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thinkstats2_test.py
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"""This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function, division
import unittest
import random
from collections import Counter
import numpy as np
import thinkstats2
import thinkplot
class Test(unittest.TestCase):
def testOdds(self):
p = 0.75
o = thinkstats2.Odds(p)
self.assertEqual(o, 3)
p = thinkstats2.Probability(o)
self.assertEqual(p, 0.75)
p = thinkstats2.Probability2(3, 1)
self.assertEqual(p, 0.75)
def testMean(self):
t = [1, 1, 1, 3, 3, 591]
mean = thinkstats2.Mean(t)
self.assertEqual(mean, 100)
def testVar(self):
t = [1, 1, 1, 3, 3, 591]
mean = thinkstats2.Mean(t)
var1 = thinkstats2.Var(t)
var2 = thinkstats2.Var(t, mean)
self.assertAlmostEqual(mean, 100.0)
self.assertAlmostEqual(var1, 48217.0)
self.assertAlmostEqual(var2, 48217.0)
def testMeanVar(self):
t = [1, 1, 1, 3, 3, 591]
mean, var = thinkstats2.MeanVar(t)
self.assertAlmostEqual(mean, 100.0)
self.assertAlmostEqual(var, 48217.0)
def testBinomialCoef(self):
res = thinkstats2.BinomialCoef(10, 3)
self.assertEqual(round(res), 120)
res = thinkstats2.BinomialCoef(100, 4)
self.assertEqual(round(res), 3921225)
def testInterpolator(self):
xs = [1, 2, 3]
ys = [4, 5, 6]
interp = thinkstats2.Interpolator(xs, ys)
y = interp.Lookup(1)
self.assertAlmostEqual(y, 4)
y = interp.Lookup(2)
self.assertAlmostEqual(y, 5)
y = interp.Lookup(3)
self.assertAlmostEqual(y, 6)
y = interp.Lookup(1.5)
self.assertAlmostEqual(y, 4.5)
y = interp.Lookup(2.75)
self.assertAlmostEqual(y, 5.75)
x = interp.Reverse(4)
self.assertAlmostEqual(x, 1)
x = interp.Reverse(6)
self.assertAlmostEqual(x, 3)
x = interp.Reverse(4.5)
self.assertAlmostEqual(x, 1.5)
x = interp.Reverse(5.75)
self.assertAlmostEqual(x, 2.75)
def testTrim(self):
t = list(range(100))
random.shuffle(t)
trimmed = thinkstats2.Trim(t, p=0.05)
n = len(trimmed)
self.assertEqual(n, 90)
def testHist(self):
hist = thinkstats2.Hist('allen')
self.assertEqual(len(str(hist)), 38)
self.assertEqual(len(hist), 4)
self.assertEqual(hist.Freq('l'), 2)
hist = thinkstats2.Hist(Counter('allen'))
self.assertEqual(len(hist), 4)
self.assertEqual(hist.Freq('l'), 2)
hist2 = thinkstats2.Hist('nella')
self.assertEqual(hist, hist2)
def testPmf(self):
pmf = thinkstats2.Pmf('allen')
# this one might not be a robust test
self.assertEqual(len(str(pmf)), 45)
self.assertEqual(len(pmf), 4)
self.assertEqual(pmf.Prob('l'), 0.4)
self.assertEqual(pmf['l'], 0.4)
self.assertEqual(pmf.Percentile(50), 'l')
pmf = thinkstats2.Pmf(Counter('allen'))
self.assertEqual(len(pmf), 4)
self.assertEqual(pmf.Prob('l'), 0.4)
pmf = thinkstats2.Pmf(pmf)
self.assertEqual(len(pmf), 4)
self.assertEqual(pmf.Prob('l'), 0.4)
pmf2 = pmf.Copy()
self.assertEqual(pmf, pmf2)
xs, ys = pmf.Render()
self.assertEqual(tuple(xs), tuple(sorted(pmf.Values())))
def testSortedItems(self):
pmf = thinkstats2.Pmf('allen')
items = pmf.SortedItems()
self.assertEqual(len(items), 4)
pmf = thinkstats2.Pmf(['a', float('nan'), 1, pmf])
# should generate a warning
items = pmf.SortedItems()
self.assertEqual(len(items), 4)
def testPmfAddSub(self):
pmf = thinkstats2.Pmf([1, 2, 3, 4, 5, 6])
pmf1 = pmf + 1
self.assertAlmostEqual(pmf1.Mean(), 4.5)
pmf2 = pmf + pmf
self.assertAlmostEqual(pmf2.Mean(), 7.0)
pmf3 = pmf - 1
self.assertAlmostEqual(pmf3.Mean(), 2.5)
