Part6 - UID_FFS.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"part6_UID_FFS.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"nK7trYtaMIoi","executionInfo":{"status":"ok","timestamp":1617547091595,"user_tz":0,"elapsed":1510,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","import os,gc,re\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import roc_auc_score,roc_curve,auc,confusion_matrix\n","from sklearn.model_selection import RandomizedSearchCV\n","import xgboost as xgb\n","import time\n","plt.style.use('seaborn-whitegrid')\n","import warnings\n","warnings.simplefilter(\"ignore\")\n","colors = [x['color'] for x in plt.rcParams['axes.prop_cycle']]\n","\n","\n","from sklearn.model_selection import GroupKFold\n","from sklearn.metrics import roc_auc_score"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WJ6uTJnhoBAJ","executionInfo":{"status":"ok","timestamp":1617547093694,"user_tz":0,"elapsed":1205,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"f3a38811-ace5-40b7-8228-433e89125dd7"},"source":["!nvidia-smi"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Sun Apr  4 14:38:12 2021       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 460.67       Driver Version: 460.32.03    CUDA Version: 11.2     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   52C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Y3rW4xVSMMJb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617547117787,"user_tz":0,"elapsed":23287,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"e97d555f-2149-42c5-8803-dd7b88de83c0"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3o0PHRyeMNtx","executionInfo":{"status":"ok","timestamp":1617547119192,"user_tz":0,"elapsed":1398,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["import os\n","os.chdir('/content/drive/My Drive/Colab Notebooks/iee-fraud detection')"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"FEbmGoyQMXix","executionInfo":{"status":"ok","timestamp":1617547164462,"user_tz":0,"elapsed":46661,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["df_train = pd.read_csv('data/train_combined.csv')\n","df_test = pd.read_csv('data/test_combined.csv')"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"UCww_CYrMXk-","executionInfo":{"status":"ok","timestamp":1617547194710,"user_tz":0,"elapsed":76903,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["# column details\n","cat_cols = (['ProductCD'] + \n","            ['card%d' % i for i in range(1, 7)] + \n","            ['addr1', 'addr2', 'P_emaildomain', 'R_emaildomain'] + \n","            ['M%d' % i for i in range(1, 10)] + \n","            ['DeviceType', 'DeviceInfo'] +\n","            ['id_%d' % i for i in range(12, 39)])\n","\n","\n","type_map = {c: str for c in cat_cols}\n","df_train[cat_cols] = df_train[cat_cols].astype(type_map, copy=False)\n","df_test[cat_cols] = df_test[cat_cols].astype(type_map, copy=False)\n","\n","######################################################################################\n","\n","id_cols = ['TransactionID', 'TransactionDT']\n","target = 'isFraud'\n","\n","numeric_cols =  [\n","    'TransactionAmt', 'dist1', 'dist2', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', \n","    'C11', 'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', \n","    'D12', 'D13', 'D14', 'D15', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', \n","    'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', \n","    'V26', 'V27', 'V28', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', \n","    'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52', 'V53', \n","    'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', \n","    'V68', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74', 'V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', \n","    'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94', 'V95', \n","    'V96', 'V97', 'V98', 'V99', 'V100', 'V101', 'V102', 'V103', 