Case Study - Microsoft Malware Prediction.ipynb

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    "# Microsoft Malware Predction\n",
    "\n",
    "Dataset from: https://www.kaggle.com/c/microsoft-malware-prediction/data\n",
    "\n",
    "# Problem Description\n",
    "\n",
    "The malware industry continues to be a well-organized, well-funded market dedicated to evading traditional security measures. Once a computer is infected by malware, criminals can hurt consumers and enterprises in many ways.\n",
    "\n",
    "With more than one *billion* enterprise and consumer customers, Microsoft takes this problem very seriously and is deeply invested in improving security.\n",
    "\n",
    "As one part of their overall strategy for doing so, Microsoft is challenging the data science community to develop techniques to predict if a machine will soon be hit with malware. As with their previous, Malware Challenge (2015), Microsoft is providing Kagglers with an unprecedented malware dataset to encourage open-source progress on effective techniques for predicting malware occurrences.\n",
    "\n",
    "Can you help protect more than one billion machines from damage BEFORE it happens?\n",
    "\n",
    "\n",
    "# Data Description: \n",
    "\n",
    "The goal of this competition is to predict a Windows machine’s probability of getting infected by various families of malware, based on different properties of that machine. The telemetry data containing these properties and the machine infections was generated by combining heartbeat and threat reports collected by Microsoft's endpoint protection solution, Windows Defender.\n",
    "\n",
    "Each row in this dataset corresponds to a machine, uniquely identified by a __MachineIdentifier. HasDetections__ is the ground truth and indicates that Malware was detected on the machine. Using the information and labels in __train.csv__, you must predict the value for __HasDetections__ for each machine in __test.csv__.\n",
    "\n",
    "The sampling methodology used to create this dataset was designed to meet certain business constraints, both in regards to user privacy as well as the time period during which the machine was running. Malware detection is inherently a time-series problem, but it is made complicated by the introduction of new machines, machines that come online and offline, machines that receive patches, machines that receive new operating systems, etc. While the dataset provided here has been roughly split by time, the complications and sampling requirements mentioned above may mean you may see imperfect agreement between your cross validation, public, and private scores! Additionally, this dataset is not representative of Microsoft customers’ machines in the wild; it has been sampled to include a much larger proportion of malware machines.\n",
    "\n",
    "\n",
    "Feature | Description\n",
    "---|---\n",
    "    MachineIdentifier | Individual machine ID\n",
    "    ProductName | Defender state information e.g. win8defender\n",
    "    EngineVersion | Defender state information e.g. 1.1.12603.0\n",
    "    AppVersion | Defender state information e.g. 4.9.10586.0\n",
    "    AvSigVersion | Defender state information e.g. 1.217.1014.0\n",
    "    IsBeta | Defender state information e.g. false\n",
    "    RtpStateBitfield | NA\n",
    "    IsSxsPassiveMode | NA\n",
    "    DefaultBrowsersIdentifier | ID for the machine's default browser\n",
    "    AVProductStatesIdentifier | ID for the specific configuration of a user's antivirus software\n",
    "    AVProductsInstalled | NA\n",
    "    AVProductsEnabled | NA\n",
    "    HasTpm | True if machine has tpm\n",
    "    CountryIdentifier | ID for the country the machine is located in\n",
    "    CityIdentifier | ID for the city the machine is located in\n",
    "    OrganizationIdentifier | ID for the organization the machine belongs in, organization ID is mapped to both specific companies and broad industries\n",
    "    GeoNameIdentifier | ID for the geographic region a machine is located in\n",
    "    LocaleEnglishNameIdentifier | English name of Locale ID of the current user\n",
    "    Platform | Calculates platform name (of OS related properties and processor property)\n",
    "    Processor | This is the process architecture of the installed operating system\n",
    "    OsVer | Version of the current operating system\n",
    "    OsBuild | Build of the current operating system\n",
    "    OsSuite | Product suite mask for the current operating system.\n",
    "    OsPlatformSubRelease | Returns the OS Platform sub-release (Windows Vista, Windows 7, Windows 8, TH1, TH2)\n",
    "    OsBuildLab | Build lab that generated the current OS. Example: 9600.17630.amd64fre.winblue_r7.150109-2022\n",
    "    SkuEdition | The goal of this feature is to use the Product Type defined in the MSDN to map to a 'SKU-Edition' name that is useful in population reporting. The valid Product Type are defined in %sdxroot%\\data\\windowseditions.xml. This API has been used since Vista and Server 2008, so there are many Product Types that do not apply to Windows 10. The 'SKU-Edition' is a string value that is in one of three classes of results. The design must hand each class.\n",
    "    IsProtected | This is a calculated field derived from the Spynet Report's AV Products field. Returns: a. TRUE if there is at least one active and up-to-date antivirus product running on this machine. b. FALSE if there is no active AV product on this machine, or if the AV is active, but is not receiving the latest updates. c. null if there are no Anti Virus Products in the report. Returns: Whether a machine is protected.\n",
    "    AutoSampleOptIn | This is the SubmitSamplesConsent value passed in from the service, available on CAMP 9+\n",
    "    PuaMode | Pua Enabled mode from the service\n",
    "    SMode | This field is set to true when the device is known to be in 'S Mode', as in, Windows 10 S mode, where only Microsoft Store apps can be installed\n",
    "    IeVerIdentifier | NA\n",
    "    SmartScreen | This is the SmartScreen enabled string value from registry. This is obtained by checking in order, HKLM\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\SmartScreenEnabled and HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled. If the value exists but is blank, the value \"ExistsNotSet\" is sent in telemetry.\n",
    "    Firewall | This attribute is true (1) for Windows 8.1 and above if windows firewall is enabled, as reported by the service.\n",
    "    UacLuaenable | This attribute reports whether or not the \"administrator in Admin Approval Mode\" user type is disabled or enabled in UAC. The value reported is obtained by reading the regkey HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA.\n",
    "    Census_MDC2FormFactor | A grouping based on a combination of Device Census level hardware characteristics. The logic used to define Form Factor is rooted in business and industry standards and aligns with how people think about their device. (Examples: Smartphone, Small Tablet, All in One, Convertible...)\n",
    "    Census_DeviceFamily | AKA DeviceClass. Indicates the type of device that an edition of the OS is intended for. Example values: Windows.Desktop, Windows.Mobile, and iOS.Phone\n",
    "    Census_OEMNameIdentifier | NA\n",
    "    Census_OEMModelIdentifier | NA\n",
    "    Census_ProcessorCoreCount | Number of logical cores in the processor\n",
    "    Census_ProcessorManufacturerIdentifier | NA\n",
    "    Census_ProcessorModelIdentifier | NA\n",
    "    Census_ProcessorClass | A classification of processors into high/medium/low. Initially used for Pricing Level SKU. No longer maintained and updated\n",
    "    Census_PrimaryDiskTotalCapacity | Amount of disk space on primary disk of the machine in MB\n",
    "    Census_PrimaryDiskTypeName | Friendly name of Primary Disk Type - HDD or SSD\n",
    "    Census_SystemVolumeTotalCapacity | The size of the partition that the System volume is installed on in MB\n",
    "    Census_HasOpticalDiskDrive | True indicates that the machine has an optical disk drive (CD/DVD)\n",
    "    Census_TotalPhysicalRAM | Retrieves the physical RAM in MB\n",
    "    Census_ChassisTypeName | Retrieves a numeric representation of what type of chassis the machine has. A value of 0 means xx\n",
    "    Census_InternalPrimaryDiagonalDisplaySizeInInches | Retrieves the physical diagonal length in inches of the primary display\n",
    "    Census_InternalPrimaryDisplayResolutionHorizontal | Retrieves the number of pixels in the horizontal direction of the internal display.\n",
    "    Census_InternalPrimaryDisplayResolutionVertical | Retrieves the number of pixels in the vertical direction of the internal display\n",
    "    Census_PowerPlatformRoleName | Indicates the OEM preferred power management profile. This value helps identify the basic form factor of the device\n",
    "    Census_InternalBatteryType | NA\n",
    "    Census_InternalBatteryNumberOfCharges | NA\n",
    "    Census_OSVersion | Numeric OS version Example - 10.0.10130.0\n",
    "    Census_OSArchitecture | Architecture on which the OS is based. Derived from OSVersionFull. Example - amd64\n",
    "    Census_OSBranch | Branch of the OS extracted from the OsVersionFull. Example - OsBranch = fbl_partner_eeap where OsVersion = 6.4.9813.0.amd64fre.fbl_partner_eeap.140810-0005\n",
    "    Census_OSBuildNumber | OS Build number extracted from the OsVersionFull. Example - OsBuildNumber = 10512 or 10240\n",
    "    Census_OSBuildRevision | OS Build revision extracted from the OsVersionFull. Example - OsBuildRevision = 1000 or 16458\n",
    "    Census_OSEdition | Edition of the current OS. Sourced from HKLM\\Software\\Microsoft\\Windows NT\\CurrentVersion@EditionID in registry. Example: Enterprise\n",
    "    Census_OSSkuName | OS edition friendly name (currently Windows only)\n",
    "    Census_OSInstallTypeName | Friendly description of what install was used on the machine i.e. clean\n",
    "    Census_OSInstallLanguageIdentifier | NA\n",
    "    Census_OSUILocaleIdentifier | NA\n",
    "    Census_OSWUAutoUpdateOptionsName | Friendly name of the WindowsUpdate auto-update settings on the machine.\n",
    "    Census_IsPortableOperatingSystem | Indicates whether OS is booted up and running via Windows-To-Go on a USB stick.\n",
    "    Census_GenuineStateName | Friendly name of OSGenuineStateID. 0 = Genuine\n",
    "    Census_ActivationChannel | Retail license key or Volume license key for a machine.\n",
    "    Census_IsFlightingInternal | NA\n",
    "    Census_IsFlightsDisabled | Indicates if the machine is participating in flighting.\n",
    "    Census_FlightRing | The ring that the device user would like to receive flights for. This might be different from the ring of the OS which is currently installed if the user changes the ring after getting a flight from a different ring.\n",
    "    Census_ThresholdOptIn | NA\n",
    "    Census_FirmwareManufacturerIdentifier | NA\n",
    "    Census_FirmwareVersionIdentifier | NA\n",
    "    Census_IsSecureBootEnabled | Indicates if Secure Boot mode is enabled.\n",
    "    Census_IsWIMBootEnabled | NA\n",
    "    Census_IsVirtualDevice | Identifies a Virtual Machine (machine learning model)\n",
    "    Census_IsTouchEnabled | Is this a touch device ?\n",
    "    Census_IsPenCapable | Is the device capable of pen input ?\n",
    "    Census_IsAlwaysOnAlwaysConnectedCapable | Retreives information about whether the battery enables the device to be AlwaysOnAlwaysConnected .\n",
    "    Wdft_IsGamer | Indicates whether the device is a gamer device or not based on its hardware combination.\n",
    "    Wdft_RegionIdentifier | NA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import feather\n",
    "import gc\n",
    "from datetime import datetime\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "from sklearn.model_selection import train_test_split, KFold, StratifiedKFold\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from numba import jit\n",
    "\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [1] Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# optimized datatypes for loading a dataframe from train/test.csv\n",
    "dtypes = {\n",
    "        'MachineIdentifier':                                    'object',\n",
    "        'ProductName':                                          'category',\n",
    "        'EngineVersion':                                        'category',\n",
    "        'AppVersion':                                           'category',\n",
    "        'AvSigVersion':                                         'category',\n",
    "        'IsBeta':                                               'int8',\n",
    "        'RtpStateBitfield':                                     'float16',\n",
    "        'IsSxsPassiveMode':                                     'int8',\n",
    "        'DefaultBrowsersIdentifier':                            'float16',\n",
    "        'AVProductStatesIdentifier':                            'float32',\n",
    "        'AVProductsInstalled':                                  'float16',\n",
    "        'AVProductsEnabled':                                    'float16',\n",
    "        'HasTpm':                                               'int8',\n",
    "        'CountryIdentifier':                                    'int16',\n",
    "        'CityIdentifier':                                       'float32',\n",
    "        'OrganizationIdentifier':                               'float16',\n",
    "        'GeoNameIdentifier':                                    'float16',\n",
    "        'LocaleEnglishNameIdentifier':                          'int8',\n",
    "        'Platform':                                             'category',\n",
    "        'Processor':                                            'category',\n",
    "        'OsVer':                                                'category',\n",
    "        'OsBuild':                                              'int16',\n",
    "        'OsSuite':                                              'int16',\n",
    "        'OsPlatformSubRelease':                                 'category',\n",
    "        'OsBuildLab':                                           'category',\n",
    "        'SkuEdition':                                           'category',\n",
    "        'IsProtected':                                          'float16',\n",
    "        'AutoSampleOptIn':                                      'int8',\n",
    "        'PuaMode':                                              'category',\n",
    "        'SMode':                                                'float16',\n",
    "        'IeVerIdentifier':                                      'float16',\n",
    "        'SmartScreen':                                          'category',\n",
    "        'Firewall':                                             'float16',\n",
    "        'UacLuaenable':                                         'float32',\n",
    "        'Census_MDC2FormFactor':                                'category',\n",
    "        'Census_DeviceFamily':                                  'category',\n",
    "        'Census_OEMNameIdentifier':                             'float16',\n",
    "        'Census_OEMModelIdentifier':                            'float32',\n",
    "        'Census_ProcessorCoreCount':                            'float16',\n",
    "        'Census_ProcessorManufacturerIdentifier':               'float16',\n",
    "        'Census_ProcessorModelIdentifier':                      'float16',\n",
    "        'Census_ProcessorClass':                                'category',\n",
    "        'Census_PrimaryDiskTotalCapacity':                      'float32',\n",
    "        'Census_PrimaryDiskTypeName':                           'category',\n",
    "        'Census_SystemVolumeTotalCapacity':                     'float32',\n",
    "        'Census_HasOpticalDiskDrive':                           'int8',\n",
    "        'Census_TotalPhysicalRAM':                              'float32',\n",
    "        'Census_ChassisTypeName':                               'category',\n",
    "        'Census_InternalPrimaryDiagonalDisplaySizeInInches':    'float16',\n",
    "        'Census_InternalPrimaryDisplayResolutionHorizontal':    'float16',\n",
    "        'Census_InternalPrimaryDisplayResolutionVertical':      'float16',\n",
    "        'Census_PowerPlatformRoleName':                         'category',\n",
    "        'Census_InternalBatteryType':                           'category',\n",
    "        'Census_InternalBatteryNumberOfCharges':                'float32',\n",
    "        'Census_OSVersion':                                     'category',\n",
    "        'Census_OSArchitecture':                                'category',\n",
    "        'Census_OSBranch':                                      'category',\n",
    "        'Census_OSBuildNumber':                                 'int16',\n",
    "        'Census_OSBuildRevision':                               'int32',\n",
    "        'Census_OSEdition':                                     'category',\n",
    "        'Census_OSSkuName':                                     'category',\n",
    "        'Census_OSInstallTypeName':                             'category',\n",
    "        'Census_OSInstallLanguageIdentifier':                   'float16',\n",
    "        'Census_OSUILocaleIdentifier':                          'int16',\n",
    "        'Census_OSWUAutoUpdateOptionsName':                     'category',\n",
    "        'Census_IsPortableOperatingSystem':                     'int8',\n",
    "        'Census_GenuineStateName':                              'category',\n",
    "        'Census_ActivationChannel':                             'category',\n",
    "        'Census_IsFlightingInternal':                           'float16',\n",
