变换预测目标

Question

我有一个数据集，其中每个观察结果可能属于不同的标签（多标签分类）。

我已经对其及其工作进行了 SVM 分类。 （这里我对查看每个类的准确性很感兴趣，所以我为每个类应用了OneVsRestClassifier，正如您将在代码中看到的那样。）

我想查看测试数据中每个项目的预测值。换句话说，我想看看模型在测试样本中的每次观察预测了哪个标签。

例如： 这是传递给模型进行预测的数据

,sentences,ADR,WD,EF,INF,SSI,DI,others
0,"extreme weight gain, short-term memory loss, hair loss.",1,0,0,0,0,0,0
1,I am detoxing from Lexapro now.,0,0,0,0,0,0,1
2,I slowly cut my dosage over several months and took vitamin supplements to help.,0,0,0,0,0,0,1
3,I am now 10 days completely off and OMG is it rough.,0,0,0,0,0,0,1
4,"I have flu-like symptoms, dizziness, major mood swings, lots of anxiety, tiredness.",0,1,0,0,0,0,1
5,I have no idea when this will end.,1,0,0,0,0,0,1

那么我的模型已经预测了这些行的标签，我想查看每一行的预测映射。

我知道我们可以在 scikit-learn 库中使用 Label Binarization 来做到这一点。

问题是fit_transform 的输入参数解释here 与我准备并传递给SVM 分类的目标数据不同。 所以我不知道如何弄清楚。

这是我的代码：

df = pd.read_csv("finalupdatedothers.csv")
categories = ['ADR','WD','EF','INF','SSI','DI','others']

train,test = train_test_split(df,random_state=42,test_size=0.3,shuffle=True)
X_train = train.sentences
X_test = test.sentences

SVC_pipeline = Pipeline([
                ('tfidf', TfidfVectorizer(stop_words=stop_words)),
                ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
            ])

for category in categories:
    print('... Processing {} '.format(category))
    SVC_pipeline.fit(X_train,train[category]
    prediction = SVC_pipeline.predict(X_test)
    print('SVM Linear Test accuracy is {} '.format(accuracy_score(test[category], prediction)))
    print 'SVM Linear f1 measurement is {} '.format(f1_score(test[category], prediction, average='weighted'))
    print "\n"

感谢您的宝贵时间。

Answer 1

这就是你想要的，我刚刚做的是，我映射了prediction，这是一个 numpy 数组，表示你的categories 列表中的类标签索引。所以这里是完整的代码。

import pandas as pd
import numpy as np
from sklearn import svm
from sklearn.datasets import samples_generator
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.multiclass import OneVsRestClassifier

from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
df = pd.read_csv("finalupdatedothers.csv")
categories = ['ADR','WD','EF','INF','SSI','DI','others']

train,test = train_test_split(df,random_state=42,test_size=0.3,shuffle=True)
X_train = train.sentences
X_test = test.sentences

SVC_pipeline = Pipeline([
                ('tfidf', TfidfVectorizer(stop_words=[])),
                ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
            ])


for category in categories:
    print('... Processing {} '.format(category))
    SVC_pipeline.fit(X_train,train[category])
    prediction = SVC_pipeline.predict(X_test)
    print([{X_test.iloc[i]:categories[prediction[i]]} for i in range(len(list(prediction)))  ])

    print('SVM Linear Test accuracy is {} '.format(accuracy_score(test[category], prediction)))
    print ('SVM Linear f1 measurement is {} '.format(f1_score(test[category], prediction, average='weighted')))
    print ("\n")

这是示例输出：

... Processing ADR 
[{'extreme weight gain, short-term memory loss, hair loss.': 'ADR'}, {'I am detoxing from Lexapro now.': 'ADR'}]
SVM Linear Test accuracy is 0.5 
SVM Linear f1 measurement is 0.3333333333333333 


... Processing WD 
[{'extreme weight gain, short-term memory loss, hair loss.': 'ADR'}, {'I am detoxing from Lexapro now.': 'ADR'}]
SVM Linear Test accuracy is 1.0 
SVM Linear f1 measurement is 1.0 

我希望这会有所帮助。