Question

我已经训练了一个SVM模型，并尝试创建一个混淆矩阵来对其进行评估。因此，我将对测试数据进行预测，并将预测结果与测试数据的目标类别进行比较。

我大约有1000条数据记录，而Test数据则是近300条数据记录。我定义了九个类/标签。

特性从-1标准化为1，并且均为浮点型。数组A的一行代表每个数据记录，目标类存储在数组B中。我将这些阵列按70:30的比例分为训练数据和测试数据。

这是一个简单的代码，但是我现在没有任何想法。一种可能是对测试数据的每个数据记录进行预测和混淆矩阵，然后将结果存储在列表中。遍历所有数据记录后，我可以建立所有存储元素的均值吗？

有人知道如何解决我的问题吗？

# -*- coding: utf-8 -*-
"""
Created on Fri Apr  5 10:50:47 2019

@author: mattdoe
"""

from data_preprocessor_db import data_storage # validation data
from sklearn.preprocessing import MinMaxScaler
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from numpy import array
import pickle



# for seperation of data_storage
# Link_ID = list()
Input, Output = list(), list()

# seperate data_storage in Input and Output data
for items in data_storage:
    # Link_ID = items[0] # identifier not needed
    Input.append((float(items[1]), float(items[2]), float(items[3]), float(items[4]), float(items[5]), float(items[6]), float(items[7]), float(items[8]), float(items[9]))) # Input: all characteristics
    Output.append(float(items[10])) # Output: scenario_class 1 to 9

# Input tuple to array
A = array(Input)

# normalise array between 0 and 1
scaler = MinMaxScaler(feature_range=(-1, 1))
scaledA = scaler.fit_transform(A)

# Output tuple to array
B = array(Output)

# split train and test data; ration: 70:30
# shuffle = False: doesn't sort data randomly
# shuffle = True: default: sorts data randomly
A_train, A_test, B_train, B_test = train_test_split(A, B, test_size=0.3, shuffle=True, random_state=40)

# create model
model = svm.SVC(kernel='linear', C = 1.0)

# fit model
model.fit(A_train, B_train)

# get support vectors
# model.support_vectors_

# get indices of support vectors
# model.support_

# get number of support vectors for each class
# model.n_support_

filename = 'ml_svm.sav'
pickle.dump(model, open(filename, 'wb'))

# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))

# test to all data records
# result = loaded_model.score(A, B)

# test with test data
# score represents the mean accuracy of given test data and labels
result = loaded_model.score(A_test, B_test) # relative 
print(result)

# confusion matrix compares true value with predicted value
# true value <--> predicted value
predicted = model.predict(A_test)
tn, fp, fn, tp = confusion_matrix(B_test, predicted, labels=[1, 2, 3, 4, 5, 6, 7, 8, 9]).ravel()

我的错误：

Traceback (most recent call last):

  File "<ipython-input-8-8649dd873bbd>", line 1, in <module>
    runfile('C:/Workspace/Master-Thesis/Programm/MapValidationML/ml_svm.py', wdir='C:/Workspace/Master-Thesis/Programm/MapValidationML')

  File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
    execfile(filename, namespace)

  File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Workspace/Master-Thesis/Programm/MapValidationML/ml_svm.py", line 75, in <module>
    tn, fp, fn, tp = confusion_matrix(B_test, predicted, labels=[1, 2, 3, 4, 5, 6, 7, 8, 9]).ravel()

ValueError: too many values to unpack (expected 4)

Answer 1

感谢elgordorafiki。使用confusion_vector = confusion_matrix（...）的解决方案效果很好。

没有.ravel（），我现在收到一个9x9矩阵。

对角线上的结果是否全部正确，对角线所有不正确的值？那么每一列每一行代表一个类吗？哪些是预科课程？列还是行？

我如何理解结果？

我的结果如下：

[[ 35   1   0   0   0   0   0   0   0]
 [  0 177   0   0   0   0   0   0   0]
 [  3   2   0   0   0   0   0   0   0]
 [  2   3   0   0   0   0   0   0   0]
 [  0   0   0   0   5   0   0   0   0]
 [  0   0   0   0   0   8   0   0   0]
 [  0   0   0   0   0   0   3   0   0]
 [  0   0   0   0   0   0   0   7   0]
 [  4   6   0   0   1   1   1   0  14]]

就我而言，第3和第4类似乎与第1和2类有关。

svm confusion_matrix'太多值无法解压'

1 个答案: