├── .gitignore ├── Chapter2_MorePyMC ├── sms_model.png ├── Screen Shot 2013-02-08 at 11.23.49 AM.png ├── README.md ├── data │ └── challenger_data.csv ├── daft_plot.py └── separation_plot.py ├── Chapter6_Priorities ├── data │ ├── table_2014.csv │ ├── table_2014.ods │ ├── data.csv │ ├── data.txt │ └── results_2014.csv ├── README.md ├── BanditsD3.html ├── other_strats.py ├── ystockquote.py └── d3bandits.js ├── ExamplesFromChapters ├── README.md ├── Chapter2 │ ├── FreqOfCheaters.py │ ├── ORingFailure.py │ └── ABtesting.py ├── Chapter1 │ └── SMS_behaviour.py └── Chapter3 │ └── ClusteringWithGaussians.py ├── Chapter4_TheGreatestTheoremNeverTold ├── reddit_comments.png ├── README.md └── top_showerthoughts_submissions.py ├── requirements.txt ├── to_latex_pdf.sh ├── Chapter1_Introduction ├── README.md └── data │ └── txtdata.csv ├── Chapter5_LossFunctions ├── README.md ├── draw_sky2.py └── data │ ├── Test_haloCounts.csv │ └── Train_Skies │ └── Train_Skies │ ├── Training_Sky200.csv │ ├── Training_Sky191.csv │ ├── Training_Sky24.csv │ ├── Training_Sky45.csv │ ├── Training_Sky216.csv │ ├── Training_Sky132.csv │ ├── Training_Sky79.csv │ ├── Training_Sky168.csv │ └── Training_Sky145.csv ├── Chapter3_MCMC ├── README.md ├── data │ ├── smoking_death.csv │ ├── github_data.csv │ └── mixture_data.csv └── github_pull.py ├── styles ├── bmh_matplotlibrc.json └── custom.css ├── sandbox ├── github_events.py └── Chapter10_ │ ├── data │ ├── gh_forks.csv │ ├── gh_stars.csv │ ├── gh_forks_02112013.csv │ └── gh_stars_02112013.csv │ └── github_datapull.py ├── LICENSE.txt ├── book_layout.txt └── Chapter7_BayesianMachineLearning ├── auc.py └── MachineLearning.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | *.pyc 3 | *~ 4 | *.png 5 | **/.ipynb_checkpoints 6 | -------------------------------------------------------------------------------- /Chapter2_MorePyMC/sms_model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/canyon289/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/sms_model.png -------------------------------------------------------------------------------- /Chapter6_Priorities/data/table_2014.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/canyon289/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/data/table_2014.csv -------------------------------------------------------------------------------- /Chapter6_Priorities/data/table_2014.ods: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/canyon289/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/data/table_2014.ods -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /Chapter4_TheGreatestTheoremNeverTold/reddit_comments.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/canyon289/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/reddit_comments.png -------------------------------------------------------------------------------- /Chapter2_MorePyMC/Screen Shot 2013-02-08 at 11.23.49 AM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/canyon289/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Screen Shot 2013-02-08 at 11.23.49 AM.