Morten Hjorth-Jensen
941 lines
40 KiB
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Applied Data Analysis and Machine Learning
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
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<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">12. Clustering and Unsupervised Learning</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations with Deep Learning</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
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<h1>Clustering and Unsupervised Learning</h1>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#codes-and-approaches">12.1. Codes and Approaches</a></li>
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="searchbox"></div>
|
||
<article class="bd-article">
|
||
|
||
<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
|
||
doconce format html clustering.do.txt --><section class="tex2jax_ignore mathjax_ignore" id="clustering-and-unsupervised-learning">
|
||
<h1><span class="section-number">12. </span>Clustering and Unsupervised Learning<a class="headerlink" href="#clustering-and-unsupervised-learning" title="Link to this heading">#</a></h1>
|
||
<p>In general terms cluster analysis, or clustering, is the task of grouping a
|
||
data-set into different distinct categories based on some measure of equality of
|
||
the data. This measure is often referred to as a <strong>metric</strong> or <strong>similarity
|
||
measure</strong> in the literature (note: sometimes we deal with a <strong>dissimilarity
|
||
measure</strong> instead). Usually, these metrics are formulated as some kind of
|
||
distance function between points in a high-dimensional space.</p>
|
||
<p>The simplest, and also the most
|
||
common is the <strong>Euclidean distance</strong>.</p>
|
||
<p>The simplest of all clustering algorithms is the <strong>k-means algorithm</strong>
|
||
, sometimes also referred to as <em>Lloyds algorithm</em>. It is the simplest and also
|
||
the most common. From its simplicity it obtains both strengths and weaknesses.
|
||
These will be discussed in more detail later. The <span class="math notranslate nohighlight">\(k\)</span>-means algorithm is a
|
||
<strong>centroid based</strong> clustering algorithm.</p>
|
||
<p>Assume, we are given <span class="math notranslate nohighlight">\(n\)</span> data points and we wish to split the data into <span class="math notranslate nohighlight">\(K < n\)</span>
|
||
different categories, or clusters. We label each cluster by an integer</p>
|
||
<div class="math notranslate nohighlight">
|
||
\[
|
||
k\in\{1, \cdots, K \}.
|
||
\]</div>
|
||
<p>In the basic k-means algorithm each point is assigned to only
|
||
one cluster <span class="math notranslate nohighlight">\(k\)</span>, and these assignments are <em>non-injective</em> i.e. many-to-one. We
|
||
can think of these mappings as an encoder <span class="math notranslate nohighlight">\(k = C(i)\)</span>, which assigns the <span class="math notranslate nohighlight">\(i\)</span>-th
|
||
data-point <span class="math notranslate nohighlight">\(\bf x_i\)</span> to the <span class="math notranslate nohighlight">\(k\)</span>-th cluster.</p>
|
||
<p><span class="math notranslate nohighlight">\(k\)</span>-means algorithm in words:</p>
|
||
<ol class="arabic simple">
|
||
<li><p>We start with guesses / random initializations of our <span class="math notranslate nohighlight">\(k\)</span> cluster centers/centroids</p></li>
|
||
<li><p>For each centroid the points that are most similar are identified</p></li>
|
||
<li><p>Then we move / replace each centroid with a coordinate average of all the points that were assigned to that centroid.</p></li>
|
||
<li><p>Iterate 2-3 until the centroids no longer move (to some tolerance)</p></li>
|
||
</ol>
|
||
<p>We assume we have <span class="math notranslate nohighlight">\(n\)</span> data-points</p>
|
||
<!-- Equation labels as ordinary links -->
|
||
<div id="eq:kmeanspoints"></div>
|
||
<div class="math notranslate nohighlight">
|
||
\[
|
||
\begin{equation}\label{eq:kmeanspoints} \tag{1}
|
||
\boldsymbol{x_i} = \{x_{i, 1}, \cdots, x_{i, p}\}\in\mathbb{R}^p.
