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LSTM_CNN.py
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LSTM_CNN.py
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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.preprocessing import LabelEncoder
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.model_selection import train_test_split
from keras.callbacks import LearningRateScheduler
from keras.optimizers import Adam
from keras import callbacks
from sklearn.metrics import roc_auc_score
from encoding import *
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
dtypes = {
'MachineIdentifier': 'category',
'ProductName': 'category',
'EngineVersion': 'category',
'AppVersion': 'category',
'AvSigVersion': 'category',
'IsBeta': 'int8',
'RtpStateBitfield': 'float16',
'IsSxsPassiveMode': 'int8',
'DefaultBrowsersIdentifier': 'float32',
'AVProductStatesIdentifier': 'float32',
'AVProductsInstalled': 'float16',
'AVProductsEnabled': 'float16',
'HasTpm': 'int8',
'CountryIdentifier': 'int16',
'CityIdentifier': 'float32',
'OrganizationIdentifier': 'float16',
'GeoNameIdentifier': 'float16',
'LocaleEnglishNameIdentifier': 'int16',
'Platform': 'category',
'Processor': 'category',
'OsVer': 'category',
'OsBuild': 'int16',
'OsSuite': 'int16',
'OsPlatformSubRelease': 'category',
'OsBuildLab': 'category',
'SkuEdition': 'category',
'IsProtected': 'float16',
'AutoSampleOptIn': 'int8',
'PuaMode': 'category',
'SMode': 'float16',
'IeVerIdentifier': 'float16',
'SmartScreen': 'category',
'Firewall': 'float16',
'UacLuaenable': 'float32',
'Census_MDC2FormFactor': 'category',
'Census_DeviceFamily': 'category',
'Census_OEMNameIdentifier': 'float32',
'Census_OEMModelIdentifier': 'float32',
'Census_ProcessorCoreCount': 'float16',
'Census_ProcessorManufacturerIdentifier': 'float16',
'Census_ProcessorModelIdentifier': 'float32',
'Census_ProcessorClass': 'category',
'Census_PrimaryDiskTotalCapacity': 'float64',
'Census_PrimaryDiskTypeName': 'category',
'Census_SystemVolumeTotalCapacity': 'float64',
'Census_HasOpticalDiskDrive': 'int8',
'Census_TotalPhysicalRAM': 'float32',
'Census_ChassisTypeName': 'category',
'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float32',
'Census_InternalPrimaryDisplayResolutionHorizontal': 'float32',
'Census_InternalPrimaryDisplayResolutionVertical': 'float32',
'Census_PowerPlatformRoleName': 'category',
'Census_InternalBatteryType': 'category',
'Census_InternalBatteryNumberOfCharges': 'float64',
'Census_OSVersion': 'category',
'Census_OSArchitecture': 'category',
'Census_OSBranch': 'category',
'Census_OSBuildNumber': 'int16',
'Census_OSBuildRevision': 'int32',
'Census_OSEdition': 'category',
'Census_OSSkuName': 'category',
'Census_OSInstallTypeName': 'category',
'Census_OSInstallLanguageIdentifier': 'float16',
'Census_OSUILocaleIdentifier': 'int16',
'Census_OSWUAutoUpdateOptionsName': 'category',
'Census_IsPortableOperatingSystem': 'int8',
'Census_GenuineStateName': 'category',
'Census_ActivationChannel': 'category',
'Census_IsFlightingInternal': 'float16',
'Census_IsFlightsDisabled': 'float16',
'Census_FlightRing': 'category',
'Census_ThresholdOptIn': 'float16',
'Census_FirmwareManufacturerIdentifier': 'float16',
'Census_FirmwareVersionIdentifier': 'float32',
'Census_IsSecureBootEnabled': 'int8',
'Census_IsWIMBootEnabled': 'float16',
'Census_IsVirtualDevice': 'float16',
'Census_IsTouchEnabled': 'int8',
'Census_IsPenCapable': 'int8',
