In this article by Bastiaan Sjardin, Luca Massaron, and Alberto Boschetti, the authors of the book Large Scale Machine Learning with Python, we will try to create new features and variables at scale in the observation matrix. We will introduce the unsupervised methods and illustrate principal component analysis (PCA)—an effective way to reduce the number of features.
(For more resources related to this topic, see here.)
Unsupervised methods
Unsupervised learning is a branch of machine learning whose algorithms reveal inferences from data without an explicit label (unlabeled data). The goal of such techniques is to extract hidden patterns and group similar data.
In these algorithms, the unknown parameters of interests of each observation (the group membership and topic composition, for instance) are often modeled as latent variables (or a series of hidden variables), hidden in the system of observed variables that cannot be observed directly, but only deduced from the past and present outputs of the system. Typically, the output of the system contains noise, which makes this operation harder.
In common problems, unsupervised methods are used in two main situations:
With labeled datasets to extract additional features to be processed by the classifier/regressor down to the processing chain. Enhanced by additional features, they may perform better.
With labeled or unlabeled datasets to extract some information about the structure of the data. This class of algorithms is commonly used during the Exploratory Data Analysis (EDA) phase of the modeling.
First at all, before starting with our illustration, let's import the modules that will be necessary along the article in our notebook:
In : import matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
%matplotlib inline
import matplotlib.cm as cm
import copy
import tempfile
import os
Feature decomposition – PCA
PCA is an algorithm commonly used to decompose the dimensions of an input signal and keep just the principal ones. From a mathematical perspective, PCA performs an orthogonal transformation of the observation matrix, outputting a set of linear uncorrelated variables, named principal components. The output variables form a basis set, where each component is orthonormal to the others. Also, it's possible to rank the output components (in order to use just the principal ones) as the first component is the one containing the largest possible variance of the input dataset, the second is orthogonal to the first (by definition) and contains the largest possible variance of the residual signal, and the third is orthogonal to the first two and contains the largest possible variance of the residual signal, and so on.
A generic transformation with PCA can be expressed as a projection to a space. If just the principal components are taken from the transformation basis, the output space will have a smaller dimensionality than the input one. Mathematically, it can be expressed as follows:
Here, X is a generic point of the training set of dimension N, T is the transformation matrix coming from PCA, and is the output vector. Note that the symbol indicates a dot product in this matrix equation. From a practical perspective, also note that all the features of X must be zero-centered before doing this operation.
Let's now start with a practical example; later, we will explain math PCA in depth. In this example, we will create a dummy dataset composed of two blobs of points—one cantered in (-5, 0) and the other one in (5,5).Let's use PCA to transform the dataset and plot the output compared to the input. In this simple example, we will use all the features, that is, we will not perform feature reduction:
In:from sklearn.datasets.samples_generator import make_blobs
from sklearn.decomposition import PCA
X, y = make_blobs(n_samples=1000, random_state=101,
centers=[[-5, 0], [5, 5]])
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
pca_comp = pca.components_.T
test_point = np.matrix([5, -2])
test_point_pca = pca.transform(test_point)
plt.subplot(1, 2, 1)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='none')
plt.quiver(0, 0, pca_comp[:,0], pca_comp[:,1], width=0.02,
scale=5, color='orange')
plt.plot(test_point[0, 0], test_point[0, 1], 'o')
plt.title('Input dataset')
plt.subplot(1, 2, 2)
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, edgecolors='none')
plt.plot(test_point_pca[0, 0], test_point_pca[0, 1], 'o')
plt.title('After "lossless" PCA')
plt.show()
As you can see, the output is more organized than the original features' space and, if the next task is a classification, it would require just one feature of the dataset, saving almost 50% of the space and computation needed. In the image, you can clearly see the core of PCA: it's just a projection of the input dataset to the transformation basis drawn in the image on the left in orange. Are you unsure about this? Let's test it:
In:print "The blue point is in", test_point[0, :]
print "After the transformation is in", test_point_pca[0, :]
print "Since (X-MEAN) * PCA_MATRIX = ", np.dot(test_point -
pca.mean_, pca_comp)
Out:The blue point is in [[ 5 -2]]
After the transformation is in [-2.34969911 -6.2575445 ]
Since (X-MEAN) * PCA_MATRIX = [[-2.34969911 -6.2575445 ]
Now, let's dig into the core problem: how is it possible to generate T from the training set? It should contain orthonormal vectors, and the vectors should be ranked according the quantity of variance (that is, the energy or information carried by the observation matrix) that they can explain. Many solutions have been implemented, but the most common implementation is based on Singular Value Decomposition (SVD).
SVD is a technique that decomposes any matrix M into three matrixes () with special properties and whose multiplication gives back M again:
Specifically, given M, a matrix of m rows and n columns, the resulting elements of the equivalence are as follows:
U is a matrix m x m (square matrix), it's unitary, and its columns form an orthonormal basis. Also, they're named left singular vectors, or input singular vectors, and they're the eigenvectors of the matrix product .
is a matrix m x n, which has only non-zero elements on its diagonal. These values are named singular values, are all non-negative, and are the eigenvalues of both and .
W is a unitary matrix n x n (square matrix), its columns form an orthonormal basis, and they're named right (or output) singular vectors. Also, they are the eigenvectors of the matrix product .
Why is this needed? The solution is pretty easy: the goal of PCA is to try and estimate the directions where the variance of the input dataset is larger. For this, we first need to remove the mean from each feature and then operate on the covariance matrix .
Given that, by decomposing the matrix X with SVD, we have the columns of the matrix W that are the principal components of the covariance (that is, the matrix T we are looking for), the diagonal of that contains the variance explained by the principal components, and the columns of U the principal components. Here's why PCA is always done with SVD.
