adding plots
26
scripts/df_pred-test_plasmAnt.csv
Normal file
|
@ -0,0 +1,26 @@
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|||
numVol,BMI_pred,BMI_test,Bpmax_pred,Bpmax_test,Bpmin_pred,Bpmin_test,CA.G_pred,CA.G_test,CA.S_pred,CA.S_test,CA_pred,CA_test,CVRI_pred,CVRI_test,DHPAA.GG_pred,DHPAA.GG_test,DHPAA.GS_pred,DHPAA.GS_test,DHPAA.G_pred,DHPAA.G_test,DHPAA.SS_pred,DHPAA.SS_test,DHPAA_pred,DHPAA_test,Fat_pred,Fat_test,Frec_pred,Frec_test,Sex,Sweetener,TFA.G_pred,TFA.G_test,TFA.S_pred,TFA.S_test,Total.CA_pred,Total.CA_test,Total.DHPAA_pred,Total.DHPAA_test,Total.TFA_pred,Total.TFA_test,Total.VA_pred,Total.VA_test,VA.GG_pred,VA.GG_test,VA.GS_pred,VA.GS_test,VA.SS_pred,VA.SS_test,VA.S_pred,VA.S_test,VA_pred,VA_test,Weight_pred,Weight_test
|
||||
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5,27.060256565656555,24.9,113.31228643578645,121.0,76.72052705627705,62.0,0.0819956928983715,0.0821428571428571,0.2826003002373969,0.274193548387097,0.3324180531650411,0.216867469879518,7.477949134199132,5.0,0.1881931008806009,0.104487179487179,0.19275288600288598,0.262755102040816,0.20406920609152757,0.0672268907563025,0.17958559482114755,0.0,0.3814207457386363,0.0634375,34.08787561327562,31.1,70.40289538239539,69.0,WOMAN,ST,0.047233140715224754,0.0032948929159802,0.04529645987998543,0.0458362905133665,0.21664822049374868,0.192411924119241,0.3590505564175776,0.0514379237783493,0.04578933962148905,0.0384839933832831,0.2701191398061112,0.100655126035837,0.2252886179790387,0.0595915963086589,0.3107858588649691,0.102514506769826,0.24228170824642706,0.191106210951856,0.16425506242964494,0.279053583855254,0.20451035344355964,0.0,74.80806410533911,70.3
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|
||||
86,28.498510606060606,27.9,130.941170995671,151.0,89.32027308802311,104.0,0.10720908962069671,0.0357142857142857,0.29651907322068616,0.193548387096774,0.38112421982301503,1.0,10.518943722943721,12.0,0.20267039973914971,0.126923076923077,0.20919494967900573,0.270408163265306,0.23680921307917113,0.121848739495798,0.180809442232809,0.0,0.4122495289953101,0.2675,27.915842063492065,26.3,67.79791233766232,86.0,MAN,SU,0.07008326597741757,0.0024711696869851,0.06296272041445924,0.0272651999053702,0.25100921624295586,0.319783197831978,0.3965301267459321,0.225157820902502,0.06439328922188704,0.0230612046317019,0.2974679671710128,0.285001518504057,0.2337376812039215,0.167435696053407,0.39356091601285004,0.514119922630561,0.2628236158773644,0.468945240720323,0.2057288240542416,0.294711203897008,0.2500046337603588,0.158608990670059,89.3390294733045,94.5
|
||||
65,27.715285606060593,26.6,117.99189754689752,125.0,77.78756096681097,82.0,0.1070339195526695,0.117857142857143,0.2802254806125774,0.17741935483871,0.3630761748291869,0.72289156626506,8.250544733044736,8.0,0.19618404812779805,0.0461538461538462,0.19769888204199426,0.038265306122449,0.22025961179926518,0.112394957983193,0.18642484699833947,0.226130653266332,0.3993587620625902,0.1575,33.096955591630596,35.8,60.347909812409796,68.0,WOMAN,SU,0.057503762818425085,0.0529928610653487,0.042493882733857895,0.0440619824934942,0.24359126984126975,0.317073170731707,0.38013185362425506,0.109188683656769,0.045316038623351054,0.0458304952807239,0.29172889833422827,0.226560805241008,0.23978196327528745,0.0780973885725506,0.360930126772003,0.266537717601547,0.2443618810035569,0.549797868430724,0.18565546382404421,0.532707028531663,0.20667854389037602,0.33587786259542,79.15016327561328,77.9
|
||||
136,28.91784188311688,31.4,125.01580375180373,125.0,82.51338961038962,82.0,0.1311627396413111,0.142857142857143,0.33815357491970394,0.241935483870968,0.3782638258662358,0.566265060240964,10.605613275613273,14.0,0.1908734129296629,0.0903846153846154,0.2089871501089614,0.122448979591837,0.26605044972534464,0.359243697478992,0.20905964345537217,0.0,0.4142151247970775,0.2978125,27.427843037518056,29.6,62.70592676767679,92.0,MAN,SA,0.09841763718617097,0.042833607907743,0.08724970251110242,0.039981074047788,0.27580104275429457,0.311653116531165,0.4027341301892974,0.273790039747487,0.08938644000226907,0.0406733482533813,0.3168836289517339,0.536162089461582,0.2402893406839141,0.441782839191047,0.43381642998093684,0.735396518375242,0.29660456695903226,0.177875780962881,0.2454875055355223,0.326026443980515,0.2508340380051575,0.438507209499576,88.42199599567098,103.9
|
||||
1,28.0542908008658,27.9,118.89352669552667,120.0,79.42954689754693,85.0,0.10065080266955269,0.0464285714285714,0.3009155960526929,0.629032258064516,0.3416553530137867,0.602409638554217,8.171947691197692,7.0,0.1855390972453472,0.0634615384615385,0.1938414327311012,0.165816326530612,0.22144989018334596,0.105042016806723,0.19091438251865386,0.0,0.36154985930735933,0.1059375,35.442314213564195,41.0,68.09911652236654,98.0,WOMAN,ST,0.060497250392843496,0.0340472267984624,0.043166906036095146,0.0079843860894251,0.23672041162691548,0.311653116531165,0.3482462271770782,0.0678045358896423,0.04639559030862429,0.0127955629074633,0.2892307368792142,0.1157967807714,0.23331817902029475,0.0796681720007854,0.37119736868547315,0.0882011605415861,0.25178911708569923,0.256155825064315,0.18795385031345366,0.0,0.21899447400210761,0.626802374893978,78.7594479437229,75.0
|
||||
27,28.633624819624835,29.7,125.12254401154397,111.0,84.33075396825397,77.0,0.11550686842918985,0.0571428571428571,0.33898644858725513,0.129032258064516,0.344094620907874,0.228915662650602,10.39781746031746,10.0,0.17324374190624187,0.0115384615384615,0.19136777578996966,0.0280612244897959,0.2496042398019813,0.0987394957983193,0.20800417128934723,0.120603015075377,0.4167630800189393,0.3684375,27.309780699855697,21.4,63.61248340548344,55.0,MAN,ST,0.08896761349273706,0.0639758374519495,0.07925807414695807,0.0295718003312042,0.2565434689520054,0.151761517615176,0.39289390430575843,0.240589198036007,0.08114216300468367,0.0358567675391651,0.29585339525737153,0.147555208468914,0.22990142198895633,0.0626349892008639,0.38953254666030285,0.499032882011605,0.29254583173574356,0.127894156560088,0.20523437275629353,0.203201113430759,0.22992556089184593,0.325699745547074,88.69147070707074,101.6
|
||||
53,27.588980122655126,28.0,116.35157070707074,109.0,76.80541486291487,70.0,0.09237265254586688,0.0142857142857143,0.2822265628636596,0.467741935483871,0.35929197308715394,0.120481927710843,8.023979437229437,7.0,0.1883873644873646,0.0897435897435898,0.19977727530406109,0.209183673469388,0.21613821210302311,0.0483193277310924,0.17337324972626486,0.0678391959798995,0.40225922201930026,0.27875,33.744931024531034,37.8,62.74108621933622,79.0,WOMAN,SU,0.06339893680667975,0.0,0.04924375659498087,0.0003548616039744,0.23205173590336162,0.151761517615176,0.3775008804940416,0.202478372691139,0.05190494886169695,0.000389218643573,0.26361592565484276,0.149203870016053,0.21047504382057947,0.0972413116041626,0.343497616396069,0.417408123791103,0.2481056563694381,0.027930907754502,0.17923426894956124,0.121433542101601,0.2067894414274822,0.368956743002545,77.86226911976912,71.8
|
||||
76,27.753418506493507,25.6,126.08652489177483,133.0,84.025382034632,88.0,0.1359727105236034,0.075,0.3239822708653354,0.451612903225806,0.38126844607868693,0.265060240963855,9.62759018759019,9.0,0.18563506447256448,0.125641025641026,0.2044143309921371,0.102040816326531,0.2552269873858636,0.032563025210084,0.20527962141153094,0.293969849246231,0.42823890771554846,0.5496875,27.362795382395376,25.9,61.54605627705628,59.0,MAN,SU,0.08947323622591333,0.0820977484898407,0.08209950932147522,0.0068015140761769,0.27767224509907423,0.227642276422764,0.40805604594662365,0.425999532382511,0.08356719980283517,0.0203366741266907,0.2982047018595242,0.287084038353074,0.23123725514591642,0.299234243078736,0.4141430126074227,0.0963249516441006,0.25012720474021477,0.205071664829107,0.22216694670635173,0.0,0.2511030109039016,0.281594571670908,83.68287958152955,73.0
