Update TDR/MoM tests - compare output files

.gitignore

@@ -44,3 +44,7 @@ Example_Systems/SmallNewEngland/ThreeZones_full/*
 
 Example_Systems/SmallNewEngland/ThreeZones_MultiStage/Inputs/*
 test_get_retirement_period.jl
+
+# Test files
+test/TDR/TDR_Results_test
+test/MethodofMorris/morris.csv

src/additional_tools/method_of_morris.jl

@@ -1,3 +1,6 @@
+const SEED = 1234
+
+
 @doc raw"""
 	morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outpath::AbstractString, OPTIMIZER)
 
@@ -14,6 +17,7 @@ struct MorrisResult{T1,T2}
     variances::T1
     elementary_effects::T2
 end
+
 function generate_design_matrix(p_range, p_steps, rng;len_design_mat,groups)
     ps = [range(p_range[i][1], stop=p_range[i][2], length=p_steps[i]) for i in 1:length(p_range)]
     indices = [rand(rng, 1:i) for i in p_steps]
@@ -74,8 +78,9 @@ function sample_matrices(p_range,p_steps, rng;num_trajectory,total_num_trajector
     reduce(hcat,matrices)
 end
 
-function my_gsa(f, p_steps, num_trajectory, total_num_trajectory, p_range::AbstractVector,len_design_mat,groups)
+function my_gsa(f, p_steps, num_trajectory, total_num_trajectory, p_range::AbstractVector, len_design_mat, groups, random)
     rng = Random.default_rng()
+    if !random; Random.seed!(SEED);  end
     design_matrices_original = sample_matrices(p_range, p_steps, rng;num_trajectory,
                                         total_num_trajectory,len_design_mat,groups)
     println(design_matrices_original)
@@ -158,7 +163,7 @@ function my_gsa(f, p_steps, num_trajectory, total_num_trajectory, p_range::Abstr
     end
     MorrisResult(reduce(hcat, means),reduce(hcat, means_star),reduce(hcat, variances),effects)
 end
-function morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outpath::AbstractString, OPTIMIZER)
+function morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outpath::AbstractString, OPTIMIZER; random=true)
 
     # Reading the input parameters
     Morris_range = load_dataframe(joinpath(path, "Method_of_morris_range.csv"))
@@ -197,7 +202,7 @@ function morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outp
     end
 
     # Perform the method of morris analysis
-    m = my_gsa(f1,p_steps,num_trajectory,total_num_trajectory,p_range,len_design_mat,groups)
+    m = my_gsa(f1,p_steps,num_trajectory,total_num_trajectory,p_range,len_design_mat,groups,random)
     println(m.means)
     println(DataFrame(m.means', :auto))
     #save the mean effect of each uncertain variable on the objective fucntion

src/time_domain_reduction/time_domain_reduction.jl

@@ -18,6 +18,9 @@ using Distances
 using CSV
 using GenX
 
+
+const SEED = 1234
+
 @doc raw"""
     rmse_score(y_true, y_pred)
 
@@ -202,7 +205,7 @@ function get_worst_period_idx(R)
 end
 
 @doc raw"""
-    cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters)
+    cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false, random=true)
 
 Get representative periods using cluster centers from kmeans or kmedoids.
 
@@ -212,12 +215,13 @@ https://juliastats.org/Clustering.jl/dev/kmeans.html
 K-Medoids:
  https://juliastats.org/Clustering.jl/stable/kmedoids.html
 """
-function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false)
+function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false, random=true)
     if ClusterMethod == "kmeans"
         DistMatrix = pairwise(Euclidean(), Matrix(ClusteringInputDF), dims=2)
         R = kmeans(Matrix(ClusteringInputDF), NClusters, init=:kmcen)
 
         for i in 1:nIters
+            if !random; Random.seed!(SEED);  end
             R_i = kmeans(Matrix(ClusteringInputDF), NClusters)
 
             if R_i.totalcost < R.totalcost
@@ -243,6 +247,7 @@ function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false)
         R = kmedoids(DistMatrix, NClusters, init=:kmcen)
 