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | ipython>=2.0 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; python_version < '3.0' 9 | praw>=2.0.0 10 | jinja2 11 | -------------------------------------------------------------------------------- /to_latex_pdf.sh: -------------------------------------------------------------------------------- 1 | find Prologue Chapter* -name "*.ipynb" | grep -v "PyMC2" | xargs ipython3 nbconvert --to pdf --template article 2 | 3 | # merge all files: 4 | pdfjoin Prologue.pdf Ch*.pdf DontOverfit.pdf MachineLearning.pdf 5 | -------------------------------------------------------------------------------- /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/Ch6_Priors_PyMC2.ipynb) 5 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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/Ch1_Introduction_PyMC2.ipynb) 5 | -------------------------------------------------------------------------------- /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/Ch2_MorePyMC_PyMC2.ipynb) 5 | -------------------------------------------------------------------------------- /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/Ch5_LossFunctions_PyMC2.ipynb) 5 | -------------------------------------------------------------------------------- /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/Ch4_LawOfLargeNumbers_PyMC2.ipynb) 4 | -------------------------------------------------------------------------------- /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/Ch3_IntroMCMC_PyMC2.ipynb) 5 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /styles/bmh_matplotlibrc.json: -------------------------------------------------------------------------------- 1 | { 2 | "lines.linewidth": 2.0, 3 | "axes.edgecolor": "#bcbcbc", 4 | "patch.linewidth": 0.5, 5 | "legend.fancybox": true, 6 | "axes.color_cycle": [ 7 | "#348ABD", 8 | "#A60628", 9 | "#7A68A6", 10 | "#467821", 11 | "#CF4457", 12 | "#188487", 13 | "#E24A33" 14 | ], 15 | "axes.facecolor": "#eeeeee", 16 | "axes.labelsize": "large", 17 | "axes.grid": true, 18 | "patch.edgecolor": "#eeeeee", 19 | "axes.titlesize": "x-large", 20 | "svg.fonttype": "path", 21 | "examples.directory": "" 22 | } 23 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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) ) -------------------------------------------------------------------------------- /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 | ax = fig.add_subplot(111, aspect='equal') 10 | n = galaxies.shape[0] 11 | for i in range(n): 12 | _g = galaxies[i,:] 13 | x,y = _g[0], _g[1] 14 | d = np.sqrt( _g[2]**2 + _g[3]**2 ) 15 | a = 1.0/ ( 1 - d ) 16 | b = 1.0/( 1 + d) 17 | theta = np.degrees( np.arctan2( _g[3], _g[2])*0.5 ) 18 | 19 | ax.add_patch( Ellipse(xy=(x, y), width=size_multiplier*a, height=size_multiplier*b, angle=theta) ) 20 | ax.autoscale_view(tight=True) 21 | 22 | return fig -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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() -------------------------------------------------------------------------------- /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/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 | -------------------------------------------------------------------------------- /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_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_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_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 | -------------------------------------------------------------------------------- /Chapter4_TheGreatestTheoremNeverTold/top_showerthoughts_submissions.