crbn4/Building
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

new file: checkpoints_tree/task1_tr_tree/best_tr_tree.pkl

Browse Source 
new file:   checkpoints_tree/task1_tr_tree/model_selection.csv
	new file:   evaluation_outputs/task1_tr_tree/evaluation_train_all_samples.csv
	new file:   evaluation_outputs/task1_tr_tree/evaluation_train_curve.png
	new file:   evaluation_outputs/task1_tr_tree/evaluation_val_all_samples.csv
	new file:   evaluation_outputs/task1_tr_tree/evaluation_val_curve.png
	new file:   evaluation_outputs/task1_tr_tree/harmonic_5mm_1.55Hz_prediction.png
	new file:   scripts_tree/__pycache__/config.cpython-310.pyc
	new file:   scripts_tree/config.py
	new file:   scripts_tree/evaluate.py
	new file:   scripts_tree/predict_single.py
	new file:   scripts_tree/train.py
This commit is contained in:
CrbnsCat10n
2026-05-06 12:32:49 +08:00
parent 484643409d
commit 02e488cd0d
12 changed files with 549 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

BIN
checkpoints_tree/task1_tr_tree/best_tr_tree.pkl Normal file
View File

Binary file not shown.

4
checkpoints_tree/task1_tr_tree/model_selection.csv Normal file
View File

@@ -0,0 +1,4 @@
model_name,train_mean_rms_error,train_median_rms_error,train_max_rms_error,val_mean_rms_error,val_median_rms_error,val_max_rms_error
random_forest,0.23011051198123578,0.1289880713751052,1.3614534820442807,0.2941758399359114,0.1350525270089895,0.8030963210135198
extra_trees,3.6456671909608616e-08,2.9450879227826572e-08,1.1542847206743363e-07,0.30134282357310554,0.1285832699374185,0.989878917287992
gradient_boosting,0.012023494394761644,0.005998110300540536,0.11812203696846045,0.3579803351621235,0.3397381373027084,0.9497835717961135
1 model_name train_mean_rms_error train_median_rms_error train_max_rms_error val_mean_rms_error val_median_rms_error val_max_rms_error
2 random_forest 0.23011051198123578 0.1289880713751052 1.3614534820442807 0.2941758399359114 0.1350525270089895 0.8030963210135198
3 extra_trees 3.6456671909608616e-08 2.9450879227826572e-08 1.1542847206743363e-07 0.30134282357310554 0.1285832699374185 0.989878917287992
4 gradient_boosting 0.012023494394761644 0.005998110300540536 0.11812203696846045 0.3579803351621235 0.3397381373027084 0.9497835717961135

37
evaluation_outputs/task1_tr_tree/evaluation_train_all_samples.csv Normal file
View File

