使用谱共聚类算法对文档进行双聚类#

此示例演示了在二十个新闻组数据集上使用谱共聚类算法。“comp.os.ms-windows.misc”类别被排除在外，因为它包含许多仅包含数据的帖子。

TF-IDF向量化的帖子形成一个词频矩阵，然后使用Dhillon的谱共聚类算法对其进行双聚类。生成的文档-单词双聚类指示在这些子集文档中更常用的一些单词子集。

对于一些最佳的双聚类，将打印其最常见的文档类别及其十个最重要的单词。最佳双聚类由它们的归一化割决定。最佳单词是通过比较它们在双聚类内部和外部的总和来确定的。

为了比较，还使用MiniBatchKMeans对文档进行聚类。从双聚类导出的文档聚类比MiniBatchKMeans找到的聚类实现了更好的V度量。

Vectorizing...
Coclustering...
Done in 1.28s. V-measure: 0.4415
MiniBatchKMeans...
Done in 2.28s. V-measure: 0.3015

Best biclusters:
----------------
bicluster 0 : 8 documents, 6 words
categories   : 100% talk.politics.mideast
words        : cosmo, angmar, alfalfa, alphalpha, proline, benson

bicluster 1 : 1948 documents, 4325 words
categories   : 23% talk.politics.guns, 18% talk.politics.misc, 17% sci.med
words        : gun, guns, geb, banks, gordon, clinton, pitt, cdt, surrender, veal

bicluster 2 : 1259 documents, 3534 words
categories   : 27% soc.religion.christian, 25% talk.politics.mideast, 25% alt.atheism
words        : god, jesus, christians, kent, sin, objective, belief, christ, faith, moral

bicluster 3 : 775 documents, 1623 words
categories   : 30% comp.windows.x, 25% comp.sys.ibm.pc.hardware, 20% comp.graphics
words        : scsi, nada, ide, vga, esdi, isa, kth, s3, vlb, bmug

bicluster 4 : 2180 documents, 2802 words
categories   : 18% comp.sys.mac.hardware, 16% sci.electronics, 16% comp.sys.ibm.pc.hardware
words        : voltage, shipping, circuit, receiver, processing, scope, mpce, analog, kolstad, umass

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from collections import Counter
from time import time

import numpy as np

from sklearn.cluster import MiniBatchKMeans, SpectralCoclustering
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.cluster import v_measure_score


def number_normalizer(tokens):
    """Map all numeric tokens to a placeholder.

    For many applications, tokens that begin with a number are not directly
    useful, but the fact that such a token exists can be relevant.  By applying
    this form of dimensionality reduction, some methods may perform better.
    """
    return ("#NUMBER" if token[0].isdigit() else token for token in tokens)


class NumberNormalizingVectorizer(TfidfVectorizer):
    def build_tokenizer(self):
        tokenize = super().build_tokenizer()
        return lambda doc: list(number_normalizer(tokenize(doc)))


# exclude 'comp.os.ms-windows.misc'
categories = [
    "alt.atheism",
    "comp.graphics",
    "comp.sys.ibm.pc.hardware",
    "comp.sys.mac.hardware",
    "comp.windows.x",
    "misc.forsale",
    "rec.autos",
    "rec.motorcycles",
    "rec.sport.baseball",
    "rec.sport.hockey",
    "sci.crypt",
    "sci.electronics",
    "sci.med",
    "sci.space",
    "soc.religion.christian",
    "talk.politics.guns",
    "talk.politics.mideast",
    "talk.politics.misc",
    "talk.religion.misc",
]
newsgroups = fetch_20newsgroups(categories=categories)
y_true = newsgroups.target

vectorizer = NumberNormalizingVectorizer(stop_words="english", min_df=5)
cocluster = SpectralCoclustering(
    n_clusters=len(categories), svd_method="arpack", random_state=0
)
kmeans = MiniBatchKMeans(
    n_clusters=len(categories), batch_size=20000, random_state=0, n_init=3
)

print("Vectorizing...")
X = vectorizer.fit_transform(newsgroups.data)

print("Coclustering...")
start_time = time()
cocluster.fit(X)
y_cocluster = cocluster.row_labels_
print(
    f"Done in {time() - start_time:.2f}s. V-measure: \
{v_measure_score(y_cocluster, y_true):.4f}"
)


print("MiniBatchKMeans...")
start_time = time()
y_kmeans = kmeans.fit_predict(X)
print(
    f"Done in {time() - start_time:.2f}s. V-measure: \
{v_measure_score(y_kmeans, y_true):.4f}"
)


feature_names = vectorizer.get_feature_names_out()
document_names = list(newsgroups.target_names[i] for i in newsgroups.target)


def bicluster_ncut(i):
    rows, cols = cocluster.get_indices(i)
    if not (np.any(rows) and np.any(cols)):
        import sys

        return sys.float_info.max
    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]
    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]
    # Note: the following is identical to X[rows[:, np.newaxis],
    # cols].sum() but much faster in scipy <= 0.16
    weight = X[rows][:, cols].sum()
    cut = X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()
    return cut / weight


bicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names)))
best_idx = np.argsort(bicluster_ncuts)[:5]

print()
print("Best biclusters:")
print("----------------")
for idx, cluster in enumerate(best_idx):
    n_rows, n_cols = cocluster.get_shape(cluster)
    cluster_docs, cluster_words = cocluster.get_indices(cluster)
    if not len(cluster_docs) or not len(cluster_words):
        continue

    # categories
    counter = Counter(document_names[doc] for doc in cluster_docs)

    cat_string = ", ".join(
        f"{(c / n_rows * 100):.0f}% {name}" for name, c in counter.most_common(3)
    )

    # words
    out_of_cluster_docs = cocluster.row_labels_ != cluster
    out_of_cluster_docs = np.where(out_of_cluster_docs)[0]
    word_col = X[:, cluster_words]
    word_scores = np.array(
        word_col[cluster_docs, :].sum(axis=0)
        - word_col[out_of_cluster_docs, :].sum(axis=0)
    )
    word_scores = word_scores.ravel()
    important_words = list(
        feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]
    )

    print(f"bicluster {idx} : {n_rows} documents, {n_cols} words")
    print(f"categories   : {cat_string}")
    print(f"words        : {', '.join(important_words)}\n")

脚本总运行时间：（0分钟6.456秒）