# Model-Free Classification Variable Selection Explained¶

In this tutorial we do a deep-dive into variable selection in a classification problem, and we illustrate that the analysis provided by the kxy package makes intuitive sense on the bank note UCI classification dataset.

First we define the required imports, and load the dataset.

In [1]:

%load_ext autoreload
import os
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import kxy

In [2]:

url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00267/' \
'data_banknote_authentication.txt'
df = pd.read_csv(url, names=['Variance', 'Skewness', 'Kurtosis', 'Entropy', 'Is Fake'])


Next, we normalize the data so that each input takes value in $$[0, 1]$$, so as to ease visualization. All input importance analyses performed by the kxy package are robust to increasing transformations, including the foregoing normalization. Nonetheless, we will take a copy of the data before normalization, which we will use for analyses, the normalized data being used only for visualization.

In [3]:

ef = df.copy() # Copy used for analysis.
# Normalization to ease vizualization
df[['Variance', 'Skewness', 'Kurtosis', 'Entropy']] = \
(df[['Variance', 'Skewness', 'Kurtosis', 'Entropy']] - \
df[['Variance', 'Skewness', 'Kurtosis', 'Entropy']].min(axis=0))
df[['Variance', 'Skewness', 'Kurtosis', 'Entropy']] /= \
df[['Variance', 'Skewness', 'Kurtosis', 'Entropy']].max(axis=0)


## Univariate Variable Importance¶

### Intuition¶

We begin by forming an intuition for what we would expect out of an individual input importance analysis.

We recall that the unvivariate variable importance analysis aims at quantifying the usefulness of each input for predicting the output, when used in isolation.

Intuitively, saying that an input $$x_i$$ is informative about a categorical label $$y$$ in isolation is the same as saying that observing the value of $$x_i$$ is useful for inferring the value of the label/class $$y$$. For this to be true, it ought to be the case that the collection of values of $$x_i$$ corresponding to a given class or value of $$y$$, should be sufficiently different from the collections of values of $$x_i$$ corresponding to the other classes.

The more these collections are different, the less ambiguity there will be in inferring $$y$$ from $$x_i$$, and therefore the more useful $$x_i$$ will be for inferring $$y$$ in isolation.

In the case of the bank note dataset, for every one of the four inputs of interest, we plot all values on the same line, and color each point red or green depending on whether the observed input came from a fake note or not.

The more distinguishable the collection of red ticks is from the collection of green ticks, the more the corresponding input is useful at predicting whether or not a bank note is a forgery.

In [4]:

import pylab as plt
import numpy as np

fig, ax = plt.subplots(1, 1, figsize=(8, 8))

y = df['Is Fake'].values.astype(bool)
v = df['Variance'].values
s = df['Skewness'].values
k = df['Kurtosis'].values
e = df['Entropy'].values

bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9)
ax.plot(v[y], 0.0*np.ones_like(v[y]), '|', color='r', linewidth=2, markersize=10,\
label='Fake')
ax.plot(v[~y], 0.0*np.ones_like(v[~y]), '|', color='g', linewidth=2, markersize=10,\
label='Geniune')
ax.text(-0.1, 0, "Variance", ha="center", va="center", size=15, bbox=bbox_props)
ax.plot(s[y], 0.1*np.ones_like(s[y]), '|', color='r', linewidth=2, markersize=10)
ax.plot(s[~y], 0.1*np.ones_like(s[~y]), '|', color='g', linewidth=2, markersize=10)
ax.text(-0.1, 0.1, "Skewness", ha="center", va="center", size=15, bbox=bbox_props)
ax.plot(k[y], 0.2*np.ones_like(k[y]), '|', color='r', linewidth=2, markersize=10)
ax.plot(k[~y], 0.2*np.ones_like(k[~y]), '|', color='g', linewidth=2, markersize=10)
ax.text(-0.1, 0.2, "Kurtosis", ha="center", va="center", size=15, bbox=bbox_props)
ax.plot(e[y], 0.3*np.ones_like(e[y]), '|', color='r', linewidth=2, markersize=10)
ax.plot(e[~y], 0.3*np.ones_like(e[~y]), '|', color='g', linewidth=2, markersize=10)
ax.text(-0.1, 0.3, "Entropy", ha="center", va="center", size=15, bbox=bbox_props)
ax.axes.yaxis.set_visible(False)
plt.legend()
plt.show()

In [5]:

'Percentage of points with a normalized kurtosis higher than 0.6: %.2f%%' %\
(100.*(df['Kurtosis'] > 0.6).mean())

Out[5]:

'Percentage of points with a normalized kurtosis higher than 0.6: 7.43%'


Clearly, from the plot above, it is visually very hard to differenciate geniune bank notes from forgeries solely using the Entropy input.