@@ -0,0 +1,37 @@
file_name,frequency_hz,true_rms,pred_rms,relative_error_percent,x_rms,pred_tr,true_tr,dominant_frequency_hz,frequency_squared,inverse_frequency_hz,log_frequency_hz,input_rms,input_peak_abs,input_peak_to_peak,crest_factor,middle_length_ratio,dominant_amplitude,dominant_energy_ratio,harmonic_fit_amplitude,harmonic_fit_residual_ratio,spectral_peak_prominence,half_power_bandwidth_hz,spectral_centroid_hz,signal_mean,sampling_rate
harmonic_5mm_1.95Hz.csv,1.95,2.063512086868286,2.042942868521436,0.996806293394051,0.4040626883506775,5.056004742383957,5.106910705566406,1.9470664262771606,3.7910678386688232,0.5135931372642517,0.6663238406181335,0.4040626883506775,1.629040002822876,2.9249401092529297,4.031651496887207,0.25,0.407725989818573,0.9061827659606934,0.44422510266304016,0.629830002784729,43.91968536376953,0.09677428752183914,2.039226531982422,-0.004885347560048103,50.000047683761295
harmonic_5mm_0.7Hz.csv,0.7,0.2675361931324005,0.2647816168893098,1.0296088207129102,0.11656410992145538,2.2715535430908202,2.295184850692749,0.6991758346557617,0.4888468384742737,1.43025541305542,-0.3578530251979828,0.11656410992145538,0.33765000104904175,0.6533100008964539,2.8966891765594482,0.25,0.1183394342660904,0.8892508745193481,0.11721993237733841,0.7034800052642822,37.61368942260742,0.06349212676286697,0.9012212753295898,-0.00016539028729312122,50.000047683761295
harmonic_5mm_0.8Hz.csv,0.8,0.3204104006290436,0.3104756411225298,3.1006357743098976,0.09064009040594101,3.4253677344322204,3.5349743366241455,0.7963964343070984,0.6342473030090332,1.2556560039520264,-0.22765816748142242,0.09064009040594101,0.43685001134872437,0.7310900092124939,4.819611549377441,0.2499212622642517,0.0584954135119915,0.7228869199752808,0.06087201461195946,0.8802145719528198,26.92901039123535,0.09451805055141449,1.1917314529418945,0.002280592219904065,50.000047683761295
harmonic_5mm_1.45Hz.csv,1.45,1.0070611238479614,1.0508663280991015,4.349805906890859,0.2516467273235321,4.175958651542664,4.0018839836120605,1.451728343963623,2.107515335083008,0.6888340711593628,0.372754842042923,0.2516467273235321,0.9840899705886841,1.9117000102996826,3.9106009006500244,0.25,0.25519290566444397,0.8691993951797485,0.26430585980415344,0.6701168417930603,45.977691650390625,0.09523818641901016,1.7056236267089844,-0.00019088915723841637,50.000047683761295
harmonic_5mm_1.1Hz.csv,1.1,0.5534171462059021,0.5779597429583696,4.434737326215095,0.1373506784439087,4.207913273572922,4.0292277336120605,1.0999548435211182,1.2099006175994873,0.9091282486915588,0.0952691063284874,0.1373506784439087,0.587689995765686,1.1190800666809082,4.2787556648254395,0.24994152784347534,0.1195499375462532,0.8312234282493591,0.11857906728982925,0.7920843362808228,37.334434509277344,0.04679461568593979,1.324162483215332,-7.055706373648718e-05,50.000047683761295
harmonic_5mm_1.65Hz.csv,1.65,0.5992732048034668,0.6282565440955618,4.836415020691626,0.38267070055007935,1.6417680872678757,1.566028356552124,1.6503766775131226,2.723743200302124,0.605922281742096,0.5010035634040833,0.38267070055007935,1.171970009803772,2.2650599479675293,3.0626070499420166,0.24991999566555023,0.4040696918964386,0.9350709319114685,0.4694216847419739,0.4976775050163269,43.57965850830078,0.0960308238863945,1.6984570026397705,-0.006530857179313898,50.000047683761295
harmonic_5mm_0.5Hz.csv,0.5,0.18624502420425415,0.19870795051296228,6.6916828312363865,0.06567567586898804,3.025594299316406,2.835829496383667,0.5023733973503113,0.25237902998924255,1.990551233291626,-0.6884115934371948,0.06567567586898804,0.22301000356674194,0.4394000172615051,3.395625352859497,0.25,0.024631651118397713,0.31016308069229126,0.026469262316823006,0.9584437608718872,16.04189109802246,0.050632961094379425,2.159400463104248,0.0017537976382300258,50.000047683761295
harmonic_5mm_0.85Hz.csv,0.85,0.5794365406036377,0.5367616244860894,7.364899022952703,0.12664538621902466,4.238303822278977,4.575267791748047,0.845729649066925,0.7152586579322815,1.1824109554290771,-0.1675555258989334,0.12664538621902466,0.5095099806785583,0.9128699898719788,4.023123264312744,0.24991869926452637,0.0956917256116867,0.6594006419181824,0.09168906509876251,0.8589633107185364,18.147825241088867,0.06506186723709106,1.4032906293869019,-0.0011623100144788623,50.000047683761295
harmonic_5mm_1.15Hz.csv,1.15,0.9153674244880676,0.8472856717514132,7.437642078505262,0.18818436563014984,4.502423295974731,4.864205360412598,1.1485493183135986,1.319165587425232,0.8706635236740112,0.13849970698356628,0.18818436563014984,0.7081000208854675,1.3734800815582275,3.7627995014190674,0.24991869926452637,0.17172867059707642,0.8639681935310364,0.183344766497612,0.7253570556640625,30.369056701660156,0.0975928083062172,1.386907696723938,-0.0019338843412697315,50.000047683761295
harmonic_5mm_2.1Hz.csv,2.1,0.9339020252227783,1.003448067990676,7.446824280235163,0.6165724992752075,1.6274616029262543,1.5146669149398804,2.10196590423584,4.4182610511779785,0.4757450819015503,0.7428730726242065,0.6165724992752075,1.728060007095337,3.2244300842285156,2.802687406539917,0.25,0.7569394707679749,0.9819692969322205,0.7959626913070679,0.40733572840690613,133.85641479492188,0.11111121624708176,2.100722312927246,-0.005007810425013304,50.000047683761295
harmonic_5mm_0.65Hz.csv,0.65,0.2034619301557541,0.22107336420719614,8.655886650618205,0.1112922951579094,1.986421107530594,1.8281761407852173,0.6577304005622864,0.43260928988456726,1.5203797817230225,-0.4189601540565491,0.1112922951579094,0.35517001152038574,0.6907100081443787,3.191326141357422,0.25,0.06641923636198044,0.5974180698394775,0.06589993834495544,0.9084223508834839,14.282920837402344,0.09230778366327286,1.486210823059082,-0.00035510817542672157,50.000047683761295
harmonic_5mm_1Hz.csv,1.0,0.3037901520729065,0.33260700774091306,9.485776767737626,0.21532277762889862,1.544690308213234,1.4108593463897705,0.9989057183265686,0.9978126883506775,1.0010954141616821,-0.0010948515264317393,0.21532277762889862,0.635129988193512,1.2681899070739746,2.94966459274292,0.2499224841594696,0.2571507394313812,0.9758802056312561,0.2638331651687622,0.49583709239959717,152.86651611328125,0.0930522009730339,1.0530376434326172,-0.0009750677854754031,50.000047683761295
harmonic_5mm_1.2Hz.csv,1.2,0.7224284410476685,0.6515492682358728,9.811237872778989,0.3020855486392975,2.156836933016777,2.391469717025757,1.1994376182556152,1.4386504888534546,0.8337240815162659,0.1818527728319168,0.3020855486392975,0.8440300226211548,1.5216000080108643,2.7940099239349365,0.24991999566555023,0.3333832025527954,0.9823232889175415,0.38784340023994446,0.4198954999446869,124.32585906982422,0.0960308238863945,1.2258714437484741,0.0011010364396497607,50.000047683761295
harmonic_5mm_1.35Hz.csv,1.35,1.256479024887085,1.1329108880206473,9.83447669391395,0.20795053243637085,5.447982627153396,6.042201519012451,1.3494648933410645,1.8210554122924805,0.7410345077514648,0.29970812797546387,0.20795053243637085,0.8708500266075134,1.7264100313186646,4.187775135040283,0.24991999566555023,0.1956998109817505,0.8739701509475708,0.19486407935619354,0.7484625577926636,37.17776870727539,0.0640205442905426,1.5017311573028564,-0.005424206610769033,50.000047683761295
harmonic_5mm_2.3Hz.csv,2.3,3.7199018001556396,3.330646787548538,10.464120654766079,0.9912338852882385,3.360101825594902,3.7527992725372314,2.301363945007324,5.296276569366455,0.43452489376068115,0.8335019946098328,0.9912338852882385,1.9464600086212158,3.861459970474243,1.963673710823059,0.24992592632770538,1.217545509338379,0.9911264181137085,1.3459256887435913,0.2792012691497803,227.761474609375,0.08891531825065613,2.3008110523223877,0.008378428407013416,50.000047683761295
harmonic_5mm_0.9Hz.csv,0.9,0.3999128043651581,0.4468199892673058,11.729353096510772,0.1796521544456482,2.4871396095752716,2.2260396480560303,0.8994534015655518,0.8090164065361023,1.1117863655090332,-0.10596802830696106,0.1796521544456482,0.5928999781608582,1.0781500339508057,3.3002665042877197,0.2499224841594696,0.2150648981332779,0.9518226981163025,0.21449175477027893,0.5359740853309631,58.302303314208984,0.06203479692339897,0.9868842959403992,-0.00024062092415988445,50.000047683761295
harmonic_5mm_1.75Hz.csv,1.75,1.59738028049469,1.4050853159227565,12.038145638831969,0.35962721705436707,3.907060559630394,4.441766738891602,1.7508330345153809,3.065416097640991,0.5711566805839539,0.5600916743278503,0.35962721705436707,1.2678200006484985,2.4300899505615234,3.5253727436065674,0.25,0.39048516750335693,0.9655163288116455,0.4300072193145752,0.5336334109306335,81.74029541015625,0.08955232053995132,1.7760101556777954,-0.000693939218763262,50.000047683761295
harmonic_5mm_2.15Hz.csv,2.15,3.0180647373199463,2.6414293752430624,12.479366576190065,0.46591538190841675,5.669332839846611,6.477710247039795,2.1465415954589844,4.607641220092773,0.4658656418323517,0.7638580203056335,0.46591538190841675,1.5632699728012085,3.0576400756835938,3.3552658557891846,0.2499212622642517,0.48979803919792175,0.9034159779548645,0.49743175506591797,0.6561670303344727,72.98995971679688,0.063012033700943,2.2847402095794678,0.002827510703355074,50.000047683761295