    "        'Census_IsFlightsDisabled':                             'float16',\n",
    "        'Census_FlightRing':                                    'category',\n",
    "        'Census_ThresholdOptIn':                                'float16',\n",
    "        'Census_FirmwareManufacturerIdentifier':                'float16',\n",
    "        'Census_FirmwareVersionIdentifier':                     'float32',\n",
    "        'Census_IsSecureBootEnabled':                           'int8',\n",
    "        'Census_IsWIMBootEnabled':                              'float16',\n",
    "        'Census_IsVirtualDevice':                               'float16',\n",
    "        'Census_IsTouchEnabled':                                'int8',\n",
    "        'Census_IsPenCapable':                                  'int8',\n",
    "        'Census_IsAlwaysOnAlwaysConnectedCapable':              'float16',\n",
    "        'Wdft_IsGamer':                                         'float16',\n",
    "        'Wdft_RegionIdentifier':                                'float16',\n",
    "        'HasDetections':                                        'int8'\n",
    "}\n",
    "\n",
    "train = pd.read_csv('train.csv', dtype=dtypes, low_memory=True)\n",
    "test  = pd.read_csv('test.csv',  dtype=dtypes, low_memory=True)\n",
    "\n",
    "# store a copy as feather file\n",
    "#train.to_feather('train.ft')\n",
    "#test.to_feather('test.ft')\n",
    "\n",
    "# load train/test feather files\n",
    "#train = feather.read_dataframe('train.ft')\n",
    "#test = feather.read_dataframe('test.ft')\n",
    "\n",
    "# store labels and unique machine ids for later use\n",
    "target = train['HasDetections']\n",
    "machine_id = test['MachineIdentifier']\n",
    "train.drop(['MachineIdentifier', 'HasDetections'], inplace=True, axis=1)\n",
    "test.drop(['MachineIdentifier'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [4] EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 63% public LB, 37% private LB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 27.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# checking missing train data\n",
    "total = train.isnull().sum().sort_values(ascending = False)\n",
    "percent = (train.isnull().sum()/train.isnull().count()*100).sort_values(ascending = False)\n",
    "missing_train_data  = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PuaMode</th>\n",
       "      <td>8919174</td>\n",
       "      <td>99.974119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorClass</th>\n",
       "      <td>8884852</td>\n",
       "      <td>99.589407</td>\n",
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       "    <tr>\n",
       "      <th>DefaultBrowsersIdentifier</th>\n",
       "      <td>8488045</td>\n",
       "      <td>95.141637</td>\n",
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       "    <tr>\n",
       "      <th>Census_IsFlightingInternal</th>\n",
       "      <td>7408759</td>\n",
       "      <td>83.044030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalBatteryType</th>\n",
       "      <td>6338429</td>\n",
       "      <td>71.046809</td>\n",
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       "    <tr>\n",
       "      <th>Census_ThresholdOptIn</th>\n",
       "      <td>5667325</td>\n",
       "      <td>63.524472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsWIMBootEnabled</th>\n",
       "      <td>5659703</td>\n",
       "      <td>63.439038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmartScreen</th>\n",
       "      <td>3177011</td>\n",
       "      <td>35.610795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrganizationIdentifier</th>\n",
       "      <td>2751518</td>\n",
       "      <td>30.841487</td>\n",
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       "    <tr>\n",
       "      <th>SMode</th>\n",
       "      <td>537759</td>\n",
       "      <td>6.027686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CityIdentifier</th>\n",
       "      <td>325409</td>\n",
       "      <td>3.647477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wdft_IsGamer</th>\n",
       "      <td>303451</td>\n",
       "      <td>3.401352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wdft_RegionIdentifier</th>\n",
       "      <td>303451</td>\n",
       "      <td>3.401352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalBatteryNumberOfCharges</th>\n",
       "      <td>268755</td>\n",
       "      <td>3.012448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_FirmwareManufacturerIdentifier</th>\n",
       "      <td>183257</td>\n",
       "      <td>2.054109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsFlightsDisabled</th>\n",
       "      <td>160523</td>\n",
       "      <td>1.799286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_FirmwareVersionIdentifier</th>\n",
       "      <td>160133</td>\n",
       "      <td>1.794915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_OEMModelIdentifier</th>\n",
       "      <td>102233</td>\n",
       "      <td>1.145919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_OEMNameIdentifier</th>\n",
       "      <td>95478</td>\n",
       "      <td>1.070203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firewall</th>\n",
       "      <td>91350</td>\n",
       "      <td>1.023933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_TotalPhysicalRAM</th>\n",
       "      <td>80533</td>\n",
       "      <td>0.902686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsAlwaysOnAlwaysConnectedCapable</th>\n",
       "      <td>71343</td>\n",
       "      <td>0.799676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_OSInstallLanguageIdentifier</th>\n",
       "      <td>60084</td>\n",
       "      <td>0.673475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IeVerIdentifier</th>\n",
       "      <td>58894</td>\n",
       "      <td>0.660137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_PrimaryDiskTotalCapacity</th>\n",
       "      <td>53016</td>\n",
       "      <td>0.594251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_SystemVolumeTotalCapacity</th>\n",
       "      <td>53002</td>\n",
       "      <td>0.594094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalPrimaryDiagonalDisplaySizeInInches</th>\n",
       "      <td>47134</td>\n",
       "      <td>0.528320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalPrimaryDisplayResolutionHorizontal</th>\n",
       "      <td>46986</td>\n",
       "      <td>0.526661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalPrimaryDisplayResolutionVertical</th>\n",
       "      <td>46986</td>\n",
       "      <td>0.526661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorModelIdentifier</th>\n",
       "      <td>41343</td>\n",
       "      <td>0.463410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorManufacturerIdentifier</th>\n",
       "      <td>41313</td>\n",
       "      <td>0.463073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorCoreCount</th>\n",
       "      <td>41306</td>\n",
       "      <td>0.462995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVProductStatesIdentifier</th>\n",
       "      <td>36221</td>\n",
       "      <td>0.405998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVProductsInstalled</th>\n",
       "      <td>36221</td>\n",
       "      <td>0.405998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVProductsEnabled</th>\n",
       "      <td>36221</td>\n",
       "      <td>0.405998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsProtected</th>\n",
       "      <td>36044</td>\n",
       "      <td>0.404014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RtpStateBitfield</th>\n",
       "      <td>32318</td>\n",
       "      <td>0.362249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsVirtualDevice</th>\n",
       "      <td>15953</td>\n",
       "      <td>0.178816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_PrimaryDiskTypeName</th>\n",
       "      <td>12844</td>\n",
       "      <td>0.143967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UacLuaenable</th>\n",
       "      <td>10838</td>\n",
       "      <td>0.121482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ChassisTypeName</th>\n",
       "      <td>623</td>\n",
       "      <td>0.006983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GeoNameIdentifier</th>\n",
       "      <td>213</td>\n",
       "      <td>0.002387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_PowerPlatformRoleName</th>\n",
       "      <td>55</td>\n",
       "      <td>0.000616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OsBuildLab</th>\n",
       "      <td>21</td>\n",
       "      <td>0.000235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Processor</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OsVer</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsTouchEnabled</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LocaleEnglishNameIdentifier</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OsSuite</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Total    Percent\n",
       "PuaMode                                            8919174  99.974119\n",
       "Census_ProcessorClass                              8884852  99.589407\n",
       "DefaultBrowsersIdentifier                          8488045  95.141637\n",
       "Census_IsFlightingInternal                         7408759  83.044030\n",
       "Census_InternalBatteryType                         6338429  71.046809\n",
       "Census_ThresholdOptIn                              5667325  63.524472\n",
       "Census_IsWIMBootEnabled                            5659703  63.439038\n",
       "SmartScreen                                        3177011  35.610795\n",
       "OrganizationIdentifier                             2751518  30.841487\n",
       "SMode                                               537759   6.027686\n",
       "CityIdentifier                                      325409   3.647477\n",
       "Wdft_IsGamer                                        303451   3.401352\n",
       "Wdft_RegionIdentifier                               303451   3.401352\n",
       "Census_InternalBatteryNumberOfCharges               268755   3.012448\n",
       "Census_FirmwareManufacturerIdentifier               183257   2.054109\n",
       "Census_IsFlightsDisabled                            160523   1.799286\n",
       "Census_FirmwareVersionIdentifier                    160133   1.794915\n",
       "Census_OEMModelIdentifier                           102233   1.145919\n",
       "Census_OEMNameIdentifier                             95478   1.070203\n",
       "Firewall                                             91350   1.023933\n",
       "Census_TotalPhysicalRAM                              80533   0.902686\n",
       "Census_IsAlwaysOnAlwaysConnectedCapable              71343   0.799676\n",
       "Census_OSInstallLanguageIdentifier                   60084   0.673475\n",
       "IeVerIdentifier                                      58894   0.660137\n",
       "Census_PrimaryDiskTotalCapacity                      53016   0.594251\n",
       "Census_SystemVolumeTotalCapacity                     53002   0.594094\n",
       "Census_InternalPrimaryDiagonalDisplaySizeInInches    47134   0.528320\n",
       "Census_InternalPrimaryDisplayResolutionHorizontal    46986   0.526661\n",
       "Census_InternalPrimaryDisplayResolutionVertical      46986   0.526661\n",
       "Census_ProcessorModelIdentifier                      41343   0.463410\n",
       "Census_ProcessorManufacturerIdentifier               41313   0.463073\n",
       "Census_ProcessorCoreCount                            41306   0.462995\n",
       "AVProductStatesIdentifier                            36221   0.405998\n",
       "AVProductsInstalled                                  36221   0.405998\n",
       "AVProductsEnabled                                    36221   0.405998\n",
       "IsProtected                                          36044   0.404014\n",
       "RtpStateBitfield                                     32318   0.362249\n",
       "Census_IsVirtualDevice                               15953   0.178816\n",
       "Census_PrimaryDiskTypeName                           12844   0.143967\n",
       "UacLuaenable                                         10838   0.121482\n",
       "Census_ChassisTypeName                                 623   0.006983\n",
       "GeoNameIdentifier                                      213   0.002387\n",
       "Census_PowerPlatformRoleName                            55   0.000616\n",
       "OsBuildLab                                              21   0.000235\n",
       "Platform                                                 0   0.000000\n",
       "Processor                                                0   0.000000\n",
       "OsVer                                                    0   0.000000\n",
       "Census_IsTouchEnabled                                    0   0.000000\n",
       "LocaleEnglishNameIdentifier                              0   0.000000\n",
       "OsSuite                                                  0   0.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_train_data.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 24.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# checking missing test data\n",
    "total = test.isnull().sum().sort_values(ascending = False)\n",
    "percent = (test.isnull().sum()/test.isnull().count()*100).sort_values(ascending = False)\n",
    "missing_test_data  = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PuaMode</th>\n",
       "      <td>7851065</td>\n",
       "      <td>99.972139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorClass</th>\n",
       "      <td>7835022</td>\n",
       "      <td>99.767854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DefaultBrowsersIdentifier</th>\n",
       "      <td>7546134</td>\n",
       "      <td>96.089277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsFlightingInternal</th>\n",
       "      <td>6673962</td>\n",
       "      <td>84.983408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalBatteryType</th>\n",
       "      <td>5979844</td>\n",
       "      <td>76.144803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMode</th>\n",
       "      <td>5831272</td>\n",
       "      <td>74.252950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ThresholdOptIn</th>\n",
       "      <td>5529515</td>\n",
       "      <td>70.410504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsWIMBootEnabled</th>\n",
       "      <td>5522707</td>\n",
       "      <td>70.323814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmartScreen</th>\n",
       "      <td>3498402</td>\n",
       "      <td>44.547170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrganizationIdentifier</th>\n",
       "      <td>2482129</td>\n",
       "      <td>31.606380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wdft_IsGamer</th>\n",
       "      <td>302857</td>\n",
       "      <td>3.856453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wdft_RegionIdentifier</th>\n",
       "      <td>302857</td>\n",
       "      <td>3.856453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalBatteryNumberOfCharges</th>\n",
       "      <td>239233</td>\n",
       "      <td>3.046292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_FirmwareManufacturerIdentifier</th>\n",
       "      <td>198634</td>\n",
       "      <td>2.529321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CityIdentifier</th>\n",
       "      <td>191962</td>\n",
       "      <td>2.444363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_FirmwareVersionIdentifier</th>\n",
       "      <td>177624</td>\n",
       "      <td>2.261789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsFlightsDisabled</th>\n",
       "      <td>125801</td>\n",
       "      <td>1.601897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_OEMModelIdentifier</th>\n",
       "      <td>95935</td>\n",
       "      <td>1.221596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_TotalPhysicalRAM</th>\n",
       "      <td>95051</td>\n",
       "      <td>1.210339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsAlwaysOnAlwaysConnectedCapable</th>\n",
       "      <td>90152</td>\n",
       "      <td>1.147957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_OEMNameIdentifier</th>\n",
       "      <td>89546</td>\n",
       "      <td>1.140241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_PrimaryDiskTotalCapacity</th>\n",
       "      <td>74701</td>\n",
       "      <td>0.951211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_SystemVolumeTotalCapacity</th>\n",
       "      <td>74690</td>\n",
       "      <td>0.951071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorModelIdentifier</th>\n",
       "      <td>61314</td>\n",
       "      <td>0.780747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorManufacturerIdentifier</th>\n",
       "      <td>61281</td>\n",
       "      <td>0.780326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ProcessorCoreCount</th>\n",
       "      <td>61277</td>\n",
       "      <td>0.780275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_OSInstallLanguageIdentifier</th>\n",
       "      <td>58743</td>\n",
       "      <td>0.748009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firewall</th>\n",
       "      <td>58472</td>\n",
       "      <td>0.744558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IeVerIdentifier</th>\n",
       "      <td>49796</td>\n",
       "      <td>0.634081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalPrimaryDiagonalDisplaySizeInInches</th>\n",
       "      <td>42610</td>\n",
       "      <td>0.542578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalPrimaryDisplayResolutionVertical</th>\n",
       "      <td>42511</td>\n",
       "      <td>0.541317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_InternalPrimaryDisplayResolutionHorizontal</th>\n",