         for i in 1:nIters
+            if !random; Random.seed!(SEED);  end
             R_i = kmedoids(DistMatrix, NClusters)
             if R_i.totalcost < R.totalcost
                 R = R_i
@@ -257,7 +262,7 @@ function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false)
         M = R.medoids # get the cluster centers - M for Medoids
     else
         println("INVALID ClusterMethod. Select kmeans or kmedoids. Running kmeans instead.")
-        return cluster("kmeans", ClusteringInputDF, NClusters, nIters)
+        return cluster("kmeans", ClusteringInputDF, NClusters, nIters, v, random)
     end
     return [R, A, W, M, DistMatrix]
 end
@@ -485,7 +490,7 @@ end
 
 
 @doc raw"""
-    cluster_inputs(inpath, settings_path, v=false, norm_plot=false, silh_plot=false, res_plots=false, indiv_plots=false, pair_plots=false)
+    cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false; random=true)
 
 Use kmeans or kmedoids to cluster raw demand profiles and resource capacity factor profiles
 into representative periods. Use Extreme Periods to capture noteworthy periods or
@@ -538,7 +543,7 @@ to separate Vre_and_stor_solar_variability.csv and Vre_and_stor_wind_variability
 and wind profiles for co-located resources will be separated into different CSV files to be read by loading the inputs 
 after the clustering of the inputs has occurred. 
 """
-function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false)
+function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false; random=true)
     if v println(now()) end
 
     ##### Step 0: Load in settings and data
@@ -781,7 +786,7 @@ function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false)
     cluster_results = []
 
     # Cluster once regardless of iteration decisions
-    push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v))
+    push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v, random))
 
     # Iteratively add worst periods as extreme periods OR increment number of clusters k
     #    until threshold is met or maximum periods are added (If chosen in inputs)
@@ -790,15 +795,15 @@ function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false)
             if IterateMethod == "cluster"
                 if v println("Adding a new Cluster! ") end
                 NClusters += 1
-                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v))
+                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v, random))
             elseif (IterateMethod == "extreme") & (UseExtremePeriods == 1)
                 if v println("Adding a new Extreme Period! ") end
                 worst_period_idx = get_worst_period_idx(last(cluster_results)[1])
                 removed_period = string(names(ClusteringInputDF)[worst_period_idx])
                 select!(ClusteringInputDF, Not(worst_period_idx))
                 push!(ExtremeWksList, parse(Int, removed_period))
                 if v println(worst_period_idx, " (", removed_period, ") ", ExtremeWksList) end
-                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v))
+                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v, random))
             elseif IterateMethod == "extreme"
                 println("INVALID IterateMethod ", IterateMethod, " because UseExtremePeriods is off. Set to 1 if you wish to add extreme periods.")
                 break

test/MethodofMorris/morris_true.csv

@@ -0,0 +1,9 @@
+Resource,Zone,ID,Lower_bound,Upper_bound,Parameter,Group,p_steps,total_num_trajectory,num_trajectory,len_design_mat,policy,mean,variance
+natural_gas_combined_cycle,1,1,-5,5,Inv_Cost_per_MWyr,ng,10,2,2,15,reference,19.876405206036726,52.14720602710861
+solar_pv,1,2,-10,10,Inv_Cost_per_MWyr,solar,20,,,,,0.01587702025497309,NaN
+onshore_wind,1,3,-7,7,Inv_Cost_per_MWyr,wind,14,,,,,0.9168892311220421,NaN
+battery,1,4,-20,20,Inv_Cost_per_MWyr,battery,40,,,,,1.2587368193702644,0.02628898806467195
+natural_gas_combined_cycle,1,1,-5,5,Fixed_OM_Cost_per_MWyr,ng,10,,,,,19.876405206036726,52.14720602710861
+solar_pv,1,2,-10,10,Fixed_OM_Cost_per_MWyr,solar,20,,,,,0.01587702025497309,NaN
+onshore_wind,1,3,-7,7,Fixed_OM_Cost_per_MWyr,wind,14,,,,,0.9168892311220421,NaN
+battery,1,4,-20,20,Fixed_OM_Cost_per_MWyr,battery,40,,,,,1.2587368193702644,0.02628898806467195