如何基于sklearn中的预测概率对实例进行排名 [英] How to rank the instances based on prediction probability in sklearn
问题描述
我正在使用sklearn的支持向量机（ SVC ）来使用 10-来获取我的实例在数据集中的预测概率，如下所示：折叠交叉验证。
I am using sklearn's support vector machine (SVC) as follows to get the prediction probability of my instances in my dataset as follows using 10-fold cross validation.
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
clf=SVC(class_weight="balanced")
proba = cross_val_predict(clf, X, y, cv=10, method='predict_proba')
print(clf.classes_)
print(proba[:,1])
print(np.argsort(proba[:,1]))
我的预期输出如下对于 print（proba [：，1]）和 print（np.argsort（proba [：，1]）），其中第一个指示类 1 的所有实例的预测概率，第二个指示数据实例的对应索引
My expected output is as follows for print(proba[:,1]) and print(np.argsort(proba[:,1])) where the first one indicates the prediction probability of all instances for class 1 and the second one indicates the corresponding index of the data instance for each probability.
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.1 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.2 0. 0. 0. 0. 0.1 0. 0. 0. 0. 0. 0. 0. 0. 0.9 1. 0.7 1.
1. 1. 1. 0.7 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.9 0.9 0.1 1.
0.6 1. 1. 1. 0.9 0. 1. 1. 1. 1. 1. 0.4 0.9 0.9 1. 1. 1. 0.9
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0.9 0.
0.1 0. 0. 0. 0. 0. 0. 0. 0.1 0. 0. 0.8 0. 0.1 0. 0.1 0. 0.1
0.3 0.2 0. 0.6 0. 0. 0. 0.6 0.4 0. 0. 0. 0.8 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. ]
[ 0 113 112 111 110 109 107 105 104 114 103 101 100 77 148 49 48 47
46 102 115 117 118 147 146 145 144 143 142 141 140 139 137 136 135 132
131 130 128 124 122 120 45 44 149 42 15 26 16 17 18 19 20 21
22 43 23 24 35 34 33 32 31 30 29 28 27 37 13 25 9 10
7 6 5 4 3 8 11 2 1 38 39 40 12 108 116 41 121 70
14 123 125 36 127 126 134 83 72 133 129 52 57 119 138 89 76 50
84 106 85 69 68 97 98 66 65 64 63 62 61 67 60 58 56 55
54 53 51 59 71 73 75 96 95 94 93 92 91 90 88 87 86 82
81 80 79 78 99 74]
我的第一个问题是；似乎 SVC 不支持 predict_proba 。因此，如果我使用 proba = cross_val_predict（clf，X，y，cv = 10，method ='decision_function'）是否正确？
My first question is; it seems like SVC does not support predict_proba. Therefore, is it correct if I use proba = cross_val_predict(clf, X, y, cv=10, method='decision_function') instead?
我的第二个问题是如何打印预测概率类别？我尝试了 clf_classes _ 。但是，我收到一个错误，提示 AttributeError：'SVC'对象没有属性'classes _'。有解决方法吗？
My second question is how to print the classes of prediction probability? I tried clf_classes_. But, I get an error saying AttributeError: 'SVC' object has no attribute 'classes_'. Is there a way to resolve this issue?
注意：我想使用交叉验证来获得所有实例的预测概率。
Note: I want to get the prediction probability for all the instances using cross validation.
编辑：
@KRKirov的回答很好。但是，我不需要 GridSearchCV ，只想使用普通的交叉验证。因此，我使用 cross_val_score 更改了他的代码。现在，我遇到错误 NotFittedError：在预测之前先进行拟合。
The answer of @KRKirov is great. However, I do not need GridSearchCV and only want to use normal cross validation. Therefore, I changed his code use cross_val_score. Now, I am getting the error NotFittedError: Call fit before prediction.
有没有解决此问题的方法？
Is there a way to resolve this issue?
如果需要，我很乐意提供更多详细信息。
I am happy to provide more details if needed.
推荐答案
Cross_val预测是一个不会在输出中返回分类器（在您的情况下为SVC）的函数。因此，您将无法访问后者及其方法和属性。
Cross_val predict is a function which does not return the classifier (in your case the SVC) as part of its output. Therefore you don't get access to the latter and its methods and attributes.
要执行交叉验证并计算概率，请使用scikit-learn的GridSearchCV或RandomizedSearchCV。如果只想进行简单的交叉验证，请传递仅包含一个参数的参数字典。一旦有了概率，就可以使用pandas或numpy根据特定类别对它们进行排序（在下面的示例中为1）。
To perform cross-validation and calculate probabilities use scikit-learn's GridSearchCV or RandomizedSearchCV. If you want just a simple cross-validation, pass a parameter dictionary with only one parameter. Once you have the probabilities you can use either pandas or numpy to sort them according to a particular class (1 in the example below).
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from sklearn import datasets
import pandas as pd
import numpy as np
iris = datasets.load_iris()
X = iris.data
y = iris.target
parameters = {'kernel':(['rbf'])}
svc = SVC(gamma="scale", probability=True)
clf = GridSearchCV(svc, parameters, cv=10)
clf.fit(iris.data, iris.target)
probabilities = pd.DataFrame(clf.predict_proba(X), columns=clf.classes_)
probabilities['Y'] = iris.target
probabilities.columns.name = 'Classes'
probabilities.head()
# Sorting in ascending order by the probability of class 1.
# Showing only the first five rows.
# Note that all information (indices, values) is in one place
probabilities.sort_values(1).head()
Out[49]:
Classes 0 1 2 Y
100 0.006197 0.000498 0.993305 2
109 0.009019 0.001023 0.989959 2
143 0.006664 0.001089 0.992248 2
105 0.010763 0.001120 0.988117 2
144 0.006964 0.001295 0.991741 2
# Alternatively using numpy
indices = np.argsort(probabilities.values[:,1])
proba = probabilities.values[indices, :]
print(indices)
[100 109 143 105 144 122 135 118 104 107 102 140 130 117 120 136 132 131
128 124 125 108 22 148 112 13 115 14 32 37 33 114 35 40 16 4
42 103 2 0 6 36 139 19 145 38 17 47 48 28 49 15 46 129
10 21 7 27 12 39 8 11 1 3 9 45 34 116 29 137 5 31
26 30 141 43 18 111 25 20 41 44 24 23 147 134 113 101 142 110
146 121 149 83 123 127 77 119 133 126 138 70 72 106 52 76 56 86
68 63 54 98 50 84 66 85 78 91 73 51 57 58 93 55 87 75
65 79 90 64 61 60 97 74 94 59 96 81 88 53 95 99 89 80
71 82 69 92 67 62]
# Showing only the first five values of the sorted probabilities for class 1
print(proba[:5, 1])
[0.00049785 0.00102258 0.00108851 0.00112034 0.00129501]
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