'V104', 'V105', 'V106', 'V107', 'V108', \n","    'V109', 'V110', 'V111', 'V112', 'V113', 'V114', 'V115', 'V116', 'V117', 'V118', 'V119', 'V120', \n","    'V121', 'V122', 'V123', 'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V130', 'V131', 'V132', \n","    'V133', 'V134', 'V135', 'V136', 'V137', 'V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', \n","    'V145', 'V146', 'V147', 'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', \n","    'V157', 'V158', 'V159', 'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166', 'V167', 'V168', \n","    'V169', 'V170', 'V171', 'V172', 'V173', 'V174', 'V175', 'V176', 'V177', 'V178', 'V179', 'V180', \n","    'V181', 'V182', 'V183', 'V184', 'V185', 'V186', 'V187', 'V188', 'V189', 'V190', 'V191', 'V192', \n","    'V193', 'V194', 'V195', 'V196', 'V197', 'V198', 'V199', 'V200', 'V201', 'V202', 'V203', 'V204', \n","    'V205', 'V206', 'V207', 'V208', 'V209', 'V210', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216', \n","    'V217', 'V218', 'V219', 'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227', 'V228', \n","    'V229', 'V230', 'V231', 'V232', 'V233', 'V234', 'V235', 'V236', 'V237', 'V238', 'V239', 'V240', \n","    'V241', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V250', 'V251', 'V252', \n","    'V253', 'V254', 'V255', 'V256', 'V257', 'V258', 'V259', 'V260', 'V261', 'V262', 'V263', 'V264', \n","    'V265', 'V266', 'V267', 'V268', 'V269', 'V270', 'V271', 'V272', 'V273', 'V274', 'V275', 'V276', \n","    'V277', 'V278', 'V279', 'V280', 'V281', 'V282', 'V283', 'V284', 'V285', 'V286', 'V287', 'V288', \n","    'V289', 'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V296', 'V297', 'V298', 'V299', 'V300', \n","    'V301', 'V302', 'V303', 'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', \n","    'V313', 'V314', 'V315', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321', 'V322', 'V323', 'V324', \n","    'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', \n","    'V337', 'V338', 'V339', 'id_01', 'id_02', 'id_03', 'id_04', 'id_05', 'id_06', 'id_07', 'id_08', \n","    'id_09', 'id_10', 'id_11'\n","]\n","\n","v_cols_included = ['V1', 'V3', 'V4', 'V6', 'V8', 'V11', 'V13', 'V14', 'V17', 'V20', \n"," 'V23', 'V26', 'V27', 'V30', 'V36', 'V37', 'V40', 'V41', 'V44', 'V47', 'V48', 'V54', 'V56', 'V59', \n"," 'V62', 'V65', 'V67', 'V68', 'V70', 'V76', 'V78', 'V80', 'V82', 'V86', 'V88', 'V89', 'V91', 'V96', \n"," 'V98', 'V99', 'V104', 'V107', 'V108', 'V111', 'V115', 'V117', 'V120', 'V121', 'V123', 'V124', 'V127', \n"," 'V129', 'V130', 'V136', 'V138', 'V139', 'V142', 'V147', 'V156', 'V162', 'V165', 'V160', 'V166', 'V178',\n"," 'V176', 'V173', 'V182', 'V187', 'V203', 'V205', 'V207', 'V215', 'V169', 'V171', 'V175', 'V180', 'V185', \n"," 'V188', 'V198', 'V210', 'V209', 'V218', 'V223', 'V224', 'V226', 'V228', 'V229', 'V235', 'V240', 'V258', \n"," 'V257', 'V253', 'V252', 'V260', 'V261', 'V264', 'V266', 'V267', 'V274', 'V277', 'V220', 'V221', 'V234', \n"," 'V238', 'V250', 'V271', 'V294', 'V284', 'V285', 'V286', 'V291',\n"," 'V297', 'V303', 'V305', 'V307', 'V309', 'V310', 'V320', 'V281', 'V283', 'V289', 'V296', 'V301', 'V314', 'V332', 'V325', 'V335', 'V338']\n","\n"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"Tmf5FN3qMXnz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617547197399,"user_tz":0,"elapsed":79584,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"ce4458ef-574e-4ac6-9917-6de35c56a6d1"},"source":["# droping v cols \n","drop_cols = [col for col in df_train.columns if col[0] == 'V' and col not in v_cols_included]\n","\n","print(f'dropping {len(drop_cols)} columns')\n","df_train = df_train.drop(columns=drop_cols)\n","df_test = df_test.drop(columns=drop_cols)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["dropping 211 