Add @info into algorithm comparison scripts

src/CompareAlgorithmClassificationRates.jl

@@ -17,6 +17,7 @@ include("mod/utils.jl")
 import .LDBUtils: repeat_experiment, dict2dataframes, aggregate_results
 
 ## ========= Setup =========
+@info "LDB Object Setup"
 # TODO: Change wavelet filter
 wt = wavelet(WT.coif2)
 # TODO: Change decomposition levels
@@ -62,6 +63,7 @@ classifiers = Dict("LDA" => LDA(), "CT" => CT())
 measures = [MisclassificationRate(), MulticlassPrecision(), MulticlassTruePositiveRate()]
 
 ## ========== Run experiments ==========
+@info "Run Experiments"
 # Set `save_data` to `true` to see the results in csv files.
 results_raw = repeat_experiment(ldbs, classifiers, measures; repeats = 100, save_data=false)
 results_by_measure = Dict("$((string ∘ typeof)(measure))" => dict2dataframes(results_raw, measure; save_data=false) for measure in measures)

src/CompareAlgorithmVisualizations.jl

@@ -64,6 +64,7 @@ include("mod/utils.jl")
 import .LDBUtils: compute_ldb_vectors, plot_coefficients
 
 ## ========== Setup ==========
+@info "LDB Object Setup"
 train_x, train_y = generateclassdata(ClassData(:tri, 33 , 33 , 33 ), false)
 test_x , test_y  = generateclassdata(ClassData(:tri, 333, 333, 333), true)
 # TODO: Change wavelet filter
@@ -104,6 +105,7 @@ ldbkemd = LocalDiscriminantBasis(wt = wt,
                                  n_features = n_features)
 
 ## ========== Display sample dataset ==========
+@info "Data Setup"
 # TODO: Change indexing if train size is changed above
 class1_mean = mean(train_x[:, 1:33], dims=2)
 class2_mean = mean(train_x[:,34:66], dims=2)
@@ -114,6 +116,7 @@ plot!(p01, class3_mean, linestyle=:dot, color=:black, label="Class 3")
 plot!(p01, title="Mean training waveform")
 
 ## ========== Fit LDB ==========
+@info "LDB Transform"
 # Fit and transform training data
 ldbk_train_features    = WaveletsExt.fit_transform(ldbk   , train_x, train_y)
 ldbkash_train_features = WaveletsExt.fit_transform(ldbkash, train_x, train_y)
@@ -144,6 +147,7 @@ p09 = plot_coefficients(ldbkash_train_features) |> p -> plot!(p, title="LDBKASH"
 p10 = plot_coefficients(ldbkemd_train_features) |> p -> plot!(p, title="LDBKEMD")
 
 ## ========== Fit features in Linear Discriminant Analysis (LDA) classifier ==========
+@info "Model Training"
 # Data wrangling to fit MLJ.jl syntax
 original_train_features = DataFrame(train_x', :auto)
 ldbk_train_features     = DataFrame(ldbk_train_features', :auto)
@@ -156,7 +160,7 @@ ldbkash_test_features  = DataFrame(ldbkash_test_features', :auto)
 ldbkemd_test_features  = DataFrame(ldbkemd_test_features', :auto)
 test_y = coerce(test_y, Multiclass)
 # Model fitting
-LDA = @load LDA pkg=MultivariateStats
+LDA = @load LDA pkg=MultivariateStats verbosity=0
 original_classifier = machine(LDA(), original_train_features, train_y)
 ldbk_classifier     = machine(LDA(), ldbk_train_features, train_y)
 ldbkash_classifier  = machine(LDA(), ldbkash_train_features, train_y)
@@ -175,19 +179,20 @@ ldbk_test_ŷ     = predict_mode(ldbk_classifier    , ldbk_test_features)
 ldbkash_test_ŷ  = predict_mode(ldbkash_classifier , ldbkash_test_features)
 ldbkemd_test_ŷ  = predict_mode(ldbkemd_classifier , ldbkemd_test_features)
 # Evaluate Model
+@info "Model Evaluation"
 original_train_accuracy = Accuracy()(original_train_ŷ, train_y)
-@info "Original LDB Train Accuracy: $original_train_accuracy"
+@info "\tOriginal LDB Train Accuracy: $original_train_accuracy"
 ldbk_train_accuracy     = Accuracy()(ldbk_train_ŷ    , train_y)
-@info "LDBK Train Accuracy: $ldbk_train_accuracy"
+@info "\tLDBK Train Accuracy: $ldbk_train_accuracy"
 ldbkash_train_accuracy  = Accuracy()(ldbkash_train_ŷ , train_y)
-@info "LDBASH Train Accuracy: $ldbkash_train_accuracy"
+@info "\tLDBASH Train Accuracy: $ldbkash_train_accuracy"
 ldbkemd_train_accuracy  = Accuracy()(ldbkemd_train_ŷ , train_y)
-@info "LDBKEMD Train Accuracy: $ldbkemd_train_accuracy"
+@info "\tLDBKEMD Train Accuracy: $ldbkemd_train_accuracy"
 original_test_accuracy = Accuracy()(original_test_ŷ, test_y)
-@info "Original LDB Test Accuracy: $original_test_accuracy"
+@info "\tOriginal LDB Test Accuracy: $original_test_accuracy"
 ldbk_test_accuracy     = Accuracy()(ldbk_test_ŷ    , test_y)
-@info "LDBK Test Accuracy: $ldbk_test_accuracy"
+@info "\tLDBK Test Accuracy: $ldbk_test_accuracy"
 ldbkash_test_accuracy  = Accuracy()(ldbkash_test_ŷ , test_y)
-@info "LDBKASH Test Accuracy: $ldbkash_test_accuracy"
+@info "\tLDBKASH Test Accuracy: $ldbkash_test_accuracy"
 ldbkemd_test_accuracy  = Accuracy()(ldbkemd_test_ŷ , test_y)
-@info "LDBKEMD Test Accuracy: $ldbkemd_test_accuracy"
+@info "\tLDBKEMD Test Accuracy: $ldbkemd_test_accuracy"