Kaggle competition: IEEE-CIS Fraud Detection
<SOS> metric: AUC Kaggle data source: ieee-cis-fraud-detection<define_variables>
import numpy as np
import pandas as pd
import os
from sklearn import preprocessing
import lightgbm as lgb
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.model_selection import train_test_split
from bayes_opt import BayesianOptimization
import seaborn as sns
from sklearn import metrics
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import mean_squared_error
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import auc
from sklearn.metrics import precision_score
from sklearn.metrics import roc_curve
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
test_dir = "/kaggle/input/deepfake-detection-challenge/test_videos/"
test_videos = sorted([x for x in os.listdir(test_dir)if x[-4:] == ".mp4"])
 len(test_videos )<import_modules>
train_id=pd.read_csv('.. /input/train_identity.csv')
 test_id=pd.read_csv('.. /input/test_identity.csv')
 train_id.head()
print("PyTorch version:", torch.__version__)
 print("CUDA version:", torch.version.cuda)
 print("cuDNN version:", torch.backends.cudnn.version() )<set_options>
train_data=pd.read_csv('.. /input/train_transaction.csv')
 test_data=pd.read_csv('.. /input/test_transaction.csv')
gpu = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 gpu<load_pretrained>
train = train_data.merge(train_id, how='left', on='TransactionID')
 test = test_data.merge(test_id, how='left',on='TransactionID')
 del train_id; gc.collect()
del test_id; gc.collect()
del train_data; gc.collect()
del test_data; gc.collect()
facedet = BlazeFace().to(gpu)
 facedet.load_weights("/kaggle/input/blazeface-pytorch/blazeface.pth")
 facedet.load_anchors("/kaggle/input/blazeface-pytorch/anchors.npy")
 _ = facedet.train(False )<load_pretrained>
frames_per_video = 64
video_reader = VideoReader()
video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video)
 face_extractor = FaceExtractor(video_read_fn, facedet )<normalization>
training_missing = train.isna().sum(axis=0)/ train.shape[0]
test_missing = test.isna().sum(axis=0)/ test.shape[0]
change =(training_missing / test_missing ).sort_values(ascending=False)
 change = change[change<1e6]
train_more=change[change>4].reset_index()
train_more.columns=['train_more_id','rate']
test_more=change[change<0.4].reset_index()
test_more.columns=['test_more_id','rate']
train_more_id=train_more['train_more_id'].values
train[train_more_id].head()
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
normalize_transform = Normalize(mean, std )<define_variables>
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
 test[['dist1','dist2','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13','C14']]=imp.fit_transform(test[['dist1','dist2','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13','C14']])
 train[['dist1','dist2','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13','C14']]=imp.fit_transform(train[['dist1','dist2','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13','C14']] )
input_size = 224<choose_model_class>
isna = train.isna().sum(axis=1)
 isna_test = test.isna().sum(axis=1)
 train['isna']=train.isna().sum(axis=1)
 test['isna']=test.isna().sum(axis=1 )
class MyResNeXt(models.resnet.ResNet):
def __init__(self, training=True):
super(MyResNeXt, self ).__init__(block=models.resnet.Bottleneck,
layers=[3, 4, 6, 3],
groups=32,
width_per_group=4)
 self.fc = nn.Linear(2048, 1 )<load_pretrained>
test[test.TransactionDT>2.5e7][train_more_id]=imp.fit_transform(test[test.TransactionDT>2.5e7][train_more_id])
 train[train_more_id]=imp.fit_transform(train[train_more_id] )
checkpoint = torch.load("/kaggle/input/deepfakes-inference-demo/resnext.pth", map_location=gpu)
 model = MyResNeXt().to(gpu)
 model.load_state_dict(checkpoint)
 _ = model.eval()
del checkpoint<predict_on_test>
START_DATE = '2017-12-01'
startdate = datetime.datetime.strptime(START_DATE, '%Y-%m-%d')
 train['TransactionDT'] = train['TransactionDT'].apply(lambda x:(startdate + datetime.timedelta(seconds = x)))
 test['TransactionDT'] = test['TransactionDT'].apply(lambda x:(startdate + datetime.timedelta(seconds = x)))
 train['hour'] = train['TransactionDT'].dt.hour
test['hour'] = test['TransactionDT'].dt.hour
train['month'] = train['TransactionDT'].dt.month
test['month'] = test['TransactionDT'].dt.month
def predict_on_video(video_path, batch_size):
try:
faces = face_extractor.process_video(video_path)
 face_extractor.keep_only_best_face(faces)
 if len(faces)> 0:
x = np.zeros(( batch_size, input_size, input_size, 3), dtype=np.uint8)
 n = 0
for frame_data in faces:
for face in frame_data["faces"]:
resized_face = isotropically_resize_image(face, input_size)
 resized_face = make_square_image(resized_face)
 if n < batch_size:
x[n] = resized_face
n += 1
else:
print("WARNING: have %d faces but batch size is %d" %(n, batch_size))
if n > 0:
x = torch.tensor(x, device=gpu ).float()
x = x.permute(( 0, 3, 1, 2))
for i in range(len(x)) :
x[i] = normalize_transform(x[i] / 255.)