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("showerthoughts") 11 | 12 | top_submissions = subreddit.get_top(limit=100) 13 | 14 | n_sub = int( sys.argv[1] ) if sys.argv[1] else 1 15 | 16 | i = 0 17 | while i < n_sub: 18 | top_submission = next(top_submissions) 19 | i+=1 20 | 21 | top_post = top_submission.title 22 | 23 | upvotes = [] 24 | downvotes = [] 25 | contents = [] 26 | 27 | for sub in top_submissions: 28 | try: 29 | ratio = reddit.get_submission(sub.permalink).upvote_ratio 30 | ups = int(round((ratio*sub.score)/(2*ratio - 1)) if ratio != 0.5 else round(sub.score/2)) 31 | upvotes.append(ups) 32 | downvotes.append(ups - sub.score) 33 | contents.append(sub.title) 34 | except Exception as e: 35 | continue 36 | votes = np.array( [ upvotes, downvotes] ).T -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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/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 an assignment, 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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_bmh = np.array( ["#eeeeee", "#348ABD"] ) 30 | 31 | 32 | fig = plt.figure( ) 33 | 34 | for i in range(M): 35 | ax = fig.add_subplot(M, 1, i+1) 36 | ix = np.argsort( p[:,i] ) 37 | #plot the different bars 38 | bars = ax.bar( np.arange(n), np.ones(n), width=1., 39 | color = colors_bmh[ y[ix].astype(int) ], 40 | edgecolor = 'none') 41 | ax.plot( np.arange(n+1), np.append(p[ix,i], p[ix,i][-1]), "k", 42 | linewidth = 1.,drawstyle="steps-post" ) 43 | #create expected value bar. 44 | ax.vlines( [(1-p[ix,i]).sum()], [0], [1] ) 45 | plt.xlim( 0, n) 46 | 47 | plt.tight_layout() 48 | 49 | return 50 | 51 | 52 | 53 | -------------------------------------------------------------------------------- /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 | Sky120,3 122 | -------------------------------------------------------------------------------- /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 -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /styles/custom.css: -------------------------------------------------------------------------------- 1 | 62 | 77 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /Chapter6_Priorities/BanditsD3.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 45 | 46 | 47 | 48 | 49 | 50 | 51 |
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89 | 90 | 91 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | 0,0,1,0,0,0,948,1,0,84,18,4 41 | 1,0,0,0,0,0,383,1,0,0,2,1 42 | 0,0,0,1,0,0,62,1,0,0,0,0 43 | 0,0,0,1,0,0,927,1,0,0,40,17 44 | 0,1,0,0,0,0,1303,1,0,0,3,0 45 | 0,1,0,0,0,0,113,1,0,0,0,0 46 | 0,0,1,0,0,0,810,1,4,5,1,0 47 | 0,0,0,0,0,1,43,1,7,11,2,0 48 | 0,0,0,0,0,1,369,1,0,0,1,0 49 | 0,1,0,0,0,0,449,1,106,4398,399,60 50 | 0,1,0,0,0,0,200,1,0,0,0,0 51 | 0,1,0,0,0,0,122,1,4,2,0,0 52 | 0,0,1,0,0,0,738,1,0,0,1,0 53 | 0,1,0,0,0,0,705,1,0,0,2,0 54 | 0,0,1,0,0,0,291,1,0,0,1,0 55 | 0,1,0,0,0,0,621,1,3,23,1,0 56 | 0,0,1,0,0,0,368,1,0,0,1,0 57 | 0,0,1,0,0,0,851,1,0,0,1,0 58 | 0,0,0,1,0,0,58,1,0,0,0,0 59 | 0,0,0,1,0,0,706,1,0,8,2,1 60 | 0,0,0,0,0,1,817,1,0,1,1,1 61 | 0,0,0,0,1,0,1475,1,0,4,1,0 62 | 0,0,1,0,0,0,584,1,0,0,1,0 