使用神经网络和ROCR包绘制神经网络曲线(Plot a Neural Net Curve Using neuralnet and ROCR package)

这里我有一个分类任务，我需要使用神经网络和ROCR包。问题是我在使用预测功能时收到错误消息。

这是我的代码：

#load packages require(neuralnet) library(ROCR) #create data set train<-read.table(file="train.txt",header=TRUE,sep=",") test<- read.table(file="test.txt",header=TRUE,sep=",") #build model and make predictions nn.sag <- neuralnet(Type ~ Area+Perimeter+Compactness+Length+Width+Asymmetry+Groove, data = train, hidden = 5, algorithm = "sag", err.fct = "sse", linear.output = FALSE) prob = compute(nn.sag, test[, -ncol(test)] ) prob.result <- prob$net.result nn.pred = prediction(prob.result, test$Type) pref <- performance(nn.pred, "tpr", "fpr") plot(pref)

在这里我得到了'预测'函数的错误信息：'$运算符对原子向量无效'

数据集看起来像（这里只有训练数据集）：

Area,Perimeter,Compactness,Length,Width,Asymmetry,Groove,Type 14.8,14.52,0.8823,5.656,3.288,3.112,5.309,1 14.79,14.52,0.8819,5.545,3.291,2.704,5.111,1 14.99,14.56,0.8883,5.57,3.377,2.958,5.175,1 19.14,16.61,0.8722,6.259,3.737,6.682,6.053,0 15.69,14.75,0.9058,5.527,3.514,1.599,5.046,1 14.11,14.26,0.8722,5.52,3.168,2.688,5.219,1 13.16,13.55,0.9009,5.138,3.201,2.461,4.783,1 16.16,15.33,0.8644,5.845,3.395,4.266,5.795,0 15.01,14.76,0.8657,5.789,3.245,1.791,5.001,1 14.11,14.1,0.8911,5.42,3.302,2.7,5,1 17.98,15.85,0.8993,5.979,3.687,2.257,5.919,0 21.18,17.21,0.8989,6.573,4.033,5.78,6.231,0 14.29,14.09,0.905,5.291,3.337,2.699,4.825,1 14.59,14.28,0.8993,5.351,3.333,4.185,4.781,1 11.42,12.86,0.8683,5.008,2.85,2.7,4.607,1 12.11,13.47,0.8392,5.159,3.032,1.502,4.519,1 15.6,15.11,0.858,5.832,3.286,2.725,5.752,0 15.38,14.66,0.899,5.477,3.465,3.6,5.439,0 18.94,16.49,0.875,6.445,3.639,5.064,6.362,0 12.36,13.19,0.8923,5.076,3.042,3.22,4.605,1 14.01,14.29,0.8625,5.609,3.158,2.217,5.132,1 17.12,15.55,0.8892,5.85,3.566,2.858,5.746,0 15.78,14.91,0.8923,5.674,3.434,5.593,5.136,1 16.19,15.16,0.8849,5.833,3.421,0.903,5.307,1 14.43,14.4,0.8751,5.585,3.272,3.975,5.144,1 13.8,14.04,0.8794,5.376,3.155,1.56,4.961,1 14.46,14.35,0.8818,5.388,3.377,2.802,5.044,1 18.59,16.05,0.9066,6.037,3.86,6.001,5.877,0 18.75,16.18,0.8999,6.111,3.869,4.188,5.992,0 15.49,14.94,0.8724,5.757,3.371,3.412,5.228,1 12.73,13.75,0.8458,5.412,2.882,3.533,5.067,1 13.5,13.85,0.8852,5.351,3.158,2.249,5.176,1 14.38,14.21,0.8951,5.386,3.312,2.462,4.956,1 14.86,14.67,0.8676,5.678,3.258,2.129,5.351,1 18.45,16.12,0.8921,6.107,3.769,2.235,5.794,0 17.32,15.91,0.8599,6.064,3.403,3.824,5.922,0 20.2,16.89,0.8894,6.285,3.864,5.173,6.187,0 20.03,16.9,0.8811,6.493,3.857,3.063,6.32,0 18.14,16.12,0.8772,6.059,3.563,3.619,6.011,0 13.99,13.83,0.9183,5.119,3.383,5.234,4.781,1 15.57,15.15,0.8527,5.92,3.231,2.64,5.879,0 16.2,15.27,0.8734,5.826,3.464,2.823,5.527,1 20.97,17.25,0.8859,6.563,3.991,4.677,6.316,0 14.16,14.4,0.8584,5.658,3.129,3.072,5.176,1 13.45,14.02,0.8604,5.516,3.065,3.531,5.097,1 15.5,14.86,0.882,5.877,3.396,4.711,5.528,1 16.77,15.62,0.8638,5.927,3.438,4.92,5.795,0 12.74,13.67,0.8564,5.395,2.956,2.504,4.869,1 14.88,14.57,0.8811,5.554,3.333,1.018,4.956,1 14.28,14.17,0.8944,5.397,3.298,6.685,5.001,1 14.34,14.37,0.8726,5.63,3.19,1.313,5.15,1 14.03,14.16,0.8796,5.438,3.201,1.717,5.001,1 19.11,16.26,0.9081,6.154,3.93,2.936,6.079,0 14.52,14.6,0.8557,5.741,3.113,1.481,5.487,1 18.43,15.97,0.9077,5.98,3.771,2.984,5.905,0 18.81,16.29,0.8906,6.272,3.693,3.237,6.053,0 13.78,14.06,0.8759,5.479,3.156,3.136,4.872,1 14.69,14.49,0.8799,5.563,3.259,3.586,5.219,1 18.85,16.17,0.9056,6.152,3.806,2.843,6.2,0 12.88,13.5,0.8879,5.139,3.119,2.352,4.607,1 12.78,13.57,0.8716,5.262,3.026,1.176,4.782,1 14.33,14.28,0.8831,5.504,3.199,3.328,5.224,1 19.46,16.5,0.8985,6.113,3.892,4.308,6.009,0 19.38,16.72,0.8716,6.303,3.791,3.678,5.965,0 15.26,14.85,0.8696,5.714,3.242,4.543,5.314,1 20.24,16.91,0.8897,6.315,3.962,5.901,6.188,0 19.94,16.92,0.8752,6.675,3.763,3.252,6.55,0 20.71,17.23,0.8763,6.579,3.814,4.451,6.451,0 16.17,15.38,0.8588,5.762,3.387,4.286,5.703,0 13.02,13.76,0.8641,5.395,3.026,3.373,4.825,1 16.53,15.34,0.8823,5.875,3.467,5.532,5.88,0 13.89,14.02,0.888,5.439,3.199,3.986,4.738,1 18.98,16.57,0.8687,6.449,3.552,2.144,6.453,0 17.08,15.38,0.9079,5.832,3.683,2.956,5.484,1 15.03,14.77,0.8658,5.702,3.212,1.933,5.439,1 16.14,14.99,0.9034,5.658,3.562,1.355,5.175,1 18.65,16.41,0.8698,6.285,3.594,4.391,6.102,0 20.1,16.99,0.8746,6.581,3.785,1.955,6.449,0 17.99,15.86,0.8992,5.89,3.694,2.068,5.837,0 15.88,14.9,0.8988,5.618,3.507,0.7651,5.091,1 13.22,13.84,0.868,5.395,3.07,4.157,5.088,1 18.3,15.89,0.9108,5.979,3.755,2.837,5.962,0 19.51,16.71,0.878,6.366,3.801,2.962,6.185,0