       "      <td>42511</td>\n",
       "      <td>0.541317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RtpStateBitfield</th>\n",
       "      <td>32222</td>\n",
       "      <td>0.410301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVProductStatesIdentifier</th>\n",
       "      <td>23767</td>\n",
       "      <td>0.302639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVProductsInstalled</th>\n",
       "      <td>23767</td>\n",
       "      <td>0.302639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVProductsEnabled</th>\n",
       "      <td>23767</td>\n",
       "      <td>0.302639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsProtected</th>\n",
       "      <td>23649</td>\n",
       "      <td>0.301136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_IsVirtualDevice</th>\n",
       "      <td>17291</td>\n",
       "      <td>0.220176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UacLuaenable</th>\n",
       "      <td>7865</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_PrimaryDiskTypeName</th>\n",
       "      <td>6807</td>\n",
       "      <td>0.086677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_ChassisTypeName</th>\n",
       "      <td>341</td>\n",
       "      <td>0.004342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GeoNameIdentifier</th>\n",
       "      <td>147</td>\n",
       "      <td>0.001872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_PowerPlatformRoleName</th>\n",
       "      <td>26</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OsBuildLab</th>\n",
       "      <td>17</td>\n",
       "      <td>0.000216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_OSEdition</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Census_GenuineStateName</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Processor</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LocaleEnglishNameIdentifier</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasTpm</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Total    Percent\n",
       "PuaMode                                            7851065  99.972139\n",
       "Census_ProcessorClass                              7835022  99.767854\n",
       "DefaultBrowsersIdentifier                          7546134  96.089277\n",
       "Census_IsFlightingInternal                         6673962  84.983408\n",
       "Census_InternalBatteryType                         5979844  76.144803\n",
       "SMode                                              5831272  74.252950\n",
       "Census_ThresholdOptIn                              5529515  70.410504\n",
       "Census_IsWIMBootEnabled                            5522707  70.323814\n",
       "SmartScreen                                        3498402  44.547170\n",
       "OrganizationIdentifier                             2482129  31.606380\n",
       "Wdft_IsGamer                                        302857   3.856453\n",
       "Wdft_RegionIdentifier                               302857   3.856453\n",
       "Census_InternalBatteryNumberOfCharges               239233   3.046292\n",
       "Census_FirmwareManufacturerIdentifier               198634   2.529321\n",
       "CityIdentifier                                      191962   2.444363\n",
       "Census_FirmwareVersionIdentifier                    177624   2.261789\n",
       "Census_IsFlightsDisabled                            125801   1.601897\n",
       "Census_OEMModelIdentifier                            95935   1.221596\n",
       "Census_TotalPhysicalRAM                              95051   1.210339\n",
       "Census_IsAlwaysOnAlwaysConnectedCapable              90152   1.147957\n",
       "Census_OEMNameIdentifier                             89546   1.140241\n",
       "Census_PrimaryDiskTotalCapacity                      74701   0.951211\n",
       "Census_SystemVolumeTotalCapacity                     74690   0.951071\n",
       "Census_ProcessorModelIdentifier                      61314   0.780747\n",
       "Census_ProcessorManufacturerIdentifier               61281   0.780326\n",
       "Census_ProcessorCoreCount                            61277   0.780275\n",
       "Census_OSInstallLanguageIdentifier                   58743   0.748009\n",
       "Firewall                                             58472   0.744558\n",
       "IeVerIdentifier                                      49796   0.634081\n",
       "Census_InternalPrimaryDiagonalDisplaySizeInInches    42610   0.542578\n",
       "Census_InternalPrimaryDisplayResolutionVertical      42511   0.541317\n",
       "Census_InternalPrimaryDisplayResolutionHorizontal    42511   0.541317\n",
       "RtpStateBitfield                                     32222   0.410301\n",
       "AVProductStatesIdentifier                            23767   0.302639\n",
       "AVProductsInstalled                                  23767   0.302639\n",
       "AVProductsEnabled                                    23767   0.302639\n",
       "IsProtected                                          23649   0.301136\n",
       "Census_IsVirtualDevice                               17291   0.220176\n",
       "UacLuaenable                                          7865   0.100150\n",
       "Census_PrimaryDiskTypeName                            6807   0.086677\n",
       "Census_ChassisTypeName                                 341   0.004342\n",
       "GeoNameIdentifier                                      147   0.001872\n",
       "Census_PowerPlatformRoleName                            26   0.000331\n",
       "OsBuildLab                                              17   0.000216\n",
       "Census_OSEdition                                         5   0.000064\n",
       "Census_GenuineStateName                                  1   0.000013\n",
       "Platform                                                 0   0.000000\n",
       "Processor                                                0   0.000000\n",
       "LocaleEnglishNameIdentifier                              0   0.000000\n",
       "HasTpm                                                   0   0.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_test_data.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load AvSigVersion Dates\n",
    "# source: https://www.kaggle.com/c/microsoft-malware-prediction/discussion/74596#483796\n",
    "dates1 = np.load('train_AvSigVersion.npy')[()]\n",
    "dates2 = np.load('train_AvSigVersion2.npy')[()]\n",
    "dates3 = np.load('test_AvSigVersion.npy')[()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process the dates, create a dictionary to store all dates\n",
    "date = {}\n",
    "for key, value in zip(dates1.keys(), dates1.values()):\n",
    "    if key not in date.keys():\n",
    "        date[key] = value\n",
    "        \n",
    "for key, value in zip(dates2.keys(), dates2.values()):\n",
    "    if key not in date.keys():\n",
    "        date[key] = value\n",
    "        \n",
    "for key, value in zip(dates3.keys(), dates3.values()):\n",
    "    if key not in date.keys():\n",
    "        date[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for stripping month, year, day, week data. try/except since there are missing dates\n",
    "def strip_month(feature):\n",
    "    try:\n",
    "        return datetime.strptime(feature, '%b %d,%Y %I:%M %p UTC').month\n",
    "    except: \n",
    "        return 0\n",
    "\n",
    "def strip_year(feature):\n",
    "    try:\n",
    "        return datetime.strptime(feature, '%b %d,%Y %I:%M %p UTC').year\n",
    "    except: \n",
    "        return 0\n",
    "\n",
    "def strip_day(feature):\n",
    "    try:\n",
    "        return datetime.strptime(feature, '%b %d,%Y %I:%M %p UTC').day\n",
    "    except: \n",
    "        return 0\n",
    "\n",
    "def strip_week(feature):\n",
    "    try:\n",
    "        # be careful, there is a leap week. apparently there is a 53rd week!\n",
    "        return datetime.strptime(feature, '%b %d,%Y %I:%M %p UTC').isocalendar()[1]\n",
    "    except: \n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 11min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# create a numerical feature that extracts month, year, day, and week information from\n",
    "train['Month'] = train['AvSigVersion'].map(date).apply(strip_month)\n",
    "test['Month'] = test['AvSigVersion'].map(date).apply(strip_month)\n",
    "train['Year'] = train['AvSigVersion'].map(date).apply(strip_year)\n",
    "test['Year'] = test['AvSigVersion'].map(date).apply(strip_year)\n",
    "train['Day'] = train['AvSigVersion'].map(date).apply(strip_day)\n",
    "test['Day'] = test['AvSigVersion'].map(date).apply(strip_day)\n",
    "train['Week'] = train['AvSigVersion'].map(date).apply(strip_week)\n",
    "test['Week'] = test['AvSigVersion'].map(date).apply(strip_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4e2797f98>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Detection Counts by Month\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(train['Month'], hue=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4820a1c18>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(test['Month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4b7bb1198>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Detection Counts by Year\n",
    "sns.countplot(train['Year'], hue=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4986eb0b8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(16,8))\n",
    "sns.countplot(test['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e48d1e0550>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Detection Counts by Week\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(train['Week'], hue=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4a258f2b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(test['Week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4f22ab668>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vSbJ1Vc1brf1X9tUff1KSFauPr7X2kdbaotbaoh122OGxHi4AAAAbuCFXt64kH09yY2vtrDHtTx7T7QVJruu3L01yXF+Zerckuye5KsnVSXbvK1lvltHiXpe21lqSK5O8qG9/QpJLxuzrhH77RUn+ufcHAACACc1bc5d1dlCSP0xybVUt7W1vymh16gUZXf58S5I/TpLW2vVV9ZkkN2S0MvYprbWVSVJVpya5PMkmSc5prV3f9/fGJBdW1V8luSajUJ7+/ZNVtSyjM8jHDXicAAAAzBGDheTW2tcz/nuDL5tkm3ckecc47ZeNt11r7Qf55eXaY9t/muTYtRkvAAAAzMjq1gAAALAhEJIBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgmzfbAwCA9d1+bzh/rfovOfP4gUYCAAzNmWQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADoBgvJVbVzVV1ZVTdW1fVV9Zrevm1VXVFVN/Xv2/T2qqqzq2pZVX2nqvYds68Tev+bquqEMe37VdW1fZuzq6omqwEAAACTmTfgvh9J8uettW9V1VZJllTVFUlenuQrrbV3VtVpSU5L8sYkRyTZvX8dkORDSQ6oqm2TvCXJoiSt7+fS1tp9vc/JSb6R5LIkhyf5Yt/neDUAAGCjtN8bzp9y34u3OnOt9r3L6deu7XBgvTVYSG6t3ZHkjn77waq6MclOSY5O8nu923lJvppRgD06yfmttZbkG1W1dVU9ufe9orW2Ikl60D68qr6a5ImttX/r7ecnOSajkDxRDQAY3K1n7D3lvp5YAsD6ZUbek1xVuyZZmOSbSX6zB+hVQXrH3m2nJLeN2Wx5b5usffk47ZmkxurjOrmqFlfV4rvvvntdDw8AAIA5YvCQXFVPSHJRkte21v5rsq7jtLV1aJ+y1tpHWmuLWmuLdthhh7XZFAAAgDlo0JBcVZtmFJD/vrX2D735zn4Zdfr3u3r78iQ7j9l8fpLb19A+f5z2yWoAAADAhIZc3bqSfDzJja21s8Y8dGmSVStUn5DkkjHtx/dVrg9M8kC/VPryJIdV1TZ9lerDklzeH3uwqg7stY5fbV/j1QAAAIAJDbm69UFJ/jDJtVW1tLe9Kck7k3ymqk5KcmuSY/tjlyU5MsmyJA8leUWStNZWVNXbk1zd+52xahGvJK9Kcm6SLTNasOuLvX2iGgAAADChIVe3/nrGf99wkjx3nP4tySkT7OucJOeM0744yV7jtN87Xg0AAACYzIysbg0AAAAbAiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAOiEZAAAAuimF5Kr6ylTaAAAAYEM2b7IHq2qLJI9Lsn1VbZOk+kNPTPKUgccGAAAAM2rSkJzkj5O8NqNAvCS/DMn/leSDA44LAAAAZtykIbm19jdJ/qaqXt1ae/8MjQkAAABmxZrOJCdJWmvvr6rfSbLr2G1aa+cPNC4AAACYcVMKyVX1ySRPS7I0ycre3JIIyQAAAMwZUwrJSRYl2bO11oYcDAAAAMymqX5O8nVJ/tuQAwEAAIDZNtUzydsnuaGqrkrys1WNrbWjBhkVAAAAzIKphuS3DjmIjd1+b5j6W7uXnHn8gCMBAADYuE11deuvDT0QAAAAmG1TXd36wYxWs06SzZJsmuTHrbUnDjUwAAAAmGlTPZO81dj7VXVMkv0HGREAAADMkqm+J/lXtNY+V1WnTfdgWLNbz9h7yn13Of3aAUcCAAAw90z1cusXjrn7Gxl9brLPTAYAAGBOmeqZ5P9rzO1HktyS5OhpHw0AAADMoqm+J/kVQw8EAAAAZttvTKVTVc2vqour6q6qurOqLqqq+UMPDgAAAGbSlEJykk8kuTTJU5LslOTzvQ0AAADmjKmG5B1aa59orT3Sv85NssOA4wIAAIAZN9WQfE9VvayqNulfL0ty75ADAwAAgJk21dWtT0zygSTvzeijn/5XEot5wRy33xvOn3LfJWceP+BIAABgZkw1JL89yQmttfuSpKq2TfLujMIzAAAAzAlTvdx6n1UBOUlaayuSLBxmSAAAADA7pnom+TeqapvVziRPdVtgI3DrGXtPue8up1874EgAAGDdTTXovifJ/6qqz2b0nuQXJ3nHYKMCAACAWTClkNxaO7+qFic5NEkleWFr7YZBRwYAAAAzbMqXTPdQLBgDAAAwZ0114S4AAACY84RkAAAA6IRkAAAA6IRkAAAA6IRkAAAA6Ka8uvXaqqpzkvx+krtaa3v1trcm+aMkd/dub2qtXdYf+4skJyVZmeRPW2uX9/bDk/xNkk2SfKy19s7evluSC5Nsm+RbSf6wtfZwVW2e5Pwk+yW5N8kftNZuGeo4AQAYzn5vOH/KfZecefyAIwE2FkOeST43yeHjtL+3tbagf60KyHsmOS7JM/o2f1tVm1TVJkk+mOSIJHsmeUnvmyR/3fe1e5L7MgrY6d/va609Pcl7ez8AAABYo8FCcmvtX5KsmGL3o5Nc2Fr7WWvt5iTLkuzfv5a11n7QWns4ozPHR1dVJTk0yWf79uclOWbMvs7rtz+b5Lm9PwAAAExqsMutJ3FqVR2fZHGSP2+t3ZdkpyTfGNNneW9LkttWaz8gyXZJ7m+tPTJO/51WbdNae6SqHuj971l9IFV1cpKTk2SXXXZ57EcGAHPcrWfsPeW+u5x+7YAjAYBhzHRI/lCStydp/ft7kpyYZLwzvS3jn+luk/TPGh771cbWPpLkI0myaNGicfsATBfvqwMAWP/NaEhurd256nZVfTTJF/rd5Ul2HtN1fpLb++3x2u9JsnVVzetnk8f2X7Wv5VU1L8mTMvXLvgEAYEaszYuniRdQYabM6EdAVdWTx9x9QZLr+u1LkxxXVZv3Vat3T3JVkquT7F5Vu1XVZhkt7nVpa60luTLJi/r2JyS5ZMy+Tui3X5Tkn3t/AAAAmNSQHwF1QZLfS7J9VS1P8pYkv1dVCzK6/PmWJH+cJK2166vqM0luSPJIklNaayv7fk5NcnlGHwF1Tmvt+l7ijUkurKq/SnJNko/39o8n+WRVLcvoDPJxQx0jAADMhIu3OjO3nnHmlPtbEwDW3WAhubX2knGaPz5O26r+70jyjnHaL0ty2TjtP8ho9evV23+a5Ni1GiwAbKDWZiGtxBNn5jYLywHTYUYvtwYAAID12Wx8BBQAMIm1Wczn4q0GHMhGyJlIAJxJBgAAgE5IBgAAgM7l1gDrIZd8AgDMDmeSAQAAoBOSAQAAoBOSAQAAoPOeZAAAYKNi7Q8mIyTDY7A2n2W65MzjBxwJAAAwHYRkmCFr84pl4lVLgMTZHgBmnpDMesWTIQA2Jv7uAUMwtzw2Fu4CAACAzplkAGBOW5v1Iy7easCBALBBEJIBAADWc17wmzkutwYAAIDOmWQGtTaveCVe9QJgw+dsD8wO//eYLs4kAwAAQCckAwAAQOdyawDYyLlEEQB+SUgGYFqsTdBacubxA44EGJL/67D2bj1j7yn33eX0awccCVPhcmsAAADonEkGNlrOhgAAsDpnkgEAAKBzJhkAgEF4HyZzmUUPf2mu/V93JhkAAAA6Z5IBgBnl7AsA6zNnkgEAAKATkgEAAKBzufVGaO0ucztzyn03hDfhA8DGbK4trgMMZ2N+a4wzyQAAANAJyQAAANC53Bo2IGtz2cuSM48fcCQAADA3CckAAABsMIZeX0FIZk5ZmzOtibOtTJ3FbgBgOP7Osj4RkgEANmAb8wq0AEMQktmoedUSAAAYS0gGAACmnasc2FD5CCgAAADonEmGOcql5AAAsPaEZGC94XOgAQCYbUIyADPOlQ4AwPpKSAYAAGBWrU8LvQnJAACwDlwVA3OT1a0BAACgcyYZAACydpd7Jj7bF+YqZ5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACgE5IBAACg8xFQwAbp1jP2nnLfXU6/dsCRAAAwlziTDAAAAJ2QDAAAAN1gIbmqzqmqu6rqujFt21bVFVV1U/++TW+vqjq7qpZV1Xeqat8x25zQ+99UVSeMad+vqq7t25xdVTVZDQAAAFiTIc8kn5vk8NXaTkvyldba7km+0u8nyRFJdu9fJyf5UDIKvEnekuSAJPsnecuY0Puh3nfVdoevoQYAAABMarCQ3Fr7lyQrVms+Osl5/fZ5SY4Z035+G/lGkq2r6slJnp/kitbaitbafUmuSHJ4f+yJrbV/a621JOevtq/xagAAAMCkZvo9yb/ZWrsjSfr3HXv7TkluG9NveW+brH35OO2T1fg1VXVyVS2uqsV33333Oh8UAAAAc8P6snBXjdPW1qF9rbTWPtJaW9RaW7TDDjus7eYAAADMMTMdku/sl0qnf7+rty9PsvOYfvOT3L6G9vnjtE9WAwAAACY10yH50iSrVqg+IcklY9qP76tcH5jkgX6p9OVJDquqbfqCXYclubw/9mBVHdhXtT5+tX2NVwMAAAAmNW+oHVfVBUl+L8n2VbU8o1Wq35nkM1V1UpJbkxzbu1+W5Mgky5I8lOQVSdJaW1FVb09yde93Rmtt1WJgr8poBe0tk3yxf2WSGgAAADCpwUJya+0lEzz03HH6tiSnTLCfc5KcM0774iR7jdN+73g1AAAAYE3Wl4W7AAAAYNYJyQAAANAJyQAAANAJyQAAANAJyQAAANAJyQAAANAJyQAAANAJyQAAANAJyQAAANDNm+0BAMDa2u8N569V/yVnHj/QSACAucaZZAAAAOiEZAAAAOhcbg0wR63NJckuRwYAGHEmGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADqfkwzAnHfrGXtPue8up1874EgAgPWdM8kAAADQCckAAADQCckAAADQeU8yAN6zCwDQOZMMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAAnZAMAAAA3ayE5Kq6paquraqlVbW4t21bVVdU1U39+za9varq7KpaVlXfqap9x+znhN7/pqo6YUz7fn3/y/q2NfNHCQAAwIZmNs8kH9JaW9BaW9Tvn5bkK6213ZN8pd9PkiOS7N6/Tk7yoWQUqpO8JckBSfZP8pZVwbr3OXnMdocPfzgAAABs6Nany62PTnJev31ekmPGtJ/fRr6RZOuqenKS5ye5orW2orV2X5IrkhzeH3tia+3fWmstyflj9gUAAAATmq2Q3JJ8uaqWVNXJve03W2t3JEn/vmNv3ynJbWO2Xd7bJmtfPk47AAAATGreLNU9qLV2e1XtmOSKqvruJH3Hez9xW4f2X9/xKKCfnCS77LLL5CMGAABgzpuVM8mttdv797uSXJzRe4rv7JdKp3+/q3dfnmTnMZvPT3L7Gtrnj9M+3jg+0lpb1FpbtMMOOzzWwwIAAGADN+MhuaoeX1Vbrbqd5LAk1yW5NMmqFapPSHJJv31pkuP7KtcHJnmgX459eZLNnIPDAAAO+0lEQVTDqmqbvmDXYUku7489WFUH9lWtjx+zLwAAAJjQbFxu/ZtJLu6fyjQvyadaa1+qqquTfKaqTkpya5Jje//LkhyZZFmSh5K8Iklaayuq6u1Jru79zmitrei3X5Xk3CRbJvli/wIAAIBJzXhIbq39IMkzx2m/N8lzx2lvSU6ZYF/nJDlnnPbFSfZ6zIMFAABgo7I+fQQUAAAAzCohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAADohGQAAALo5G5Kr6vCq+l5VLauq02Z7PAAAAKz/5mRIrqpNknwwyRFJ9kzykqrac3ZHBQAAwPpuTobkJPsnWdZa+0Fr7eEkFyY5epbHBAAAwHquWmuzPYZpV1UvSnJ4a+2V/f4fJjmgtXbqav1OTnJyv/vbSb63DuW2T3LPYxiuehtPvbl8bOqpp97s1ZvLx6aeeurNTi311Jur9f57a22HNXWatw473hDUOG2/9mpAa+0jST7ymApVLW6tLXos+1Bv46g3l49NPfXUm716c/nY1FNPvdmppZ56G3u9uXq59fIkO4+5Pz/J7bM0FgAAADYQczUkX51k96rarao2S3JckktneUwAAACs5+bk5dattUeq6tQklyfZJMk5rbXrByr3mC7XVm+jqjeXj0099dSbvXpz+djUU0+92amlnnobdb05uXAXAAAArIu5erk1AAAArDUhGQAAADoheR1V1eFV9b2qWlZVp81AvXOq6q6qum4Gau1cVVdW1Y1VdX1VvWbgeltU1VVV9e1e721D1htTd5OquqaqvjADtW6pqmuramlVLZ6BeltX1Wer6rv93/F/H7DWb/fjWvX1X1X12qHq9Zqv678r11XVBVW1xYC1XtPrXD/UcY33/7uqtq2qK6rqpv59m4HrHduP8RdVNW0fqTBBrTP77+Z3quriqtp64Hpv77WWVtWXq+opQ9Yb89jrq6pV1fZD1quqt1bVD8f8HzxyyHq9/dX9b+D1VfWuIetV1afHHNstVbV04HoLquobq+brqtp/4HrPrKp/638jPl9VT5ymWuP+LR9qbpmk3lBzy0T1BplfJqk3yPwyUb0xj0/r/DLJ8Q0yv0x2fEPML5Mc3yDzyyT1pn1+maTWUHPLuM/ba7Rg8jf73PLpGi2ePGS9U2uUw6b17+yjWmu+1vIro8XAvp/kqUk2S/LtJHsOXPPgJPsmuW4Gju/JSfbtt7dK8u9DHl9Gn2v9hH570yTfTHLgDBznnyX5VJIvzECtW5JsP3SdMfXOS/LKfnuzJFvPUN1NkvxnRh/UPlSNnZLcnGTLfv8zSV4+UK29klyX5HEZLXT4T0l2H6DOr/3/TvKuJKf126cl+euB6+2R5LeTfDXJooFrHZZkXr/91zNwbE8cc/tPk3x4yHq9feeMFo/8j+n8vz/B8b01yeun+/dyknqH9P8Lm/f7Ow798xzz+HuSnD7w8X05yRH99pFJvjpwvauTPKffPjHJ26ep1rh/y4eaWyapN9TcMlG9QeaXSeoNMr9MVK/fn/b5ZZLjG2R+maTeIPPLZD/PMX2mbX6Z5PimfX6ZpNZQc8u4z9szej52XG//cJJXDVxvYZJdM9BzbGeS183+SZa11n7QWns4yYVJjh6yYGvtX5KsGLLGmFp3tNa+1W8/mOTGjILJUPVaa+1H/e6m/WvQFeWqan6S/zPJx4asMxv6K4UHJ/l4krTWHm6t3T9D5Z+b5Puttf8YuM68JFtW1byMAuxQn4O+R5JvtNYeaq09kuRrSV4w3UUm+P99dEYvdqR/P2bIeq21G1tr35uuGmuo9eX+80ySb2T0WfZD1vuvMXcfn2mcXyaZm9+b5P+dzlprqDeICeq9Ksk7W2s/633uGrhekqSqKsmLk1wwcL2WZNUZlydlGueXCer9dpJ/6bevSPJ/T1Otif6WDzK3TFRvwLllonqDzC+T1BtkflnDc7Fpn19m4bnfRPUGmV/WdHzTPb9MUm/a55dJag01t0z0vP3QJJ/t7dM5t4xbr7V2TWvtlumoMR4hed3slOS2MfeXZ8CJZDZV1a4ZvVLzzYHrbNIvcbkryRWttUHrJXlfRn9gfjFwnVVaki9X1ZKqOnngWk9NcneST9TocvKPVdXjB665ynGZxiew42mt/TDJu5PcmuSOJA+01r48ULnrkhxcVdtV1eMyetV354Fqre43W2t3JKM/gEl2nKG6M+3EJF8cukhVvaOqbkvy0iSnD1zrqCQ/bK19e8g6qzm1X/J5Tk3jpfkT+K0kz+6X1X2tqp41cL1Vnp3kztbaTQPXeW2SM/vvy7uT/MXA9a5LclS/fWwGmGNW+1s++NwyU88dplBvkPll9XpDzy9j683E/DLOz3PQ+WW1eoPPLxP8vgw2v6xWb9D5ZbVag80tqz9vz+gK2/vHvEA1rdloFnKCkLyOapy2OfdZWlX1hCQXJXntaq+UTrvW2srW2oKMXvHdv6r2GqpWVf1+krtaa0uGqjGOg1pr+yY5IskpVXXwgLXmZXQ534daawuT/DijS+oG1d97clSS/zlwnW0yOhOyW5KnJHl8Vb1siFqttRszulzviiRfyuitFY9MuhFTVlV/mdHP8++HrtVa+8vW2s691qlD1ekvpvxlBg7iq/lQkqclWZDRC0fvGbjevCTbZHS52xuSfKafhRnaSzLwi3Ddq5K8rv++vC79qpwBnZjR34UlGV0q+fB07nwm/5avT/WGml/Gqzfk/DK2XkbHM+j8Ms7xDTq/jFNv0Pllkt/PQeaXceoNNr+MU2uwuWX15+0ZXXn3a92GqjdkTlhFSF43y/Orr8bMz3CXe86Kqto0o/9of99a+4eZqtsvC/5qksMHLHNQkqOq6paMLpU/tKr+x4D10lq7vX+/K8nFGU0oQ1meZPmYV9k+m1FoHtoRSb7VWrtz4DrPS3Jza+3u1trPk/xDkt8Zqlhr7eOttX1bawdndJnk0GexVrmzqp6cJP37tF3Suj6oqhOS/H6Sl7bWZvJFxk9lmi45m8DTMnoB59t9jpmf5FtV9d+GKthau7M/gfhFko9m2PklGc0x/9Avgbsqoytypn/RlDH6WytemOTTQ9bpTshoXklGL/oN+vNsrX23tXZYa22/jJ6kf3+69j3B3/LB5paZfu4wUb2h5pcpHN+0zi/j1Bt0fhnv+IacXyb4eQ42v0zy+zLI/DJBvUHmlwn+7QabW1YZ87z9wCRb959lMlA2mqGckERIXldXJ9m9r+K2WUaXmF46y2OaNv0Vu48nubG1dtYM1Nuh+uqTVbVlRiHou0PVa639RWttfmtt14z+7f65tTbImcgkqarHV9VWq25ntKjIYKuUt9b+M8ltVfXbvem5SW4Yqt4YM3WW59YkB1bV4/rv6nMzev/NIKpqx/59l4z+iM7EMSajOeWEfvuEJJfMUN3BVdXhSd6Y5KjW2kMzUG/3MXePyrDzy7WttR1ba7v2OWZ5Rguq/OdQNVcFnu4FGXB+6T6X0XvPUlW/ldHigPcMXPN5Sb7bWls+cJ1k9MTuOf32oRn4hbExc8xvJPn/MlrwZjr2O9Hf8kHmlll47jBuvaHml0nqDTK/jFdvyPllkuMbZH6Z5PdlkPllDb+f0z6/TFJv2ueXSf7thppbxnvefmOSK5O8qHebzrllRnPCo9o0rwS2sXxl9N7Ef8/oVZm/nIF6F2R0mcvPM5oUTxqw1u9mdInEd5Is7V9HDlhvnyTX9HrXZRpXLp1C7d/LwKtbZ/Qe4W/3r+tn6PdlQZLF/Wf6uSTbDFzvcUnuTfKkGfp3e1tGE+R1ST6ZvgrmQLX+NaMXGb6d5LkD1fi1/99JtkvylYz+gH4lybYD13tBv/2zJHcmuXzAWssyWtdh1fwynatNj1fvov678p0kn89osZ3B6q32+C2Z3tWtxzu+Tya5th/fpUmePHC9zZL8j/4z/VaSQ4f+eSY5N8mfTFedNRzf7yZZ0v/PfzPJfgPXe01Gzyf+Pck7k9Q01Rr3b/lQc8sk9YaaWyaqN8j8Mkm9QeaXieqt1mfa5pdJjm+Q+WWSeoPML5P9PIeYXyY5vmmfXyapNdTcMu7z9oye717V/w/+z0zTc7NJ6v1pn1seyejFh49N579h9SIAAACw0XO5NQAAAHRCMgAAAHRCMgAAAHRCMgAAAHRCMgAAAHTz1twFANiQVNXKjD62ZdOMPh7jvCTva639YlYHBgAbACEZAOaen7TWFiRJVe2Y5FNJnpTkLbM6KgDYALjcGgDmsNbaXUlOTnJqjexaVf9aVd/qX7+TJFX1yao6etV2VfX3VXXUbI0bAGZLtdZmewwAwDSqqh+11p6wWtt9Sf63JA8m+UVr7adVtXuSC1pri6rqOUle11o7pqqelGRpkt1ba4/M+AEAwCxyuTUAbByqf980yQeqakGSlUl+K0laa1+rqg/2y7NfmOQiARmAjZGQDABzXFU9NaNAfFdG70u+M8kzM3rb1U/HdP1kkpcmOS7JiTM8TABYLwjJADCHVdUOST6c5AOttdYvpV7eWvtFVZ2QZJMx3c9NclWS/2ytXT/zowWA2SckA8Dcs2VVLc0vPwLqk0nO6o/9bZKLqurYJFcm+fGqjVprd1bVjUk+N8PjBYD1hoW7AIAkSVU9LqPPV963tfbAbI8HAGaDj4ACAFJVz0vy3STvF5AB2Jg5kwwAAACdM8kAAADQCckAAADQCckAAADQCckAAADQCckAAADQ/f8ehkw+dfMsAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Detection