|
||||
117,29.505141017316024,33.6,121.46919877344871,120.0,80.63337085137088,83.0,0.08853982426303852,0.0392857142857143,0.275288734115347,0.0,0.342712677550027,0.204819277108434,8.885298340548339,9.0,0.1675886747511747,0.0487179487179487,0.19072368639014045,0.26530612244898,0.23517504767058328,0.0399159663865546,0.16886411585343747,0.376884422110553,0.3952797743055556,0.6221875,36.203470851370845,43.0,55.13406890331891,61.0,WOMAN,SA,0.0448524912377878,0.0,0.04829011333637841,0.0055594984622663,0.22513038925843795,0.0921409214092141,0.36775784237714304,0.476502221183072,0.047833403117484054,0.0046706237228763,0.30395832758142416,0.263829233372381,0.24670686160927668,0.243716866287061,0.3841662602817341,0.373694390715667,0.24156482489089984,0.332598309445057,0.20364091908246407,0.0,0.22425210391813447,0.0,84.20416565656569,93.6
|
||||
25,27.147951154401156,25.0,118.43159235209232,117.0,79.2832878787879,75.0,0.1274373260667903,0.0714285714285714,0.3024815610017223,0.387096774193548,0.35605710721674577,0.180722891566265,9.131540764790765,7.0,0.19715915681540686,0.0333333333333333,0.22667399118738404,0.150510204081633,0.22964822482023112,0.186974789915966,0.23918934046132542,0.0,0.4202202932224025,0.355625,27.38655999278499,17.2,65.66359054834057,53.0,MAN,ST,0.06222380221371162,0.0886875343218012,0.0597541569928901,0.0332387035722735,0.2610660603323205,0.195121951219512,0.40570548203906887,0.260462941314005,0.06035335055541584,0.0432519217670526,0.2847275383793996,0.158358280185691,0.22802953281447047,0.11314549381504,0.3772421433176751,0.397292069632495,0.23021210556767333,0.0893054024255788,0.1991890126034177,0.148921363952679,0.23082002810119862,0.108566581849025,81.09617972582971,72.5
|
|
|
@ -15,6 +15,7 @@ from sklearn.ensemble import RandomForestRegressor
|
|||
sns.set_theme()
|
||||
|
||||
|
||||
|
||||
def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True, pipeline = True, plot = True):
|
||||
|
||||
# extract model name
|
||||
|
@ -51,26 +52,26 @@ def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,
|
|||
|
||||
#X_test.to_csv("X_test_"+modelname+".csv")
|
||||
|
||||
scores = []
|
||||
|
||||
for n in y_test.columns:
|
||||
scores = pd.Series()
|
||||
metabs = pd.Series(y_test.columns)
|
||||
for n in metabs:
|
||||
print(" ----------------- " + n + " MODEL EVALUATION ----------------- ")
|
||||
|
||||
n_scores = cross_val_score(model, X_test, y_test[n], scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
|
||||
n_scores = np.absolute(n_scores)
|
||||
scores.append(n_scores)
|
||||
print('Full model ' + modelname + ' for metabolite '+n+ ' MAE: %.3f (%.3f)' % (np.mean(n_scores), np.std(n_scores)))
|
||||
scores._append(pd.Series(n_scores))
|
||||
print('Full model ' + df_name + ' for metabolite '+n+ ' MAE: %.3f (%.3f)' % (np.mean(n_scores), np.std(n_scores)))
|
||||
|
||||
# full model evaluation
|
||||
print(" ----------------- MEAN PERFORMANCE EVALUATION ----------------- ")
|
||||
|
||||
n_scores = cross_val_score(model, X_test, y_test, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
|
||||
n_scores = np.absolute(n_scores)
|
||||
scores.append = n_scores
|
||||
metabs = y_test.columns.append("Mean Performance")
|
||||
scores._append(pd.Series(n_scores))
|
||||
metabs._append(pd.Series(["Mean Performance"]))
|
||||