harmonic_5mm_1.5Hz.csv,1.5,1.2808952331542969,1.110302433240289,13.318247698830973,0.45307496190071106,2.450593227624893,2.827115535736084,1.5013059377670288,2.2539196014404297,0.666086733341217,0.40633538365364075,0.45307496190071106,1.1585400104522705,2.150049924850464,2.5570602416992188,0.25,0.5677548050880432,0.9871437549591064,0.5882158875465393,0.3955877423286438,194.53369140625,0.08695660531520844,1.5181952714920044,0.0008904285496100783,50.000047683761295
harmonic_5mm_1.3Hz.csv,1.3,0.9550632834434509,0.8240282575900374,13.72003595206496,0.2030215710401535,4.058821204900742,4.704245567321777,1.3018183708190918,1.6947312355041504,0.7681562900543213,0.26376205682754517,0.2030215710401535,0.7108299732208252,1.34552001953125,3.501253366470337,0.24993710219860077,0.20925211906433105,0.9342537522315979,0.22753745317459106,0.6098353862762451,55.399173736572266,0.07549075782299042,1.397734522819519,-0.00013690412743017077,50.000047683761295
harmonic_5mm_1.6Hz.csv,1.6,1.1856828927993774,1.011715705384904,14.672319932333666,0.22232241928577423,4.550668837785721,5.333168029785156,1.60205078125,2.5665667057037354,0.6241999268531799,0.47128453850746155,0.22232241928577423,0.8100799918174744,1.6100399494171143,3.643717050552368,0.2499159723520279,0.20403607189655304,0.9129946231842041,0.22558331489562988,0.6967292428016663,54.344844818115234,0.10087434202432632,1.6988458633422852,0.0011404975084587932,50.000047683761295
harmonic_5mm_2.45Hz.csv,2.45,4.167365074157715,3.5243981540885345,15.428619970357008,0.5156936049461365,6.83428710436821,8.081088066101074,2.4505887031555176,6.00538444519043,0.40806522965431213,0.8963282704353333,0.5156936049461365,1.7411500215530396,3.2864298820495605,3.376326560974121,0.25,0.5547626614570618,0.9294771552085876,0.5594695210456848,0.6415255665779114,73.60872650146484,0.06451619416475296,2.469957113265991,0.0013848240487277508,50.000047683761295
harmonic_5mm_1.05Hz.csv,1.05,0.36426904797554016,0.42230971018971875,15.933459770119113,0.2716720998287201,1.5544831819534302,1.3408409357070923,1.0530656576156616,1.1089472770690918,0.9496083855628967,0.051705583930015564,0.2716720998287201,0.7901600003242493,1.5045499801635742,2.908506393432617,0.24991999566555023,0.3438712954521179,0.9576663374900818,0.3379869759082794,0.4775521755218506,72.97195434570312,0.0640205442905426,1.126065731048584,-0.0008716708398424089,50.000047683761295
harmonic_5mm_1.8Hz.csv,1.8,1.6704485416412354,1.3841861638257655,17.136856998552837,0.5062151551246643,2.734383097410202,3.2998785972595215,1.8012458086013794,3.2444865703582764,0.5551713109016418,0.5884785652160645,0.5062151551246643,1.4020500183105469,2.7015299797058105,2.769672155380249,0.25,0.6510956883430481,0.9815194606781006,0.6610633730888367,0.3853244483470917,126.02283477783203,0.05555560812354088,1.819625735282898,0.0018060111906379461,50.000047683761295
harmonic_5mm_2.4Hz.csv,2.4,4.107017993927002,3.396024555134596,17.311670897077725,0.9670910835266113,3.5115870810747145,4.246774673461914,2.4017505645751953,5.768405437469482,0.4163629710674286,0.876197874546051,0.9670910835266113,2.3246400356292725,4.529560089111328,2.403744697570801,0.25,1.1933718919754028,0.9901480078697205,1.3087358474731445,0.2874786853790283,216.48052978515625,0.10344837605953217,2.4013094902038574,-8.548868208890781e-05,50.000047683761295
harmonic_5mm_2.2Hz.csv,2.2,1.0718663930892944,1.2945454730228163,20.774891476140446,0.6085917353630066,2.1271164194345475,1.7612240314483643,2.201188325881958,4.845230579376221,0.45430004596710205,0.7889974117279053,0.6085917353630066,1.7638200521469116,3.3627500534057617,2.8981993198394775,0.25,0.7086807489395142,0.9761964082717896,0.7861699461936951,0.4083978533744812,121.00875854492188,0.07894744724035263,2.1985433101654053,0.0010015374282374978,50.000047683761295
harmonic_5mm_0.6Hz.csv,0.6,0.16537058353424072,0.2013263053238447,21.742513705382912,0.09397729486227036,2.142286662101746,1.7596864700317383,0.5964841246604919,0.3557933568954468,1.6764904260635376,-0.5167025923728943,0.09397729486227036,0.3058300018310547,0.5446599721908569,3.2542967796325684,0.2499224841594696,0.08657485246658325,0.8864659667015076,0.09075998514890671,0.7302541732788086,35.94514465332031,0.0930522009730339,0.8173193335533142,0.001446693786419928,50.000047683761295
harmonic_5mm_2.25Hz.csv,2.25,2.1223104000091553,1.6541852357127729,22.057337338325393,0.45399144291877747,3.643648490548134,4.67478084564209,2.2518274784088135,5.070727348327637,0.4440837502479553,0.8117421269416809,0.45399144291877747,1.468690037727356,2.881589889526367,3.235060930252075,0.24992366135120392,0.4949429929256439,0.961538553237915,0.5433851480484009,0.5339846014976501,91.52095031738281,0.09163112193346024,2.2496755123138428,-0.001197385834529996,50.000047683761295
harmonic_5mm_1.9Hz.csv,1.9,0.5868141055107117,0.7590829336435948,29.356626999099728,0.5143030285835266,1.4759449030160905,1.1409889459609985,1.9012991189956665,3.614938497543335,0.5259561538696289,0.6425374150276184,0.5143030285835266,1.3888100385665894,2.6350998878479004,2.7003729343414307,0.25,0.6525437235832214,0.9834353923797607,0.670233428478241,0.38811808824539185,157.31686401367188,0.06250005960464478,1.9118845462799072,-0.0027283257804811,50.000047683761295
harmonic_5mm_2.05Hz.csv,2.05,0.6199118494987488,0.82180861364958,32.568624767228094,0.7315996885299683,1.1233036680221558,0.8473374843597412,2.0498123168945312,4.201730251312256,0.48784953355789185,0.71774822473526,0.7315996885299683,1.6353000402450562,3.2010200023651123,2.235239028930664,0.24991999566555023,0.9387410283088684,0.9784685373306274,0.9522533416748047,0.3911336362361908,110.50621795654297,0.0640205442905426,2.0580592155456543,-0.004247209522873163,50.000047683761295
harmonic_5mm_0.55Hz.csv,0.55,0.1971234530210495,0.2787700623838687,41.41902351624325,0.109112448990345,2.5548877782821657,1.8066083192825317,0.5490954518318176,0.30150580406188965,1.8211770057678223,-0.5994830131530762,0.109112448990345,0.40608999133110046,0.751579999923706,3.72175669670105,0.25,0.06793846935033798,0.5611140727996826,0.07492320239543915,0.874541163444519,13.549410820007324,0.08955232053995132,1.263006329536438,-0.0011556772515177727,50.000047683761295
harmonic_5mm_1.7Hz.csv,1.7,0.6067864894866943,0.8862511380642422,46.05650478703942,0.32135581970214844,2.7578499710559843,1.8882076740264893,1.700693964958191,2.892360210418701,0.5879952311515808,0.531036376953125,0.32135581970214844,1.1075899600982666,2.123849868774414,3.446615695953369,0.25,0.3294788897037506,0.9566228985786438,0.3822683095932007,0.5411480069160461,79.01654815673828,0.09523818641901016,1.7275936603546143,0.0009157023159787059,50.000047683761295
harmonic_5mm_1.4Hz.csv,1.4,0.3850800693035126,0.6183495861111026,60.57688657569331,0.2212747484445572,2.7944878051280977,1.740280270576477,1.3995026350021362,1.9586076736450195,0.7145395278930664,0.3361169397830963,0.2212747484445572,0.8126099705696106,1.4906599521636963,3.6724026203155518,0.2499212622642517,0.2121341973543167,0.9263311624526978,0.2458721548318863,0.6176945567131042,50.935482025146484,0.09451805055141449,1.501712679862976,0.0024810773320496082,50.000047683761295
harmonic_5mm_2.5Hz.csv,2.5,0.6538841724395752,1.1258108364036792,72.17282262749896,0.5744112730026245,1.9599386177062987,1.1383553743362427,2.5015766620635986,6.257885932922363,0.39974790811538696,0.9169211983680725,0.5744112730026245,1.5267499685287476,2.5278899669647217,2.6579387187957764,0.2499280571937561,0.7362810969352722,0.9838377833366394,0.7437620162963867,0.4004853665828705,234.6715087890625,0.05757058039307594,2.530334234237671,-0.01246642041951418,50.000047683761295
harmonic_5mm_2.35Hz.csv,2.35,0.7861254215240479,1.6179925774542805,105.81863060954142,0.991222620010376,1.6323200709819794,0.7930866479873657,2.351473808288574,5.529428958892822,0.42526522278785706,0.8550422787666321,0.991222620010376,2.2557199001312256,4.422339916229248,2.2756946086883545,0.2499212622642517,1.1917885541915894,0.9816745519638062,1.3294483423233032,0.31509023904800415,127.34346771240234,0.09451805055141449,2.3649213314056396,-0.004312594421207905,50.000047683761295
harmonic_5mm_2Hz.csv,2.0,0.5447156429290771,1.2863206517188583,136.14534820442807,0.7751342058181763,1.6594812124967575,0.7027371525764465,2.0002939701080322,4.001175880432129,0.49992653727531433,0.693294107913971,0.7751342058181763,1.559309959411621,3.019509792327881,2.011664390563965,0.24990099668502808,0.8933139443397522,0.9935725927352905,1.0516541004180908,0.28168511390686035,319.8121337890625,0.11885906755924225,2.0122017860412598,0.008680449798703194,50.000047683761295
