scikit-learn/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.
"""
# Authors: Chirag Nagpal
#          Christos Aridas
print(__doc__)

import numpy as np
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.utils.metaestimators import if_delegate_has_method


N_SAMPLES = 5000
RANDOM_STATE = 42


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

    @if_delegate_has_method(delegate='classifier_')
    def predict(self, X):
        return self.classifier_.predict(X)

    @if_delegate_has_method(delegate='classifier_')
    def decision_function(self, X):
        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)


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

# Plotting decision regions
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))

Z = inductive_learner.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z, alpha=0.4)
plt.title("Classify unknown instances")

plt.show()