Model_CatBoost.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,mean_squared_error,roc_curve
import warnings
import lightgbm as lgb

import pickle
from sklearn.model_selection import KFold
from xgboost import XGBClassifier

warnings.simplefilter(action='ignore', category=FutureWarning)

dtypes = {
        'MachineIdentifier':                                    'category',
        'ProductName':                                          'category',
        'EngineVersion':                                        'category',
        'AppVersion':                                           'category',
        'AvSigVersion':                                         'category',
        'IsBeta':                                               'int8',
        'RtpStateBitfield':                                     'float16',
        'IsSxsPassiveMode':                                     'int8',
        'DefaultBrowsersIdentifier':                            'float16',
        'AVProductStatesIdentifier':                            'float32',
        'AVProductsInstalled':                                  'float16',
        'AVProductsEnabled':                                    'float16',
        'HasTpm':                                               'int8',
        'CountryIdentifier':                                    'int16',
        'CityIdentifier':                                       'float32',
        'OrganizationIdentifier':                               'float16',
        'GeoNameIdentifier':                                    'float16',
        'LocaleEnglishNameIdentifier':                          'int8',
        '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':                             'float16',
        'Census_OEMModelIdentifier':                            'float32',
        'Census_ProcessorCoreCount':                            'float16',
        'Census_ProcessorManufacturerIdentifier':               'float16',
        'Census_ProcessorModelIdentifier':                      'float16',
        'Census_ProcessorClass':                                'category',
        'Census_PrimaryDiskTotalCapacity':                      'float32',
        'Census_PrimaryDiskTypeName':                           'category',
        'Census_SystemVolumeTotalCapacity':                     'float32',
        'Census_HasOpticalDiskDrive':                           'int8',
        'Census_TotalPhysicalRAM':                              'float32',
        'Census_ChassisTypeName':                               'category',
        'Census_InternalPrimaryDiagonalDisplaySizeInInches':    'float16',
        'Census_InternalPrimaryDisplayResolutionHorizontal':    'float16',
        'Census_InternalPrimaryDisplayResolutionVertical':      'float16',
        'Census_PowerPlatformRoleName':                         'category',
        'Census_InternalBatteryType':                           'category',
        'Census_InternalBatteryNumberOfCharges':                'float32',
        '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'
        }

# start = time.time()
# train_df = pd.read_csv('train.csv', dtype=dtypes)
# print('Train data loaded in '+str(round(time.time()-start,2))+' seconds.\n')

# column_names=list(train_df.columns.values)
# print('Columns originally in Train: '+str(len(column_names))+'\n')

# RemoveColumns = ['PuaMode', 'Census_ProcessorClass', 'DefaultBrowsersIdentifier', 'Census_IsFlightingInternal', 'Census_InternalBatteryType', 'Census_ThresholdOptIn','Census_IsWIMBootEnabled', 'SmartScreen', 'OrganizationIdentifier', 'SMode']
# #SMode due to it being removed in Test data

# train_df.drop(RemoveColumns, axis=1, inplace=True)

# train_column_names=list(train_df.columns.values)
# print('Columns after removing in Train: '+str(len(train_column_names))+'\n')
# print('Columns removed: ',RemoveColumns,'\n')

# def fill_with_max(df):
#     x = df.value_counts().argmax()
#     df.fillna(x,inplace = True)

# column_names=list(train_df.columns.values)
# for column in column_names:
#     fill_with_max(train_df[column])

# train_df_category = train_df.select_dtypes(include='category')

# #start = time.time()
# for i in range(len(train_df_category.columns)):
#     labelencoder_X_i = LabelEncoder()
#     train_df[train_df_category.columns[i]] = labelencoder_X_i.fit_transform(train_df[train_df_category.columns[i]])
    #print(train_df.columns[i]) 
#print(time.time()-start)

# for i in range(len(train_df_category.columns)):
#     train_df[train_df_category.columns[i]] = train_df[train_df_category.columns[i]].astype(np.int8)

#print(train_df.info())
#print(train_df.columns)
#print(len(train_df.columns))

# start = time.time()
# onehotencoder = OneHotEncoder(categorical_features=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],sparse = False)
# train_df_final_coded = onehotencoder.fit_transform(train_df_final)
# print(time.time()-start)

#train_df.to_csv('ModelTrain.csv', index=False)

start = time.time()
train = pd.read_csv('ModelTrain.csv')
#,dtype=dtypes
print('Train data loaded in '+str(round(time.time()-start,2))+' seconds.\n')

#train['MachineIdentifier'] = train.index.astype('uint32')

#gc.collect()

X = train.drop('HasDetections',axis=1)
X = X.iloc[:].values
Y = train['HasDetections'].values

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.5, random_state = 0)

def plotRoc(testLabels, y_pred_probabilities, label):
    oneHotEncoding = np.zeros([testLabels.shape[0], 10])
    y_pred_probabilities = y_pred_probabilities.T
    for i in range(testLabels.shape[0]):
        oneHotEncoding[i][testLabels[i]] = 1
    plt.figure()
    oneHotEncoding = oneHotEncoding.T
    ClassSize = 0
    for i in range (1):
        auc_score = roc_auc_score(oneHotEncoding[i], y_pred_probabilities[i])
        fpr, tpr, thresholds = roc_curve(oneHotEncoding[i], y_pred_probabilities[i])
        ClassSize+=1
        plt.plot(fpr, tpr, label="Class: " + str(i) + ": " + str(auc_score)[0:5], linewidth=0.5)
    
    print(ClassSize)
    plt.legend(loc="lower right")
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.plot([0, 1], [0, 1], 'k--', linewidth=2)
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC Curves for ' + label)
    plt.savefig(label+'.png')

m = CatBoostRegressor(iterations=100,learning_rate=0.02,depth=12,eval_metric='RMSE',random_seed = 23,bagging_temperature = 0.2,od_type='Iter',metric_period = 75,od_wait=100)
m.fit(X_train, Y_train,eval_set=(X_test,Y_test),cat_features=categorical_features_pos,use_best_model=True,verbose=True)

m = lgb.train(params,d_train,100)
dict1={}
modelname="CatBoost_Model.pkl"
dict1['model']=m
joblib.dump(dict1,open(modelname,'wb'))

#m.fit(X_train[:100], Y_train[:100])
YProb = m.predict_proba(X_test)
YPred = m.predict(X_test)

print(confusion_matrix(Y_test, YPred))
print(np.mean(Y_test==YPred)*100)
plotRoc(Y_test,YProb,'ROC_Curve')