|
||
\end{equation}
|
||
\]</div>
|
||
<p>which we wish to group into <span class="math notranslate nohighlight">\(K < n\)</span> clusters. For our dissimilarity measure we
|
||
use the <em>squared Euclidean distance</em></p>
|
||
<!-- Equation labels as ordinary links -->
|
||
<div id="eq:squaredeuclidean"></div>
|
||
<div class="math notranslate nohighlight">
|
||
\[
|
||
\begin{equation}\label{eq:squaredeuclidean} \tag{2}
|
||
d(\boldsymbol{x_i}, \boldsymbol{x_i'}) = \sum_{j=1}^p(x_{ij} - x_{i'j})^2
|
||
= ||\boldsymbol{x_i} - \boldsymbol{x_{i'}}||^2
|
||
\end{equation}
|
||
\]</div>
|
||
<p>We define the so called <em>within-cluster point scatter</em> which gives us a
|
||
measure of how close each data point assigned to the same cluster tends to be to
|
||
the all the others.</p>
|
||
<!-- Equation labels as ordinary links -->
|
||
<div id="eq:withincluster"></div>
|
||
<div class="math notranslate nohighlight">
|
||
\[
|
||
\begin{equation}\label{eq:withincluster} \tag{3}
|
||
W(C) = \frac{1}{2}\sum_{k=1}^K\sum_{C(i)=k}
|
||
\sum_{C(i')=k}d(\boldsymbol{x_i}, \boldsymbol{x_{i'}}) =
|
||
\sum_{k=1}^KN_k\sum_{C(i)=k}||\boldsymbol{x_i} - \boldsymbol{\overline{x_k}}||^2
|
||
\end{equation}
|
||
\]</div>
|
||
<p>where <span class="math notranslate nohighlight">\(\boldsymbol{\overline{x_k}}\)</span> is the mean vector associated with the <span class="math notranslate nohighlight">\(k\)</span>-th
|
||
cluster, and <span class="math notranslate nohighlight">\(N_k = \sum_{i=1}^nI(C(i) = k)\)</span>, where the <span class="math notranslate nohighlight">\(I()\)</span> notation is
|
||
similar to the Kronecker delta (<em>Commonly used in statistics, it just means that
|
||
when <span class="math notranslate nohighlight">\(i = k\)</span> we have the encoder <span class="math notranslate nohighlight">\(C(i)\)</span></em>). In other words, the within-cluster
|
||
scatter measures the compactness of each cluster with respect to the data points
|
||
assigned to each cluster. This is the quantity that the <span class="math notranslate nohighlight">\(k\)</span>-means algorithm aims
|
||
to minimize. We refer to this quantity <span class="math notranslate nohighlight">\(W(C)\)</span> as the within cluster scatter
|
||
because of its relation to the <em>total scatter</em>.</p>
|
||
<p>We have</p>
|
||
<!-- Equation labels as ordinary links -->
|
||
<div id="eq:totalscatter"></div>
|
||
<div class="math notranslate nohighlight">
|
||
\[
|
||
\begin{equation}\label{eq:totalscatter} \tag{4}
|
||
T = W(C) + B(C) = \frac{1}{2}\sum_{i=1}^n
|
||
\sum_{i'=1}^nd(\boldsymbol{x_i}, \boldsymbol{x_{i'}})
|
||
= \frac{1}{2}\sum_{k=1}^K\sum_{C(i)=k}
|
||
\Big(\sum_{C(i') = k}d(\boldsymbol{x_i}, \boldsymbol{x_{i'}})
|
||
+ \sum_{C(i')\neq k}d(\boldsymbol{x_i}, \boldsymbol{x_{i'}})\Big).
|
||
\end{equation}
|
||
\]</div>
|
||
<p>This is a quantity that is conserved throughout the <span class="math notranslate nohighlight">\(k\)</span>-means algorithm. It can
|
||
be thought of as the total amount of information in the data, and it is composed
|
||
of the aforementioned within-cluster scatter and the <em>between-cluster scatter</em>
|
||
<span class="math notranslate nohighlight">\(B(C)\)</span>. In methods such as principle component analysis the total scatter is not
|
||
conserved.</p>
|
||
<p>Given a cluster mean <span class="math notranslate nohighlight">\(\boldsymbol{m_k}\)</span> we define the <strong>total cluster variance</strong></p>
|
||
<!-- Equation labels as ordinary links -->
|
||
<div id="eq:totalclustervariance"></div>
|
||
<div class="math notranslate nohighlight">
|
||
\[
|
||
\begin{equation}\label{eq:totalclustervariance} \tag{5}
|
||
\min_{C, \{\boldsymbol{m_k}\}_1^K}\sum_{k=1}^KN_k\sum||\boldsymbol{x_i} - \boldsymbol{m_k}||^2
|
||
\end{equation}
|
||
\]</div>
|
||
<p>Now we have all the pieces necessary to formally revisit the <span class="math notranslate nohighlight">\(k\)</span>-means algorithm.</p>