1 file_name frequency_hz true_rms pred_rms relative_error_percent x_rms pred_tr true_tr dominant_frequency_hz frequency_squared inverse_frequency_hz log_frequency_hz input_rms input_peak_abs input_peak_to_peak crest_factor middle_length_ratio dominant_amplitude dominant_energy_ratio harmonic_fit_amplitude harmonic_fit_residual_ratio spectral_peak_prominence half_power_bandwidth_hz spectral_centroid_hz signal_mean sampling_rate
2 harmonic_5mm_1.95Hz.csv 1.95 2.063512086868286 2.042942868521436 0.996806293394051 0.4040626883506775 5.056004742383957 5.106910705566406 1.9470664262771606 3.7910678386688232 0.5135931372642517 0.6663238406181335 0.4040626883506775 1.629040002822876 2.9249401092529297 4.031651496887207 0.25 0.407725989818573 0.9061827659606934 0.44422510266304016 0.629830002784729 43.91968536376953 0.09677428752183914 2.039226531982422 -0.004885347560048103 50.000047683761295
3 harmonic_5mm_0.7Hz.csv 0.7 0.2675361931324005 0.2647816168893098 1.0296088207129102 0.11656410992145538 2.2715535430908202 2.295184850692749 0.6991758346557617 0.4888468384742737 1.43025541305542 -0.3578530251979828 0.11656410992145538 0.33765000104904175 0.6533100008964539 2.8966891765594482 0.25 0.1183394342660904 0.8892508745193481 0.11721993237733841 0.7034800052642822 37.61368942260742 0.06349212676286697 0.9012212753295898 -0.00016539028729312122 50.000047683761295
4 harmonic_5mm_0.8Hz.csv 0.8 0.3204104006290436 0.3104756411225298 3.1006357743098976 0.09064009040594101 3.4253677344322204 3.5349743366241455 0.7963964343070984 0.6342473030090332 1.2556560039520264 -0.22765816748142242 0.09064009040594101 0.43685001134872437 0.7310900092124939 4.819611549377441 0.2499212622642517 0.0584954135119915 0.7228869199752808 0.06087201461195946 0.8802145719528198 26.92901039123535 0.09451805055141449 1.1917314529418945 0.002280592219904065 50.000047683761295
5 harmonic_5mm_1.45Hz.csv 1.45 1.0070611238479614 1.0508663280991015 4.349805906890859 0.2516467273235321 4.175958651542664 4.0018839836120605 1.451728343963623 2.107515335083008 0.6888340711593628 0.372754842042923 0.2516467273235321 0.9840899705886841 1.9117000102996826 3.9106009006500244 0.25 0.25519290566444397 0.8691993951797485 0.26430585980415344 0.6701168417930603 45.977691650390625 0.09523818641901016 1.7056236267089844 -0.00019088915723841637 50.000047683761295
6 harmonic_5mm_1.1Hz.csv 1.1 0.5534171462059021 0.5779597429583696 4.434737326215095 0.1373506784439087 4.207913273572922 4.0292277336120605 1.0999548435211182 1.2099006175994873 0.9091282486915588 0.0952691063284874 0.1373506784439087 0.587689995765686 1.1190800666809082 4.2787556648254395 0.24994152784347534 0.1195499375462532 0.8312234282493591 0.11857906728982925 0.7920843362808228 37.334434509277344 0.04679461568593979 1.324162483215332 -7.055706373648718e-05 50.000047683761295
7 harmonic_5mm_1.65Hz.csv 1.65 0.5992732048034668 0.6282565440955618 4.836415020691626 0.38267070055007935 1.6417680872678757 1.566028356552124 1.6503766775131226 2.723743200302124 0.605922281742096 0.5010035634040833 0.38267070055007935 1.171970009803772 2.2650599479675293 3.0626070499420166 0.24991999566555023 0.4040696918964386 0.9350709319114685 0.4694216847419739 0.4976775050163269 43.57965850830078 0.0960308238863945 1.6984570026397705 -0.006530857179313898 50.000047683761295
8 harmonic_5mm_0.5Hz.csv 0.5 0.18624502420425415 0.19870795051296228 6.6916828312363865 0.06567567586898804 3.025594299316406 2.835829496383667 0.5023733973503113 0.25237902998924255 1.990551233291626 -0.6884115934371948 0.06567567586898804 0.22301000356674194 0.4394000172615051 3.395625352859497 0.25 0.024631651118397713 0.31016308069229126 0.026469262316823006 0.9584437608718872 16.04189109802246 0.050632961094379425 2.159400463104248 0.0017537976382300258 50.000047683761295
9 harmonic_5mm_0.85Hz.csv 0.85 0.5794365406036377 0.5367616244860894 7.364899022952703 0.12664538621902466 4.238303822278977 4.575267791748047 0.845729649066925 0.7152586579322815 1.1824109554290771 -0.1675555258989334 0.12664538621902466 0.5095099806785583 0.9128699898719788 4.023123264312744 0.24991869926452637 0.0956917256116867 0.6594006419181824 0.09168906509876251 0.8589633107185364 18.147825241088867 0.06506186723709106 1.4032906293869019 -0.0011623100144788623 50.000047683761295
10 harmonic_5mm_1.15Hz.csv 1.15 0.9153674244880676 0.8472856717514132 7.437642078505262 0.18818436563014984 4.502423295974731 4.864205360412598 1.1485493183135986 1.319165587425232 0.8706635236740112 0.13849970698356628 0.18818436563014984 0.7081000208854675 1.3734800815582275 3.7627995014190674 0.24991869926452637 0.17172867059707642 0.8639681935310364 0.183344766497612 0.7253570556640625 30.369056701660156 0.0975928083062172 1.386907696723938 -0.0019338843412697315 50.000047683761295
11 harmonic_5mm_2.1Hz.csv 2.1 0.9339020252227783 1.003448067990676 7.446824280235163 0.6165724992752075 1.6274616029262543 1.5146669149398804 2.10196590423584 4.4182610511779785 0.4757450819015503 0.7428730726242065 0.6165724992752075 1.728060007095337 3.2244300842285156 2.802687406539917 0.25 0.7569394707679749 0.9819692969322205 0.7959626913070679 0.40733572840690613 133.85641479492188 0.11111121624708176 2.100722312927246 -0.005007810425013304 50.000047683761295
12 harmonic_5mm_0.65Hz.csv 0.65 0.2034619301557541 0.22107336420719614 8.655886650618205 0.1112922951579094 1.986421107530594 1.8281761407852173 0.6577304005622864 0.43260928988456726 1.5203797817230225 -0.4189601540565491 0.1112922951579094 0.35517001152038574 0.6907100081443787 3.191326141357422 0.25 0.06641923636198044 0.5974180698394775 0.06589993834495544 0.9084223508834839 14.282920837402344 0.09230778366327286 1.486210823059082 -0.00035510817542672157 50.000047683761295
13 harmonic_5mm_1Hz.csv 1.0 0.3037901520729065 0.33260700774091306 9.485776767737626 0.21532277762889862 1.544690308213234 1.4108593463897705 0.9989057183265686 0.9978126883506775 1.0010954141616821 -0.0010948515264317393 0.21532277762889862 0.635129988193512 1.2681899070739746 2.94966459274292 0.2499224841594696 0.2571507394313812 0.9758802056312561 0.2638331651687622 0.49583709239959717 152.86651611328125 0.0930522009730339 1.0530376434326172 -0.0009750677854754031 50.000047683761295
14 harmonic_5mm_1.2Hz.csv 1.2 0.7224284410476685 0.6515492682358728 9.811237872778989 0.3020855486392975 2.156836933016777 2.391469717025757 1.1994376182556152 1.4386504888534546 0.8337240815162659 0.1818527728319168 0.3020855486392975 0.8440300226211548 1.5216000080108643 2.7940099239349365 0.24991999566555023 0.3333832025527954 0.9823232889175415 0.38784340023994446 0.4198954999446869 124.32585906982422 0.0960308238863945 1.2258714437484741 0.0011010364396497607 50.000047683761295
15 harmonic_5mm_1.35Hz.csv 1.35 1.256479024887085 1.1329108880206473 9.83447669391395 0.20795053243637085 5.447982627153396 6.042201519012451 1.3494648933410645 1.8210554122924805 0.7410345077514648 0.29970812797546387 0.20795053243637085 0.8708500266075134 1.7264100313186646 4.187775135040283 0.24991999566555023 0.1956998109817505 0.8739701509475708 0.19486407935619354 0.7484625577926636 37.17776870727539 0.0640205442905426 1.5017311573028564 -0.005424206610769033 50.000047683761295
16 harmonic_5mm_2.3Hz.csv 2.3 3.7199018001556396 3.330646787548538 10.464120654766079 0.9912338852882385 3.360101825594902 3.7527992725372314 2.301363945007324 5.296276569366455 0.43452489376068115 0.8335019946098328 0.9912338852882385 1.9464600086212158 3.861459970474243 1.963673710823059 0.24992592632770538 1.217545509338379 0.9911264181137085 1.3459256887435913 0.2792012691497803 227.761474609375 0.08891531825065613 2.3008110523223877 0.008378428407013416 50.000047683761295
17 harmonic_5mm_0.9Hz.csv 0.9 0.3999128043651581 0.4468199892673058 11.729353096510772 0.1796521544456482 2.4871396095752716 2.2260396480560303 0.8994534015655518 0.8090164065361023 1.1117863655090332 -0.10596802830696106 0.1796521544456482 0.5928999781608582 1.0781500339508057 3.3002665042877197 0.2499224841594696 0.2150648981332779 0.9518226981163025 0.21449175477027893 0.5359740853309631 58.302303314208984 0.06203479692339897 0.9868842959403992 -0.00024062092415988445 50.000047683761295
18 harmonic_5mm_1.75Hz.csv 1.75 1.59738028049469 1.4050853159227565 12.038145638831969 0.35962721705436707 3.907060559630394 4.441766738891602 1.7508330345153809 3.065416097640991 0.5711566805839539 0.5600916743278503 0.35962721705436707 1.2678200006484985 2.4300899505615234 3.5253727436065674 0.25 0.39048516750335693 0.9655163288116455 0.4300072193145752 0.5336334109306335 81.74029541015625 0.08955232053995132 1.7760101556777954 -0.000693939218763262 50.000047683761295
19 harmonic_5mm_2.15Hz.csv 2.15 3.0180647373199463 2.6414293752430624 12.479366576190065 0.46591538190841675 5.669332839846611 6.477710247039795 2.1465415954589844 4.607641220092773 0.4658656418323517 0.7638580203056335 0.46591538190841675 1.5632699728012085 3.0576400756835938 3.3552658557891846 0.2499212622642517 0.48979803919792175 0.9034159779548645 0.49743175506591797 0.6561670303344727 72.98995971679688 0.063012033700943 2.2847402095794678 0.002827510703355074 50.000047683761295
