.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_inductive_clustering.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_cluster_plot_inductive_clustering.py: ==================== Inductive Clustering ==================== Clustering can be expensive, especially when our dataset contains millions of datapoints. Many clustering algorithms are not :term:`inductive` and so cannot be directly applied to new data samples without recomputing the clustering, which may be intractable. Instead, we can use clustering to then learn an inductive model with a classifier, which has several benefits: - it allows the clusters to scale and apply to new data - unlike re-fitting the clusters to new samples, it makes sure the labelling procedure is consistent over time - it allows us to use the inferential capabilities of the classifier to describe or explain the clusters This example illustrates a generic implementation of a meta-estimator which extends clustering by inducing a classifier from the cluster labels. .. GENERATED FROM PYTHON SOURCE LINES 22-130 .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_inductive_clustering_001.png :alt: Ward Linkage, Unknown instances, Classify unknown instances :srcset: /auto_examples/cluster/images/sphx_glr_plot_inductive_clustering_001.png :class: sphx-glr-single-img .. code-block:: Python # 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.408 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_inductive_clustering.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_inductive_clustering.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/cluster/plot_inductive_clustering.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_inductive_clustering.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_inductive_clustering.py ` .. include:: plot_inductive_clustering.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_