|
||
<p>The <span class="math notranslate nohighlight">\(k\)</span>-means clustering algorithm goes as follows</p>
|
||
<ol class="arabic simple">
|
||
<li><p>For a given cluster assignment <span class="math notranslate nohighlight">\(C\)</span>, and <span class="math notranslate nohighlight">\(k\)</span> cluster means <span class="math notranslate nohighlight">\(\left\{m_1, \cdots, m_k\right\}\)</span>. We minimize the total cluster variance with respect to the cluster means <span class="math notranslate nohighlight">\(\{m_k\}\)</span> yielding the means of the currently assigned clusters.</p></li>
|
||
<li><p>Given a current set of <span class="math notranslate nohighlight">\(k\)</span> means <span class="math notranslate nohighlight">\(\{m_k\}\)</span> the total cluster variance is minimized by assigning each observation to the closest (current) cluster mean. That is $<span class="math notranslate nohighlight">\(C(i) = \underset{1\leq k\leq K}{\mathrm{argmin}} ||\boldsymbol{x_i} - \boldsymbol{m_k}||^2\)</span>$</p></li>
|
||
<li><p>Steps 1 and 2 are repeated until the assignments do not change.</p></li>
|
||
</ol>
|
||
<section id="codes-and-approaches">
|
||
<h2><span class="section-number">12.1. </span>Codes and Approaches<a class="headerlink" href="#codes-and-approaches" title="Link to this heading">#</a></h2>
|
||
<ol class="arabic simple">
|
||
<li><p>Before we start we specify a number <span class="math notranslate nohighlight">\(k\)</span> which is the number of clusters we want to try to separate our data into.</p></li>
|
||
<li><p>We initially choose <span class="math notranslate nohighlight">\(k\)</span> random data points in our data as our initial centroids, <em>or means</em> (this is where the name comes from).</p></li>
|
||
<li><p>Assign each data point to their closest centroid, based on the squared Euclidean distance.</p></li>
|
||
<li><p>For each of the <span class="math notranslate nohighlight">\(k\)</span> cluster we update the centroid by calculating new mean values for all the data points in the cluster.</p></li>
|
||
<li><p>Iteratively minimize the within cluster scatter by performing steps (3, 4) until the new assignments stop changing (can be to some tolerance) or until a maximum number of iterations have passed.</p></li>
|
||
</ol>
|
||
<p>Let us now program the most basic version of the algorithm using nothing but
|
||
Python with numpy arrays. This code is kept intentionally simple to gradually
|
||
progress our understanding. There is no vectorization of any kind, and even most
|
||
helper functions are not utilized.</p>
|
||
<p>We need first a dataset to do our cluster analysis on. In our case
|
||
this is a plain <em>vanilla</em> data set using random numbers using a
|
||
Gaussian distribution.</p>
|
||
<div class="cell docutils container">
|
||
<div class="cell_input docutils container">
|
||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
|
||
|
||
import time
|
||
import numpy as np
|
||
import tensorflow as tf
|
||
from matplotlib import image
|
||
import matplotlib.pyplot as plt
|
||
from sklearn.cluster import KMeans
|
||
from IPython.display import display
|
||
|
||
np.random.seed(2021)
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p>Next we define functions, for ease of use later, to generate Gaussians and to
|
||
set up our toy data set.</p>
|
||
<div class="cell docutils container">
|
||
<div class="cell_input docutils container">
|
||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def gaussian_points(dim=2, n_points=1000, mean_vector=np.array([0, 0]),
|
||
sample_variance=1):
|
||
"""
|
||
Very simple custom function to generate gaussian distributed point clusters
|
||
with variable dimension, number of points, means in each direction
|
||
(must match dim) and sample variance.
|
||
|
||
Inputs:
|
||
dim (int)
|
||
n_points (int)
|
||
mean_vector (np.array) (where index 0 is x, index 1 is y etc.)