df_scores = pd.concat([metabs, scores])
|
||||
df_scores.columns = ["Metabolite modeled", "MAE score"]
|
||||
df_scores.to_csv("scores_" + modelname +".csv", sep = ",")
|
||||
df_scores.to_csv("scores_" + df_name +".csv", sep = ",")
|
||||
|
||||
print("Mean performance of all model" + modelname + " MAE: %.3f (%.3f)" % (np.mean(n_scores), np.std(n_scores)))
|
||||
|
||||
|
@ -93,13 +94,14 @@ def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,
|
|||
df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0)
|
||||
df_predTest["Sex"] = dummies_sex
|
||||
df_predTest["Sweetener"] = dummies_sweetener
|
||||
df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("df_pred-test_"+modelname+".csv", sep = ",")
|
||||
df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("df_pred-test_"+df_name+".csv", sep = ",")
|
||||
|
||||
# print plots
|
||||
metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener')))
|
||||
|
||||
print(" ----------------- STARTING PLOTTING ----------------- ")
|
||||
|
||||
|
||||
|
||||
for metab in metabs:
|
||||
#sns.regplot(data = pred_prueba_DF, x=metab+"_pred", y=metab+"_test", )
|
||||
|
||||
|
@ -117,25 +119,24 @@ def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,
|
|||
|
||||
sns.lmplot(data = df_predTest, x=metab+"_pred", y=metab+"_test")
|
||||
plt.title("TEST VS PREDICTED lmplot " + metab + " " + modelname)
|
||||
plt.savefig("testVsPredicted_lmplot_"+ metab+"_"+modelname+".png")
|
||||
plt.savefig("testVsPredicted_lmplot_"+ metab+"_"+df_name+".png")
|
||||
plt.close()
|
||||
|
||||
print(" ----------------- PREDICTED VS RESIDUALS " + metab + " ----------------- ")
|
||||
|
||||
plt.figure()
|
||||
|
||||
|
||||
y_pred_metab = y_pred[metab]
|
||||
residuals = y_test[metab]-y_pred[metab]
|
||||
df_all = pd.concat([y_pred_metab, residuals], axis=1)
|
||||
df_all.columns = ["Predicted", "Residuals"]
|
||||
|
||||
sns.scatterplot(data = df_all, x="Predicted", y="Residuals")
|
||||
plt.title("RESIDUALS VS PREDICTED " + metab + " " + modelname)
|
||||
plt.savefig("residualsVsPredicted_"+ metab+"_"+modelname+".png")
|
||||
plt.axhline(y=0)
|
||||
plt.close()
|
||||
|
||||
'''
|
||||
if (residPlot):
|
||||
print(" ----------------- PREDICTED VS RESIDUALS " + metab + " ----------------- ")
|
||||
plt.figure()
|
||||
y_pred_metab = y_pred[metab]
|
||||
residuals = y_test[metab]-y_pred[metab]
|
||||
df_all = pd.concat([y_pred_metab, residuals], axis=1)
|
||||
df_all.columns = ["Predicted", "Residuals"]
|
||||
|
||||
sns.scatterplot(data = df_all, x="Predicted", y="Residuals")
|
||||
plt.title("RESIDUALS VS PREDICTED " + metab + " " + modelname)
|
||||
plt.savefig("residualsVsPredicted_"+ metab+"_"+df_name+".png")
|
||||
plt.axhline(y=0)
|
||||
plt.close()
|
||||
'''
|
||||
|
||||
def RF_Fit(df, df_name, full = True, eval = True, plot = True):
|
||||
|
||||
|
|
|
@ -18,19 +18,19 @@ paths = ["../data/" + s for s in paths]
|
|||
|
||||
|
||||
# execution
|
||||
test = False
|
||||
exec = False
|
||||
test = True
|
||||
execution = False
|
||||
if (test):
|
||||
df, df_name = fullRead(paths[0], sep = ",", full = True)
|
||||
df, df_name = fullRead(paths[1], sep = ",", full = True)
|
||||
df_name = re.sub("../", "", df_name)
|
||||
df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
|
||||
|
||||
y_metTest = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/X_metTest_RF_Met_lightTrainplasmFlav.csv")
|
||||
X_metTest = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/y_metTest_RF_Met_lightTrainplasmFlav.csv")
|
||||
|
||||
evaluateNPlot(modelPath = "RF_Met_lightTrainplasmFlav.pkl", df = df, df_name = df_name, y_metTest =y_metTest, X_metTest=X_metTest, pipeline = False)
|
||||
#y_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/X_metTest_RF_Met_lightTrainplasmFlav.csv")
