├── .gitignore ├── Chapter1_Introduction ├── Chapter1.ipynb ├── README.md └── data │ └── txtdata.csv ├── Chapter2_MorePyMC ├── Chapter2.ipynb ├── README.md ├── Screen Shot 2013-02-08 at 11.23.49 AM.png ├── daft_plot.py ├── data │ └── challenger_data.csv ├── separation_plot.py └── sms_model.png ├── Chapter3_MCMC ├── Chapter3.ipynb ├── README.md ├── data │ ├── github_data.csv │ ├── mixture_data.csv │ └── smoking_death.csv └── github_pull.py ├── Chapter4_TheGreatestTheoremNeverTold ├── Chapter4.ipynb ├── README.md ├── data │ └── census_data.csv ├── reddit_comments.png └── top_pic_comments.py ├── Chapter5_LossFunctions ├── Chapter5.ipynb ├── DarkWorldsMetric.py ├── README.md ├── data │ ├── Test_haloCounts.csv │ ├── Train_Skies │ │ └── Train_Skies │ │ │ ├── Training_Sky1.csv │ │ │ ├── Training_Sky10.csv │ │ │ ├── Training_Sky100.csv │ │ │ ├── Training_Sky101.csv │ │ │ ├── Training_Sky102.csv │ │ │ ├── Training_Sky103.csv │ │ │ ├── Training_Sky104.csv │ │ │ ├── Training_Sky105.csv │ │ │ ├── Training_Sky106.csv │ │ │ ├── Training_Sky107.csv │ │ │ ├── Training_Sky108.csv │ │ │ ├── Training_Sky109.csv │ │ │ ├── Training_Sky11.csv │ │ │ ├── Training_Sky110.csv │ │ │ ├── Training_Sky111.csv │ │ │ ├── Training_Sky112.csv │ │ │ ├── Training_Sky113.csv │ │ │ ├── Training_Sky114.csv │ │ │ ├── Training_Sky115.csv │ │ │ ├── Training_Sky116.csv │ │ │ ├── Training_Sky117.csv │ │ │ ├── Training_Sky118.csv │ │ │ ├── Training_Sky119.csv │ │ │ ├── Training_Sky12.csv │ │ │ ├── Training_Sky120.csv │ │ │ ├── Training_Sky121.csv │ │ │ ├── Training_Sky122.csv │ │ │ ├── Training_Sky123.csv │ │ │ ├── Training_Sky124.csv │ │ │ ├── Training_Sky125.csv │ │ │ ├── Training_Sky126.csv │ │ │ ├── Training_Sky127.csv │ │ │ ├── Training_Sky128.csv │ │ │ ├── Training_Sky129.csv │ │ │ ├── Training_Sky13.csv │ │ │ ├── Training_Sky130.csv │ │ │ ├── Training_Sky131.csv │ │ │ ├── Training_Sky132.csv │ │ │ ├── Training_Sky133.csv │ │ │ ├── Training_Sky134.csv │ │ │ ├── Training_Sky135.csv │ │ │ ├── Training_Sky136.csv │ │ │ ├── Training_Sky137.csv │ │ │ ├── Training_Sky138.csv │ │ │ ├── Training_Sky139.csv │ │ │ ├── Training_Sky14.csv │ │ │ ├── Training_Sky140.csv │ │ │ ├── Training_Sky141.csv │ │ │ ├── Training_Sky142.csv │ │ │ ├── Training_Sky143.csv │ │ │ ├── Training_Sky144.csv │ │ │ ├── Training_Sky145.csv │ │ │ ├── Training_Sky146.csv │ │ │ ├── Training_Sky147.csv │ │ │ ├── Training_Sky148.csv │ │ │ ├── Training_Sky149.csv │ │ │ ├── Training_Sky15.csv │ │ │ ├── Training_Sky150.csv │ │ │ ├── Training_Sky151.csv │ │ │ ├── Training_Sky152.csv │ │ │ ├── Training_Sky153.csv │ │ │ ├── Training_Sky154.csv │ │ │ ├── Training_Sky155.csv │ │ │ ├── Training_Sky156.csv │ │ │ ├── Training_Sky157.csv │ │ │ ├── Training_Sky158.csv │ │ │ ├── Training_Sky159.csv │ │ │ ├── Training_Sky16.csv │ │ │ ├── Training_Sky160.csv │ │ │ ├── Training_Sky161.csv │ │ │ ├── Training_Sky162.csv │ │ │ ├── Training_Sky163.csv │ │ │ ├── Training_Sky164.csv │ │ │ ├── Training_Sky165.csv │ │ │ ├── Training_Sky166.csv │ │ │ ├── Training_Sky167.csv │ │ │ ├── Training_Sky168.csv │ │ │ ├── Training_Sky169.csv │ │ │ ├── Training_Sky17.csv │ │ │ ├── Training_Sky170.csv │ │ │ ├── Training_Sky171.csv │ │ │ ├── Training_Sky172.csv │ │ │ ├── Training_Sky173.csv │ │ │ ├── Training_Sky174.csv │ │ │ ├── Training_Sky175.csv │ │ │ ├── Training_Sky176.csv │ │ │ ├── Training_Sky177.csv │ │ │ ├── Training_Sky178.csv │ │ │ ├── Training_Sky179.csv │ │ │ ├── Training_Sky18.csv │ │ │ ├── Training_Sky180.csv │ │ │ ├── Training_Sky181.csv │ │ │ ├── Training_Sky182.csv │ │ │ ├── Training_Sky183.csv │ │ │ ├── Training_Sky184.csv │ │ │ ├── Training_Sky185.csv │ │ │ ├── Training_Sky186.csv │ │ │ ├── Training_Sky187.csv │ │ │ ├── Training_Sky188.csv │ │ │ ├── Training_Sky189.csv │ │ │ ├── Training_Sky19.csv │ │ │ ├── Training_Sky190.csv │ │ │ ├── Training_Sky191.csv │ │ │ ├── Training_Sky192.csv │ │ │ ├── Training_Sky193.csv │ │ │ ├── Training_Sky194.csv │ │ │ ├── Training_Sky195.csv │ │ │ ├── Training_Sky196.csv │ │ │ ├── Training_Sky197.csv │ │ │ ├── Training_Sky198.csv │ │ │ ├── Training_Sky199.csv │ │ │ ├── Training_Sky2.csv │ │ │ ├── Training_Sky20.csv │ │ │ ├── Training_Sky200.csv │ │ │ ├── Training_Sky201.csv │ │ │ ├── Training_Sky202.csv │ │ │ ├── Training_Sky203.csv │ │ │ ├── Training_Sky204.csv │ │ │ ├── Training_Sky205.csv │ │ │ ├── Training_Sky206.csv │ │ │ ├── Training_Sky207.csv │ │ │ ├── Training_Sky208.csv │ │ │ ├── Training_Sky209.csv │ │ │ ├── Training_Sky21.csv │ │ │ ├── Training_Sky210.csv │ │ │ ├── Training_Sky211.csv │ │ │ ├── Training_Sky212.csv │ │ │ ├── Training_Sky213.csv │ │ │ ├── Training_Sky214.csv │ │ │ ├── Training_Sky215.csv │ │ │ ├── Training_Sky216.csv │ │ │ ├── Training_Sky217.csv │ │ │ ├── Training_Sky218.csv │ │ │ ├── Training_Sky219.csv │ │ │ ├── Training_Sky22.csv │ │ │ ├── Training_Sky220.csv │ │ │ ├── Training_Sky221.csv │ │ │ ├── Training_Sky222.csv │ │ │ ├── Training_Sky223.csv │ │ │ ├── Training_Sky224.csv │ │ │ ├── Training_Sky225.csv │ │ │ ├── Training_Sky226.csv │ │ │ ├── Training_Sky227.csv │ │ │ ├── Training_Sky228.csv │ │ │ ├── Training_Sky229.csv │ │ │ ├── Training_Sky23.csv │ │ │ ├── Training_Sky230.csv │ │ │ ├── Training_Sky231.csv │ │ │ ├── Training_Sky232.csv │ │ │ ├── Training_Sky233.csv │ │ │ ├── Training_Sky234.csv │ │ │ ├── Training_Sky235.csv │ │ │ ├── Training_Sky236.csv │ │ │ ├── Training_Sky237.csv │ │ │ ├── Training_Sky238.csv │ │ │ ├── Training_Sky239.csv │ │ │ ├── Training_Sky24.csv │ │ │ ├── Training_Sky240.csv │ │ │ ├── Training_Sky241.csv │ │ │ ├── Training_Sky242.csv │ │ │ ├── Training_Sky243.csv │ │ │ ├── Training_Sky244.csv │ │ │ ├── Training_Sky245.csv │ │ │ ├── Training_Sky246.csv │ │ │ ├── Training_Sky247.csv │ │ │ ├── Training_Sky248.csv │ │ │ ├── Training_Sky249.csv │ │ │ ├── Training_Sky25.csv │ │ │ ├── Training_Sky250.csv │ │ │ ├── Training_Sky251.csv │ │ │ ├── Training_Sky252.csv │ │ │ ├── Training_Sky253.csv │ │ │ ├── Training_Sky254.csv │ │ │ ├── Training_Sky255.csv │ │ │ ├── Training_Sky256.csv │ │ │ ├── Training_Sky257.csv │ │ │ ├── Training_Sky258.csv │ │ │ ├── Training_Sky259.csv │ │ │ ├── Training_Sky26.csv │ │ │ ├── Training_Sky260.csv │ │ │ ├── Training_Sky261.csv │ │ │ ├── Training_Sky262.csv │ │ │ ├── Training_Sky263.csv │ │ │ ├── Training_Sky264.csv │ │ │ ├── Training_Sky265.csv │ │ │ ├── Training_Sky266.csv │ │ │ ├── Training_Sky267.csv │ │ │ ├── Training_Sky268.csv │ │ │ ├── Training_Sky269.csv │ │ │ ├── Training_Sky27.csv │ │ │ ├── Training_Sky270.csv │ │ │ ├── Training_Sky271.csv │ │ │ ├── Training_Sky272.csv │ │ │ ├── Training_Sky273.csv │ │ │ ├── Training_Sky274.csv │ │ │ ├── Training_Sky275.csv │ │ │ ├── Training_Sky276.csv │ │ │ ├── Training_Sky277.csv │ │ │ ├── Training_Sky278.csv │ │ │ ├── Training_Sky279.csv │ │ │ ├── Training_Sky28.csv │ │ │ ├── Training_Sky280.csv │ │ │ ├── Training_Sky281.csv │ │ │ ├── Training_Sky282.csv │ │ │ ├── Training_Sky283.csv │ │ │ ├── Training_Sky284.csv │ │ │ ├── Training_Sky285.csv │ │ │ ├── Training_Sky286.csv │ │ │ ├── Training_Sky287.csv │ │ │ ├── Training_Sky288.csv │ │ │ ├── Training_Sky289.csv │ │ │ ├── Training_Sky29.csv │ │ │ ├── Training_Sky290.csv │ │ │ ├── Training_Sky291.csv │ │ │ ├── Training_Sky292.csv │ │ │ ├── Training_Sky293.csv │ │ │ ├── Training_Sky294.csv │ │ │ ├── Training_Sky295.csv │ │ │ ├── Training_Sky296.csv │ │ │ ├── Training_Sky297.csv │ │ │ ├── Training_Sky298.csv │ │ │ ├── Training_Sky299.csv │ │ │ ├── Training_Sky3.csv │ │ │ ├── Training_Sky30.csv │ │ │ ├── Training_Sky300.csv │ │ │ ├── Training_Sky31.csv │ │ │ ├── Training_Sky32.csv │ │ │ ├── Training_Sky33.csv │ │ │ ├── Training_Sky34.csv │ │ │ ├── Training_Sky35.csv │ │ │ ├── Training_Sky36.csv │ │ │ ├── Training_Sky37.csv │ │ │ ├── Training_Sky38.csv │ │ │ ├── Training_Sky39.csv │ │ │ ├── Training_Sky4.csv │ │ │ ├── Training_Sky40.csv │ │ │ ├── Training_Sky41.csv │ │ │ ├── Training_Sky42.csv │ │ │ ├── Training_Sky43.csv │ │ │ ├── Training_Sky44.csv │ │ │ ├── Training_Sky45.csv │ │ │ ├── Training_Sky46.csv │ │ │ ├── Training_Sky47.csv │ │ │ ├── Training_Sky48.csv │ │ │ ├── Training_Sky49.csv │ │ │ ├── Training_Sky5.csv │ │ │ ├── Training_Sky50.csv │ │ │ ├── Training_Sky51.csv │ │ │ ├── Training_Sky52.csv │ │ │ ├── Training_Sky53.csv │ │ │ ├── Training_Sky54.csv │ │ │ ├── Training_Sky55.csv │ │ │ ├── Training_Sky56.csv │ │ │ ├── Training_Sky57.csv │ │ │ ├── Training_Sky58.csv │ │ │ ├── Training_Sky59.csv │ │ │ ├── Training_Sky6.csv │ │ │ ├── Training_Sky60.csv │ │ │ ├── Training_Sky61.csv │ │ │ ├── Training_Sky62.csv │ │ │ ├── Training_Sky63.csv │ │ │ ├── Training_Sky64.csv │ │ │ ├── Training_Sky65.csv │ │ │ ├── Training_Sky66.csv │ │ │ ├── Training_Sky67.csv │ │ │ ├── Training_Sky68.csv │ │ │ ├── Training_Sky69.csv │ │ │ ├── Training_Sky7.csv │ │ │ ├── Training_Sky70.csv │ │ │ ├── Training_Sky71.csv │ │ │ ├── Training_Sky72.csv │ │ │ ├── Training_Sky73.csv │ │ │ ├── Training_Sky74.csv │ │ │ ├── Training_Sky75.csv │ │ │ ├── Training_Sky76.csv │ │ │ ├── Training_Sky77.csv │ │ │ ├── Training_Sky78.csv │ │ │ ├── Training_Sky79.csv │ │ │ ├── Training_Sky8.csv │ │ │ ├── Training_Sky80.csv │ │ │ ├── Training_Sky81.csv │ │ │ ├── Training_Sky82.csv │ │ │ ├── Training_Sky83.csv │ │ │ ├── Training_Sky84.csv │ │ │ ├── Training_Sky85.csv │ │ │ ├── Training_Sky86.csv │ │ │ ├── Training_Sky87.csv │ │ │ ├── Training_Sky88.csv │ │ │ ├── Training_Sky89.csv │ │ │ ├── Training_Sky9.csv │ │ │ ├── Training_Sky90.csv │ │ │ ├── Training_Sky91.csv │ │ │ ├── Training_Sky92.csv │ │ │ ├── Training_Sky93.csv │ │ │ ├── Training_Sky94.csv │ │ │ ├── Training_Sky95.csv │ │ │ ├── Training_Sky96.csv │ │ │ ├── Training_Sky97.csv │ │ │ ├── Training_Sky98.csv │ │ │ └── Training_Sky99.csv │ └── Training_halos.csv └── draw_sky2.py ├── Chapter6_Priorities ├── BanditsD3.html ├── Chapter6.ipynb ├── README.md ├── d3bandits.js ├── data │ ├── data.csv │ ├── data.txt │ ├── results_2014.csv │ ├── table_2014.csv │ └── table_2014.ods ├── other_strats.py └── ystockquote.py ├── Chapter7_BayesianMachineLearning ├── DontOverfit.ipynb ├── MachineLearning.ipynb └── auc.py ├── ExamplesFromChapters ├── Chapter1 │ └── SMS_behaviour.py ├── Chapter2 │ ├── ABtesting.py │ ├── FreqOfCheaters.py │ └── ORingFailure.py ├── Chapter3 │ └── ClusteringWithGaussians.py └── README.md ├── LICENSE.txt ├── Prologue └── Prologue.ipynb ├── README.md ├── book_layout.txt ├── requirements.txt ├── sandbox ├── ABCtests.ipynb ├── Chapter10_ │ ├── More hacking with PyMC.ipynb │ ├── data │ │ ├── gh_forks.csv │ │ ├── gh_forks_02112013.csv │ │ ├── gh_stars.csv │ │ └── gh_stars_02112013.csv │ └── github_datapull.py ├── CommitDataForChapter1.ipynb ├── GithubUsers.ipynb ├── SpaceShuttleBayesFactor.ipynb ├── Sum of Gaussian Distributions + noise.ipynb └── github_events.py ├── styles ├── bmh_matplotlibrc.json ├── custom.css └── matplotlibrc └── to_latex_pdf.sh /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | *.pyc 3 | *~ 4 | *.png 5 | **/.ipynb_checkpoints 6 | -------------------------------------------------------------------------------- /Chapter1_Introduction/README.md: -------------------------------------------------------------------------------- 1 | Chapter 1: Introduction to Bayesian Methods 2 | =========== 3 | 4 | ### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1.ipynb) 5 | -------------------------------------------------------------------------------- /Chapter1_Introduction/data/txtdata.csv: -------------------------------------------------------------------------------- 1 | 1.300000000000000000e+01 2 | 2.400000000000000000e+01 3 | 8.000000000000000000e+00 4 | 2.400000000000000000e+01 5 | 7.000000000000000000e+00 6 | 3.500000000000000000e+01 7 | 1.400000000000000000e+01 8 | 1.100000000000000000e+01 9 | 1.500000000000000000e+01 10 | 1.100000000000000000e+01 11 | 2.200000000000000000e+01 12 | 2.200000000000000000e+01 13 | 1.100000000000000000e+01 14 | 5.700000000000000000e+01 15 | 1.100000000000000000e+01 16 | 1.900000000000000000e+01 17 | 2.900000000000000000e+01 18 | 6.000000000000000000e+00 19 | 1.900000000000000000e+01 20 | 1.200000000000000000e+01 21 | 2.200000000000000000e+01 22 | 1.200000000000000000e+01 23 | 1.800000000000000000e+01 24 | 7.200000000000000000e+01 25 | 3.200000000000000000e+01 26 | 9.000000000000000000e+00 27 | 7.000000000000000000e+00 28 | 1.300000000000000000e+01 29 | 1.900000000000000000e+01 30 | 2.300000000000000000e+01 31 | 2.700000000000000000e+01 32 | 2.000000000000000000e+01 33 | 6.000000000000000000e+00 34 | 1.700000000000000000e+01 35 | 1.300000000000000000e+01 36 | 1.000000000000000000e+01 37 | 1.400000000000000000e+01 38 | 6.000000000000000000e+00 39 | 1.600000000000000000e+01 40 | 1.500000000000000000e+01 41 | 7.000000000000000000e+00 42 | 2.000000000000000000e+00 43 | 1.500000000000000000e+01 44 | 1.500000000000000000e+01 45 | 1.900000000000000000e+01 46 | 7.000000000000000000e+01 47 | 4.900000000000000000e+01 48 | 7.000000000000000000e+00 49 | 5.300000000000000000e+01 50 | 2.200000000000000000e+01 51 | 2.100000000000000000e+01 52 | 3.100000000000000000e+01 53 | 1.900000000000000000e+01 54 | 1.100000000000000000e+01 55 | 1.800000000000000000e+01 56 | 2.000000000000000000e+01 57 | 1.200000000000000000e+01 58 | 3.500000000000000000e+01 59 | 1.700000000000000000e+01 60 | 2.300000000000000000e+01 61 | 1.700000000000000000e+01 62 | 4.000000000000000000e+00 63 | 2.000000000000000000e+00 64 | 3.100000000000000000e+01 65 | 3.000000000000000000e+01 66 | 1.300000000000000000e+01 67 | 2.700000000000000000e+01 68 | 0.000000000000000000e+00 69 | 3.900000000000000000e+01 70 | 3.700000000000000000e+01 71 | 5.000000000000000000e+00 72 | 1.400000000000000000e+01 73 | 1.300000000000000000e+01 74 | 2.200000000000000000e+01 75 | -------------------------------------------------------------------------------- /Chapter2_MorePyMC/README.md: -------------------------------------------------------------------------------- 1 | Chapter 2: A little more on PyMC 2 | ================================= 3 | 4 | ### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Chapter2.ipynb) 5 | -------------------------------------------------------------------------------- /Chapter2_MorePyMC/Screen Shot 2013-02-08 at 11.23.49 AM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmportilla/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/78f81617e241aa15b84c05097f1089a818d8d2d8/Chapter2_MorePyMC/Screen Shot 2013-02-08 at 11.23.49 AM.png -------------------------------------------------------------------------------- /Chapter2_MorePyMC/daft_plot.py: -------------------------------------------------------------------------------- 1 | #daft drawing for SMS example 2 | import matplotlib.pyplot as plt 3 | 4 | 5 | 6 | try: 7 | import daft 8 | except ImportError: 9 | print "python library Daft required." 10 | 11 | 12 | pgm = daft.PGM([9, 4], origin=[.5,.5]) 13 | pgm.add_node(daft.Node("tau", r"$\tau$", 4.0, 3.5)) 14 | pgm.add_node(daft.Node("alpha", r"$\alpha$", 6, 4.0)) 15 | pgm.add_node(daft.Node("lambda1", r"$\lambda_1$", 5.5, 3.2,)) 16 | pgm.add_node(daft.Node("lambda2", r"$\lambda_2$", 6.5, 3.2)) 17 | pgm.add_node(daft.Node("lambda", r"$\lambda$", 5.0, 2.0)) 18 | pgm.add_node(daft.Node("obs", "obs", 5.0, 1.0, 1.2, observed=True)) 19 | 20 | 21 | 22 | pgm.add_edge("tau", "lambda") 23 | pgm.add_edge("alpha", "lambda1") 24 | pgm.add_edge("alpha", "lambda2") 25 | pgm.add_edge("lambda1", "lambda") 26 | pgm.add_edge("lambda2", "lambda") 27 | 28 | pgm.add_edge("lambda", "obs") 29 | pgm.render() 30 | plt.figure( figsize=(12,5) ) 31 | plt.show() -------------------------------------------------------------------------------- /Chapter2_MorePyMC/data/challenger_data.csv: -------------------------------------------------------------------------------- 1 | Date,Temperature,Damage Incident 2 | 04/12/1981,66,0 3 | 11/12/1981,70,1 4 | 3/22/82,69,0 5 | 6/27/82,80,NA 6 | 01/11/1982,68,0 7 | 04/04/1983,67,0 8 | 6/18/83,72,0 9 | 8/30/83,73,0 10 | 11/28/83,70,0 11 | 02/03/1984,57,1 12 | 04/06/1984,63,1 13 | 8/30/84,70,1 14 | 10/05/1984,78,0 15 | 11/08/1984,67,0 16 | 1/24/85,53,1 17 | 04/12/1985,67,0 18 | 4/29/85,75,0 19 | 6/17/85,70,0 20 | 7/29/85,81,0 21 | 8/27/85,76,0 22 | 10/03/1985,79,0 23 | 10/30/85,75,1 24 | 11/26/85,76,0 25 | 01/12/1986,58,1 26 | 1/28/86,31,Challenger Accident 27 | -------------------------------------------------------------------------------- /Chapter2_MorePyMC/separation_plot.py: -------------------------------------------------------------------------------- 1 | # separation plot 2 | # Author: Cameron Davidson-Pilon,2013 3 | # see http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf 4 | 5 | 6 | import matplotlib.pyplot as plt 7 | import numpy as np 8 | 9 | 10 | 11 | def separation_plot( p, y, **kwargs ): 12 | """ 13 | This function creates a separation plot for logistic and probit classification. 14 | See http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf 15 | 16 | p: The proportions/probabilities, can be a nxM matrix which represents M models. 17 | y: the 0-1 response variables. 18 | 19 | """ 20 | assert p.shape[0] == y.shape[0], "p.shape[0] != y.shape[0]" 21 | n = p.shape[0] 22 | 23 | try: 24 | M = p.shape[1] 25 | except: 26 | p = p.reshape( n, 1 ) 27 | M = p.shape[1] 28 | 29 | #colors = np.array( ["#fdf2db", "#e44a32"] ) 30 | colors_bmh = np.array( ["#eeeeee", "#348ABD"] ) 31 | 32 | 33 | fig = plt.figure( )#figsize = (8, 1.3*M) ) 34 | 35 | for i in range(M): 36 | ax = fig.add_subplot(M, 1, i+1) 37 | ix = np.argsort( p[:,i] ) 38 | #plot the different bars 39 | bars = ax.bar( np.arange(n), np.ones(n), width=1., 40 | color = colors_bmh[ y[ix].astype(int) ], 41 | edgecolor = 'none') 42 | ax.plot( np.arange(n+1), np.append(p[ix,i], p[ix,i][-1]), "k", 43 | linewidth = 1.,drawstyle="steps-post" ) 44 | #create expected value bar. 45 | ax.vlines( [(1-p[ix,i]).sum()], [0], [1] ) 46 | #ax.grid(False) 47 | #ax.axis('off') 48 | plt.xlim( 0, n) 49 | 50 | plt.tight_layout() 51 | 52 | return 53 | 54 | 55 | 56 | -------------------------------------------------------------------------------- /Chapter2_MorePyMC/sms_model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmportilla/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/78f81617e241aa15b84c05097f1089a818d8d2d8/Chapter2_MorePyMC/sms_model.png -------------------------------------------------------------------------------- /Chapter3_MCMC/README.md: -------------------------------------------------------------------------------- 1 | Chapter 3: Opening the black box of MCMC (Markov Chain Monte Carlo) 2 | ================================================================== 3 | 4 | ### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Chapter3.ipynb) 5 | -------------------------------------------------------------------------------- /Chapter3_MCMC/data/github_data.csv: -------------------------------------------------------------------------------- 1 | Python, Javascript, Ruby, Java, Shell, PHP, days_since_creation, has_wiki, author_following, author_followers, starred_count, forked_count 2 | 0,1,0,0,0,0,523,1,0,0,2,1 3 | 0,1,0,0,0,0,778,1,193,95,1,0 4 | 0,0,0,1,0,0,531,1,1,4,3,0 5 | 0,0,1,0,0,0,396,1,1,2,2,0 6 | 0,0,0,0,1,0,846,1,3,5,1,0 7 | 0,0,1,0,0,0,520,1,155,56,1,0 8 | 0,1,0,0,0,0,384,1,0,0,2,0 9 | 1,0,0,0,0,0,705,1,33,9,1,0 10 | 0,0,0,0,0,1,726,1,0,7,11,1 11 | 1,0,0,0,0,0,413,1,0,2,1,0 12 | 1,0,0,0,0,0,141,1,3,0,1,0 13 | 1,0,0,0,0,0,1410,1,65,82,1,0 14 | 0,0,0,0,0,1,175,1,0,0,0,0 15 | 0,0,1,0,0,0,365,1,49,85,1,0 16 | 0,0,0,0,0,1,580,1,9,4,1,0 17 | 0,0,0,1,0,0,471,1,1,0,1,0 18 | 0,1,0,0,0,0,353,1,5,2,2,1 19 | 0,0,1,0,0,0,973,1,0,0,1,0 20 | 0,0,1,0,0,0,591,1,0,0,1,0 21 | 0,0,1,0,0,0,85,1,0,0,0,0 22 | 0,1,0,0,0,0,431,1,0,0,1,0 23 | 1,0,0,0,0,0,188,1,1,1,0,0 24 | 0,0,1,0,0,0,144,1,0,0,0,0 25 | 0,1,0,0,0,0,102,1,0,0,0,0 26 | 0,1,0,0,0,0,249,1,0,0,2,0 27 | 0,1,0,0,0,0,395,1,2,0,1,0 28 | 0,1,0,0,0,0,433,1,12,69,1,0 29 | 0,0,0,1,0,0,615,1,0,0,1,0 30 | 0,0,1,0,0,0,707,1,0,0,1,0 31 | 0,0,0,1,0,0,256,1,0,1,1,0 32 | 0,0,1,0,0,0,363,1,0,0,1,0 33 | 0,0,1,0,0,0,1818,1,98,162,16,6 34 | 0,1,0,0,0,0,86,1,13,7,0,0 35 | 0,1,0,0,0,0,284,1,1,3,2,1 36 | 0,0,0,1,0,0,867,1,9,5,2,0 37 | 0,0,1,0,0,0,1203,1,1,0,1,0 38 | 0,1,0,0,0,0,436,1,34,4,0,0 39 | 