Made some timing experiments

time_pls.py

@@ -95,6 +95,14 @@
             time = cross_val_cpu_pls(pls, X, Y, n_splits, fit_params, n_jobs=n_jobs, verbose=1)
         print(f"Time: {time}")
 
+    try:
+        with open("timings/timings.csv", "x") as f:
+            f.write("model,n_components,n_splits,n,m,k,time\n")
+            f.write(f"{model},{n_components},{n_splits},{n},{m},{k},{time}\n")
+    except FileExistsError:
+        with open("timings/timings.csv", "a") as f:
+            f.write(f"{model},{n_components},{n_splits},{n},{m},{k},{time}\n")
+
     # Freeze values:
     # 1. n_components = 30
     # 2. n_splits = {1, LOOCV} # The overhead of JIT-compilation is already negligible at 1e4 samples.
@@ -103,7 +111,7 @@
     # 5. k = {1, 10}
 
     # Dynamic values:
-    # 1. n_components = {10, 20, 50, 100, 200, 500, 1000}
+    # 1. n_components = {10, 20, 30, 50, 100, 200, 500, 1000}
     # 1. n = {1e1, 1e2, 1e3, 1e4, 1e5, 1e6}
     # 3. m = {20, 50, 100, 500, 1000, 5000, 10000}
 

