python - 带有 CountVectorizer 和附加预测器的 sklearn DecisionTreeClassifier

Question

我已经使用 sklearn 的 DecisionTreeClassifier 构建了一个文本分类模型，并想添加另一个预测器。我的数据位于 pandas 数据框中，列标记为 'Impression'(文本)、'Volume'( float )和 'Cancer'(标签)。我一直只使用印象来预测癌症，但想改用印象和体积来预测癌症。

我之前运行的代码没有问题:

X_train, X_test, y_train, y_test = train_test_split(data['Impression'], data['Cancer'], test_size=0.2)

vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)

dt = DecisionTreeClassifier(class_weight='balanced', max_depth=6, min_samples_leaf=3, max_leaf_nodes=20)
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)

我尝试了几种不同的方法来添加音量预测器(更改以粗体显示):

1) 仅fit_transform Impressions

X_train, X_test, y_train, y_test = train_test_split(data[['Impression', 'Volume']], data['Cancer'], test_size=0.2)

vectorizer = CountVectorizer()
X_train['Impression'] = vectorizer.fit_transform(X_train['Impression'])
X_test = vectorizer.transform(X_test)

dt = DecisionTreeClassifier(class_weight='balanced', max_depth=6, min_samples_leaf=3, max_leaf_nodes=20)
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)

这会引发错误

TypeError: float() argument must be a string or a number, not 'csr_matrix'
...
ValueError: setting an array element with a sequence.

2) 对 Impressions 和 Volumes 调用 fit_transform。除了 fit_transform 行之外，与上面的代码相同:

X_train = vectorizer.fit_transform(X_train)

这当然会引发错误:

ValueError: Number of labels=1800 does not match number of samples=2
...
X_train.shape
(2, 2)
y_train.shape
(1800,)

我非常确定方法 #1 是正确的方法，但我找不到任何教程或解决方案来说明如何将 float 预测器添加到此文本分类模型。

如有任何帮助，我们将不胜感激!

Answer 1

ColumnTransformer()将完全解决这个问题。我们可以在 ColumnTransformer 中将 remainder 参数设置为 passthrough，而不是手动将 CountVectorizer 的输出附加到其他列>。

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.compose import make_column_transformer
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
from sklearn import set_config

set_config(print_changed_only='True', display='diagram')

data = pd.DataFrame({'Impression': ['this is the first text',
                                    'second one goes like this',
                                    'third one is very short',
                                    'This is the final statement'],
                     'Volume': [123, 1, 2, 123],
                     'Cancer': [1, 0, 0, 1]})

X_train, X_test, y_train, y_test = train_test_split(
    data[['Impression', 'Volume']], data['Cancer'], test_size=0.5)

ct = make_column_transformer(
    (CountVectorizer(), 'Impression'), remainder='passthrough')

pipeline = make_pipeline(ct, DecisionTreeClassifier())
pipeline.fit(X_train, y_train)
pipeline.score(X_test, y_test)

使用0.23.0版本，查看管道对象的视觉效果(set_config中的display参数)