with torch.no_grad() :
y_pred = model(x)
 y_pred = torch.sigmoid(y_pred.squeeze())
 return y_pred[:n].mean().item()
except Exception as e:
print("Prediction error on video %s: %s" %(video_path, str(e)))
 return 0.5<predict_on_test>
x=test['hour']<7
x1=test['hour']>19
x.astype(int)
 x1.astype(int)
 x2=np.add(x,x1)
 x2.astype(int)
 test['night']=x2.astype(int )
def predict_on_video_set(videos, num_workers):
def process_file(i):
filename = videos[i]
y_pred = predict_on_video(os.path.join(test_dir, filename), batch_size=frames_per_video)
 return y_pred
with ThreadPoolExecutor(max_workers=num_workers)as ex:
predictions = ex.map(process_file, range(len(videos)))
 return list(predictions )<predict_on_test>
x=train['hour']<7
x1=train['hour']>19
x.astype(int)
 x1.astype(int)
 x2=np.add(x,x1)
 x2.astype(int)
 train['night']=x2.astype(int )
%%time
predictions = predict_on_video_set(test_videos, num_workers=4 )<save_to_csv>
def set_missing_card2(df):
give_df = df[['card2', 'TransactionID', 'card1', 'TransactionAmt', 'C1', 'C2', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13']]
known_card2 = give_df[give_df.card2.notnull() ].as_matrix()
unknown_card2 = give_df[give_df.card2.isnull() ].as_matrix()
y = known_card2[:, 0]
X = known_card2[:, 1:]
rfr = RandomForestRegressor(random_state=0, n_estimators=10, n_jobs=-1)
 rfr.fit(X, y)
 predictedcard2= rfr.predict(unknown_card2[:, 1::])
 df.loc[(df.card2.isnull()), 'card2']= predictedcard2
return df, rfr
train, rfr = set_missing_card2(train)
 test, rfr = set_missing_card2(test )
submission_df_resnext = pd.DataFrame({"filename": test_videos, "label": predictions})
 submission_df_resnext.to_csv("submission_resnext.csv", index=False )<install_modules>
def set_missing_card3(df):
give_df = df[['card3','TransactionID','card1', 'TransactionAmt','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13']]
known_card3 = give_df[give_df.card3.notnull() ].as_matrix()
unknown_card3 = give_df[give_df.card3.isnull() ].as_matrix()
y = known_card3[:, 0]
X = known_card3[:, 1:]
rfr = RandomForestRegressor(random_state=0, n_estimators=10, n_jobs=-1)
 rfr.fit(X, y)
 predictedcard3 = rfr.predict(unknown_card3[:, 1::])
 df.loc[(df.card3.isnull()), 'card3' ] = predictedcard3
return df, rfr
train,rfr=set_missing_card3(train)
 test,rfr=set_missing_card3(test )
!pip install.. /input/deepfake-xception-trained-model/pytorchcv-0.0.55-py2.py3-none-any.whl --quiet<define_variables>
def set_missing_card5(df):
give_df = df[['card5','TransactionID','card1', 'TransactionAmt','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13']]
known_card5 = give_df[give_df.card5.notnull() ].as_matrix()
unknown_card5 = give_df[give_df.card5.isnull() ].as_matrix()
y = known_card5[:, 0]
X = known_card5[:, 1:]
rfr = RandomForestRegressor(random_state=0, n_estimators=10, n_jobs=-1)
 rfr.fit(X, y)
 predictedcard5 = rfr.predict(unknown_card5[:, 1::])
 df.loc[(df.card5.isnull()), 'card5' ] = predictedcard5
return df, rfr
train,rfr=set_missing_card5(train)
 test,rfr=set_missing_card5(test )
input_size = 150<choose_model_class>
def set_missing_addr1(df):
give_df = df[['addr1','TransactionID','card1', 'TransactionAmt','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13']]
known_addr1 = give_df[give_df.addr1.notnull() ].as_matrix()
unknown_addr1 = give_df[give_df.addr1.isnull() ].as_matrix()
y = known_addr1[:, 0]
X = known_addr1[:, 1:]
rfr = RandomForestRegressor(random_state=0, n_estimators=10, n_jobs=-1)
 rfr.fit(X, y)
 predictedaddr1 = rfr.predict(unknown_addr1[:, 1::])
 df.loc[(df.addr1.isnull()), 'addr1' ] = predictedaddr1
return df, rfr
train,rfr=set_missing_addr1(train)
 test,rfr=set_missing_addr1(test )
model = get_model("xception", pretrained=False)
 model = nn.Sequential(*list(model.children())[:-1])
 class Pooling(nn.Module):
def __init__(self):
super(Pooling, self ).__init__()
self.p1 = nn.AdaptiveAvgPool2d(( 1,1))
self.p2 = nn.AdaptiveMaxPool2d(( 1,1))
def forward(self, x):
x1 = self.p1(x)
 x2 = self.p2(x)
 return(x1+x2)* 0.5
