Prefer to use @info instead of internal debug command

MilesCranmer · Dec 23, 2023 · d732afd · d732afd
1 parent f5ccffd
commit d732afd
Show file tree

Hide file tree

Showing 4 changed files with 27 additions and 45 deletions.
diff --git a/src/Configure.jl b/src/Configure.jl
@@ -95,25 +95,18 @@ function test_dataset_configuration(
         )
     end
 
-    if size(dataset.X, 2) > 10000
-        if !options.batching
-            debug(
-                verbosity > 0,
-                "Note: you are running with more than 10,000 datapoints. You should consider turning on batching (`options.batching`), and also if you need that many datapoints. Unless you have a large amount of noise (in which case you should smooth your dataset first), generally < 10,000 datapoints is enough to find a functional form.",
-            )
-        end
+    if size(dataset.X, 2) > 10000 && !options.batching && verbosity > 0
+        @info "Note: you are running with more than 10,000 datapoints. You should consider turning on batching (`options.batching`), and also if you need that many datapoints. Unless you have a large amount of noise (in which case you should smooth your dataset first), generally < 10,000 datapoints is enough to find a functional form."
     end
 
-    if !(typeof(options.elementwise_loss) <: SupervisedLoss)
-        if dataset.weighted
-            if !(3 in [m.nargs - 1 for m in methods(options.elementwise_loss)])
-                throw(
-                    AssertionError(
-                        "When you create a custom loss function, and are using weights, you need to define your loss function with three scalar arguments: f(prediction, target, weight).",
-                    ),
-                )
-            end
-        end
+    if !(typeof(options.elementwise_loss) <: SupervisedLoss) &&
+        dataset.weighted &&
+        !(3 in [m.nargs - 1 for m in methods(options.elementwise_loss)])
+        throw(
+            AssertionError(
+                "When you create a custom loss function, and are using weights, you need to define your loss function with three scalar arguments: f(prediction, target, weight).",
+            ),
+        )
     end
 end
 
@@ -202,14 +195,15 @@ end
 
 function copy_definition_to_workers(op, procs, options::Options, verbosity)
     name = nameof(op)
-    debug_inline(verbosity > 0, "Copying definition of $op to workers...")
+    verbosity > 0 && @info "Copying definition of $op to workers..."
     src_ms = methods(op).ms
     # Thanks https://discourse.julialang.org/t/easy-way-to-send-custom-function-to-distributed-workers/22118/2
     @everywhere procs @eval function $name end
     for m in src_ms
         @everywhere procs @eval $m
     end
-    return debug(verbosity > 0, "Finished!")
+    verbosity > 0 && @info "Finished!"
+    return nothing
 end
 
 function test_function_on_workers(example_inputs, op, procs)
@@ -223,7 +217,7 @@ function test_function_on_workers(example_inputs, op, procs)
 end
 
 function activate_env_on_workers(procs, project_path::String, options::Options, verbosity)
-    debug(verbosity > 0, "Activating environment on workers.")
+    verbosity > 0 && @info "Activating environment on workers."
     @everywhere procs begin
         Base.MainInclude.eval(
             quote
@@ -237,7 +231,7 @@ end
 function import_module_on_workers(procs, filename::String, options::Options, verbosity)
     included_local = !("SymbolicRegression" in [k.name for (k, v) in Base.loaded_modules])
     if included_local
-        debug_inline(verbosity > 0, "Importing local module ($filename) on workers...")
+        verbosity > 0 && @info "Importing local module ($filename) on workers..."
         @everywhere procs begin
             # Parse functions on every worker node
             Base.MainInclude.eval(
@@ -247,18 +241,18 @@ function import_module_on_workers(procs, filename::String, options::Options, ver
                 end,
             )
         end
-        debug(verbosity > 0, "Finished!")
+        verbosity > 0 && @info "Finished!"
     else
-        debug_inline(verbosity > 0, "Importing installed module on workers...")
+        verbosity > 0 && @info "Importing installed module on workers..."
         @everywhere procs begin
             Base.MainInclude.eval(using SymbolicRegression)
         end
-        debug(verbosity > 0, "Finished!")
+        verbosity > 0 && @info "Finished!"
     end
 end
 