0,1,0,0,0,0,720,1,6,73,6,0 40 | 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2.171688340729648985e+02 298 | 2.423196685427484454e+02 299 | 1.963990296752487268e+02 300 | 2.228190673247157747e+02 301 | -------------------------------------------------------------------------------- /Chapter3_MCMC/data/smoking_death.csv: -------------------------------------------------------------------------------- 1 | age,smoke_cigars_pipe,smoke_both, smoke_cigarettes, population, deaths 2 | 1,0,0,0,656,18 3 | 2,0,0,0,359,22 4 | 3,0,0,0,249,19 5 | 4,0,0,0,632,55 6 | 5,0,0,0,1067,117 7 | 6,0,0,0,897,170 8 | 7,0,0,0,668,179 9 | 8,0,0,0,361,120 10 | 9,0,0,0,274,120 11 | 1,1,0,0,145,2 12 | 2,1,0,0,104,4 13 | 3,1,0,0,98,3 14 | 4,1,0,0,372,38 15 | 5,1,0,0,846,113 16 | 6,1,0,0,949,173 17 | 7,1,0,0,824,212 18 | 8,1,0,0,667,243 19 | 9,1,0,0,537,253 20 | 1,0,1,0,4531,149 21 | 2,0,1,0,3030,169 22 | 3,0,1,0,2267,193 23 | 4,0,1,0,4682,576 24 | 5,0,1,0,6052,1001 25 | 6,0,1,0,3880,901 26 | 7,0,1,0,2033,613 27 | 8,0,1,0,871,337 28 | 9,0,1,0,345,189 29 | 1,0,0,1,3410,124 30 | 2,0,0,1,2239,140 31 | 3,0,0,1,1851,187 32 | 4,0,0,1,3270,514 33 | 5,0,0,1,3791,778 34 | 6,0,0,1,2421,689 35 | 7,0,0,1,1195,432 36 | 8,0,0,1,436,214 37 | 9,0,0,1,113,63 38 | -------------------------------------------------------------------------------- /Chapter3_MCMC/github_pull.py: -------------------------------------------------------------------------------- 1 | #github data scrapper 2 | 3 | """ 4 | variables of interest: 5 | indp. variables 6 | - language, given as a binary variable. Need 4 positions for 5 langagues 7 | - #number of days created ago, 1 position 8 | - has wiki? Boolean, 1 position 9 | - followers, 1 position 10 | - following, 1 position 11 | - constant 12 | 13 | dep. variables 14 | -stars/watchers 15 | -forks 16 | 17 | """ 18 | from json import loads 19 | import datetime 20 | import numpy as np 21 | from requests import get 22 | 23 | 24 | MAX = 8000000 25 | today = datetime.datetime.today() 26 | randint = np.random.randint 27 | N = 120 #sample size. 28 | auth = ("username", "password" ) 29 | 30 | language_mappings = {"Python": 0, "JavaScript": 1, "Ruby": 2, "Java":3, "Shell":4, "PHP":5} 31 | 32 | #define data matrix: 33 | X = np.zeros( (N , 12), dtype = int ) 34 | 35 | for i in xrange(N): 36 | is_fork = True 37 | is_valid_language = False 38 | 39 | while is_fork == True or is_valid_language == False: 40 | is_fork = True 41 | is_valid_language = False 42 | 43 | params = {"since":randint(0, MAX ) } 44 | r = get("https://api.github.com/repositories", params = params, auth=auth ) 45 | results = loads( r.text )[0] 46 | #im only interested in the first one, and if it is not a fork. 47 | is_fork = results["fork"] 48 | 49 | r = get( results["url"], auth = auth) 50 | 51 | #check the language 52 | repo_results = loads( r.text ) 53 | try: 54 | language_mappings[ repo_results["language" ] ] 55 | is_valid_language = True 56 | except: 57 | pass 58 | 59 | #languages 60 | X[ i, language_mappings[ repo_results["language" ] ] ] = 1 61 | 62 | #delta time 63 | X[ i, 6] = ( today - datetime.datetime.strptime( repo_results["created_at"][:10], "%Y-%m-%d" ) ).days 64 | 65 | #haswiki 66 | X[i, 7] = repo_results["has_wiki"] 67 | 68 | #get user information 69 | r = get( results["owner"]["url"] , auth = auth) 70 | user_results = loads( r.text ) 71 | X[i, 8] = user_results["following"] 72 | X[i, 9] = user_results["followers"] 73 | 74 | #get dep. data 75 | X[i, 10] = repo_results["watchers_count"] 76 | X[i, 11] = repo_results["forks_count"] 77 | print 78 | print " -------------- " 79 | print i, ": ", results["full_name"], repo_results["language" ], repo_results["watchers_count"], repo_results["forks_count"] 80 | print " -------------- " 81 | print 82 | 83 | np.savetxt("data/github_data.csv", X, delimiter=",", fmt="%d" ) 84 | -------------------------------------------------------------------------------- /Chapter4_TheGreatestTheoremNeverTold/README.md: -------------------------------------------------------------------------------- 1 | Chapter 4: The Greatest Theorem Never Told 2 | ==== 3 | ### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Chapter4.ipynb) 4 | -------------------------------------------------------------------------------- /Chapter4_TheGreatestTheoremNeverTold/reddit_comments.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmportilla/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/78f81617e241aa15b84c05097f1089a818d8d2d8/Chapter4_TheGreatestTheoremNeverTold/reddit_comments.png -------------------------------------------------------------------------------- /Chapter4_TheGreatestTheoremNeverTold/top_pic_comments.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | import numpy as np 4 | from IPython.core.display import Image 5 | 6 | import praw 7 | 8 | 9 | reddit = praw.Reddit("BayesianMethodsForHackers") 10 | subreddit = reddit.get_subreddit( "pics" ) 11 | 12 | top_submissions = subreddit.get_top() 13 | 14 | 15 | n_pic = int( sys.argv[1] ) if sys.argv[1] else 1 16 | 17 | i = 0 18 | while i < n_pic: 19 | top_submission = top_submissions.next() 20 | while "i.imgur.com" not in top_submission.url: 21 | #make sure it is linking to an image, not a webpage. 22 | top_submission = top_submissions.next() 23 | i+=1 24 | 25 | print "Title of submission: \n", top_submission.title 26 | top_post_url = top_submission.url 27 | #top_submission.replace_more_comments(limit=5, threshold=0) 28 | print top_post_url 29 | 30 | upvotes = [] 31 | downvotes = [] 32 | contents = [] 33 | _all_comments = top_submission.comments 34 | all_comments=[] 35 | for comment in _all_comments: 36 | try: 37 | upvotes.append( comment.ups ) 38 | downvotes.append( comment.downs ) 39 | contents.append( comment.body ) 40 | except Exception as e: 41 | continue 42 | 43 | votes = np.array( [ upvotes, downvotes] ).T 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | -------------------------------------------------------------------------------- /Chapter5_LossFunctions/README.md: -------------------------------------------------------------------------------- 1 | Chapter 5: Would you rather lose an arm or a leg? 2 | ==== 3 | 4 | ### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Chapter5.ipynb) 5 | -------------------------------------------------------------------------------- /Chapter5_LossFunctions/data/Test_haloCounts.csv: -------------------------------------------------------------------------------- 1 | SkyID,NumberHalos 2 | Sky1,1 3 | Sky2,1 4 | Sky3,1 5 | Sky4,1 6 | Sky5,1 7 | Sky6,1 8 | Sky7,1 9 | Sky8,1 10 | Sky9,1 11 | Sky10,1 12 | Sky11,1 13 | Sky12,1 14 | Sky13,1 15 | Sky14,1 16 | Sky15,1 17 | Sky16,1 18 | Sky17,1 19 | Sky18,1 20 | Sky19,1 21 | Sky20,1 22 | Sky21,1 23 | Sky22,1 24 | Sky23,1 25 | Sky24,1 26 | Sky25,1 27 | Sky26,1 28 | Sky27,1 29 | Sky28,1 30 | Sky29,1 31 | Sky30,1 32 | Sky31,1 33 | Sky32,1 34 | Sky33,1 35 | Sky34,1 36 | Sky35,1 37 | Sky36,1 38 | Sky37,1 39 | Sky38,1 40 | Sky39,1 41 | Sky40,1 42 | Sky41,2 43 | Sky42,2 44 | Sky43,2 45 | Sky44,2 46 | Sky45,2 47 | Sky46,2 48 | Sky47,2 49 | Sky48,2 50 | Sky49,2 51 | Sky50,2 52 | Sky51,2 53 | Sky52,2 54 | Sky53,2 55 | Sky54,2 56 | Sky55,2 57 | Sky56,2 58 | Sky57,2 59 | Sky58,2 60 | Sky59,2 61 | Sky60,2 62 | Sky61,2 63 | Sky62,2 64 | Sky63,2 65 | Sky64,2 66 | Sky65,2 67 | Sky66,2 68 | Sky67,2 69 | Sky68,2 70 | Sky69,2 71 | Sky70,2 72 | Sky71,2 73 | Sky72,2 74 | Sky73,2 75 | Sky74,2 76 | Sky75,2 77 | Sky76,2 78 | Sky77,2 79 | Sky78,2 80 | Sky79,2 81 | Sky80,2 82 | Sky81,3 83 | Sky82,3 84 | Sky83,3 85 | Sky84,3 86 | Sky85,3 87 | Sky86,3 88 | Sky87,3 89 | Sky88,3 90 | Sky89,3 91 | Sky90,3 92 | Sky91,3 93 | Sky92,3 94 | Sky93,3 95 | Sky94,3 96 | Sky95,3 97 | Sky96,3 98 | Sky97,3 99 | Sky98,3 100 | Sky99,3 101 | Sky100,3 102 | Sky101,3 103 | Sky102,3 104 | Sky103,3 105 | Sky104,3 106 | Sky105,3 107 | Sky106,3 108 | Sky107,3 109 | Sky108,3 110 | Sky109,3 111 | Sky110,3 112 | Sky111,3 113 | Sky112,3 114 | Sky113,3 115 | Sky114,3 116 | Sky115,3 117 | Sky116,3 118 | Sky117,3 119 | Sky118,3 120 | Sky119,3 121 | Sky120,3 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185 | Galaxy184,3981.68,1756.12,0.199829,0.155748 186 | Galaxy185,1848.21,4141.11,-0.213418,0.052246 187 | Galaxy186,191.59,3434.65,-0.138117,-0.107815 188 | Galaxy187,984.42,4144.86,-0.450513,0.020876 189 | Galaxy188,1737.76,2593.93,-0.374679,0.121389 190 | Galaxy189,766.72,518.03,-0.048888,-0.003454 191 | Galaxy190,1341.57,1683.82,0.277980,0.155423 192 | Galaxy191,3617.45,3098.19,-0.041724,-0.005124 193 | Galaxy192,703.95,2097.09,-0.099777,0.443211 194 | Galaxy193,3286.09,209.16,0.039314,-0.022571 195 | Galaxy194,348.73,2970.10,0.188389,-0.109593 196 | Galaxy195,1482.43,1790.91,0.165213,-0.322425 197 | Galaxy196,4046.22,4124.58,0.206650,0.020593 198 | Galaxy197,2420.47,2203.81,0.200592,0.304594 199 | Galaxy198,2267.50,2354.71,0.180244,-0.330609 200 | Galaxy199,697.96,2230.92,-0.266932,0.126898 201 | Galaxy200,3616.60,3019.57,0.133426,0.088902 202 | Galaxy201,2054.26,1143.86,-0.095747,0.126982 203 | Galaxy202,624.72,4032.13,0.057488,0.062488 204 | Galaxy203,1357.21,124.40,0.141634,0.143941 205 | Galaxy204,1549.02,3343.44,0.050292,0.002864 206 | Galaxy205,1439.58,1307.58,0.065786,0.061814 207 | Galaxy206,1432.30,2664.46,0.231571,-0.107493 208 | Galaxy207,2565.97,781.24,0.158356,0.238912 209 | Galaxy208,2100.02,2799.49,-0.270209,0.203871 210 | Galaxy209,3866.89,3874.08,-0.002106,-0.147734 211 | Galaxy210,2828.54,940.90,-0.038175,0.203001 212 | Galaxy211,1733.22,3783.55,-0.087186,-0.230283 213 | Galaxy212,1834.83,2285.95,0.021040,-0.007755 214 | Galaxy213,2898.93,1955.81,0.043995,-0.162454 215 | Galaxy214,2642.26,1700.91,-0.224128,-0.190959 216 | Galaxy215,3499.29,4013.86,0.131707,-0.208682 217 | Galaxy216,3037.55,1059.89,-0.012412,-0.017858 218 | Galaxy217,2491.75,1392.10,0.287406,0.479956 219 | Galaxy218,3246.80,1158.24,0.521521,-0.170803 220 | Galaxy219,2723.07,608.65,0.077419,-0.216203 221 | Galaxy220,411.22,3270.86,0.028996,-0.259367 222 | Galaxy221,3610.23,2857.23,-0.375060,0.306263 223 | Galaxy222,3719.90,669.52,-0.060062,-0.135059 