model[0].final_block.pool = nn.Sequential(nn.AdaptiveAvgPool2d(( 1,1)))
 class Head(torch.nn.Module):
def __init__(self, in_f, out_f):
super(Head, self ).__init__()
self.f = nn.Flatten()
self.l = nn.Linear(in_f, 512)
 self.d = nn.Dropout(0.5)
 self.o = nn.Linear(512, out_f)
 self.b1 = nn.BatchNorm1d(in_f)
 self.b2 = nn.BatchNorm1d(512)
 self.r = nn.ReLU()
def forward(self, x):
x = self.f(x)
 x = self.b1(x)
 x = self.d(x)
 x = self.l(x)
 x = self.r(x)
 x = self.b2(x)
 x = self.d(x)
 out = self.o(x)
 return out
class FCN(torch.nn.Module):
def __init__(self, base, in_f):
super(FCN, self ).__init__()
self.base = base
self.h1 = Head(in_f, 1)
 def forward(self, x):
x = self.base(x)
 return self.h1(x )<load_pretrained>
def set_missing_addr2(df):
give_df = df[['addr2','TransactionID','card1', 'TransactionAmt','C1','C2','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13']]
known_addr2 = give_df[give_df.addr2.notnull() ].as_matrix()
unknown_addr2 = give_df[give_df.addr2.isnull() ].as_matrix()
y = known_addr2[:, 0]
X = known_addr2[:, 1:]
rfr = RandomForestRegressor(random_state=0, n_estimators=10, n_jobs=-1)
 rfr.fit(X, y)
 predictedaddr2 = rfr.predict(unknown_addr2[:, 1::])
 df.loc[(df.addr2.isnull()), 'addr2' ] = predictedaddr2
return df, rfr
train,rfr=set_missing_addr2(train)
 test,rfr=set_missing_addr2(test)3q
checkpoint = torch.load('.. /input/deepfake-xception-trained-model/model.pth', map_location=gpu)
 model = FCN(model, 2048 ).to(gpu)
 model.load_state_dict(checkpoint)
 _ = model.eval()
del checkpoint<predict_on_test>
train=train.drop('TransactionDT',axis=1)
 test=test.drop('TransactionDT',axis=1 )
%%time
predictions = predict_on_video_set(test_videos, num_workers=4 )<save_to_csv>
labels = {np.nan: -999, 'nan': -999, 't': 1, 'f': 2, 'm2': 3, 'm0': 4, 'm1': 5, 'gmail.com': 6, 'outlook.com': 7, 'yahoo.com': 8, 'mail.com': 9, 'anonymous.com': 10, 'hotmail.com': 11, 'verizon.net': 12, 'aol.com': 13, 'me.com': 14, 'comcast.net': 15, 'optonline.net': 16, 'cox.net': 17, 'charter.net': 18, 'rocketmail.com': 19, 'prodigy.net.mx': 20, 'embarqmail.com': 21, 'icloud.com': 22, 'live.com.mx': 23, 'gmail': 24, 'live.com': 25, 'att.net': 26, 'juno.com': 27, 'ymail.com': 28, 'sbcglobal.net': 29, 'bellsouth.net': 30, 'msn.com': 31, 'q.com': 32, 'yahoo.com.mx': 33, 'centurylink.net': 34, 'servicios-ta.com': 35, 'earthlink.net': 36, 'hotmail.es': 37, 'cfl.rr.com': 38, 'roadrunner.com': 39, 'netzero.net': 40, 'gmx.de': 41, 'suddenlink.net': 42, 'frontiernet.net': 43, 'windstream.net': 44, 'frontier.com': 45, 'outlook.es': 46, 'mac.com': 47, 'netzero.com': 48, 'aim.com': 49, 'web.de': 50, 'twc.com': 51, 'cableone.net': 52, 'yahoo.fr': 53, 'yahoo.de': 54, 'yahoo.es': 55, 'sc.rr.com': 56, 'ptd.net': 57, 'live.fr': 58, 'yahoo.co.uk': 59, 'hotmail.fr': 60, 'hotmail.de': 61, 'hotmail.co.uk': 62, 'protonmail.com': 63, 'yahoo.co.jp': 64, 'scranton.edu': 65, 'w': 66, 'h': 67, 'c': 68, 's': 69, 'r': 70, 'discover': 71, 'mastercard': 72, 'visa': 73, 'american express': 74, 'credit': 75, 'debit': 76, 'debit or credit': 77, 'charge card': 78, 'notfound': 81, 'found': 82, 'new': 83, 'unknown': 84, 'ip_proxy:transparent': 85, 'ip_proxy:anonymous': 86, 'ip_proxy:hidden': 87, 'android 7.0': 88, 'ios 11.1.2': 89, 'mac os x 10_11_6': 90, 'windows 10': 91, 'android': 284, 'linux': 93, 'ios 11.0.3': 94, 'mac os x 10_7_5': 95, 'mac os x 10_12_6': 96, 'mac os x 10_13_1': 97, 'ios 11.1.0': 98, 'mac os x 10_9_5': 99, 'windows 7': 100, 'windows 8.1': 101, 'mac': 102, 'ios 10.3.3': 103, 'mac os x 10.12': 104, 'mac os x 10_10_5': 105, 'mac os x 10_11_5': 106, 'ios 9.3.5': 107, 'android 5.1.1': 108, 'android 7.1.1': 109, 'android 6.0': 110, 'ios 10.3.1': 111, 'mac os x 10.9': 112, 'ios 11.1.1': 113, 'windows vista': 114, 'ios 10.3.2': 115, 'ios 11.0.2': 116, 'mac os x 10.11': 117, 'android 8.0.0': 118, 'ios 10.2.0': 119, 'ios 10.2.1': 120, 'ios 11.0.0': 121, 'mac os x 10.10': 122, 'mac os x 10_12_3': 123, 'mac os x 10_12': 124, 'android 6.0.1': 125, 'ios': 126, 'mac os x 10.13': 127, 'mac os x 10_12_5': 128, 'mac os x 10_8_5': 129, 