As for the Kurtosis variable, while a normalized kurtosis higher than 0.6 is a strong indication that the bank note is a forgery, this only happens about 7% of the time. When the normalized kurtosis is lower than 0.6 on the other hand, it is very hard to distinguish geniune notes from forgeries using the kurtosis alone.

The Skewness input is visually more useful than the previously mentioned two inputs, but the Variance input is clearly the most useful. Geniune bank notes tend to have a higher variance, and forgeries tend to have a lower variance.

### Validation¶

Let’s see if the univariate variable importancee analysis of the kxy package is consistent with these observations.

In [6]:

var_selection_analysis = ef.kxy.variable_selection_analysis('Is Fake')

In [7]:

var_selection_analysis.data[['Univariate Achievable R^2',\
'Univariate Achievable True Log-Likelihood Per Sample', \
'Univariate Achievable Accuracy', \
'Univariate Mutual Information (nats)']].style.use(\
var_selection_analysis.export())

Out[7]:

Univariate Achievable R^2 Univariate Achievable True Log-Likelihood Per Sample Univariate Achievable Accuracy Univariate Mutual Information (nats)
Variable
Variance 0.527296 -0.312355 0.906 0.374643
Skewness 0.182617 -0.586174 0.727 0.100824
Kurtosis 0.036426 -0.668445 0.611 0.018553
Entropy 0.00186626 -0.686064 0.559 0.000934

The columns Univariate Achievable * represent the performance that can be achieved by using the corresponding variable in isolation.

The Univariate Mutual Information column represents the mutual information (in nats) between the corresponding variable and the label.

We note that both the ranking and the magnitudes of univariate performance scores calculated by the kxy package are indeed consistent with our crude visual analysis.

## Multivariate Variable Selection¶

### Intuition¶

Moving on to marginal input importance, the objective of this analysis is to quantify the marginal usefulness of each input for predicting the label. Specifically, we are no longer exclusively interested in determining how good inputs can be when used in isolation to predict the label, but rather in studying what happens when some inputs are used together. Of particular interest is the ability to detect inputs that are redundant and inputs that are complementary.

Redundant inputs should be avoided, as they might result in overfitting during the training phase, either as a result of ill-conditioning (e.g. the effect of linearly dependent inputs in linear regression), or as a result of the model complexity of the classifier used increasing as a function of the number of inputs (i.e. adding redundant inputs could artificially increase model complexity for the same effective number of inputs, to the point of overfitting). On the other hand, an input that is complementary to other inputs could shed some light where said inputs are not sufficiently informative to accurately predict the label.

Let’s try to form an intuitive understanding of the marginal usefulness of our four inputs for predicting whether a bank note is a forgery. As the previous analysis suggested, clearly Variance is the input with the highest usefulness when used in isolation.

To figure out which of the three remaining inputs would complement Variance the best, we make three 2D scatter plots with Variance as the x-axis and the other input as the y-axis and, as always, we color dots green (resp. red) when the associated inputs came from a geniune (resp. fake) bank note. Intuitively, the input that complements Variance the best is the one where the collections of green and red points are the most distinguishable. The more distinguishable these two collections, the more accurate it would be to predict whether the bank note is a forgery. The more the two collections overlap, the more ambiguous our prediction will be.

In [8]:

fig, ax = plt.subplots(2, 2, figsize=(15, 10))
df.plot.scatter(ax=ax[0, 0], x='Variance', y='Skewness', c=df['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
df.plot.scatter(ax=ax[0, 1], x='Variance', y='Kurtosis', c=df['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
df.plot.scatter(ax=ax[1, 0], x='Variance', y='Entropy', c=df['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
theta = np.arange(0, 2*np.pi, 0.01)
bound_x = 0.55 + 0.15*np.cos(theta)
bound_y = 0.6 + 0.15*np.sin(theta)
ax[0, 0].plot(bound_x, bound_y, '.', c='b')

selector = (((df['Variance']-0.55)/0.15)**2 + ((df['Skewness']-0.6)/0.15)**2) <=1
cf = df[selector]
cf.plot.scatter(ax=ax[1, 1], x='Variance', y='Skewness', c=cf['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
ax[1, 1].plot(bound_x, bound_y, '.', c='b')
plt.show()


As it can be seen above, it is in the plot Variance x Skewness that the collections of green and red points overlap the least. Thus, one would conclude that Skewness is the input that should be expected to complement Variance the most.