20 harmonic_5mm_1.5Hz.csv 1.5 1.2808952331542969 1.110302433240289 13.318247698830973 0.45307496190071106 2.450593227624893 2.827115535736084 1.5013059377670288 2.2539196014404297 0.666086733341217 0.40633538365364075 0.45307496190071106 1.1585400104522705 2.150049924850464 2.5570602416992188 0.25 0.5677548050880432 0.9871437549591064 0.5882158875465393 0.3955877423286438 194.53369140625 0.08695660531520844 1.5181952714920044 0.0008904285496100783 50.000047683761295
21 harmonic_5mm_1.3Hz.csv 1.3 0.9550632834434509 0.8240282575900374 13.72003595206496 0.2030215710401535 4.058821204900742 4.704245567321777 1.3018183708190918 1.6947312355041504 0.7681562900543213 0.26376205682754517 0.2030215710401535 0.7108299732208252 1.34552001953125 3.501253366470337 0.24993710219860077 0.20925211906433105 0.9342537522315979 0.22753745317459106 0.6098353862762451 55.399173736572266 0.07549075782299042 1.397734522819519 -0.00013690412743017077 50.000047683761295
22 harmonic_5mm_1.6Hz.csv 1.6 1.1856828927993774 1.011715705384904 14.672319932333666 0.22232241928577423 4.550668837785721 5.333168029785156 1.60205078125 2.5665667057037354 0.6241999268531799 0.47128453850746155 0.22232241928577423 0.8100799918174744 1.6100399494171143 3.643717050552368 0.2499159723520279 0.20403607189655304 0.9129946231842041 0.22558331489562988 0.6967292428016663 54.344844818115234 0.10087434202432632 1.6988458633422852 0.0011404975084587932 50.000047683761295
23 harmonic_5mm_2.45Hz.csv 2.45 4.167365074157715 3.5243981540885345 15.428619970357008 0.5156936049461365 6.83428710436821 8.081088066101074 2.4505887031555176 6.00538444519043 0.40806522965431213 0.8963282704353333 0.5156936049461365 1.7411500215530396 3.2864298820495605 3.376326560974121 0.25 0.5547626614570618 0.9294771552085876 0.5594695210456848 0.6415255665779114 73.60872650146484 0.06451619416475296 2.469957113265991 0.0013848240487277508 50.000047683761295
24 harmonic_5mm_1.05Hz.csv 1.05 0.36426904797554016 0.42230971018971875 15.933459770119113 0.2716720998287201 1.5544831819534302 1.3408409357070923 1.0530656576156616 1.1089472770690918 0.9496083855628967 0.051705583930015564 0.2716720998287201 0.7901600003242493 1.5045499801635742 2.908506393432617 0.24991999566555023 0.3438712954521179 0.9576663374900818 0.3379869759082794 0.4775521755218506 72.97195434570312 0.0640205442905426 1.126065731048584 -0.0008716708398424089 50.000047683761295
25 harmonic_5mm_1.8Hz.csv 1.8 1.6704485416412354 1.3841861638257655 17.136856998552837 0.5062151551246643 2.734383097410202 3.2998785972595215 1.8012458086013794 3.2444865703582764 0.5551713109016418 0.5884785652160645 0.5062151551246643 1.4020500183105469 2.7015299797058105 2.769672155380249 0.25 0.6510956883430481 0.9815194606781006 0.6610633730888367 0.3853244483470917 126.02283477783203 0.05555560812354088 1.819625735282898 0.0018060111906379461 50.000047683761295
26 harmonic_5mm_2.4Hz.csv 2.4 4.107017993927002 3.396024555134596 17.311670897077725 0.9670910835266113 3.5115870810747145 4.246774673461914 2.4017505645751953 5.768405437469482 0.4163629710674286 0.876197874546051 0.9670910835266113 2.3246400356292725 4.529560089111328 2.403744697570801 0.25 1.1933718919754028 0.9901480078697205 1.3087358474731445 0.2874786853790283 216.48052978515625 0.10344837605953217 2.4013094902038574 -8.548868208890781e-05 50.000047683761295
27 harmonic_5mm_2.2Hz.csv 2.2 1.0718663930892944 1.2945454730228163 20.774891476140446 0.6085917353630066 2.1271164194345475 1.7612240314483643 2.201188325881958 4.845230579376221 0.45430004596710205 0.7889974117279053 0.6085917353630066 1.7638200521469116 3.3627500534057617 2.8981993198394775 0.25 0.7086807489395142 0.9761964082717896 0.7861699461936951 0.4083978533744812 121.00875854492188 0.07894744724035263 2.1985433101654053 0.0010015374282374978 50.000047683761295
28 harmonic_5mm_0.6Hz.csv 0.6 0.16537058353424072 0.2013263053238447 21.742513705382912 0.09397729486227036 2.142286662101746 1.7596864700317383 0.5964841246604919 0.3557933568954468 1.6764904260635376 -0.5167025923728943 0.09397729486227036 0.3058300018310547 0.5446599721908569 3.2542967796325684 0.2499224841594696 0.08657485246658325 0.8864659667015076 0.09075998514890671 0.7302541732788086 35.94514465332031 0.0930522009730339 0.8173193335533142 0.001446693786419928 50.000047683761295
29 harmonic_5mm_2.25Hz.csv 2.25 2.1223104000091553 1.6541852357127729 22.057337338325393 0.45399144291877747 3.643648490548134 4.67478084564209 2.2518274784088135 5.070727348327637 0.4440837502479553 0.8117421269416809 0.45399144291877747 1.468690037727356 2.881589889526367 3.235060930252075 0.24992366135120392 0.4949429929256439 0.961538553237915 0.5433851480484009 0.5339846014976501 91.52095031738281 0.09163112193346024 2.2496755123138428 -0.001197385834529996 50.000047683761295
30 harmonic_5mm_1.9Hz.csv 1.9 0.5868141055107117 0.7590829336435948 29.356626999099728 0.5143030285835266 1.4759449030160905 1.1409889459609985 1.9012991189956665 3.614938497543335 0.5259561538696289 0.6425374150276184 0.5143030285835266 1.3888100385665894 2.6350998878479004 2.7003729343414307 0.25 0.6525437235832214 0.9834353923797607 0.670233428478241 0.38811808824539185 157.31686401367188 0.06250005960464478 1.9118845462799072 -0.0027283257804811 50.000047683761295
31 harmonic_5mm_2.05Hz.csv 2.05 0.6199118494987488 0.82180861364958 32.568624767228094 0.7315996885299683 1.1233036680221558 0.8473374843597412 2.0498123168945312 4.201730251312256 0.48784953355789185 0.71774822473526 0.7315996885299683 1.6353000402450562 3.2010200023651123 2.235239028930664 0.24991999566555023 0.9387410283088684 0.9784685373306274 0.9522533416748047 0.3911336362361908 110.50621795654297 0.0640205442905426 2.0580592155456543 -0.004247209522873163 50.000047683761295
32 harmonic_5mm_0.55Hz.csv 0.55 0.1971234530210495 0.2787700623838687 41.41902351624325 0.109112448990345 2.5548877782821657 1.8066083192825317 0.5490954518318176 0.30150580406188965 1.8211770057678223 -0.5994830131530762 0.109112448990345 0.40608999133110046 0.751579999923706 3.72175669670105 0.25 0.06793846935033798 0.5611140727996826 0.07492320239543915 0.874541163444519 13.549410820007324 0.08955232053995132 1.263006329536438 -0.0011556772515177727 50.000047683761295
33 harmonic_5mm_1.7Hz.csv 1.7 0.6067864894866943 0.8862511380642422 46.05650478703942 0.32135581970214844 2.7578499710559843 1.8882076740264893 1.700693964958191 2.892360210418701 0.5879952311515808 0.531036376953125 0.32135581970214844 1.1075899600982666 2.123849868774414 3.446615695953369 0.25 0.3294788897037506 0.9566228985786438 0.3822683095932007 0.5411480069160461 79.01654815673828 0.09523818641901016 1.7275936603546143 0.0009157023159787059 50.000047683761295
34 harmonic_5mm_1.4Hz.csv 1.4 0.3850800693035126 0.6183495861111026 60.57688657569331 0.2212747484445572 2.7944878051280977 1.740280270576477 1.3995026350021362 1.9586076736450195 0.7145395278930664 0.3361169397830963 0.2212747484445572 0.8126099705696106 1.4906599521636963 3.6724026203155518 0.2499212622642517 0.2121341973543167 0.9263311624526978 0.2458721548318863 0.6176945567131042 50.935482025146484 0.09451805055141449 1.501712679862976 0.0024810773320496082 50.000047683761295
35 harmonic_5mm_2.5Hz.csv 2.5 0.6538841724395752 1.1258108364036792 72.17282262749896 0.5744112730026245 1.9599386177062987 1.1383553743362427 2.5015766620635986 6.257885932922363 0.39974790811538696 0.9169211983680725 0.5744112730026245 1.5267499685287476 2.5278899669647217 2.6579387187957764 0.2499280571937561 0.7362810969352722 0.9838377833366394 0.7437620162963867 0.4004853665828705 234.6715087890625 0.05757058039307594 2.530334234237671 -0.01246642041951418 50.000047683761295
36 harmonic_5mm_2.35Hz.csv 2.35 0.7861254215240479 1.6179925774542805 105.81863060954142 0.991222620010376 1.6323200709819794 0.7930866479873657 2.351473808288574 5.529428958892822 0.42526522278785706 0.8550422787666321 0.991222620010376 2.2557199001312256 4.422339916229248 2.2756946086883545 0.2499212622642517 1.1917885541915894 0.9816745519638062 1.3294483423233032 0.31509023904800415 127.34346771240234 0.09451805055141449 2.3649213314056396 -0.004312594421207905 50.000047683761295
37 harmonic_5mm_2Hz.csv 2.0 0.5447156429290771 1.2863206517188583 136.14534820442807 0.7751342058181763 1.6594812124967575 0.7027371525764465 2.0002939701080322 4.001175880432129 0.49992653727531433 0.693294107913971 0.7751342058181763 1.559309959411621 3.019509792327881 2.011664390563965 0.24990099668502808 0.8933139443397522 0.9935725927352905 1.0516541004180908 0.28168511390686035 319.8121337890625 0.11885906755924225 2.0122017860412598 0.008680449798703194 50.000047683761295