'ios 11.0.1': 130, 'ios 10.0.2': 131, 'android 5.0.2': 132, 'windows xp': 133, 'ios 11.2.0': 134, 'mac os x 10.6': 135, 'windows 8': 136, 'mac os x 10_6_8': 137, 'mac os x 10_11_4': 138, 'mac os x 10_12_1': 139, 'ios 10.1.1': 140, 'mac os x 10_11_3': 141, 'mac os x 10_12_4': 142, 'mac os x 10_13_2': 143, 'android 4.4.2': 144, 'mac os x 10_12_2': 145, 'android 5.0': 146, 'func': 147, 'android 7.1.2': 148, 'android 8.1.0': 149, 'other': 150, 'mac os x 10_13_3': 151, 'ios 11.2.1': 152, 'ios 11.2.5': 153, 'windows': 154, 'ios 11.2.2': 155, 'ios 11.3.0': 156, 'ios 11.2.6': 157, 'mac os x 10_13_4': 158, 'mac os x 10_13_5': 159, 'ios 11.4.0': 160, 'ios 11.3.1': 161, 'ios 11.4.1': 162, 'android 9': 163, 'ios 12.0.0': 164, 'mac os x 10_13_6': 165, 'mac os x 10.14': 166, 'mac os x 10_14_0': 167, 'mac os x 10_14': 168, 'ios 12.1.0': 169, 'mac os x 10_14_1': 170, 'ios 12.0.1': 171, 'ios 12.1.1': 172, 'mac os x 10_14_2': 173, 'ios 12.1.2': 174, 'samsung browser 6.2': 175, 'mobile safari 11.0': 176, 'chrome 62.0': 177, 'chrome 62.0 for android': 178, 'edge 15.0': 179, 'mobile safari generic': 180, 'chrome 49.0': 181, 'chrome 61.0': 182, 'edge 16.0': 183, 'safari generic': 184, 'edge 14.0': 185, 'chrome 56.0 for android': 186, 'firefox 57.0': 187, 'chrome 54.0 for android': 188, 'mobile safari uiwebview': 189, 'chrome': 190, 'chrome 62.0 for ios': 191, 'firefox': 192, 'chrome 60.0 for android': 193, 'mobile safari 10.0': 194, 'chrome 61.0 for android': 195, 'ie 11.0 for desktop': 196, 'ie 11.0 for tablet': 197, 'mobile safari 9.0': 198, 'chrome generic': 199, 'chrome 59.0 for android': 200, 'firefox 56.0': 201, 'android webview 4.0': 202, 'chrome 55.0': 203, 'opera 49.0': 204, 'ie': 205, 'chrome 55.0 for android': 206, 'firefox 52.0': 207, 'chrome 57.0 for android': 208, 'chrome 56.0': 209, 'chrome 46.0 for android': 210, 'chrome 58.0': 211, 'firefox 48.0': 212, 'chrome 59.0': 213, 'samsung browser 4.0': 214, 'edge 13.0': 215, 'chrome 53.0 for android': 216, 'chrome 58.0 for android': 217, 'chrome 60.0': 218, 'mobile safari 8.0': 219, 'firefox generic': 220, 'generic/android 7.0': 221, 'mobile': 222, 'samsung/sm-g532m': 223, 'chrome 50.0 for android': 224, 'chrome 51.0 for android': 225, 'chrome 63.0': 226, 'chrome 52.0 for android': 227, 'chrome 51.0': 228, 'firefox 55.0': 229, 'edge': 230, 'opera': 231, 'chrome generic for android': 232, 'aol': 233, 'samsung browser 5.4': 234, 'samsung/sch': 235, 'silk': 236, 'chrome 57.0': 237, 'firefox 47.0': 238, 'chrome 63.0 for android': 239, 'samsung/sm-g531h': 240, 'chrome 43.0 for android': 241, 'waterfox': 242, 'nokia/lumia': 243, 'chrome 63.0 for ios': 244, 'puffin': 245, 'microsoft/windows': 246, 'cyberfox': 247, 'generic/android': 248, 'samsung': 849, 'opera generic': 250, 'chrome 49.0 for android': 251, 'zte/blade': 252, 'safari': 253, 'android browser 4.0': 254, 'samsung browser 5.2': 255, 'palemoon': 256, 'maxthon': 257, 'line': 258, 'lg/k-200': 259, 'iron': 260, 'blu/dash': 261, 'seamonkey': 262, 'firefox 58.0': 263, 'chrome 64.0 for android': 264, 'chrome 64.0': 265, 'firefox 59.0': 266, 'chrome 64.0 for ios': 267, 'm4tel/m4': 268, 'comodo': 269, 'lanix/ilium': 270, 'samsung browser generic': 271, 'chromium': 272, 'opera 51.0': 273, 'inco/minion': 274, 'samsung browser 7.0': 275, 'mozilla/firefox': 276, 'samsung browser 4.2': 277, 'samsung browser 6.4': 278, 'chrome 65.0': 279, 'chrome 65.0 for android': 280, 'chrome 65.0 for ios': 281, 'cherry': 282, 'icedragon': 283, 'edge 17.0': 285, 'chrome 66.0': 286, 'chrome 66.0 for android': 287, 'safari 11.0': 288, 'safari 9.0': 289, 'safari 10.0': 290, 'google': 291, 'chrome 66.0 for 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'lg-h831': 3188, 'oneplus a3003': 3189, 'lg-m320': 3190, 'motorola one': 3191, 'lml413dl': 3192, 'sm-j327t1': 3193, 'sm-n950f': 3194, 'eml-l29': 3195, 'oneplus a5010': 3196, 'ane-lx1': 3197, 'sm-j510mn': 3198, 'sm-s727vl': 3199, 'sm-n910v': 3200, 'sm-j320m': 3201, 'oneplus a3010': 3202, 'sm-g955n': 3203, 'nexus 5': 3204, '9003a': 3205, 