pmf4 = pmf - pmf
self.assertAlmostEqual(pmf4.Mean(), 0)
def testPmfMulDiv(self):
pmf = thinkstats2.Pmf([1, 2, 3, 4, 5, 6])
pmf1 = pmf * 2
self.assertAlmostEqual(pmf1.Mean(), 7)
pmf2 = pmf * pmf
self.assertAlmostEqual(pmf2.Mean(), 12.25)
pmf3 = pmf / 2
self.assertAlmostEqual(pmf3.Mean(), 1.75)
pmf4 = pmf / pmf
self.assertAlmostEqual(pmf4.Mean(), 1.4291667)
def testPmfProbLess(self):
d6 = thinkstats2.Pmf(range(1,7))
self.assertEqual(d6.ProbLess(4), 0.5)
self.assertEqual(d6.ProbGreater(3), 0.5)
two = d6 + d6
three = two + d6
# Pmf no longer supports magic comparators
self.assertAlmostEqual(two.ProbGreater(three), 0.15200617284)
self.assertAlmostEqual(two.ProbLess(three), 0.778549382716049)
def testPmfMax(self):
d6 = thinkstats2.Pmf(range(1,7))
two = d6 + d6
three = two + d6
cdf = three.Max(6)
thinkplot.Cdf(cdf)
self.assertAlmostEqual(cdf[14], 0.558230962626)
def testCdf(self):
t = [1, 2, 2, 3, 5]
pmf = thinkstats2.Pmf(t)
hist = thinkstats2.Hist(t)
cdf = thinkstats2.Cdf(pmf)
self.assertEqual(len(str(cdf)), 33)
self.assertEqual(cdf[0], 0)
self.assertAlmostEqual(cdf[1], 0.2)
self.assertAlmostEqual(cdf[2], 0.6)
self.assertAlmostEqual(cdf[3], 0.8)
self.assertAlmostEqual(cdf[4], 0.8)
self.assertAlmostEqual(cdf[5], 1)
self.assertAlmostEqual(cdf[6], 1)
xs = list(range(-1, 7))
ps = cdf.Probs(xs)
for p1, p2 in zip(ps, [0, 0, 0.2, 0.6, 0.8, 0.8, 1, 1]):
self.assertAlmostEqual(p1, p2)
self.assertEqual(cdf.Value(0), 1)
self.assertEqual(cdf.Value(0.1), 1)
self.assertEqual(cdf.Value(0.2), 1)
self.assertEqual(cdf.Value(0.3), 2)
self.assertEqual(cdf.Value(0.4), 2)
self.assertEqual(cdf.Value(0.5), 2)
self.assertEqual(cdf.Value(0.6), 2)
self.assertEqual(cdf.Value(0.7), 3)
self.assertEqual(cdf.Value(0.8), 3)
self.assertEqual(cdf.Value(0.9), 5)
self.assertEqual(cdf.Value(1), 5)
ps = np.linspace(0, 1, 11)
xs = cdf.ValueArray(ps)
self.assertTrue((xs == [1, 1, 1, 2, 2, 2, 2, 3, 3, 5, 5]).all())
np.random.seed(17)
xs = cdf.Sample(7)
self.assertListEqual(xs.tolist(), [2, 2, 1, 1, 3, 3, 3])
# when you make a Cdf from a Pdf, you might get some floating
# point representation error
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertAlmostEqual(cdf[2], 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromPmf(pmf)
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromItems(pmf.Items())
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(pmf.d)
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromDict(pmf.d)
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(hist)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromHist(hist)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(t)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromList(t)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(Counter(t))
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf2 = cdf.Copy()
self.assertEqual(cdf2.Prob(2), 0.6)
self.assertEqual(cdf2.Value(0.6), 2)
def testShift(self):
t = [1, 2, 2, 3, 5]
cdf = thinkstats2.Cdf(t)
cdf2 = cdf.Shift(1)
self.assertEqual(cdf[1], cdf2[2])
def testScale(self):
t = [1, 2, 2, 3, 5]
cdf = thinkstats2.Cdf(t)
cdf2 = cdf.Scale(2)
self.assertEqual(cdf[2], cdf2[4])
def testCdfRender(self):
t = [1, 2, 2, 3, 5]
cdf = thinkstats2.Cdf(t)
xs, ps = cdf.Render()
self.assertEqual(xs[0], 1)
self.assertEqual(ps[2], 0.2)
self.assertEqual(sum(xs), 22)
self.assertEqual(sum(ps), 4.2)