To qualitatively determine which of Entropy or Kurtosis would complement the pair (Variance, Skewness) the most, we identity values of the pair (Variance, Skewness) that are jointly inconclusive about whether the bank note is a forgery. This is the region of the Variance x Skewness plane where green dots and red dots overlap. We have crudely idendified this region in the top-left plot with the blue ellipse, a zoomed-in version thereof is displayed in the bottom right plot.

We then seek to know which of Entropy and Kurtosis can best help alleviate the ambiguity inherent to that region. To do so, we consider all the bank notes that fall within the blue ellipse above, and we plot them on the four planes Variance x Kurtosis, Variance x Entropy, Skewness x Kurtosis, and Skewness x Entropy, in an attempt to figure out how much ambiguity we can remove at a glance by knowing Entropy or Kurtosis.

In [9]:

fig, ax = plt.subplots(2, 2, figsize=(15, 10))
cf.plot.scatter(ax=ax[0, 0], x='Variance', y='Kurtosis', c=cf['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
cf.plot.scatter(ax=ax[0, 1], x='Variance', y='Entropy', c=cf['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
cf.plot.scatter(ax=ax[1, 0], x='Skewness', y='Kurtosis', c=cf['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
cf.plot.scatter(ax=ax[1, 1], x='Skewness', y='Entropy', c=cf['Is Fake']\
.apply(lambda x: 'r' if x == 1. else 'g'))
plt.show()


As it turns out, knowing the value of the input Kurtosis allows us to almost perfectly differentiate geniune notes from forgeries, among all notes that were previously ambiguous to tell apart solely using Variance and Skewness. On the other hand, although it helps alleviate some ambiguity, the input Entropy appears not as effective as Kurtosis.

Thus, the third input in decreasing order of marginal usefulness is expected to be Kurtosis, Entropy being the least marginally useful.

### Validation¶

Let’s see if the incremental input importance analysis of the kxy package is consistent with these observations.

In [10]:

var_selection_analysis

Out[10]:

Selection Order Univariate Achievable R^2 Maximum Marginal R^2 Increase Running Achievable R^2 Running Achievable R^2 (%) Univariate Achievable Accuracy Maximum Marginal Accuracy Increase Running Achievable Accuracy Univariate Mutual Information (nats) Conditional Mutual Information (nats) Univariate Achievable True Log-Likelihood Per Sample Maximum Marginal True Log-Likelihood Per Sample Increase Running Achievable True Log-Likelihood Per Sample Running Mutual Information (nats) Running Mutual Information (%)
Variable
Variance 1 0.527296 0.527296 0.527296 77.0074 0.906 0.351 0.906 0.374643 0.374643 -0.312355 0.374643 -0.312355 0.374643 64.9103
Skewness 2 0.182617 0.0355081 0.562804 82.1931 0.727 0.016 0.922 0.100824 0.039044 -0.586174 0.039044 -0.273311 0.413687 71.6751
Kurtosis 3 0.036426 0.0750919 0.637896 93.1596 0.611 0.034 0.956 0.018553 0.094225 -0.668445 0.094225 -0.179086 0.507912 88.0004
Entropy 4 0.00186626 0.0468384 0.684734 100 0.559 0.021 0.977 0.000934 0.069258 -0.686064 0.069258 -0.109828 0.57717 100

The kxy package selected variables in the same order as our qualitative analysis (see the Selection Order column).

The Runing * columns represent the performance that can be achieved by using the variable in the corresponding row, and all variables selected prior to it (i.e. with a smaller Selection Order).

The Maximum Marginal * Increase columns represent the performance increase that can be achieved by adding the variable in the corresponding row to the set of variables selected prior to it (i.e. with a smaller Selection Order).

The Conditional Mutual Information column represents the mutual information between the variable in the corresponding row and the label, conditional on all variables selected prior to it (i.e. with a smaller Selection Order).