columns\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"piCv7VhnMXrE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617547198139,"user_tz":0,"elapsed":80316,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"3b4eb6e0-f431-4818-9aea-8e58f72c3e33"},"source":["## Train and test split¶\n","y_train = df_train['isFraud']\n","X_train = df_train.drop(columns=['isFraud'])\n","X_test = df_test.copy()\n","\n","print(X_train.shape)\n","print(X_test.shape)\n","gc.collect()"],"execution_count":9,"outputs":[{"output_type":"stream","text":["(590540, 222)\n","(506691, 222)\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["103"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"3_0PJYm5MmGb","executionInfo":{"status":"ok","timestamp":1617547219624,"user_tz":0,"elapsed":101794,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["# Label encoding all cat features\n","for col in X_train.columns:\n","    \n","    if col in cat_cols:\n","        # label encode all cat columns\n","        dff = pd.concat([X_train[col],X_test[col]])\n","        dff,_ = pd.factorize(dff,sort=True)\n","        if dff.max()>32000: \n","            print(col,'needs int32 datatype')\n","            \n","        X_train[col] = dff[:len(X_train)].astype('int16')\n","        X_test[col] = dff[len(X_train):].astype('int16')"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"LLXlmcvvMqMb","executionInfo":{"status":"ok","timestamp":1617547219625,"user_tz":0,"elapsed":101789,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["# normalize d cols\n","norm_cols =  [15, 4, 2, 11, 10]\n","for i in range(1,16):\n","  if i in norm_cols:\n","      X_train['D'+str(i)+'n'] =  X_train['D'+str(i)] - X_train.TransactionDT/np.float32(24*60*60)\n","      X_test['D'+str(i)+'n'] = X_test['D'+str(i)] - X_test.TransactionDT/np.float32(24*60*60)\n","\n","# hour column\n","\n","X_train['hour'] = (X_train['TransactionDT']//(60*60))%24\n","X_test['hour'] = (X_test['TransactionDT']//(60*60))%24\n"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xg8f7jtY8a2U","executionInfo":{"status":"ok","timestamp":1617547233710,"user_tz":0,"elapsed":115867,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"7618e337-9221-4a78-cfb3-9e1575c18ee6"},"source":["# frequency encode columns\n","def frequency_encode(df1, df2, cols):\n","    \"\"\"\n","    it combines train and test dataframes and then encodes\n","    \"\"\"\n","    for col in cols:\n","        df = pd.concat([df1[col],df2[col]])\n","        vc = df.value_counts(dropna=True, normalize=True).to_dict()\n","        vc[-1] = -1\n","        nm = col+'_FE'\n","        df1[nm] = df1[col].map(vc)\n","        df1[nm] = df1[nm].astype('float32')\n","        df2[nm] = df2[col].map(vc)\n","        df2[nm] = df2[nm].astype('float32')\n","        print(f\"'{nm}'\",\", \",end=\" \")\n","\n","def label_encode(df_train,df_test,col):\n","    \"\"\"\n","    label encode a particular column\n","    Also memory usage is reduced\n","    \"\"\"\n","    \n","    dff = pd.concat([df_train[col],df_test[col]])\n","    # sort = True just make sure that the values are sorted before label encoding\n","    # ['b', 'b', 'a', 'c'] will be [1,1,0,2] with sort and without sort it will be [0,0,1,2]\n","    dff,_ = pd.factorize(dff,sort=True)\n","    if dff.max() > 32000:\n","        df_train[col] = dff[0:len(df_train)].astype('int32')\n","        df_test[col] = dff[len(df_train):].astype('int32')\n","    else:\n","        df_train[col] = dff[0:len(df_train)].astype('int16')\n","        df_test[col] = dff[len(df_train):].astype('int16')\n","    del dff,_\n","    gc.collect()\n","\n","def combine_cols(col1,col2,df_train,df_test):\n","    name = col1 + '_' + col2\n","    df_train[name] = df_train[col1].astype(str)+'_'+df_train[col2].astype(str)\n","    df_test[name] = df_test[col1].astype(str)+'_'+df_test[col2].astype(str)\n","    label_encode(df_train,df_test,name)\n","    print(f\"'{name}'\",\", \",end=\" \")\n","\n","###################################################################################################\n","new_cols =['card4_addr1_R_emaildomain', 'card2_FE','card1_FE', 'card1_addr1_R_emaildomain', 'card3_addr1_P_emaildomain', 'card1_addr1','card4_addr1_P_emaildomain_FE']\n","\n","# 'card2_FE','card1_FE'\n","frequency_encode(X_train,X_test,['card1','card2'])\n","\n","# 'card1_addr1_R_emaildomain', 'card3_addr1_P_emaildomain', 'card1_addr1'\n","combine_cols('card1','addr1',X_train,X_test)\n","combine_cols('card3','addr1',X_train,X_test)\n","combine_cols('card1_addr1','R_emaildomain',X_train,X_test)\n","combine_cols('card3_addr1','P_emaildomain',X_train,X_test)\n","\n","#'card4_addr1_R_emaildomain , 'card4_addr1_P_emaildomain_FE\n","combine_cols('card4','addr1',X_train,X_test)\n","combine_cols('card4_addr1','R_emaildomain',X_train,X_test)\n","combine_cols('card4_addr1','P_emaildomain',X_train,X_test)\n","frequency_encode(X_train,X_test,['card4_addr1_P_emaildomain'])\n","\n","\n","drop_cols = ['card3_addr1','card4_addr1','card4_addr1_P_emaildomain']\n","print('--'*50)\n","print(drop_cols)\n","\n","X_train = X_train.drop(columns=drop_cols)\n","X_test = X_test.drop(columns=drop_cols)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["'card1_FE' ,  'card2_FE' ,  'card1_addr1' ,  'card3_addr1' ,  'card1_addr1_R_emaildomain' ,  'card3_addr1_P_emaildomain' ,  'card4_addr1' ,  'card4_addr1_R_emaildomain' ,  'card4_addr1_P_emaildomain' ,  'card4_addr1_P_emaildomain_FE' ,  ----------------------------------------------------------------------------------------------------\n","['card3_addr1', 'card4_addr1', 'card4_addr1_P_emaildomain']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"-lHByQ8LDLry"},"source":["## UID + More encoding"]},{"cell_type":"code","metadata":{"id":"T7kFFqVFPG4N","executionInfo":{"status":"ok","timestamp":1617547236645,"user_tz":0,"elapsed":115929,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["def aggregate_cols(num_cols,cat_cols,aggs,X_train=X_train,X_test=X_test,use_nan_value=True,fill_nan=True):\n","    for num_col in num_cols:\n","        for cat_col in cat_cols:\n","            for agg_type in aggs:\n","                cols = [num_col,cat_col]\n","                new_col_name = num_col + '_' + cat_col + '_' + agg_type\n","                df = pd.concat([X_train[cols],X_test[cols]])\n","                if use_nan_value: \n","                    df.loc[df[num_col]==-1,num_col] = np.nan\n","            \n","                tmp_df = df.groupby(by=[cat_col])[num_col].agg([agg_type])[agg_type].reset_index().rename(\n","                                                        columns={agg_type: new_col_name})\n","                tmp_df.index = list(tmp_df[cat_col])\n","                tmp_dict = tmp_df[new_col_name].to_dict()\n","                X_train[new_col_name] = X_train[cat_col].map(tmp_dict).astype('float32')\n","                X_test[new_col_name]  = X_test[cat_col].map(tmp_dict).astype('float32')\n","            \n","                if fill_nan:\n","                    X_train[new_col_name].fillna(-1,inplace=True)\n","                    X_test[new_col_name].fillna(-1,inplace=True)\n","                \n","                print(f\"'{new_col_name}'\",\", \",end=\" \")\n","\n","\n","def aggregate_cols_cat(num_cols,cat_cols,X_train=X_train,X_test=X_test):\n","  for num_col in num_cols:\n","    for cat_col in cat_cols:\n","        cols = [num_col,cat_col]\n","        new_col_name = num_col + '_' + cat_col + '_ct'\n","        df = pd.concat([X_train[cols],X_test[cols]])\n","        mp = df.groupby(cat_col)[num_col].agg(['nunique'])['nunique'].to_dict()\n","        X_train[new_col_name] = X_train[cat_col].map(mp).astype('float32')\n","        X_test[new_col_name] = X_test[cat_col].map(mp).astype('float32')\n","        print(f\"'{new_col_name}'\",\", \",end=\" \")\n","\n","\n","#######################################################\n","\n","import datetime\n","START_DATE = datetime.datetime.strptime('2017-11-30', '%Y-%m-%d')\n","X_train['DT_M'] = X_train['TransactionDT'].apply(lambda