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bi-mcmc/src/net/thegeez/bi_mcmc/clusters.clj
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(ns net.thegeez.bi-mcmc.clusters | |
(:require [incanter.charts :as charts] | |
[incanter.core :as inc-core] | |
[incanter.stats :as stats] | |
[net.thegeez.bi-mcmc.mcmc :as mcmc] | |
[net.thegeez.bi-mcmc.net :as net] | |
[net.thegeez.bi-mcmc.distributions :as dists] | |
[clojure.data.csv :as csv] | |
[clojure.java.io :as io] | |
[clojure.pprint :as pprint])) | |
;; chapter 3 from | |
;; http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/IntroMCMC.ipynb | |
;; from matkelcey | |
;; from pymc import * | |
;; from pymc.Matplot import plot | |
;; #data = [normal(100, 20) for _i in xrange(1000)] | |
;; data = map(float, open('data', 'r').readlines()) | |
;; >>> min(data) | |
;; 40.3058046075 | |
;; >>> max(data) | |
;; 172.745689773 | |
;; mean = Uniform('mean', lower=min(data), upper=max(data)) | |
;; precision = Uniform('precision', lower=0.0001, upper=1.0) | |
;; process = Normal('process', mu=mean, tau=precision, value=data, observed=True) | |
;; # do posterior sampling | |
;; m = pymc.MCMC([mean, precision, process],verbose=10) | |
;; #m.sample(iter=500, verbose=10) | |
;; m.sample(iter=10, verbose=10) | |
;; print(m.stats()) | |
;; >>> m.stats() | |
;; {'precision': {'95% HPD interval': array([ 0.00307265, 0.03616355]), 'n': 500, 'quantiles': {2.5: 0.0030726520461263188, 25: 0.0056304478695005343, 50: 0.0056304478695005343, 75: 0.0056304478695005343, 97.5: 0.036163554933286587}, 'standard deviation': 0.024166450376244385, 'mc error': 0.0022530354801112954, 'mean': 0.012143625932209764}, 'mean': {'95% HPD interval': array([ 100.00015441, 117.23326122]), 'n': 500, 'quantiles': {2.5: 100.00015440990551, 25: 100.00015440990551, 50: 102.89806412433191, 75: 102.89806412433191, 97.5: 130.95207326510638}, 'standard deviation': 8.3417552405533666, 'mc error': 0.82388925092850029, 'mean': 103.90355067943328}} | |
;; >>> | |
(defonce data (let [file-path "data/matkelcey_simple.csv"] | |
(doall | |
(map (comp read-string first) (csv/read-csv (slurp (io/resource file-path))))))) | |
(def structure {:mean {:type :stochastic | |
:parents [] | |
:children [:process] | |
:dist-fn (constantly | |
(dists/uniform-distribution 40.304 172.745) | |
#_(dists/normal-distribution 80.0 20.0) | |
) | |
:n 1 | |
} | |
:std-dev {:type :stochastic | |
:n 1 | |
:parents [] | |
:children [:process] | |
:dist-fn (constantly | |
(dists/uniform-distribution 1.0 50.0) | |
#_(dists/uniform-distribution 19.9 20.1))} | |
:process {:type :stochastic | |
:parents [:mean :std-dev] | |
:children [] | |
:dist-fn (fn [{:keys [mean std-dev] :as s}] | |
(dists/normal-distribution mean std-dev)) | |
:n (count data) | |
:observed data} | |
}) | |
(def structure-tau {:mean {:type :stochastic | |
:parents [] | |
:children [:process] | |
:dist-fn (constantly | |
#_(dists/uniform-distribution 99.00 101.00) | |
(dists/uniform-distribution 40.304 172.745) | |
#_(dists/normal-distribution 80.0 20.0) | |
) | |
:n 1 | |
} | |
:tau {:type :stochastic | |
:n 1 | |
:parents [] | |
:children [:std-dev] | |
:dist-fn (constantly | |
(dists/uniform-distribution 0.0001 1.0) | |
#_(dists/uniform-distribution 0.0024 0.0026))} | |
:std-dev {:type :deterministic | |
:n 1 | |
:parents [:tau] | |
:children [:process] | |
:value-fn (fn [{:keys [tau]}] | |
(/ 1.0 (Math/sqrt tau)))} | |
:process {:type :stochastic | |
:parents [:mean :std-dev] | |
:children [] | |
:dist-fn (fn [{:keys [mean std-dev] :as s}] | |
(dists/normal-distribution mean std-dev)) | |
:n (count data) | |
:observed data} | |
}) | |
(defn go [] | |
(let [model (mcmc/mcmc structure #_-tau)] | |
(println "model" model) | |
(let [model (mcmc/sample model :iter 5000)] | |
(mcmc/pr-model model) | |
(def res model) | |
(pprint/pprint (mcmc/stats res))) | |
)) | |
(defn go-tau [] | |
(let [model (mcmc/mcmc structure-tau)] | |
(println "model" model) | |
(let [model (mcmc/sample model :iter 5000)] | |
(mcmc/pr-model model) | |
(def res model)) | |
)) | |
(comment | |
(let [model res | |
mean-vals (->> model | |
:samples | |
(map (comp :value :mean))) | |
std-vals (->> model | |
:samples | |
(map (comp :value :precision)))] | |
(doto (charts/xy-plot (range 500) mean-vals | |
:y-label "mean") | |
(charts/add-points (range 500) std-vals | |
:series-label "std") | |
#_(charts/add-function lin -3 3) | |
;; show biggest gains when p is low | |
#_(charts/add-function (fn [x] | |
(- (pax_p x) | |
(lin x))) 0 1) | |