|
||
sample_variance (float)
|
||
|
||
Returns:
|
||
data (np.array): with dimensions (dim x n_points)
|
||
"""
|
||
|
||
mean_matrix = np.zeros(dim) + mean_vector
|
||
covariance_matrix = np.eye(dim) * sample_variance
|
||
data = np.random.multivariate_normal(mean_matrix, covariance_matrix,
|
||
n_points)
|
||
return data
|
||
|
||
|
||
|
||
def generate_simple_clustering_dataset(dim=2, n_points=1000, plotting=True,
|
||
return_data=True):
|
||
"""
|
||
Toy model to illustrate k-means clustering
|
||
"""
|
||
|
||
data1 = gaussian_points(mean_vector=np.array([5, 5]))
|
||
data2 = gaussian_points()
|
||
data3 = gaussian_points(mean_vector=np.array([1, 4.5]))
|
||
data4 = gaussian_points(mean_vector=np.array([5, 1]))
|
||
data = np.concatenate((data1, data2, data3, data4), axis=0)
|
||
|
||
if plotting:
|
||
fig, ax = plt.subplots()
|
||
ax.scatter(data[:, 0], data[:, 1], alpha=0.2)
|
||
ax.set_title('Toy Model Dataset')
|
||
plt.show()
|
||
|
||
|
||
if return_data:
|
||
return data
|
||
|
||
|
||
data = generate_simple_clustering_dataset()
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p>With the above dataset we start
|
||
implementing the <span class="math notranslate nohighlight">\(k\)</span>-means algorithm.</p>
|
||
<div class="cell docutils container">
|
||
<div class="cell_input docutils container">
|
||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>
|
||
n_samples, dimensions = data.shape
|
||
n_clusters = 4
|
||
|
||
# we randomly initialize our centroids
|
||
np.random.seed(2021)
|
||
centroids = data[np.random.choice(n_samples, n_clusters, replace=False), :]
|
||
distances = np.zeros((n_samples, n_clusters))
|
||
|
||
# first we need to calculate the distance to each centroid from our data
|
||
for k in range(n_clusters):
|
||
for n in range(n_samples):
|
||
dist = 0
|
||
for d in range(dimensions):
|
||
dist += np.abs(data[n, d] - centroids[k, d])**2
|
||
distances[n, k] = dist
|
||
|
||
# we initialize an array to keep track of to which cluster each point belongs
|
||
# the way we set it up here the index tracks which point and the value which
|
||
# cluster the point belongs to
|
||
cluster_labels = np.zeros(n_samples, dtype='int')
|
||
|
||
# next we loop through our samples and for every point assign it to the cluster
|
||
# to which it has the smallest distance to
|
||
for n in range(n_samples):
|
||
# tracking variables (all of this is basically just an argmin)
|
||
smallest = 1e10
|
||
smallest_row_index = 1e10
|
||
for k in range(n_clusters):
|
||
if distances[n, k] < smallest:
|
||
smallest = distances[n, k]
|
||
smallest_row_index = k
|
||
|
||
cluster_labels[n] = smallest_row_index
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="cell docutils container">
|
||
<div class="cell_input docutils container">
|
||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>fig = plt.figure()
|
||
ax = fig.add_subplot()
|
||
unique_cluster_labels = np.unique(cluster_labels)
|
||
for i in unique_cluster_labels:
|
||
ax.scatter(data[cluster_labels == i, 0],
|
||
data[cluster_labels == i, 1],
|
||
label = i,
|
||
alpha = 0.2)
|
||
ax.scatter(centroids[:, 0], centroids[:, 1], c='black')
|
||
|
||
ax.set_title("First Grouping of Points to Centroids")
|
||
|
||
plt.show()
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p>So what do we have so far? We have ‘picked’ <span class="math notranslate nohighlight">\(k\)</span> centroids at random from our
|
||
data points. There are other ways of more intelligently choosing their
|
||
initializations, however for our purposes randomly is fine. Then we have
|
||
initialized an array ‘distances’ which holds the information of the distance,
|
||
<em>or dissimilarity</em>, of every point to of our centroids. Finally, we have
|
||
initialized an array ‘cluster_labels’ which according to our distances array
|
||
holds the information of to which centroid every point is assigned. This was the
|
||
first pass of our algorithm. Essentially, all we need to do now is repeat the
|
||
distance and assignment steps above until we have reached a desired convergence
|
||
or a maximum amount of iterations.</p>
|
||
<div class="cell docutils container">
|
||
<div class="cell_input docutils container">
|
||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>
|
||
max_iterations = 100
|
||
tolerance = 1e-8
|
||
|
||
for iteration in range(max_iterations):