Counts by Day\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(train['Day'], hue=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e4f62a3438>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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McnmS/ZKc1903jv5emuSCqvqtJB/OLJRnfH1rVW3L7AryKROeJwAAAGvEZCG5u/9nFv5s8GVL7POqJK9aoP2yhfbr7k/ma7drz7f/U5LnPZDjBQAAgBWZ3RoAAAAeDiaf3RoAgLXj1HfeMkm/b3/u4ZP0C/BAuZIMAAAAgyvJAMCK+omLLp6k3z87+Scn6ReAfYsryQAAADAIyQAAADC43RpgjXr2JZsn6ffSE/9ikn4BAPYGriQDAADA4EoyAMDD2MkXf2iSfi/6yadN0i/A3s6VZAAAABiEZAAAABiEZAAAABiEZAAAABiEZAAAABjMbg3ss37n7c+cpN9fPfXySfoFAGB6riQDAADA4EoyPATv+8NnTdb3MT/355P1DQAALExIBhb1tjdPczvy8/+t25EBANg7CckAPOyc8K7/d7K+L3vOyybrGwDY+/lMMgAAAAxCMgAAAAxCMgAAAAw+k8yacs1//YnJ+v7+n/+zyfoGAAD2Dq4kAwAAwCAkAwAAwCAkAwAAwCAkAwAAwLBHIbmqrtyTNgAAAHg4W3J266p6VJLHJDmoqg5MUmPVY5N868THBgD7pGddfO5kff/5T542Wd8AsBbs7k9A/XySl2QWiK/N10Ly3yd5w4THBQAAACtuyZDc3f8lyX+pql/s7tev0DEBAAA8IJ/5ndsn6ffQXz1kkn7Ze+3uSnKSpLtfX1U/mOTw+X26+y0THRcAAACsuD0KyVX11iTfkeS6JPeN5k4iJAMAALBm7FFITrIpyRHd3VMeDAAAAKymPf07yTck+ZYpDwQAAABW255eST4oyceq6uok9+xs7O5nT3JUAAAAsAr2NCS/YsqDYGXd/saXTtLvIf/3qyfpFwAAYKXs6ezWfzn1gQC7d8l5x0/S74kvfM8k/QIAwMPNns5u/aXMZrNOkv2TPDLJP3b3Y6c6MAAAAFhpe3ol+Zvnn1fVSUmOmuSIAAAAYJXs6WeSv053/2lVnbXcB8Pas+33T5ys7yefeclkfQMAAPumPb3d+rlzTx+R2d9N9jeTAYC93okXTTPvwiUnTzNPBACra0+vJP/E3PK9SW5JMt0lQgAAAFgFj9iTjbr7Z+ce/1d3v6q771xqn6o6rKreX1U3VdWNVfXi0f74qrqiqm4eXw8c7VVVr6uqbVV1fVU9ba6vLWP7m6tqy1z706vqo2Of11VVLVUDAAAAlrJHIbmqNlTVu6rqzqq6o6ourqoNu9nt3iS/0t3fk+ToJGdU1RFJzkpyZXdvTHLleJ4kxyfZOB6nJzl71H58kpcn+YHMJgt7+VzoPXtsu3O/zaN9sRoAAACwqD293fqPkvxJkueN5z892v7PxXbo7tuT3D6Wv1RVNyU5NLPbtH9kbHZ+kg8keelof0t3d5IPVtUBVXXI2PaK7r4rSarqiiSbq+oDSR7b3X892t+S5KQk71miBgAAsAJufNMdk/T7vb/wxEn6hZ32NCSv7+4/mnv+5qp6yZ4WqarDkzw1yVVJnjgCdLr79qo6eGx2aJJb53bbPtqWat++QHuWqAEAAMBu3Pn690/S78G/+KOT9Luc9jQkf66qfjrJ28fzU5N8fk92rKpvSnJxkpd099+Pjw0vuOkCbf0g2vdYVZ2e2e3a+bZv+7YHsisAAMCKueP3rpmk3ye+5Psn6ffhbI8+k5zkhUl+KslnM7uF+uQkP7u7narqkZkF5Ld19ztH8x3jNuqMrzsnANue5LC53TckuW037RsWaF+qxtfp7nO6e1N3b1q/fv3uTgcAAIA1bk9D8iuTbOnu9d19cGah+RVL7TBmmj43yU3d/btzqy5NsnOG6i1JLplrf8GY5froJF8ct0xfnuS4qjpwTNh1XJLLx7ovVdXRo9YLdulroRoAAACwqD293fpfdvfdO590911V9dTd7POMJD+T5KNVdd1o+49JfjvJhVV1WpJP52uTgV2W5IQk25J8OeNK9aj1yiQ77y/4zZ2TeCV5UZI3J3l0ZhN2vWe0L1YDAAAAFrWnIfkRVXXgzqA8/izTkvt29//Mwp8bTpJjF9i+k5yxSF/nJTlvgfatSZ6yQPvnF6oBAAAAS9nTkPyaJP+rqi7KbHKsn0ryqsmOCgAAAFbBHoXk7n5LVW1NckxmV4ef290fm/TIAAAAYIXt6ZXkjFAsGAMAALBm7ens1gAAALDmCckAAAAwCMkAAAAwCMkAAAAwCMkAAAAwCMkAAAAwCMkAAAAwCMkAAAAwrFvtAwAAAHi4+exrPj5Jv9/yK989Sb/sOVeSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYJgsJFfVeVV1Z1XdMNf2iqr6TFVdNx4nzK17WVVtq6pPVNUz59o3j7ZtVXXWXPuTquqqqrq5qt5RVfuP9m8Yz7eN9YdPdY4AAACsLVNeSX5zks0LtL+2u48cj8uSpKqOSHJKku8d+7yxqvarqv2SvCHJ8UmOSHLq2DZJXj362pjk7iSnjfbTktzd3U9O8tqxHQAAAOzWZCG5u/8qyV17uPmJSS7o7nu6+1NJtiU5ajy2dfcnu/srSS5IcmJVVZJjklw09j8/yUlzfZ0/li9KcuzYHgAAAJa0Gp9JPrOqrh+3Yx842g5NcuvcNttH22LtT0jyhe6+d5f2r+trrP/i2P5+qur0qtpaVVt37Njx0M8MAACAh7WVDslnJ/mOJEcmuT3Ja0b7Qld6+0G0L9XX/Ru7z+nuTd29af369UsdNwAAAPuAFQ3J3X1Hd9/X3V9N8geZ3U6dzK4EHza36YYkty3R/rkkB1TVul3av66vsf5x2fPbvgEAANiHrWhIrqpD5p4+J8nOma8vTXLKmJn6SUk2Jrk6yTVJNo6ZrPfPbHKvS7u7k7w/yclj/y1JLpnra8tYPjnJ+8b2AAAAsKR1u9/kwamqtyf5kSQHVdX2JC9P8iNVdWRmtz/fkuTnk6S7b6yqC5N8LMm9Sc7o7vtGP2cmuTzJfknO6+4bR4mXJrmgqn4ryYeTnDvaz03y1qraltkV5FOmOkcAAADWlslCcnefukDzuQu07dz+VUletUD7ZUkuW6D9k/na7drz7f+U5HkP6GABAAAgqzO7NQAAAOyVhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYhGQAAAAYJgvJVXVeVd1ZVTfMtT2+qq6oqpvH1wNHe1XV66pqW1VdX1VPm9tny9j+5qraMtf+9Kr66NjndVVVS9UAAACA3ZnySvKbk2zepe2sJFd298YkV47nSXJ8ko3jcXqSs5NZ4E3y8iQ/kOSoJC+fC71nj2137rd5NzUAAABgSZOF5O7+qyR37dJ8YpLzx/L5SU6aa39Lz3wwyQFVdUiSZya5orvv6u67k1yRZPNY99ju/uvu7iRv2aWvhWoAAADAklb6M8lP7O7bk2R8PXi0H5rk1rntto+2pdq3L9C+VI37qarTq2prVW3dsWPHgz4pAAAA1oa9ZeKuWqCtH0T7A9Ld53T3pu7etH79+ge6OwAAAGvMSofkO8at0hlf7xzt25McNrfdhiS37aZ9wwLtS9UAAACAJa10SL40yc4ZqrckuWSu/QVjluujk3xx3Cp9eZLjqurAMWHXcUkuH+u+VFVHj1mtX7BLXwvVAAAAgCWtm6rjqnp7kh9JclBVbc9slurfTnJhVZ2W5NNJnjc2vyzJCUm2Jflykp9Nku6+q6pemeSasd1vdvfOycBelNkM2o9O8p7xyBI1AAAAYEmTheTuPnWRVccusG0nOWORfs5Lct4C7VuTPGWB9s8vVAMAAAB2Z2+ZuAsAAABWnZAMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAg5AMAAAAw7rVPgAAAAD2bXe+4c8m6ffgM37iAe/jSjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMQjIAAAAMqxKSq+qWqvpoVV1XVVtH2+Or6oqqunl8PXC0V1W9rqq2VdX1VfW0uX62jO1vrqotc+1PH/1vG/vWyp8lAAAADzereSX5R7v7yO7eNJ6fleTK7t6Y5MrxPEmOT7JxPE5PcnYyC9VJXp7kB5IcleTlO4P12Ob0uf02T386AAAAPNztTbdbn5jk/LF8fpKT5trf0jMfTHJAVR2S5JlJrujuu7r77iRXJNk81j22u/+6uzvJW+b6AgAAgEWtVkjuJO+tqmur6vTR9sTuvj1JxteDR/uhSW6d23f7aFuqffsC7fdTVadX1daq2rpjx46HeEoAAAA83K1bpbrP6O7bqurgJFdU1ceX2HahzxP3g2i/f2P3OUnOSZJNmzYtuA0AAAD7jlW5ktzdt42vdyZ5V2afKb5j3Cqd8fXOsfn2JIfN7b4hyW27ad+wQDsAAAAsacVDclV9Y1V9887lJMcluSHJpUl2zlC9JcklY/nSJC8Ys1wfneSL43bsy5McV1UHjgm7jkty+Vj3pao6esxq/YK5vgAAAGBRq3G79ROTvGv8VaZ1Sf6ku/+iqq5JcmFVnZbk00meN7a/LMkJSbYl+XKSn02S7r6rql6Z5Jqx3W92911j+UVJ3pzk0UneMx4AAACwpBUPyd39ySTft0D755Mcu0B7Jzljkb7OS3LeAu1bkzzlIR8sAAAA+5S96U9AAQAAwKoSkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGAQkgEAAGBYsyG5qjZX1SeqaltVnbXaxwMAAMDeb02G5KraL8kbkhyf5Igkp1bVEat7VAAAAOzt1mRITnJUkm3d/cnu/kqSC5KcuMrHBAAAwF6uunu1j2HZVdXJSTZ398+N5z+T5Ae6+8xdtjs9yenj6Xcl+cSDKHdQks89hMNVb9+pt5bPTT311Fu9emv53NRTT73VqaWeemu13r/o7vW722jdg+j44aAWaLvfuwHdfU6Scx5Soaqt3b3pofSh3r5Rby2fm3rqqbd69dbyuamnnnqrU0s99fb1emv1duvtSQ6be74hyW2rdCwAAAA8TKzVkHxNko1V9aSq2j/JKUkuXeVjAgAAYC+3Jm+37u57q+rMJJcn2S/Jed1940TlHtLt2urtU/XW8rmpp556q1dvLZ+beuq1RI43AAAL4ElEQVSptzq11FNvn663JifuAgAAgAdjrd5uDQAAAA+YkAwAAACDkPwgVdXmqvpEVW2rqrNWoN55VXVnVd2wArUOq6r3V9VNVXVjVb144nqPqqqrq+ojo95vTFlvru5+VfXhqnr3CtS6pao+WlXXVdXWFah3QFVdVFUfH/+O/8eEtb5rnNfOx99X1Uumqjdq/tL4Wbmhqt5eVY+asNaLR50bpzqvhf5/V9Xjq+qKqrp5fD1w4nrPG+f41apatj+psEit3xk/m9dX1buq6oCJ671y1Lquqt5bVd86Zb25df++qrqqDpqyXlW9oqo+M/d/8IQp6432Xxy/A2+sqv80Zb2qesfcud1SVddNXO/IqvrgzvG6qo6auN73VdVfj98Rf1ZVj12mWgv+Lp9qbFmi3lRjy2L1Jhlflqg3yfiyWL259cs6vixxfpOML0ud3xTjyxLnN8n4skS9ZR9flqg11diy4Ov2mk2YfNUYW95Rs8mTp6x3Zs1y2LL+nv1n3e3xAB+ZTQb2t0m+Pcn+ST6S5IiJa/5QkqcluWEFzu+QJE8by9+c5G+mPL/M/q71N43lRya5KsnRK3Cev5zkT5K8ewVq3ZLkoKnrzNU7P8nPjeX9kxywQnX3S/LZzP5Q+1Q1Dk3yqSSPHs8vTPJvJ6r1lCQ3JHlMZhMd/vckGyeoc7//30n+U5KzxvJZSV49cb3vSfJdST6QZNPEtY5Lsm4sv3oFzu2xc8v/Lsmbpqw32g/LbPLIv1vO//uLnN8rkvz75f65XKLej47/C98wnh889fdzbv1rkvz6xOf33iTHj+UTknxg4nrXJPnhsfzCJK9cploL/i6famxZot5UY8ti9SYZX5aoN8n4sli98XzZx5clzm+S8WWJepOML0t9P+e2WbbxZYnzW/bxZYlaU40tC75uz+z12Cmj/U1JXjRxvacmOTwTvcZ2JfnBOSrJtu7+ZHd/JckFSU6csmB3/1WSu6asMVfr9u7+0Fj+UpKbMgsmU9Xr7v6H8fSR4zHpjHJVtSHJs5L84ZR1VsN4p/CHkpybJN39le7+wgqVPzbJ33b3301cZ12SR1fVuswC7FR/B/17knywu7/c3fcm+cskz1nuIov8/z4xszc7Mr6eNGW97r6puz+xXDV2U+u94/uZJB/M7G/ZT1nv7+eefmOWcXxZYmx+bZL/sJy1dlNvEovUe1GS3+7ue8Y2d05cL0lSVZXkp5K8feJ6nWTnFZfHZRnHl0XqfVeSvxrLVyT5yWWqtdjv8knGlsXqTTi2LFZvkvFliXqTjC+7eS227OPLKrz2W6zeJOPL7s5vuceXJeot+/iyRK2pxpbFXrcfk+Si0b6cY8uC9br7w919y3LUWIiQ/OAcmuTWuefbM+FAspqq6vDM3qm5auI6+41bXO5MckV3T1ovye9l9gvmqxPX2amTvLeqrq2q0yeu9e1JdiT5o5rdTv6HVfWNE9fc6ZQs4wvYhXT3Z5L85ySfTnJ7ki9293snKndDkh+qqidU1WMye9f3sIlq7eqJ3X17MvsFmOTgFaq70l6Y5D1TF6mqV1XVrUmen+TXJ6717CSf6e6PTFlnF2eOWz7Pq2W8NX8R35nkX4/b6v6yqr5/4no7/eskd3T3zRPXeUmS3xk/L/85ycsmrndDkmeP5edlgjFml9/lk48tK/XaYQ/qTTK+7Fpv6vFlvt5KjC8LfD8nHV92qTf5+LLIz8tk48su9SYdX3apNdnYsuvr9szusP3C3BtUy5qNViEnCMkPUi3Qtub+llZVfVOSi5O8ZJd3Spddd9/X3Udm9o7vUVX1lKlqVdWPJ7mzu6+dqsYCntHdT0tyfJIzquqHJqy1LrPb+c7u7qcm+cfMbqmb1PjsybOT/LeJ6xyY2ZWQJyX51iTfWFU/PUWt7r4ps9v1rkjyF5l9tOLeJXdij1XVr2X2/Xzb1LW6+9e6+7BR68yp6ow3U34tEwfxXZyd5DuSHJnZG0evmbjeuiQHZna7268muXBchZnaqZn4TbjhRUl+afy8/FLGXTkTemFmvxeuzexWya8sZ+cr+bt8b6o31fiyUL0px5f5epmdz6TjywLnN+n4skC9SceXJX4+JxlfFqg32fiyQK3JxpZdX7dndufd/Tabqt6UOWEnIfnB2Z6vfzdmQ6a73XNVVNUjM/uP9rbufudK1R23BX8gyeYJyzwjybOr6pbMbpU/pqr+eMJ66e7bxtc7k7wrswFlKtuTbJ97l+2izELz1I5P8qHuvmPiOj+W5FPdvaO7/3eSdyb5wamKdfe53f207v6hzG6TnPoq1k53VNUhSTK+LtstrXuDqtqS5MeTPL+7V/JNxj/JMt1ytojvyOwNnI+MMWZDkg9V1bdMVbC77xgvIL6a5A8y7fiSzMaYd45b4K7O7I6c5Z80Zc74aMVzk7xjyjrDlszGlWT2pt+k38/u/nh3H9fdT8/sRfrfLlffi/wun2xsWenXDovVm2p82YPzW9bxZYF6k44vC53flOPLIt/PycaXJX5eJhlfFqk3yfiyyL/dZGPLTnOv249OcsD4XiYTZaMVyglJhOQH65okG8csbvtndovppat8TMtmvGN3bpKbuvt3V6De+hqzT1bVozMLQR+fql53v6y7N3T34Zn9272vuye5EpkkVfWNVfXNO5czm1RkslnKu/uzSW6tqu8aTccm+dhU9eas1FWeTyc5uqoeM35Wj83s8zeTqKqDx9dvy+yX6EqcYzIbU7aM5S1JLlmhupOrqs1JXprk2d395RWot3Hu6bMz7fjy0e4+uLsPH2PM9swmVPnsVDV3Bp7hOZlwfBn+NLPPnqWqvjOzyQE/N3HNH0vy8e7ePnGdZPbC7ofH8jGZ+I2xuTHmEUn+n8wmvFmOfhf7XT7J2LIKrx0WrDfV+LJEvUnGl4XqTTm+LHF+k4wvS/y8TDK+7Obnc9nHlyXqLfv4ssS/3VRjy0Kv229K8v4kJ4/NlnNsWdGc8M96mWcC21cemX028W8ye1fm11ag3tszu83lf2c2KJ42Ya1/ldktEtcnuW48Tpiw3r9M8uFR74Ys48yle1D7RzLx7NaZfUb4I+Nx4wr9vByZZOv4nv5pkgMnrveYJJ9P8rgV+nf7jcwGyBuSvDVjFsyJav2PzN5k+EiSYyeqcb//30mekOTKzH6BXpnk8RPXe85YvifJHUkun7DWtszmddg5viznbNML1bt4/Kxcn+TPMptsZ7J6u6y/Jcs7u/VC5/fWJB8d53dpkkMmrrd/kj8e39MPJTlm6u9nkjcn+YXlqrOb8/tXSa4d/+evSvL0ieu9OLPXE3+T5LeT1DLVWvB3+VRjyxL1phpbFqs3yfiyRL1JxpfF6u2yzbKNL0uc3yTjyxL1Jhlflvp+TjG+LHF+yz6+LFFrqrFlwdftmb3evXr8H/xvWabXZkvU+3djbLk3szcf/nA5/w1rFAEAAIB9ntutAQAAYBCSAQAAYBCSAQAAYBCSAQAAYBCSAQAAYFi3+00AgIeTqrovsz/b8sjM/jzG+Ul+r7u/uqoHBgAPA0IyAKw9/193H5kkVXVwkj9J8rgkL1/VowKAhwG3WwPAGtbddyY5PcmZNXN4Vf2PqvrQePxgklTVW6vqxJ37VdXbqurZq3XcALBaqrtX+xgAgGVUVf/Q3d+0S9vdSb47yZeSfLW7/6mqNiZ5e3dvqqofTvJL3X1SVT0uyXVJNnb3vSt+AgCwitxuDQD7hhpfH5nk96vqyCT3JfnOJOnuv6yqN4zbs5+b5GIBGYB9kZAMAGtcVX17ZoH4zsw+l3xHku/L7GNX/zS36VuTPD/JKUleuMKHCQB7BSEZANawqlqf5E1Jfr+7e9xKvb27v1pVW5LsN7f5m5NcneSz3X3jyh8tAKw+IRkA1p5HV9V1+dqfgHprkt8d696Y5OKqel6S9yf5x507dfcdVXVTkj9d4eMFgL2GibsAgCRJVT0ms7+v/LTu/uJqHw8ArAZ/AgoASFX9WJKPJ3m9gAzAvsyVZAAAABhcSQYAAIBBSAYAAIBBSAYAAIBBSAYAAIBBSAYAAIDh/wcaNSknjDsVowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(test['Day'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using AvSigVersion as a date proxy, we can see some valuable information.\n",
    "\n",
    "Based on the charts above, we can see that the majority of train and test resides in 2018. Train has a large density of data points in July, August, and September. Whereas, test has a large density of data points in September, October, November. Furthermore, HasDetection ratio in train is higher in months with higher data density. This can be confirmed by viewing Weekly representation as well. Based on all these charts, we can see that July, August, September is not a random sample, instead they contain upsampled data points based on HasDetections. In all other months, detection ratio is less than July, August and September. Therefore the natural rate of HasDetection will be close to months outside of July - Sept. However, we do not know how public test or private test was sampled, so we cannot make any assumptions here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [5] Feature Engineering, Missing Value Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 SmartScreen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean up some values in SmartScreen for train\n",
    "# replace 0's as NaN, only 4 zero values per train\n",
    "train['SmartScreen'] = train['SmartScreen'].str.lower()\n",
    "train['SmartScreen'].replace(\n",
    "    {'promt': 'prompt', \n",
    "    'promprt': 'prompt',\n",
    "    'enabled': 'on',\n",
    "    'of': 'off',\n",
    "    'deny': '0',\n",
    "    '00000000': '0',\n",
    "    'requiredadmin': 'requireadmin'},\n",
    "     inplace=True)\n",
    "train['SmartScreen'].replace({'0': np.nan}, inplace=True)\n",
    "train['SmartScreen'] = train['SmartScreen'].astype('category')\n",
    "\n",
    "test['SmartScreen'] = test['SmartScreen'].str.lower()\n",
    "test['SmartScreen'].replace(\n",
    "    {'promt': 'prompt', \n",
    "    'promprt': 'prompt',\n",
    "    'enabled': 'on',\n",
    "    'of': 'off',\n",
    "    'deny': '0',\n",
    "    '00000000': '0',\n",
    "    'requiredadmin': 'requireadmin'},\n",
    "     inplace=True)\n",
    "test['SmartScreen'].replace({'0': np.nan}, inplace=True)\n",
    "test['SmartScreen'] = test['SmartScreen'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 BatteryType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_batt(x):\n",
    "    x = x.lower()\n",
    "    if 'li' in x:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "train['li'] = train['Census_InternalBatteryType'].apply(group_batt).astype('int8')\n",
    "test['li'] = test['Census_InternalBatteryType'].apply(group_batt).astype('int8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions\n",
    "def isnan(feature):\n",
    "    return 1 if np.isnan(float(feature)) else 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.1 Missing Numericals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 105 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# count -1 as a missing value for train\n",
    "train['Census_InternalPrimaryDisplayResolutionHorizontal'].replace({-1: np.nan}, inplace=True)\n",
    "train['Census_InternalPrimaryDisplayResolutionVertical'].replace({-1: np.nan}, inplace=True)\n",
    "\n",
    "# count -1 as a missing value for test\n",
    "test['Census_InternalPrimaryDisplayResolutionHorizontal'].replace({-1: np.nan}, inplace=True)\n",
    "test['Census_InternalPrimaryDisplayResolutionVertical'].replace({-1: np.nan}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Create a binary feature indicating that a missing value has been imputed for train/test\n",
    "train['core_nan'] = train['Census_ProcessorCoreCount'].apply(isnan).astype('uint8')\n",
    "train['ram_nan'] = train['Census_TotalPhysicalRAM'].apply(isnan).astype('uint8')\n",
    "train['primary_nan'] = train['Census_PrimaryDiskTotalCapacity'].apply(isnan).astype('uint8')\n",
    "train['system_nan'] = train['Census_SystemVolumeTotalCapacity'].apply(isnan).astype('uint8')\n",
    "train['diagonal_nan'] = train['Census_InternalPrimaryDiagonalDisplaySizeInInches'].apply(isnan).astype('uint8')\n",
    "train['horizonal_nan'] = train['Census_InternalPrimaryDisplayResolutionHorizontal'].apply(isnan).astype('uint8')\n",
    "train['vertical_nan'] = train['Census_InternalPrimaryDisplayResolutionVertical'].apply(isnan).astype('uint8')\n",
    "train['charges_nan'] = train['Census_InternalBatteryNumberOfCharges'].apply(isnan).astype('uint8')\n",
    "\n",
    "test['core_nan'] = test['Census_ProcessorCoreCount'].apply(isnan).astype('uint8')\n",
    "test['ram_nan'] = test['Census_TotalPhysicalRAM'].apply(isnan).astype('uint8')\n",
    "test['primary_nan'] = test['Census_PrimaryDiskTotalCapacity'].apply(isnan).astype('uint8')\n",
    "test['system_nan'] = test['Census_SystemVolumeTotalCapacity'].apply(isnan).astype('uint8')\n",
    "test['diagonal_nan'] = test['Census_InternalPrimaryDiagonalDisplaySizeInInches'].apply(isnan).astype('uint8')\n",
    "test['horizonal_nan'] = test['Census_InternalPrimaryDisplayResolutionHorizontal'].apply(isnan).astype('uint8')\n",
    "test['vertical_nan'] = test['Census_InternalPrimaryDisplayResolutionVertical'].apply(isnan).astype('uint8')\n",
    "test['charges_nan'] = test['Census_InternalBatteryNumberOfCharges'].apply(isnan).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute some median values first\n",
    "# median is may be more robust to outliers (if they exist)\n",
    "# only use SimpleImputer for Diagonal, NumberOfCharges\n",
    "imputer = SimpleImputer(strategy='median')\n",
    "\n",
    "train['Census_InternalBatteryNumberOfCharges'] = imputer.fit_transform(train['Census_InternalBatteryNumberOfCharges'].values.reshape(-1,1))\n",
    "test['Census_InternalBatteryNumberOfCharges'] = imputer.transform(test['Census_InternalBatteryNumberOfCharges'].values.reshape(-1,1))\n",
    "train['Census_InternalPrimaryDiagonalDisplaySizeInInches'] = imputer.fit_transform(train['Census_InternalPrimaryDiagonalDisplaySizeInInches'].values.reshape(-1,1))\n",
    "test['Census_InternalPrimaryDiagonalDisplaySizeInInches'] = imputer.transform(test['Census_InternalPrimaryDiagonalDisplaySizeInInches'].values.reshape(-1,1))\n",
    "train['Census_ProcessorCoreCount'] = imputer.fit_transform(train['Census_ProcessorCoreCount'].values.reshape(-1,1))\n",