'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',
'Wdft_IsGamer': 'float16',
'Wdft_RegionIdentifier': 'float16',
'HasDetections': 'int8'
}
train=pd.read_csv("../data/train.csv",dtype=dtypes)
train.drop(['MachineIdentifier', 'LocaleEnglishNameIdentifier','Census_InternalBatteryType','Census_InternalBatteryNumberOfCharges','Census_OSInstallLanguageIdentifier','Census_InternalPrimaryDiagonalDisplaySizeInInches','Census_InternalPrimaryDisplayResolutionHorizontal','Census_InternalPrimaryDisplayResolutionVertical','Census_OSUILocaleIdentifier','Census_IsTouchEnabled','Census_IsPenCapable','Census_IsAlwaysOnAlwaysConnectedCapable','Wdft_RegionIdentifier'], axis = 1,inplace=True) #d
train.drop(['PuaMode', 'Census_ProcessorClass','DefaultBrowsersIdentifier','Census_IsFlightingInternal'], axis = 1,inplace=True) # droping features with more than 80% empty values
train.drop(['Census_IsWIMBootEnabled', 'IsBeta','Census_IsFlightsDisabled','AutoSampleOptIn','Census_ThresholdOptIn','SMode','Census_IsPortableOperatingSystem','Census_DeviceFamily','UacLuaenable','Census_IsVirtualDevice'], axis = 1,inplace=True) # droping features unbalancedness >= 99
trans_dict = {
'off': 'Off', '': '2', '': '1', 'on': 'On', 'requireadmin': 'RequireAdmin', 'OFF': 'Off',
'Promt': 'Prompt', 'requireAdmin': 'RequireAdmin', 'prompt': 'Prompt', 'warn': 'Warn',
'00000000': '0', '': '3', np.nan: 'RequireAdmin'
}
train.replace({'SmartScreen': trans_dict}, inplace=True)
train.replace({'OrganizationIdentifier': {np.nan: 0}}, inplace=True)
train['SmartScreen'] = train.SmartScreen.astype('category')
category_cols = train.select_dtypes(include='category').columns.tolist()
train=train.dropna()
FE = ['AppVersion','AvSigVersion','Census_OSVersion']
OHE = [ 'IsSxsPassiveMode',
'AVProductStatesIdentifier','AVProductsInstalled', 'AVProductsEnabled',
'CountryIdentifier', 'CityIdentifier',
'GeoNameIdentifier', 'OsBuild', 'OsSuite',
'SmartScreen','Census_MDC2FormFactor',
'Census_OEMNameIdentifier',
'Census_ProcessorCoreCount',
'Census_ProcessorModelIdentifier',
'Census_PrimaryDiskTotalCapacity', 'Census_PrimaryDiskTypeName',
'Census_HasOpticalDiskDrive',
'Census_TotalPhysicalRAM', 'Census_ChassisTypeName',
'Census_PowerPlatformRoleName',
'Census_OSEdition',
'Census_GenuineStateName','Census_ActivationChannel',
'Census_FirmwareManufacturerIdentifier', 'Wdft_IsGamer',]
cols = []; dd = []
for x in FE:
cols += encode_FE(train,x)
for x in OHE:
tmp = encode_OHE(train,x,0.005,5)
cols += tmp[0]; dd.append(tmp[1])
print('Encoded',len(cols),'new variables')
for x in FE+OHE:
del train[x]
print('Removed original',len(FE+OHE),'variables')
train.drop(['EngineVersion', 'RtpStateBitfield','IeVerIdentifier','Platform','SkuEdition','Census_OSSkuName','Census_OSBuildNumber','Processor'], axis = 1,inplace=True) # droping features with more correlation coefficient > 0.90
X_train, X_val, Y_train, Y_val = train_test_split(train[cols],train['HasDetections'] ,test_size = 0.5)
model = Sequential()
model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=1))
model.add(LSTM(256))
model.add(Dense(1,activation='sigmoid'))
model.compile(optimizer=Adam(lr=0.01), loss="binary_crossentropy", metrics=["accuracy"])
annealer = LearningRateScheduler(lambda x: 1e-2 * 0.95 ** x)
model.fit(X_train,Y_train, batch_size=1024, epochs = 20, callbacks=[annealer,
printAUC(X_train, Y_train)], validation_data = (X_val,Y_val), verbose=1)