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purity.py
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""" | |
The MIT License (MIT) | |
Copyright (c) 2017 David Mugisha | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is | |
furnished to do so, subject to the following conditions: | |
The above copyright notice and this permission notice shall be included in all | |
copies or substantial portions of the Software. | |
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
SOFTWARE. | |
""" | |
""" | |
Computation of purity score with sklearn. | |
""" | |
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
from sklearn.metrics import accuracy_score | |
import numpy as np | |
def purity_score(y_true, y_pred): | |
"""Purity score | |
To compute purity, each cluster is assigned to the class which is most frequent | |
in the cluster [1], and then the accuracy of this assignment is measured by counting | |
the number of correctly assigned documents and dividing by the number of documents. | |
We suppose here that the ground truth labels are integers, the same with the predicted clusters i.e | |
the clusters index. | |
Args: | |
y_true(np.ndarray): n*1 matrix Ground truth labels | |
y_pred(np.ndarray): n*1 matrix Predicted clusters | |
Returns: | |
float: Purity score | |
References: | |
[1] https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html | |
""" | |
# matrix which will hold the majority-voted labels | |
y_voted_labels = np.zeros(y_true.shape) | |
# Ordering labels | |
## Labels might be missing e.g with set like 0,2 where 1 is missing | |
## First find the unique labels, then map the labels to an ordered set | |
## 0,2 should become 0,1 | |
labels = np.unique(y_true) | |
ordered_labels = np.arange(labels.shape[0]) | |
for k in range(labels.shape[0]): | |
y_true[y_true==labels[k]] = ordered_labels[k] | |
# Update unique labels | |
labels = np.unique(y_true) | |
# We set the number of bins to be n_classes+2 so that | |
# we count the actual occurence of classes between two consecutive bin | |
# the bigger being excluded [bin_i, bin_i+1[ | |
bins = np.concatenate((labels, [np.max(labels)+1]), axis=0) | |
for cluster in np.unique(y_pred): | |
hist, _ = np.histogram(y_true[y_pred==cluster], bins=bins) | |
# Find the most present label in the cluster | |
winner = np.argmax(hist) | |
y_voted_labels[y_pred==cluster] = winner | |
return accuracy_score(y_true, y_voted_labels) |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
import numpy as np | |
from purity import purity_score | |
y_true = np.random.randint(1,6, size=(20,1)) | |
y_pred = np.random.randint(5, size=(20,1)) | |
purity_score(y_true, y_pred) | |
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