归纳聚类#

聚类的成本可能很高，尤其是当我们的数据集包含数百万个数据点时。许多聚类算法不是归纳的，因此不能在不重新计算聚类的情况下直接应用于新的数据样本，这可能是难以处理的。相反，我们可以使用聚类来学习一个带有分类器的归纳模型，这有几个好处

它允许集群扩展并应用于新数据
与将集群重新拟合到新样本不同，它确保了标记过程随时间推移保持一致
它允许我们使用分类器的推理能力来描述或解释集群

此示例说明了元估计器的通用实现，该估计器通过从集群标签中引入分类器来扩展聚类。

Ward Linkage, Unknown instances, Classify unknown instances

# Authors: Chirag Nagpal
#          Christos Aridas

import matplotlib.pyplot as plt

from sklearn.base import BaseEstimator, clone
from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.utils.metaestimators import available_if
from sklearn.utils.validation import check_is_fitted

N_SAMPLES = 5000
RANDOM_STATE = 42


def _classifier_has(attr):
    """Check if we can delegate a method to the underlying classifier.

    First, we check the first fitted classifier if available, otherwise we
    check the unfitted classifier.
    """
    return lambda estimator: (
        hasattr(estimator.classifier_, attr)
        if hasattr(estimator, "classifier_")
        else hasattr(estimator.classifier, attr)
    )


class InductiveClusterer(BaseEstimator):
    def __init__(self, clusterer, classifier):
        self.clusterer = clusterer
        self.classifier = classifier

    def fit(self, X, y=None):
        self.clusterer_ = clone(self.clusterer)
        self.classifier_ = clone(self.classifier)
        y = self.clusterer_.fit_predict(X)
        self.classifier_.fit(X, y)
        return self

    @available_if(_classifier_has("predict"))
    def predict(self, X):
        check_is_fitted(self)
        return self.classifier_.predict(X)

    @available_if(_classifier_has("decision_function"))
    def decision_function(self, X):
        check_is_fitted(self)
        return self.classifier_.decision_function(X)


def plot_scatter(X, color, alpha=0.5):
    return plt.scatter(X[:, 0], X[:, 1], c=color, alpha=alpha, edgecolor="k")


# Generate some training data from clustering
X, y = make_blobs(
    n_samples=N_SAMPLES,
    cluster_std=[1.0, 1.0, 0.5],
    centers=[(-5, -5), (0, 0), (5, 5)],
    random_state=RANDOM_STATE,
)


# Train a clustering algorithm on the training data and get the cluster labels
clusterer = AgglomerativeClustering(n_clusters=3)
cluster_labels = clusterer.fit_predict(X)

plt.figure(figsize=(12, 4))

plt.subplot(131)
plot_scatter(X, cluster_labels)
plt.title("Ward Linkage")


# Generate new samples and plot them along with the original dataset
X_new, y_new = make_blobs(
    n_samples=10, centers=[(-7, -1), (-2, 4), (3, 6)], random_state=RANDOM_STATE
)

plt.subplot(132)
plot_scatter(X, cluster_labels)
plot_scatter(X_new, "black", 1)
plt.title("Unknown instances")


# Declare the inductive learning model that it will be used to
# predict cluster membership for unknown instances
classifier = RandomForestClassifier(random_state=RANDOM_STATE)
inductive_learner = InductiveClusterer(clusterer, classifier).fit(X)

probable_clusters = inductive_learner.predict(X_new)


ax = plt.subplot(133)
plot_scatter(X, cluster_labels)
plot_scatter(X_new, probable_clusters)

# Plotting decision regions
DecisionBoundaryDisplay.from_estimator(
    inductive_learner, X, response_method="predict", alpha=0.4, ax=ax
)
plt.title("Classify unknown instances")

plt.show()

脚本总运行时间：（0 分钟 2.680 秒）