BIN
evaluation_outputs/task1_tr_tree/evaluation_train_curve.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 165 KiB

6
evaluation_outputs/task1_tr_tree/evaluation_val_all_samples.csv Normal file
View File

@@ -0,0 +1,6 @@
file_name,frequency_hz,true_rms,pred_rms,relative_error_percent,x_rms,pred_tr,true_tr,dominant_frequency_hz,frequency_squared,inverse_frequency_hz,log_frequency_hz,input_rms,input_peak_abs,input_peak_to_peak,crest_factor,middle_length_ratio,dominant_amplitude,dominant_energy_ratio,harmonic_fit_amplitude,harmonic_fit_residual_ratio,spectral_peak_prominence,half_power_bandwidth_hz,spectral_centroid_hz,signal_mean,sampling_rate
harmonic_5mm_0.75Hz.csv,0.75,0.39182430505752563,0.4018011530100954,2.546255508857473,0.11687792837619781,3.4377846920490267,3.3524234294891357,0.7515671849250793,0.5648532509803772,1.3305530548095703,-0.285594642162323,0.11687792837619781,0.4074699878692627,0.8070399761199951,3.4862868785858154,0.24992701411247253,0.10824807733297348,0.8746803998947144,0.11673416197299957,0.7099294662475586,36.5,0.08761690557003021,0.8937567472457886,-0.001955104758962989,50.000047683761295
harmonic_5mm_0.95Hz.csv,0.95,0.5053804516792297,0.48249583166035853,4.528196518648947,0.1381060928106308,3.4936607201099394,3.6593642234802246,0.9513587951660156,0.9050835967063904,1.0511281490325928,-0.04986399784684181,0.1381060928106308,0.5125100016593933,1.0134000778198242,3.7109878063201904,0.25,0.09424196928739548,0.7232213020324707,0.11362786591053009,0.8133426308631897,20.55999183654785,0.09677428752183914,1.3476505279541016,0.00015772903861943632,50.000047683761295
harmonic_5mm_1.85Hz.csv,1.85,1.185911774635315,1.0257513926611004,13.50525270089895,0.6145902872085571,1.6690003308057786,1.9295974969863892,1.8481981754302979,3.4158363342285156,0.5410675406455994,0.6142112016677856,0.6145902872085571,1.3770899772644043,2.6619200706481934,2.240663528442383,0.25,0.7274389863014221,0.9514397978782654,0.789516270160675,0.42080339789390564,85.657958984375,0.09677428752183914,1.8637527227401733,-0.002481284085661173,50.000047683761295
harmonic_5mm_1.55Hz.csv,1.55,1.4177086353302002,0.7627473327797605,46.198583138198344,0.392645925283432,1.942583084821701,3.610654354095459,1.5492842197418213,2.4002816677093506,0.6454593539237976,0.4377930164337158,0.392645925283432,1.105049967765808,2.1589999198913574,2.8143677711486816,0.25,0.4746793210506439,0.9668325185775757,0.5014731884002686,0.43158969283103943,69.77510833740234,0.09836074709892273,1.584490418434143,-0.0011147483019158244,50.000047683761295
harmonic_5mm_1.25Hz.csv,1.25,0.3802441656589508,0.6856168561865096,80.30963210135198,0.15971125662326813,4.292852430582046,2.3808226585388184,1.2483434677124023,1.5583614110946655,0.8010615706443787,0.22181743383407593,0.15971125662326813,0.5707299709320068,1.0413799285888672,3.5735113620758057,0.24992592632770538,0.1439778357744217,0.8661479949951172,0.14893445372581482,0.7513272762298584,56.41539764404297,0.05927687883377075,1.4980274438858032,-0.0021986484061926603,50.000047683761295
1 file_name frequency_hz true_rms pred_rms relative_error_percent x_rms pred_tr true_tr dominant_frequency_hz frequency_squared inverse_frequency_hz log_frequency_hz input_rms input_peak_abs input_peak_to_peak crest_factor middle_length_ratio dominant_amplitude dominant_energy_ratio harmonic_fit_amplitude harmonic_fit_residual_ratio spectral_peak_prominence half_power_bandwidth_hz spectral_centroid_hz signal_mean sampling_rate
2 harmonic_5mm_0.75Hz.csv 0.75 0.39182430505752563 0.4018011530100954 2.546255508857473 0.11687792837619781 3.4377846920490267 3.3524234294891357 0.7515671849250793 0.5648532509803772 1.3305530548095703 -0.285594642162323 0.11687792837619781 0.4074699878692627 0.8070399761199951 3.4862868785858154 0.24992701411247253 0.10824807733297348 0.8746803998947144 0.11673416197299957 0.7099294662475586 36.5 0.08761690557003021 0.8937567472457886 -0.001955104758962989 50.000047683761295
3 harmonic_5mm_0.95Hz.csv 0.95 0.5053804516792297 0.48249583166035853 4.528196518648947 0.1381060928106308 3.4936607201099394 3.6593642234802246 0.9513587951660156 0.9050835967063904 1.0511281490325928 -0.04986399784684181 0.1381060928106308 0.5125100016593933 1.0134000778198242 3.7109878063201904 0.25 0.09424196928739548 0.7232213020324707 0.11362786591053009 0.8133426308631897 20.55999183654785 0.09677428752183914 1.3476505279541016 0.00015772903861943632 50.000047683761295
4 harmonic_5mm_1.85Hz.csv 1.85 1.185911774635315 1.0257513926611004 13.50525270089895 0.6145902872085571 1.6690003308057786 1.9295974969863892 1.8481981754302979 3.4158363342285156 0.5410675406455994 0.6142112016677856 0.6145902872085571 1.3770899772644043 2.6619200706481934 2.240663528442383 0.25 0.7274389863014221 0.9514397978782654 0.789516270160675 0.42080339789390564 85.657958984375 0.09677428752183914 1.8637527227401733 -0.002481284085661173 50.000047683761295
5 harmonic_5mm_1.55Hz.csv 1.55 1.4177086353302002 0.7627473327797605 46.198583138198344 0.392645925283432 1.942583084821701 3.610654354095459 1.5492842197418213 2.4002816677093506 0.6454593539237976 0.4377930164337158 0.392645925283432 1.105049967765808 2.1589999198913574 2.8143677711486816 0.25 0.4746793210506439 0.9668325185775757 0.5014731884002686 0.43158969283103943 69.77510833740234 0.09836074709892273 1.584490418434143 -0.0011147483019158244 50.000047683761295
6 harmonic_5mm_1.25Hz.csv 1.25 0.3802441656589508 0.6856168561865096 80.30963210135198 0.15971125662326813 4.292852430582046 2.3808226585388184 1.2483434677124023 1.5583614110946655 0.8010615706443787 0.22181743383407593 0.15971125662326813 0.5707299709320068 1.0413799285888672 3.5735113620758057 0.24992592632770538 0.1439778357744217 0.8661479949951172 0.14893445372581482 0.7513272762298584 56.41539764404297 0.05927687883377075 1.4980274438858032 -0.0021986484061926603 50.000047683761295

