Kaggle competition: IEEE-CIS Fraud Detection
<SOS> metric: AUC Kaggle data source: ieee-cis-fraud-detection<compute_test_metric>
fake_users = [3675092,
3675220,
3675222,
3675224,
3675664,
3675671,
3676611,
3678832,
3678915,
3680489,
3686898,
3696177,
3696197,
3706230,
3706233,
3728046,
3728371,
3728378,
3728380,
3736552,
3739847,
3746563,
3751307,
3751312,
3752776,
3754293,
3754888,
3755063,
3766462,
3773321,
3773324,
3773327,
3773331,
3774072,
3774628,
3774641,
3778645,
3778835,
3780007,
3782022,
3782029,
3784627,
3786361,
3793491,
3802384,
3804591,
3804598,
3804602,
3804605,
3804610,
3804614,
3813532,
3814787,
3870028,
3870030,
3870160,
3872008,
3874367,
3878358,
3885616,
3885782,
3886152,
3886162,
3890934,
3890938,
3890942,
3890943,
3890951,
3890953,
3891400,
3919708,
3920857,
3941138,
3942591,
3950989,
3951346,
3953392,
3954152,
3956200,
3956208,
3956212,
3957622,
3957646,
3957647,
3962111,
3963078,
3964729,
3974966,
3976537,
3976549,
3976581,
3978840,
3981018,
3986769,
3990744,
3991684,
3991785,
3992349,
3992365,
3992371,
3994323,
3994685,
4001389,
4001670,
4001792,
4001969,
4003304,
4003306,
4003349,
4008952,
4009339,
4011062,
4011789,
4012423,
4012512,
4013026,
4015663,
4017735,
4018119,
4023200,
4023810,
4023849,
4025569,
4025585,
4033888,
4038711,
4039648,
4043140,
4044092,
4046075,
4046088,
4047811,
4047814,
4049130,
4049455,
4052338,
4052382,
4052416,
4052838,
4052894,
4054484,
4063978,
4065054,
4069816,
4072304,
4072318,
4074954,
4076564,
4077862,
4077896,
4080555,
4080560,
4080750,
4080758,
4080767,
4083310,
4084704,
4086831,
4089011,
4089018,
4091311,
4091608,
4098204,
4102399,
4104099,
4104546,
4105756,
4106102,
4106104,
4106265,
4108498,
4112290,
4113512,
4113992,
4114021,
4114073,
4120021,
4122335,
4122503,
4123436,
4124809,
4124830,
4126213,
4127131,
4129482,
4133128,
4133382,
4137019,
4137651,
4138422,
4139290,
4139736,
4140415,
4140866,
4140876,
4140885,
4140888,
4142722,
4142857,
4146328,
4147304,
4149421,
4149429,
4154121,
4154484,
4155526,
4157307,
4158172,
4159899,
4159911,
4161494,
4161824,
4162840,
4162863,
4162881,
4163437,
4163633,
4163827,
4165162,
4165203,
4165205,
4165211,
4165225,
4167358,
4168182,
4168420]
score = model.evaluate(X_val, Y_val, verbose=0)
 print("Val_Loss :",score[0])
 print("Val_Acc :",score[1] )<predict_on_test>
fraud_tran_users = [3671888,
3676402,
3676410,
3677776,
3679661,
3679667,
3682263,
3685848,
3686652,
3689369,
3689599,
3697831,
3697893,
3698063,
3700660,
3700663,
3701086,
3703681,
3707336,
3708386,
3711944,
3711956,
3715012,
3729146,
3729719,
3730919,
3736667,
3742126,
3753511,
3756488,
3762286,
3764738,
3764741,
3764742,
3764746,
3765703,
3777492,
3779360,
3790961,
3791376,
3791418,
3800966,
3804339,
3804974,
3811777,
3811834,
3817323,
3817332,
3817337,
3819508,
3820639,
3825564,
3830237,
3830645,
3830648,
3833416,
3839619,
3839621,
3839628,
3840265,
3853868,
3855783,
3856122,
3856126,
3858739,
3858741,
3858913,
3864705,
3865304,
3869334,
3878178,
3878818,
3880519,
3880795,
3880938,
3880978,
3884824,
3885935,
3888408,
3891513,
3893682,
3895696,
3899183,
3909492,
3910095,
3911943,
3918744,
3918752,
3919563,
3919569,
3919574,
3919867,
3920437,
3922735,
3929343,
3936498,
3938032,
3939861,
3940245,
3941736,
3942864,
3948602,
3950394,
3950401,
3950403,
3950415,
3950418,
3950421,
3950671,
3957368,
3958894,
3960455,
3961144,
3961650,
3962873,
3962877,
3962881,
3962909,
3962956,
3962959,
3964831,
3969750,
3970268,
3970688,
3971072,
3971571,
3975220,
3980008,
3980013,
3980645,
3985298,
3985306,
3985313,
3985672,
3985677,
3985682,
3990116,
3990119,
3991837,
3994482,
3999698,
3999702,
3999703,
4002412,
4004976,
4007409,
4007428,
4007433,
4009468,
4009473,
4010733,
4011164,
4011181,
4013047,
4014514,
4015014,
4015787,
4018153,
4018173,
4022610,
4023223,
4023849,
4025540,
4025641,
4026248,
4029004,
4029005,
4030751,
4044848,
4045018,
4045803,
4050099,
4050105,
4050117,
4050130,
4050636,
4059726,
4061664,
4063724,
4067067,
4076997,
4077309,
4078114,
4080429,
4082621,
4082894,
4085298,
4089316,
4094551,
4095410,
4096324,
4097033,
4097039,
4097272,
4098072,
4098491,
4098947,
4098974,
4101056,
4101425,
4101526,
4106264,
4106703,
4108065,
4108286,
4109485,
4113667,
4114458,
4117756,
4117771,
4118261,
4120923,
4122948,
4124677,
4125805,
4126117,
4126222,
4126226,
4126254,
4126258,
4127375,
4127380,
4127395,
4127590,
4131644,
4132416,
4132885,
4133012,
4133331,
4133359,
4133411,
4134322,
4134495,
4134499,
4134509,
4134642,
4136286,
4136534,
4137182,
4138950,
4139722,
4140204,
4141069,
4142428,
4152461,
4154580,
4156319,
4158363,
4158526,
4158615,
4158777,
4159713,
4162245,
4162612,
4163621,
4164780,
4168047,
4169416]
probabilities = model.predict(test)
 y_pred = np.argmax(probabilities, axis=1)
 print(y_pred.shape )<save_to_csv>
df = pd.read_csv(".. /input/009429/stack_median(1 ).csv")
submission = pd.DataFrame({'ImageId': np.arange(1, 28001), 'Label': y_pred})
 submission.to_csv("submission.csv", index = False)
 print("Your submission was successfully saved!" )<set_options>
dff = df.copy()
np.random.seed(42)
 %matplotlib inline<define_variables>
dff.loc[~dfr['TransactionID'].isin(inter), 'isFraud'] = 0
model_path='.'
path='/kaggle/input/histopathologic-cancer-detection/'
train_folder=f'{path}train'
test_folder=f'{path}test'
train_lbl=f'{path}train_labels.csv'
bs=64
num_workers=None
sz=96<set_options>
inter = list(set(fake_users + fraud_tran_users))
print(torch.cuda.is_available())
 print(torch.backends.cudnn.enabled )<load_from_csv>
dfr.loc[dfr['isFraud'] < 0.001, 'isFraud'] = 0
dfr.loc[dfr['isFraud'] > 0.98, 'isFraud'] = 1
<count_values><EOS> 
dff.to_csv("subbb.csv", index=False )