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3 - apprentissage.py
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# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
dtypes = {
'MachineIdentifier': 'category',
'ProductName': 'category',
'EngineVersion': 'category',
'AppVersion': 'category',
'AvSigVersion': 'category',
'IsBeta': 'int8',
'RtpStateBitfield': 'float16',
'IsSxsPassiveMode': 'int8',
'DefaultBrowsersIdentifier': 'float16',
'AVProductStatesIdentifier': 'float32',
'AVProductsInstalled': 'float16',
'AVProductsEnabled': 'float16',
'HasTpm': 'int8',
'CountryIdentifier': 'int16',
'CityIdentifier': 'float32',
'OrganizationIdentifier': 'float16',
'GeoNameIdentifier': 'float16',
'LocaleEnglishNameIdentifier': 'int8',
'Platform': 'category',
'Processor': 'category',
'OsVer': 'category',
'OsBuild': 'int16',
'OsSuite': 'int16',
'OsPlatformSubRelease': 'category',
'OsBuildLab': 'category',
'SkuEdition': 'category',
'IsProtected': 'float16',
'AutoSampleOptIn': 'int8',
'PuaMode': 'category',
'SMode': 'float16',
'IeVerIdentifier': 'float16',
'SmartScreen': 'category',
'Firewall': 'float16',
'UacLuaenable': 'float32',
'Census_MDC2FormFactor': 'category',
'Census_DeviceFamily': 'category',
'Census_OEMNameIdentifier': 'float16',
'Census_OEMModelIdentifier': 'float32',
'Census_ProcessorCoreCount': 'float16',
'Census_ProcessorManufacturerIdentifier': 'float16',
'Census_ProcessorModelIdentifier': 'float16',
'Census_ProcessorClass': 'category',
'Census_PrimaryDiskTotalCapacity': 'float32',
'Census_PrimaryDiskTypeName': 'category',
'Census_SystemVolumeTotalCapacity': 'float32',
'Census_HasOpticalDiskDrive': 'int8',
'Census_TotalPhysicalRAM': 'float32',
'Census_ChassisTypeName': 'category',
'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float16',
'Census_InternalPrimaryDisplayResolutionHorizontal': 'float16',
'Census_InternalPrimaryDisplayResolutionVertical': 'float16',
'Census_PowerPlatformRoleName': 'category',
'Census_InternalBatteryType': 'category',
'Census_InternalBatteryNumberOfCharges': 'float32',
'Census_OSVersion': 'category',
'Census_OSArchitecture': 'category',
'Census_OSBranch': 'category',
'Census_OSBuildNumber': 'int16',
'Census_OSBuildRevision': 'int32',
'Census_OSEdition': 'category',
'Census_OSSkuName': 'category',
'Census_OSInstallTypeName': 'category',
'Census_OSInstallLanguageIdentifier': 'float16',
'Census_OSUILocaleIdentifier': 'int16',
'Census_OSWUAutoUpdateOptionsName': 'category',
'Census_IsPortableOperatingSystem': 'int8',
'Census_GenuineStateName': 'category',
'Census_ActivationChannel': 'category',
'Census_IsFlightingInternal': 'float16',
'Census_IsFlightsDisabled': 'float16',
'Census_FlightRing': 'category',
'Census_ThresholdOptIn': 'float16',
'Census_FirmwareManufacturerIdentifier': 'float16',
'Census_FirmwareVersionIdentifier': 'float32',
'Census_IsSecureBootEnabled': 'int8',
'Census_IsWIMBootEnabled': 'float16',
'Census_IsVirtualDevice': 'float16',
'Census_IsTouchEnabled': 'int8',
'Census_IsPenCapable': 'int8',
'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',
'Wdft_IsGamer': 'float16',
'Wdft_RegionIdentifier': 'float16',
'HasDetections': 'int8'
}
train = pd.read_csv("data/train.csv", dtype=dtypes)
train = train[['AVProductsInstalled', 'Census_IsAlwaysOnAlwaysConnectedCapable',
'IsProtected', 'Census_ProcessorCoreCount', 'Wdft_IsGamer', 'HasDetections']]
train2 = train.dropna(subset = ['AVProductsInstalled', 'Census_IsAlwaysOnAlwaysConnectedCapable', 'IsProtected',
'Census_ProcessorCoreCount', 'Wdft_IsGamer', 'HasDetections'])
X = train2.iloc[:, :-1].values
y = train2.iloc[:, 5].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
#TEST#
'''
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)
'''
#TEST#
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
#######################################################################################################
test = pd.read_csv("data/test.csv", dtype=dtypes)
ID = test[['MachineIdentifier']]
ID.info()
test = test[['AVProductsInstalled', 'Census_IsAlwaysOnAlwaysConnectedCapable',
'IsProtected', 'Census_ProcessorCoreCount', 'Wdft_IsGamer']]
test2 = test.fillna(value=0)
y_pred = regressor.predict(test2)
y_pred2 = np.reshape(y_pred, (7853253,1))
# ID : 7 853 253 lignes
# y_pred : 7 853 253 lignes
id2 = ID.values
id3 = id2.astype(object)
sub = np.concatenate((id3, y_pred2), axis=1)
print(sub)
##########################
columns = ['MachineIdentifier', 'HasDetections']
df = pd.DataFrame(sub, columns=columns, index=None)
print(df)
df.to_csv(path_or_buf= 'submission.csv', index=False)