BIN
evaluation_outputs/task1_tr_tree/evaluation_val_curve.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 103 KiB

BIN
evaluation_outputs/task1_tr_tree/harmonic_5mm_1.55Hz_prediction.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 264 KiB

BIN
scripts_tree/__pycache__/config.cpython-310.pyc Normal file
View File

Binary file not shown.

50
scripts_tree/config.py Normal file
View File

@@ -0,0 +1,50 @@
from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
import sys
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from scripts.config import CORE_FEATURE_NAMES, DataConfig
@dataclass
class TreeModelConfig:
candidate_models: tuple[str, ...] = ("extra_trees", "random_forest", "gradient_boosting")
random_state: int = 42
@dataclass
class TrainConfig:
checkpoint_dir: str = "checkpoints_tree"
summary_name: str = "model_selection.csv"
best_model_name: str = "best_tr_tree.pkl"
@dataclass
class ExperimentConfig:
data: DataConfig = field(default_factory=DataConfig)
model: TreeModelConfig = field(default_factory=TreeModelConfig)
train: TrainConfig = field(default_factory=TrainConfig)
def make_experiment_config() -> ExperimentConfig:
return ExperimentConfig()
def checkpoint_dir(config: ExperimentConfig) -> Path:
path = config.data.project_root / config.train.checkpoint_dir / "task1_tr_tree"
path.mkdir(parents=True, exist_ok=True)
return path
def evaluation_dir(config: ExperimentConfig) -> Path:
path = config.data.project_root / "evaluation_outputs" / "task1_tr_tree"
path.mkdir(parents=True, exist_ok=True)
return path
FEATURE_NAMES = CORE_FEATURE_NAMES

177
scripts_tree/evaluate.py Normal file
View File

@@ -0,0 +1,177 @@
from __future__ import annotations
import argparse
import pickle
from pathlib import Path
import sys
import matplotlib.pyplot as plt
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from scripts.dataset import build_datasets, report_to_text
try:
from .config import FEATURE_NAMES, evaluation_dir, make_experiment_config
except ImportError:
from config import FEATURE_NAMES, evaluation_dir, make_experiment_config
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Evaluate tree model on train/val/test splits.")
parser.add_argument("--split", choices=("train", "val", "test"), default="val")
parser.add_argument("--sample-index", type=int, default=0)
parser.add_argument("--all-samples", action="store_true")
parser.add_argument("--checkpoint", type=str, default=None)
return parser.parse_args()
def resolve_checkpoint_path(project_root: Path, checkpoint_arg: str | None) -> Path:
if checkpoint_arg:
return Path(checkpoint_arg).resolve()
return project_root / "checkpoints_tree" / "task1_tr_tree" / "best_tr_tree.pkl"
def relative_percent_error(true_value: float, pred_value: float) -> float:
return abs(pred_value - true_value) / max(abs(true_value), 1e-12) * 100.0
def evaluate_record(model, record) -> dict[str, float | str]:
feature_matrix = [record.features.tolist()]
pred_tr = max(float(model.predict(feature_matrix)[0]), 1e-6)
x_rms = float(record.x_rms.item())
y_rms = float(record.y_rms.item())
pred_rms = pred_tr * x_rms
row = {
"file_name": record.file_path.name,
"frequency_hz": float(record.frequency_hz),
"true_rms": y_rms,
"pred_rms": pred_rms,
"relative_error_percent": relative_percent_error(y_rms, pred_rms),
"x_rms": x_rms,
"pred_tr": pred_tr,
"true_tr": float(record.target_y_rms.item()),
}
for name, value in zip(FEATURE_NAMES, record.features.tolist()):
row[name] = float(value)
row["sampling_rate"] = float(record.sampling_rate)
return row
def save_all_samples_plot(result_df: pd.DataFrame, split: str, save_dir: Path) -> Path | None:
if result_df["frequency_hz"].isna().any():
return None
plot_df = result_df.sort_values("frequency_hz").reset_index(drop=True)
fig, axes = plt.subplots(2, 1, figsize=(12, 8))
fig.suptitle(f"Task1 TR Tree Evaluation | {split}")
axes[0].plot(plot_df["frequency_hz"], plot_df["true_rms"], marker="o", label="True RMS")
axes[0].plot(plot_df["frequency_hz"], plot_df["pred_rms"], marker="o", label="Pred RMS")
axes[0].set_xlabel("Frequency (Hz)")
axes[0].set_ylabel("RMS")
axes[0].grid(True, alpha=0.3)
axes[0].legend()
axes[1].bar(plot_df["frequency_hz"].astype(str), plot_df["relative_error_percent"], color="tab:orange")
axes[1].set_xlabel("Frequency (Hz)")
axes[1].set_ylabel("Relative Error (%)")
axes[1].grid(True, axis="y", alpha=0.3)
axes[1].tick_params(axis="x", labelrotation=45)
plt.tight_layout()
figure_path = save_dir / f"evaluation_{split}_curve.png"
plt.savefig(figure_path, dpi=180, bbox_inches="tight")
plt.close(fig)
return figure_path
def save_single_sample_plot(record, result: dict[str, float | str], split: str, save_dir: Path, sample_index: int) -> Path:
time_middle = record.time_middle.detach().cpu().numpy().reshape(-1)
x_middle = record.x_middle.detach().cpu().numpy().reshape(-1)
y_middle = record.y_middle.detach().cpu().numpy().reshape(-1)
frequency_hz = float(result["frequency_hz"])
pred_rms = float(result["pred_rms"])
true_rms = float(result["true_rms"])
fig, axes = plt.subplots(3, 1, figsize=(12, 10))
fig.suptitle(f"Task1 TR Tree | {split} | {result['file_name']}")
axes[0].plot(time_middle, x_middle, color="tab:blue")
axes[0].set_title("Input Base Excitation (Middle Segment)")
axes[0].set_xlabel("Time")
axes[0].set_ylabel("Acceleration")
axes[0].grid(True, alpha=0.3)
axes[1].plot(time_middle, y_middle, color="tab:green")
axes[1].set_title("True Top Response (Middle Segment)")
axes[1].set_xlabel("Time")
axes[1].set_ylabel("Acceleration")
axes[1].grid(True, alpha=0.3)
axes[2].bar(["True RMS", "Pred RMS"], [true_rms, pred_rms], color=["tab:green", "tab:orange"])
axes[2].set_title(
f"Freq: {frequency_hz:.4f} Hz | True RMS: {true_rms:.6f} | Pred RMS: {pred_rms:.6f} | "
f"Error: {float(result['relative_error_percent']):.2f}%"
)
axes[2].set_ylabel("RMS")
axes[2].grid(True, axis="y", alpha=0.3)
plt.tight_layout()
figure_path = save_dir / f"evaluation_{split}_s{sample_index}.png"
plt.savefig(figure_path, dpi=180, bbox_inches="tight")
plt.close(fig)
return figure_path
def main() -> None:
args = parse_args()
config = make_experiment_config()
ckpt_path = resolve_checkpoint_path(config.data.project_root, args.checkpoint)
with ckpt_path.open("rb") as handle:
bundle = pickle.load(handle)
model = bundle["model"]
_, raw_records, reports = build_datasets(config.data)
print(report_to_text(reports))
records = raw_records[args.split]
save_dir = evaluation_dir(config)
if args.all_samples:
rows = [evaluate_record(model, record) for record in records]
result_df = pd.DataFrame(rows).sort_values(["relative_error_percent", "file_name"]).reset_index(drop=True)
csv_path = save_dir / f"evaluation_{args.split}_all_samples.csv"
result_df.to_csv(csv_path, index=False)
fig_path = save_all_samples_plot(result_df, args.split, save_dir)
summary = {
"count": len(result_df),
"mean_error_percent": float(result_df["relative_error_percent"].mean()),
"median_error_percent": float(result_df["relative_error_percent"].median()),
"max_error_percent": float(result_df["relative_error_percent"].max()),
"min_error_percent": float(result_df["relative_error_percent"].min()),
}
print(f"Checkpoint: {ckpt_path}")
print(f"Model: {bundle['model_name']}")
print(f"Summary: {summary}")
print(f"CSV saved to: {csv_path}")
if fig_path is not None:
print(f"Figure saved to: {fig_path}")
print(result_df.to_string(index=False))
return
record = records[args.sample_index]
result = evaluate_record(model, record)
fig_path = save_single_sample_plot(record, result, args.split, save_dir, args.sample_index)
print(f"Checkpoint: {ckpt_path}")
print(f"Model: {bundle['model_name']}")
print(f"Sample file: {result['file_name']}")
print(f"True RMS: {float(result['true_rms']):.6f}")
print(f"Pred RMS: {float(result['pred_rms']):.6f}")
print(f"Relative RMS Error (%): {float(result['relative_error_percent']):.4f}")
print(f"Figure saved to: {fig_path}")
if __name__ == "__main__":
main()

