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

iris = load_iris()
X = iris['data']
y = ['setosa']*50+['versicolor']*50+['virginica']*50
decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
decision_tree =, y)
r = export_text(decision_tree, feature_names=iris['feature_names'])
|--- 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)
|--- 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.

Friday, May 17, 2019

Feelings toward immigration of people from other EU Member States in November 2018

In this post we will see a snippet about how to plot a part of the results of the eurobarometer survey released last March. In particular, we will focus on the responses to the following question:
Please tell me whether the following statement evokes a positive or negative feeling for you: Immigration of people from other EU Member States.
The data from the main spreadsheet reporting the results country by country was isolated in a csv file (then uploaded on github) so that it could be easily loaded in Pandas as follows:
import pandas as pd

# github gist
gist = ''
gist += '2c25b9b153192baf573ce3b744ea6a65/raw/'
gist += '5f3888f7f42caca58b2418ec5822425083b6d559/'
gist += 'immigration_from_EU_eurobarometer_2018.csv'
df = pd.read_csv(gist, index_col=0)
df = df[ x: not '\n' in x)]
df.sort_values(by=["Total 'Positive'"], inplace=True)

# from
country_names = {'BE' : 'Belgium',
'BG' : 'Bulgaria',
'CZ' : 'Czechia',
'DK' : 'Denmark',
'DE' : 'Germany',
'EE' : 'Estonia',
'IE' : 'Ireland',
'EL' : 'Greece',
'ES' : 'Spain',
'FR' : 'France',
'HR' : 'Croatia',
'IT' : 'Italy',
'CY' : 'Cyprus',
'LV' : 'Latvia',
'LT' : 'Lithuania',
'LU' : 'Luxembourg',
'HU' : 'Hungary',
'MT' : 'Malta',
'NL' : 'Netherlands',
'AT' : 'Austria',
'PL' : 'Poland',
'PT' : 'Portugal',
'RO' : 'Romania',
'SI' : 'Slovenia',
'SK' : 'Slovakia',
'FI' : 'Finland',
'SE' : 'Sweden',
'UK' : 'United Kingdom'}

df.index =
The idea is to create a bar chart with two sides, positive responses on the right and negative on the left. To do this, we can use the function barh and the attribute left can be used to stack the two subsets of responses ("Fairly positive/ negative" and "Very positive/negative"). The xticks also need to be adapted to reflect that the left side of the axis doesn't report values below zero. Here's the snippet:
import matplotlib.pyplot as plt
import numpy as np

country_idx = range(len(df))

plt.figure(figsize=(11, 14))
plt.barh(country_idx, df['Fairly positive'],
         color='deepskyblue',label='Fairly positive')
plt.barh(country_idx, df['Very positive'], left=df['Fairly positive'],
         color='dodgerblue', label='Very positive')
plt.barh(country_idx, -df['Fairly negative'],
         color='tomato', label='Fairly negative')
plt.barh(country_idx, -df['Very negative'], left=-df['Fairly negative'],
         color='firebrick', label='Very negative')

plt.yticks(country_idx, df.index)
plt.xlim([-100, 100])
plt.xticks(np.arange(-100, 101, 25), np.abs(np.arange(-100, 101, 25)))
plt.ylim([-.5, len(df)-.5])
title = 'Feelings toward immigration of people from\n'
title += 'other EU Member States in November 2018'
xlbl = 'negative            <<<       % responses       >>>            positive'
plt.legend(loc='lower right')

bbox_props = dict(fc="white", ec="k", lw=2) 
plt.text(-95, 27, 'twitter: @justglowing \n',
         ha="left", va="center", size=11, bbox=bbox_props)

From the chart we note that the percentage of positive responses per country is mostly above 50% while the negative ones reach 50% only in two cases. We also see that Ireland and Sweden are the countries with the most positive responses, while Czechia (yes, that's Chech Republic :) is the country with most negative responses, though Cypriots also gave a similar number of "Very negative" responses.

Wednesday, April 17, 2019

Visualizing atmospheric carbon dioxide

Let's have a look at how to create a visualization that shows how CO2 concentrations evolved in the atmosphere. First, we fetched from the Earth System Research Laboratory website like follows:
import pandas as pd

data_url = ''
co2_data = pd.read_csv(data_url, sep='\s+', comment='#', na_values=-999.99,
                       names=['year', 'month', 'day', 'decimal', 'ppm', 
                       'days', '1_yr_ago',  '10_yr_ago', 'since_1800'])

co2_data['timestamp'] = co2_data.apply(lambda x: pd.Timestamp(year=int(x.year),
co2_data = co2_data[['timestamp', 'ppm']].set_index('timestamp').ffill()
Then, we group the it by year and month at the same time storing the result in a matrix where each element represents the concentration in a specific year and month:
import numpy as np
import matplotlib.pyplot as plt
from calendar import month_abbr

co2_data = co2_data['1975':'2018']
n_years = co2_data.index.year.max() - co2_data.index.year.min()
z = np.ones((n_years +1 , 12)) * np.min(co2_data.ppm)
for d, y in co2_data.groupby([co2_data.index.year, co2_data.index.month]):
  z[co2_data.index.year.max() - d[0], d[1] - 1] = y.mean()[0]
plt.figure(figsize=(10, 14))
plt.pcolor(np.flipud(z), cmap='hot_r')
plt.yticks(np.arange(0, n_years+1)+.5,
           range(co2_data.index.year.min(), co2_data.index.year.max()+1));
plt.xticks(np.arange(13)-.5, month_abbr)
plt.xlim((0, 12))
plt.colorbar().set_label('Atmospheric Carbon Dioxide in ppm')

This visualization makes us able to compare the CO2 levels month by month with single glance. For example, we see that the period from April to June gets dark quicker than other periods, meaning that it contains the highest levels every year. Conversely, the period that goes from September to October gets darker more slowly, meaning that it's the period with the lowest CO2 levels. Also, looking at the color bar we note that in 43 years there was a 80 ppm increase.

Is this bad for the planet earth? Reading Hansen et al. (2008) we can classify CO2 levels less than 300 ppm as safe, levels between 300 and 350 ppm as dangerous, while levels beyond 350 ppm are considered catastrophic. According to this, the chart is a sad picture of how the levels transitioned from dangerous to catastrophic!

Concerned by this situation I created the CO2 Forecast twitter account where I'll publish short and long term forecasts of CO2 levels in the atmosphere.