 function test_module_on_workers(procs, options::Options, verbosity)
-    debug_inline(verbosity > 0, "Testing module on workers...")
+    verbosity > 0 && @info "Testing module on workers..."
     futures = []
     for proc in procs
         push!(
@@ -269,14 +263,15 @@ function test_module_on_workers(procs, options::Options, verbosity)
     for future in futures
         fetch(future)
     end
-    return debug(verbosity > 0, "Finished!")
+    verbosity > 0 && @info "Finished!"
+    return nothing
 end
 
 function test_entire_pipeline(
     procs, dataset::Dataset{T}, options::Options, verbosity
 ) where {T<:DATA_TYPE}
     futures = []
-    debug_inline(verbosity > 0, "Testing entire pipeline on workers...")
+    verbosity > 0 && @info "Testing entire pipeline on workers..."
     for proc in procs
         push!(
             futures,
@@ -307,7 +302,8 @@ function test_entire_pipeline(
     for future in futures
         fetch(future)
     end
-    return debug(verbosity > 0, "Finished!")
+    verbosity > 0 && @info "Finished!"
+    return nothing
 end
 
 function configure_workers(;

diff --git a/src/SingleIteration.jl b/src/SingleIteration.jl
@@ -3,7 +3,6 @@ module SingleIterationModule
 import DynamicExpressions: Node, string_tree, simplify_tree, combine_operators
 import ..CoreModule: Options, Dataset, RecordType, DATA_TYPE, LOSS_TYPE
 import ..ComplexityModule: compute_complexity
-import ..UtilsModule: debug
 import ..PopMemberModule: generate_reference
 import ..PopulationModule: Population, finalize_scores, best_sub_pop
 import ..HallOfFameModule: HallOfFame

diff --git a/src/SymbolicRegression.jl b/src/SymbolicRegression.jl
@@ -192,8 +192,7 @@ import .CoreModule:
     erf,
     erfc,
     atanh_clip
-import .UtilsModule:
-    debug, debug_inline, is_anonymous_function, recursive_merge, json3_write
+import .UtilsModule: is_anonymous_function, recursive_merge, json3_write
 import .ComplexityModule: compute_complexity
 import .CheckConstraintsModule: check_constraints
 import .AdaptiveParsimonyModule:
@@ -529,7 +528,7 @@ function equation_search(
                 end
             ) / 1024^2
         )
-        heap_size_hint_in_bytes === nothing &&
+        _verbosity > 0 && heap_size_hint_in_bytes === nothing &&
             @info "Automatically setting --heap-size-hint=$(heap_size_hint_in_megabytes)M on each Julia process. You can set this manually with `heap_size_hint_in_bytes`."
 
         `--heap-size=$(heap_size_hint_in_megabytes)M`
@@ -755,7 +754,7 @@ function _equation_search(
         allPops[j][i] = updated_pop
     end
 
-    debug(verbosity > 0, "Started!")
+    verbosity > 0 && @info "Started!"
     start_time = time()
     total_cycles = options.populations * niterations
     cycles_remaining = [total_cycles for j in 1:nout]

diff --git a/src/Utils.jl b/src/Utils.jl
@@ -4,18 +4,6 @@ module UtilsModule
 import Printf: @printf
 import MacroTools: splitdef, combinedef
 
-function debug(verbosity, string...)
-    if verbosity > 0
-        println(string...)
-    end
-end
-
-function debug_inline(verbosity, string...)
-    if verbosity > 0
-        print(string...)
-    end
-end
-
 const pseudo_time = Ref(0)
 
 function get_birth_order(; deterministic=false)::Int