具有異質資料來源的列轉換器#

資料集通常會包含需要不同特徵提取和處理流程的組件。當發生以下情況時，可能會發生這種情況：

您的資料集由異質資料類型組成（例如，點陣影像和文字標題），
您的資料集儲存在pandas.DataFrame中，且不同的欄位需要不同的處理流程。

此範例展示如何在包含不同類型特徵的資料集上使用ColumnTransformer。特徵的選擇並非特別有用，但可用於說明此技術。

# Authors: The scikit-learn developers
# SPDX-License-Identifier: 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 新聞群組資料集#

我們將使用20 新聞群組資料集，其中包含來自 20 個主題新聞群組的貼文。此資料集根據特定日期之前和之後發佈的訊息，分成訓練和測試子集。我們將只使用 2 個類別的貼文以加快執行時間。

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,
)

每個特徵都包含有關該貼文的元資訊，例如主題和新聞貼文的本文。

print(X_train[0])

From: mccall@mksol.dseg.ti.com (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

建立轉換器#

首先，我們需要一個轉換器，可以提取每個貼文的主題和本文。由於這是無狀態轉換（不需要來自訓練資料的狀態資訊），我們可以定義一個執行資料轉換的函數，然後使用FunctionTransformer來建立一個 scikit-learn 轉換器。

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)

我們還將建立一個轉換器，可以提取文字的長度和句子數。

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


text_stats_transformer = FunctionTransformer(text_stats)

分類流程#

下面的流程使用 SubjectBodyExtractor 從每個貼文中提取主題和本文，產生一個 (n_samples, 2) 陣列。然後，此陣列使用 ColumnTransformer 計算主題和本文的標準詞袋特徵以及本文上的文字長度和句子數。我們將它們與權重結合，然後在組合的特徵集上訓練分類器。

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,
)

最後，我們將流程擬合到訓練資料上，並使用它來預測 X_test 的主題。然後會列印我們流程的效能指標。

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

指令碼的總執行時間：(0 分鐘 2.562 秒)