Add progress bar to HMC, PG and Gibbs

REQUIRE

@@ -5,3 +5,4 @@ PDMats 0.5.1
 ForwardDiff
 Mamba
 Stats
+ProgressMeter

src/Turing.jl

@@ -2,6 +2,7 @@ module Turing
 
 using Distributions
 using ForwardDiff: Dual, npartials  # for automatic differentiation
+using ProgressMeter
 
 abstract InferenceAlgorithm{P}
 abstract Sampler{T<:InferenceAlgorithm}

src/samplers/gibbs.jl

@@ -53,12 +53,11 @@ function Base.run(model, data, spl::Sampler{Gibbs})
   # initialization
   task = current_task()
   n = spl.gibbs.n_iters
-  t_start = time()  # record the start time of HMC
   varInfo = VarInfo()
   ref_particle = nothing
 
-  # HMC steps
-  for i = 1:n
+  # Gibbs steps
+  @showprogress 1 "[Gibbs] Sampling..." for i = 1:n
     dprintln(2, "Gibbs stepping...")
 
     for local_spl in spl.samplers
@@ -92,18 +91,6 @@ function Base.run(model, data, spl::Sampler{Gibbs})
       end
     end
     spl.samples[i].value = Sample(varInfo).value
-
-    if VERBOSITY > 0
-      if i == n
-        println("100% Done")
-      elseif i % floor(n / 100) == 0
-        print("$(i / floor(n / 100))% ")
-      end
-    end
-  end
-
-  if VERBOSITY > 0
-    println("[Gibbs]: Finshed within $(time() - t_start) seconds")
   end
 
   Chain(0, spl.samples)    # wrap the result by Chain

src/samplers/hmc.jl

@@ -115,12 +115,11 @@ function sample(model::Function, alg::HMC, chunk_size::Int)
   # initialization
   n =  spl.alg.n_samples
   task = current_task()
-  t_start = time()  # record the start time of HMC
   accept_num = 0    # record the accept number
   varInfo = VarInfo()
 
   # HMC steps
-  for i = 1:n
+  @showprogress 1 "[HMC] Sampling..." for i = 1:n
     dprintln(2, "recording old θ...")
     old_vals = deepcopy(varInfo.vals)
     dprintln(2, "HMC stepping...")
@@ -136,10 +135,6 @@ function sample(model::Function, alg::HMC, chunk_size::Int)
 
   accept_rate = accept_num / n    # calculate the accept rate
 
-  if VERBOSITY > 0
-      println("[HMC]: Finshed with accept rate = $(accept_rate) within $(time() - t_start) seconds")
-  end
-
   Chain(0, spl.samples)    # wrap the result by Chain
 end
 

src/samplers/pgibbs.jl

@@ -66,22 +66,18 @@ function sample(model, alg::PG)
   global sampler = ParticleSampler{PG}(alg);
   spl = sampler
   n = spl.alg.n_iterations
-  t_start = time()  # record the start time of PG
   samples = Vector{Sample}()
   logevidence = Vector{Float64}(n)
 
   ## custom resampling function for pgibbs
   ## re-inserts reteined particle after each resampling step
   ref_particle = nothing
-  for i = 1:n
+  @showprogress 1 "[PG] Sampling..." for i = 1:n
     ref_particle, s = step(model, spl, VarInfo(), ref_particle)
     logevidence[i] = spl.particles.logE
     push!(samples, Sample(1/n, s.value))
   end
-
-  if VERBOSITY > 0
-    println("[PG]: Finshed within $(time() - t_start) seconds")
-  end
+
   chain = Chain(exp(mean(logevidence)), samples)
 end