'sm-t560nu': 3206, 'ane-lx2': 3207, 'kfsuwi': 3208, 'vtr-l09': 3209, 'sm-t713': 3210, 'redmi 5 plus': 3211, 'sm-g920f': 3212, 'lg-m250': 3213, 'blade l7': 3214, 'tr10rs1': 3215, 'sm-j700t': 3216, 'sm-g900f build/njh47f': 3217, 'sm-t385m': 3218, '6045o': 3219, 'hisense t963': 3220, 'redmi 6': 3221, 'm4_b3': 3222, 'clt-l29': 3223, 'zte blade v8 mini': 3224, 'lenovo p2a42': 3225, 'sm-n960f': 3226, 'alp-l09': 3227, 'atu-l11': 3228, 'asus_a009': 3229, 'mi a2': 3230, 'lg-h650': 3231, 'm5': 3232, 'sm-j200g build/lmy47x': 3233, '5012g': 3234, 'htc desire 650': 3235, '5059a': 3236, 'rne-l22': 3237, 'alcatel_6060c': 3238, 'hisense hi 3 build/nrd90m': 3239, 'kfaswi': 3240, 'sm-t700': 3241, 'sm-g550t1': 3242, 'g3121': 3243, 'sm-j610g': 3244, 'lenovo k33b36': 3245, 'frd-l04': 3246, 'sm-n910c': 3247, 'moto e5 plus': 3248, 'zte blade a6': 3249, 'blade l2 plus': 3250, 'lg-x210': 3251, 'sm-g925f': 3253, 'dli-l22': 3254, 'lenovoa3300-gv': 3255, 'sm-n900w8': 3256, 'lgl84vl': 3257, '5033a': 3258, 'lg-k240': 3259, 'lg-h634': 3260, 'lg-us998': 3261, 'moto e5 play': 3262, 'z982': 3263, 'sm-t710': 3264, 'iphone 6plus': 3265, 'lg-x150': 3266, 'm4 ss4458': 3267, 'lg-h870ds': 3268, 'd6653': 3269, 'ta-1039': 3270, 'ta-1033': 3271, 'lm-g710vm': 3272, 'y635-l03': 3273, 'z981': 3274, 'moto e5 play build/opg28.54-19': 3275, 'samsung-sm-j320a': 3276, 'cro-l03': 3277, 'sne-lx3': 3278, 'sm-g900t build/lmy47x': 3279, 'eva-l19': 3280, 'vs990': 3281, 'lgms330': 3282, 'ta-1028': 3283, 'blade a520': 3284, 'lgls775': 3285, 'sm-n7505': 3286, 'shift4.2': 3287, 'e6883': 3288, '5049w': 3289, 'alpha 950': 3290, 'sm-j400f': 3291, 'ilium x510': 3292, 'g3412': 3293, 'lg-m430': 3294, 'max10 build/lmy47i': 3295, '5054s': 3296, 'chc-u03': 3297, 'lenovo pb2-650y': 3298, '5015a': 3299, 'hi6210sft': 3300, '5025g': 3301, 'sm-g530h': 3302, 's60': 3303, 'htc 10': 3304, 'lg-h700': 3305, 'lm-q610(fgn': 3306, 'sm-j700h': 3307, 'lg-x230': 3308, 'sm-j320fn': 3309, 'samsung sm-t580 build/m1ajq': 3310, 'blade a6 max build/n2g47h': 3311, 'sm-t530nu': 3312, 'ldn-l21': 3313, 'lg-d280': 3314, 'sm-a500m': 3315, 'sm-j337t': 3316, 'm4 ss4457': 3317, 'sm-a300fu': 3318, 'blade v9': 3319, 'lg-h918': 3320, 'hisense f20': 3321, 'sm-j111m': 3322, 'huawei g7-l03': 3323, 'ridge': 3324, 'hs20502-16': 3325, 'lenovo pb2-670y': 3326, 'lg-x165g': 3327, 'kffowi': 3328, 'asus_x00lda': 3329, 'cmr-al09': 3330, 'bluboo': 3331, 'lg-d852g': 3332, '2ps64': 3333, 'lm-q710.fg': 3334, 'samsung-sm-j327a': 3335, 'xt1585': 3336, 'mi a2 lite': 3337, 'lg-m151': 3338, '5010g': 3339, 'sm-j327v': 3340, 'sm-j710mn': 3341, 'vs988': 3342, 'lgls676': 3343, 'z988': 3344, 'lgl158vl': 3345, 'moto e5 cruise': 3346, 'sm-g892u': 3347, 'lg-h500': 3348, 'rv:27.0': 3349, 'lm-v350': 3350, 'sm-j200m': 3351, 'samsung-sm-g920a': 3352, '8_plus': 3353, 'kfarwi': 3354, '5044t': 3355, 'lld-l21': 3356, 'sm-s337tl': 3357, 'sm-j727r4': 3358, 'onix': 3359, 'sm-t900': 3360, 'redmi s2': 3361, 'a502dl': 3362, 'lg-m153': 3363, '4009a': 3364, 'vs995': 3365, 'lg-k425': 3366, 'moto g(6)build/opss27.82-87-3': 3367, 'bln-l21': 3368, 'h3123': 3369, 'p5525a': 3370, '5041c': 3371, 'sm-g9500': 3372, 'gt-i9060i': 3373, 'gt-n8013': 3374, 'lg-h910': 3375, 'gravity': 3376, 'sm-t357w': 3377, 'lg-x220': 3378, 'shift': 3379, 'bla-l29': 3380, 'moto g(5)plus build/opss28.85-13-3': 3381, '5099a': 3382, 'pocophone': 3383, 'sm-c5010': 3384, 'lya-l09': 3385, '8050g': 3386, 'rex build/mra58k': 3387, 'lg-ls777': 3388, 'sm-g930u': 3389, 'pixel 3 xl': 3390, 'p5526a': 3391, 'lg-d120 build/kot49i.v10a': 3392, 'frd-l09': 3393, 'hisense l675': 3394, 'sm-t110': 3395, 'kfapwi': 3396, 'gt-i8190l': 3397, 'moto e(4)plus build/nma26.42-167': 3398, 'sm-t378v': 3399, 'a10': 3400, 'sm-g925t': 3401, 'sm-j710f': 3402, 'n9517': 3403, 'ax921': 3404, 'mxg401': 3405, '6016a': 3406, 'lg-tp260': 3407, 'sm-g355m': 3408, 'alp-l29': 3409, 'lgms550': 3410, 'lg-d415': 3411, 'e6782': 3412, 'k81': 3413, 'sm-n900p': 3414, 'lg-h542': 3415, 'sm-j327u': 3416, 'lg-h811': 3417, '2014819': 3418, 'ghia_axis7': 3419, 'm4 ss4458-r': 3420, 'samsung-sm-g928a': 