XGB_solver.py 文件源码

def use_model_to_predict(test_df, model):
    test_df.drop(['label'], axis=1, inplace=True)
    print 'Fix Missing App Count Value...'
    model_miss = joblib.load('XGB_missing.model')
    test_df = fix_missing_appcounts(test_df, model_miss)
    '''print 'Fix Missing Age Value...'
    model_age = joblib.load('XGB_age.model')
    test_df = fix_missing_age(test_df, model_age)'''
    test_df.drop(['marriageStatus','haveBaby','sitesetID', 'positionType'], axis=1, inplace=True)
    print 'Done'
    print test_df.info()
    print test_df.describe()
    print test_df.isnull().sum()
    test_np = test_df.as_matrix()
    X = test_np[:, 1:]
    print 'Use Model To Predict...'
    predicts = model.predict(X)
    result = pd.DataFrame({'instanceID':test_df['instanceID'].as_matrix(), 'prob':predicts})
    #print predicts#, predicts.min(axis=0), predicts.max(axis=0), predicts.sum(axis=1)
    return result