Here I have a classification task and I need to use neuralnet and ROCR packages. The problem is that I got the error messages when I use prediction function.

Here is my code:

And here I got the error message for the 'prediction' function: '$ operator is invalid for atomic vectors'

The dataset looks like (only training dataset here):

最满意答案

在R中的neuralnet和ROCR包中都可以使用prediction()函数。所以不要将两个包加载到一起。首先加载神经网络，训练你的模型，然后使用detach()分离它，然后加载ROCR包。请尝试以下代码：

#load packages require(neuralnet) #create data set train<-read.table(file="train.txt",header=TRUE,sep=",") test<- read.table(file="test.txt",header=TRUE,sep=",") #build model and make predictions nn.sag <- neuralnet(Type ~ Area+Perimeter+Compactness+Length+Width+Asymmetry+Groove, data = train, hidden = 5, algorithm = "sag", err.fct = "sse", linear.output = FALSE) prob = compute(nn.sag, test[, -ncol(test)] ) prob.result <- prob$net.result detach(package:neuralnet,unload = T) library(ROCR) nn.pred = prediction(prob.result, test$Type) pref <- performance(nn.pred, "tpr", "fpr") plot(pref)

The prediction() function is available in both neuralnet and ROCR package in R. So do not load both packages together. First load neuralnet, train your model and then detach it using detach() and then load ROCR package. Try following code:

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