def testPmfFromCdf(self):
t = [1, 2, 2, 3, 5]
pmf = thinkstats2.Pmf(t)
cdf = thinkstats2.Cdf(pmf)
pmf2 = thinkstats2.Pmf(cdf)
for x in pmf.Values():
self.assertAlmostEqual(pmf[x], pmf2[x])
pmf3 = cdf.MakePmf()
for x in pmf.Values():
self.assertAlmostEqual(pmf[x], pmf3[x])
def testNormalPdf(self):
pdf = thinkstats2.NormalPdf(mu=1, sigma=2)
self.assertEqual(len(str(pdf)), 29)
self.assertAlmostEqual(pdf.Density(3), 0.12098536226)
pmf = pdf.MakePmf()
self.assertAlmostEqual(pmf[1.0], 0.0239951295619)
xs, ps = pdf.Render()
self.assertEqual(xs[0], -5.0)
self.assertAlmostEqual(ps[0], 0.0022159242059690038)
pmf = thinkstats2.Pmf(pdf)
self.assertAlmostEqual(pmf[1.0], 0.0239951295619)
xs, ps = pmf.Render()
self.assertEqual(xs[0], -5.0)
self.assertAlmostEqual(ps[0], 0.00026656181123)
cdf = thinkstats2.Cdf(pdf)
self.assertAlmostEqual(cdf[1.0], 0.51199756478094904)
xs, ps = cdf.Render()
self.assertEqual(xs[0], -5.0)
self.assertAlmostEqual(ps[0], 0.0)
def testExponentialPdf(self):
pdf = thinkstats2.ExponentialPdf(lam=0.5)
self.assertEqual(len(str(pdf)), 24)
self.assertAlmostEqual(pdf.Density(3), 0.11156508007421491)
pmf = pdf.MakePmf()
self.assertAlmostEqual(pmf[1.0], 0.02977166586593202)
xs, ps = pdf.Render()
self.assertEqual(xs[0], 0.0)
self.assertAlmostEqual(ps[0], 0.5)
def testEstimatedPdf(self):
pdf = thinkstats2.EstimatedPdf([1, 2, 2, 3, 5])
self.assertEqual(len(str(pdf)), 30)
self.assertAlmostEqual(pdf.Density(3)[0], 0.19629968)
pmf = pdf.MakePmf()
self.assertAlmostEqual(pmf[1.0], 0.010172282816895044)
pmf = pdf.MakePmf(low=0, high=6)
self.assertAlmostEqual(pmf[0.0], 0.0050742294053582942)
def testEvalNormalCdf(self):
p = thinkstats2.EvalNormalCdf(0)
self.assertAlmostEqual(p, 0.5)
p = thinkstats2.EvalNormalCdf(2, 2, 3)
self.assertAlmostEqual(p, 0.5)
p = thinkstats2.EvalNormalCdf(1000, 0, 1)
self.assertAlmostEqual(p, 1.0)
p = thinkstats2.EvalNormalCdf(-1000, 0, 1)
self.assertAlmostEqual(p, 0.0)
x = thinkstats2.EvalNormalCdfInverse(0.95, 0, 1)
self.assertAlmostEqual(x, 1.64485362695)
x = thinkstats2.EvalNormalCdfInverse(0.05, 0, 1)
self.assertAlmostEqual(x, -1.64485362695)
def testEvalPoissonPmf(self):
p = thinkstats2.EvalPoissonPmf(2, 1)
self.assertAlmostEqual(p, 0.1839397205)
def testCov(self):
t = [0, 4, 7, 3, 8, 1, 6, 2, 9, 5]
a = np.array(t)
t2 = [5, 4, 3, 0, 8, 9, 7, 6, 2, 1]
self.assertAlmostEqual(thinkstats2.Cov(t, a), 8.25)
self.assertAlmostEqual(thinkstats2.Cov(t, -a), -8.25)
self.assertAlmostEqual(thinkstats2.Corr(t, a), 1)
self.assertAlmostEqual(thinkstats2.Corr(t, -a), -1)
self.assertAlmostEqual(thinkstats2.Corr(t, t2), -0.1878787878)
self.assertAlmostEqual(thinkstats2.SpearmanCorr(t, -a), -1)
self.assertAlmostEqual(thinkstats2.SpearmanCorr(t, t2), -0.1878787878)
def testReadStataDct(self):
dct = thinkstats2.ReadStataDct('2002FemPreg.dct')
self.assertEqual(len(dct.variables), 243)
self.assertEqual(len(dct.colspecs), 243)
self.assertEqual(len(dct.names), 243)
# TODO: This fails now; investigate what changed
# self.assertEqual(dct.colspecs[-1][1], -1)
def testCdfProbs(self):
t = [-1, 1, 2, 2, 3, 5]
cdf = thinkstats2.Cdf(t)
ps = cdf.Probs(t)
print(ps)
def testPmfOfHist(self):
bowl1 = thinkstats2.Hist(dict(vanilla=30, chocolate=10))
bowl2 = thinkstats2.Hist(dict(vanilla=20, chocolate=20))
pmf = thinkstats2.Pmf([bowl1, bowl2])
pmf.Print()
if __name__ == "__main__":
unittest.main()