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

Question

我已经用 sklearn 建立了一个文本分类模型，DecisionTreeClassifier并想添加另一个预测器。我的数据位于 pandas 数据框中，其中的列标记为'Impression'（文本）、'Volume'（浮点数）和'Cancer'（标签）。我一直只使用印象来预测癌症，但想使用印象和体积来预测癌症。

我之前运行没有问题的代码：

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（更改为粗体）：

1）只有fit_transform印象

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) 调用fit_transform印象数和销量。与上面相同的代码，除了fit_transform行：

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,)

我很确定方法 #1 是正确的方法，但我无法找到任何教程或解决方案来说明如何将浮点预测器添加到此文本分类模型中。

任何帮助，将不胜感激！

标签： pythonmachine-learningscikit-learndecision-tree

Answer 1

ColumnTransformer()正好解决这个问题。我们可以将参数设置为in ，而不是手动将输出附加到CountVectorizer其他列中。remainderpassthroughColumnTransformer

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（display参数 in set_config）