224 | Galaxy223,203.04,2509.10,-0.457299,-0.313297 225 | Galaxy224,2304.09,3611.73,0.209046,-0.056485 226 | Galaxy225,453.99,2290.03,0.005734,0.009596 227 | Galaxy226,2075.51,1763.43,-0.244898,-0.331087 228 | Galaxy227,494.84,1105.45,-0.116584,0.196238 229 | Galaxy228,1600.39,3249.89,0.369859,-0.222707 230 | Galaxy229,2037.56,3171.04,-0.042582,-0.244995 231 | Galaxy230,1500.41,2299.80,-0.344525,0.462705 232 | Galaxy231,3035.34,1513.24,0.098203,0.065147 233 | Galaxy232,3840.93,2874.31,0.389604,0.272858 234 | Galaxy233,1045.89,783.12,0.126115,-0.259653 235 | Galaxy234,3284.13,3612.35,0.106593,-0.156612 236 | Galaxy235,1317.64,3306.80,-0.128025,-0.025713 237 | Galaxy236,3364.36,1722.42,0.108838,-0.124385 238 | Galaxy237,965.20,3004.29,-0.567660,0.218672 239 | Galaxy238,151.22,3573.65,-0.630198,-0.274249 240 | Galaxy239,220.44,1710.74,-0.279939,-0.180480 241 | Galaxy240,1169.25,4178.38,0.183532,0.045251 242 | Galaxy241,4067.46,3605.63,-0.041220,0.114528 243 | Galaxy242,2212.77,1683.45,0.249006,0.363035 244 | Galaxy243,1962.99,1024.79,-0.372270,-0.144197 245 | Galaxy244,858.55,1191.13,0.137072,-0.017281 246 | Galaxy245,1819.87,3063.26,0.156792,0.421663 247 | Galaxy246,3432.19,2897.86,0.165120,-0.106949 248 | Galaxy247,1726.68,1933.48,0.026344,0.280095 249 | Galaxy248,2878.67,2445.44,-0.032010,-0.031350 250 | Galaxy249,3119.91,826.45,-0.066528,-0.169526 251 | Galaxy250,2816.43,1707.67,-0.194538,0.012642 252 | Galaxy251,306.95,3140.00,-0.408528,0.247290 253 | Galaxy252,2978.08,3684.42,0.024853,-0.119773 254 | Galaxy253,2745.88,2595.54,-0.072008,-0.364359 255 | Galaxy254,166.12,1299.86,0.251731,-0.031076 256 | Galaxy255,1115.66,2596.72,0.247405,-0.346954 257 | Galaxy256,1551.48,1062.65,-0.465718,-0.266508 258 | Galaxy257,1806.20,2648.41,0.005053,-0.191388 259 | Galaxy258,2123.72,3302.00,0.000784,0.264660 260 | Galaxy259,1607.72,1848.07,-0.158682,-0.377357 261 | Galaxy260,1789.21,353.49,-0.097706,0.126320 262 | Galaxy261,4192.02,3625.75,-0.033171,0.063867 263 | Galaxy262,3697.41,1699.24,-0.109245,-0.147616 264 | Galaxy263,3819.48,1894.45,0.005503,0.146136 265 | Galaxy264,3530.45,1559.68,0.333658,0.046486 266 | Galaxy265,473.85,3625.84,0.345073,-0.365009 267 | Galaxy266,3744.43,1576.82,0.103219,-0.305736 268 | Galaxy267,2015.40,132.39,-0.395797,0.034220 269 | Galaxy268,3994.41,3601.75,-0.065515,-0.189400 270 | Galaxy269,1817.55,971.07,-0.274426,0.089548 271 | Galaxy270,3678.98,240.94,-0.234879,-0.105125 272 | Galaxy271,3247.73,2716.67,-0.204339,-0.159215 273 | Galaxy272,2374.43,2479.10,-0.322343,0.067825 274 | Galaxy273,3054.66,3637.24,0.351115,-0.417399 275 | Galaxy274,949.41,2925.69,-0.208260,0.082958 276 | Galaxy275,607.80,1910.84,0.184830,0.155393 277 | Galaxy276,4179.52,2093.29,0.000943,-0.327880 278 | Galaxy277,3791.67,3439.40,-0.203535,-0.016425 279 | Galaxy278,3941.55,1074.07,-0.225548,0.060171 280 | Galaxy279,3340.52,3594.11,-0.027063,0.094426 281 | Galaxy280,3126.45,1313.70,0.230585,0.237522 282 | Galaxy281,1493.87,3338.79,0.406936,0.287537 283 | Galaxy282,1202.36,2392.14,-0.253936,-0.220457 284 | Galaxy283,179.71,316.37,-0.129686,0.217637 285 | Galaxy284,2221.75,2836.22,0.452013,0.171711 286 | Galaxy285,768.90,2054.25,0.013105,0.170166 287 | Galaxy286,1692.58,2665.40,0.297594,0.103773 288 | Galaxy287,2487.19,3808.68,-0.013511,0.575862 289 | Galaxy288,2913.38,3642.91,0.346218,-0.533800 290 | Galaxy289,4091.02,1225.47,0.140202,0.102776 291 | Galaxy290,681.56,1606.04,0.089748,0.275250 292 | Galaxy291,3151.08,219.31,0.405692,-0.162915 293 | Galaxy292,1974.51,2033.76,-0.027568,0.118460 294 | Galaxy293,98.51,3733.41,0.384794,-0.397892 295 | Galaxy294,3781.26,1642.29,-0.033442,0.058281 296 | Galaxy295,1632.58,3908.57,0.104266,0.061275 297 | Galaxy296,3444.82,2519.43,-0.310237,0.074373 298 | Galaxy297,3790.16,3098.03,-0.198856,0.130362 299 | Galaxy298,769.86,2382.02,-0.033856,-0.053595 300 | Galaxy299,3390.12,2526.21,-0.033844,-0.198775 301 | Galaxy300,2586.21,3713.30,-0.019323,-0.095208 302 | Galaxy301,3267.23,1941.37,0.404544,-0.098516 303 | Galaxy302,1995.75,3615.91,-0.112023,-0.288812 304 | Galaxy303,1781.25,1878.79,0.135262,0.314139 305 | Galaxy304,3968.99,1620.50,0.157059,0.211680 306 | Galaxy305,871.53,3943.94,-0.227895,-0.016396 307 | Galaxy306,2188.83,2364.70,0.006323,-0.269327 308 | Galaxy307,4127.69,555.46,-0.072670,-0.100969 309 | Galaxy308,731.85,1747.20,0.211320,-0.170121 310 | Galaxy309,2151.98,2766.58,0.139111,0.384373 311 | Galaxy310,3471.14,318.45,-0.168283,0.418351 312 | Galaxy311,1676.68,3576.49,0.229743,0.064735 313 | Galaxy312,69.29,2912.62,-0.010464,0.015118 314 | Galaxy313,2547.81,2658.26,-0.159092,-0.288962 315 | Galaxy314,4.04,163.93,-0.234044,-0.068929 316 | Galaxy315,180.94,4039.41,-0.090073,0.122891 317 | Galaxy316,3811.09,309.06,-0.046749,0.388647 318 | Galaxy317,1187.24,2197.97,0.014247,-0.083501 319 | Galaxy318,69.13,4121.07,-0.160088,-0.059881 320 | Galaxy319,563.12,2930.24,-0.038796,0.094123 321 | Galaxy320,1982.68,2876.95,0.080327,-0.198012 322 | Galaxy321,2450.05,768.50,-0.257380,0.109321 323 | Galaxy322,437.07,625.17,-0.104630,-0.136037 324 | Galaxy323,358.18,1812.04,-0.133812,-0.012610 325 | Galaxy324,3115.11,173.95,0.329686,0.139691 326 | Galaxy325,1300.56,1161.01,0.013458,0.184725 327 | Galaxy326,3938.30,960.64,-0.391015,0.235916 328 | Galaxy327,3061.74,3040.04,-0.040431,-0.086680 329 | -------------------------------------------------------------------------------- /Chapter5_LossFunctions/draw_sky2.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | from matplotlib.patches import Ellipse 3 | import numpy as np 4 | 5 | def draw_sky( galaxies ): 6 | """adapted from Vishal Goklani""" 7 | size_multiplier = 45 8 | fig = plt.figure(figsize=(10,10)) 9 | #fig.patch.set_facecolor("blue") 10 | ax = fig.add_subplot(111, aspect='equal') 11 | n = galaxies.shape[0] 12 | for i in xrange(n): 13 | _g = galaxies[i,:] 14 | x,y = _g[0], _g[1] 15 | d = np.sqrt( _g[2]**2 + _g[3]**2 ) 16 | a = 1.0/ ( 1 - d ) 17 | b = 1.0/( 1 + d) 18 | theta = np.degrees( np.arctan2( _g[3], _g[2])*0.5 ) 19 | 20 | ax.add_patch( Ellipse(xy=(x, y), width=size_multiplier*a, height=size_multiplier*b, angle=theta) ) 21 | ax.autoscale_view(tight=True) 22 | 23 | return fig -------------------------------------------------------------------------------- /Chapter6_Priorities/BanditsD3.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 45 | 46 | 47 | 48 | 49 | 50 | 51 |
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89 | 90 | 91 | -------------------------------------------------------------------------------- /Chapter6_Priorities/README.md: -------------------------------------------------------------------------------- 1 | Chapter 6: Getting our priorities straight 2 | =========== 3 | 4 | ### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Chapter6.ipynb) 5 | -------------------------------------------------------------------------------- /Chapter6_Priorities/d3bandits.js: -------------------------------------------------------------------------------- 1 | var ARMS = [0,0,0,0,0,0]; 2 | var _PROBS = [ rbeta(2,9), rbeta(5,9), rbeta(5,9) ]; 3 | var BB_RUN = 0; 4 | 5 | d3.select( "#reveal-div" ) 6 | .selectAll("p") 7 | .data( _PROBS ) 8 | .enter() 9 | .append("p") 10 | .text( function(d,i){ return "Arm " + (i + 1) + ": " +d.toFixed(4); } ) 11 | 12 | 13 | function pdfbeta(x_array,a,b){ 14 | //x is an array 15 | _beta = Beta(a,b); 16 | function _pdfbeta(x){ 17 | return ( Math.pow(x,a-1)*Math.pow(1-x, b - 1) )/_beta 18 | } 19 | 20 | return x_array.map( _pdfbeta ) 21 | } 22 | 23 | function Beta(a,b){ 24 | //stirlings approx 25 | // use logs and exponentials to avoid underflow 26 | // with logs is still giving me errors 27 | 28 | //var n = Math.pow(a, a - 0.5)*Math.pow(b, b-0.5) 29 | 30 | var log_n = Math.log(a)*(a - 0.5) + Math.log(b)*( b-0.5) 31 | //var d = Math.pow( a + b, a+ b-0.5) 32 | var log_d = Math.log( a + b)*(a+ b-0.5) 33 | return Math.sqrt( 2*Math.PI)*Math.exp(log_n - log_d) 34 | } 35 | 36 | 37 | function rbeta(a,b){ 38 | //from Simulation and MC, Wiley 39 | 40 | var p = a/b; 41 | if (Math.min(a,b) <= 1){ 42 | var lambda = Math.min(a,b) 43 | }else{ 44 | var lambda = Math.sqrt( (2*a*b - a - b)/(a+b-2) ) 45 | } 46 | 47 | while (1){ 48 | var R1 = Math.random(); 49 | var R2 = Math.random(); 50 | var y = Math.pow( ( 1./R1 - 1.), 1./lambda ); 51 | if ( 4*R1*R2*R2 < (Math.pow(y, a - lambda)*Math.pow( (1.+ p)/(1 + p*y) , a + b ) )){ 52 | return (p*y)/(1+ p*y) 53 | } 54 | } 55 | } 56 | 57 | function rbeta_array( arm_counts){ 58 | // to be used with ARMS with uniform prior. 59 | samples = [] 60 | for (var i=0; i < arm_counts.length/2; i++){ 61 | samples.push( 62 | rbeta(arm_counts[2*i + 1]+1, 1+arm_counts[2*i] - arm_counts[2*i+1] ) 63 | ) 64 | } 65 | return samples 66 | } 67 | 68 | function draw_arm( p ){ 69 | if ( Math.random() < p){ return 1 } else { return 0 } 70 | } 71 | 72 | 73 | function update_arm( arm_number ){ 74 | var result = draw_arm(_PROBS[arm_number] ); 75 | ARMS[2*arm_number] += 1; 76 | ARMS[2*arm_number+1] += result; 77 | redraw(arm_number); 78 | return 79 | } 80 | 81 | 82 | function bayesian_bandits(){ 83 | //for (var i = 0; i < n_pulls; i++ ){ 84 | //sample from Beta distributions 85 | var samples = rbeta_array(ARMS); 86 | var select = samples.indexOf( d3.max( samples) ); 87 | update_arm( select ); 88 | if (BB_RUN < 300){ 89 | BB_RUN += 1; 90 | window.setTimeout( bayesian_bandits, 100 ) 91 | } 92 | else{ 93 | return 94 | } 95 | //} 96 | } 97 | 98 | var x_array = []; 99 | var _N = 100; 100 | var max_data = 10 101 | for ( var i =0; i < _N; i++ ){ 102 | x_array.push( .01*i ) 103 | } 104 | 105 | var colors = ["#348ABD", "#A60628", "#7A68A6"]; 106 | var fill_colors = [ "rgba(52, 128, 189,0.1)", "rgba(166, 6, 40, 0.1 )", "rgba( 122, 104, 166,0.1 )"]; 107 | 108 | var w = 600, 109 | h = 150, 110 | margin = 15, 111 | y = d3.scale.linear().domain([0, max_data]).range([h - margin,0 + margin ]), 112 | x = d3.scale.linear().domain([0,_N]).range([0 + margin, w - margin]) 113 | 114 | var vis = d3.select("#beta-graphs") 115 | .append("svg:svg") 116 | .attr("width", w ) 117 | .attr("height", h ) 118 | 119 | var g = vis.append("svg:g") 120 | 121 | var line = d3.svg.line() 122 | .x(function(d, i) { return x(i); }) 123 | .y(y) 124 | 125 | 126 | for ( var i =0; i < 3; i++){ 127 | var _data = pdfbeta(x_array, 1 + ARMS[2*i+1],1+ARMS[2*i] - ARMS[2*i+1] ); 128 | g.selectAll('path.line') 129 | .data( [_data] ) 130 | .enter() 131 | .append("svg:path") 132 | .attr("stroke", colors[i] ) 133 | //.attr("fill", fill_colors[i] ) 134 | //.attr("fill", fill_colors[i] ) 135 | //.attr("stroke-width", 0 ) 136 | .attr("d", line ) 137 | .attr("id", "line-" + i ); 138 | } 139 | 140 | 141 | g.append("svg:line") 142 | .attr("x1", x(0)) 143 | .attr("y1", y(0)) 144 | .attr("x2", x(w)) 145 | .attr("y2", y(0)) 146 | 147 | g.append("svg:line") 148 | .attr("x1", x(0)) 149 | .attr("y1", y(0)) 150 | .attr("x2", x(0)) 151 | .attr("y2", y(max_data)) 152 | 153 | g.selectAll(".xLabel") 154 | .data( d3.range(0,1.2,.2) ) 155 | .enter().append("svg:text") 156 | .attr("class", "xLabel") 157 | .text(String) 158 | .attr("x", function(d) { return x(100*d) }) 159 | .attr("y", h) 160 | .attr("text-anchor", "middle") 161 | .attr("dy", 0.0 ) 162 | /* 163 | g.selectAll(".yLabel") 164 | .data(y.ticks(4)) 165 | .enter().append("svg:text") 166 | .attr("class", "yLabel") 167 | .text(String) 168 | .attr("x", 0) 169 | .attr("y", function(d) { return y(d) }) 170 | .attr("text-anchor", "right") 171 | .attr("dy", 4) 172 | */ 173 | g.selectAll(".xTicks") 174 | .data(x.ticks(5)) 175 | .enter().append("svg:line") 176 | .attr("class", "xTicks") 177 | .attr("x1", function(d) { return x(d); }) 178 | .attr("y1", y(0)) 179 | .attr("x2", function(d) { return x(d); }) 180 | .attr("y2", y(-0.1)) 181 | 182 | vis.append("text") 183 | .attr("x", (w / 2)) 184 | .attr("y", 15 ) 185 | .attr("text-anchor", "middle") 186 | .style("font-size", "17px") 187 | .text("Posterior Distributions"); 188 | 189 | /* 190 | g.selectAll(".yTicks") 191 | .data(y.ticks(4)) 192 | .enter().append("svg:line") 193 | .attr("class", "yTicks") 194 | .attr("y1", function(d) { return -1 * y(d); }) 195 | .attr("x1", x(-0.3)) 196 | .attr("y2", function(d) { return -1 * y(d); }) 197 | .attr("x2", x(0)) 198 | */ 199 | 200 | 201 | 202 | 203 | 204 | 205 | var data = ARMS; 206 | var labellist = ["Arm 1", "", "Arm 2", "", "Arm 3", ""]; 207 | 208 | var w_bar = 600, 209 | h_bar = 170, 210 | labelpad = 50, 211 | x_bar = d3.scale.linear().domain([0, 100]).range([0, w_bar]), 212 | y_bar = d3.scale.ordinal().domain(d3.range(data.length)).rangeBands([0, h_bar], .2); 213 | 214 | var vis = d3.select("#paired-bar-chart") 215 | .append("svg:svg") 216 | .attr("width", w_bar + 40) 217 | .attr("height", h_bar + 20) 218 | .append("svg:g") 219 | 220 | var bars = vis.selectAll("g.bar") 221 | .data(data) 222 | .enter().append("svg:g") 223 | .attr("class", "bar") 224 | .attr("transform", function(d, i) { return "translate(" + labelpad + "," + y_bar(i) + ")"; }) 225 | 226 | 227 | bars.append("svg:rect") 228 | .attr("fill", function(d, i) { return (i%2)? colors[i]: fill_colors[i]; } ) //Alternate colors 229 | .attr("width", function(d,i){ return x_bar(d)*0.5 }) 230 | .attr("height", y_bar.rangeBand()); 231 | 232 | bars.append("svg:text") 233 | .attr("x", 0) 234 | .attr("y", 10 + y_bar.rangeBand() / 2) 235 | .attr("dx", -6) 236 | .attr("dy", ".50em") 237 | .attr("text-anchor", "end") 238 | .text(function(d, i) { return labellist[i]; }); 239 | 240 | var counts = bars.append("svg:text") 241 | .attr("x", 0) 242 | .attr("y", 10 + y_bar.rangeBand() / 2) 243 | .attr("dx", -6) 244 | .attr("dy", "-.40em") 245 | .attr("text-anchor", "end") 246 | .text(function(d, i) { return ""; }); 247 | 248 | 249 | var rules = vis.selectAll("g.rule") 250 | .data(x.ticks(10)) 251 | .enter().append("svg:g") 252 | .attr("class", "rule") 253 | .attr("transform", function(d) { return "translate(" + x_bar(d) + ", 0)"; }); 254 | 255 | 256 | rules.append("svg:line") 257 | .attr("y1", 0) 258 | .attr("y2", h_bar) 259 | .attr("x1", labelpad) 260 | .attr("x2", labelpad) 261 | .attr("stroke", "white") 262 | .attr("stroke-opacity", .3); 263 | 264 | 265 | 266 | function redraw(arm_number){ 267 | 268 | var _data = [] 269 | for ( var i =0; i < 3; i++){ 270 | _data.push( pdfbeta(x_array, 1 + ARMS[2*i+1],1+ARMS[2*i] - ARMS[2*i+1] ) ); 271 | 272 | } 273 | //update what is max. 274 | max_data = d3.max( [ 275 | 10, 276 | d3.max(_data[0]), 277 | d3.max(_data[1]), 278 | d3.max(_data[2]) ]) 279 | 280 | y = d3.scale.linear().domain([0, max_data]).range([h - margin,0 + margin ]) 281 | line = d3.svg.line() 282 | .x(function(d, i) { return x(i); }) 283 | .y(y) 284 | 285 | for ( var i =0; i < 3; i++){ 286 | g.select("#line-" + i) 287 | .data( [_data[i]] ) 288 | .attr("d", line ) 289 | } 290 | 291 | 292 | 293 | 294 | 295 | 296 | bars.data(ARMS) 297 | .enter().append("svg:g") 298 | .attr("class", "bar") 299 | .attr("transform", function(d, i) { return "translate(" + labelpad + "," + y(i) + ")"; }); 300 | 301 | 302 | bars.append("svg:rect") 303 | .attr("fill", function(d, ix) {_ix = Math.floor(ix/2); return (ix%2)? fill_colors[_ix]: colors[_ix]; } ) //Alternate colors 304 | .attr("width", function(d,i){ return x_bar(d)*0.5 }) 305 | .attr("height", y_bar.rangeBand()); 306 | 307 | 308 | counts 309 | .attr("x", 0) 310 | .attr("y", 10 + y_bar.rangeBand() / 2) 311 | .attr("dx", function( d,i) { return !(i%2) ? 50 + 3.2*data[i] : 67 + 3.2*data[i] ;}) 312 | .attr("dy", "-.40em") 313 | .attr("text-anchor", "end") 314 | .text(function(d, i) { return !(i%2) ? data[i] + " pulls" : data[i] + " rewards" ;}); 315 | 316 | //update scoreboard 317 | var rewards = ARMS[1] + ARMS[3] + ARMS[5]; 318 | var pulls = ARMS[0] + ARMS[2] + ARMS[4]; 319 | document.getElementById("rewards").innerHTML = rewards ; 320 | document.getElementById("pulls").innerHTML = pulls ; 321 | document.getElementById("ratio").innerHTML = (rewards/pulls).toFixed(3) ; 322 | 323 | } 324 | 325 | 326 | d3.select( "#reveal-div" ) 327 | .selectAll("p") 328 | .data( _PROBS ) 329 | .enter() 330 | .append("span") 331 | .attr( "style", "margin-left:15; margin-right: 30px; margin-top:0" ) 332 | .text( function(d,i){ return d.toFixed(4) ; } ) 333 | //redraw() //to initialize 334 | -------------------------------------------------------------------------------- /Chapter6_Priorities/data/data.csv: -------------------------------------------------------------------------------- 1 | Pos,Team,Pl,w,D,L,PF,PA,DF,TF,TA,Pts 2 | 1,Ireland,5,4,0,1,132,49,83,16,4,8 3 | 2,England,5,4,0,1,138,65,73,14,5,8 4 | 3,Wales,5,3,0,2,122,79,43,11,6,6 5 | 4,France,5,3,0,2,101,100,1,9,10,6 6 | 5,Scotland,5,1,0,4,47,138,-91,4,15,2 7 | 6,Italy,5,0,0,5,63,172,-109,7,21,0 8 | -------------------------------------------------------------------------------- /Chapter6_Priorities/data/data.txt: -------------------------------------------------------------------------------- 1 | 2 | Possition Nation Games Points Tries Table 3 | points 4 | P:q 5 | layed Won Drawn Lost For Against Difference 6 | 1 Ireland 5 4 0 1 132 49 +83 16 8 7 | 2 England 5 4 0 1 138 65 +73 14 8 8 | 3 Wales 5 3 0 2 122 79 +43 11 6 9 | 4 France 5 3 0 2 101 100 +1 9 6 10 | 5 Scotland 5 1 0 4 47 138 −91 4 2 11 | 6 Italy 5 0 0 5 63 172 −109 7 0 12 | 13 | -------------------------------------------------------------------------------- /Chapter6_Priorities/data/results_2014.csv: -------------------------------------------------------------------------------- 1 | home_team,away_team,home_score,away_score 2 | Wales,Italy,23,15 3 | France,England,26,24 4 | Ireland,Scotland,28,6 5 | Ireland,Wales,26,3 6 | Scotland,England,0,20 7 | France,Italy,30,10 8 | Wales,France,27,6 9 | Italy,Scotland,20,21 10 | England,Ireland,13,10 11 | Ireland,Italy,46,7 12 | Scotland,France,17,19 13 | England,Wales,29,18 14 | Italy,England,11,52 15 | Wales,Scotland,51,3 16 | France,Ireland,20,22 17 | -------------------------------------------------------------------------------- /Chapter6_Priorities/data/table_2014.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmportilla/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/78f81617e241aa15b84c05097f1089a818d8d2d8/Chapter6_Priorities/data/table_2014.csv -------------------------------------------------------------------------------- /Chapter6_Priorities/data/table_2014.ods: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmportilla/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/78f81617e241aa15b84c05097f1089a818d8d2d8/Chapter6_Priorities/data/table_2014.ods -------------------------------------------------------------------------------- /Chapter6_Priorities/other_strats.py: -------------------------------------------------------------------------------- 1 | #other strats. 2 | # TODO: UBC strat, epsilon-greedy 3 | 4 | import scipy.stats as stats 5 | import numpy as np 6 | from pymc import rbeta 7 | 8 | rand = np.random.rand 9 | beta = stats.beta 10 | 11 | 12 | class GeneralBanditStrat( object ): 13 | 14 | """ 15 | Implements a online, learning strategy to solve 16 | the Multi-Armed Bandit problem. 17 | 18 | parameters: 19 | bandits: a Bandit class with .pull method 20 | choice_function: accepts a self argument (which gives access to all the variables), and 21 | returns and int between 0 and n-1 22 | methods: 23 | sample_bandits(n): sample and train on n pulls. 