115
scripts_tree/predict_single.py Normal file
View File

@@ -0,0 +1,115 @@
from __future__ import annotations
import argparse
import pickle
from pathlib import Path
import sys
import matplotlib.pyplot as plt
import numpy as np
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from scripts.dataset import build_record_from_file
try:
from .config import FEATURE_NAMES, evaluation_dir, make_experiment_config
except ImportError:
from config import FEATURE_NAMES, evaluation_dir, make_experiment_config
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Predict task1 RMS from a single waveform CSV with tree model.")
parser.add_argument("--file", type=str, required=True)
parser.add_argument("--checkpoint", type=str, default=None)
return parser.parse_args()
def resolve_checkpoint_path(project_root: Path, checkpoint_arg: str | None) -> Path:
if checkpoint_arg:
return Path(checkpoint_arg).resolve()
return project_root / "checkpoints_tree" / "task1_tr_tree" / "best_tr_tree.pkl"
def save_prediction_figure(file_path: Path, record, pred_rms: float, true_rms: float | None, output_dir: Path) -> Path:
time_middle = record.time_middle.detach().cpu().numpy().reshape(-1)
x_middle = record.x_middle.detach().cpu().numpy().reshape(-1)
sampling_rate = record.sampling_rate
fft_values = np.fft.rfft(x_middle)
freqs = np.fft.rfftfreq(x_middle.size, d=1.0 / sampling_rate)
amplitudes = np.abs(fft_values)
fig, axes = plt.subplots(3, 1, figsize=(12, 10))
fig.suptitle(f"Task1 TR Tree Single-File Prediction | {file_path.name}")
axes[0].plot(time_middle, x_middle, color="tab:blue")
axes[0].set_title("Input Base Excitation (Middle Segment)")
axes[0].set_xlabel("Time")
axes[0].set_ylabel("Acceleration")
axes[0].grid(True, alpha=0.3)
axes[1].plot(freqs, amplitudes, color="tab:purple")
axes[1].axvline(record.frequency_hz, color="tab:red", linestyle="--", label=f"Dominant freq = {record.frequency_hz:.4f} Hz")
axes[1].set_xlim(0.0, 5.0)
axes[1].set_title("Input Spectrum")
axes[1].set_xlabel("Frequency (Hz)")
axes[1].set_ylabel("Amplitude")
axes[1].grid(True, alpha=0.3)
axes[1].legend()
labels = ["Pred RMS"] if true_rms is None else ["True RMS", "Pred RMS"]
values = [pred_rms] if true_rms is None else [true_rms, pred_rms]
colors = ["tab:orange"] if true_rms is None else ["tab:green", "tab:orange"]
axes[2].bar(labels, values, color=colors)
title = f"Predicted RMS = {pred_rms:.6f}"
if true_rms is not None:
error_percent = abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0
title = f"True RMS = {true_rms:.6f} | Pred RMS = {pred_rms:.6f} | Error = {error_percent:.2f}%"
axes[2].set_title(title)
axes[2].set_ylabel("RMS")
axes[2].grid(True, axis="y", alpha=0.3)
plt.tight_layout()
output_dir.mkdir(parents=True, exist_ok=True)
figure_path = output_dir / f"{file_path.stem}_prediction.png"
plt.savefig(figure_path, dpi=180, bbox_inches="tight")
plt.close(fig)
return figure_path
def main() -> None:
args = parse_args()
config = make_experiment_config()
ckpt_path = resolve_checkpoint_path(config.data.project_root, args.checkpoint)
with ckpt_path.open("rb") as handle:
bundle = pickle.load(handle)
model = bundle["model"]
file_path = Path(args.file).resolve()
record = build_record_from_file(file_path, split="predict", config=config.data)
pred_tr = max(float(model.predict([record.features.tolist()])[0]), 1e-6)
pred_rms = pred_tr * float(record.x_rms.item())
true_rms = float(record.y_rms.item()) if record.y_rms.numel() > 0 else None
out_dir = evaluation_dir(config)
fig_path = save_prediction_figure(file_path, record, pred_rms, true_rms, out_dir)
print(f"Checkpoint: {ckpt_path}")
print(f"Model: {bundle['model_name']}")
print(f"Input file: {file_path}")
print(f"Extracted frequency (Hz): {record.frequency_hz:.6f}")
for feature_name, feature_value in zip(FEATURE_NAMES, record.features.tolist()):
print(f"{feature_name}: {feature_value:.6f}")
print(f"Predicted TR: {pred_tr:.6f}")
print(f"Predicted RMS: {pred_rms:.6f}")
if true_rms is not None:
error_percent = abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0
print(f"True RMS: {true_rms:.6f}")
print(f"Relative RMS Error (%): {error_percent:.4f}")
print(f"Figure saved to: {fig_path}")
if __name__ == "__main__":
main()

160
scripts_tree/train.py Normal file
View File

@@ -0,0 +1,160 @@
from __future__ import annotations
import pickle
from dataclasses import asdict
from pathlib import Path
from typing import Any
import sys
import pandas as pd
from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingRegressor, RandomForestRegressor
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from scripts.dataset import build_datasets, report_to_text
try:
from .config import FEATURE_NAMES, ExperimentConfig, checkpoint_dir, make_experiment_config
except ImportError:
from config import FEATURE_NAMES, ExperimentConfig, checkpoint_dir, make_experiment_config
def build_regressor(model_name: str, random_state: int):
if model_name == "extra_trees":
return ExtraTreesRegressor(
n_estimators=600,
max_depth=None,
min_samples_leaf=1,
min_samples_split=2,
random_state=random_state,
)
if model_name == "random_forest":
return RandomForestRegressor(
n_estimators=500,
max_depth=None,
min_samples_leaf=1,
min_samples_split=2,
random_state=random_state,
)
if model_name == "gradient_boosting":
return GradientBoostingRegressor(
n_estimators=300,
learning_rate=0.03,
max_depth=3,
random_state=random_state,
loss="huber",
)
raise ValueError(f"Unsupported model: {model_name}")
def records_to_frame(records: list[Any]) -> pd.DataFrame:
rows = []
for record in records:
row = {
"file_name": record.file_path.name,
"frequency_hz": float(record.frequency_hz),
"x_rms": float(record.x_rms.item()),
"y_rms": float(record.y_rms.item()),
"target_tr": float(record.target_y_rms.item()),
}
for name, value in zip(FEATURE_NAMES, record.features.tolist()):
row[name] = float(value)
rows.append(row)
return pd.DataFrame(rows)
def evaluate_frame(model, frame: pd.DataFrame) -> dict[str, float]:
feature_matrix = frame.loc[:, FEATURE_NAMES].to_numpy()
pred_tr = model.predict(feature_matrix)
pred_tr = pred_tr.clip(min=1e-6)
pred_rms = pred_tr * frame["x_rms"].to_numpy()
true_rms = frame["y_rms"].to_numpy()
relative_error = abs(pred_rms - true_rms) / true_rms.clip(min=1e-6)
return {
"mean_rms_error": float(relative_error.mean()),
"median_rms_error": float(pd.Series(relative_error).median()),
"max_rms_error": float(relative_error.max()),
}
def serialize_for_checkpoint(value: Any) -> Any:
if isinstance(value, Path):
return str(value)
if isinstance(value, dict):
return {key: serialize_for_checkpoint(sub_value) for key, sub_value in value.items()}
if isinstance(value, tuple):
return [serialize_for_checkpoint(item) for item in value]
if isinstance(value, list):
return [serialize_for_checkpoint(item) for item in value]
return value
def train() -> None:
config = make_experiment_config()
datasets, raw_records, reports = build_datasets(config.data)
del datasets
print(report_to_text(reports))
train_df = records_to_frame(raw_records["train"])
val_df = records_to_frame(raw_records["val"])
feature_matrix = train_df.loc[:, FEATURE_NAMES].to_numpy()
target = train_df["target_tr"].to_numpy()
results: list[dict[str, Any]] = []
best_bundle: dict[str, Any] | None = None
best_val_error = float("inf")
for model_name in config.model.candidate_models:
model = build_regressor(model_name, config.model.random_state)
model.fit(feature_matrix, target)
train_metrics = evaluate_frame(model, train_df)
val_metrics = evaluate_frame(model, val_df)
row = {
"model_name": model_name,
"train_mean_rms_error": train_metrics["mean_rms_error"],
"train_median_rms_error": train_metrics["median_rms_error"],
"train_max_rms_error": train_metrics["max_rms_error"],
"val_mean_rms_error": val_metrics["mean_rms_error"],
"val_median_rms_error": val_metrics["median_rms_error"],
"val_max_rms_error": val_metrics["max_rms_error"],
}
results.append(row)
print(
f"{model_name}: train_mean={train_metrics['mean_rms_error']:.6f} | "
f"val_mean={val_metrics['mean_rms_error']:.6f}"
)
if val_metrics["mean_rms_error"] < best_val_error:
best_val_error = val_metrics["mean_rms_error"]
best_bundle = {
"model_name": model_name,
"model": model,
"config": serialize_for_checkpoint(asdict(config)),
"feature_names": list(FEATURE_NAMES),
"val_metrics": val_metrics,
"train_metrics": train_metrics,
}
summary_df = pd.DataFrame(results).sort_values("val_mean_rms_error").reset_index(drop=True)
ckpt_dir = checkpoint_dir(config)
summary_path = ckpt_dir / config.train.summary_name
summary_df.to_csv(summary_path, index=False)
print(f"Model selection saved to: {summary_path}")
print(summary_df.to_string(index=False))
if best_bundle is None:
raise RuntimeError("No valid model trained.")
best_path = ckpt_dir / config.train.best_model_name
with best_path.open("wb") as handle:
pickle.dump(best_bundle, handle)
print(f"Best tree model saved to: {best_path}")
print(f"Best model: {best_bundle['model_name']} | val_mean_rms_error={best_val_error:.6f}")
if __name__ == "__main__":
train()