|
||||
#X_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/y_metTest_RF_Met_lightTrainplasmFlav.csv")
|
||||
residPlot = False
|
||||
evaluateNPlot(modelPath = "../models/RF_Full_lightTrainplasmAnt.pkl", df = df, df_name = df_name, pipeline = False)
|
||||
|
||||
elif (exec):
|
||||
elif (execution):
|
||||
for path in paths:
|
||||
df, df_name = fullRead(path, sep = ",", full = True)
|
||||
df_name = re.sub("../", "", df_name)
|
||||
|
|
27
scripts/scores_plasmAnt.csv
Normal file
|
@ -0,0 +1,27 @@
|
|||
,0
|
||||
0,CA
|
||||
1,CA.G
|
||||
2,CA.S
|
||||
3,Total.CA
|
||||
4,DHPAA
|
||||
5,DHPAA.G
|
||||
6,DHPAA.GG
|
||||
7,DHPAA.GS
|
||||
8,DHPAA.SS
|
||||
9,Total.DHPAA
|
||||
10,TFA.G
|
||||
11,TFA.S
|
||||
12,Total.TFA
|
||||
13,VA
|
||||
14,VA.GG
|
||||
15,VA.S
|
||||
16,VA.GS
|
||||
17,VA.SS
|
||||
18,Total.VA
|
||||
19,Weight
|
||||
20,BMI
|
||||
21,Fat
|
||||
22,CVRI
|
||||
23,Bpmin
|
||||
24,Bpmax
|
||||
25,Frec
|
|
BIN
scripts/testVsPredicted_lmplot_BMI_plasmAnt.png
Normal file
After Width: | Height: | Size: 36 KiB |
BIN
scripts/testVsPredicted_lmplot_Bpmax_plasmAnt.png
Normal file
After Width: | Height: | Size: 36 KiB |
BIN
scripts/testVsPredicted_lmplot_Bpmin_plasmAnt.png
Normal file
After Width: | Height: | Size: 35 KiB |
BIN
scripts/testVsPredicted_lmplot_CA.G_plasmAnt.png
Normal file
After Width: | Height: | Size: 29 KiB |
BIN
scripts/testVsPredicted_lmplot_CA.S_plasmAnt.png
Normal file
After Width: | Height: | Size: 30 KiB |
BIN
scripts/testVsPredicted_lmplot_CA_plasmAnt.png
Normal file
After Width: | Height: | Size: 30 KiB |
BIN
scripts/testVsPredicted_lmplot_CVRI_plasmAnt.png
Normal file
After Width: | Height: | Size: 34 KiB |
BIN
scripts/testVsPredicted_lmplot_DHPAA.GG_plasmAnt.png
Normal file
After Width: | Height: | Size: 35 KiB |
BIN
scripts/testVsPredicted_lmplot_DHPAA.GS_plasmAnt.png
Normal file
After Width: | Height: | Size: 34 KiB |
BIN
scripts/testVsPredicted_lmplot_DHPAA.G_plasmAnt.png
Normal file
After Width: | Height: | Size: 30 KiB |
BIN
scripts/testVsPredicted_lmplot_DHPAA.SS_plasmAnt.png
Normal file
After Width: | Height: | Size: 32 KiB |
BIN
scripts/testVsPredicted_lmplot_DHPAA_plasmAnt.png
Normal file
After Width: | Height: | Size: 34 KiB |
BIN
scripts/testVsPredicted_lmplot_Fat_plasmAnt.png
Normal file
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scripts/testVsPredicted_lmplot_Frec_plasmAnt.png
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scripts/testVsPredicted_lmplot_TFA.G_plasmAnt.png
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scripts/testVsPredicted_lmplot_TFA.S_plasmAnt.png
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scripts/testVsPredicted_lmplot_Total.CA_plasmAnt.png
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scripts/testVsPredicted_lmplot_Total.DHPAA_plasmAnt.png
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scripts/testVsPredicted_lmplot_Total.TFA_plasmAnt.png
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scripts/testVsPredicted_lmplot_Total.VA_plasmAnt.png
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scripts/testVsPredicted_lmplot_VA.GG_plasmAnt.png
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scripts/testVsPredicted_lmplot_VA.GS_plasmAnt.png
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scripts/testVsPredicted_lmplot_VA.SS_plasmAnt.png
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scripts/testVsPredicted_lmplot_VA.S_plasmAnt.png
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scripts/testVsPredicted_lmplot_VA_plasmAnt.png
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scripts/testVsPredicted_lmplot_Weight_plasmAnt.png
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