24 | 25 | attributes: 26 | N: the cumulative number of samples 27 | choices: the historical choices as a (N,) array 28 | bb_score: the historical score as a (N,) array 29 | 30 | """ 31 | 32 | def __init__(self, bandits, choice_function): 33 | 34 | self.bandits = bandits 35 | n_bandits = len( self.bandits ) 36 | self.wins = np.zeros( n_bandits ) 37 | self.trials = np.zeros(n_bandits ) 38 | self.N = 0 39 | self.choices = [] 40 | self.score = [] 41 | self.choice_function = choice_function 42 | 43 | def sample_bandits( self, n=1 ): 44 | 45 | score = np.zeros( n ) 46 | choices = np.zeros( n ) 47 | 48 | for k in range(n): 49 | #sample from the bandits's priors, and select the largest sample 50 | choice = self.choice_function(self) 51 | 52 | #sample the chosen bandit 53 | result = self.bandits.pull( choice ) 54 | 55 | #update priors and score 56 | self.wins[ choice ] += result 57 | self.trials[ choice ] += 1 58 | score[ k ] = result 59 | self.N += 1 60 | choices[ k ] = choice 61 | 62 | self.score = np.r_[ self.score, score ] 63 | self.choices = np.r_[ self.choices, choices ] 64 | return 65 | 66 | 67 | def bayesian_bandit_choice(self): 68 | return np.argmax( rbeta( 1 + self.wins, 1 + self.trials - self.wins) ) 69 | 70 | def max_mean( self ): 71 | """pick the bandit with the current best observed proportion of winning """ 72 | return np.argmax( self.wins / ( self.trials +1 ) ) 73 | 74 | def lower_credible_choice( self ): 75 | """pick the bandit with the best LOWER BOUND. See chapter 5""" 76 | def lb(a,b): 77 | return a/(a+b) - 1.65*np.sqrt( (a*b)/( (a+b)**2*(a+b+1) ) ) 78 | a = self.wins + 1 79 | b = self.trials - self.wins + 1 80 | return np.argmax( lb(a,b) ) 81 | 82 | def upper_credible_choice( self ): 83 | """pick the bandit with the best LOWER BOUND. See chapter 5""" 84 | def lb(a,b): 85 | return a/(a+b) + 1.65*np.sqrt( (a*b)/( (a+b)**2*(a+b+1) ) ) 86 | a = self.wins + 1 87 | b = self.trials - self.wins + 1 88 | return np.argmax( lb(a,b) ) 89 | 90 | def random_choice( self): 91 | return np.random.randint( 0, len( self.wins ) ) 92 | 93 | 94 | def ucb_bayes( self ): 95 | C = 0 96 | n = 10000 97 | alpha =1 - 1./( (self.N+1) ) 98 | return np.argmax( beta.ppf( alpha, 99 | 1 + self.wins, 100 | 1 + self.trials - self.wins ) ) 101 | 102 | 103 | 104 | 105 | class Bandits(object): 106 | """ 107 | This class represents N bandits machines. 108 | 109 | parameters: 110 | p_array: a (n,) Numpy array of probabilities >0, <1. 111 | 112 | methods: 113 | pull( i ): return the results, 0 or 1, of pulling 114 | the ith bandit. 115 | """ 116 | def __init__(self, p_array): 117 | self.p = p_array 118 | self.optimal = np.argmax(p_array) 119 | 120 | def pull( self, i ): 121 | #i is which arm to pull 122 | return rand() < self.p[i] 123 | 124 | def __len__(self): 125 | return len(self.p) 126 | -------------------------------------------------------------------------------- /Chapter6_Priorities/ystockquote.py: -------------------------------------------------------------------------------- 1 | # 2 | # ystockquote : Python module - retrieve stock quote data from Yahoo Finance 3 | # 4 | # Copyright (c) 2007,2008,2013 Corey Goldberg (cgoldberg@gmail.com) 5 | # 6 | # license: GNU LGPL 7 | # 8 | # This library is free software; you can redistribute it and/or 9 | # modify it under the terms of the GNU Lesser General Public 10 | # License as published by the Free Software Foundation; either 11 | # version 2.1 of the License, or (at your option) any later version. 12 | # 13 | # Requires: Python 2.7/3.2+ 14 | 15 | 16 | __version__ = '0.2.2' 17 | 18 | 19 | try: 20 | # py3 21 | from urllib.request import Request, urlopen 22 | from urllib.parse import urlencode 23 | except ImportError: 24 | # py2 25 | from urllib2 import Request, urlopen 26 | from urllib import urlencode 27 | 28 | 29 | def _request(symbol, stat): 30 | url = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (symbol, stat) 31 | req = Request(url) 32 | resp = urlopen(req) 33 | return str(resp.read().decode('utf-8').strip()) 34 | 35 | 36 | def get_all(symbol): 37 | """ 38 | Get all available quote data for the given ticker symbol. 39 | 40 | Returns a dictionary. 41 | """ 42 | values = _request(symbol, 'l1c1va2xj1b4j4dyekjm3m4rr5p5p6s7').split(',') 43 | return dict( 44 | price=values[0], 45 | change=values[1], 46 | volume=values[2], 47 | avg_daily_volume=values[3], 48 | stock_exchange=values[4], 49 | market_cap=values[5], 50 | book_value=values[6], 51 | ebitda=values[7], 52 | dividend_per_share=values[8], 53 | dividend_yield=values[9], 54 | earnings_per_share=values[10], 55 | fifty_two_week_high=values[11], 56 | fifty_two_week_low=values[12], 57 | fifty_day_moving_avg=values[13], 58 | two_hundred_day_moving_avg=values[14], 59 | price_earnings_ratio=values[15], 60 | price_earnings_growth_ratio=values[16], 61 | price_sales_ratio=values[17], 62 | price_book_ratio=values[18], 63 | short_ratio=values[19], 64 | ) 65 | 66 | 67 | def get_price(symbol): 68 | return _request(symbol, 'l1') 69 | 70 | 71 | def get_change(symbol): 72 | return _request(symbol, 'c1') 73 | 74 | 75 | def get_volume(symbol): 76 | return _request(symbol, 'v') 77 | 78 | 79 | def get_avg_daily_volume(symbol): 80 | return _request(symbol, 'a2') 81 | 82 | 83 | def get_stock_exchange(symbol): 84 | return _request(symbol, 'x') 85 | 86 | 87 | def get_market_cap(symbol): 88 | return _request(symbol, 'j1') 89 | 90 | 91 | def get_book_value(symbol): 92 | return _request(symbol, 'b4') 93 | 94 | 95 | def get_ebitda(symbol): 96 | return _request(symbol, 'j4') 97 | 98 | 99 | def get_dividend_per_share(symbol): 100 | return _request(symbol, 'd') 101 | 102 | 103 | def get_dividend_yield(symbol): 104 | return _request(symbol, 'y') 105 | 106 | 107 | def get_earnings_per_share(symbol): 108 | return _request(symbol, 'e') 109 | 110 | 111 | def get_52_week_high(symbol): 112 | return _request(symbol, 'k') 113 | 114 | 115 | def get_52_week_low(symbol): 116 | return _request(symbol, 'j') 117 | 118 | 119 | def get_50day_moving_avg(symbol): 120 | return _request(symbol, 'm3') 121 | 122 | 123 | def get_200day_moving_avg(symbol): 124 | return _request(symbol, 'm4') 125 | 126 | 127 | def get_price_earnings_ratio(symbol): 128 | return _request(symbol, 'r') 129 | 130 | 131 | def get_price_earnings_growth_ratio(symbol): 132 | return _request(symbol, 'r5') 133 | 134 | 135 | def get_price_sales_ratio(symbol): 136 | return _request(symbol, 'p5') 137 | 138 | 139 | def get_price_book_ratio(symbol): 140 | return _request(symbol, 'p6') 141 | 142 | 143 | def get_short_ratio(symbol): 144 | return _request(symbol, 's7') 145 | 146 | 147 | def get_historical_prices(symbol, start_date, end_date): 148 | """ 149 | Get historical prices for the given ticker symbol. 150 | Date format is 'YYYY-MM-DD' 151 | 152 | Returns a nested list (first item is list of column headers). 153 | """ 154 | params = urlencode({ 155 | 's': symbol, 156 | 'a': int(start_date[5:7]) - 1, 157 | 'b': int(start_date[8:10]), 158 | 'c': int(start_date[0:4]), 159 | 'd': int(end_date[5:7]) - 1, 160 | 'e': int(end_date[8:10]), 161 | 'f': int(end_date[0:4]), 162 | 'g': 'd', 163 | 'ignore': '.csv', 164 | }) 165 | url = 'http://ichart.yahoo.com/table.csv?%s' % params 166 | req = Request(url) 167 | resp = urlopen(req) 168 | content = str(resp.read().decode('utf-8').strip()) 169 | days = content.splitlines() 170 | return [day.split(',') for day in days] -------------------------------------------------------------------------------- /Chapter7_BayesianMachineLearning/MachineLearning.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "metadata": { 3 | "name": "MachineLearning" 4 | }, 5 | "nbformat": 3, 6 | "nbformat_minor": 0, 7 | "worksheets": [ 8 | { 9 | "cells": [ 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "List of topics to cover:\n", 15 | "\n", 16 | "- Bayesian solution to overfitting\n", 17 | " - Salisman's solution to the Don't Overfit\n", 18 | "- Predictive distributions; \"how do I evaluate testing data?\"\n", 19 | "- model fitting, BIC + visualization tools\n", 20 | "- Gaussian Processes\n", 21 | "\n", 22 | "\n", 23 | "Would be nice/cool to cover:\n", 24 | "\n", 25 | "- classification models (using the books text)\n", 26 | "- Bayesian networks?" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "collapsed": false, 32 | "input": [], 33 | "language": "python", 34 | "metadata": {}, 35 | "outputs": [], 36 | "prompt_number": 6 37 | }, 38 | { 39 | "cell_type": "code", 40 | "collapsed": false, 41 | "input": [], 42 | "language": "python", 43 | "metadata": {}, 44 | "outputs": [], 45 | "prompt_number": 6 46 | }, 47 | { 48 | "cell_type": "code", 49 | "collapsed": false, 50 | "input": [], 51 | "language": "python", 52 | "metadata": {}, 53 | "outputs": [], 54 | "prompt_number": 6 55 | }, 56 | { 57 | "cell_type": "code", 58 | "collapsed": false, 59 | "input": [], 60 | "language": "python", 61 | "metadata": {}, 62 | "outputs": [], 63 | "prompt_number": 6 64 | }, 65 | { 66 | "cell_type": "code", 67 | "collapsed": false, 68 | "input": [ 69 | "from IPython.core.display import HTML\n", 70 | "\n", 71 | "\n", 72 | "def css_styling():\n", 73 | " styles = open(\"../styles/custom.css\", \"r\").read()\n", 74 | " return HTML(styles)\n", 75 | "css_styling()" 76 | ], 77 | "language": "python", 78 | "metadata": {}, 79 | "outputs": [ 80 | { 81 | "html": [ 82 | "\n", 127 | "" 142 | ], 143 | "output_type": "pyout", 144 | "prompt_number": 10, 145 | "text": [ 146 | "" 147 | ] 148 | } 149 | ], 150 | "prompt_number": 10 151 | }, 152 | { 153 | "cell_type": "code", 154 | "collapsed": false, 155 | "input": [], 156 | "language": "python", 157 | "metadata": {}, 158 | "outputs": [] 159 | } 160 | ], 161 | "metadata": {} 162 | } 163 | ] 164 | } -------------------------------------------------------------------------------- /Chapter7_BayesianMachineLearning/auc.py: -------------------------------------------------------------------------------- 1 | #contributed by Ben Hammer, 2013 2 | 3 | 4 | def tied_rank(x): 5 | """ 6 | Computes the tied rank of elements in x. 7 | 8 | This function computes the tied rank of elements in x. 9 | 10 | Parameters 11 | ---------- 12 | x : list of numbers, numpy array 13 | 14 | Returns 15 | ------- 16 | score : list of numbers 17 | The tied rank f each element in x 18 | 19 | """ 20 | sorted_x = sorted(zip(x,range(len(x)))) 21 | r = [0 for k in x] 22 | cur_val = sorted_x[0][0] 23 | last_rank = 0 24 | for i in range(len(sorted_x)): 25 | if cur_val != sorted_x[i][0]: 26 | cur_val = sorted_x[i][0] 27 | for j in range(last_rank, i): 28 | r[sorted_x[j][1]] = float(last_rank+1+i)/2.0 29 | last_rank = i 30 | if i==len(sorted_x)-1: 31 | for j in range(last_rank, i+1): 32 | r[sorted_x[j][1]] = float(last_rank+i+2)/2.0 33 | return r 34 | 35 | def auc(actual, posterior): 36 | """ 37 | Computes the area under the receiver-operater characteristic (AUC) 38 | 39 | This function computes the AUC error metric for binary classification. 40 | 41 | Parameters 42 | ---------- 43 | actual : list of binary numbers, numpy array 44 | The ground truth value 45 | posterior : same type as actual 46 | Defines a ranking on the binary numbers, from most likely to 47 | be positive to least likely to be positive. 48 | 49 | Returns 50 | ------- 51 | score : double 52 | The mean squared error between actual and posterior 53 | 54 | """ 55 | r = tied_rank(posterior) 56 | num_positive = len([0 for x in actual if x==1]) 57 | num_negative = len(actual)-num_positive 58 | sum_positive = sum([r[i] for i in range(len(r)) if actual[i]==1]) 59 | auc = ((sum_positive - num_positive*(num_positive+1)/2.0) / 60 | (num_negative*num_positive)) 61 | return auc -------------------------------------------------------------------------------- /ExamplesFromChapters/Chapter1/SMS_behaviour.py: -------------------------------------------------------------------------------- 1 | import pymc as pm 2 | import numpy as np 3 | 4 | count_data = np.loadtxt("../../Chapter1_Introduction/data/txtdata.csv") 5 | n_count_data = len(count_data) 6 | 7 | alpha = 1.0 / count_data.mean() # recall count_data is 8 | # the variable that holds our txt counts 9 | 10 | lambda_1 = pm.Exponential("lambda_1", alpha) 11 | lambda_2 = pm.Exponential("lambda_2", alpha) 12 | 13 | tau = pm.DiscreteUniform("tau", lower=0, upper=n_count_data) 14 | 15 | 16 | @pm.deterministic 17 | def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2): 18 | out = np.zeros(n_count_data) 19 | out[:tau] = lambda_1 # lambda before tau is lambda1 20 | out[tau:] = lambda_2 # lambda after tau is lambda2 21 | return out 22 | 23 | observation = pm.Poisson("obs", lambda_, value=count_data, observed=True) 24 | model = pm.Model([observation, lambda_1, lambda_2, tau]) 25 | 26 | 27 | mcmc = pm.MCMC(model) 28 | mcmc.sample(100000, 50000, 1) 29 | -------------------------------------------------------------------------------- /ExamplesFromChapters/Chapter2/ABtesting.py: -------------------------------------------------------------------------------- 1 | """ 2 | This is an example of using Bayesian A/B testing 3 | 4 | """ 5 | 6 | import pymc as pm 7 | 8 | # these two quantities are unknown to us. 9 | true_p_A = 0.05 10 | true_p_B = 0.04 11 | 12 | # notice the unequal sample sizes -- no problem in Bayesian analysis. 13 | N_A = 1500 14 | N_B = 1000 15 | 16 | # generate data 17 | observations_A = pm.rbernoulli(true_p_A, N_A) 18 | observations_B = pm.rbernoulli(true_p_B, N_B) 19 | 20 | 21 | # set up the pymc model. Again assume Uniform priors for p_A and p_B 22 | p_A = pm.Uniform("p_A", 0, 1) 23 | p_B = pm.Uniform("p_B", 0, 1) 24 | 25 | 26 | # define the deterministic delta function. This is our unknown of interest. 27 | 28 | @pm.deterministic 29 | def delta(p_A=p_A, p_B=p_B): 30 | return p_A - p_B 31 | 32 | 33 | # set of observations, in this case we have two observation datasets. 34 | obs_A = pm.Bernoulli("obs_A", p_A, value=observations_A, observed=True) 35 | obs_B = pm.Bernoulli("obs_B", p_B, value=observations_B, observed=True) 36 | 37 | # to be explained in chapter 3. 38 | mcmc = pm.MCMC([p_A, p_B, delta, obs_A, obs_B]) 39 | mcmc.sample(20000, 1000) 40 | -------------------------------------------------------------------------------- /ExamplesFromChapters/Chapter2/FreqOfCheaters.py: -------------------------------------------------------------------------------- 1 | import pymc as pm 2 | 3 | p = pm.Uniform("freq_cheating", 0, 1) 4 | 5 | 6 | @pm.deterministic 7 | def p_skewed(p=p): 8 | return 0.5 * p + 0.25 9 | 10 | yes_responses = pm.Binomial( 11 | "number_cheaters", 100, p_skewed, value=35, observed=True) 12 | 13 | model = pm.Model([yes_responses, p_skewed, p]) 14 | 15 | # To Be Explained in Chapter 3! 16 | mcmc = pm.MCMC(model) 17 | mcmc.sample(50000, 25000) 18 | -------------------------------------------------------------------------------- /ExamplesFromChapters/Chapter2/ORingFailure.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pymc as pm 3 | 4 | 5 | challenger_data = np.genfromtxt( 6 | "../../Chapter2_MorePyMC/data/challenger_data.csv", 7 | skip_header=1, usecols=[1, 2], missing_values="NA", delimiter=",") 8 | # drop the NA values 9 | challenger_data = challenger_data[~np.isnan(challenger_data[:, 1])] 10 | 11 | 12 | temperature = challenger_data[:, 0] 13 | D = challenger_data[:, 1] # defect or not? 14 | 15 | beta = pm.Normal("beta", 0, 0.001, value=0) 16 | alpha = pm.Normal("alpha", 0, 0.001, value=0) 17 | 18 | 19 | @pm.deterministic 20 | def p(temp=temperature, alpha=alpha, beta=beta): 21 | return 1.0 / (1. + np.exp(beta * temperature + alpha)) 22 | 23 | 24 | observed = pm.Bernoulli("bernoulli_obs", p, value=D, observed=True) 25 | 26 | model = pm.Model([observed, beta, alpha]) 27 | 28 | # mysterious code to be explained in Chapter 3 29 | map_ = pm.MAP(model) 30 | map_.fit() 31 | mcmc = pm.MCMC(model) 32 | mcmc.sample(260000, 220000, 2) 33 | -------------------------------------------------------------------------------- /ExamplesFromChapters/Chapter3/ClusteringWithGaussians.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pymc as pm 3 | 4 | 5 | data = np.loadtxt("../../Chapter3_MCMC/data/mixture_data.csv", delimiter=",") 6 | 7 | 8 | p = pm.Uniform("p", 0, 1) 9 | 10 | assignment = pm.Categorical("assignment", [p, 1 - p], size=data.shape[0]) 11 | 12 | taus = 1.0 / pm.Uniform("stds", 0, 100, size=2) ** 2 # notice the size! 13 | centers = pm.Normal("centers", [150, 150], [0.001, 0.001], size=2) 14 | 15 | """ 16 | The below deterministic functions map a assingment, in this case 0 or 1, 17 | to a set of parameters, located in the (1,2) arrays `taus` and `centers.` 18 | """ 19 | 20 | 21 | @pm.deterministic 22 | def center_i(assignment=assignment, centers=centers): 23 | return centers[assignment] 24 | 25 | 26 | @pm.deterministic 27 | def tau_i(assignment=assignment, taus=taus): 28 | return taus[assignment] 29 | 30 | # and to combine it with the observations: 31 | observations = pm.Normal("obs", center_i, tau_i, 32 | value=data, observed=True) 33 | 34 | # below we create a model class 35 | model = pm.Model([p, assignment, taus, centers]) 36 | 37 | 38 | map_ = pm.MAP(model) 39 | map_.fit() 40 | mcmc = pm.MCMC(model) 41 | mcmc.sample(100000, 50000) 42 | -------------------------------------------------------------------------------- /ExamplesFromChapters/README.md: -------------------------------------------------------------------------------- 1 | ## Read Me 2 | 3 | 4 | Included is all PyMC examples and models *out of context*, this is for users to easily view the entire Python program. 5 | 6 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2013 Cameron Davidson-Pilon 2 | 3 | Permission is hereby granted, free of charge, to any person 4 | obtaining a copy of this software and associated documentation 5 | files (the "Software"), to deal in the Software without 6 | restriction, including without limitation the rights to use, 7 | copy, modify, merge, publish, distribute, sublicense, and/or sell 8 | copies of the Software, and to permit persons to whom the 9 | Software is furnished to do so, subject to the following 10 | conditions: 11 | 12 | The above copyright notice and this permission notice shall be 13 | included in all copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, 16 | EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES 17 | OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND 18 | NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT 19 | HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 20 | WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING 21 | FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR 22 | OTHER DEALINGS IN THE SOFTWARE. 23 | -------------------------------------------------------------------------------- /book_layout.txt: -------------------------------------------------------------------------------- 1 | # Bayesian Methods for Hackers Layout 2 | 3 | \section{ Preamble} 4 | 5 | 6 | \chapter1{ Introduction } 7 | 8 | 9 | \chapter2{More PyMC / Modeling in PyMC} 10 | #flexible about what this section is. Basically it's more intro to the 11 | syntax of PyMC, with examples + distributions. 12 | 13 | \chapter3{ Intro to MCMC and Diagnogstics } 14 | 15 | 16 | \chapter4{ The greatest theorem never told } 17 | #This is about the law of large numbers and how a bayesian uses it for estimates. 18 | 19 | 20 | 21 | \chapter5{ Would you rather lose an arm or a leg? } 22 | #Introduction to loss functions and point estimation. 23 | 24 | 25 | 26 | >>>>>>>>> 27 | Below is subject to change 28 | 29 | \chapter6{What should my prior look like?} 30 | \subsection{Noninformative priors...} 31 | \subsection{Noninformative priors do not exist} 32 | \subsection{Good choices of priors } 33 | 34 | \chapter7{ Bayesian Networks } 35 | #I do not know too much about this. 36 | 37 | 38 | \chapter8{ Gaussian Processes } 39 | # pymc.gp 40 | 41 | 42 | \chapter9{ Large Scale systems } 43 | #how can we scale PyMC to larger systems/datasets? 44 | 45 | \chapter10{More hacking with PyMC} 46 | #some examples from PyMC. 47 | # Potential class? 48 | 49 | 50 | 51 | 52 | \section{Appendix} 53 | \subsection{A} 54 | #Chart of distributions and their support 55 | \subsection{B} 56 | #Appendix on MCMC 57 | \section{C} 58 | #Proofs 59 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | ipython>=0.13.2 2 | matplotlib>=1.2.1 3 | numpy>=1.7.1 4 | pymc==2.3.4 5 | pyzmq>=13.1.0 6 | scipy>=0.12.0 7 | tornado>=3.0.2 8 | wsgiref>=0.1.2 9 | praw>=2.0.0 10 | jinja2 11 | -------------------------------------------------------------------------------- /sandbox/Chapter10_/data/gh_forks.csv: -------------------------------------------------------------------------------- 1 | 0.000000000000000000e+00 2 | 1.000000000000000000e+00 3 | 2.000000000000000000e+00 4 | 4.000000000000000000e+00 5 | 8.000000000000000000e+00 6 | 1.600000000000000000e+01 7 | 3.200000000000000000e+01 8 | 6.400000000000000000e+01 9 | 1.280000000000000000e+02 10 | 2.560000000000000000e+02 11 | 5.120000000000000000e+02 12 | 1.024000000000000000e+03 13 | 2.048000000000000000e+03 14 | 4.096000000000000000e+03 15 | 8.192000000000000000e+03 16 | 1.638400000000000000e+04 17 | 3.276800000000000000e+04 18 | 2.738548000000000000e+06 19 | 3.345390000000000000e+05 20 | 1.592060000000000000e+05 21 | 7.483600000000000000e+04 22 | 3.653200000000000000e+04 23 | 1.794800000000000000e+04 24 | 8.394000000000000000e+03 25 | 3.841000000000000000e+03 26 | 1.580000000000000000e+03 27 | 6.050000000000000000e+02 28 | 2.220000000000000000e+02 29 | 6.900000000000000000e+01 30 | 1.700000000000000000e+01 31 | 4.000000000000000000e+00 32 | 2.000000000000000000e+00 33 | 0.000000000000000000e+00 34 | 0.000000000000000000e+00 35 | -------------------------------------------------------------------------------- /sandbox/Chapter10_/data/gh_forks_02112013.csv: -------------------------------------------------------------------------------- 1 | 0.000000000000000000e+00 2 | 1.000000000000000000e+00 3 | 2.000000000000000000e+00 4 | 4.000000000000000000e+00 5 | 8.000000000000000000e+00 6 | 1.600000000000000000e+01 7 | 3.200000000000000000e+01 8 | 6.400000000000000000e+01 9 | 1.280000000000000000e+02 10 | 2.560000000000000000e+02 11 | 5.120000000000000000e+02 12 | 1.024000000000000000e+03 13 | 2.048000000000000000e+03 14 | 4.096000000000000000e+03 15 | 8.192000000000000000e+03 16 | 1.638400000000000000e+04 17 | 3.276800000000000000e+04 18 | 2.305360000000000000e+06 19 | 2.473584000000000000e+06 20 | 2.528307000000000000e+06 21 | 2.569002000000000000e+06 22 | 2.594631000000000000e+06 23 | 2.609437000000000000e+06 24 | 2.617785000000000000e+06 25 | 2.621941000000000000e+06 26 | 2.624019000000000000e+06 27 | 2.624923000000000000e+06 28 | 2.625293000000000000e+06 29 | 2.625435000000000000e+06 30 | 2.625486000000000000e+06 31 | 2.625494000000000000e+06 32 | 2.625496000000000000e+06 33 | 2.625498000000000000e+06 34 | 2.625498000000000000e+06 35 | -------------------------------------------------------------------------------- /sandbox/Chapter10_/data/gh_stars.csv: -------------------------------------------------------------------------------- 1 | 0.000000000000000000e+00 2 | 1.000000000000000000e+00 3 | 2.000000000000000000e+00 4 | 4.000000000000000000e+00 5 | 8.000000000000000000e+00 6 | 1.600000000000000000e+01 7 | 3.200000000000000000e+01 8 | 6.400000000000000000e+01 9 | 1.280000000000000000e+02 10 | 2.560000000000000000e+02 11 | 5.120000000000000000e+02 12 | 1.024000000000000000e+03 13 | 2.048000000000000000e+03 14 | 4.096000000000000000e+03 15 | 8.192000000000000000e+03 16 | 1.638400000000000000e+04 17 | 3.276800000000000000e+04 18 | 2.738541000000000000e+06 19 | 1.704779000000000000e+06 20 | 4.935290000000000000e+05 21 | 2.120990000000000000e+05 22 | 1.069730000000000000e+05 23 | 5.810100000000000000e+04 24 | 3.187700000000000000e+04 25 | 1.737000000000000000e+04 26 | 9.239000000000000000e+03 27 | 4.578000000000000000e+03 28 | 2.150000000000000000e+03 29 | 8.720000000000000000e+02 30 | 2.860000000000000000e+02 31 | 8.400000000000000000e+01 32 | 2.200000000000000000e+01 33 | 5.000000000000000000e+00 34 | 1.000000000000000000e+00 35 | -------------------------------------------------------------------------------- /sandbox/Chapter10_/data/gh_stars_02112013.csv: -------------------------------------------------------------------------------- 1 | 0.000000000000000000e+00 2 | 1.000000000000000000e+00 3 | 2.000000000000000000e+00 4 | 4.000000000000000000e+00 5 | 8.000000000000000000e+00 6 | 1.600000000000000000e+01 7 | 3.200000000000000000e+01 8 | 6.400000000000000000e+01 9 | 1.280000000000000000e+02 10 | 2.560000000000000000e+02 11 | 5.120000000000000000e+02 12 | 1.024000000000000000e+03 13 | 2.048000000000000000e+03 14 | 4.096000000000000000e+03 15 | 8.192000000000000000e+03 16 | 1.638400000000000000e+04 17 | 3.276800000000000000e+04 18 | 9.241220000000000000e+05 19 | 2.138996000000000000e+06 20 | 2.337881000000000000e+06 21 | 2.461031000000000000e+06 22 | 2.531702000000000000e+06 23 | 2.571640000000000000e+06 24 | 2.595213000000000000e+06 25 | 2.608768000000000000e+06 26 | 2.616559000000000000e+06 27 | 2.621043000000000000e+06 28 | 2.623388000000000000e+06 29 | 2.624607000000000000e+06 30 | 2.625171000000000000e+06 31 | 2.625367000000000000e+06 32 | 2.625423000000000000e+06 33 | 2.625440000000000000e+06 34 | 2.625444000000000000e+06 35 | -------------------------------------------------------------------------------- /sandbox/Chapter10_/github_datapull.py: -------------------------------------------------------------------------------- 1 | 2 | try: 3 | import numpy as np 4 | from requests import get 5 | from bs4 import BeautifulSoup 6 | 7 | 8 | 9 | 10 | stars_to_explore = ( 2**np.arange( -1, 16 ) ).astype("int") 11 | forks_to_explore = ( 2**np.arange( -1, 16 ) ).astype("int") 12 | repo_with_stars = np.ones_like( stars_to_explore ) 13 | repo_with_forks = np.ones_like( forks_to_explore ) 14 | 15 | URL = "https://github.com/search" 16 | print "Scrapping data from Github. Sorry Github..." 17 | print "The data is contained in variables `foo_to_explore` and `repo_with_foo`" 18 | print 19 | print "stars first..." 20 | payload = {"q":""} 21 | for i, _star in enumerate(stars_to_explore): 22 | payload["q"] = "stars:>=%d"%_star 23 | r = get( URL, params = payload ) 24 | soup = BeautifulSoup( r.text ) 25 | try: 26 | h3 = soup.find( class_="sort-bar").find( "h3" ).text #hopefully the github search results page plays nicely. 27 | value = int( h3.split(" ")[2].replace(",", "" ) ) 28 | except AttributeError as e: 29 | #there might be less than 10 repos, so I'll count the number of display results 30 | value = len( soup.findAll(class_= "mega-icon-public-repo" ) ) 31 | 32 | repo_with_stars[i] = value 33 | print "number of repos with greater than or equal to %d stars: %d"%(_star, value ) 34 | 35 | #repo_with_stars = repo_with_stars.astype("float")/repo_with_stars[0] 36 | 37 | 38 | print 39 | print "forks second..." 40 | payload = {"q":""} 41 | for i, _fork in enumerate(stars_to_explore): 42 | payload["q"] = "forks:>=%d"%_fork 43 | r = get( URL, params = payload ) 44 | soup = BeautifulSoup( r.text ) 45 | try: 46 | h3 = soup.find( class_="sort-bar").find( "h3" ).text #hopefully the github search results page plays nicely. 47 | value = int( h3.split(" ")[2].replace(",", "" ) ) 48 | except AttributeError as e: 49 | #there might be less than 10 repos, so I'll count the number of display results 50 | value = len( soup.findAll(class_= "mega-icon-public-repo" ) ) 51 | 52 | repo_with_forks[i] = value 53 | print "number of repos with greater than or equal to %d forks: %d"%(_fork, value ) 54 | 55 | #repo_with_forks = repo_with_forks.astype("float")/repo_with_forks[0] 56 | 57 | np.savetxt( "data/gh_forks.csv", np.concatenate( [forks_to_explore, repo_with_forks], axis=1) ) 58 | np.savetxt( "data/gh_stars.csv", np.concatenate( [stars_to_explore, repo_with_stars], axis=1) ) 59 | 60 | except ImportError as e: 61 | print e 62 | print "requests / BeautifulSoup not found. Using data pulled on Feburary 11, 2013" 63 | _data = np.genfromtxt( "data/gh_forks.csv", delimiter = "," ) #cehck this. 64 | forks_to_explore = _data[:,0] 65 | repo_with_forks = _data[:,1] 66 | 67 | _data = np.genfromtxt( "data/gh_stars.csv", delimiter = "," ) #cehck this. 68 | stars_to_explore = _data[:,0] 69 | repo_with_stars = _data[:,1] 70 | 71 | 72 | -------------------------------------------------------------------------------- /sandbox/github_events.py: -------------------------------------------------------------------------------- 1 | #github_events.py 2 | 3 | try: 4 | from json import loads 5 | 6 | import numpy as np 7 | from requests import get 8 | 9 | except ImportError as e: 10 | raise e 11 | 12 | 13 | URL = "https://api.github.com/events" 14 | 15 | #github allows up to 10 pages of 30 events, but we will only keep the unique ones. 16 | ids = np.empty(300, dtype=int) 17 | 18 | k = 0 19 | for page in range(10,0, -1): 20 | 21 | r = get( URL, params = {"page":page} ) 22 | data = loads(r.text) 23 | for event in data: 24 | ids[k] = ( event["actor"]["id"] ) 25 | k+=1 26 | 27 | ids = np.unique( ids.astype(int) ) -------------------------------------------------------------------------------- /styles/bmh_matplotlibrc.json: -------------------------------------------------------------------------------- 1 | { 2 | "lines.linewidth": 2.0, 3 | "examples.download": true, 4 | "axes.edgecolor": "#bcbcbc", 5 | "patch.linewidth": 0.5, 6 | "legend.fancybox": true, 7 | "axes.color_cycle": [ 8 | "#348ABD", 9 | "#A60628", 10 | "#7A68A6", 11 | "#467821", 12 | "#CF4457", 13 | "#188487", 14 | "#E24A33" 15 | ], 16 | "axes.facecolor": "#eeeeee", 17 | "axes.labelsize": "large", 18 | "axes.grid": true, 19 | "patch.edgecolor": "#eeeeee", 20 | "axes.titlesize": "x-large", 21 | "svg.embed_char_paths": "path", 22 | "examples.directory": "" 23 | } 24 | -------------------------------------------------------------------------------- /styles/custom.css: -------------------------------------------------------------------------------- 1 | 62 | 77 | -------------------------------------------------------------------------------- /to_latex_pdf.sh: -------------------------------------------------------------------------------- 1 | cd Chapter1_Introduction/ && ipython nbconvert Chapter1.ipynb --to latex --post PDF --template article 2 | cd ../Chapter2_MorePyMC/ && ipython nbconvert Chapter2.ipynb --to latex --post PDF --template article 3 | cd ../Chapter3_MCMC/ && ipython nbconvert Chapter3.ipynb --to latex --post PDF --template article 4 | cd ../Chapter4_TheGreatestTheoremNeverTold/ && ipython nbconvert Chapter4.ipynb --to latex --post PDF --template article 5 | cd ../Chapter5_LossFunctions/ && ipython nbconvert Chapter5.ipynb --to latex --post PDF --template article 6 | cd ../Chapter6_Priorities/ && ipython nbconvert Chapter6.ipynb --to latex --post PDF --template article 7 | --------------------------------------------------------------------------------