Question

我已经开发了用于多标签分类的文本模型。 OneVsRestClassifier LinearSVC模型使用sklearns Pipeline和FeatureUnion进行模型准备。

主要输入功能包括一个名为response的文本列，以及一个由t1_prob-t5_prob预测的5个可能标签的5个主题概率（从以前的LDA主题模型生成）。管道中还有其他功能创建步骤，用于生成TfidfVectorizer。

我最终用ItemSelector调用了每一列，并对这些主题概率列分别执行了5次ArrayCaster（有关功能定义，请参见下面的代码）。是否有更好的方法使用FeatureUnion选择管道中的多个列？（因此我不必做5次）

我想知道是否有必要复制topic1_feature-topic5_feature代码，或者是否可以更简洁地选择多个列？

我要输入的数据是Pandas dataFrame：

id response label_1 label_2 label3  label_4 label_5     t1_prob t2_prob t3_prob t4_prob t5_prob
1   Text from response...   0.0 0.0 0.0 0.0 0.0 0.0     0.0625  0.0625  0.1875  0.0625  0.1250
2   Text to model with...   0.0 0.0 0.0 0.0 0.0 0.0     0.1333  0.1333  0.0667  0.0667  0.0667  
3   Text to work with ...   0.0 0.0 0.0 0.0 0.0 0.0     0.1111  0.0938  0.0393  0.0198  0.2759  
4   Free text comments ...  0.0 0.0 1.0 1.0 0.0 0.0     0.2162  0.1104  0.0341  0.0847  0.0559

x_train为response，并包含5个主题概率列（t1_prob，t2_prob，t3_prob，t4_prob，t5_prob）。

y_train是5个label列，我在它们上面调用了.values以返回数据帧的numpy表示形式。（label_1，label_2，label3，label_4，label_5）

示例数据框：

import pandas as pd
column_headers = ["id", "response", 
                  "label_1", "label_2", "label3", "label_4", "label_5",
                  "t1_prob", "t2_prob", "t3_prob", "t4_prob", "t5_prob"]

input_data = [
    [1, "Text from response",0.0,0.0,1.0,0.0,0.0,0.0625,0.0625,0.1875,0.0625,0.1250],
    [2, "Text to model with",0.0,0.0,0.0,0.0,0.0,0.1333,0.1333,0.0667,0.0667,0.0667],
    [3, "Text to work with",0.0,0.0,0.0,0.0,0.0,0.1111,0.0938,0.0393,0.0198,0.2759],
    [4, "Free text comments",0.0,0.0,1.0,1.0,1.0,0.2162,0.1104,0.0341,0.0847,0.0559]
    ]

df = pd.DataFrame(input_data, columns = column_headers)
df = df.set_index('id')
df

我认为我的实现有点麻烦，因为FeatureUnion在组合它们时只能处理二维数组，因此其他任何类型（如DataFrame）对我来说都是成问题的。但是，此示例有效-我只是在寻找改进它并使它更干燥的方法。

from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.base import BaseEstimator, TransformerMixin

class ItemSelector(BaseEstimator, TransformerMixin):
    def __init__(self, column):
        self.column = column

    def fit(self, X, y=None):
        return self

    def transform(self, X, y=None):
        return X[self.column]

class ArrayCaster(BaseEstimator, TransformerMixin):
    def fit(self, x, y=None):
        return self

    def transform(self, data):
        return np.transpose(np.matrix(data))


def basic_text_model(trainX, testX, trainY, testY, classLabels, plotPath):
    '''OneVsRestClassifier for multi-label prediction''' 
pipeline = Pipeline([
    ('features', FeatureUnion([
            ('topic1_feature', Pipeline([
                ('selector', ItemSelector(column='t1_prob')),
                ('caster', ArrayCaster())
            ])),
            ('topic2_feature', Pipeline([
                ('selector', ItemSelector(column='t2_prob')),
                ('caster', ArrayCaster())
            ])),
            ('topic3_feature', Pipeline([
                ('selector', ItemSelector(column='t3_prob')),
                ('caster', ArrayCaster())
            ])),
            ('topic4_feature', Pipeline([
                ('selector', ItemSelector(column='t4_prob')),
                ('caster', ArrayCaster())
            ])),
            ('topic5_feature', Pipeline([
                ('selector', ItemSelector(column='t5_prob')),
                ('caster', ArrayCaster())
            ])),
           ('word_features', Pipeline([
                    ('vect', CountVectorizer(analyzer="word", stop_words='english')), 
                    ('tfidf', TfidfTransformer(use_idf = True)),
            ])),
     ])),
    ('clf', OneVsRestClassifier(svm.LinearSVC(random_state=random_state))) 
])

# Fit the model
pipeline.fit(trainX, trainY)
predicted = pipeline.predict(testX)

我从这个answer开始将ArrayCaster合并到流程中。

Answer 1

我使用了受{Marcus V针对此FunctionTransformer解决方案的启发question得出的答案。修改后的管道更加简洁。

from sklearn.preprocessing import FunctionTransformer

get_numeric_data = FunctionTransformer(lambda x: x[['t1_prob', 't2_prob', 't3_prob', 't4_prob', 't5_prob']], validate=False)

pipeline = Pipeline([
    ('features', FeatureUnion([
            ('numeric_features', Pipeline([
                ('selector', get_numeric_data)
            ])),
             ('word_features', Pipeline([
                ('vect', CountVectorizer(analyzer="word", stop_words='english')), 
                ('tfidf', TfidfTransformer(use_idf = True)),
            ])),
         ])),
           ('clf', OneVsRestClassifier(svm.LinearSVC(random_state=random_state))) 
     ])

如何使用sklearn Pipeline和FeatureUnion选择多个（数字和文本）列以进行文本分类？

1 个答案: