"""This file contains code used in "Think Bayes",
by Allen B. Downey, available from greenteapress.com
Copyright 2012 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function, division
import csv
import math
import numpy
import sys
import matplotlib
import matplotlib.pyplot as pyplot
import thinkbayes2
import thinkplot
def ReadScale(filename='sat_scale.csv', col=2):
"""Reads a CSV file of SAT scales (maps from raw score to standard score).
Args:
filename: string filename
col: which column to start with (0=Reading, 2=Math, 4=Writing)
Returns: thinkbayes2.Interpolator object
"""
def ParseRange(s):
"""Parse a range of values in the form 123-456
s: string
"""
t = [int(x) for x in s.split('-')]
return 1.0 * sum(t) / len(t)
fp = open(filename)
reader = csv.reader(fp)
raws = []
scores = []
for t in reader:
try:
raw = int(t[col])
raws.append(raw)
score = ParseRange(t[col+1])
scores.append(score)
except ValueError:
pass
raws.sort()
scores.sort()
return thinkbayes2.Interpolator(raws, scores)
def ReadRanks(filename='sat_ranks.csv'):
"""Reads a CSV file of SAT scores.
Args:
filename: string filename
Returns:
list of (score, freq) pairs
"""
fp = open(filename)
reader = csv.reader(fp)
res = []
for t in reader:
try:
score = int(t[0])
freq = int(t[1])
res.append((score, freq))
except ValueError:
pass
return res
def DivideValues(pmf, denom):
"""Divides the values in a Pmf by denom.
Returns a new Pmf.
"""
new = thinkbayes2.Pmf()
denom = float(denom)
for val, prob in pmf.Items():
x = val / denom
new.Set(x, prob)
return new
class Exam(object):
"""Encapsulates information about an exam.
Contains the distribution of scaled scores and an
Interpolator that maps between scaled and raw scores.
"""
def __init__(self):
self.scale = ReadScale()
scores = ReadRanks()
score_pmf = thinkbayes2.Pmf(dict(scores))
self.raw = self.ReverseScale(score_pmf)
self.max_score = max(self.raw.Values())
self.prior = DivideValues(self.raw, denom=self.max_score)
center = -0.05
width = 1.8
self.difficulties = MakeDifficulties(center, width, self.max_score)
def CompareScores(self, a_score, b_score, constructor):
"""Computes posteriors for two test scores and the likelihood ratio.
a_score, b_score: scales SAT scores
constructor: function that instantiates an Sat or Sat2 object
"""
a_sat = constructor(self, a_score)
b_sat = constructor(self, b_score)
a_sat.PlotPosteriors(b_sat)
if constructor is Sat:
PlotJointDist(a_sat, b_sat)
top = TopLevel('AB')
top.Update((a_sat, b_sat))
top.Print()
ratio = top.Prob('A') / top.Prob('B')
print('Likelihood ratio', ratio)
posterior = ratio / (ratio + 1)
print('Posterior', posterior)
if constructor is Sat2:
ComparePosteriorPredictive(a_sat, b_sat)
def MakeRawScoreDist(self, efficacies):
"""Makes the distribution of raw scores for given difficulty.
efficacies: Pmf of efficacy
"""
pmfs = thinkbayes2.Pmf()
for efficacy, prob in efficacies.Items():
scores = self.PmfCorrect(efficacy)
pmfs.Set(scores, prob)
mix = thinkbayes2.MakeMixture(pmfs)
return mix
def CalibrateDifficulty(self):
"""Make a plot showing the model distribution of raw scores."""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
cdf = thinkbayes2.Cdf(self.raw, label='data')
thinkplot.Cdf(cdf)
efficacies = thinkbayes2.MakeNormalPmf(0, 1.5, 3)
pmf = self.MakeRawScoreDist(efficacies)
cdf = thinkbayes2.Cdf(pmf, label='model')
thinkplot.Cdf(cdf)
thinkplot.Save(root='sat_calibrate',
xlabel='raw score',
ylabel='CDF',
formats=['pdf', 'eps'])
def PmfCorrect(self, efficacy):
"""Returns the PMF of number of correct responses.
efficacy: float
"""
pmf = PmfCorrect(efficacy, self.difficulties)
return pmf
def Lookup(self, raw):
"""Looks up a raw score and returns a scaled score."""
return self.scale.Lookup(raw)
def Reverse(self, score):
"""Looks up a scaled score and returns a raw score.
Since we ignore the penalty, negative scores round up to zero.
"""
raw = self.scale.Reverse(score)
return raw if raw > 0 else 0
def ReverseScale(self, pmf):
"""Applies the reverse scale to the values of a PMF.
Args:
pmf: Pmf object
scale: Interpolator object
Returns:
new Pmf
"""
new = thinkbayes2.Pmf()
for val, prob in pmf.Items():
raw = self.Reverse(val)
new.Incr(raw, prob)
return new
class Sat(thinkbayes2.Suite):
"""Represents the distribution of p_correct for a test-taker."""
def __init__(self, exam, score):
self.exam = exam
self.score = score
thinkbayes2.Suite.__init__(self, exam.prior)
self.Update(score)
def Likelihood(self, data, hypo):
"""Computes the likelihood of a test score, given efficacy."""
p_correct = hypo
score = data
k = self.exam.Reverse(score)
n = self.exam.max_score
like = thinkbayes2.EvalBinomialPmf(k, n, p_correct)
return like
def PlotPosteriors(self, other):
"""Plots posterior distributions of efficacy.
self, other: Sat objects.
"""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
cdf1 = thinkbayes2.Cdf(self, label='posterior %d' % self.score)
cdf2 = thinkbayes2.Cdf(other, label='posterior %d' % other.score)
thinkplot.Cdfs([cdf1, cdf2])
thinkplot.Save(xlabel='p_correct',
ylabel='CDF',
axis=[0.7, 1.0, 0.0, 1.0],
root='sat_posteriors_p_corr',
formats=['pdf', 'eps'])
class Sat2(thinkbayes2.Suite):
"""Represents the distribution of efficacy for a test-taker."""
def __init__(self, exam, score):
self.exam = exam
self.score = score
efficacies = thinkbayes2.MakeNormalPmf(0, 1.5, 3)
thinkbayes2.Suite.__init__(self, efficacies)
self.Update(score)
def Likelihood(self, data, hypo):
"""Computes the likelihood of a test score, given efficacy."""
efficacy = hypo
score = data
raw = self.exam.Reverse(score)
pmf = self.exam.PmfCorrect(efficacy)
like = pmf.Prob(raw)
return like
def MakePredictiveDist(self):
"""Returns the distribution of raw scores expected on a re-test."""
raw_pmf = self.exam.MakeRawScoreDist(self)
return raw_pmf
def PlotPosteriors(self, other):
"""Plots posterior distributions of efficacy.
self, other: Sat objects.
"""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
cdf1 = thinkbayes2.Cdf(self, label='posterior %d' % self.score)
cdf2 = thinkbayes2.Cdf(other, label='posterior %d' % other.score)
thinkplot.Cdfs([cdf1, cdf2])
thinkplot.Save(xlabel='efficacy',
ylabel='CDF',
axis=[0, 4.6, 0.0, 1.0],
root='sat_posteriors_eff',
formats=['pdf', 'eps'])
def PlotJointDist(pmf1, pmf2, thresh=0.8):
"""Plot the joint distribution of p_correct.
pmf1, pmf2: posterior distributions
thresh: lower bound of the range to be plotted
"""
def Clean(pmf):
"""Removes values below thresh."""