|
||
prev_centroids = centroids.copy()
|
||
for k in range(n_clusters):
|
||
# this array will be used to update our centroid positions
|
||
vector_mean = np.zeros(dimensions)
|
||
mean_divisor = 0
|
||
for n in range(n_samples):
|
||
if cluster_labels[n] == k:
|
||
vector_mean += data[n, :]
|
||
mean_divisor += 1
|
||
|
||
# update according to the k means
|
||
centroids[k, :] = vector_mean / mean_divisor
|
||
|
||
# we find the dissimilarity
|
||
for k in range(n_clusters):
|
||
for n in range(n_samples):
|
||
dist = 0
|
||
for d in range(dimensions):
|
||
dist += np.abs(data[n, d] - centroids[k, d])**2
|
||
distances[n, k] = dist
|
||
|
||
# assign each point
|
||
for n in range(n_samples):
|
||
smallest = 1e10
|
||
smallest_row_index = 1e10
|
||
for k in range(n_clusters):
|
||
if distances[n, k] < smallest:
|
||
smallest = distances[n, k]
|
||
smallest_row_index = k
|
||
|
||
cluster_labels[n] = smallest_row_index
|
||
|
||
# convergence criteria
|
||
centroid_difference = np.sum(np.abs(centroids - prev_centroids))
|
||
if centroid_difference < tolerance:
|
||
print(f'Converged at iteration {iteration}')
|
||
break
|
||
|
||
elif iteration == max_iterations:
|
||
print(f'Did not converge in {max_iterations} iterations')
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p>We now have a simple , un-optimized <span class="math notranslate nohighlight">\(k\)</span>-means
|
||
clustering implementation. Lets plot the final result</p>
|
||
<div class="cell docutils container">
|
||
<div class="cell_input docutils container">
|
||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>fig = plt.figure()
|
||
ax = fig.add_subplot()
|
||
unique_cluster_labels = np.unique(cluster_labels)
|
||
for i in unique_cluster_labels:
|
||
ax.scatter(data[cluster_labels == i, 0],
|
||
data[cluster_labels == i, 1],
|
||
label = i,
|
||
alpha = 0.2)
|
||
ax.scatter(centroids[:, 0], centroids[:, 1], c='black')
|
||
|
||
ax.set_title("Final Result of K-means Clustering")
|
||
|
||
plt.show()
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="cell docutils container">
|
||
<div class="cell_input docutils container">
|
||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def naive_kmeans(data, n_clusters=4, max_iterations=100, tolerance=1e-8):
|
||
start_time = time.time()
|
||
|
||
n_samples, dimensions = data.shape
|
||
n_clusters = 4
|
||
#np.random.seed(2021)
|
||
centroids = data[np.random.choice(n_samples, n_clusters, replace=False), :]
|
||
distances = np.zeros((n_samples, n_clusters))
|
||
|
||
for k in range(n_clusters):
|
||
for n in range(n_samples):
|
||
dist = 0
|
||
for d in range(dimensions):
|
||
dist += np.abs(data[n, d] - centroids[k, d])**2
|
||
distances[n, k] = dist
|
||
|
||
cluster_labels = np.zeros(n_samples, dtype='int')
|
||
|
||
for n in range(n_samples):
|
||
smallest = 1e10
|
||
smallest_row_index = 1e10
|
||
for k in range(n_clusters):
|
||
if distances[n, k] < smallest:
|
||
smallest = distances[n, k]
|
||
smallest_row_index = k
|
||
|
||
cluster_labels[n] = smallest_row_index
|
||
|
||
for iteration in range(max_iterations):
|
||
prev_centroids = centroids.copy()
|
||
for k in range(n_clusters):
|
||
vector_mean = np.zeros(dimensions)
|
||
mean_divisor = 0
|
||
for n in range(n_samples):
|
||
if cluster_labels[n] == k:
|
||
vector_mean += data[n, :]
|
||
mean_divisor += 1
|
||
|
||
centroids[k, :] = vector_mean / mean_divisor
|
||
|
||
for k in range(n_clusters):
|
||
for n in range(n_samples):
|
||
dist = 0
|
||
for d in range(dimensions):
|
||
dist += np.abs(data[n, d] - centroids[k, d])**2
|
||
distances[n, k] = dist
|
||
|
||
for n in range(n_samples):
|
||
smallest = 1e10
|
||
smallest_row_index = 1e10
|
||
for k in range(n_clusters):
|
||
if distances[n, k] < smallest:
|
||
smallest = distances[n, k]
|
||
smallest_row_index = k
|
||
|
||
cluster_labels[n] = smallest_row_index
|
||
|
||
centroid_difference = np.sum(np.abs(centroids - prev_centroids))
|
||
if centroid_difference < tolerance:
|
||
print(f'Converged at iteration {iteration}')
|
||
print(f'Runtime: {time.time() - start_time} seconds')
|
||
|
||
return cluster_labels, centroids
|
||
|
||
print(f'Did not converge in {max_iterations} iterations')
|
||
print(f'Runtime: {time.time() - start_time} seconds')
|
||
|
||
return cluster_labels, centroids
|
||
</pre></div>
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