diff --git a/docs/examples/Biclustering/a_demo_of_the_spectral_clustering_algorithm.md b/docs/examples/Biclustering/a_demo_of_the_spectral_clustering_algorithm.md new file mode 100644 index 0000000000000000000000000000000000000000..88fc3971a9e3624e7f92d484769f659eeaaea9d9 --- /dev/null +++ b/docs/examples/Biclustering/a_demo_of_the_spectral_clustering_algorithm.md @@ -0,0 +1,78 @@ + +# 频谱双聚类算法的演示 + +> 翻译者：[@N!no](https://github.com/lovelybuggies) +> 校验者：待校验 + +这个例子演示了如何使用光谱聚类算法生成棋盘数据集并对其进行聚类处理。 + +数据是用`make_checkerboard`函数生成的，然后打乱顺序并传递给光谱双聚类算法。变换后的矩阵的行和列被重新排列，以显示该算法找到的双聚类。 + +行和列标签向量的外积表示棋盘结构。 + +![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_001.png) + +![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_002.png) + +![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_003.png) + +![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_004.png) + +``` +consensus score: 1.0 +``` + + +```python +print(__doc__) + +# Author: Kemal Eren +# License: BSD 3 clause + +import numpy as np +from matplotlib import pyplot as plt + +from sklearn.datasets import make_checkerboard +from sklearn.cluster import SpectralBiclustering +from sklearn.metrics import consensus_score + + +n_clusters = (4, 3) +data, rows, columns = make_checkerboard( + shape=(300, 300), n_clusters=n_clusters, noise=10, + shuffle=False, random_state=0) + +plt.matshow(data, cmap=plt.cm.Blues) +plt.title("Original dataset") + +# 打乱聚类顺序 +rng = np.random.RandomState(0) +row_idx = rng.permutation(data.shape[0]) +col_idx = rng.permutation(data.shape[1]) +data = data[row_idx][:, col_idx] + +plt.matshow(data, cmap=plt.cm.Blues) +plt.title("Shuffled dataset") + +model = SpectralBiclustering(n_clusters=n_clusters, method='log', + random_state=0) +model.fit(data) +score = consensus_score(model.biclusters_, + (rows[:, row_idx], columns[:, col_idx])) + +print("consensus score: {:.1f}".format(score)) + +fit_data = data[np.argsort(model.row_labels_)] +fit_data = fit_data[:, np.argsort(model.column_labels_)] + +plt.matshow(fit_data, cmap=plt.cm.Blues) +plt.title("After biclustering; rearranged to show biclusters") + +plt.matshow(np.outer(np.sort(model.row_labels_) + 1, + np.sort(model.column_labels_) + 1), + cmap=plt.cm.Blues) +plt.title("Checkerboard structure of rearranged data") + +plt.show() +``` + diff --git a/docs/examples/Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md b/docs/examples/Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md new file mode 100644 index 0000000000000000000000000000000000000000..61508caea3c470f0c9b93eb127d27104c368ebd6 --- /dev/null +++ b/docs/examples/Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md @@ -0,0 +1,66 @@ + +# 频谱共聚算法演示 + +> 翻译者：[@N!no](https://github.com/lovelybuggies) +> 校验者：待校验 + +这个例子演示了如何使用谱协聚类算法生成数据集并对其进行双聚类处理。 + +数据集是使用 `make_biclusters` 函数生成的，该函数创建一个小值矩阵，并将大值植入双聚类。然后将行和列打乱并传递给光谱协聚算法。通过重新排列变换后的矩阵可以使双聚类连续，这展示出该算法找到双聚类的准确性。 + +![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_coclustering_001.png) + +![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_coclustering_002.png) + +![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_coclustering_003.png) + +``` +consensus score: 1.0 +``` + + +```python +print(__doc__) + +# Author: Kemal Eren +# License: BSD 3 clause + +import numpy as np +from matplotlib import pyplot as plt + +from sklearn.datasets import make_biclusters +from sklearn.cluster import SpectralCoclustering +from sklearn.metrics import consensus_score + +data, rows, columns = make_biclusters( + shape=(300, 300), n_clusters=5, noise=5, + shuffle=False, random_state=0) + +plt.matshow(data, cmap=plt.cm.Blues) +plt.title("Original dataset") + +# 打乱聚类的位置 +rng = np.random.RandomState(0) +row_idx = rng.permutation(data.shape[0]) +col_idx = rng.permutation(data.shape[1]) +data = data[row_idx][:, col_idx] + +plt.matshow(data, cmap=plt.cm.Blues) +plt.title("Shuffled dataset") + +model = SpectralCoclustering(n_clusters=5, random_state=0) +model.fit(data) +score = consensus_score(model.biclusters_, + (rows[:, row_idx], columns[:, col_idx])) + +print("consensus score: {:.3f}".format(score)) + +fit_data = data[np.argsort(model.row_labels_)] +fit_data = fit_data[:, np.argsort(model.column_labels_)] + +plt.matshow(fit_data, cmap=plt.cm.Blues) +plt.title("After biclustering; rearranged to show biclusters") + +plt.show() +``` + diff --git a/docs/examples/Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md b/docs/examples/Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md new file mode 100644 index 0000000000000000000000000000000000000000..ebea66adf9f0ede688eb310b8724e95e3617bbd6 --- /dev/null +++ b/docs/examples/Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md @@ -0,0 +1,174 @@ + +# 使用频谱共聚算法对文档进行聚合 + +> 翻译者：[@N!no](https://github.com/lovelybuggies) +> 校验者：待校验 + +这个例子演示了20个新闻组数据集上的光谱协聚类算法。‘comp.os.ms-windows.misc’ 类别被排除在外，因为它包含许多只包含数据的帖子。 + +TF-IDF 矢量帖构成一个词频矩阵，然后使用 Dhillon 光谱协聚算法对其进行重组。由此产生的文档词双聚类表明在这些子集文档中被使用频率更高的子集词。 + +对于一些最好的双聚类来说，它最常见的文档类别和十个最重要的单词会被打印出来。最佳双类别由其归一化的切割决定。最好的单词是通过比较它们在两区内和两区外的总和来确定的。 + +为了进行比较，我们还使用 MiniBatchKMeans 对文档进行集群。从双聚类衍生出的文档聚类比使用 MiniBatchKMeans 得到的聚类具有更好的 V-measure。 + +``` +Vectorizing... +Coclustering... +Done in 2.75s. V-measure: 0.4387 +MiniBatchKMeans... +Done in 5.69s. V-measure: 0.3344 + +Best biclusters: +---------------- +bicluster 0 : 1829 documents, 2524 words +categories : 22% comp.sys.ibm.pc.hardware, 19% comp.sys.mac.hardware, 18% comp.graphics +words : card, pc, ram, drive, bus, mac, motherboard, port, windows, floppy + +bicluster 1 : 2391 documents, 3275 words +categories : 18% rec.motorcycles, 17% rec.autos, 15% sci.electronics +words : bike, engine, car, dod, bmw, honda, oil, motorcycle, behanna, ysu + +bicluster 2 : 1887 documents, 4232 words +categories : 23% talk.politics.guns, 19% talk.politics.misc, 13% sci.med +words : gun, guns, firearms, geb, drugs, banks, dyer, amendment, clinton, cdt + +bicluster 3 : 1146 documents, 3263 words +categories : 29% talk.politics.mideast, 26% soc.religion.christian, 25% alt.atheism +words : god, jesus, christians, atheists, kent, sin, morality, belief, resurrection, marriage + +bicluster 4 : 1732 documents, 3967 words +categories : 26% sci.crypt, 23% sci.space, 17% sci.med +words : clipper, encryption, key, escrow, nsa, crypto, keys, intercon, secure, wiretap +``` + + +```python +from collections import defaultdict +import operator +from time import time + +import numpy as np + +from sklearn.cluster import SpectralCoclustering +from sklearn.cluster import MiniBatchKMeans +from sklearn.datasets import fetch_20newsgroups +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.cluster import v_measure_score + +print(__doc__) + + +def number_normalizer(tokens): + """ 将所有数字标记映射到占位符。 + + 对于许多应用程序来说，以数字开头的令牌并没有直接的用处，但是这样的令牌存在的事实可能是相关的。通过应用这种降维形式，一些方法可能会表现得更好。 + """ + 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))) + + +# 不包含 'comp.os.ms-windows.misc' 类别 +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) + +print("Vectorizing...") +X = vectorizer.fit_transform(newsgroups.data) + +print("Coclustering...") +start_time = time() +cocluster.fit(X) +y_cocluster = cocluster.row_labels_ +print("Done in {:.2f}s. V-measure: {:.4f}".format( + time() - start_time, + v_measure_score(y_cocluster, y_true))) + +print("MiniBatchKMeans...") +start_time = time() +y_kmeans = kmeans.fit_predict(X) +print("Done in {:.2f}s. V-measure: {:.4f}".format( + time() - start_time, + v_measure_score(y_kmeans, y_true))) + +feature_names = vectorizer.get_feature_names() +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] + # 注意：接下来的操作等同于 X[rows[:, np.newaxis], cols].sum() + # 但是会针对于 scipy <= 0.16 的版本更快一些 + weight = X[rows][:, cols].sum() + cut = (X[row_complement][:, cols].sum() + + X[rows][:, col_complement].sum()) + return cut / weight + + +def most_common(d): + """默认字典有最大值的项。 + + 在 Python >= 2.7 中类似于 Counter.most_common 。 + """ + return sorted(d.items(), key=operator.itemgetter(1), reverse=True) + + +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 + + # 种类 + counter = defaultdict(int) + for i in cluster_docs: + counter[document_names[i]] += 1 + cat_string = ", ".join("{:.0f}% {}".format(float(c) / n_rows * 100, name) + for name, c in most_common(counter)[:3]) + + # 单词 + 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("bicluster {} : {} documents, {} words".format( + idx, n_rows, n_cols)) + print("categories : {}".format(cat_string)) + print("words : {}\n".format(', '.join(important_words))) +``` + diff --git a/docs/examples/README.md b/docs/examples/README.md index d101fc70d57b3a4f5315b69d44b5edcc6e46e9ad..8d63516abebeca0ba3cdaaef618316b4d479b640 100644 --- a/docs/examples/README.md +++ b/docs/examples/README.md @@ -13,13 +13,13 @@ scikit-learn 的 Miscellaneous 和入门示例。 | ![](img/sphx_glr_plot_kernel_ridge_regression_thumb.png)
[内核岭回归和SVR的比较](https://scikit-learn.org/stable/auto_examples/plot_kernel_ridge_regression.html#sphx-glr-auto-examples-plot-kernel-ridge-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id10) | ![](img/sphx_glr_plot_kernel_approximation_thumb.png)
[RBF内核的显式特征图逼近](https://scikit-learn.org/stable/auto_examples/plot_kernel_approximation.html#sphx-glr-auto-examples-plot-kernel-approximation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id11) | -## 双集群 +## 双聚类 有关`sklearn.cluster.bicluster`模块的示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_spectral_coclustering_thumb.png)
[频谱共聚算法演示](https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-coclustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id12) | ![](img/sphx_glr_plot_spectral_biclustering_thumb.png)
[频谱二值化算法的演示](https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id13) | ![](img/sphx_glr_plot_bicluster_newsgroups_thumb.png)
[使用频谱共同聚类算法对文档进行聚类](https://scikit-learn.org/stable/auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id14) | +| ![](img/sphx_glr_plot_spectral_coclustering_thumb.png)
[频谱共聚算法演示](https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-coclustering-py)[](Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md) | ![](img/sphx_glr_plot_spectral_biclustering_thumb.png)
[频谱双聚类算法的演示](https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py)[](Biclustering/a_demo_of_the_spectral_clustering_algorithm.md) | ![](img/sphx_glr_plot_bicluster_newsgroups_thumb.png)
[使用频谱共聚算法对文档进行聚合](https://scikit-learn.org/stable/auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py)[](Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md) || ## 校准 @@ -41,7 +41,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | ![](img/sphx_glr_plot_lda_qda_thumb.png)
[线性和二次判别分析与协方差椭球](https://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id23) | -## 多集群 +## 多聚类 有关[`sklearn.cluster`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster "sklearn.cluster")模块的示例。 diff --git a/docs/examples/SUMMARY.md b/docs/examples/SUMMARY.md index c7d1ade6f397cd133dd9fac5902bdc65f1c26046..c49f225d7225ee68ae6c9e548e181feec160e9f0 100644 --- a/docs/examples/SUMMARY.md +++ b/docs/examples/SUMMARY.md @@ -1,8 +1,8 @@ * 其他示例 -* 双集群 +* 双聚类 * 校准 * 分类 -* 多集群 +* 多聚类 * 协方差估计 * 交叉分解 * 数据集示例