timings/timings.csv

@@ -0,0 +1,118 @@
+model,n_components,n_splits,n,m,k,time
+sk,10,1,10000,500,1,0.19273439201060683
+sk,10,10000,10000,500,1,481.8888097489835
+sk,10,1,10000,500,10,0.6096877630043309
+sk,10,10000,10000,500,10,3083.1409927269997
+sk,20,1,10000,500,1,0.383961057000306
+sk,30,1,10000,500,1,0.538925515000301
+sk,50,1,10000,500,1,0.8993592769993484
+sk,100,1,10000,500,1,1.7544596559991987
+sk,200,1,10000,500,1,3.4300453200003176
+sk,500,1,10000,500,1,8.231237813999542
+sk,20,1,10000,500,10,1.2046825039997202
+sk,30,1,10000,500,10,1.7152136909999172
+sk,50,1,10000,500,10,2.5382029529991996
+sk,100,1,10000,500,10,4.446280705999925
+sk,200,1,10000,500,10,8.997756613000092
+sk,500,1,10000,500,10,26.541843658999824
+sk,30,1,10,500,1,0.0027361649999875226
+sk,30,1,100,500,1,0.009425168000234407
+sk,30,1,1000,500,1,0.049496089999593096
+sk,30,1,10000,500,1,0.5260668050004824
+sk,30,1,100000,500,1,4.879484715999752
+sk,30,1,1000000,500,1,43.23828121799943
+sk,30,1,10,500,10,0.005154452999704517
+sk,30,1,100,500,10,0.04722639000010531
+sk,30,1,1000,500,10,0.22319066199997906
+sk,30,1,10000,500,10,1.568664624000121
+sk,30,1,100000,500,10,59.655239371999414
+sk,30,1,1000000,500,10,636.2466903430004
+sk,30,1,10000,30,1,0.03232301800017012
+sk,30,1,10000,50,1,0.05948806400010653
+sk,30,1,10000,100,1,0.098399309999877
+sk,30,1,10000,500,1,0.503990841999439
+sk,30,1,10000,1000,1,1.0616326409999601
+sk,30,1,10000,5000,1,4.715606132000175
+sk,30,1,10000,10000,1,7.55188517500028
+sk,30,1,10000,30,10,0.10395021400017868
+sk,30,1,10000,50,10,0.31709428099929937
+sk,30,1,10000,100,10,0.3963616490000277
+sk,30,1,10000,500,10,1.6708524519999628
+sk,30,1,10000,1000,10,10.25260551700012
+sk,30,1,10000,5000,10,122.55457919299988
+sk,30,1,10000,10000,10,291.9931363270007
+np1,10,1,10000,500,1,0.02829229299823055
+np1,20,1,10000,500,1,0.025495142999716336
+np1,30,1,10000,500,1,0.03573968899945612
+np1,50,1,10000,500,1,0.057505740001943195
+np1,100,1,10000,500,1,0.11449231099686585
+np1,200,1,10000,500,1,0.26500373399903765
+np1,500,1,10000,500,1,1.077820430000429
+np1,30,1,10,500,1,0.0035715200028789695
+np1,30,1,100,500,1,0.006363390999467811
+np1,30,1,1000,500,1,0.009307193002314307
+np1,30,1,10000,500,1,0.03354567700080224
+np1,30,1,100000,500,1,0.8221006140011013
+np1,30,1,1000000,500,1,8.44086087300093
+np1,30,1,10000,30,1,0.00863133999882848
+np1,30,1,10000,50,1,0.012853928998083575
+np1,30,1,10000,100,1,0.014647433999925852
+np1,30,1,10000,500,1,0.0354511840014311
+np1,30,1,10000,1000,1,0.10847483099860256
+np1,30,1,10000,5000,1,0.7698168780007109
+np1,30,1,10000,10000,1,1.5100300060003065
+np1,10,1,10000,500,10,0.028714912001305493
+np1,20,1,10000,500,10,0.028550800001539756
+np1,30,1,10000,500,10,0.04005221999977948
+np1,50,1,10000,500,10,0.05554433799989056
+np1,100,1,10000,500,10,0.11898328399911406
+np1,200,1,10000,500,10,0.2757587380001496
+np1,500,1,10000,500,10,1.080385589000798
+np1,30,1,10,500,10,0.004644275999453384
+np1,30,1,100,500,10,0.008309045999340015
+np1,30,1,1000,500,10,0.011847425997984828
+np1,30,1,10000,500,10,0.03908856699854368
+np1,30,1,100000,500,10,0.8213651980004215
+np1,30,1,1000000,500,10,8.418709229001252
+np1,30,1,10000,30,10,0.013260746000014478
+np1,30,1,10000,50,10,0.02961625599709805
+np1,30,1,10000,100,10,0.024874249000276905
+np1,30,1,10000,500,10,0.03835980800067773
+np1,30,1,10000,1000,10,0.11690236999857007
+np1,30,1,10000,5000,10,0.7834515440008545
+np1,30,1,10000,10000,10,1.5617745100025786
+np2,10,1,10000,500,1,0.06693687100050738
+jax1,10,1,10000,500,1,2.850088137998682
+jax2,10,1,10000,500,1,2.4174978740011284
+diffjax1,10,1,10000,500,1,2.488450716002262
+diffjax2,10,1,10000,500,1,2.3965144389985653
+np2,20,1,10000,500,1,0.07027839900183608
+jax1,20,1,10000,500,1,3.256795921999583
+jax2,20,1,10000,500,1,3.063774940001167
+diffjax1,20,1,10000,500,1,3.2046105150002404
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+np2,30,1,10000,500,1,0.07769091299996944
+jax1,30,1,10000,500,1,4.127460686999257
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+diffjax1,30,1,10000,500,1,4.099335901999439
+diffjax2,30,1,10000,500,1,3.7981624040003226
+np2,50,1,10000,500,1,0.07905295599994133
+jax1,50,1,10000,500,1,5.888724919001106
+jax2,50,1,10000,500,1,5.242987173998699
+diffjax1,50,1,10000,500,1,5.947335015000135
+diffjax2,50,1,10000,500,1,5.3286497320004855
+np2,100,1,10000,500,1,0.10478619800051092
+np2,200,1,10000,500,1,0.193939386997954
+np2,500,1,10000,500,1,0.787515349999012
+jax1,100,1,10000,500,1,11.4029962229979
+jax1,200,1,10000,500,1,25.6140492409977
+jax1,500,1,10000,500,1,108.06587946400032
+jax2,100,1,10000,500,1,9.864700633999746
+jax2,200,1,10000,500,1,21.71426913200048
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+diffjax1,100,1,10000,500,1,11.364623696998024
+diffjax1,200,1,10000,500,1,25.757944255998154
+diffjax1,500,1,10000,500,1,110.90607149599964
+diffjax2,100,1,10000,500,1,9.760836130000826
+diffjax2,200,1,10000,500,1,21.620848131999082
+diffjax2,500,1,10000,500,1,92.68657545600217