    "test['Census_ProcessorCoreCount'] = imputer.transform(test['Census_ProcessorCoreCount'].values.reshape(-1,1))\n",
    "train['Census_TotalPhysicalRAM'] = imputer.fit_transform(train['Census_TotalPhysicalRAM'].values.reshape(-1,1))\n",
    "test['Census_TotalPhysicalRAM'] = imputer.transform(test['Census_TotalPhysicalRAM'].values.reshape(-1,1))\n",
    "train['Census_PrimaryDiskTotalCapacity'] = imputer.fit_transform(train['Census_PrimaryDiskTotalCapacity'].values.reshape(-1,1))\n",
    "test['Census_PrimaryDiskTotalCapacity'] = imputer.transform(test['Census_PrimaryDiskTotalCapacity'].values.reshape(-1,1))\n",
    "train['Census_SystemVolumeTotalCapacity'] = imputer.fit_transform(train['Census_SystemVolumeTotalCapacity'].values.reshape(-1,1))\n",
    "test['Census_SystemVolumeTotalCapacity'] = imputer.transform(test['Census_SystemVolumeTotalCapacity'].values.reshape(-1,1))\n",
    "train['Census_InternalPrimaryDisplayResolutionHorizontal'] = imputer.fit_transform(train['Census_InternalPrimaryDisplayResolutionHorizontal'].values.reshape(-1,1))\n",
    "test['Census_InternalPrimaryDisplayResolutionHorizontal'] = imputer.transform(test['Census_InternalPrimaryDisplayResolutionHorizontal'].values.reshape(-1,1))\n",
    "train['Census_InternalPrimaryDisplayResolutionVertical'] = imputer.fit_transform(train['Census_InternalPrimaryDisplayResolutionVertical'].values.reshape(-1,1))\n",
    "test['Census_InternalPrimaryDisplayResolutionVertical'] = imputer.transform(test['Census_InternalPrimaryDisplayResolutionVertical'].values.reshape(-1,1))\n",
    "train['AVProductsEnabled'] = imputer.fit_transform(train['AVProductsEnabled'].values.reshape(-1,1))\n",
    "test['AVProductsEnabled'] = imputer.transform(test['AVProductsEnabled'].values.reshape(-1,1))\n",
    "train['AVProductsInstalled'] = imputer.fit_transform(train['AVProductsInstalled'].values.reshape(-1,1))\n",
    "test['AVProductsInstalled'] = imputer.transform(test['AVProductsInstalled'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Numerical Engineered Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total disk capacity remaining\n",
    "train['disk_remain'] = train['Census_PrimaryDiskTotalCapacity'] - train['Census_SystemVolumeTotalCapacity']\n",
    "test['disk_remain'] = test['Census_PrimaryDiskTotalCapacity'] - test['Census_SystemVolumeTotalCapacity']\n",
    "train['disk_remain'] = train['disk_remain'].astype('float32')\n",
    "test['disk_remain'] = test['disk_remain'].astype('float32')\n",
    "\n",
    "# Ram-to-CPU ratio\n",
    "train['ram_cpu_ratio'] = train['Census_TotalPhysicalRAM'] / train['Census_ProcessorCoreCount']\n",
    "test['ram_cpu_ratio'] = test['Census_TotalPhysicalRAM'] / test['Census_ProcessorCoreCount']\n",
    "\n",
    "# Pixel Per Inch PPI sqrt(horizonal**2 + vertical**2) / diagonal\n",
    "train['ppi'] = np.sqrt(train['Census_InternalPrimaryDisplayResolutionHorizontal']**2 + train['Census_InternalPrimaryDisplayResolutionVertical']**2) / train['Census_InternalPrimaryDiagonalDisplaySizeInInches']\n",
    "test['ppi'] = np.sqrt(test['Census_InternalPrimaryDisplayResolutionHorizontal']**2 + test['Census_InternalPrimaryDisplayResolutionVertical']**2) / test['Census_InternalPrimaryDiagonalDisplaySizeInInches']\n",
    "\n",
    "# PPI squared\n",
    "train['ppi2'] = train.ppi ** 2\n",
    "test['ppi2'] = test.ppi ** 2\n",
    "\n",
    "# Screen aspect ratio = Horizonal / Vertical\n",
    "train['aspect_ratio'] = train['Census_InternalPrimaryDisplayResolutionHorizontal'] / train['Census_InternalPrimaryDisplayResolutionVertical']\n",
    "test['aspect_ratio'] = test['Census_InternalPrimaryDisplayResolutionHorizontal'] / test['Census_InternalPrimaryDisplayResolutionVertical']\n",
    "\n",
    "# Pixel count = Horizonal * Vertical\n",
    "train['pixel_count'] = train['Census_InternalPrimaryDisplayResolutionHorizontal'] * train['Census_InternalPrimaryDisplayResolutionVertical']\n",
    "test['pixel_count'] = test['Census_InternalPrimaryDisplayResolutionHorizontal'] * test['Census_InternalPrimaryDisplayResolutionVertical']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6] Remove categories in train missing from test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_categorical = [\n",
    "'RtpStateBitfield','DefaultBrowsersIdentifier', 'AVProductStatesIdentifier', \n",
    "'CityIdentifier' , 'OrganizationIdentifier','GeoNameIdentifier', \n",
    "'IeVerIdentifier', 'UacLuaenable', 'Census_OEMNameIdentifier', \n",
    "'Census_OEMModelIdentifier', 'Census_ProcessorManufacturerIdentifier', \n",
    "'Census_ProcessorModelIdentifier', 'Census_OSInstallLanguageIdentifier', \n",
    "'Census_FirmwareManufacturerIdentifier', 'Census_FirmwareVersionIdentifier', \n",
    "'Wdft_RegionIdentifier', 'LocaleEnglishNameIdentifier', 'OsBuild', 'OsSuite', \n",
    "'Census_OSBuildNumber', 'CountryIdentifier', 'Census_OSUILocaleIdentifier', 'Census_OSBuildRevision'\n",
    "]\n",
    "categorical = [\n",
    "'ProductName', 'Platform', 'Processor', 'OsVer', 'OsPlatformSubRelease', \n",
    "'OsBuildLab', 'SkuEdition', 'PuaMode', 'SmartScreen', 'Census_MDC2FormFactor', \n",
    "'Census_DeviceFamily', 'Census_ProcessorClass', 'Census_PrimaryDiskTypeName', \n",
    "'Census_ChassisTypeName', 'Census_PowerPlatformRoleName', 'Census_InternalBatteryType', \n",
    "'Census_OSArchitecture', 'Census_OSBranch', 'Census_OSEdition', 'Census_OSSkuName', \n",
    "'Census_OSInstallTypeName', 'Census_OSWUAutoUpdateOptionsName', 'Census_GenuineStateName', \n",
    "'Census_ActivationChannel', 'Census_FlightRing', \n",
    "'AvSigVersion', 'AppVersion', 'EngineVersion', 'Census_OSVersion' \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 27.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#inspired by: https://www.kaggle.com/c/microsoft-malware-prediction/discussion/83946#490456\n",
    "# values that occur in train but not test will not be useful for predicting test\n",
    "for col in numerical_categorical:\n",
    "    train.loc[~train[col].isin(test[col].unique()), col] = -1\n",
    "\n",
    "for col in categorical:\n",
    "    train[col] = train[col].cat.add_categories([-1])\n",
    "    train.loc[~train[col].isin(test[col].unique()), col] = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [7] Encode Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 23/23 [01:53<00:00,  4.46s/it]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 29/29 [00:48<00:00,  1.71s/it]\n"
     ]
    }
   ],
   "source": [
    "# Frequency Encode & Label Encode\n",
    "# source: https://www.kaggle.com/artgor/is-this-malware-eda-fe-and-lgb-updated\n",
    "\n",
    "def frequency_encoding(variable):\n",
    "    t = pd.concat([train[variable], test[variable]]).value_counts().reset_index()\n",
    "    t = t.reset_index()\n",
    "    t.loc[t[variable] == 1, 'level_0'] = np.nan\n",
    "    t.set_index('index', inplace=True)\n",
    "    max_label = t['level_0'].max() + 1\n",
    "    t.fillna(max_label, inplace=True)\n",
    "    return t.to_dict()['level_0']\n",
    "\n",
    "for col in tqdm(numerical_categorical):\n",
    "    freq_enc_dict = frequency_encoding(col)\n",
    "    train[col] = train[col].map(lambda x: freq_enc_dict.get(x, np.nan)).astype('float32')\n",
    "    test[col] = test[col].map(lambda x: freq_enc_dict.get(x, np.nan)).astype('float32')\n",
    "    \n",
    "for col in tqdm(categorical):\n",
    "    freq_enc_dict = frequency_encoding(col)\n",
    "    train[col] = train[col].map(lambda x: freq_enc_dict.get(x, np.nan)).astype('float32')\n",
    "    test[col] = test[col].map(lambda x: freq_enc_dict.get(x, np.nan)).astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [8] Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fast & efficient way to calculate mean using numba\n",
    "@jit(parallel=True)\n",
    "def mean_score(array):\n",
    "    return np.mean(array)\n",
    "\n",
    "# fast & efficient way to calculate division using numba\n",
    "@jit(parallel=True)\n",
    "def jit_div(array, divisor):\n",
    "    return np.divide(array, divisor)\n",
    "\n",
    "def run_lgb(train, target, test):\n",
    "    global clf\n",
    "    \n",
    "    kf = KFold(n_splits=3, shuffle=True)\n",
    "\n",
    "    train_scores = []\n",
    "    cv_scores = []\n",
    "    train_predictions = np.zeros(train.shape[0], dtype='float32')\n",
    "    test_predictions = np.zeros(test.shape[0], dtype='float32')\n",
    "    \n",
    "    for (train_index, test_index) in tqdm(kf.split(train, target)):\n",
    "        x_train, y_train = train.iloc[train_index], target.iloc[train_index]\n",
    "        x_test, y_test = train.iloc[test_index], target.iloc[test_index]\n",
    "\n",
    "        clf = lgb.LGBMClassifier(boosting_type='gbdt', \n",
    "                                 num_leaves=250, \n",
    "                                 n_estimators=2000, \n",
    "                                 learning_rate=0.02)\n",
    "\n",
    "        clf.fit(x_train, y_train, eval_metric='auc',  \n",
    "                eval_set=[(x_test, y_test)], \n",
    "                verbose=200,\n",
    "                early_stopping_rounds=100)\n",
    "        pred = clf.predict(x_train)\n",
    "        score = roc_auc_score(y_train, pred)\n",
    "        train_scores.append(score)\n",
    "\n",
    "        pred = clf.predict(x_test)\n",
    "        score = roc_auc_score(y_test, pred)\n",
    "        cv_scores.append(score)\n",
    "        \n",
    "        # predict probabilities for train\n",
    "        pred_prob = clf.predict_proba(train)[:,1]\n",
    "        train_predictions = np.add(train_predictions, pred_prob).astype('float32')\n",
    "        \n",
    "        # predict probabilities for test\n",
    "        pred_prob = clf.predict_proba(test)[:,1]\n",
    "        test_predictions = np.add(test_predictions, pred_prob).astype('float32')\n",
    "    \n",
    "    print(f'training sklearn auc: {mean_score(train_scores):.4f}')\n",
    "    print(f'3-fold CV sklearn auc: {mean_score(cv_scores):.4f}')\n",
    "\n",
    "    return jit_div(train_predictions, 3), jit_div(test_predictions, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid_0's auc: 0.719308\tvalid_0's binary_logloss: 0.612475\n",
      "[400]\tvalid_0's auc: 0.729573\tvalid_0's binary_logloss: 0.603843\n",
      "[600]\tvalid_0's auc: 0.734061\tvalid_0's binary_logloss: 0.600052\n",
      "[800]\tvalid_0's auc: 0.736116\tvalid_0's binary_logloss: 0.598314\n",
      "[1000]\tvalid_0's auc: 0.737049\tvalid_0's binary_logloss: 0.597533\n",
      "[1200]\tvalid_0's auc: 0.737687\tvalid_0's binary_logloss: 0.596991\n",
      "[1400]\tvalid_0's auc: 0.738194\tvalid_0's binary_logloss: 0.596565\n",
      "[1600]\tvalid_0's auc: 0.738705\tvalid_0's binary_logloss: 0.596132\n",
      "[1800]\tvalid_0's auc: 0.739084\tvalid_0's binary_logloss: 0.595807\n",
      "[2000]\tvalid_0's auc: 0.739376\tvalid_0's binary_logloss: 0.595555\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[2000]\tvalid_0's auc: 0.739376\tvalid_0's binary_logloss: 0.595555\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "1it [48:57, 2937.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid_0's auc: 0.719304\tvalid_0's binary_logloss: 0.612383\n",
      "[400]\tvalid_0's auc: 0.729929\tvalid_0's binary_logloss: 0.603569\n",
      "[600]\tvalid_0's auc: 0.734279\tvalid_0's binary_logloss: 0.599952\n",
      "[800]\tvalid_0's auc: 0.73638\tvalid_0's binary_logloss: 0.598194\n",
      "[1000]\tvalid_0's auc: 0.737403\tvalid_0's binary_logloss: 0.597337\n",
      "[1200]\tvalid_0's auc: 0.737992\tvalid_0's binary_logloss: 0.596838\n",
      "[1400]\tvalid_0's auc: 0.738424\tvalid_0's binary_logloss: 0.59647\n",
      "[1600]\tvalid_0's auc: 0.738876\tvalid_0's binary_logloss: 0.596082\n",
      "[1800]\tvalid_0's auc: 0.739258\tvalid_0's binary_logloss: 0.595748\n",
      "[2000]\tvalid_0's auc: 0.739611\tvalid_0's binary_logloss: 0.595446\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[2000]\tvalid_0's auc: 0.739611\tvalid_0's binary_logloss: 0.595446\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "2it [1:37:56, 2937.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid_0's auc: 0.7195\tvalid_0's binary_logloss: 0.612325\n",
      "[400]\tvalid_0's auc: 0.729654\tvalid_0's binary_logloss: 0.603806\n",
      "[600]\tvalid_0's auc: 0.734078\tvalid_0's binary_logloss: 0.600086\n",
      "[800]\tvalid_0's auc: 0.736216\tvalid_0's binary_logloss: 0.598273\n",
      "[1000]\tvalid_0's auc: 0.737222\tvalid_0's binary_logloss: 0.597422\n",
      "[1200]\tvalid_0's auc: 0.737822\tvalid_0's binary_logloss: 0.596904\n",
      "[1400]\tvalid_0's auc: 0.738346\tvalid_0's binary_logloss: 0.596454\n",
      "[1600]\tvalid_0's auc: 0.738706\tvalid_0's binary_logloss: 0.59615\n",
      "[1800]\tvalid_0's auc: 0.739042\tvalid_0's binary_logloss: 0.595864\n",
      "[2000]\tvalid_0's auc: 0.739393\tvalid_0's binary_logloss: 0.595563\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[2000]\tvalid_0's auc: 0.739393\tvalid_0's binary_logloss: 0.595563\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "3it [2:26:03, 2922.81s/it]\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.6833\n",
      "3-fold CV sklearn auc: 0.6713\n",
      "Wall time: 2h 26min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_predictions, test_predictions = run_lgb(train, target, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e486b8ae80>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display feature importances\n",
    "feat_imp = pd.Series(clf.feature_importances_, index=train.columns)\n",
    "feat_imp.nlargest(30).plot(kind='barh', figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the kernel densities of predicted train probabilities\n",
    "sns.kdeplot(train_predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XkTdE5IiI7DzH8+NFpFJEtrZ8POH8mMpqb6/NwS7C7Xr5o0vdMiweQacCVu3nyMh9HnB1K9usNMYMafl4qv2xlDuprq1nwcZ8Jg2KJSYsyOo4Xq1n12Am9I3mg415nGpotDqO8mCtlrsxZgVQ1gFZlJt6f0Me1acamDU2xeooPuGOUUkcPV7Hkh1FVkdRHsxZx9wzRGSbiHwuIgOd9JrKDdQ1NPHGqhwyUiIYFKcrLXWEsamRpER24q01esequnDOKPfNQKIx5mLgb8Cic20oIrNEJFNEMktLdR4NT/DptsMUV9Uya5yO2juKzSbckZHI1vwKtuVXWB1Heah2l7sxpsoYc7zl8RLAX0Qiz7HtHGNMujEmPSpKV+5xd8YYXluZTd/uutJSR7spLY5OAXbeWpNjdRTlodpd7iISIy13tIjI8JbXPNbe11XW+25/KXuLq7n30hS9aamDhQb5c1NaHP/cXsTR46esjqM8kCOXQr4PrAX6ikiBiNwjIrNFZHbLJlOAnSKyDXgemGaMMa6LrDrKq99lE9MliOsv1puWrHBHRhJ1jU18sEFvalJt1+pk3MaYW1t5/gXgBaclUm5hR0Ela7OP8dikfgT46b1uVkiN7szY3pG8uy6P+8b1wl9XvFJtoD8t6qxeXXGQzoF+3DoiweooPu3OjCSKq2r5aleJ1VGUh9FyVz+SX1bDkh1FTB+RQJcgf6vj+LQJ/aKJ6xasJ1ZVm2m5qx+ZuzIbu024a3SS1VF8nr3lssgNOWXsPlxldRzlQbTc1Q8cqa7lg435/HRIT2LDgq2Oo4Cb0+MJ8rfx9tocq6MoD6Llrn7gtRXZ1Dc28eCEVKujqBZdQwK44ZKeLNpaSEVNndVxlIfQclf/cuz4Kd5dl8fkIT1JiuxkdRx1mjsykqitb2Jhps4WqRyj5a7+Ze6qQ9Q2NOqo3Q31j+3C8ORw3l6bS2OT3kaiWqflrgCoqKnj7TU5XDsoltTozlbHUWcxc1QSBeUn+VaX4VMO0HJXALyx6hAn6hr5+WU6andXVw7oTmxYkF4WqRyi5a6oqq3nzTU5XD0whn4xXayOo87Bz25j+vAEVmUdJefoCavjKDen5a54a3UO1bUNOmr3ADcPi8duE97X+WZUK7TcfdzxUw28vvoQE/tFc1FPXYzD3XXvEsTl/aP5cFOBLsOnzkvL3ce9szaXipp6HprY2+ooykHTRyRSdqKOL3W+GXUeWu4+rKaugbkrs7m0TxRD4rtaHUc5aGxqJPHhwcxfr8vwqXPTcvdh89fncexEHb+cqMfaPYnNJkwblsC67DKyjhy3Oo5yU1ruPqq2vpFXV2QzqlcEaYnhVsdRbTQ1PQ4/PbGqzkPL3Uct2JhPafUpHrpMj7V7oujQIK4c2J2PNxdQW68nVtWPabn7oFMNjby8/CDDk8IZmaKjdk81fXgiFTX1fL6zyOooyg1pufugjzYVUFxVy0MTU3Xhaw82qlcEiREhzF+vh2bUj2m5+5j6xiZeWnaQIfFdGZMaaXUc1Q42mzB9eAIbc8rZX1JtdRzlZrTcfcwnmwsprDjJLyf21lG7F5iSFkeA3aajd/UjWu4+pKGxiReXZzGoZxjj+0ZZHUc5QUTnQK4Y2J1FWwv1jlX1A1ruPmTxtsPkHqvhocv0WLs3uTk9noqaepbu1qmA1b9pufuIxibDC8uy6BcTyhUDulsdRznRmNRIYsOC+HCTrtKk/k3L3Ucs2VFEdukJHrpMj7V7G7tNuGloHCv2l1JcWWt1HOUmtNx9QFOT4W/fHqB3dGcmXRRjdRzlAlPS4mgy8PHmAqujKDeh5e4DvtpdzP6S4/z8slRsNh21e6OkyE4MTw7nw8x8jNE1VpWWu9czxvD8N1kkR3biusE9rI6jXOjm9HhyjtWQmVtudRTlBrTcvdw3e46wu6iKByekYtdRu1e7ZlAMnQLsLNyoJ1aVlrtXM6b5WHt8eDCTh+io3duFBPhx3eAefLajiBOnGqyOoyym5e7FvttfyraCSh4cn4q/Xb/VvmBqehw1dY18tkMnE/N1+hvvpZpH7Vn07BrMjUPjrI6jOkhaYjdSIjvxYaYemvF1Wu5eam32MTblljN7XAoBfvpt9hUiwtT0eDbmlJNdqqs0+TL9rfdSL3ybRXRoIFPT462OojrYjUN7YpPmqZ2V72q13EXkDRE5IiI7z/G8iMjzIpIlIttFZKjzY6q22JRbxpqDx5h1aQpB/nar46gO1r1LEOP7RvPx5gIam/Sad1/lyMh9HnD1eZ6fBPRu+ZgFvNz+WKo9Xvg2i/BOAUwfkWB1FGWRqWlxlFSdYsWBUqujKIu0Wu7GmBVA2Xk2mQy8bZqtA7qKSKyzAqq22VlYybJ9pdwzJpmQAD+r4yiLTOzfnfBOAXyUqYdmfJUzjrn3BE4/NV/Q8jVlgRe+zaJLkB93ZCRaHUVZKMDPxuQhPfh6dwnlJ+qsjqMs4IxyP9ttj2c90Ccis0QkU0QyS0v1z0Vn21dczRe7ipk5OpnQIH+r4yiLTU2Lp66xiX9sLbQ6irKAM8q9ADj9kow44PDZNjTGzDHGpBtj0qOidCUgZ3txWRadAuzcNSrJ6ijKDQzo0YVBPcNYqIdmfJIzyn0xcEfLVTMjgUpjjN4e18GyS4/zz+2HuT0jkW6dAqyOo9zE1PQ4dhdVsbOw0uooqoM5cink+8BaoK+IFIjIPSIyW0Rmt2yyBMgGsoDXgAdcllad08vLD+Jvt/GzMSlWR1Fu5PqLexDgZ9Nr3n1Qq5dTGGNubeV5AzzotESqzfLLavhkSyG3j0wkKjTQ6jjKjXQNCeDKAc0LaD92TT8C/fS+B1+hd6h6gVdXHEQE7huno3b1Y7qAtm/ScvdwJVW1LNxYwJS0eGLDgq2Oo9zQ6JYFtBfqZGI+Rcvdw81ZkU2jMdw/rpfVUZSbstuEKWlxrDxQSlHlSavjqA6i5e7Bjh0/xXvrc5k8pAcJESFWx1FubGpaPE0GPtTLIn2GlrsHe3N1DqcamnhgvI7a1fklRIQwJjWSBRvzdTIxH6Hl7qGqa+t5e20OVw2IITU61Oo4ygNMH5FAYcVJnUzMR2i5e6j56/Ooqm3gfh21Kwdd3r87kZ0DmL8+z+ooqgNouXug2vpG5q46xOjUCC6O72p1HOUhAvxsTEmL59u9RyiurLU6jnIxLXcP9PfNhZRWn+L+calWR1EeZtqweBqbjF4W6QO03D1MQ2MTr644yOC4MEanRlgdR3mYpMhOjO0dyfz1edQ3NlkdR7mQlruH+XxnMbnHanhgfC9EzjbbslLnN3NUEsVVtXy+s9jqKMqFtNw9iDGGl5YfJCWqE1cOiLE6jvJQE/pGkxQRwpurD1kdRbmQlrsH+W5/KXuKqpg9rhc2m47a1YWx2YSZo5LYklfB1vwKq+MoF9Fy9yAvLT9IbFgQPx2iqxiq9pmSHk9ooJ+O3r2YlruH2JRbxoZDZfxsbAoBfvptU+3TOdCPqenxfLa9iJIqvSzSG2lLeIiXlx+ka4g/tw6Pb31jpRwwc1QSjcbwztpcq6MoF9By9wD7iqtZuucIM0clERLQ6voqSjkkISKEif26M39DHrX1jVbHUU6m5e4BXvnuICEBdmbqwtfKye4ek0TZiToWbz3rmvbKg2m5u7n8shoWbzvM9OEJdA3Rha+Vc2WkRNAvJpQ3Vh+iecVM5S203N3cayuzsQncMzbZ6ijKC4kId41OYm9xNeuyy6yOo5xIy92NlVafYsHGfG68JE6X0FMuM3lITyI6BfDqioNWR1FOpOXuxuatOURdY5MufK1cKsjfzt1jklm+r5SdhZVWx1FOouXuppoX48hl0kUxpER1tjqO8nIzMhIJDfLjpeVZVkdRTqLl7qbeXZdHdW2DTuurOkSXIH/uzEji853FZB2ptjqOcgItdzdUW9/I66sOMbZ3JIPiwqyOo3zEXaOTCPSz8dJyPfbuDbTc3dBHmwo4evyULqGnOlRE50BuG5HIoi2FZB05bnUc1U5a7m6mobGJOSuyGRLflYwUXYxDdaz7x/ci2N/OX77eZ3UU1U5a7m7msx1F5JXVcL8uxqEsENk5kHvGprBkRzHbC3Q6YE+m5e5GjDG8vPwgvaM7c0X/7lbHUT7q3rHJdAvx55kvdfTuybTc3ciyfUfYW1zNfboYh7JQaJA/D05IZeWBo6w8UGp1HHWBtNzdhDGGF77NIq5bMJOH9LA6jvJxt49MJD48mN/+czcNupC2R9JydxNrs4+xOa+C+8b1wt+u3xZlrSB/O49f05/9Jcd5f0Oe