具有异构数据源的列转换器#

数据集通常包含需要不同特征提取和处理管道的组件。这种情况可能发生在

您的数据集包含异构数据类型（例如栅格图像和文本标题），
您的数据集存储在 pandas.DataFrame 中，并且不同的列需要不同的处理管道。

此示例演示了如何在包含不同类型特征的数据集上使用 ColumnTransformer。特征的选择并不特别有用，但用于说明该技术。

# Author: Matt Terry <[email protected]>
#
# License: BSD 3 clause

import numpy as np

from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import PCA
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.svm import LinearSVC

20 个新闻组数据集#

我们将使用 20 个新闻组数据集，它包含来自 20 个主题的新闻组的帖子。此数据集根据特定日期之前和之后发布的消息分为训练集和测试集。为了加快运行时间，我们只使用来自 2 个类别的帖子。

categories = ["sci.med", "sci.space"]
X_train, y_train = fetch_20newsgroups(
    random_state=1,
    subset="train",
    categories=categories,
    remove=("footers", "quotes"),
    return_X_y=True,
)
X_test, y_test = fetch_20newsgroups(
    random_state=1,
    subset="test",
    categories=categories,
    remove=("footers", "quotes"),
    return_X_y=True,
)

每个特征都包含有关该帖子的元信息，例如主题和新闻帖子的正文。

print(X_train[0])

From: [email protected] (fred j mccall 575-3539)
Subject: Re: Metric vs English
Article-I.D.: mksol.1993Apr6.131900.8407
Organization: Texas Instruments Inc
Lines: 31

American, perhaps, but nothing military about it.  I learned (mostly)
slugs when we talked English units in high school physics and while
the teacher was an ex-Navy fighter jock the book certainly wasn't
produced by the military.

[Poundals were just too flinking small and made the math come out
funny; sort of the same reason proponents of SI give for using that.]

--
"Insisting on perfect safety is for people who don't have the balls to live
 in the real world."   -- Mary Shafer, NASA Ames Dryden

创建转换器#

首先，我们希望有一个转换器来提取每个帖子的主题和正文。由于这是一个无状态转换（不需要来自训练数据的状态信息），我们可以定义一个执行数据转换的函数，然后使用 FunctionTransformer 创建一个 scikit-learn 转换器。

def subject_body_extractor(posts):
    # construct object dtype array with two columns
    # first column = 'subject' and second column = 'body'
    features = np.empty(shape=(len(posts), 2), dtype=object)
    for i, text in enumerate(posts):
        # temporary variable `_` stores '\n\n'
        headers, _, body = text.partition("\n\n")
        # store body text in second column
        features[i, 1] = body

        prefix = "Subject:"
        sub = ""
        # save text after 'Subject:' in first column
        for line in headers.split("\n"):
            if line.startswith(prefix):
                sub = line[len(prefix) :]
                break
        features[i, 0] = sub

    return features


subject_body_transformer = FunctionTransformer(subject_body_extractor)

我们还将创建一个转换器来提取文本的长度和句子数量。

def text_stats(posts):
    return [{"length": len(text), "num_sentences": text.count(".")} for text in posts]


text_stats_transformer = FunctionTransformer(text_stats)

分类管道#

下面的管道使用 SubjectBodyExtractor 从每个帖子中提取主题和正文，生成一个 (n_samples, 2) 数组。然后使用 ColumnTransformer，此数组用于计算主题和正文的标准词袋特征，以及正文的文本长度和句子数量。我们将它们组合起来，并使用权重，然后在组合的特征集上训练分类器。

pipeline = Pipeline(
    [
        # Extract subject & body
        ("subjectbody", subject_body_transformer),
        # Use ColumnTransformer to combine the subject and body features
        (
            "union",
            ColumnTransformer(
                [
                    # bag-of-words for subject (col 0)
                    ("subject", TfidfVectorizer(min_df=50), 0),
                    # bag-of-words with decomposition for body (col 1)
                    (
                        "body_bow",
                        Pipeline(
                            [
                                ("tfidf", TfidfVectorizer()),
                                ("best", PCA(n_components=50, svd_solver="arpack")),
                            ]
                        ),
                        1,
                    ),
                    # Pipeline for pulling text stats from post's body
                    (
                        "body_stats",
                        Pipeline(
                            [
                                (
                                    "stats",
                                    text_stats_transformer,
                                ),  # returns a list of dicts
                                (
                                    "vect",
                                    DictVectorizer(),
                                ),  # list of dicts -> feature matrix
                            ]
                        ),
                        1,
                    ),
                ],
                # weight above ColumnTransformer features
                transformer_weights={
                    "subject": 0.8,
                    "body_bow": 0.5,
                    "body_stats": 1.0,
                },
            ),
        ),
        # Use a SVC classifier on the combined features
        ("svc", LinearSVC(dual=False)),
    ],
    verbose=True,
)

最后，我们在训练数据上拟合我们的管道，并使用它来预测 X_test 的主题。然后打印我们管道性能指标。

pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
print("Classification report:\n\n{}".format(classification_report(y_test, y_pred)))

[Pipeline] ....... (step 1 of 3) Processing subjectbody, total=   0.0s
[Pipeline] ............. (step 2 of 3) Processing union, total=   0.4s
[Pipeline] ............... (step 3 of 3) Processing svc, total=   0.0s
Classification report:

              precision    recall  f1-score   support

           0       0.84      0.87      0.86       396
           1       0.87      0.84      0.85       394

    accuracy                           0.86       790
   macro avg       0.86      0.86      0.86       790
weighted avg       0.86      0.86      0.86       790

脚本的总运行时间：（0 分钟 2.505 秒）