3421, 'z916bl': 3422, 'lg-ls997': 3423, 'lgmp450': 3424, 'sm-g950n': 3425, 'c5': 3426, 'lgms210': 3427, 'rocket': 3428, 'sm-j720f': 3429, 'sm-g930r4': 3430, 'sch-i545': 3431, 'samsung sm-j610g build/m1ajq': 3432, 'lg-m710': 3433, 'sm-n960u1': 3434, 'sgp311': 3435, 'vk410': 3436, 'hi6250': 3437, 'vs986': 3438, 'sm-j327p': 3439, 'nexus 6p': 3441, 'kfthwi': 3442, 'sm-j337v': 3443, 'htc one m9plus': 3444, 'kftbwi': 3445, 'sm-j737t': 3446, 'sm-t337t': 3447, 'sm-n960w': 3448, 'im6': 3449, 'sm-j737v': 3450, 'kfkawi': 3451, 'huawei y560-l03': 3452, 'sm-j415g': 3453, 'asus_a006': 3454, 'vie-l29': 3455, 'sm-g610m build/m1ajq': 3456, 'moto e5 play build/opgs28.54-19-2': 3457, 'lg-d690': 3458, 'moto e5 play build/ocps27.91-51-3-3': 3459, 'lg-d631': 3460, 'redmi note 6 pro': 3461, 'kyocera-c6742': 3462, 'gt-i9060l': 3463, 'moto z3': 3464, 'vs501': 3465, 'g3311': 3466, 'htl23': 3467, 'q01a': 3468, 'samsung sm-t820 build/r16nw': 3469, 'sm-g930vl': 3470, 'sm-a750g': 3471, '6062w': 3472, 'redmi 4a': 3473, 'p10': 3474, 'infinit_lite_2': 3475, 'sm-t827v': 3476, 'sm-j737p': 3477, 'moto e(4)build/nma26.42-167': 3478, 'a622gl': 3479, 'sm-j327vpp': 3480, 'sm-a600t1': 3481, 'e2306': 3482, 'lm-q710.fgn': 3483, 'jkm-lx3': 3484, 'sm-a700k': 3485, 'slate': 3486, 'sm-j327t': 3487, 'moto g(5)build/opps28.85-13-2': 3488, 'hma-l09': 3489, 'ale-l02': 3490, 'rv:65.0': 3491, 'rct6513w87': 3492, 'cph1835': 3493, '5085b': 3494, 'lgl164vl': 3495, 'asus_x00pd': 3496, 'rct6303w87m7': 3497, 'ghia_axis7p': 3498, 'samsung-sm-t337a': 3499, 'mha-l29': 3500, 'sm-g532g': 3501, 'samsung-sm-g530az': 3502, 'mb520': 3503, 'lg-h990': 3504, 'lg-m327': 3505, 'lifesize': 3506, 'pspc550': 3507, '8080': 3508, 'oneplus a6013': 3509, '7048a': 3510, 'sm-n950u1': 3511, 'tab8': 3512, 'sm-n9200': 3513, 'c2105': 3514, 'sm-j320v': 3515, 'kftt': 3516, 'samsung sm-g610m build/m1ajq': 3517, 'verykools5702': 3518, 'sm-j105b': 3519, 'max10': 3520, 'sm-g600s': 3521, 'cpu': 3522, 'lm-v405': 3523, '4027a': 3524, 'sm-g360p': 3525, 'n9560': 3526, 'z956': 3527, 'c6902': 3528, 'z818l': 3529, 'sm-p350': 3530, 'a462c': 3531, 'sm-j600gt': 3532, 'sm-t837v': 3533, 'e6910': 3534, 'z851m': 3535, 'sm-a920f': 3536, 'asus_a002a': 3537, 'pic-lx9': 3538, 'bla-al00': 3539, 'neffos_c9a': 3540, 'lenovo tb-7104f build/o11019': 3541, 'sgp621': 3542, 'sm-g903f': 3543, '5086a': 3544, 'sm-t387v': 3545, 'sm-g611f': 3546, 'lg-d325': 3547, 'sm-t597v': 3548, 'neffos_c7': 3549, 'sm-g925p': 3550, 'lg-m210': 3551, 'zte-z999': 3552, 'p002': 3553, 'lm-x410pm': 3554, 'che2-l23': 3555, 'q05a': 3556, 'azumi_doshi_a55_ql': 3557, 'lg-k430': 3558, 'hk-914581': 3559, 'sm-t817v': 3560, 'verykools5036': 3561, 'rct6b03w13': 3562, 'sm-j810y': 3563, 'z899vl': 3564, 'sm-s367vl': 3565, 'sm-p905v': 3566, 'infinity': 3567, 'sm-g935r4': 3568, '6043a': 3569, 'atu-l21': 3570, 'd2303': 3571, 'lgl157bl': 3572, 'phone': 3573, 'lg-q710al': 3574, 'n5702l': 3575, 'so-02h': 3576, 'verykools5027': 3577, 'bah-l09': 3578, 'sm-j120fn': 3579, 'asus_x008dc': 3580, 'ags-l09': 3581, 'techpad_10y': 3582, 'dig-l03': 3583, 'sm-j530l': 3584, 'asus_z017dc': 3585, 'motoe2': 3586, 'f3116': 3587, 'lava_r1': 3588, 'sm-j260m': 3589, '8082': 3590, 'lg-k540': 3591, 'zebra': 3592, 'armor': 3593, 'sm-t320': 3594, 'lm-q850': 3595, 'pspcl20a0': 3596, 'aura': 3597, 'azumi_iro_a5_q': 3598, 'kyocera-c6742a': 3599, 'alcatel_5098o': 3600, 'centurylink': 3601, 'spectrum': 3602, 'us': 3603, 'mx': 3604, 'yahoo': 3605, 'de': 3606, 'jp': 3607, 'uk': 3608, 'fr': 3609, 'com': 3610, 'es': 3611, 'apple': 3612,'0':0}
submission_df_xception = pd.DataFrame({"filename": test_videos, "label": predictions})
 submission_df_xception.to_csv("submission_xception.csv", index=False )<create_dataframe>
for c1, c2 in train.dtypes.reset_index().values:
if c2=='O':
train[c1] = train[c1].map(lambda x: labels[str(x ).lower() ])
 test[c1] = test[c1].map(lambda x: labels[str(x ).lower() ])
 train.fillna(-999, inplace=True)
 test.fillna(-999, inplace=True )
submission_df = pd.DataFrame({"filename": test_videos} )<feature_engineering>