1XHUBdAWcRMvLssiOjSQqWlxVkdRCoCrBsaQkRLBn7/eT0VNndVxVBtpubuBzXnlrM46xr1jUwjyt1sdRymgecbIJ34ygKqT9fzl6/1Wx1Ft5FC5i8jVIrJPRLJE5NGzPD9TREpFZGvLx8+cH9V7vfhtFl1D/Jk+IsHqKEr9QP/YLtyRkcQ763LZlKtTAnuSVstdROzAi8AkYABwq4gMOMumC4wxQ1o+5jo5p9fafbiKb/Ye4e7RyXQK1CX0lPv5r6v60iMsmF9/vINTDbocn6dwZOQ+HMgyxmQbY+qAD4DJro3lO15cnkXnQD/uzEiyOopSZ9U50I//veEiso4c58VvddZIT+FIufcE8k/7vKDla2e6SUS2i8hHIhLvlHRe7mDpcZbsKGJGRiJhIf5Wx1HqnMb3jeaGS3ry0vKDZObo4RlP4Ei5n+1umjMXW/wUSDLGDAaWAm+d9YVEZolIpohklpbqzREvLz9IoJ+Ne8boEnrK/T15/UB6dgvmgfc2c6S61uo4qhWOlHsBcPpIPA74wVLpxphjxphTLZ++BqSd7YWMMXOMMenGmPSoqKgLyes18o7VsGhLIdOGJRDZOdDqOEq1KizYn1duT6Oqtp4H39tMvd7c5NYcKfeNQG8RSRaRAGAasPj0DUQk9rRPrwf2OC+id3rumwPYbcIDOq2v8iD9Y7vwx5sGszGnnF99uI3GpjP/iFfuotXLM4wxDSLyc+BLwA68YYzZJSJPAZnGmMXAL0TkeqABKANmujCzxztYepxPthRw9+hkorsEWR1HqTaZPKQnBeUneebLfYgIf5p6MXadC8ntOHTtnTFmCbDkjK89cdrjx4DHnBvNez239ABB/nZm66hdeagHJ6RijOFPX+2nscnwzNTBBPrpDXjuRC+s7mD7iqv5dPthZo/rpcfalUf7+WW9sdts/PGLvRyuOMkrM9L0Z9qN6PQDHezZpfvpFODHrLEpVkdRqt3uH9+LF6cPZefhSia/sJoteeVWR1IttNw70K7DlXy+s5i7xyTTrVOA1XGUcoprB8fy4X2jAJjyylqeW3pApwl2A1ruHeivX++nS5CfXteuvM6guDA+f3gsPxkcy1+X7mfScytZvO2wXk1jIS33DrI1v4Kle44w69IUwoL1blTlfboE+fPstEt45fahAPzi/S1M+NNynly8i6W7SyipqsUYLfuOoidUO8ifv9pHtxB/Zo7WUbvybldfFMuVA2JYsrOIhZkFfLAxj3lrcgDoEuRHcmQnYsOCie0aRI+W/8aGBdOjaxBRnQPx08VqnELLvQMs33eElQeO8ptr+9NZZ35UPsBmE64b3IPrBvfgVEMjW/Mq2FdSzf6SanKP1ZBVepyVB0o5UffDWSYD/Gz0j+3CxXFhDE8OJyMlggi9AueCiFV/JqWnp5vMzExL3rsjNTQ2Mem5ldQ3NvHVf4wjwE9HJUpB87rBVbUNFFWepKiylqKKWg4dPc72gkp2Flb+q/gH9Qxj0qAYrh0US2JEJ4tTW09ENhlj0lvbToeRLrYgM58DR47zyu1DtdiVOo2IEBbsT1iwP/1iuvzguYbGJnYUVrLm4DG+3l3C01/s4+kv9jGxXzQPTOhFWmK4Rak9h47cXaiqtp4JzyynV1RnFtw3EhG9RVupC1FYcZKPMguYt+YQ5TX1jO0dyePX9v/RPwq+wNGRuw4lXehPX+6jrKaO31zXX4tdqXbo2TWYX17em9WPXsbj1/Rne0El1zy3ksf+voOq2nqr47klLXcX2ZxXzjvrcrkzI4nBcV2tjqOUVwgJ8OPeS1P47lfjuWt0Mgsz85n07ErWHjwkrFIsAAAKLklEQVRmdTS3o+XuAvWNTfz333fQPTSIR67sY3UcpbxO15AA/ue6AXw0O4MAPxu3vraO3/1zN7X1usbr97TcXeD1VYfYW1zNk9cPJDRIb1hSylUuSejGZ78Yw4yRicxddYif/G0VOwsrrY7lFrTcnSy/rIZnl+7nigHdufqiGKvjKOX1QgL8+O1PL+Ktu4dTebKeG19ew4KNeVbHspyWuxMZY3h80U7sIvzf6wdaHUcpnzKuTxRfPHwpI5LD+fXHO3j04+0+fZhGy92JPt1exIr9pTxyZV96dA22Oo5SPie8UwDz7hrOgxN68cHGfKa+spaC8hqrY1lCy91Jyk7U8dSnuxgcF8ado5KsjqOUz7LbhF9d1Y85M9LIOXqC6/62im/3llgdq8NpuTuBMYbH/r6dqpMN/PGmwbqepFJu4MqBMSx+aAwxXYK4e14mj368nWofuiZey90JPt5cyJe7Snjkyj70j/W9O+aUclfJkZ34x89HM3tcLxZm5nP1sytZk3XU6lgdQsu9nfLLanhy8S5GJIfzM106Tym3E+hn59FJ/fhw9igC/GxMn7ueJxfv4mSdd59s1XJvh5N1jdz3ziZE4M83X6yHY5RyY2mJ3Vjyi7HMHJXEvDU5XPP8Sjblllkdy2W03C/Q98fZ9xRX8dy0IcR1C7E6klKqFcEBdp68fiDz7x1BXUMTU19Zyx8+3+OVl0xquV+gN1bnsGjrYf7z8j5c1q+71XGUUm0wqlckXzw8lluGxfPqd9lc/8IqdhR4152tWu4XYMmOIn732W6uGtidByekWh1HKXUBQoP8+cONg3nzrmFUnqznhpdW8+66XKtjOY2WexutOXiUhz/YSlpCN5695RJsepxdKY82oW80Xz08jrG9I/nNop38ZtEO6hubrI7VblrubbA5r5xZb28iKTKEuXemExxgtzqSUsoJwkL8mXvnMO4bl8K76/K4e95GTpxqsDpWu2i5O2jDoTJmzF1PZOcA3rp7OF1DAqyOpJRyIrtNeGxSf56+aTCrs45y29z1lJ+oszrWBdNyd8CK/aXc+cYGYsKCWHBfBrFhOm+MUt7q5mHxvHx7GruLqrhlzlqOVNVaHemCaLm34t11udw1byOJESF8MCuD7l2CrI6klHKxqwbG8NZdwykoP8ktc9ZxuOKk1ZHaTMv9HOoamnhy8S5+s2gnl/aO5MPZGUSFBlodSynVQTJ6RfDOPcM5Wn2Km19dS36ZZ80uqeV+FnnHapj66lrmrcnh7tHJzL1zmK6opJQPSksM5717R1Bd28DNr64lu/S41ZEcpuV+GmMMH2bmc+3zK8kuPc5Ltw3liZ8M0GkFlPJhg+O68v69I6lraOKWOevYX1JtdSSHaLm3yC+r4c43N/Krj7bTLzaUJb8YyzWDYq2OpZRyAwN6dOGDWSMR4KaX1vDFziKrI7XKoXIXkatFZJ+IZInIo2d5PlBEFrQ8v15Ekpwd1FXKTtTxu3/uZuJfvmNTThlPTR7IglkZxIfrXDFKqX/r3T2Uvz8wipTozsx+dzNPfbrbra+F92ttAxGxAy8CVwAFwEYRWWyM2X3aZvcA5caYVBGZBvwRuMUVgZ3BGMOm3HI+2lTAp9sOc7K+kZuGxvEfV/TR5fGUUucU1y2ED+/L4H8/280bqw+xeNthfjExlZvT4wnyd6+bGsUYc/4NRDKAJ40xV7V8/hiAMeYPp23zZcs2a0XEDygGosx5Xjw9Pd1kZmY6YRf+zRiDMdBkDE0Gahsaqa5toPxEHQXlNeQcq2FLXjkbc8opO1FHSICdawbFMuvSFPp0D3VqFqWUd9uUW87TX+xl/aEyQgLsjEmNZGzvSJIjO5MQHkLnID8C/WwE+tnwszvvCLiIbDLGpLe2Xasjd6AnkH/a5wXAiHNtY4xpEJFKIAJw+pInX+ws5j8Xbv1XgZuW/za1FHtr4sODmdA3mtGpEVw1MIZOgY78L1BKqR9KS+zGB7NGsjb7GEt2FPHNniN8tfvsa7XabUKA3Ya0XJvxszHJ/OeVfV2az5FmO9ulImfWqCPbICKzgFktnx4XkX0OvL+jInHgH5NcYBXwFye+sUUc2l8vofvqnXx2Xx9p+bhAiY5s5Ei5FwDxp30eBxw+xzYFLYdlwoAfLXFijJkDzHEkWFuJSKYjf6p4C1/aX91X76T76lqOHAjaCPQWkWQRCQCmAYvP2GYxcGfL4ynAt+c73q6UUsq1Wh25txxD/znwJWAH3jDG7BKRp4BMY8xi4HXgHRHJonnEPs2VoZVSSp2fQ2cTjTFLgCVnfO2J0x7XAlOdG63NXHK4x4350v7qvnon3VcXavVSSKWUUp5Hpx9QSikv5HHl7s1TIZzJgX39TxHZLSLbReQbEXHoEil31dr+nrbdFBExIuKxV1o4sq8icnPL93eXiMzv6IzO4sDPcYKILBORLS0/y9dYkdMZROQNETkiIjvP8byIyPMt/y+2i8hQl4VpvqvTMz5oPqF7EEgBAoBtwIAztnkAeKXl8TRggdW5XbivE4CQlsf3e+q+Orq/LduFAiuAdUC61bld+L3tDWwBurV8Hm11bhfu6xzg/pbHA4Acq3O3Y38vBYYCO8/x/DXA5zTfGzQSWO+qLJ42ch8OZBljso0xdcAHwOQztpkMvNXy+CNgooh44py9re6rMWaZMeb7FQTW0XwPgqdy5HsL8FvgacAz1z5r5si+3gu8aIwpBzDGHOngjM7iyL4aoEvL4zB+fB+NxzDGrOAs9/icZjLwtmm2DugqIi6ZftbTyv1sUyH0PNc2xpgG4PupEDyNI/t6untoHhF4qlb3V0QuAeKNMf/syGAu4Mj3tg/QR0RWi8g6Ebm6w9I5lyP7+iRwu4gU0HxV3kMdE80Sbf29vmCeNrGK06ZC8AAO74eI3A6kA+Ncmsi1zru/ImID/grM7KhALuTI99aP5kMz42n+i2yliFxkjKlwcTZnc2RfbwXmGWP+3DJR4Tst+9rk+ngdrsP6ydNG7m2ZCoHzTYXgARzZV0TkcuBx4HpjzKkOyuYKre1vKHARsFxEcmg+XrnYQ0+qOvpz/A9jTL0x5hCwj+ay9zSO7Os9wEIAY8xaIIjmuVi8kUO/187gaeXuS1MhtLqvLYcpXqW52D31mOz3zru/xphKY0ykMSbJGJNE8zmG640xzp03umM48nO8iOYT5ohIJM2HabI7NKVzOLKvecBEABHpT3O5l3Zoyo6zGLij5aqZkUClMcY1yzpZfXb5As5GXwPsp/kM/OMtX3uK5l90aP7B+BDIAjYAKVZnduG+LgVKgK0tH4utzuzK/T1j2+V46NUyDn5vhebJS3cDO4BpVmd24b4OAFbTfCXNVuBKqzO3Y1/fB4qAeppH6fcAs4HZp31fX2z5f7HDlT/DeoeqUkp5IU87LKOUUsoBWu5KKeWFtNyVUsoLabkrpZQX0nJXSikvpOWulFJeSMtdKaW8kJa7Ukp5of8PVig4wrRvDuEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the kernel densities of predicted test probabilities\n",
    "sns.kdeplot(test_predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Misc: Adversarial Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load train/test feather files, perform adversarial validation from untouched data\n",
    "#train = feather.read_dataframe('train.ft')\n",
    "#test = feather.read_dataframe('test.ft')\n",
    "\n",
    "# Create Pseudo-Label\n",
    "train['label'] = 1\n",
    "test['label'] = 0\n",
    "train['label'] = train['label'].astype('uint8')\n",
    "test['label'] = test['label'].astype('uint8')\n",
    "\n",
    "# combine train and test \n",
    "total = pd.concat([train, test], axis=0)\n",
    "psuedo_label = total['label']\n",
    "total.drop(['label'], inplace=True, axis=1)\n",
    "total.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in categorical:\n",
    "    total[col] = total[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adversarial_valid(train, target):\n",
    "    kf = KFold(n_splits=5, shuffle=True)\n",
    "\n",
    "    train_scores = []\n",
    "    cv_scores = []\n",
    "    \n",
    "    for (train_index, test_index) in tqdm(kf.split(train, target)):\n",
    "        x_train, y_train = train.iloc[train_index], target.iloc[train_index]\n",
    "        x_test, y_test = train.iloc[test_index], target.iloc[test_index]\n",
    "\n",
    "        clf = lgb.LGBMClassifier()\n",
    "\n",
    "        clf.fit(x_train, y_train)\n",
    "        pred = clf.predict(x_train)\n",
    "        score = roc_auc_score(y_train, pred)\n",
    "        train_scores.append(score)\n",
    "\n",
    "        pred = clf.predict(x_test)\n",
    "        score = roc_auc_score(y_test, pred)\n",
    "        cv_scores.append(score)\n",
    "    \n",
    "    print(f'training sklearn auc: {mean_score(train_scores):.4f}')\n",
    "    print(f'3-fold CV sklearn auc: {mean_score(cv_scores):.4f}')\n",
    "    \n",
    "    feat_imp = pd.Series(clf.feature_importances_, index=total.columns)\n",
    "    feat_imp.nlargest(30).plot(kind='barh', figsize=(8,10))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [16:40, 200.19s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.9389\n",
      "3-fold CV sklearn auc: 0.9388\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adversarial_valid(total, psuedo_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [16:47, 202.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.9347\n",
      "3-fold CV sklearn auc: 0.9346\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping avsigversion and re-run adversarial validation\n",
    "total.drop(['AvSigVersion'], axis=1, inplace=True)\n",
    "adversarial_valid(total, psuedo_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [16:28, 196.97s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.9343\n",
      "3-fold CV sklearn auc: 0.9343\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping Census_OSVersion\n",
    "total.drop(['Census_OSVersion'], axis=1, inplace=True)\n",
    "adversarial_valid(total, psuedo_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [16:21, 196.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.9021\n",
      "3-fold CV sklearn auc: 0.9020\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping EngineVersion\n",
    "total.drop(['EngineVersion'], axis=1, inplace=True)\n",
    "adversarial_valid(total, psuedo_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [16:13, 194.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.8745\n",
      "3-fold CV sklearn auc: 0.8745\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping AppVersion\n",
    "total.drop(['AppVersion'], axis=1, inplace=True)\n",
    "adversarial_valid(total, psuedo_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [16:09, 193.41s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.8502\n",
      "3-fold CV sklearn auc: 0.8501\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZmXVUXZfaVbzQ+3bg6og4KNsagc1Z+h7yrvYzYANg+4hYIulw4LZMMD8GfA4YFBGLJG0CrL2S8zUCTcDdKzmOLW8osAB4BCAiLint+wLw24j4cX7+OB2Uf8eKiLdXUZxmZmY9Qr0rnvsCi8sJQERMi4iHJJ0kaXJWEk+FdyphT0q6LKuMEyStl/tGZcV0hqQbs22Z6p+kmTnG+pLGS5qebSNqBSepN3A4cHxELMn4rgQWAZ8A+gFzI2JR7psbES/WGGeBpNNzvkmSNs/2f8n5p0t6UNLawGnAiKy+jZC0m6RHshL3iKQPd+ZCS7pD0pS8biM7ENu2+XmypNMkLcj2dyqt+fkCSYfl9o+y/0xJ4zIhQ9KQ/L08KmlspZopqVd+rvyej6oR93pZEZ8h6SZgvdK+YTnmE5JultQn2+dIOjXbW7Ka2QAcDRyf13avyt9HVpiPA74h6YHKdSnN09bf4kXAE8BWnfm9mJmZ9WT1Tjx3BqZUN0oaBvQHdqOoAA6WtHfu7g9cGBE7Aa8AX8r20cCuETGQIsFoy6eBFyNil4jYGbinlX7bAc9HxKtV7c3ATsAEYCtJT0u6SNI+rYyzPjApInYBHgSOzPYfAf+U7QdExJvZdlNENEbETcBTwN4RsWvu+1k759aaIyJiMEU1dZSkjduJ7VfAryJiCLBcMt2KCyJiSF7T9SiqwQBXAkdHxO7AklL/rwPzco4hwJGSql/zeQywMH+vpwODAbK6/ENgv4gYRPE7OaF03Nxsvxg4MSLmAJcA5+S1fajSMSLuLu3btzx5O3+LHwauiYhdI+K56oshaaSkZknNSxbOa+fSmZmZ9TzvloeLhuXPVIpq0g4U/+cP8GxETMvtKUBDbs8ArpN0CPBWO+O3APtJOkvSXhHRWlYgIFprj4gFFInQSOBl4KZK9a/Km0ClSliO+WHgKklHAr1aiaEvcHNWCc+hSHg7Y5Sk6cAkiupc5Xq2FtvuwM25fX0H59hX0mOSWigqwjtJ2hDYICIq90yWxxoGHCppGvAYsHEproq9gWsBImIGxe8ZitscdgQezuO/BmxdOu62GufUGW39LT4XEZNaOzAixkVEU0Q09erddyVCMDMz657q/XVKs4ADa7QLOCMiLl2msVguXVRqWsLSpdf9KZKUA4B/k7QTRQJaTqbXBYiIpyUNBj4LnCFpQkScViOOvwBbS9ogIuaX2gcBv8uxlgATgYmZcH0NuKpqnMURUUlgl5DXOSKOVnGv6P7ANBX3t1b7CfBARAzP859Yo0+bJA0F9gN2j4iFkiaS16K12NpQ85pKWhe4CGiKiP+SNCb3qa3QgO9ExL1V8TZU9Wst+f99RHyllbErfycdOae2tPW3+NpKjGtmZtbj1bvieT+wTlb8gOJ+QOBV4IjSPXtbStqstUEkrQFsFREPACcDGwJ9gDkUSSKSBgHb5PYWFMu31wJnV/pUi4jXgKuBX0rqlcceCvQG7pf0YUnlCl0jsNySaxtxbxsRj0XEj4C5FJXI+RQPM1X0BV7I7cM6OnaVvsA/MuncgaJa2J5JLL2N4aBS+3PAjpLWkdQX+GS2VxLZufl7OxAgIv4BzJf0sRpj3QscI2ktAEnbS1q/Ko4HgYNz/87AwFJ8e0jaLvf1VvvfhlB9bTviXlbgb9HMzMw6rq4Vz4gIScOBcyWNBt6gSBaPo7h/89F8PmUBcAjL3h9Y1gu4NhMhUdyr94qkW1m6lDuZpU/KDwDGSnobWExxH2Frvk+RnD6d/Z8ChmfsfYDzczn5LYoK6cjWh1rO2ExcBdwHTAeeB0ZnzGcAPweulnQCRaLeET+UdFzp87bA0ZJmAH+iSNracxzFNf0uMB6YB5DVzH+nWPL+M8USNHm9L6O4jWEOxfWu+DpwmaTXKCq2lVsbLqdYBn9CxS/6ZYqny8suBq7M2KcBj+d8L+dtDTdIWqdy3rT9bQi/A26R9HngOx24BkTEBEkfoeN/izUN2LIvzf4SeDMzs2Vo6aqr9WQqnuh/PRPsg4CvRMTnOzlWn7wflvwPjH4RcewqDPddr6mpKZqbm7s6DDMzs7qQNCUi2v3ebb8y0yoGAxdkJfIV4IiVGGt/Sd+n+Pt6js7fMmBmZmbdSI9NPCXdTt4DWvK96gdfeor8uqFdVtFYNwE3rYqxzMzMrPvosYlnRAzv6hjMzMzMepJ3y/d4mpmZmVk358TTzMzMzOrCiaeZmZmZ1YUTTzMzMzOrix77cJHZ6tTywjwaRo/v9PFz/OXzZmbWDbni2YUkfVDSbyX9WdIzkn4lae02+i+RNE3STEk355e+I2lBO/NsKOmbVW1jJc2SNHbVnM0yY39O0lRJ0yXNlnRUO/2HSrqrlX1zJLVImiHpD5K27sD8cyRt0tn4zczMbPVw4tlF8ovabwPuiIj+wPYU75s/vY3DXo+IxojYGXgTOLqD020IfLOq7ShgUESc1MF4O1Qdz/ewjwP+OSJ2AXaleG3mytg3IgbmOD9cybHMzMysizjx7DqfAN6IiCsBImIJcDxwhKSdJD2e1c0Z+X73ag8B25UbJPWRdJ+kJ7JKWHnl5ZnAtjneWEl3AusDj0kaIWnrPG5G/vuhHO8qSb+U9ABwlqQxkq6WNCGril+U9POc655MOjeguIXj73leiyLiT6XxDizFW67Uvk/S7VkhvURSrb/NR4EtS8cfUrpOl0rqVX1Aa30kXSypOau+p5b6n5kxzJB0drZtKulWSZPzZ48asZmZmVk7nHh2nZ2AKeWGiHgVeB64APhVRDQCTcB/l/tl9fEzQEvVmG8AwyNiELAv8IusrI4Gnslq6UkRcQBLq6c35XzXZFXxOuC80pjbA/tFxHfz87bA/sDngWuBByJiAPA6sH9E/B9wJ/CcpBskHdxKElltN+C7wICc44s1+nwauCOvwUeAEcAeeZ2WAAdXXae2+pyS75QdCOwjaaCkjYDhwE55LX6afX8FnBMRQ4AvAZd34HzMzMysih8u6joCopX2icAPJH0QuC0i/pz71pM0LbcfAn5d49ifSdobeJuiOrh5B2LZnaWJ3m+An5f23ZzV2Ir/iIjFklqAXsA92d4CNABExDckDQD2A04EPkX772t/PCL+CiDpBmBP4Jbc94CkzYGXWLrU/kmK98tPLnJr1sv9ZW31+bKkkRT/G+gH7AjMpkjeL5c0Hqjcd7ofsGOOAUV1doOImF+eLMcbCdDrfZu2c7pmZmY9jxPPrjOLonr2DknvA7YCxlJUE/cH7pX0jYi4n6xStjHmwcCmwOBMDucA63YitnJC/FrVvkUAEfG2pMURUen7NqW/p4hoAVok/QZ4liLxfIussmcltvwgVXUSXv68b8ZxFXAacAJFkn11RHy/jfOo2UfSNhQJ8ZCI+Iekq4B1I+ItSbtRJKwHAd+muCViDWD3iHi9jbmIiHEU97eyTr/+tf6jwszMrEfzUnvXuQ/oLelQgLz38BcUydUHgL9GxHkUy9YDOzhmX+ClTDr3BSpPgM+nuPeyNY9QJFpQJK9/XIHzWEbeZzq01NQIPJfbcygqkFAs1a9V6rebpG1yWX5EdQyZ9B0HHJpL4vcBB0raLOfdqMYT7631eR9FIjsvK6mfqcQO9I2Iu3OuSpI/gSIJrZxjW8m/mZmZtcKJZxfJSuFw4F8k/Rl4mmKZ9wcUidfMXFbfAbimg8NeBzRJaqZIIJ/Kuf4OPKzia5hqfX3SKOBwSTOAfwWO7fyZIeBkSX/K+E9l6TL7ZRT3Uz4OfJRlq6mPUjwENZOiQnp79cAR8TfgBuBbETGbYtl9Qsb9e4ol83L/mn0iYjowlaLqfAXwcB6yAXBX9v0DxcNeUFyfpnzgaDYd/zYBMzMzK9HSlVIzW1Wampqiubm5q8MwMzOrC0lT8qHdNrniaWZmZmZ14cTTzMzMzOrCiaeZmZmZ1YUTTzMzMzOrCyeeZmZmZlYXTjzNzMzMrC6ceJqZmZlZXTjxNDMzM7O68LvazVaDlhfm0TB6/EqPM+fM/VdBNGZmZu8Ornh2E5I2l3S9pL9KmiLpUUnDOzFOg6SQ9J1S2wWSDlulAS8/70RJy73xQNJhki7o5JgbSvpm6fMWkm4pfb4hX4N5vKTTJO3XuejNzMysI1zx7AYkCbgDuDoivpptWwMHdHLIl4BjJV0aEW+uojC7wobAN4GLACLiReBAAEkfAD4eEVt3ZmBJa0bEW6sqUDMzs57AFc/u4RPAmxFxSaUhIp6LiPMl9ZI0VtLkrO4dBUWymu0zJbVIGlEa72XgPuBr1RNJOjLHmi7pVkm9s/0qSRdLeiCrrvtIukLSk5KuKh0/LKuxT0i6WVKfGnMcLulpSX8A9ii1b5pzTs6fPbJ9TM41MecelYecCWwraVqea4OkmblvArBZ7tsr468kpYMl/SErx/dK6pftEyX9LOM6dgV/R2ZmZj2eE8/uYSfgiVb2fR2YFxFDgCHAkZK2Ab4INAK7APsBYysJVjoT+K6kXlXj3RYRQyJiF+DJHL/i/RRJ8PHA74BzMrYBkholbQL8ENgvIgYBzcAJ5cEzhlMpEs5PATuWdv8KOCfP5UvA5aV9OwD/BOwG/FjSWsBo4JmIaIyIk6rO44DSvodK868FnA8cGBGDgSuA00vHbRgR+0TELzAzM7MV4qX2bkjShcCewJvAc8DASjUP6Av0z/03RMQS4H+zijcEmAEQEc9Kehz4atXwO0v6KcUydh/g3tK+30VESGoB/jciWjKeWUAD8EGKRPLh4u4A1gYerRr/o8DEiHg5j70J2D737QfsmMcCvE/SBrk9PiIWAYskvQRs3tHrVeXDwM7A73OeXsDfSvtvau1ASSOBkQC93rdpJ6c3MzPrvpx4dg+zKCqAAETEt7K62Aw8D3wnIsoJIpI+24FxfwbcAjxYarsK+EJETM8HjoaW9i3Kf98ubVc+rwksAX4fEV9pZ95opX0NYPeIeL3cmAlieb4ldP5vW8CsiNi9lf2vtXZgRIwDxgGs069/a+dgZmbWY3mpvXu4H1hX0jGltt75773AMbmEjKTtJa1PkUyOyHtANwX2Bh4vDxoRTwGzgc+VmjcA/pbjHbyCcU4C9pC0XcbSW9L2VX0eA4ZK2jjn+JfSvgnAtysfJDW2M9/8jHdF/AnYVNLuOcdaknZawTHMzMysBiee3UBEBPAFYB9JlSXyq4HvUdwHORt4Ih+suZSiGng7xbL6dIrE9eSI+J8aw59OsURe8W8UyeHvgadWMM6XgcOAGyTNoEhEd6jq8zdgDMUS/H+y7L2ro4CmfEhqNnB0O/P9nWJZf6aksR2M8U2KJ9/PkjQdmAZ8vCPHmpmZWdtU5Cxmtio1NTVFc3NzV4dhZmZWF5KmRMRy38ddzRVPMzMzM6sLJ55mZmZmVhdOPM3MzMysLpx4mpmZmVldOPE0MzMzs7pw4mlmZmZmdeHE08zMzMzqwomnmZmZmdWF39Vuthq0vDCPhtHjV9l4c87cf5WNZWZm1lVc8bR3PUlLJE0r/TRIapJ0Xh1jmCNpk9xeUK95zczMuhNXPO294PWIaKxqmwMs905KSWtGxFt1icrMzMxWiCue9p4kaaiku3J7jKRxkiYA10jqJWmspMmSZkg6KvtdJOmA3L5d0hW5/XVJP83tOyRNkTRL0sguOj0zM7NuyRVPey9YT9K03H42IobX6DMY2DMiXs+EcV5EDJG0DvBwJqUPAnsBdwJbAv3y2D2BG3P7iIj4P0nrAZMl3RoRf19dJ2ZmZtaTOPG094JaS+3V7oyI13N7GDBQ0oH5uS/QH3gIOE7SjsBs4P2S+gG7A6Oy7yhJlcR2qzyuQ4lnJrwjAXq9b9OOHGJmZtajOPG07uK10raA70TEvdWdJL0f+DRF9XMj4MvAgoiYL2kosB+we0QslDQRWLejAUTEOGAcwDr9+kcnz8PMzKzb8j2e1h3dCxwjaS0ASdtLWj/3PQocR5F4PgScmP9CURn9RyadOwAfq2/YZmZm3ZsTT+uOLqdYSn9C0kzgUpZW9x8C1oyIvwBPUFQ9K4nnPcCakmYAPwEm1TVqMzOzbk4RXhE0W9XW6dc/+n3t3FU2nr9A3szM3s0kTYmIpvb6+R5Ps9VgwJZ9aXayaGZmtgwvtZuS8NmlAAAgAElEQVSZmZlZXTjxNDMzM7O6cOJpZmZmZnXhxNPMzMzM6sKJp5mZmZnVhRNPMzMzM6sLJ55mZmZmVhdOPM3MzMysLvwF8marQcsL82gYPX6Vj+s3GJmZ2XuZK55mZmZmVhdOPGuQ9AFJN0p6RtJsSXdL2r6r46qQtKakn0n6s6Rp+XPKaphnC0m3rMTxn5M0VdL0vI5HZfsXJO3YgeM72m+MpIWSNiu1Lehs3GZmZrZ6OPGsIknA7cDEiNg2InYEfgBs3rWRLeOnwBbAgIhoBPYC1lrVk0TEixFxYGeOlbQWMA7454jYBdgVmJi7vwC0m1CuQD+AucB3VzBMMzMzqyMnnsvbF1gcEZdUGiJiWkQ8JOkkSZMlzZB0KoCkBklPSrpM0ixJEyStl/tGZaVvhqQbs22MpBMrY0uamWOsL2l8VgdnShpRKzhJvYEjge9ExBsZ3/yIGFPqc4ikx7MSeqmkXtm+QNLpOcckSZtn+1WSDiwdv6B0bjNz+zBJt0m6JyutPy/1HybpUUlPSLpZUh9gA4p7iP+eMS6KiD9J+jhwADA249tW0pF5XadLulVS71b6bZvzT5H0kKQdSpfmCmCEpI1qXLM78phZkkaWz1PSWbnvPyXtJmmipL9KOiD79JI0tvR7P6rmX03Rd6SkZknNSxbOa62bmZlZj+XEc3k7A1OqGyUNA/oDuwGNwGBJe+fu/sCFEbET8ArwpWwfDewaEQOBo9uZ99PAixGxS0TsDNzTSr/tgOcjYn6tnZI+AowA9shq6BLg4Ny9PjApK5APUiSwK6Ixxx5AkeRtJWkT4IfAfhExCGgGToiI/wPuBJ6TdIOkgyWtERGPZPtJEdEYEc8At0XEkIzrSeDrrfQbR5FwDwZOBC4qxbaAIvk8tkbcR+QxTcAoSRuXrsfE3DefopL8KWA4cFr2+TowLyKGAEOAIyVtU+viRMS4iGiKiKZevfuuwGU1MzPrGfxUe8cNy5+p+bkPRcL5PPBsREzL9ilAQ27PAK6TdAdwRzvjtwBnSzoLuCsiHupIUJIOp0i2NgY+DnwSGAxMLu4aYD3gpez+JnBXKc5PdWSOkvsiYl7OOxvYGtiQYjn84ZxvbeBRgIj4hqQBwH4UieKngMNqjLuzpJ/mWH2Ae2ucZ588v5tzHoB1qrqdB0yT9Iuq9lGShuf2VhS/t79TXI9Kgt8CLIqIxZJaWPo7HAYMLFWE++bxz9Y4DzMzM2uDE8/lzQJq3dco4IyIuHSZRqkBWFRqWkKR7AHsD+xNsWT8b5J2At5i2UrzugAR8bSkwcBngTMkTYiI01jeX4APSdogl9ivBK7MJfFeGefVEfH9GscujogoxVn5/b8TU97junaNY6lxnmvmfL+PiK/UOiAiWoAWSb+hSNYOq9HtKuALETFd0mHA0Bp91gBeySpuTRHxiqTrgW9W2iQNpUh8d4+IhZImktecZa/H25Xzi4i3JVWujSiqrMslw2ZmZrZivNS+vPuBdSS9swwtaQjwKnBEVt6QtKVKT1FXk7QGsFVEPACczNJq3hxgUPYZBGyT21sACyPiWuDsSp9qEbEQ+DVwgaR189heLE0W7wMOrMQmaSNJW7dzznMoqqQAn2fFHlSaBOwhabucr7ek7SX1yaSvohF4LrfnU9wDWrEB8DcVDyQdXGp/p19EvAo8K+lfch5J2qVGPL8EjmJpUt0X+EcmnTsAH1uBc4Oi+npMxkae2/orOIaZmZnhiudyIiJyWfZcSaOBNygSs+Mo7t98NJd6FwCHUFT+aukFXCupL0XV7JysyN0KHCppGjAZeDr7D6B4kOZtYDFwTBthngL8BJgpaT7wOnA1xT2ib0r6ITAhk9/FwLdYmvTVchnwW0mPUySur7XRdxkR8XJWKW+QVFn6/iHwN+BkSZdmfK+xtNp5I3CZpFEU1eV/Ax7LGFtYmpRW9zsYuDjPb63cP70qnrmSbgeOz6Z7gKMlzQD+RJEor4jLKZbdn8hq8MsUT9u3acCWfWn2l72bmZktQ0tXGs1sVWlqaorm5uauDsPMzKwuJE2JiKb2+nmp3czMzMzqwkvt72K5ZFz91T3f84MuZmZm9l7kxPNdLCKGt9/LzMzM7L3BS+1mZmZmVhdOPM3MzMysLpx4mpmZmVldOPE0MzMzs7rww0Vmq0HLC/NoGD1+tc4xx19Qb2Zm7zGueJqZmZlZXTjx7KYkfUDSjZKekTRb0t2Stu/quCokrS3p3Izvz5J+K+mDpf2nSJolaYakaZI+mu0TJf0p26ZJuqWV8Rfkv1tU+khqlPTZUp8D8rWoZmZmVgdeau+G8p3itwNXR8RB2dYIbM7Sd8N3tZ9RvJN9+4hYIulw4LZMMD8GfA4YFBGLJG0CrF069uCI6ND7KCPiRYr3vAM0Ak3A3bnvTuDOVXI2ZmZm1i5XPLunfYHFEXFJpSEipkXEQ5JOkjQ5K4mnAkhqkPSkpMuyyjhB0nq5b1RWTGdIujHbxkg6sTK2pJk5xvqSxkuanm0jagUnqTdwOHB8RCzJ+K4EFgGfAPoBcyNiUe6bmwlkqyRtI+nRPLeflNobMpa1gdOAEVkpHSHpMEkXZL+tJd2X53mfpA9l+1WSzpP0iKS/SjqwdgQgaaSkZknNSxbOaytcMzOzHsmJZ/e0MzClulHSMKA/sBtF9W+wpL1zd3/gwojYCXgF+FK2jwZ2jYiBwNHtzPtp4MWI2CUidgbuaaXfdsDzEfFqVXszsBMwAdhK0tOSLpK0T1W/60pL7WOz7VfAxRExBPif6gkj4k3gR8BNEdEYETdVdbkAuCbP8zrgvNK+fsCeFFXYM1s7+YgYFxFNEdHUq3ff1rqZmZn1WE48e5Zh+TMVeALYgSLhBHg2Iqbl9hSgIbdnUCR6hwBvtTN+C7CfpLMk7RURrZX9BERr7RGxABgMjAReBm6SdFip38GZPDZGxEnZtgdwQ27/pp04a9kduL50/J6lfXdExNsRMZvidgUzMzPrBCee3dMsisStmoAzSknbdhHx69y3qNRvCUvv/90fuDDHmyJpTYoEtPy3sy5ARDyd/VqAMyT9qJX4/gJsLWmDqvZBwOwca0lETIyIHwPfZmkFti21ktnOKo9VvjZahXOYmZn1KE48u6f7gXUkHVlpkDQEeBU4QlKfbNtS0matDSJpDWCriHgAOBnYEOgDzKFIEpE0CNgmt7cAFkbEtcDZlT7VIuI14Grgl5J65bGHAr2B+yV9WFL/0iGNwHPtnPPDwEG5fXArfeZTPNBUyyNVx/+xnfnMzMxsBfmp9m4oIkLScODc/LqgNyiSxeMo7t98tHjwnQXAIRQVzlp6AddK6ktR6TsnIl6RdCtwqKRpwGSWPik/ABgr6W1gMXBMG2F+nyI5fTr7PwUMz9j7AOdL2pCiuvoXimX3iuskvZ7bcyNiP+BY4HpJxwK3tjLnA8DojPuMqn2jgCsknUSxvH94G7G3a8CWfWn2F7ybmZktQxGrcnXSzACampqiublD3/hkZmb2nidpSkQ0tdfPS+1mZmZmVhdearfVStLt5D2gJd+LiHu7Ih4zMzPrOk48bbWKiOFdHYOZmZm9O3ip3czMzMzqwomnmZmZmdWFE08zMzMzqwsnnmZmZmZWF364yGw1aHlhHg2jx9dlrjn+onozM3uPcMUTkLRgNY49R9ImHejTImla/py3KuaT9Eg7fWuet6QxkhaWX6e5Oq9Rjt8gaWYr+yZKavdLaVs5dqikj5c+H52v50TSDnm9p0ratr3rZWZmZivHFc93j30jYu6qHDAiPt5+r1bNBb4LfG8VhdNVhlK8GvQRgIi4pLTvC8BvI+LH+bnD10vFO0cVEW+vojjNzMy6PVc8WyFpa0n3SZqR/34o2zeXdLuk6fnz8Wy/Q9IUSbMkjWxlzEMkPZ5Vtksl9WonhomSzspjnpa0V7b3lvTvGdtNkh6rVRGsVCkl9ZP0YM47szJO7js9z2OSpM1Lh18BjJC0UY1xa56rpAUZ7xRJ/ylptzyHv0o6IPv0kjRW0uSM/6ga468n6cbK+QHrlfYNk/SopCck3Zzvda9Uek/N9pasZjYARwPH57nvldXcEyV9luLd9d+Q9ED5euX2SaUYT822BklPSroIeALYqq3fn5mZmS3LiWfrLgCuiYiBwHVAZfn7POAPEbELMAiYle1HRMRgoAkYJWnj8mCSPgKMAPaIiEZgCXBwqcsDpaX240vta0bEbhRJUqUy903gHxnbT4DB7ZzLV4F7c95dgGnZvj4wKc/lQeDI0jELKJLPY2uM19q5rg9MzH3zgZ8CnwKGA6dln68D8yJiCDAEOFJS9ZuNjgEW5vmdXjm/vIXgh8B+ETEIaAZOKB03N9svBk6MiDnAJcA5EdEYEQ9VOkbE3aV9+5YnlzQM6A/sBjQCgyXtnbs/TPF3sWtEPFd13EhJzZKalyycV+OymZmZ9Wxeam/d7sAXc/s3wM9z+xPAoQARsQSoZBijJFXe0rMVReLy99J4n6RIoCYXq7SsB7xU2t/aUvtt+e8UoCG39wR+lTHMlDSjnXOZDFwhaS3gjoioJJ5vAneVxv9U1XHnAdMk/aKqvbVzfRO4J9tbgEURsVhSSyn2YcBASQfm5755/NOl8ffOuYmIGaXz+xiwI/BwXsO1gUdLx5Wv1RfpvGH5MzU/98kYnweei4hJtQ6KiHHAOIB1+vWPlZjfzMysW3Li2XGtJhKShgL7AbtHxEJJE4F1q7sBV0fE91dw3kX57xKW/r60IgNExINZsdsf+I2ksRFxDbA4IirnVR6/ctwrkq6nqLAWE7d9ruXx3q7EHhFvSyrH/p3qd7Xnsvgy09c4FQG/j4ivtHKqta5VZwg4IyIurRHjaysxrpmZWY/mpfbWPQIclNsHA3/M7fsoloIr9yu+j6Jq949MxHagqMxVuw84UPmkuKSNJG3dydj+CHw5x9kRGNBW55znpYi4DPg1xS0CHfVL4CiWJnIdOde23Asck9VXJG0vaf2qPg+StyFI2hkYmO2TgD0kbZf7ekvavp355gMbdCLGI0r3j26p0hP+ZmZm1jlOPAu9Jf136ecEYBRweC7z/itL73U8Ftg3l4+nADtRLC+vmX1/QpEgLSMiZlPcnzgh+/0e6FfqUr7H85p24r0I2DTH+R4wg6VL/rUMpVgynwp8iVym74hc/r8dWCeb2j3XdlwOzAaeUPH1SZeyfHXyYqBPznEy8HjG8jJwGHBD7psE7NDOfL8DhlceLupIgBExAbgeeDR/z7ew4smrmZmZVdHSlVF7r1DxNPxaEfGGpG0pqqnbR8SbXRyapaampmhubu7qMMzMzOpC0pSIaPc7t32P53tTb4oK6VoU9yMe46TTzMzM3u2ceL4HRcR8iq8yMjMzM3vP8D2eZmZmZlYXTjzNzMzMrC6ceJqZmZlZXTjxNDMzM7O6cOJpZmZmZnXhxNPMzMzM6sJfp2S2GrS8MI+G0eO7ZO45Z+7fJfOamZm1xxVPMzMzM6sLJ56ApA9IulHSM5JmS7pb0vZdHVeFpLUlnZvx/VnSbyV9sLT/FEmzJM3Id5J/NNvnSNqk1G+opLty+zBJF+T2GEknrqbY35mnM30kLVjJubcofb5c0o65/S+SnpT0gKQmSed1dh4zMzPrmB6/1C5JwO3A1RFxULY1ApsDT3dlbCU/AzageB/7EkmHA7dlgvkx4HPAoIhYlInm2l0Y67vJYcBM4EWAiPhGad/XgW9GxAP5ucMvVpe0ZkS8taqCNDMz6ylc8YR9gcURcUmlISKmRcRDkk6SNDkriacCSGrIStllWWWcIGm93DcqK6YzJN2YbctUEyXNzDHWlzRe0vRsG1ErOEm9gcOB4yNiScZ3JbAI+ATQD5gbEYty39yIeHFVXBhJJ2RsMyUdV2o/NM9xuqTfZNs/S3pM0lRJ/ylp8xrjbSrp1rymkyXtUaPPNpIezf0/qdrX4d+HpAMpXit6XVaB15M0MaubPwL2BC6RNLaqEry+pCtynqmSPp/th0m6WdLvgAmtXK+RkpolNS9ZOK+TV93MzKz7cuIJOwNTqhslDQP6A7sBjcBgSXvn7v7AhRGxE/AK8KVsHw3sGhEDgaPbmffTwIsRsUtE7Azc00q/7YDnI+LVqvZmYCeKJGgrSU9LukjSPu3M2yGSBlMkvJWq6pGSdpW0E3AK8ImI2AU4Ng/5I/CxiNgVuBE4ucawvwLOiYghFNfs8lb6XJx9/qcUzwr9PiLiFoprdHBENEbE65WxIuK00r6TquY/Bbg/598XGCtp/dy3O/C1iPhErWsWEeMioikimnr17luri5mZWY/W45fa2zAsf6bm5z4UCc7zwLMRMS3bpwANuT2DosJ2B3BHO+O3AGdLOgu4KyIeaqWfgGitPSIWZJK4F0WidJOk0RFxVSvH1WqrZU/g9oh4DUDSbTlHALdExFyKAP4v+38w5+5HsdT/bI0x9wN2LO5uAOB9kjao6rMHSxP53wBn5XZnfh+dMQw4oFSlXhf4UG7/vnS+ZmZmtoJc8YRZwOAa7QLOyGpZY0RsFxG/zn2LSv2WsDSB3x+4MMebImlN4C2Wvc7rAkTE09mvBTgjl39r+QuwdY0EbRAwO8daEhETI+LHwLdZmrj9HXh/6ZiNgLmtzFNNbbTXSl7PBy6IiAHAUeR5VlkD2L10TbeMiPk1+rWWaK/o76MzRFExrczzoYh4Mve9thLjmpmZ9XhOPOF+YB1JR1YaJA0BXgWOkNQn27aUtFlrg0haA9gqH1Y5GdiQoio3hyJJRNIgYJvc3gJYGBHXAmdX+lTLiuPVwC8l9cpjDwV6A/dL+rCk/qVDGoHncnsi8K95TC/gEOABOuZB4AuSeudS83DgIeA+4MuSNs5xN8r+fYEXcvtrrYw5gSIxJo9trNHnYeCg3D641H4vK/D7SPMpHspaEfcC38mHzpC06woeb2ZmZq3o8UvtERGShgPnShoNvEGRLB5Hcb/go5mDLKBI3Ja0MlQv4FpJfSmqZudExCuSbgUOlTQNmMzSJ+UHUNw/+DawGDimjTC/T5GcPp39nwKGZ+x9gPMlbUhRXf0LMDKP+wlwsaTpGdM9wLWtzPHD8gNEEfFBSVcBj2fT5RExFUDS6cAfJC2hWPo+DBgD3CzpBWASmWBXGQVcKGkGxd/egyx/L+yxwPWSjgVuLcUzQdJH6PjvA+AqigeIXqe4P7MjfgKcC8zI5HMOxbcGrJABW/al2V/kbmZmtgxFdPSWPzPrqKampmhu7vA3NJmZmb2nSZoSEU3t9fNSu5mZmZnVRY9fan83kXQ7yy9Rfy8i7u2KeMzMzMxWJSee7yIRMbyrYzAzMzNbXbzUbmZmZmZ14cTTzMzMzOrCiaeZmZmZ1YUTTzMzMzOrCz9cZLYatLwwj4bR47s0hjn+AnszM3uXccXTOk3SByTdKOkZSbMl3S1pb0m35P5GSZ/twDhjJJ1Yo71B0syViO84Sb1Ln+/ONzwhaZSkJyVdJ+mAfGuVmZmZrUaueFqn5OskbweujoiDsq0R2CAiDsxujUATcHfXRMlxFK8IXQgQEeUk+JvAZyLi2fx8Z0cHlbRmRLy1yqI0MzPrIVzxtM7aF1gcEZdUGiJiGvBfkmZKWhs4DRghaZqkEZL+LGlTAElrSPqLpE3Kg0oaLGm6pEeBb5Xae0kaK2mypBmSjsr2oZImSrpF0lNZwZSkUcAWwAOSHsi+cyRtIukS4P8Bd0o6XtJhki7IPptKujXnmSxpj2wfI2mcpAnANavropqZmXVnrnhaZ+0MTGltZ0S8KelHQFNEfBtA0g7AwcC5wH7A9IiYWxRP33El8J2I+IOksaX2rwPzImKIpHWAhzMJ5P+zd+9xf8/3/ccfT0FIQrSjiiGdxpmcrlDHiqWplrVNMTRmaGVs5UdHm1WntFsxWtqhGmrRUVLFptI16SpxjMOVSK4kjjXpOra2qSbEIYjn74/P+ytfl+uc5HuR63m/3XLL5/v+vA+vz5fe+vJ6fz7fDzAC2A14FrgX2M/2dyR9ARhje0mr2E6WdEjtnKTj605/G7jE9j2StgOmA7uUc6OA/W2/3LWvKCIiIuol8YxGugb4d6rE80SqJPNNkgYDm9m+szT9K/CxcjwO2FNSbRt/MDAUeBV40Pb/lDnmAUOAe3oY41hg17pkeFNJm5Tj2zpKOiVNBCYC9Nt0ix4uHxERse5K4hk9tQg4otNedWz/WtJvJB0M7E1V/awnwO0MF1Ul9C3vrZd0ELCirmklq/fv9XrAPq0TzJKIvtjRQNuTgckA/bca2t51RERE9Fm5xzN66g6gv6STag2SRgPb1/V5Adik1birqR74+ZHtlfUnbC8FlknavzTVJ6bTgVMkbVDW2lHSwE5ibGv9zswAPl/7UB6YioiIiDUgiWf0iG0D44GPlJ9TWgScS3WfZc1Mqm3reZKOKm23AYNotc1e5wTg8vJwUX3V8WrgEWBu+Yml79F5ZXMy8B+1h4u66DSgqTzA9AhwcjfGRkRERAdU5Q8RjSGpierhnQN6O5a1qampyc3Nzb0dRkRERENImmO7qbN+ucczGqb8SPspvP3ezoiIiOgDstUeDWP7Atvb2+7pE+cRERHxLpbEMyIiIiIaIolnRERERDREEs+IiIiIaIgknhERERHREEk8IyIiIqIhknhGREREREPkdzwj1oIFzyxjyKRpvR0GAIsvOLS3Q4iIiABS8XxXkjRekiXtXD4/LWmnVn0ulfRFSQdJWibpYUmPSvrqaq49RdIRPRz75S70mVXebtTduYdL+ngX+h0k6fZyfLyky7q5zmJJm3c3voiIiEji+W51DHAPcHT5fGPdMZLWA44Appamu22PAJqAYyWNqp9MUqMq350mnqthONBp4hkRERG9J4nnu4ykQcB+wGdZlWzeUHcMcCCw2Pav6sfafhGYA+xQqn03SfoJMEOViyQtlLRA0lFlPUm6TNIjkqYB76uL5c3qn6QmSbNqMUr6lzJPi6TDJV0AbCxpnqTrJQ2UNE3S/LLmUW1c63JJ/1j63C9py9J+ZBkzX9JdkjYEvgYcVeY/StJeku4rld77WleE21hrC0k3S3qo/NmvtP+RpBllnu8B6so/p4iIiHi7JJ7vPp8Cfmb7CeA5SSNttwBvSBpW+hxNlYy+haQ/Aj4ELCpN+wB/aftg4NNUVcNhwFjgIklbAeOBnYA9gJOAfbsQ498Dy2zvYXtP4A7bk4CXbQ+3PQE4BHjW9jDbuwM/a2OegcD9tocBd5X1Ac4BPlraP2H71dI2tcw/FXgMOLBUes8BvtFJzN8GLrE9GjgcuLq0fxW4p8xzG7BdF64/IiIi2pCHi959jgEuLcc3ls9zKVVPSYuAT1IlWzUHSHoYeAO4wPYiSaOBn9t+rvTZH7jB9krgN5LuBEZTVU9r7c9KuqMLMY6lrgJr+w9t9FkAXCzpQuB223e30edV4PZyPAf4SDm+F5gi6UfALe3EMBi4VtJQwMAGXYh5V+nNguamkjahuv5Pl+uYJqmtawFA0kRgIkC/TbfoZLmIiIi+J4nnu0ipWB4M7C7JQD/Akr5IlXjOAO4EWmz/tm7o3bYPa2PKF+un72Bpt9P+Oquq5hu1mqu9MdWE9hPlXtOPA+dLmmH7a626vWa7Ns9Kyr+vtk+WtDdwKDBP0vA2lvg6MNP2eElDgFkdxVOuYx/bL9c3lkS0w2upu6bJwGSA/lsN7dKYiIiIviRb7e8uRwA/sL297SG2twWeBva3/RTwe+AC2thm74K7qO6R7CdpC6pK34Ol/ejSvhUwpm7MYqD2oNLhde0zgM/XPkh6Tzl8TdIGpW1r4CXb1wEXAyO7GqikHWw/YPscYAmwLfACsEldt8HAM+X4+C5M2zrmWjJ7FzChtH0MeM/bh0ZERERXJPF8dzkGuLVV283AZ8rxDcDObfTpiluBFmA+cAfwRdv/V9qfpNoa/y5VRbXmPODbku6mqkjW/APwntoDQKxKVicDLZKup7pn9EFJ84Czy5iuuqg8uLSQKjGcD8yk2iqfVx5U+ieqSuq9VJXhzpwGNJWHoR4BTq67xgMlzQXGAf/djTgjIiKijlbtZEbEmtLU1OTm5ubeDiMiIqIhJM2x3envcKfiGRERERENkcQzIiIiIhoiiWdERERENEQSz4iIiIhoiCSeEREREdEQSTwjIiIioiGSeEZEREREQyTxjIiIiIiGyLvaI9aCBc8sY8ikab0dxlssvuDQ3g4hIiL6uFQ8o0OSlndyfpakj7ZqO13SFd1cZ7GkzdtoP1fSmd2Zq27sEEmfqfvcJOk75bi/pP+svWJT0tWSdu3JOhEREdE1qXjG6roBOBqYXtd2NHBWVwZLEqC1EBfAEKr32P8QwHYzUHuP5QhgA9vDy+ep3ZlYUj/bKzvvGRERETWpeEaXSTpL0kOSWiSdV5p/DBwmqX/pMwTYGrinvTGlEvloqYrOBbZttc7Zkh6X9J/ATnXtO0j6maQ5ku6WtHNpnyLpO5Luk/Rfko4oQy4ADihVzTMkHSTpdknvA64DhpdzO5TKbVOZb5yk2ZLmSrpJ0qDSvljSOZLuAY5c099vRETEui6JZ3SJpHHAUGAvYDgwStKBtn8PPAgcUroeDUy17fbGlH47AT+wPcL2r+rWGVXmGAF8GhhdF8Zk4FTbo4Azgfrt/K2A/YHDqBJOgEnA3baH276k1tH2b4HP1Z17qm79zYGvAGNtj6SqkH6hbp1XbO9v+8Yuf3kREREBZKs9um5c+fNw+TyIKqm8i1Xb7f9e/j6xkzH/DfzK9v1trHMAcKvtlwAk3Vb+HgTsC9xU7c4D0L9u3L/ZfgN4RNKWq3GdHwJ2Be4t62wIzK473+6WvKSJwESAfptusRohRERErJuSeEZXCTjf9vfaOPdvwLckjQQ2tj23ozFlO/7FDtZyG23rAUvr7slsbUWrWHtKwPc0cu8AACAASURBVM9tH9PO+Xbjtj2ZqipL/62GtnUNERERfVq22qOrpgMn1t3vuE25VxLby4FZwDVU1c9Ox3TgLmC8pI0lbQL8WVnjeeBpSUeWuSRpWCdzvQBs0o1rBLgf2E/SB8s6AyTt2M05IiIiog1JPKNLbM+gejp8tqQFVA8V1Sd1NwDDgBu7MaatdeZSbWfPA24G7q47PQH4rKT5wCLgk52E3QK8Lmm+pDM6vchq/d8BxwM3SGqhSkR37srYiIiI6Jjs7AhGrGlNTU1ubm7uvGNERMQ6QNIc202d9UvFMyIiIiIaIolnRERERDREEs+IiIiIaIgknhERERHREEk8IyIiIqIhknhGREREREMk8YyIiIiIhkjiGRERERENkXe1R6wFC55ZxpBJ03o7jDYtvuDQ3g4hIiL6qFQ8Y62StLyT84slLSivtZwh6f3dnP90SQN6GNtwSR/vwbhZkjp9O0NERES8VRLPeCcYY3sY0Ax8ufVJSf06GHs60KPEExgOdDvxjIiIiJ5J4hkNIWkrSXdJmidpoaQD2uh2F/DB0n+5pK9JegDYR9KfSnq4VEevkdRf0mnA1sBMSTPLuHGSZkuaK+kmSYNK+2hJ95XK6oOSBgNfA44qMR0laWCZ+6Gy1ifL2I0l3SipRdJUYOO1/41FRESse5J4RqN8BphuezgwDJjXRp/DgAXleCCw0PbeVJXQKcBRtvegujf5FNvfAZ6lqpiOkbQ58BVgrO2RZdwXJG0ITAX+X6msjgVeBM4BptoebnsqcDZwh+3RwBjgIkkDgVOAl2zvCfwjMGqNfjMRERF9RB4uikZ5CLhG0gbAv9muTzxnSloJtFAljgArgZvL8U7A07afKJ+vBf4GuLTVGh8CdgXulQSwITC7jP9f2w8B2H4eoPSpNw74hKQzy+eNgO2AA4HvlLEtklraukBJE4GJAP023aKj7yIiIqJPSuIZDWH7LkkHAocC/yrpIts/KKfH2F7SasgrtleW47dliO0Q8HPbx7ylUdoTcBfHH2778Vbj6cp425OByQD9txralfUiIiL6lGy1R0NI2h74re2rgO8DI7sx/DFgiKQPls9/AdxZjl8ANinH9wP71fpJGiBpxzJ+a0mjS/smktZvNRZgOnCqSqYpaURpvwuYUNp2B/bsRuwRERFRJPGMRjkImCfpYeBw4NtdHWj7FeAE4CZJC4A3gCvL6cnAf0iaaft3wPHADWU7/H5gZ9uvAkcB/yxpPvBzqm30mcCutYeLgK8DGwAtkhaWzwDfBQaVOb8IPNjD7yAiIqJPk50dwYg1rampyc3Nzb0dRkRERENImmO709+4TsUzIiIiIhoiiWdERERENEQSz4iIiIhoiCSeEREREdEQSTwjIiIioiGSeEZEREREQyTxjIiIiIiGSOIZEREREQ2RxDMiIiIiGmL93g4gYl204JllDJk0rbfD6NTiCw7t7RAiIqIPScXzHULSJZJOr/s8XdLVdZ+/KekLrcZMkXREOT5A0qLy3vFdJH2mk/UOkrRM0sOSHpN08WrG/1NJm/Vw7JvX0UaMt69GTF9u9fm+uuOLyvd1kaSTJR3X03UiIiKia5J4vnPcB+wLIGk9YHNgt7rz+wL3djB+AnCx7eHAlkCHiWdxt+0RwAjgMEn79SRwANsft720p+PXkrcknrb3rfv4V8BI22fZvtL2D7o6qaTsFERERPRAEs93jnspiSdVwrkQeEHSeyT1B3YB5km6TNIjkqYB7wOQ9Dngz4FzJF0PXAAcUKqfZ3S2sO2XgXnANmW+gZKukfRQqYh+srQPkPQjSS2Spkp6QFJTObdY0ubl+AuSFpY/p5e2IZIelXRVqTTOkLRx61gkHVIqsPcAn65rby+m4yXdIulnkp6U9E+l/QJg4/IdXF/alpe/bwMGAg9IOkrSuZLOLOd2KHPNkXS3pJ1L+xRJ35I0E7iw83+cERER0VoqN+8Qtp+V9Lqk7agS0NlUieA+wDKgBTgU2AnYg6qq+Qhwje2rJe0P3G77x5IOAs60fVhX1pb0HmAocFdpOhu4w/aJZfv8QUn/CZwC/MH2npJ2p0pWW881CjgB2BsQVXJ3J/CHssYxtk+S9CPgcOC6urEbAVcBBwO/BKbWTd1eTADDqaq2K4DHJf2z7UmSPl8qwK2/609IWl47J+ncutOTgZNtPylpb+CKEg/AjsBY2yvb+R4nAhMB+m26RVtdIiIi+rRUPN9ZalXPWuI5u+7zfcCBwA22V9p+FrhjNdc7QFIL8H9USev/lfZxwCRJ84BZwEbAdsD+wI0AthdSJcOt7Q/cavtF28uBW4ADyrmnbdeS1TnAkFZjdy59nrRt6pLSDmIC+IXtZbZfoUrGt+/Wt1BIGkT1Xd9U1vkesFVdl5vaSzoBbE+23WS7qd+AwT0JISIiYp2Wiuc7S+0+zz2ottp/Dfwt8DxwDfCngNfgenfbPkzSjsA9km4tiaGAw20/Xt9ZkrowZ0d9VtQdrwTettVO+9fXXkx7tzFvT/+9Xg9Y2laVtHixh/NGREQEqXi+09wLHAY8V6qazwGbUW23z6baCj9aUj9JWwFj2pnnBWCTri5q+wngfOBLpWk6cGot0ZQ0orTfQ3UvKZJ2pUqQW7sL+FS5H3QgMB64u4uhPAZ8QNIO5fMxdefai6kjr0naoItrY/t54GlJR5Y1JGlYV8dHREREx5J4vrMsoHqa/f5WbctsLwFuBZ4sbd8F7mxnnhbgdUnzu/JwUXElcKCkDwBfBzYAWiQtLJ+hut9xi7I9/6WyzrL6SWzPBaYADwIPAFfbfrgrAZSt8onAtPJw0a/qTrcXU0cml/7Xd2X9YgLwWUnzgUXAJ7sxNiIiIjqg6la6iM5J6gdsYPuVUpX8BbCj7Vd7ObR3nKamJjc3N/d2GBEREQ0haY7tps765R7P6I4BwMyyfS3glCSdERER0VVJPNdxkj7K23938mnb47s7l+0XgE7/ayYiIiKiLUk813G2p1M9mBMRERHRq/JwUUREREQ0RBLPiIiIiGiIJJ4RERER0RBJPCMiIiKiIZJ4RkRERERD5Kn2iLVgwTPLGDJpWm+H0anFFxza2yFEREQfkopnRERERDREEs+1TNL7Jd0o6SlJj0j6qaQdezuuGkkbSrq0xPekpH+X9Md158+WtEhSi6R5kvYu7bMkPV7aHpU0sQtrnSzpuHI8RdIRbfQ5SNLt5fh4SW9I2rPu/EJJQ1b/ykHS8jUxT0RERHRNttrXIkkCbgWutX10aRsObAk80Zux1fkGsAnVO9dXSjoBuKUkmB8CDgNG2l4haXNgw7qxE2w3S3ov8JSkKR29QtP2lT2I73+As4GjejB2rZG0vu3XezuOiIiId5NUPNeuMcBr9QmX7Xm275Z0lqSHSiXxPABJQ0r18KpSZZwhaeNy7rRSMW2RdGNpO1fSmbW5a9VASQMlTZM0v7S1mbRJGgCcAJxhe2WJ71+AFcDBwFbAEtsryrkltp9tY6pBwIvAyjLvm5VESUdImtJWvHV9DpH0mKR7gE+3On07sJukndoY1946UyR9V9JMSf8l6cOSrinf7ZRWc3xT0lxJv5C0RWnbQdLPJM2RdLeknevm/Zakmbz9NaRImiipWVLzypeWtfE1RURE9G1JPNeu3YE5rRsljQOGAnsBw4FRkg4sp4cCl9veDVgKHF7aJwEjbO8JnNzJuocAz9oeZnt34Gft9Psg8N+2n2/V3gzsBswAtpX0hKQrJH24Vb/rJbUAjwNfryWv3SFpI+Aq4M+AA4D3t+ryBvBPwJe7OfV7qJLnM4CfAJdQXdMepeoMMBCYa3skcCfw1dI+GTjV9ijgTOCKunl3BMba/tvWC9qebLvJdlO/AYO7GW5ERMS6L4ln7xhX/jwMzAV2pko4AZ62Pa8czwGGlOMWqkTvWKCzLd4FwFhJF0o6wHZ75TcBbq/d9nJgFDAR+B0wVdLxdf0mlER4O+BMSdt3Eldbdqa65idtG7iujT4/BD4k6QPdmPcnZb4FwG9sL7D9BrCIVd/pG8DUcnwdsL+kQcC+wE2S5gHfo6r81tzUkwQ7IiIikniubYuoErfWBJxve3j580Hb3y/nVtT1W8mq+3APBS4v882RtD5VAlr/z3AjANtPlH4LgPMlndNOfL8Etpe0Sav2kcAjZa6VtmfZ/irweVZVYN9k+3dUCfTetabWMXWireS3fv7XgW8CX+pgXOt1at/jG7z1O32D9u9tNtX3ubTun81w27vU9Xmxo1gjIiKifUk81647gP6STqo1SBoNPA+cWKprSNpG0vvam0TSesC2tmcCXwQ2o7qvcjFVkoikkcAHyvHWwEu2rwMurvVpzfaLwLXAtyT1K2OPAwYAd0jaSdLQuiHDgV+1Ed8AYATwVGn6jaRdStzj2/96AHgM+ICkHcrnY9rpNwUYC2xR19adddqyHlB7sv4zwD3ltoOnJR0J1QNikob1YO6IiIhoJU+1r0W2LWk8cKmkScArVMni6VT3b86uHnxnOXAs5eGcNvQDrpM0mKpaeontpZJuBo4rW8IPsepJ+T2AiyS9AbwGnNJBmH9HlZw+Ufo/BowvsQ8C/lnSZlTV1V9SbbvXXC/pZaA/MMV27X7WSVQPBf0aWEiVJLf3Hb2i6qeYpklaAtxDdW9s636vSvoO8O265i6v044XqR5cmgMsY9WT8xOA70r6CrABcCMwvzsT77HNYJrz4+wRERFvoeo2uIhYk5qamtzc3NzbYURERDSEpDm2mzrrl632iIiIiGiIbLX3EZJupdwDWudLtqf3RjwRERHR9yTx7CNs9+Thm4iIiIg1JlvtEREREdEQSTwjIiIioiGSeEZEREREQyTxjIiIiIiGyMNFEWvBgmeWMWTStN4Oo9sW50fvIyJiLUrFM5C0UtI8SQsl/aS8qQhJQyR9potznC1pkaSWMtfepf308krNzsZ3td8sSY+XNR4tbz2qnfuppM3Kn79uNe6iEt9Fkk4urwbtaJ1zJZ3ZRvsQSQs7izMiIiLeLolnALxse7jt3YHngL8p7UOo3mHeIUn7AIcBI23vSfVO9V+X06dTvfu9M13tBzDB9nBgP+BCSRsC2P647aVU77L/61Zj/qrEd5btK23/oItrRURExBqSrfZobTawZzm+ANilvAv+WuAPwHiqd7N/APih7fOArYAltlcA2F4CIOk0YGtgpqQltsdI+i4wGtgY+LHtr7bTbxxwXlnrKeAE28tbxTqI6n3rK8t6i4GmEvcOJe6fAzsBA4EHJJ0P7AIst32xpB2Ay4EtgJeAk2w/Vr+IpFHANeX8PT39YiMiIvq6VDzjTZL6AX8K3FaaJgF3l2roJaVtL2ACMBw4UlITMAPYVtITkq6Q9GEA298BngXG2B5Txp9d3uW6J/BhSXu27idpc+ArwFjbI4Fm4At1oV4vqQV4HPi67ZWtLmUS8FSJ+yzbn2BVVXdqq76TgVNtjwLOBK5o46v5F+A02/t08v1NlNQsqXnlS8s66hoREdEnJfEMgI1LdfD3wHupqoTt+bnt39t+GbgF2L9UIkcBE4HfAVMlHd/O+D+XNBd4GNgN2LWNPh8q7feWuP4S2L7u/ISypb8dcKak7duYo1OSBgH7AjeVdb5HVb2t7zMY2Mz2naXpX9ubz/Zk2022m/oNGNyTkCIiItZp2WoPKNXAkmTdTnWP53fa6eu2Ppeq4yxglqQFVMnilPqOkj5AVVUcbfsPkqYAG7WxhqgS3GM6Ctr270oSuzfwq476tmM9YGm5X7Q94u3XHBERET2Qime8yfYy4DSqKuIGwAvAJq26fUTSeyVtDHyKqiq5k6ShdX2GsyoRrJ9jU6p7MpdJ2hL4WN2Y+n73A/tJ+iCApAGSdmwdb3kKfgTVPaD12oq7ret9Hnha0pFlPkka1qrP0hLv/qVpQmfzRkRERNuSeMZb2H4YmA8cDbQAr0uaL+mM0uUequ3mecDNtpupHvK5VtIj5d7LXYFzS//JwH9Imml7PtUW+yKqh3XurVu6vt/vgOOBG8p89wM71/W9vmyNzwGm2J7T6hp+T5UQL5R0USeXPAH4rKT5Ja5PttHnBOBySbOBlzuZLyIiItohO7uI0TXlvs0m25/v7Vje6Zqamtzc3NzbYURERDSEpDnl4eEOpeIZEREREQ2Rh4uiy2xPodUDQxERERFdlYpnRERERDREEs+IiIiIaIgknhERERHREEk8IyIiIqIhknhGREREREMk8YyIiIiIhsjPKUWsBQueWcaQSdN6O4w1bvEFh/Z2CBER8S6WimdERERENEQSz3WMpPdLulHSU+Xd6T+VtGNvx1UjaZakxyXNK3+OKO3Ly99bS/pxF+ZZ3k77pyTtWvd5iqSny1rzJf1p3bmr6/tGRETE2pWt9nWIJAG3AtfaPrq0DQe2BJ7ozdhamWC7zReZ234WOGI15v4UcDvwSF3bWbZ/LGkMMBkYWtb63GqsExEREd2Uiue6ZQzwmu0raw2259m+W9JZkh6S1CLpPABJQyQ9KukqSYskzZC0cTl3WqmYtki6sbSdK+nM2tySFpY5BkqaViqKCyUd1dMLKPMtLMcDJP2oxDBV0gOSmur6/mNZ835JW0raF/gEcFGpcO7QavrZwDZ142fV5pO0vPV8pX2H8vkhSV9rr9Ja+k6U1CypeeVLy3r6FURERKyzkniuW3YH5rRulDSOqsq3FzAcGCXpwHJ6KHC57d2ApcDhpX0SMML2nsDJnax7CPCs7WG2dwd+1kn/6+u22v+og35/DfyhxPB1YFTduYHA/baHAXcBJ9m+D7iNqsI53PZTbcT5b+2s9bb5Svu3gW/bHg0829FF2Z5su8l2U78BgzvqGhER0Scl8ewbxpU/DwNzgZ0p283A07bnleM5wJBy3EKVIB4LvN7J/AuAsZIulHSA7c7KfRNKYjjc9u876Lc/cCOA7YUlpppXqbbUW8fdlosk/RdwHfCNdvq0N98+wE3l+IcdrBERERGdSOK5blnEW6uCNQLOr0v2Pmj7++Xcirp+K1l13++hwOVlvjmS1qdKQOv/ndkIwPYTpd8C4HxJ56yh61EH516z7TbibstZwAeBrwDXroH5IiIiogeSeK5b7gD6S6ptEyNpNPA8cKKkQaVtG0nva28SSesB29qeCXwR2AwYBCwGRpY+I4EPlOOtgZdsXwdcXOuzBtwD/HlZY1dgjy6MeQHYpHWj7Teots3Xk/TRbsRwP6tuPzi6G+MiIiKilVR11iG2LWk8cKmkScArVMni6VT3b86uHnxnOXAsVWWvLf2A6yQNpqo6XmJ7qaSbgeMkzQMeYtWT8ntQbWe/AbwGnLKGLukK4FpJLVS3CbQAnW3j3whcJek0Wj0dX76ff6BKpqd3MYbTqb6LvwWmdWF9APbYZjDN+bH1iIiIt9Cq3cWIdxZJ/YANbL9SnlD/BbCj7VcbGMMA4OWStB4NHGP7k52Na2pqcnNzm78YFRERsc6RNMd2U2f9UvGMd7IBwExJG1BVXk9pZNJZjAIuK7+RuhQ4scHrR0RErDOSeMZaIelWyj2gdb5ku6tb3Nh+Aej0v57WJtt3A8N6M4aIiIh1RRLPWCtsj+/tGCIiIuKdJU+1R0RERERDJPGMiIiIiIZI4hkRERERDZHEMyIiIiIaIg8XRawFC55ZxpBJ03o7jLVmcX4cPyIieiAVz4iIiIhoiCSe3STp/ZJulPSUpEck/VTSjr0dVz1JW0h6TdJfdaHvQZL2rft8sqTjerju8eW97bXPV5d3rPeIpOMkLZS0qHzXZ5b2WZJ65fc9y/d1e2+sHRER8W6XxLMbyttrbgVm2d7B9q7Al4EtezeytzkSuB84pgt9DwLeTDxtX2n7Bz1c93jgzcTT9udsP9KTiSR9jOo96eNs7waMpIvvSY+IiIh3piSe3TMGeM32lbUG2/Ns3y3pLEkPSWqRdB6ApCGSHpV0VanazZC0cTl3WqnitUi6sbSdW6vqlc8LyxwDJU2TNL+0HdVJnMcAfwv8saRt6uY7RNLcMs8vJA0BTgbOkDRP0gG1GCTtIunBurFDJLWU43PKtS6UNFmVI6jeMnR9mWvj+sqkpGMkLShjLqybd7mkfywx3S+plsT/HXCm7WfL9/yK7avqrvFISQ9KekLSAXUx3l2ucW6tkluqlLMk/VjSY5KuL/8RgaTFks4r/RdI2rm0D5R0TbnOhyV1+n72iIiI6FgSz+7ZHZjTulHSOGAosBcwHBgl6cByeihweanaLQUOL+2TgBG296RK/jpyCPCs7WG2dwd+1l5HSdsC77f9IPAj4KjSvgVwFXC47WHAkbYXA1cCl9geXl4PCYDtR4ENJf1JaTqqzAdwme3RJZaNgcNs/xhoBiaUuV6ui2lr4ELg4PL9jJb0qXJ6IHB/ieku4KTS3uZ3XWd923tRVUW/Wtp+C3zE9sgS73fq+o8ofXcF/gTYr+7ckjLmu0At8T8buMP2aKr/4LhI0sAO4kHSREnNkppXvpTibERERGtJPNeMceXPw8BcYGeqhBPgadvzyvEcYEg5bqGqDh4LvN7J/AuAsZIulHSA7Y6ymqNZlSDeyKrt9g8Bd9l+GsD2c124rh8Bf16OjwKmluMxkh6QtIAqmdytk3lGU92e8DvbrwPXA7XE/FWgds9k/ffTmVvaGLMBcFWJ6yaqJLPmQdv/Y/sNYF6rddqaaxwwSdI8YBawEbBdRwHZnmy7yXZTvwGDu3gZERERfUd+Tql7FgFHtNEu4Hzb33tLY7WVvaKuaSVVhRDgUKrk6xPA30vajSoBrf+PgY0AbD8haRTwceB8STNsf62dGI8BtpQ0oXzeWtLQEqO7cpF1pgI3SbqlCsNPStoIuAJosv1rSefW4uyAOjj3mu1aXCtZ9e/kImAUcEc742rfa/2YM4DfAMOovsdX2ujfekx7c4mqOvz4Wy5k1a0AERER0U2peHbPHUB/SbXtYCSNBp4HTpQ0qLRtI+l97U0iaT1gW9szgS8CmwGDgMVUD9EgaSTwgXK8NfCS7euAi2t92ph3J2Cg7W1sD7E9BDifqgo6G/iwpNqc7y3DXgA2aWs+209RJWN/z6pqZy3JXFKutz4Rb2+uB8ram0vqR5Uc39nml7PK+cA/SXp/ibe/pNM6GTMY+N9S1fwLoF8n/TsyHTi17l7QEasxV0RERJCKZ7fYtqTxwKWSJlFV1BZT3Tu4FJhd8pTlwLFUSVtb+gHXSRpMVVm7xPZSSTcDx5Xt3YeAJ0r/PajuMXwDeA04pZ15j6F66r7ezcCNtr8uaSJwS0l8fwt8BPgJ8OPy8Mypbcw5FbiIkgSXOK+i2v5fXOKsmQJcKellYJ9ao+3/lfR3wMxyvT+1/e/tXENtzE9LdfE/S/Jn4JqOxlBVYm+WdGRZ68VO+nfk68ClQEtZfzFwWFcH77HNYJrzI+sRERFvoVW7nBGxpjQ1Nbm5ubm3w4iIiGgISXNsd/ob29lqj4iIiIiGyFb7u5SkWynb33W+ZHt6b8QTERER0Zkknu9Stsf3dgwRERER3ZGt9oiIiIhoiCSeEREREdEQSTwjIiIioiGSeEZEREREQ+Thooi1YMEzyxgyaVpvh9FQi/OD+RER0YlUPGO1SDpb0iJJLZLmSdp7Lazx5UavGREREWteKp7RY5L2oXqN5EjbKyRtDmy4BucX1Ss2vwx8Y3XXlLS+7dfXVHwRERHRPal4xurYClhiewWA7SW2n5W0WNI3JM2W1CxppKTpkp6SdDKApEGSfiFprqQF5V3xSBoi6VFJVwBzge8DG5fK5vXtrVnGjpZ0n6T5kh6UtImk4yXdJOknwIzS7yxJD5WK6Xm1i5F0bBk3T9L3JPUr7csl/WOZ9/7yDvmIiIjopiSesTpmANtKekLSFZI+XHfu17b3Ae4GpgBHAB8CvlbOvwKMtz0SGAN8s1Q4AXYCfmB7hO0TgJdtD7c9ob01JW0ITAX+n+1