In [1]:
using Distributions
using Turing
using Stan

# Load data; loaded data is a list of dict named `ldastandata`
include(Pkg.dir("Turing")*"/example-models/stan-models/lda-stan.data.jl")
topicdata = ldastandata[1]

# Load model
include(Pkg.dir("Turing")*"/example-models/stan-models/lda.model.jl")
#= NOTE: loaded model is defined as below
@model ldamodel(K, V, M, N, w, doc, beta, alpha) = begin
  theta = Vector{Vector{Real}}(M)
  for m = 1:M
    theta[m] ~ Dirichlet(alpha)
  end

  phi = Vector{Vector{Real}}(K)
  for k = 1:K
    phi[k] ~ Dirichlet(beta)
  end

  phi_dot_theta = [log([dot(map(p -> p[i], phi), theta[m]) for i = 1:V]) for m=1:M]
  for n = 1:N
    Turing.acclogp!(vi, phi_dot_theta[doc[n]][w[n]])
  end
end
=#
[Turing]: AD chunk size is set as 40
Out[1]:
ldamodel_vec (generic function with 9 methods)
In [2]:
setchunksize(100)    # increase AD chunk-size to 100
[Turing]: AD chunk size is set as 100
Out[2]:
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0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0),Partials(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0),Partials(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0))
In [3]:
samples = sample(ldamodel(data=topicdata), NUTS(1000, 0.65))
[Turing]:  Assume - `theta` is a parameter
  in @~(::Any, ::Any) at compiler.jl:76
[Turing]:  Assume - `phi` is a parameter
  in @~(::Any, ::Any) at compiler.jl:76
[Turing] looking for good initial eps...
[Turing.NUTS] found initial ϵ: 0.5
[Turing.WARNING]: Numerical error has been found in gradients.
  in verifygrad(::Array{Float64,1}) at ad.jl:87
[Turing]:  Adapted ϵ = 4.539410952991489e-76, 200 HMC iterations is used for adaption.
  in adapt_step_size(::Turing.WarmUpManager, ::Float64) at adapt.jl:54
[NUTS] Finished with
  Running time        = 249.17596517799976;
  #lf / sample        = 62.761;
  #evals / sample     = 62.763;
  pre-cond. diag mat  = [0.999999,0.999999,0.999998,0.....
Out[3]:
Object of type "Turing.Chain"

Iterations = 1:1000
Thinning interval = 1
Chains = 1
Samples per chain = 1000

[0.0117366 0.988263 … 0.405007 0.594993; 0.0117366 0.988263 … 0.405007 0.594993; … ; 0.0117366 0.988263 … 0.405007 0.594993; 0.0117366 0.988263 … 0.405007 0.594993]
In [4]:
# Load visualization script for topic models; visualization function is called `vis_topic_res`
include(Pkg.dir("Turing")*"/example-models/stan-models/topic_model_vis_helper.jl")

@doc vis_topic_res  # show the usage of the visualization function
Out[4]:

Function for visualization topic models.

Usage:

vis_topic_res(samples, K, V, avg_range)
  • samples is the chain return by sample()
  • K is the number of topics
  • V is the size of vocabulary
  • avg_range is the end point of the running average
In [5]:
vis_topic_res(samples, topicdata["K"], topicdata["V"], 1000)
Out[5]:
Word -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 -6.0 -5.8 -5.6 -5.4 -5.2 -5.0 -4.8 -4.6 -4.4 -4.2 -4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4 11.6 11.8 12.0 -10 0 10 20 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 0.6 0.8 0.2 0.4 0.0 Probability -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 -2.5 -2.4 -2.3 -2.2 -2.1 -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.0 -2.5 0.0 2.5 5.0 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 Topic
In [6]:
# Load data; loaded data is a list of dict named `nbstandata`
include(Pkg.dir("Turing")*"/example-models/stan-models/MoC-stan.data.jl")
topicdata2 = nbstandata[1]

# Load model
include(Pkg.dir("Turing")*"/example-models/stan-models/MoC.model.jl")
#= NOTE: loaded model is defined as below
@model nbmodel(K, V, M, N, z, w, doc, alpha, beta) = begin
  theta ~ Dirichlet(alpha)

  phi = Array{Any}(K)
  for k = 1:K
    phi[k] ~ Dirichlet(beta)
  end

  log_theta = log(theta)
  Turing.acclogp!(vi, sum(log_theta[z[1:M]]))

  log_phi = map(x->log(x), phi)
  for n = 1:N
    Turing.acclogp!(vi, log_phi[z[doc[n]]][w[n]])
  end

  phi
end

=#
Out[6]:
nbmodel (generic function with 10 methods)
In [7]:
samples2 = sample(nbmodel(data=topicdata2), NUTS(1000, 0.65))
[Turing]:  Assume - `theta` is a parameter
  in @~(::Any, ::Any) at compiler.jl:76
[Turing]:  Assume - `phi` is a parameter
  in @~(::Any, ::Any) at compiler.jl:76
[Turing] looking for good initial eps...
[Turing.WARNING]: Numerical error has been found in gradients.
  in verifygrad(::Array{Float64,1}) at ad.jl:87
[Turing.NUTS] found initial ϵ: 0.605
[Turing]:  Adapted ϵ = 1.1348527382479003e-76, 200 HMC iterations is used for adaption.
  in adapt_step_size(::Turing.WarmUpManager, ::Float64) at adapt.jl:54
[NUTS] Finished with
  Running time        = 2.253066046;
  #lf / sample        = 1.003;
  #evals / sample     = 1.005;
  pre-cond. diag mat  = [0.999997,0.999994,0.999994,0.....
Out[7]:
Object of type "Turing.Chain"

Iterations = 1:1000
Thinning interval = 1
Chains = 1
Samples per chain = 1000

[0.0394719 0.67126 … 1.10815e-7 0.0209499; 0.0394719 0.67126 … 1.10815e-7 0.0209499; … ; 0.0394719 0.67126 … 1.10815e-7 0.0209499; 0.0394719 0.67126 … 1.10815e-7 0.0209499]
In [8]:
vis_topic_res(samples2, topicdata2["K"], topicdata2["V"], 1000)
Out[8]:
Word -20 -15 -10 -5 0 5 10 15 20 25 30 35 -15.0 -14.5 -14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -7.0 -6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0 20.5 21.0 21.5 22.0 22.5 23.0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 28.5 29.0 29.5 30.0 -20 0 20 40 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1.00 0.25 0.75 0.50 0.00 Probability -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 -5.0 -4.8 -4.6 -4.4 -4.2 -4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4 9.6 9.8 10.0 -5 0 5 10 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 Topic
In [9]:
using ProgressMeter
In [10]:
x,n = 1,10
p = Progress(n)
for iter = 1:10
    x *= 2
    sleep(0.5)
    ProgressMeter.next!(p; showvalues = [(:iter,iter), (:x,x)])
end