Wednesday, September 11, 2019

Organizing movie covers with Neural Networks

In this post we will see how to organize a set of movie covers by similarity on a 2D grid using a particular type of Neural Network called Self Organizing Map (SOM). First, let's load the movie covers of the top 100 movies according to IMDB (the files can be downloaded here) and convert the images in samples that we can use to feed the Neural Network:
import numpy as np
import imageio
from glob import glob
from sklearn.preprocessing import StandardScaler

# covers of the top 100 movies on www.imdb.com/chart/top
# (the 13th of August 2019)
data = []
all_covers = glob('movie_covers/*.jpg')
for cover_jpg in all_covers:
data.append(cover.reshape(np.prod(cover.shape)))

scaler = StandardScaler()
data = scaler.fit_transform(data)
In the snippet above we load every image and for each of them we stack the color values of each pixel in a one dimensional vector. After loading all the images a standard scaling is applied to have all the values with mean 0 and standard deviation equal to 1. This scaling strategies often turns out to be quite successful when working with SOMs. Now we can train our model:
from minisom import MiniSom

w = 10
h = 10
som = MiniSom(h, w, len(data), learning_rate=0.5,
sigma=3, neighborhood_function='triangle')

som.train_random(data, 2500, verbose=True)
win_map = som.win_map(data)
Here we use Minisom, a lean implementation of the SOM, to implement a 10-by-10 map of neurons. Each movie cover is mapped in a neuron and we can display the results as follows:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid

fig = plt.figure(figsize=(30, 20))
grid = ImageGrid(fig, 111,

def place_image(i, img):
img = (scaler.inverse_transform(img)).astype(int)
grid[i].imshow(img.reshape(original_shape))
grid[i].axis('off')

to_fill = []
collided = []

for i in range(w*h):
position = np.unravel_index(i, (h, w))
if position in win_map:
img = win_map[position]
collided += win_map[position][1:]
place_image(i, img)
else:
to_fill.append(i)

collided = collided[::-1]
for i in to_fill:
position = np.unravel_index(i, (h, w))
img = collided.pop()
place_image(i, img)

plt.show()
Since some images can be mapped in the same neuron, we first draw all the covers picking only one per neuron, then we fill the empty spaces of the map with covers that have been mapped in nearby neurons but have not been plotted yet.

This is the result:

Where to go next:
• Read more about how Self Organizing Maps work here.
• Check out how to install Minisom here.

Sunday, August 11, 2019

Visualizing distributions with scatter plots in matplotlib

Let's say that we want to study the time between the end of a marked point and next serve in a tennis game. After gathering our data, the first thing that we can do is to draw a histogram of the variable that we are interested in:

import pandas as pd
import matplotlib.pyplot as plt

url = 'https://raw.githubusercontent.com/fivethirtyeight'
url += '/data/master/tennis-time/serve_times.csv'

plt.hist(event.seconds_before_next_point, bins=10)
plt.xlabel('Seconds before next serve')
plt.show()

The histogram reveals some interesting aspects of the distribution, indeed we can see that data is slightly skewed to the right and that on average the server takes 20 seconds. However, we couldn't tell how many time the serves happens before 10 seconds or after 35. Of course, one could increase the bins of the histogram, but this would lead to a chart which is not particularly elegant and that might hide some other details.

To have a better understanding of the situation we can draw a scatter plot of the variable we are studying:
import numpy as np
from scipy.stats.kde import gaussian_kde

def distribution_scatter(x, symmetric=True, cmap=None, size=None):
"""
Plot the distribution of x showing all the points.
The x axis represents the samples in x
and the y axis is function of the probability of x
and random assignment.

