Think Bayes

This notebook presents code and exercises from Think Bayes, second edition.

Copyright 2016 Allen B. Downey

MIT License:

In [1]:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline

# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'

import math
import numpy as np

from thinkbayes2 import Pmf, Cdf, Suite
import thinkplot

The flea beetle problem

Different species of flea beetle can be distinguished by the width and angle of the aedeagus. The data below includes measurements and know species classification for 74 specimens.

Suppose you discover a new specimen under conditions where it is equally likely to be any of the three species. You measure the aedeagus and width 140 microns and angle 15 (in multiples of 7.5 degrees). What is the probability that it belongs to each species?

This problem is based on this data story on DASL

Datafile Name: Flea Beetles

Datafile Subjects: Biology

Story Names: Flea Beetles

Reference: Lubischew, A.A. (1962) On the use of discriminant functions in taxonomy. Biometrics, 18, 455-477. Also found in: Hand, D.J., et al. (1994) A Handbook of Small Data Sets, London: Chapman & Hall, 254-255.

Authorization: Contact Authors

Description: Data were collected on the genus of flea beetle Chaetocnema, which contains three species: concinna (Con), heikertingeri (Hei), and heptapotamica (Hep). Measurements were made on the width and angle of the aedeagus of each beetle. The goal of the original study was to form a classification rule to distinguish the three species.

Number of cases: 74

Variable Names:

Width: The maximal width of aedeagus in the forpart (in microns)

Angle: The front angle of the aedeagus (1 unit = 7.5 degrees)

Species: Species of flea beetle from the genus Chaetocnema

We can read the data from this file:

In [2]:
import pandas as pd

df = pd.read_csv('../data/flea_beetles.csv', delimiter='\t')
Width Angle Species
0 150 15 Con
1 147 13 Con
2 144 14 Con
3 144 16 Con
4 153 13 Con

Here's what the distributions of width look like.

In [3]:
def plot_cdfs(df, col):
    for name, group in df.groupby('Species'):
        cdf = Cdf(group[col], label=name)
                       loc='lower right')
In [4]:
plot_cdfs(df, 'Width')

And the distributions of angle.

In [5]:
plot_cdfs(df, 'Angle')

I'll group the data by species and compute summary statistics.

In [6]:
grouped = df.groupby('Species')
<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7f33a1dd59b0>

Here are the means.

In [7]:
means = grouped.mean()
Width Angle
Con 146.190476 14.095238
Hei 124.645161 14.290323
Hep 138.272727 10.090909

And the standard deviations.

In [8]:
stddevs = grouped.std()
Width Angle
Con 5.626891 0.889087
Hei 4.622758 1.101319
Hep 4.142484 0.971454

And the correlations.

In [9]:
for name, group in grouped:
    corr = group.Width.corr(group.Angle)
    print(name, corr)
Con -0.193701151757636
Hei -0.06420611481268008
Hep -0.12478515405529574

Those correlations are small enough that we can get an acceptable approximation by ignoring them, but we might want to come back later and write a complete solution that takes them into account.

The likelihood function

To support the likelihood function, I'll make a dictionary for each attribute that contains a norm object for each species.

In [10]:
from scipy.stats import norm

dist_width = {}
dist_angle = {}
for name, group in grouped:
    dist_width[name] = norm(group.Width.mean(), group.Width.std())
    dist_angle[name] = norm(group.Angle.mean(), group.Angle.std())

Now we can write the likelihood function concisely.

In [11]:
class Beetle(Suite):
    def Likelihood(self, data, hypo):
        data: sequence of width, height
        hypo: name of species
        width, angle = data
        name = hypo
        like = dist_width[name].pdf(width)
        like *= dist_angle[name].pdf(angle)
        return like

The hypotheses are the species names:

In [12]:
hypos = grouped.groups.keys()
dict_keys(['Con', 'Hei', 'Hep'])

We'll start with equal priors

In [13]:
suite = Beetle(hypos)
Con 0.3333333333333333
Hei 0.3333333333333333
Hep 0.3333333333333333

Now we can update with the data and print the posterior.

In [14]:
suite.Update((140, 15))
Con 0.9902199258865487
Hei 0.009770186966082915
Hep 9.887147368342703e-06

Based on these measurements, the specimen is very likely to be an example of Chaetocnema concinna.