python - 神经网络数据标准化引发 ValueError : Input contains NaN, 无穷大或值对于 float64 来说太大

Question

我的数据集具有指数形式的值，如下所示:

2.15E-06    -0.000556462    0.000197385 -0.000919   -0.000578077....

这是代码:

    ####----Data----####
quest=pd.read_csv("inputFile.csv", names=["A1","A2",..."A200","Sim"])
print(quest.head())
    ####----Set up Data and Label----####
X=quest.drop('Sim',axis=1)
y=quest['Sim']
    ####----Train Test Split----####
X_train, X_test, y_train, y_test = train_test_split(X, y)
np.isfinite(X_train).any(), np.isfinite(y_train).any(),np.isfinite(X_test).any()
np.isnan(X_train).any(), np.isnan(y_train).any(), np.isnan(X_test).any()
    ####----Data Pre-Processing----####
scaler=StandardScaler()
 # Fit only to the training data
X_scaled=scaler.fit(X_train)
 # Now apply the transformations to the data:
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
    ####----Training the Model----####
mlp=MLPClassifier(hidden_layer_sizes=(13,13,13), max_iter=500)
mlp.fit(X_train,y_train)
print(mlp)
    ####----Predictions and Evaluation----####
predictions=mlp.predict(X_test)

print(confusion_matrix(y_test,predictions))
print(classification_report(y_test,predictions))

我收到此错误:

回溯(最近一次调用最后一次): 文件“E:\thesis\sk-ANN.py”，第 67 行，位于

X_scaled=scaler.fit(X_train)...
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

拟合和预测模型“mlp.fit(X_train,y_train”和“predictions=mlp.predict(X_test)”时会出现相同的错误

如何消除此错误？

Answer 1

我认为你需要预处理你的数据集。您可以将 Nan 值替换为一些有意义的值。一些有用的答案可以在这里看到 - numpy array: replace nan values with average of columns