hwFjg5TLfPsBf2j5Y0jhgKLAXMBwYJelASbsARwH72R4OrAQmlPEDgfvLvHcBJ62Rby8iIqKPyVZ79Jjt5ZJGAQdQJY9TJU0qp28rfy8ABtl+AXhB0iuSNgNeBL4h6UDgDWAboFZJ/JXt+7u55hzgf20/VPo9D1By2Z/bfq5MMa78ebh8HkSViO4JjAIeKmM2Bn5b+rwK3F6O5wAfaSs2SROBiQD9Nt2iva8tIiKiz0riGavF9kpgFjBL0gLgL8upFeXvN+qOa5/Xp6ombgGMsv2apMXARqXPiz1Ycy7gdobUzyfgfNvfq+8g6VTgWtt/18b412zX5l5JO/+7sT0ZmAzQf6uh7cUSERHRZ2WrPXpM0k6ShtY1DQd+1cXhg4HflqRzDLB9B31fk7RBJ2s+BmwtaXTpt4mkthLE6cCJkgaVfttIeh/wC+CIcoyk90rqKKaIiIjoplQ8Y3UMAv65bJ2/DvySaqv5sC6MvR74iaRmYB5V4tieyUCLpLnAt9pa0/arko4q5zamur9zbOuJbM8o93POLlvqy4FjbT8i6SvADEnrAa8Bf0PXE+mIiIjohFbtIEbEmtLU1OTm5ubeDiMiIqIhJM2x3dRZv2y1R0RERERDJPGMiIiIiIZI4hkRERERDZHEMyIiIiIaIolnRERERDREEs+IiIiIaIgknhERERHREEk8IyIiIqIh8uaiiLVgwTPLGDJpWm+H0esWX3Bob4cQERHvIKl4RkRERERDJPGMDkm6RNLpdZ+nS7q67vM3JX2h1Zgpko4oxwdIWiRpnqRdJH2mk/UOknR7J32GSrpd0lOS5kiaKenAnl1hRERENEoSz+jMfcC+AJLWAzYHdqs7vy9wbwfjJwAX2x4ObAl0mHh2RtJGwDRgsu0dbI8CTgX+ZHXm7WYMuUUlIiKiB/J/oNGZe4FLyvFuwEJgK0nvAV4CdgHmSboMOBh4GhCApM8Bfw58VNJYk983SgAAIABJREFUYAdgF0nzgGttX0IHJH0Y+Hb5aODAMt9s27fV+tleWOJC0l7ApcDGwMvACbYfl3Q88CmgH7A78E1gQ+AvgBXAx20/J2kH4HJgi3J9J9l+TNIU4DlgBDAX+NtufIcRERFBEs/ohO1nJb0uaTuq6uZsYBtgH2AZ0AIcCuwE7EFV1XwEuMb21ZL2B263/WNJBwFn2j6si8ufCfyN7XslDQJeoUp+53Yw5jHgQNuvl2T3G8Dh5dzuVInjRsAvgS/ZHiHpEuA4qoR1MnCy7Scl7Q1cQZVQA+wIjLW9sq2FJU0EJgL023SLLl5iRERE35HEM7riXqqkc1/gW1SJ575Uied9VJXIG0pC9qykO9bgut+SdD1wi+3/kfSWDpJuBYYCT9j+NDAYuFbSUKoq6QZ13WfafgF4QdIy4CelfQGwZ0lu9wVuqlunf934m9pLOgFsT6ZKXOm/1VD35IIjIiLWZbnHM7qidp/nHlRb2vdTVTzr7+9c44mW7QuAz1Ftm98vaWdgETCyrs944HjgvaXp61QJ5u7An1FVN2tW1B2/Uff5Dar/CFsPWGp7eN2fXerGvLimri0iIqIvSuIZXXEvcBjwnO2Vtp8DNqNKPmcDdwFHS+onaStgTDvzvABs0tVFJe1ge4HtC4FmYGfgh8B+kj5R13VA3fFg4JlyfHxX1wKw/TzwtKQjy/qSNKw7c0RERET7stUeXbGA6mn2H7ZqG2R7SdnuPri0PQHc2c48LcDrkuYDUzp7uAg4XdIYYCXVfaP/YXuFpMOotuAvBX5DldD+QxnzT1Rb7V8AerLlPwH4rqSvUG3T3wjM7+4ke2wzmOb8eHpERMRbyM6taBFrWlNTk5ubm3s7jIiIiIaQNMd2U2f9stUeEREREQ2RrfboFZI+ClzYqvnp8rBQRERErIOSeEavsD0dmN7bcURERETjZKs9IiIiIhoiiWdERERENEQSz4iIiIhoiCSeEREREdEQebgoYi1Y8Mwyhkya1tthvKMtzg/sR0T0Oal4RkRERERDJPFcR0l6v6QbJT0l6RFJP5W0Y2/HVSNpQ0mXlvielPTvkv647vzZkhZJapE0T9LepX2WpMdL26OSJjYg1sWSNl/b60RERKzrstW+DpIk4FbgWttHl7bhwJZU71J/J/gGsAmwo+2Vkk4AbikJ5oeAw4CR5d3smwMb1o2dYLtZ0nuBpyRNsf1q/eSS+tle2aBriYiIiC5IxXPdNAZ4zfaVtQbb82zfLeksSQ+VSuJ5AJKGlOrhVaXKOEPSxuXcaaVi2iLpxtJ2rqQza3NLWljmGChpmqT5pe2otoKTNAA4ATijlhza/hdgBXAwsBWwxPaKcm6J7WfbmGoQ8CKwssy7XNLXJD0A7CPpnHKtCyVNLgl5rWp6oaQHJT0h6YDS3k/SxZIWlOs9tW6tUyXNLed27u4/kIiIiEjiua7aHZjTulHSOGAosBcwHBgl6cByeihwue3dgKXA4aV9EjDC9p7AyZ2sewjwrO1htncHftZOvw8C/237+VbtzcBuwAxg25IUXiHpw636XS+pBXgc+HpdZXMgsND23rbvAS6zPbrEsjFVFbVmfdt7AacDXy1tE4EP1F3v9XX9l9geCXwXOJM2SJooqVlS88qXlrVz6REREX1XEs++ZVz58zAwF9iZKuGE6j3p88rxHGBIOW6hSvSOBV7vZP4FwNhSTTzAdnvZlwC31257OTCKKhH8HTBV0vF1/SaUxHA74ExJ25f2lcDNdf3GSHpA0gKqSupudeduaeNaxwJX2n6dKpDnOun/FrYn226y3dRvwOC2ukRERPRpSTzXTYuoErfWBJxve3j580Hb3y/nVtT1W8mq+38PBS4v882RtD5VAlr/785GALafKP0WAOdLOqed+H4JbC9pk1btI4FHylwrbc+y/VXg86yqwL7J9u+oEui9S9MrteqnpI2AK4AjbO8BXFWLs9X11l9rewlxe/0jIiKiG5J4rpvuAPpLOqnWIGk08DxwoqRBpW0bSe9rbxJJ6wHb2p4JfBHYjOq+ysVUSSKSRlJtTyNpa+Al29cBF9f6tGb7ReBa4FuS+pWxxwEDgDsk7SRpaN2Q4cCv2ohvADACeKqNZWpJ5pJyvUe0d511ZgAnl+Sa8vBSRERErCGp3KyDbFvSeOBSSZOAV6iSxdOp7t+cXZ6zWQ4cS3k4pw39gOskDaaqBl5ie6mkm4HjJM0DHmLVk/J7ABdJegN4DTilgzD/jio5faL0fwwYX2IfBPyzpM2oqqu/pNp2r7le0stAf2CK7bfdz1rivIqq+rq4xNmZq4EdgRZJr1FVSS/rwri32WObwTTnB9IjIiLeQnZ7O4sR0VNNTU1ubm7u7TAiIiIaQtIc202d9ctWe0REREQ0RLbaY62SdCvlHtA6X7I9vTfiiYiIiN6TxDPWKtvjezuGiIiIeGfIVntERERENEQSz4iIiIhoiCSeEREREdEQSTwjIiIioiHycFHEWrDgmWUMmTStt8N411ucH+GPiFinpOIZEREREQ2RxLMPkfR+STdKekrSI5J+KmnHNTj/QZL27cG4cyWd2Ub7EEkLVyOe08v73Guff1pew4mk0yQ9Kul6SZ8orxaNiIiItShb7X2Eqpez3wpca/vo0jYc2JJV71pfXQdRvf/9vjbWX9/262tona46HbgOeAnA9sfrzv018DHbT5fPt3V10l66loiIiHe9VDz7jjHAa7avrDXYngfcI+kiSQslLZB0FLxZvby91lfSZZKOL8eLJZ0naW4Zs7OkIcDJwBmS5kk6QNIUSd+SNBO4SNKTkrYoc6wn6ZeSNq8PUtIoSfMlzQb+pq69X4nzIUktkv6qLs5Zkn4s6bFSwZSk04CtgZll/Vrcm0u6EvgT4DZJZ0g6XtJlpc8Wkm4u6zwkab/Sfq6kyZJmAD9Yc/9YIiIi+o5UPPuO3YE5bbR/GhgODAM2Bx6SdFcX5ltie6SkvwbOtP25ktAtt30xgKTPAjsCY22vlLQUmABcCowF5tteUhVj3/QvwKm275R0UV37Z4FltkdL6g/cW5JAgBHAbsCzwL3Afra/I+kLwBjbS+oXsH2ypENq52oJdfFt4BLb90jaDpgO7FLOjQL2t/1yW1+IpInARIB+m27Rha8wIiKib0nFM/YHbrC90vZvgDuB0V0Yd0v5ew4wpIN+N9leWY6vAY4rxydSJZlvkjQY2Mz2naXpX+tOjwOOkzQPeAD4I2BoOfeg7f+x/QYwr5N4OjMWuKyscxuwqaRNyrnb2ks6AWxPtt1ku6nfgMGrEUJERMS6KRXPvmMRcEQb7WqjDeB13vofJhu1Or+i/L2Sjv89erF2YPvXkn4j6WBgb6rqZ+tY3M48oqqETn9Lo3RQXSxdiacz6wH7tE4wS1X2xTZHRERERJek4tl33AH0l3RSrUHSaOAPwFHlHsotgAOBB4FfAbtK6l8qkX/ahTVeADbppM/VVA/8/KiuEgqA7aXAMkn7l6b6xHQ6cIqkDUrsO0oauAbiaW0G8Pnah/IAVkRERKwBSTz7CNsGxgMfKT+ntAg4F/gh0ALMp0pOv2j7/2z/GvhROXc98HAXlvkJML72cFE7fW4DBtFqm73OCcDl5eGi+qrj1cAjwNzyE0vfo/PK5mTgP2oPF3XRaUBTeYDpEaoHpiIiImINUJWPRDSGpCaqh3faS0zXCU1NTW5ubu7tMCIiIhpC0hzbTZ31yz2e0TDlR9pP4e33dkZEREQfkK32aBjbF9je3vY9vR1LRERENF4Sz4iIiIhoiCSeEREREdEQSTwjIiIioiGSeEZEREREQyTxjIiIiIiGSOIZEREREQ2R3/GMWAsWPLOMIZOm9XYYfdriCw7t7RAiIqKVVDzjXUvS2ZIWlddbzpO0t6RZkv5bkur6/Zuk5d2ce4qkI9Z81BEREX1XKp7xriRpH+AwYKTtFZI2BzYsp5cC+wH3SNoM2KqXwoyIiIg6qXjGu9VW/7+9Ow+zo6rXPf59jUCAYFAmkSkogcgYkgZBAgbk4XIEQRQMCBeCQ5wQh4MYxYMgF0HxCCoIIjIjgyAawSNRICTM6ZCQhBkhiuARUAmESQjv/aPWJpXd3enOtJuk38/z5Onaq1at+tXau+HXv6raBTxt+2UA20/bfqKsuww4sCx/GPhVYyNVTpE0U9IMSaNq7adLulfStcDatW2GS7pJ0hRJ10lKIhsREbEIknjGsmo8sIGkByX9RNL7auuuB3aR1I8qAb28tu7DwFBgG2B34JSSSO4HbAZsBXwKeC+ApBWAHwP72x4OnAuc2FlAksZIapfUPveF2UvwUCMiIpYPOdUeyyTbcyQNB3YGdgUulzS2rJ4L3AyMAla2Pat2yecI4FLbc4G/S7oJ2A7Ypdb+hKQbSv/NgC2BP5Qx+gF/6yKms4GzAVZad7CX5PFGREQsD5J4xjKrJIkTgAmSZgCH1VZfBlwNHNe0mehaZ8migHts77jokUZERATkVHssoyRtJmlwrWko8Ofa60nAScClTZtOBEZJ6idpLapK552l/cDSvi5VFRXgAWCtcjMTklaQtMWSP6KIiIjlXyqesawaAPy43LX+KvAwMAa4EsC2ge93st3VwI7A3VQVzqNt/6+kq4HdgBnAg8BNZZx/l69V+pGkgVS/M6cB9yzFY4uIiFguqfr/c0QsSW1tbW5vb+/tMCIiIlpC0hTbbd31y6n2iIiIiGiJJJ4RERER0RJJPCMiIiKiJZJ4RkRERERLJPGMiIiIiJZI4hkRERERLZHEMyIiIiJaIolnRERERLREnlwUsRTMeHw2g8Ze29thRC+YdfJevR1CRMQbViqeEREREdESPUo8Jb1d0mWS/iTpXkm/k7Tp0g6upyStKOm0Et9Dkn4jaf3a+mMk3SNpuqRpkt4jaV9Jv671+bqkh2uvPyhpXFme07S/0ZJOb2q7W9KlPYx3qKQP9KDfcZKOamqbJWnNnuyn9B8p6ZolEU/pO0LSnZLuL//G9GCb+caXtI+ksT3ZX0+Vebmq9np/SecvyX1ERETE4uk28ZQk4Gpggu132d4c+AawztIObiF8B1gN2NT2YODXwK9U2RHYGxhme2tgd+Ax4FZgx9oYOwLPSlq7vH4vcEtPdi7p3VRzuYukVXuwyVCgR4lei/QoHklvB34BfMb2EGAE8GlJ3Z1bnG982+Nsn7wY8XalTdIWS2HciIiIWAJ6UvHcFXjF9lmNBtvTbE+S9FVJk0sl8XgASYMk3SfpZ6XKOF7SymXdkaViOl3SZaVtvqqepJlljFUlXVsqiTMljeosOEmrAIcDX7Y9t8R3HvAysBuwLvC07ZfLuqdtP2H7KWC2pE3KUOsBV1ElnJSft/ZkEoGPARcB44F9arFNkNRWltcsVbkVgW8Do0r1dZSkt0n6dZmX2yVt3d0OyxzdL+mCst2VZS6QtGdZdzPw4do220u6VdLU8nOzLuJZVdK55b2dKmnfMsTngfNt39WYS+BoYGwZ/3xJZ0maJOlBSXt3Mf7rFWNJG0m6vhzD9ZI2rI31oxLnI5L2L+3rSppYxpopaefatHyf6o+i5rnqcNylfXSZ999KelTSEZK+UvrdLultpd+7JP1e0pRybEO6eE/GSGqX1D73hdndvYURERF9Tk8Szy2BKc2NkvYABgPbU1W0hkvapaweDJxhewvgGeAjpX0ssG2pPH6mm/3uCTxhexvbWwK/76LfJsBfbD/b1N4ObEGVDG5QEqGfSHpfrc+twHtLIvIQcHt5/WZga2ByNzE2jAIuBy4FDlpQR9v/Bo4FLrc91PblwPHA1DIv3wAu7OF+NwPOLts9C3xOUn/gZ8AHgZ2Bt9f63w/sYnvbEsN3uojnGOAG29tR/eFxSqnkbkHHz0JjnhsGAe8D9gLOovqMNY9fdzpwYTmGS4Af1datS1VV3RtoVEg/BlxneyiwDTCt1v8KYFjtj4kuj7u2bssy5vbAicALpd9twKGlz9nAF2wPB44CfkInbJ9tu812W79VBnbWJSIiok9bnJuL9ij/pgJ3AUOoEk6AR203EoIpVMkIwHTgEkmHAK92M/4MYHdJ35W0s+2uSkgC3FW77TnAcGAM8BRwuaTRpc8tVJXN91IlGncC7wG2BR6w/dIC4jOApO2Ap2z/GbieKvF5azfH1mwEVcUU2zcAa0ga2MVxUWt/zHbjcoCLyzhDqOb/Idsu7Q0DgV9KmgmcyvwJY90ewFhJ04AJQH9gQ7qe63rbFbZfs/0Q8EiJZ0F2pDp9D9UcjKit+3UZ617mXdoxGThc0nHAVrafq/WfC5wCfL1pHws67httP9eogAO/Le0zgEGSBlB9Pn5Z5uOnVAlxRERELKSeJJ73UCVuzQScVKpYQ21vYvvnZd3LtX5zmfe1TXsBZ5TxppTK4qtNcfQHsP1g6TcDOEnSsV3E9zCwkaTVmtqHAfeWsebanmD7W8ARzKvA3kot8SxJTH9gJPNf3/liOWXc8Dbg6bJ8EDBE0izgT8BbauPXj61/F/FDNZfNDPwDaE5iV6OqIjf6NG/TWXvDCVSJ1pZUFdGuYhLwkdp7u6Ht+6g+C21NfYdT5rmbmHqq3r/+ORKA7YnALsDjwEWSDmV+F5X1G9baFnTc9X28Vnv9GtXn9k3AM7W5GGr73Qt5TBEREUHPEs8bgJUkfarRUKp8zwIfLxUhJK2neTfmdCDpTcAGtm+kui5wdWAAMIsqSUTSMGDjsvwOqtOeF1Nduzess3FtPw9cAPxAUr+y7aHAKsAN5TrGwbVNhgJ/Lsv3Au+gOiU9tbRNo7oMoH59503AIWXslYGPAjeWYzoA2Nr2INuDgH2Zd7p9FvOS9v1r4z1HlUA2TAQOLuOPpLom9dnSvk8jqZb0YeDuxrWswIaqbp6i7PNmqtPKG0t6V629YSBVwgYwegHxXAd8QZLKfrct7WcAoyUNLe1rAN8Fvlfb9gBJbyr7fyfwQCfj190KHFiWDy7H0CVJGwFP2v4Z8HOaPhe2X6Gqan6p1tzVcXervA+PSjqg7F+StlmYMSIiIqLS7RfI27ak/YDTVH0FzktUCdWXqCpvt5X8ZA5Vcja3i6H6AReXU8gCTrX9jKqvwDm0nMacDDxY+m9FdW3ha8ArwGcXEObXqZLTB0v/+4H9SuwDgB9LWp2qAvkw1Wn3xrHdAQwsCQtUp9zHMH/i+UXgp5KOLLFfaHtiSRIft/14re9EYHNJ65aYrpD0f6kS+IYbmXcq+yTgOOA8SdOBF4DDSnzTy004N0sy8CTwydo49wGHSfop1TWqZ9p+SdVXHF0r6WmqRG7L0v97wAWSvtJNPCcApwHTS/I5C9jb9t/KZRI/K8mwgNNs/7Y21gNUifo6VHe/vySpefy6I4FzJX2V6lKIw1mwkcBXJb1C9ZlrrnhClZB+s/a6q+PuqYOBMyV9E1gBuAy4e0EbbLXeQNrzReIRERHzUXUZYCxrJA0Crimnj98QVH1v5jW2r+ztWHpbW1ub29vbezuMiIiIlpA0xXbz5Xgd5MlFEREREdESy9Sz2iVdTbkGtOZrtq/rjXh6k+1ZzDuF/oZge3RvxxARERFvXMtU4ml7v96OISIiIiIWTU61R0RERERLJPGMiIiIiJZI4hkRERERLZHEMyIiIiJaYpm6uShiWTHj8dkMGnttb4cRbxCz8jCBiAggFc/FJmmupGmS7pF0t6SvlEdpdrfdKWWbUxZxv3PKz0GSPlZrHylpdolpuqQ/LuhRpm8UkkaXpzR1tm7OYo77jtrrcyRtXpYPkHSfpBsltUn60aLuJyIiIrqXiufie9F249nlawO/oHo2+Le62e7TwFq2X17M/Q8CPlb22zDJ9t4lppOAzzfHI+nNtl9dzH0vNEn9as+ab4XRwEzgCQDb9UeOfgL4nO0by+seP2qot+YvIiJiWZaK5xJk+0mq57wfoUq/UtmcXKqPnwaQNA5YFbhD0ihJH5R0h6SppUK5Tul3nKSjGuNLmlkelVl3MrBzqXB+ub6iPGd9NeBftfHOljQeuFBSf0nnSZpR9r1r6fc7SVuX5amSji3LJ0j6pKR1JU0s+5wpaeeyfg9Jt0m6S9IvJQ0o7bMkHSvpZuAASUdKurfMyWXN8yhp4zLOZEknNK37am0+jy9tg0rl8melijxe0sqS9gfagEtKrCtLmlCqm8cCI4Czyns0UtI1ZbxVJZ1b9jNV0r6lfXQ5rt8C43v4sYiIiIgiFc8lzPYj5VT72sC+wGzb20laCbhF0njb+0iaU6uUvhXYwbYlfRI4GvjPHu5yLHBUrcI5kpKIAmsAzwPfqPUfDoyw/aKk/ywxbyVpCDBe0qbAxDLGLOBVYKey7QjgYqoK63W2T5TUD1hF0prAN4HdbT8v6WvAV4Bvl21fsj2ixPgEsLHtlyWt3skx/RA40/aFkj7faJS0BzAY2B4QME7SLsBfSvtBtj8l6QrgI7YvlnREmZ/2MgblmL8tabfGujJvDccAN9j+eInvTkl/LOt2BLa2/c/moCWNofrDg35vWauTw4qIiOjbUvFcOlR+7gEcWpLAO6gSwcGd9F8fuE7SDOCrwBaLuf9Jtofa3gA4D/hebd042y+W5RHARQC27wf+DGwKTAJ2KeuvBQZIWgUYZPsBYDJwuKTjgK1sPwfsAGxOlVxPAw4DNqrt9/La8nSqKuQhVIlts52AS8vyRbX2Pcq/qcBdwBDmzeejtqeV5SlUlyAsqj2AseU4JgD9gQ3Luj90lnQC2D7bdpvttn6rDFyM3UdERCyfUvFcwiS9E5gLPEmVgH6hB8+S/zHwA9vjSuXtuNL+KvP/cdB/EUIaB1xVe/18PdwutplMdYr6EeAPwJrAp6gSOmxPLJXGvYCLVN0g9S+qpOygLsas73cvqsR2H+C/JHWWaLuTNgEn2f7pfI3V5Qf1a2XnAit3EUdPiKpi+kDTft7D/McRERERCyEVzyVI0lrAWcDptg1cB3xW0gpl/aaSVu1k04HA42X5sFr7LGBY2XYYsHEn2z5HdR1nV0YAf+pi3UTg4EZsVFW9B2z/G3gM+ChwO1UF9KjyE0kbAU/a/hnw8xLj7cBOkjYpfVYpY86nXIawQbmh52hgdWBAU7dbgAPL8sG19uuAj9euHV1P3d+x3938dOY64AvlGlkkbbuQ20dEREQnUvFcfCuXU7IrUFUoLwJ+UNadQ3XK966SxDwFfKiTMY4DfinpcaoErpFgXsW8U/WTgQc72XY68Kqku4HzqU5DN67xFDAb+GQn2wH8hOrmmhkl9tG1u+wnAe+3/YKkSVSXA0wq60YCX5X0CjAHONT2U5JGA5eW61mhuuazOeZ+wMWSBpb4TrX9TOPay+KLwC8kfZFatdb2eEnvBm4r/ecAh1BVOLtyfjnGF6muz+yJE4DTgOnlfZsF7N3DbSMiIqILqgpzEbEktbW1ub29x9/OFBERsUyTNMV2W3f9cqo9IiIiIloiiWdEREREtEQSz4iIiIhoiSSeEREREdESSTwjIiIioiWSeEZERERESyTxjIiIiIiWSOIZERERES2RJxdFLAUzHp/NoLHX9nYYsQyadfJevR1CRMRSk4pnRERERLREEs9OSHq7pMsk/UnSvZJ+J2nT3o6rQdKKkk4r8T0k6TeS1q+tP0bSPZKmS5om6T2lfYKkbh9n1cn+hkr6QA/6jZR0TVkeLen0Esu08m9ubfnIhY1jAfvdXZIl/Uet7feSRiypfURERMTiy6n2JpIEXA1cYPvA0jYUWAd4sDdjq/kOsBqwqe25kg4HflUSzB2AvYFhtl+WtCaw4mLubyjQBvxuYTe0fSJwIoCkObaHLmYsXXkM+CbwP0tp/IiIiFhMqXh2tCvwiu2zGg22p9meJOmrkiaXSuLxAJIGSbpP0s9KlXG8pJXLuiNLxXS6pMtK23GSjmqMLWlmGWNVSddKuru0jeosOEmrAIcDX7Y9t8R3HvAysBuwLvC07ZfLuqdtP9HJOHMknVj2d7ukdUr7AWX/d0uaKGlF4NvAqFKpHCVpe0m3Sppafm62sJMsaXVJj0h6c+31o5L6Sbq5VHRvkzSjUaWVNEDS+ZLuLPv+YG3Iu4CXJO3ayb6OL+/bTElnlT8uKPv5gaRJ5X1qk3R1qSIfV9v+sLLPaZJ+Iim/NxEREYsg/wPtaEtgSnOjpD2AwcD2VBXA4ZJ2KasHA2fY3gJ4BvhIaR8LbGt7a+Az3ex3T+AJ29vY3hL4fRf9NgH+YvvZpvZ2YAtgPLCBpAdLkvS+LsZZFbjd9jbAROBTpf1Y4P+U9n1s/7u0XW57qO3LgfuBXWxvW9Z9p5tj68D2M8At5bgBPgZc0UimgZVs7wh8ETinFtvvbW9PlWT/t6T+tWFPpKp6Nvuh7e2ArYCBtX0CvGh7Z+DnwK+p3qetgDElGd4S2A94b6nWvhk4sLNjkjRGUruk9rkvzO7xXERERPQVSTx7bo/ybypVdW0IVcIJ8KjtaWV5CjCoLE8HLpF0CPBqN+PPAHaX9F1JO9vuKnMR4K7abc8BhgNjgKeAyyWN7qT/v4FrOon5FuB8SZ8C+nURw0Dgl5JmAqdSJbyL4hyq6i3l53m1dZcC2L4BWFvSAKr5P0bSNOBGoD+wYWOD0re/pB2b9vN+SXcCdwPva4p3XPk5A5hh+++2XwJmAesDuwPbAe1lv+8D3tXZwdg+23ab7bZ+qwzs+SxERET0EUk8O7qHKnFrJuCkUvUbansT2z8v616u9ZvLvGtn9wLOKONNKaeVX2X+ee8PYPvB0m8GcJKkY7uI72FgI0mrNbUPA+4tY821PcH2t4AjmFeBrXvFdiOBfT1m25+hqhpuAEyTtEYn254A3Fgqsx9sHMPCsn0TsGk5Pf6K7fvrq5u7U70HH6q9BxuWeas7ETim8aJcmnA6sF+pPJ/bFG/jvXuN+d/H16jmRMC5tX1uZvuERTneiIiIvi5s1iF5AAAPaUlEQVSJZ0c3ACuVih8AkrYDngU+XipvSFpP0tpdDVKuA9zA9o3A0cDqwACqStqw0mcYsHFZfgfwgu2Lge83+jSz/TxwAfADSf3KtocCqwA3SNpM0uDaJkOBP/f04CW9y/Ydto8FnqZKQJ+jupmpYSDweFke3dOxu3AxcAnzVzsBRpV4RgJ/L8d9HfD63fCStm0ezPbvgLczr6q5MlUS+XRJ1jtLwhfkj8BHy01aSFpD0obdbBMRERGdyF3tTWxb0n7AaZLGAo3Trl+iun7ztnJvyhzgEKpqYWf6ARdLGkhVNTvV9jOSrgIOLadtJzPvTvmtgFMkvQa8Anx2AWF+nSo5fbD0v5+qoueSGP9Y0upU1dWHqU6799QpJXEVcD3V6em/AGNLzCcB3wMukPQVqkR9cVxCuYa0qf1ZSbdSJbyN0/HHU70vM6j+aHoY2LeTMb8DXAVg+x+SLgBmUiXgdyxMcLZnqLqR7I/lj4lXqK4D/cuCtttqvYG054vAIyIi5qN5Z1sjWk/SgVQ3Mx1ea7sZOKJ23ewyp62tze3t7b0dRkREREtImmK72+8KT8Uzeo2kM6lu3tmzu74RERGx7Evi+QYm6WrKNaA1X7N9XW/Es6TZ7vRyAtt54lBERMRyKInnG5jt/Xo7hoiIiIglJXe1R0RERERLJPGMiIiIiJZI4hkRERERLZHEMyIiIiJaIjcXRSwFMx6fzaCx1/Z2GLEcmZUHEkTEciAVz8UkaT9JljSkvH5U0mZNfU6TdLSkkZJmS5oq6T5J31rMfZ8vaf9F3PYbTa+PkXSPpOmSpkl6T2n/UnneeXfj9ahfF9seJ+moTtoHSZq5KGN2FpOk35UnOiHpyPIeXCJpn/KUqoiIiFiKknguvoOAm4EDy+vLasuNZ7bvz7xHQk6yvS3QBhwiaXh9MEmtqkK/nnhK2hHYGxhme2uqL3V/rKz+EtVz4LvT036tNF9Mtj9g+5ny8nPAB2wfbHuc7ZN7OmgL36OIiIjlShLPxVCei74T8AnmJZuX1pYBdgFm2f5zfVvbzwNTgHdJGi3pl5J+C4xX5RRJMyXNkDSq7E+STpd0r6RrgbVrscyStGZZbpM0oRGjpPPKONMlfUTSycDKpbJ5CbAu8LTtl0tsT9t+QtKRwDuAGyXdWMY7U1J7qY4eX9o667eHpNsk3VWObUBpP7nEP13S9zuZ0+GS7pZ0G/D5Wnu/MieTy7afLu0jJU2QdKWk+0sFU13ENEvSmpLOAt4JjJP05TL/p5c+a0m6quxnsqSdSvtxks6WNB64sNsPR0RERHSQys3i+RDwe9sPSvqnpGG275L0mqRtbN9NlYRe2ryhpDWAHYATgO2AHYGtbf9T0keAocA2wJrAZEkTS5/NgK2AdYB7gXO7ifG/gNm2tyr7favtqyQdYXtoaRsAHCvpQeCPwOW2b7L9I0lfAXa1/XQZ75gSYz/geklbN/crCfA3gd1tPy/pa8BXSnK3HzDEthunvZucB3zB9k2STqm1f6Icx3aSVgJuKUkgwLbAFsATwC3ATl3EDoDtz0jasxbv6NrqHwKn2r5Z0obAdcC7y7rhwAjbL3Yz5xEREdGJVDwXz0FUp9YpPw8qy5cCB5ZTsvsCv6xts7OkqcB44GTb95T2P9j+Z1keAVxqe67tvwM3USWnu9TanwBu6EGMuwNnNF7Y/ldzB9tzqJKqMcBTwOVNyVjdRyXdBUylSvY276TPDqX9FknTgMOAjYBngZeAcyR9GHihvpGkgcDqtm8qTRfVVu8BHFrGuwNYAxhc1t1p+6+2XwOmAYO6iL0ndgdOL/sZB7xF0mpl3bgFJZ2SxpRqcPvcF2YvRggRERHLp1Q8F1GpWO4GbCnJQD/Ako6mSjzHUyWM020/Wdt0ku29Oxny+frwC9i1u2h/lXl/SPRvGqurbeYNas8FJgATJM2gShbPr/eRtDFwFLCd7X9JOr9pX/V9/sH2QR1WSNsD76eqBB9BNYc9iVVUldD5nlMvaSTwcq1pLov3uX4TsGNzgikJ5n+POrB9NnA2wErrDu52ziMiIvqaVDwX3f7AhbY3sj3I9gbAo1SnYv8E/AM4mU5Os/fARGBUua5xLapK552l/cDSvi6wa22bWVRVS4CP1NrHUyV4QHWqvSy+ImmF0raZpMG1bYYCjWtSnwMaFb+3UCVfsyWtA/xHbZt6v9uBnSRtUsZfRdKm5ZT+QNu/o7rxZ2j9oMuNP7MljShNB9dWXwd8thbzppJWZcHqMfVU83wNXUDfiIiIWAhJPBfdQcDVTW1XAR8ry5cCQzrp0xNXA9OBu6lOpx9t+39L+0PADOBMqopqw/HADyVNoqr6Nfw/4K2qblS6m3nJ6tnA9HJz0QDggsZNP1SnyY+r9fsfSTeWa1anAvdQXVt6S20/9X5PAaOBS8t4t5e5WA24prTdBHy5k2M/HDij3FxUrzqeQ3VN612qvmLpp3Rf2Xw9pm761R0JtJUbmO4FPrMQ20ZERMQCyM4ZwYglra2tze3t7b0dRkREREtImmK7rbt+qXhGREREREsk8YyIiIiIlkjiGREREREtkcQzIiIiIloiiWdEREREtEQSz4iIiIhoiSSeEREREdESSTwjIiIioiXyrPaIpWDG47MZNPba3g4jIiKig1kn79Vr+07FM5YqSetL+o2khyT9SdIPJa24gP5zJU2TdLekuyS9twf7OEfS5mV5lqQ1O+lznKSjyvL5kvbvYfyDyiM6IyIiYjEl8YylRpKAXwG/tj0Y2JTqufAnLmCzF20Ptb0N8HXgpO72Y/uTtu9dEjFHRETE0pPEM5am3YCXbJ8HYHsu8GXg45K2kHRnqW5OlzS4k+3fAvwLQNJISdc0Vkg6XdLosjxBUofnw0o6RtIDkv4IbLagQCUNkHR9qbLOkLRvbfWbJV1Q4rxS0ioLNw0REREBucYzlq4tgCn1BtvPSvoLcDrwQ9uXlFPv/UqXlSVNA/oD61IlrwtN0nDgQGBbqs/5Xc2xNHkJ2K/EtyZwu6RxZd1mwCds3yLpXOBzwPcXJa6IiIi+LBXPWJoEuIv2CcA3JH0N2Mj2i2Vd41T7EGBP4MJyyn5h7QxcbfsF288C47rpL+A7kqYDfwTWA9Yp6x6zfUtZvhgY0ekA0hhJ7ZLa574wexFCjoiIWL4l8Yyl6R5gvlPgkt4CbACcAuwDvAhcJ6lDZdP2bcCawFrAq8z/ee3fg/13lvR25eCyn+G2hwJ/r+2jeZxOx7V9tu022239Vhm4ELuOiIjoG5J4xtJ0PbCKpEMBJPUD/hs4H3g78IjtH1FVI7du3ljSEKpT8P8A/gxsLmklSQOB93ez74nAfpJWlrQa8MFu+g8EnrT9iqRdgY1q6zaUtGNZPgi4uZuxIiIiohNJPGOpsW1gP+AASQ8BD1JdS/kNYBQws1zPOQS4sGy2crnhaBpwOXCY7bm2HwOuAKYDlwBTu9n3XWX7acBVwKSmLj+V9Nfy77YyZpukdqrq5/21vvcBh5XT8G8DzlyE6YiIiOjzVOUGEbEktbW1ub29vbfDiIiIaAlJU2x3+IaZZql4RkRERERLJPGMiIiIiJZI4hkRERERLZHEMyIiIiJaIjcXRSwFkp4DHujtON5g1gSe7u0g3mAyJx1lTjrKnHSUOemot+dkI9trddcpj8yMWDoe6MndfX2JpPbMyfwyJx1lTjrKnHSUOeloWZmTnGqPiIiIiJZI4hkRERERLZHEM2LpOLu3A3gDypx0lDnpKHPSUeako8xJR8vEnOTmooiIiIhoiVQ8IyIiIqIlknhGLEGS9pT0gKSHJY3t7XhaRdK5kp6UNLPW9jZJf5D0UPn51tIuST8qczRd0rDei3zpkbSBpBsl3SfpHklfLO19dl4k9Zd0p6S7y5wcX9o3lnRHmZPLJa1Y2lcqrx8u6wf1ZvxLk6R+kqZKuqa87tNzImmWpBmSpklqL2199ncHQNLqkq6UdH/578qOy+KcJPGMWEIk9QPOAP4D2Bw4SNLmvRtVy5wP7NnUNha43vZg4PryGqr5GVz+jQHObFGMrfYq8J+23w3sAHy+fB768ry8DOxmextgKLCnpB2A7wKnljn5F/CJ0v8TwL9sbwKcWvotr74I3Fd7nTmBXW0PrX1FUF/+3QH4IfB720OAbag+L8vcnCTxjFhytgcetv2I7X8DlwH79nJMLWF7IvDPpuZ9gQvK8gXAh2rtF7pyO7C6pHVbE2nr2P6b7bvK8nNU/5NYjz48L+XY5pSXK5R/BnYDriztzXPSmKsrgfdLUovCbRlJ6wN7AeeU16KPz0kX+uzvjqS3ALsAPwew/W/bz7AMzkkSz4glZz3gsdrrv5a2vmod23+DKgkD1i7tfW6eyunQbYE76OPzUk4pTwOeBP4A/Al4xvarpUv9uF+fk7J+NrBGayNuidOAo4HXyus1yJwYGC9piqQxpa0v/+68E3gKOK9cknGOpFVZBuckiWfEktNZ1SFfG9FRn5onSQOAq4Av2X52QV07aVvu5sX2XNtDgfWpzhK8u7Nu5edyPyeS9gaetD2l3txJ1z4zJ8VOtodRnTL+vKRdFtC3L8zJm4FhwJm2twWeZ95p9c68YeckiWfEkvNXYIPa6/WBJ3opljeCvzdO7ZSfT5b2PjNPklagSjovsf2r0tzn5wWgnCacQHX96+qSGo9wrh/363NS1g+k4yUdy7qdgH0kzaK6PGc3qgpoX54TbD9Rfj4JXE31R0pf/t35K/BX23eU11dSJaLL3Jwk8YxYciYDg8vdqCsCBwLjejmm3jQOOKwsHwb8ptZ+aLnrcgdgduNU0fKkXHf3c+A+2z+oreqz8yJpLUmrl+WVgd2prn29Edi/dGuek8Zc7Q/c4OXsy6dtf932+rYHUf034wbbB9OH50TSqpJWaywDewAz6cO/O7b/F3hM0mal6f3AvSyDc5IvkI9YgiR9gKpa0Q841/aJvRxSS0i6FBgJrAn8HfgW8GvgCmBD4C/AAbb/WRKy06nugn8BONx2e2/EvTRJGgFMAmYw79q9b1Bd59kn50XS1lQ3QPSjKnxcYfvbkt5JVe17GzAVOMT2y5L6AxdRXR/7T+BA24/0TvRLn6SRwFG29+7Lc1KO/ery8s3AL2yfKGkN+ujvDoCkoVQ3oK0IPAIcTvk9YhmakySeEREREdESOdUeERERES2RxDMiIiIiWiKJZ0RERES0RBLPiIiIiGiJJJ4RERER0RJJPCMiIiKiJZJ4RkRERERLJPGMiIiIiJb4/y5E55SAift7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping Census_OSBuildRevision\n",
    "total.drop(['Census_OSBuildRevision'], axis=1, inplace=True)\n",
    "adversarial_valid(total, psuedo_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [15:12, 182.77s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.8495\n",
      "3-fold CV sklearn auc: 0.8495\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping OsBuildLab\n",
    "total.drop(['OsBuildLab'], axis=1, inplace=True)\n",
    "adversarial_valid(total, psuedo_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5it [15:09, 182.46s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training sklearn auc: 0.8495\n",
      "3-fold CV sklearn auc: 0.8495\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping AVProductStatesIdentifier\n",
    "total.drop(['AVProductStatesIdentifier'], axis=1, inplace=True)\n",
    "adversarial_valid(total, psuedo_label)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}