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Column Transformer with Heterogeneous Data Sources#

Datasets can often contain components that require different feature extraction and processing pipelines. This scenario might occur when:

your dataset consists of heterogeneous data types (e.g. raster images and text captions),
your dataset is stored in a pandas.DataFrame and different columns require different processing pipelines.

This example demonstrates how to use ColumnTransformer on a dataset containing different types of features. The choice of features is not particularly helpful, but serves to illustrate the technique.

# Author: Matt Terry <matt.terry@gmail.com>
#
# 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 newsgroups dataset#

We will use the 20 newsgroups dataset, which comprises posts from newsgroups on 20 topics. This dataset is split into train and test subsets based on messages posted before and after a specific date. We will only use posts from 2 categories to speed up running time.

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,
)

Each feature comprises meta information about that post, such as the subject, and the body of the news post.

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

Creating transformers#

First, we would like a transformer that extracts the subject and body of each post. Since this is a stateless transformation (does not require state information from training data), we can define a function that performs the data transformation then use FunctionTransformer to create a scikit-learn transformer.

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)

We will also create a transformer that extracts the length of the text and the number of sentences.

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


text_stats_transformer = FunctionTransformer(text_stats)

Classification pipeline#

The pipeline below extracts the subject and body from each post using SubjectBodyExtractor, producing a (n_samples, 2) array. This array is then used to compute standard bag-of-words features for the subject and body as well as text length and number of sentences on the body, using ColumnTransformer. We combine them, with weights, then train a classifier on the combined set of features.

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,
)

Finally, we fit our pipeline on the training data and use it to predict topics for X_test. Performance metrics of our pipeline are then printed.

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=   0.3s
[Pipeline] ............... (step 3 of 3) Processing svc, total=   0.1s
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

Total running time of the script: (0 minutes 2.249 seconds)