63 | 1,0,0,0,0,0,841,1,0,7,1,0 64 | 0,0,0,0,0,1,382,1,0,74,1,0 65 | 0,0,1,0,0,0,213,1,1,0,1,0 66 | 0,0,1,0,0,0,93,1,0,0,0,0 67 | 0,1,0,0,0,0,356,1,1,10,2,0 68 | 0,0,1,0,0,0,123,1,0,1,0,0 69 | 0,0,1,0,0,0,113,1,35,63,0,0 70 | 0,0,0,0,0,1,302,1,13,2,3,1 71 | 0,1,0,0,0,0,550,0,19,18,2,0 72 | 0,0,1,0,0,0,721,1,2,0,1,0 73 | 0,0,1,0,0,0,160,1,5,6,11,6 74 | 1,0,0,0,0,0,399,1,7,6,1,0 75 | 0,1,0,0,0,0,206,1,15,116,2,0 76 | 0,0,0,1,0,0,319,1,0,0,2,1 77 | 0,1,0,0,0,0,111,1,2,3,0,0 78 | 0,0,0,0,0,1,632,1,10,3,2,1 79 | 0,0,0,0,0,1,1044,1,16,23,3,1 80 | 0,0,0,0,0,1,194,1,0,0,0,0 81 | 0,1,0,0,0,0,908,1,56,503,43,8 82 | 0,0,1,0,0,0,89,1,0,0,0,0 83 | 0,1,0,0,0,0,1311,1,107,119,4,1 84 | 0,1,0,0,0,0,78,1,0,0,0,0 85 | 0,0,1,0,0,0,564,1,0,2,1,0 86 | 0,0,1,0,0,0,1049,1,0,0,1,0 87 | 0,0,0,1,0,0,322,1,0,0,2,1 88 | 0,0,1,0,0,0,1443,1,11,7,3,8 89 | 0,0,1,0,0,0,310,1,1,0,1,0 90 | 0,0,0,0,0,1,515,1,0,2,1,0 91 | 0,0,1,0,0,0,563,1,0,0,1,0 92 | 1,0,0,0,0,0,344,1,0,0,1,0 93 | 0,1,0,0,0,0,132,1,2,2,0,0 94 | 0,0,1,0,0,0,837,1,0,0,1,0 95 | 0,1,0,0,0,0,459,1,1,4,1,0 96 | 0,0,0,0,0,1,768,1,8,2,3,0 97 | 0,0,0,0,0,1,410,1,0,4,2,1 98 | 1,0,0,0,0,0,380,1,0,0,9,0 99 | 0,0,0,1,0,0,859,1,14,22,1,0 100 | 1,0,0,0,0,0,628,1,0,1,1,0 101 | 0,0,1,0,0,0,206,0,55,50,21,2 102 | 0,0,0,1,0,0,117,1,1,2,0,0 103 | 0,0,0,0,1,0,237,1,0,0,1,0 104 | 0,0,1,0,0,0,462,1,0,1,1,0 105 | 1,0,0,0,0,0,628,1,2,3,1,0 106 | 0,0,0,1,0,0,504,1,1,6,1,0 107 | 0,0,1,0,0,0,283,1,0,0,1,0 108 | 0,1,0,0,0,0,654,0,4,7,1,0 109 | 0,1,0,0,0,0,97,1,0,0,0,0 110 | 1,0,0,0,0,0,396,1,3,11,1,0 111 | 0,0,0,0,1,0,179,1,0,0,0,0 112 | 0,1,0,0,0,0,1743,1,3,0,1,0 113 | 0,0,1,0,0,0,949,1,1,4,75,24 114 | 0,0,0,1,0,0,307,1,0,0,1,1 115 | 0,0,1,0,0,0,264,1,0,0,1,0 116 | 0,0,1,0,0,0,193,1,4,5,0,0 117 | 0,0,0,0,1,0,1148,1,4,37,2,1 118 | 0,0,1,0,0,0,150,1,0,0,0,0 119 | 0,0,0,0,1,0,250,1,19,9,1,0 120 | 0,0,1,0,0,0,1726,1,7,287,1,0 121 | 0,1,0,0,0,0,340,1,0,0,1,0 122 | -------------------------------------------------------------------------------- /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 | 7 | rand = np.random.rand 8 | beta = stats.beta 9 | 10 | 11 | class GeneralBanditStrat(object): 12 | 13 | """ 14 | Implements a online, learning strategy to solve 15 | the Multi-Armed Bandit problem. 16 | 17 | parameters: 18 | bandits: a Bandit class with .pull method 19 | choice_function: accepts a self argument (which gives access to all the variables), and 20 | returns and int between 0 and n-1 21 | methods: 22 | sample_bandits(n): sample and train on n pulls. 23 | 24 | attributes: 25 | N: the cumulative number of samples 26 | choices: the historical choices as a (N,) array 27 | bb_score: the historical score as a (N,) array 28 | 29 | """ 30 | 31 | def __init__(self, bandits, choice_function): 32 | 33 | self.bandits = bandits 34 | n_bandits = len(self.bandits) 35 | self.wins = np.zeros(n_bandits) 36 | self.trials = np.zeros(n_bandits) 37 | self.N = 0 38 | self.choices = [] 39 | self.score = [] 40 | self.choice_function = choice_function 41 | 42 | def sample_bandits(self, n=1): 43 | 44 | score = np.zeros(n) 45 | choices = np.zeros(n) 46 | 47 | for k in range(n): 48 | #sample from the bandits's priors, and select the largest sample 49 | choice = self.choice_function(self) 50 | 51 | #sample the chosen bandit 52 | result = self.bandits.pull(choice) 53 | 54 | #update priors and score 55 | self.wins[choice] += result 56 | self.trials[choice] += 1 57 | score[k] = result 58 | self.N += 1 59 | choices[k] = choice 60 | 61 | self.score = np.r_[self.score, score] 62 | self.choices = np.r_[self.choices, choices] 63 | return 64 | 65 | 66 | def bayesian_bandit_choice(self): 67 | return np.argmax(np.random.beta(1 + self.wins, 1 + self.trials - self.wins)) 68 | 69 | def max_mean(self): 70 | """pick the bandit with the current best observed proportion of winning """ 71 | return np.argmax(self.wins / (self.trials +1)) 72 | 73 | def lower_credible_choice( self ): 74 | """pick the bandit with the best LOWER BOUND. See chapter 5""" 75 | def lb(a,b): 76 | return a/(a+b) - 1.65*np.sqrt((a*b)/( (a+b)**2*(a+b+1))) 77 | a = self.wins + 1 78 | b = self.trials - self.wins + 1 79 | return np.argmax(lb(a,b)) 80 | 81 | def upper_credible_choice(self): 82 | """pick the bandit with the best LOWER BOUND. See chapter 5""" 83 | def lb(a,b): 84 | return a/(a+b) + 1.65*np.sqrt((a*b)/((a+b)**2*(a+b+1))) 85 | a = self.wins + 1 86 | b = self.trials - self.wins + 1 87 | return np.argmax(lb(a,b)) 88 | 89 | def random_choice(self): 90 | return np.random.randint(0, len(self.wins)) 91 | 92 | 93 | def ucb_bayes(self): 94 | C = 0 95 | n = 10000 96 | alpha =1 - 1./((self.N+1)) 97 | return np.argmax(beta.ppf(alpha, 98 | 1 + self.wins, 99 | 1 + self.trials - self.wins)) 100 | 101 | 102 | 103 | 104 | class Bandits(object): 105 | """ 106 | This class represents N bandits machines. 107 | 108 | parameters: 109 | p_array: a (n,) Numpy array of probabilities >0, <1. 110 | 111 | methods: 112 | pull( i ): return the results, 0 or 1, of pulling 113 | the ith bandit. 114 | """ 115 | def __init__(self, p_array): 116 | self.p = p_array 117 | self.optimal = np.argmax(p_array) 118 | 119 | def pull(self, i): 120 | #i is which arm to pull 121 | return rand() < self.p[i] 122 | 123 | def __len__(self): 124 | return len(self.p) 125 | -------------------------------------------------------------------------------- /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 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "List of topics to cover:\n", 8 | "\n", 9 | "- Bayesian solution to overfitting\n", 10 | " - Salisman's solution to the Don't Overfit\n", 11 | "- Predictive distributions; \"how do I evaluate testing data?\"\n", 12 | "- model fitting, BIC + visualization tools\n", 13 | "- Gaussian Processes\n", 14 | "\n", 15 | "\n", 16 | "Would be nice/cool to cover:\n", 17 | "\n", 18 | "- classification models (using the books text)\n", 19 | "- Bayesian networks?" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 6, 25 | "metadata": { 26 | "collapsed": false 27 | }, 28 | "outputs": [], 29 | "source": [] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 6, 34 | "metadata": { 35 | "collapsed": false 36 | }, 37 | "outputs": [], 38 | "source": [] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 6, 43 | "metadata": { 44 | "collapsed": false 45 | }, 46 | "outputs": [], 47 | "source": [] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 6, 52 | "metadata": { 53 | "collapsed": false 54 | }, 55 | "outputs": [], 56 | "source": [] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 10, 61 | "metadata": { 62 | "collapsed": false 63 | }, 64 | "outputs": [ 65 | { 66 | "data": { 67 | "text/html": [ 68 | "\n", 113 | "" 128 | ], 129 | "text/plain": [ 130 | "" 131 | ] 132 | }, 133 | "execution_count": 10, 134 | "metadata": {}, 135 | "output_type": "execute_result" 136 | } 137 | ], 138 | "source": [ 139 | "from IPython.core.display import HTML\n", 140 | "\n", 141 | "\n", 142 | "def css_styling():\n", 143 | " styles = open(\"../styles/custom.css\", \"r\").read()\n", 144 | " return HTML(styles)\n", 145 | "css_styling()" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": { 152 | "collapsed": false 153 | }, 154 | "outputs": [], 155 | "source": [] 156 | } 157 | ], 158 | "metadata": { 159 | "kernelspec": { 160 | "display_name": "Python 2", 161 | "language": "python", 162 | "name": "python2" 163 | }, 164 | "language_info": { 165 | "codemirror_mode": { 166 | "name": "ipython", 167 | "version": 2 168 | }, 169 | "file_extension": ".py", 170 | "mimetype": "text/x-python", 171 | "name": "python", 172 | "nbconvert_exporter": "python", 173 | "pygments_lexer": "ipython2", 174 | "version": "2.7.10" 175 | } 176 | }, 177 | "nbformat": 4, 178 | "nbformat_minor": 0 179 | } 180 | -------------------------------------------------------------------------------- /Chapter3_MCMC/data/mixture_data.csv: -------------------------------------------------------------------------------- 1 | 1.158567914166645352e+02 2 | 1.522615371577288670e+02 3 | 1.788744905891352914e+02 4 | 1.629350081470792588e+02 5 | 1.070282069704478687e+02 6 | 1.051914114638578326e+02 7 | 1.183828850125410668e+02 8 | 1.253769803023669596e+02 9 | 1.028805401104259118e+02 10 | 2.067132613581522378e+02 11 | 1.393689112736414870e+02 12 | 1.554104808778958500e+02 13 | 1.487593071755844676e+02 14 | 8.375957653422238991e+01 15 | 1.399077526119093307e+02 16 | 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1.946361653458499745e+02 276 | 2.421374635348616948e+02 277 | 2.110007927344491918e+02 278 | 1.662765715442542671e+02 279 | 1.949143298584492641e+02 280 | 1.875771759652629953e+02 281 | 2.219149956242236783e+02 282 | 1.876747717865994218e+02 283 | 1.971850332862213122e+02 284 | 1.482140589550791105e+02 285 | 2.064803608972869711e+02 286 | 2.134641290909328859e+02 287 | 1.919518147378720414e+02 288 | 1.800465916326361935e+02 289 | 2.203791380076491464e+02 290 | 2.223561248500241163e+02 291 | 2.278082046953573752e+02 292 | 2.181685881622520640e+02 293 | 1.863762249820889565e+02 294 | 1.775192919731025256e+02 295 | 1.764703438636786075e+02 296 | 2.315749158793883566e+02 297 | 2.171688340729648985e+02 298 | 2.423196685427484454e+02 299 | 1.963990296752487268e+02 300 | 2.228190673247157747e+02 301 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /Chapter5_LossFunctions/data/Train_Skies/Train_Skies/Training_Sky200.csv: -------------------------------------------------------------------------------- 1 | GalaxyID,x,y,e1,e2 2 | Galaxy1,1977.18,2284.96,0.135144,-0.075196 3 | Galaxy2,4187.01,3033.95,-0.289684,-0.009205 4 | Galaxy3,1849.42,1870.43,-0.128524,0.188735 5 | Galaxy4,1305.95,2440.36,-0.160642,-0.158346 6 | Galaxy5,2750.99,628.40,0.144453,-0.207262 7 | Galaxy6,232.89,2303.53,0.078057,-0.437543 8 | Galaxy7,2612.18,3763.64,-0.244929,-0.170409 9 | 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