Let's see it now on a real example. Let's test it on the Iris dataset, extracting the first two principal components (that is, passing from a dataset composed by four features to one composed by two):
In:from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
print "Iris dataset contains", X.shape[1], "features"
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
print "After PCA, it contains", X_pca.shape[1], "features"
print "The variance is [% of original]:",
sum(pca.explained_variance_ratio_)
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, edgecolors='none')
plt.title('First 2 principal components of Iris dataset')
plt.show()
Out:Iris dataset contains 4 features
After PCA, it contains 2 features
The variance is [% of original]: 0.977631775025
This is the analysis of the outputs of the process:
The explained variance is almost 98% of the original variance from the input. The number of features has been halved, but only 2% of the information is not in the output, hopefully just noise.
From a visual inspection, it seems that the different classes, composing the Iris dataset, are separated from each other. This means that a classifier working on such a reduced set will have comparable performance in terms of accuracy, but will be faster to train and run prediction.
As a proof of the second point, let's now try to train and test two classifiers, one using the original dataset and another using the reduced set, and print their accuracy:
In:from sklearn.linear_model import SGDClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
def test_classification_accuracy(X_in, y_in):
X_train, X_test, y_train, y_test =
train_test_split(X_in, y_in, random_state=101,
train_size=0.50)
clf = SGDClassifier('log', random_state=101)
clf.fit(X_train, y_train)
return accuracy_score(y_test, clf.predict(X_test))
print "SGDClassifier accuracy on Iris set:",
test_classification_accuracy(X, y)
print "SGDClassifier accuracy on Iris set after PCA (2 compo-nents):",
test_classification_accuracy(X_pca, y)
Out:SGDClassifier accuracy on Iris set: 0.586666666667
SGDClassifier accuracy on Iris set after PCA (2 components): 0.72
As you can see, this technique not only reduces the complexity and space of the learner down in the chain, but also helps achieve generalization (exactly as a Ridge or Lasso regularization).
Now, if you are unsure how many components should be in the output, typically as a rule of thumb, choose the minimum number that is able to explain at least 90% (or 95%) of the input variance. Empirically, such a choice usually ensures that only the noise is cut off.
So far, everything seems perfect: we found a great solution to reduce the number of features, building some with very high predictive power, and we also have a rule of thumb to guess the right number of them. Let's now check how scalable this solution is: we're investigating how it scales when the number of observations and features increases. The first thing to note is that the SVD algorithm, the core piece of PCA, is not stochastic; therefore, it needs the whole matrix in order to be able to extract its principal components. Now, let's see how scalable PCA is in practice on some synthetic datasets with an increasing number of features and observations. We will perform a full (lossless) decomposition (the augment while instantiating the object PCA is None), as asking for a lower number of features doesn't impact the performance (it's just a matter of slicing the output matrixes of SVD).
In the following code, we first create matrices with 10 thousand points and 20, 50, 100, 250, 1,000, and 2,500 features to be processed by PCA. Then, we create matrixes with 100 features and 1, 5, 10, 25, 50, and 100 thousands observations to be processed with PCA:
In:import time
def check_scalability(test_pca):
pylab.rcParams['figure.figsize'] = (10, 4)
# FEATURES
n_points = 10000
n_features = [20, 50, 100, 250, 500, 1000, 2500]
time_results = []
for n_feature in n_features:
X, _ = make_blobs(n_points, n_features=n_feature,
random_state=101)
pca = copy.deepcopy(test_pca)
tik = time.time()
pca.fit(X)
time_results.append(time.time()-tik)
plt.subplot(1, 2, 1)
plt.plot(n_features, time_results, 'o--')
plt.title('Feature scalability')
plt.xlabel('Num. of features')
plt.ylabel('Training time [s]')
# OBSERVATIONS
n_features = 100
n_observations = [1000, 5000, 10000, 25000, 50000, 100000]
time_results = []
for n_points in n_observations:
X, _ = make_blobs(n_points, n_features=n_features,
random_state=101)
pca = copy.deepcopy(test_pca)
tik = time.time()
pca.fit(X)
time_results.append(time.time()-tik)
plt.subplot(1, 2, 2)
plt.plot(n_observations, time_results, 'o--')
plt.title('Observations scalability')
plt.xlabel('Num. of training observations')
plt.ylabel('Training time [s]')
plt.show()
check_scalability(PCA(None))
Out:
As you can clearly see, PCA based on SVD is not scalable: if the number of features increases linearly, the time needed to train the algorithm increases exponentially. Also, the time needed to process a matrix with a few hundred observations becomes too high and (not shown in the image) the memory consumption makes the problem unfeasible for a domestic computer (with 16 or less GB of RAM).It seems clear that a PCA based on SVD is not the solution for big data: fortunately, in the recent years, many workarounds have been introduced.
Summary
In this article, we've introduced a popular unsupervised learner able to scale to cope with big data. PCA is able to reduce the number of features by creating ones containing the majority of variance (that is, the principal ones).
You can also refer the following books on the similar topics:
R Machine Learning Essentials: https://www.packtpub.com/big-data-and-business-intelligence/r-machine-learning-essentials
R Machine Learning By Example: https://www.packtpub.com/big-data-and-business-intelligence/r-machine-learning-example
Machine Learning with R - Second Edition: https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-r-second-edition
Resources for Article:
Further resources on this subject:
Machine Learning Tasks [article]
Introduction to Clustering and Unsupervised Learning [article]
Clustering and Other Unsupervised Learning Methods [article]
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