(size=[hidden_size])
#----------------------(Activation Function)----------
def relu(x):
return np.maximum(x, 0, x)
#--------------------------(Calculations)----------
def hidden_nodes(X):
G = np.dot(X, input_weights)
G = G + biases
H = relu(G)
return H
#Output weights 
output_weights = np.dot(pinv2(hidden_nodes(X_train)), y_train)

#------------------------(Def prediction)---------
def predict(X):
out = hidden_nodes(X)
out = np.dot(out, output_weights)
return out
#------------------------------------(Make_PREDICTION)--------------
prediction = predict(X_test_folds)

消息错误:

引发KeyError(f"None of [{key}] are in the [{axis_name}]")

KeyError: "None of [Int64Index([112, 113, 114, 115, 116, 117, 118, 119, 120, 121，n…]n 55,551, 552, 553, 554, 555, 556, 557, 558, 559]，n dtype='int64'， length=448)]都在[columns]">

Answer 1

您应该使用train_test_split()或KFold()中的任何一个来分割数据。Not the Both

如KFold()文档所述:

您应该在KFold.split()中只使用X。所以用这个:

kfolds = KFold(n_splits=5, random_state=16, shuffle=False)   
for train_index, test_index in kfolds.split(X):
X_train_folds, X_test_folds = X[train_index], X[test_index]
y_train_folds, y_test_folds = y[train_index], y[test_index]

同时，擦除所有的X_train和y_train，因为它是不需要的。

input_size = X.shape[1]
def relu(x):
return np.maximum(x, 0)
output_weights = np.dot(pinv2(hidden_nodes(X_train_folds)), y_train_folds)

如果代码仍然因为KFold()导致错误，则应考虑使用train_test_split()，并将KFold()的列、测试变量替换为train_test_split()的变量

Fortrain_test_split():

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2)
input_size = X_train.shape[1]
def relu(x):
return np.maximum(x, 0)
output_weights = np.dot(pinv2(hidden_nodes(X_train)), y_train)
prediction = predict(X_test)