train = train.fillna(-999)
 test = test.fillna(-999)
 for f in test.columns:
if train[f].dtype=='object' or test[f].dtype=='object':
print(f)
 lbl = preprocessing.LabelEncoder()
lbl.fit(list(train[f].values)+ list(test[f].values))
train[f] = lbl.transform(list(train[f].values))
test[f] = lbl.transform(list(test[f].values))
r1 = 0.46441
r2 = 0.52189
total = r1 + r2
r11 = r1/total
r22 = r2/total<feature_engineering>
train['TransactionAmt']=np.log(train['TransactionAmt'])
 test['TransactionAmt']=np.log(test['TransactionAmt'] )
submission_df["label"] = r22*submission_df_resnext["label"] + r11*submission_df_xception["label"]<save_to_csv>
submission_df.to_csv("submission.csv", index=False )<set_options>
%matplotlib inline 
<define_variables>
test_dir = "/kaggle/input/deepfake-detection-challenge/test_videos/"
test_videos = sorted([x for x in os.listdir(test_dir)if x[-4:] == ".mp4"])
 len(test_videos )<import_modules>
y_train=train['isFraud']
train=train.drop('isFraud',axis=1)
 train=train.drop('month',axis=1)
 test=test.drop('month',axis=1)
 X_test = test.copy()
del test; gc.collect()
print("PyTorch version:", torch.__version__)
 print("CUDA version:", torch.version.cuda)
 print("cuDNN version:", torch.backends.cudnn.version() )<set_options>
cols_to_drop=['V108', 'id_22', 'id_26', 'id_25', 'C3', 'V14', 'id_24', 'id_07', 'V121', 'V65', 'V305', 'V311', 'V286', 'V111', 'V300', 'V115', 'V113', 'V301', 'V25', 'V123', 'V118', 'V109', 'id_23', 'V112', 'V114', 'V120', 'V88', 'V107', 'V117', 'V122', 'V116', 'id_08', 'id_27', 'id_21', 'V110', 'V119']
train = train.drop(cols_to_drop, axis=1)
 X_test = X_test.drop(cols_to_drop, axis=1 )
gpu = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 gpu<load_pretrained>
def fast_auc(y_true, y_prob):

 y_true = np.asarray(y_true)
 y_true = y_true[np.argsort(y_prob)]
nfalse = 0
auc = 0
n = len(y_true)
 for i in range(n):
y_i = y_true[i]
nfalse +=(1 - y_i)
 auc += y_i * nfalse
auc /=(nfalse *(n - nfalse))
return auc
def eval_auc(y_true, y_pred):

 return 'auc', fast_auc(y_true, y_pred), True
def group_mean_log_mae(y_true, y_pred, types, floor=1e-9):

 maes =(y_true-y_pred ).abs().groupby(types ).mean()
return np.log(maes.map(lambda x: max(x, floor)) ).mean()
def train_model_classification(X, X_test, y, params, folds, model_type='lgb', eval_metric='auc', columns=None, plot_feature_importance=False, model=None,
verbose=10000, early_stopping_rounds=200, n_estimators=50000, splits=None, n_folds=3, averaging='usual', n_jobs=-1):

 columns = X.columns if columns is None else columns
n_splits = folds.n_splits if splits is None else n_folds
X_test = X_test[columns]
metrics_dict = {'auc': {'lgb_metric_name': eval_auc,
'catboost_metric_name': 'AUC',
'sklearn_scoring_function': metrics.roc_auc_score},
}
result_dict = {}
if averaging == 'usual':
oof = np.zeros(( len(X), 1))
prediction = np.zeros(( len(X_test), 1))
elif averaging == 'rank':
oof = np.zeros(( len(X), 1))
prediction = np.zeros(( len(X_test), 1))
scores = []
feature_importance = pd.DataFrame()
for fold_n,(train_index, valid_index)in enumerate(folds.split(X)) :
print(f'Fold {fold_n + 1} started at {time.ctime() }')
 if type(X)== np.ndarray:
X_train, X_valid = X[columns][train_index], X[columns][valid_index]
y_train, y_valid = y[train_index], y[valid_index]
else:
X_train, X_valid = X[columns].iloc[train_index], X[columns].iloc[valid_index]
y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]
if model_type == 'lgb':
model = lgb.LGBMClassifier(**params, n_estimators=n_estimators, n_jobs = n_jobs)
 model.fit(X_train, y_train,
eval_set=[(X_train, y_train),(X_valid, y_valid)], eval_metric=metrics_dict[eval_metric]['lgb_metric_name'],
verbose=verbose, early_stopping_rounds=early_stopping_rounds)
 y_pred_valid = model.predict_proba(X_valid)[:, 1]
y_pred = model.predict_proba(X_test, num_iteration=model.best_iteration_)[:, 1]
if model_type == 'xgb':
train_data = xgb.DMatrix(data=X_train, label=y_train, feature_names=X.columns)
 valid_data = xgb.DMatrix(data=X_valid, label=y_valid, feature_names=X.columns)