Returns the position on the y axis.
"""
pdf = gaussian_kde(x)
w = np.random.rand(len(x))
if symmetric:
w = w*2-1
pseudo_y = pdf(x) * w
if cmap:
plt.scatter(x, pseudo_y, c=x, cmap=cmap, s=size)
else:
plt.scatter(x, pseudo_y, s=size)
return pseudo_y

In this chart each sample is represented with a point and the spread of the points in the y direction depends on the probability of occurrence. In this case we can easily see that 4 serves happened before 10 seconds and 3 after 35.

Since we're not really interested on the values on y axis but only on the spread, we can remove the axis and add few details on the outliers to enrich the chart:

url = 'https://raw.githubusercontent.com/fivethirtyeight'
url += '/data/master/tennis-time/serve_times.csv'

plt.figure(figsize=(7, 11))
title = 'Time in seconds between'
title += '\nend of marked point and next serve'
title += '\nat 2015 French Open'
plt.title(title, loc='left', fontsize=18, color='gray')
py = distribution_scatter(event.seconds_before_next_point, cmap='cool');

cut_h = np.percentile(event.seconds_before_next_point, 98)
outliers = event.seconds_before_next_point> cut_h

ha = {True: 'right', False: 'left'}
for x, y, c in zip(event[outliers].seconds_before_next_point,
py[outliers],
event[outliers].server):
plt.text(x, y+.0005, c,
ha=ha[x<0], va='bottom', fontsize=12)

plt.xlabel('Seconds before next serve', fontsize=15)
plt.gca().spines['left'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['top'].set_visible(False)
plt.yticks([])
plt.xticks(np.arange(5, 41, 5))
plt.xlim([5, 40])
plt.show()

Where to go next:

Friday, June 7, 2019

Exporting Decision Trees in textual format with sklearn

In the past we have covered Decision Trees showing how interpretable these models can be (see the tutorials here). In the previous tutorials we have exported the rules of the models using the function export_graphviz from sklearn and visualized the output of this function in a graphical way with an external tool which is not easy to install in some cases. Luckily, since version 0.21.2, scikit-learn offers the possibility to export Decision Trees in a textual format (I implemented this feature personally ^_^) and in this post we will see an example how of to use this new feature.

Let's train a tree with 2 layers on the famous iris dataset using all the data and print the resulting rules using the brand new function export_text:
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree.export import export_text
from sklearn.datasets import load_iris

X = iris['data']
y = ['setosa']*50+['versicolor']*50+['virginica']*50
decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
decision_tree = decision_tree.fit(X, y)
r = export_text(decision_tree, feature_names=iris['feature_names'])
print(r)
|--- petal width (cm) <= 0.80
|   |--- class: setosa
|--- petal width (cm) >  0.80
|   |--- petal width (cm) <= 1.75
|   |   |--- class: versicolor
|   |--- petal width (cm) >  1.75
|   |   |--- class: virginica
Reading the them we note that if the feature petal width is less or equal than 80mm the samples are always classified as setosa. Otherwise if the petal width is less or equal than 1.75cm they're classified as versicolor or as virginica if the petal width is more than 1.75cm. This model might well suffer from overfitting but tells us some important details of the data. It's easy to note that the petal width is the only feature used, we could even say that the petal width is small for setosa samples, medium for versicolor and large for virginica.

To understand how the rules separate the labels we can also print the number of samples from each class (class weights) on the leaves:
r = export_text(decision_tree, feature_names=iris['feature_names'],
decimals=0, show_weights=True)
print(r)
|--- petal width (cm) <= 1
|   |--- weights: [50, 0, 0] class: setosa
|--- petal width (cm) >  1
|   |--- petal width (cm) <= 2
|   |   |--- weights: [0, 49, 5] class: versicolor
|   |--- petal width (cm) >  2
|   |   |--- weights: [0, 1, 45] class: virginica
Here we have the number of samples per class among square brackets. Recalling that we have 50 samples per class, we see that all the samples labeled as setosa are correctly modelled by the tree while for 5 virginica and 1 versicolor the model fails to capture the information given by the label.

Check out the documentation of the function export_text to discover all its capabilities here.