1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
|
"""
====================
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
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()
|
