bug fixes & verbose test

Author: PyDataBlog

.travis.yml

@@ -5,6 +5,7 @@ os:
   - osx
 julia:
   - 1.3
+  - 1.4
   - nightly
 after_success:
   - julia -e 'using Pkg; Pkg.add("Coverage"); using Coverage; Coveralls.submit(process_folder())'
@@ -14,7 +15,7 @@ jobs:
   fast_finish: true
   include:
     - stage: Documentation
-      julia: 1.3
+      julia: 1.4
       script: julia --project=docs -e '
           using Pkg;
           Pkg.develop(PackageSpec(path=pwd()));

docs/src/index.md

@@ -59,11 +59,11 @@ Taking advantage of Julia's brilliant multiple dispatch system, the package expo
 ```julia
 using ParallelKMeans
 
-# Use only 1 core of CPU
-results = kmeans(X, 3, ParallelKMeans.SingleThread(), tol=1e-6, max_iters=300)
-
 # Use all available CPU cores
-multi_results = kmeans(X, 3, ParallelKMeans.MultiThread(), tol=1e-6, max_iters=300)
+multi_results = kmeans(X, 3; max_iters=300)
+
+# Use only 1 core of CPU
+results = kmeans(X, 3; n_threads=1, max_iters=300)
 ```
 
 ### Practical Usage Examples
@@ -80,7 +80,7 @@ iris = dataset("datasets", "iris");
 # features to use for clustering
 features = collect(Matrix(iris[:, 1:4])'); 
 
-result = kmeans(features, 3, ParallelKMeans.MultiThread()); 
+result = kmeans(features, 3); 
 
 # plot with the point color mapped to the assigned cluster index
 scatter(iris.PetalLength, iris.PetalWidth, marker_z=result.assignments,
@@ -102,13 +102,12 @@ c = [ParallelKMeans.kmeans(X, i; tol=1e-6, max_iters=300, verbose=false).totalco
 
 # Single Thread Implementation plus a modified version of Elkan's triangiulity of inequaltiy
 # to boost speed
-e = [ParallelKMeans.kmeans(ParallelKMeans.LightElkan(), X, i, 
-                           n_threads=1, tol=1e-6, max_iters=300, 
-                           verbose=false).totalcost for i = 2:10]
+e = [ParallelKMeans.kmeans(LightElkan(), X, i; 
+                           n_threads=1, tol=1e-6, max_iters=300, verbose=false).totalcost for i = 2:10]
 
 # Multi Thread Implementation plus a modified version of Elkan's triangiulity of inequaltiy
 # to boost speed
-d = [ParallelKMeans.kmeans(ParallelKMeans.LightElkan(), X, i;
+d = [ParallelKMeans.kmeans(LightElkan(), X, i;
                            tol=1e-6, max_iters=300, verbose=false).totalcost for i = 2:10]
 ```
 

src/kmeans.jl

@@ -128,7 +128,7 @@ function kmeans!(alg, containers, design_matrix, k;
 
         if verbose
             # Show progress and terminate if J stopped decreasing.
-            println("Iteration $iter: Jclust = $J")
+            @info("Iteration $niters: Jclust = $J")
         end
 
         # Check for convergence
@@ -145,7 +145,7 @@ function kmeans!(alg, containers, design_matrix, k;
 
     # Terminate algorithm with the assumption that K-means has converged
     if verbose & converged
-        println("Successfully terminated with convergence.")
+        @info("Successfully terminated with convergence.")
     end
 
     # TODO empty placeholder vectors should be calculated

test/test05_verbose.jl

@@ -0,0 +1,17 @@
+module TestVerbosity
+
+using ParallelKMeans
+using Random
+using Test
+
+
+@testset "Testing verbosity of implementation" begin
+    Random.seed!(2020)
+    X = rand(4, 150)
+    Random.seed!(2020)
+    @test_logs (:info, "Iteration 1: Jclust = 0.31023197229652544") kmeans(X, 3, n_threads=1, max_iters=1)
+    
+end
+
+end # module
+