 watchlist = [(train_data, 'train'),(valid_data, 'valid_data')]
model = xgb.train(dtrain=train_data, num_boost_round=n_estimators, evals=watchlist, early_stopping_rounds=early_stopping_rounds, verbose_eval=verbose, params=params)
 y_pred_valid = model.predict(xgb.DMatrix(X_valid, feature_names=X.columns), ntree_limit=model.best_ntree_limit)
 y_pred = model.predict(xgb.DMatrix(X_test, feature_names=X.columns), ntree_limit=model.best_ntree_limit)
 if model_type == 'sklearn':
model = model
model.fit(X_train, y_train)
 y_pred_valid = model.predict(X_valid ).reshape(-1,)
 score = metrics_dict[eval_metric]['sklearn_scoring_function'](y_valid, y_pred_valid)
 print(f'Fold {fold_n}.{eval_metric}: {score:.4f}.')
 print('')
 y_pred = model.predict_proba(X_test)
 if model_type == 'cat':
model = CatBoostClassifier(iterations=n_estimators, eval_metric=metrics_dict[eval_metric]['catboost_metric_name'], **params,
loss_function=metrics_dict[eval_metric]['catboost_metric_name'])
 model.fit(X_train, y_train, eval_set=(X_valid, y_valid), cat_features=[], use_best_model=True, verbose=False)
 y_pred_valid = model.predict(X_valid)
 y_pred = model.predict(X_test)
 if averaging == 'usual':
oof[valid_index] = y_pred_valid.reshape(-1, 1)
 scores.append(metrics_dict[eval_metric]['sklearn_scoring_function'](y_valid, y_pred_valid))
prediction += y_pred.reshape(-1, 1)
 elif averaging == 'rank':
oof[valid_index] = y_pred_valid.reshape(-1, 1)
 scores.append(metrics_dict[eval_metric]['sklearn_scoring_function'](y_valid, y_pred_valid))
prediction += pd.Series(y_pred ).rank().values.reshape(-1, 1)
 if model_type == 'lgb' and plot_feature_importance:
fold_importance = pd.DataFrame()
fold_importance["feature"] = columns
fold_importance["importance"] = model.feature_importances_
fold_importance["fold"] = fold_n + 1
feature_importance = pd.concat([feature_importance, fold_importance], axis=0)
 prediction /= n_splits
print('CV mean score: {0:.4f}, std: {1:.4f}.'.format(np.mean(scores), np.std(scores)))
 result_dict['oof'] = oof
result_dict['prediction'] = prediction
result_dict['scores'] = scores
if model_type == 'lgb':
if plot_feature_importance:
feature_importance["importance"] /= n_splits
cols = feature_importance[["feature", "importance"]].groupby("feature" ).mean().sort_values(
 by="importance", ascending=False)[:50].index
best_features = feature_importance.loc[feature_importance.feature.isin(cols)]
plt.figure(figsize=(16, 12)) ;
sns.barplot(x="importance", y="feature", data=best_features.sort_values(by="importance", ascending=False)) ;
plt.title('LGB Features(avg over folds)');
result_dict['feature_importance'] = feature_importance
result_dict['top_columns'] = cols
return result_dict
facedet = BlazeFace().to(gpu)
 facedet.load_weights("/kaggle/input/helper/blazeface-pytorch/blazeface.pth")
 facedet.load_anchors("/kaggle/input/helper/blazeface-pytorch/anchors.npy")
 _ = facedet.train(False )<load_pretrained>
n_fold = 7
folds = TimeSeriesSplit(n_splits=n_fold)
 folds = KFold(n_splits=5 )
frames_per_video = 17
video_reader = VideoReader()
video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video)
 face_extractor = FaceExtractor(video_read_fn, facedet )<define_variables>
params = {'num_leaves': 256,
'min_child_samples': 79,
'objective': 'binary',
'max_depth': 13,
'learning_rate': 0.03,
"boosting_type": "gbdt",
"subsample_freq": 3,
"subsample": 0.9,
"bagging_seed": 11,
"metric": 'auc',
"verbosity": -1,
'reg_alpha': 0.3,
'reg_lambda': 0.3,
'colsample_bytree': 0.9,
'tree_method':'gpu_hist'
}
result_dict_lgb = train_model_classification(X=train, X_test=X_test, y=y_train, params=params, folds=folds, model_type='lgb', eval_metric='auc', plot_feature_importance=True,
verbose=500, early_stopping_rounds=200, n_estimators=5000, averaging='usual', n_jobs=-1 )
input_size = 224<normalization>
sample_submission = pd.read_csv('.. /input/sample_submission.csv')
 sample_submission['isFraud'] = result_dict_lgb['prediction']
sample_submission.to_csv('submission_IEEE_.csv',index=False )