#_(charts/add-points [from to] [p-from p-to]) | |
#_(charts/add-points [from to] [logp-from logp-to]) | |
inc-core/view)) | |
(let [model res | |
mean-vals (->> model | |
:samples | |
(map (comp :value :mean)))] | |
(-> (charts/histogram mean-vals | |
:title "mean" | |
:nbins 30 | |
:density true | |
) | |
#_(add-points [-10 20] [0.01 0.01]) | |
inc-core/view)) | |
(let [model res | |
mean-vals (->> model | |
:samples | |
(map (comp :value :mean))) | |
std-vals (->> model | |
:samples | |
(map (comp :value :precision))) | |
] | |
(stats/quantile mean-vals :probs [0.025 0.975])) | |
(let [model res | |
std-vals (->> model | |
:samples | |
(map (comp :value :precision))) | |
] | |
(stats/quantile std-vals :probs [0.025 0.975])) | |
(doto (charts/function-plot (fn [s] | |
(/ 1.0 (Math/sqrt s))) 0.001 50.0) | |
inc-core/view) | |
(doto (charts/function-plot (fn [t] | |
(/ 1.0 (* t t))) 0.001 1.0) | |
inc-core/view) | |
) | |
(defonce data-two (let [file-path "data/matkelcey_two.csv"] | |
(doall | |
(map (comp read-string first) (csv/read-csv (slurp (io/resource file-path))))))) | |
(def structure-two (let [n (count data-two) | |
min (apply min data-two) | |
max (apply max data-two)] | |
{:theta {:type :stochastic | |
:dist-fn (constantly | |
#_(dists/uniform-distribution 0.31 0.36) | |
(dists/uniform-distribution 0.0 1.0)) | |
:n 1 | |
} | |
:bern {:type :stochastic | |
:parents [:theta] | |
:dist-fn (fn [{theta :theta}] | |
(dists/binomial-distribution 1 theta)) | |
:n (count data-two) | |
:step-scale 0.152003093 ;; log(1.0 - 0.1) / log 0.5 | |
} | |
:mean1 {:type :stochastic | |
:dist-fn (constantly | |
(dists/uniform-distribution min max)) | |
:n 1} | |
:mean2 {:type :stochastic | |
:dist-fn (constantly | |
(dists/uniform-distribution min max)) | |
:n 1} | |
:std-dev {:type :stochastic | |
:dist-fn (constantly | |
#_(dists/uniform-distribution 18.0 22.0) | |
(dists/uniform-distribution 0.0 50.0)) | |
:n 1 | |
} | |
:mean {:type :deterministic | |
:parents [:bern :mean1 :mean2] | |
:value-fn (fn [{:keys [bern mean1 mean2]}] | |
;; (if (= bern 1) mean1 mean2) | |
(+ (* bern mean1) | |
(* (- 1 bern) mean2))) | |
:n n} | |
:process {:type :stochastic | |
:parents [:mean :std-dev] | |
:dist-fn (fn [{:keys [mean std-dev]}] | |
(dists/normal-distribution mean std-dev)) | |
:n n | |
:observed data-two}})) | |
(defn chart-go-two [res] | |
(let [model res | |
mean1-vals (->> model | |
:samples | |
(map (comp :value :mean1))) | |
mean2-vals (->> model | |
:samples | |
(map (comp :value :mean2))) | |
std-vals (->> model | |
:samples | |
(map (comp :value :std-dev))) | |
theta-vals (->> model | |
:samples | |
(map (comp :value :theta)))] | |
(doto (charts/xy-plot (range) mean1-vals | |
:series-label "mean1" | |
:legend true) | |
(charts/add-points (range) mean2-vals | |
:series-label "mean2") | |
(charts/add-points (range) std-vals | |
:series-label "std") | |
inc-core/view) | |
(doto (charts/xy-plot (range) theta-vals | |
:series-label "theta" | |
:legend true) | |
inc-core/view))) | |
(defn go-two [] | |
(let [model (mcmc/mcmc structure-two)] | |
(println "model" model) | |
(let [model (time (mcmc/sample model | |
:iter 100000 :burn 0 #_10000 :thin 1000 | |
;; :iter 100 :burn 10 :thin 5 | |
))] | |
(mcmc/pr-model model) | |
(def res model) | |
(pprint/pprint (mcmc/stats res)) | |
(chart-go-two res))) | |
) | |
(comment | |
(chart-go-two res) | |
) | |
;; {:theta {:std-dev 0.013403034589704833, :mean 0.712831870202466, :quantiles {0.975 0.7333746636976199, 0.75 0.7210089328056395, 0.5 0.7143299420175615, 0.25 0.70497601670011, 0.025 0.68798796011754}, :mc-error 7.232812366720091E-4, :n 10000, :accepted 185, :rejected 9814}, :bern {:n 10000, :accepted 0, :rejected 9999}, :mean1 {:std-dev 2.9354356240079986, :mean 132.50650257579827, :quantiles {0.975 135.37761623300153, 0.75 133.3180231268265, 0.5 132.31519770405492, 0.25 131.72547871936558, 0.025 129.23042183191617}, :mc-error 0.12904597314788857, :n 10000, :accepted 106, :rejected 9893}, :mean2 {:std-dev 2.9444003351822747, :mean 136.96123383660253, :quantiles {0.975 141.73971252295715, 0.75 138.8813821712648, 0.5 137.1255005968757, 0.25 134.9752034278358, 0.025 132.8474174171206}, :mc-error 0.17057574621581284, :n 10000, :accepted 251, :rejected 9748}, :std-dev {:std-dev 0.9298255428806178, :mean 49.40049384311787, :quantiles {0.975 49.990456566240994, 0.75 49.79866575538405, 0.5 49.53601803303591, 0.25 49.21113425754308, 0.025 48.18724144586106}, :mc-error 0.050223705760840245, :n 10000, :accepted 209, :rejected 9790}} |