vals = [val for val in pmf.Values() if val < thresh]
[pmf.Remove(val) for val in vals]
Clean(pmf1)
Clean(pmf2)
pmf = thinkbayes2.MakeJoint(pmf1, pmf2)
thinkplot.Figure(figsize=(6, 6))
thinkplot.Contour(pmf, contour=False, pcolor=True)
thinkplot.Plot([thresh, 1.0], [thresh, 1.0],
color='gray', alpha=0.2, linewidth=4)
thinkplot.Save(root='sat_joint',
xlabel='p_correct Alice',
ylabel='p_correct Bob',
axis=[thresh, 1.0, thresh, 1.0],
formats=['pdf', 'eps'])
def ComparePosteriorPredictive(a_sat, b_sat):
"""Compares the predictive distributions of raw scores.
a_sat: posterior distribution
b_sat:
"""
a_pred = a_sat.MakePredictiveDist()
b_pred = b_sat.MakePredictiveDist()
a_like = thinkbayes2.PmfProbGreater(a_pred, b_pred)
b_like = thinkbayes2.PmfProbLess(a_pred, b_pred)
c_like = thinkbayes2.PmfProbEqual(a_pred, b_pred)
print('Posterior predictive')
print('A', a_like)
print('B', b_like)
print('C', c_like)
def PlotPriorDist(pmf):
"""Plot the prior distribution of p_correct.
pmf: prior
"""
thinkplot.Clf()
thinkplot.PrePlot(num=1)
cdf1 = thinkbayes2.Cdf(pmf, label='prior')
thinkplot.Cdf(cdf1)
thinkplot.Save(root='sat_prior',
xlabel='p_correct',
ylabel='CDF',
formats=['pdf', 'eps'])
class TopLevel(thinkbayes2.Suite):
"""Evaluates the top-level hypotheses about Alice and Bob.
Uses the bottom-level posterior distribution about p_correct
(or efficacy).
"""
def Update(self, data):
a_sat, b_sat = data
a_like = thinkbayes2.PmfProbGreater(a_sat, b_sat)
b_like = thinkbayes2.PmfProbLess(a_sat, b_sat)
c_like = thinkbayes2.PmfProbEqual(a_sat, b_sat)
a_like += c_like / 2
b_like += c_like / 2
self.Mult('A', a_like)
self.Mult('B', b_like)
self.Normalize()
def ProbCorrect(efficacy, difficulty, a=1):
"""Returns the probability that a person gets a question right.
efficacy: personal ability to answer questions
difficulty: how hard the question is
Returns: float prob
"""
return 1 / (1 + math.exp(-a * (efficacy - difficulty)))
def BinaryPmf(p):
"""Makes a Pmf with values 1 and 0.
p: probability given to 1
Returns: Pmf object
"""
pmf = thinkbayes2.Pmf()
pmf.Set(1, p)
pmf.Set(0, 1-p)
return pmf
def PmfCorrect(efficacy, difficulties):
"""Computes the distribution of correct responses.
efficacy: personal ability to answer questions
difficulties: list of difficulties, one for each question
Returns: new Pmf object
"""
pmf0 = thinkbayes2.Pmf([0])
ps = [ProbCorrect(efficacy, difficulty) for difficulty in difficulties]
pmfs = [BinaryPmf(p) for p in ps]
dist = sum(pmfs, pmf0)
return dist
def MakeDifficulties(center, width, n):
"""Makes a list of n difficulties with a given center and width.
Returns: list of n floats between center-width and center+width
"""
low, high = center-width, center+width
return numpy.linspace(low, high, n)
def ProbCorrectTable():
"""Makes a table of p_correct for a range of efficacy and difficulty."""
efficacies = [3, 1.5, 0, -1.5, -3]
difficulties = [-1.85, -0.05, 1.75]
for eff in efficacies:
print('%0.2f & ' % eff, end=' ')
for diff in difficulties:
p = ProbCorrect(eff, diff)
print('%0.2f & ' % p, end=' ')
print(r'\\')
def main(script):
ProbCorrectTable()
exam = Exam()
PlotPriorDist(exam.prior)
exam.CalibrateDifficulty()
exam.CompareScores(780, 740, constructor=Sat)
exam.CompareScores(780, 740, constructor=Sat2)
if __name__ == '__main__':
main(*sys.argv)
