我开发了一个用于多标签分类的文本模型。这OneVsRest分类器 http://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.htmlLinearSVC模型使用sklearnsPipeline and FeatureUnion用于模型准备。
主要输入功能由一个名为的文本列组成response还有 5 个主题概率（从之前的 LDA 主题模型生成），称为t1_prob - t5_prob预测 5 个可能的标签。管道中还有其他特征创建步骤用于生成TfidfVectorizer.
我最终用以下方式调用每一列项目选择器 http://scikit-learn.org/stable/auto_examples/hetero_feature_union.html并对这些主题概率列分别执行 ArrayCaster（函数定义请参阅下面的代码）5 次。有没有更好的使用方法特征联盟 http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion选择管道中的多个列？ （所以我不必做5次）
我想知道是否有必要复制topic1_feature -topic5_feature代码或者是否可以以更简洁的方式选择多列？
我输入的数据是 Pandas 数据帧：
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 是response以及 5 个主题概率列（t1_prob、t2_prob、t3_prob、t4_prob、t5_prob）。
y_train 是 5label我称之为的专栏.values返回 DataFrame 的 numpy 表示。 （标签_1、标签_2、标签3、标签_4、标签_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绕，因为 FeatureUnion 在组合二维数组时只会处理它们，所以像 DataFrame 这样的任何其他类型对我来说都是有问题的。然而，这个例子是有效的——我只是在寻找改进它并使其更加干燥的方法。
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)
我将 ArrayCaster 合并到这个过程中就是源于此answer https://stackoverflow.com/questions/25795511/unable-to-use-featureunion-in-scikit-learn-due-to-different-dimensions.