x: (START_DATE + datetime.timedelta(seconds = x)))\n","X_train['DT_M'] = (X_train['DT_M'].dt.year-2017)*12 + X_train['DT_M'].dt.month \n","\n","X_test['DT_M'] = X_test['TransactionDT'].apply(lambda x: (START_DATE + datetime.timedelta(seconds = x)))\n","X_test['DT_M'] = (X_test['DT_M'].dt.year-2017)*12 + X_test['DT_M'].dt.month \n","\n","\n","X_train['day'] = X_train.TransactionDT / (24*60*60)\n","X_train['uid'] = X_train.card1_addr1.astype(str)+'_'+np.floor(X_train.day-X_train.D1).astype(str)\n","\n","X_test['day'] = X_test.TransactionDT / (24*60*60)\n","X_test['uid'] = X_test.card1_addr1.astype(str)+'_'+np.floor(X_test.day-X_test.D1).astype(str)\n"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dPNTt40K8a-f","executionInfo":{"status":"ok","timestamp":1617547290942,"user_tz":0,"elapsed":169801,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"8c987c18-34a8-4122-9e08-935360936e80"},"source":["# FREQUENCY ENCODE UID\n","frequency_encode(X_train,X_test,['uid'])\n","aggregate_cols(['M'+str(x) for x in range(1,10)],['uid'],['mean','std'],use_nan_value=True,fill_nan=True)\n"],"execution_count":14,"outputs":[{"output_type":"stream","text":["'uid_FE' ,  'M1_uid_mean' ,  'M1_uid_std' ,  'M2_uid_mean' ,  'M2_uid_std' ,  'M3_uid_mean' ,  'M3_uid_std' ,  'M4_uid_mean' ,  'M4_uid_std' ,  'M5_uid_mean' ,  'M5_uid_std' ,  'M6_uid_mean' ,  'M6_uid_std' ,  'M7_uid_mean' ,  'M7_uid_std' ,  'M8_uid_mean' ,  'M8_uid_std' ,  'M9_uid_mean' ,  'M9_uid_std' ,  "],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"skYVbraVia5v","executionInfo":{"status":"ok","timestamp":1617547382324,"user_tz":0,"elapsed":260512,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"b32328ba-b8b0-4d92-a2d6-3adbb030b05d"},"source":["# AGGREGATE \n","tmp = ['D'+str(i) for i in range(1,16)]\n","tmp.append('TransactionAmt')\n","aggregate_cols(tmp,['uid'],['mean','std'],use_nan_value=True,fill_nan=True)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["'D1_uid_mean' ,  'D1_uid_std' ,  'D2_uid_mean' ,  'D2_uid_std' ,  'D3_uid_mean' ,  'D3_uid_std' ,  'D4_uid_mean' ,  'D4_uid_std' ,  'D5_uid_mean' ,  'D5_uid_std' ,  'D6_uid_mean' ,  'D6_uid_std' ,  'D7_uid_mean' ,  'D7_uid_std' ,  'D8_uid_mean' ,  'D8_uid_std' ,  'D9_uid_mean' ,  'D9_uid_std' ,  'D10_uid_mean' ,  'D10_uid_std' ,  'D11_uid_mean' ,  'D11_uid_std' ,  'D12_uid_mean' ,  'D12_uid_std' ,  'D13_uid_mean' ,  'D13_uid_std' ,  'D14_uid_mean' ,  'D14_uid_std' ,  'D15_uid_mean' ,  'D15_uid_std' ,  'TransactionAmt_uid_mean' ,  'TransactionAmt_uid_std' ,  "],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GyGjo2KeibTX","executionInfo":{"status":"ok","timestamp":1617547463814,"user_tz":0,"elapsed":341506,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"3db2699d-2bcb-459e-8988-2bbf7e54dadb"},"source":["tmp = ['C'+str(i) for i in range(1,15)]\n","aggregate_cols(tmp,['uid'],['mean','std'],use_nan_value=True,fill_nan=True)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["'C1_uid_mean' ,  'C1_uid_std' ,  'C2_uid_mean' ,  'C2_uid_std' ,  'C3_uid_mean' ,  'C3_uid_std' ,  'C4_uid_mean' ,  'C4_uid_std' ,  'C5_uid_mean' ,  'C5_uid_std' ,  'C6_uid_mean' ,  'C6_uid_std' ,  'C7_uid_mean' ,  'C7_uid_std' ,  'C8_uid_mean' ,  'C8_uid_std' ,  'C9_uid_mean' ,  'C9_uid_std' ,  'C10_uid_mean' ,  'C10_uid_std' ,  'C11_uid_mean' ,  'C11_uid_std' ,  'C12_uid_mean' ,  'C12_uid_std' ,  'C13_uid_mean' ,  'C13_uid_std' ,  'C14_uid_mean' ,  'C14_uid_std' ,  "],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Kq4hm6nlZILR","executionInfo":{"status":"ok","timestamp":1617547505006,"user_tz":0,"elapsed":382324,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"b80a3232-f5fb-4936-db0d-6ed8efdcdc1e"},"source":["tmp = ['C'+str(i) for i in range(1,15)]\n","aggregate_cols_cat(tmp, ['uid'], X_train, X_test)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["'C1_uid_ct' ,  'C2_uid_ct' ,  'C3_uid_ct' ,  'C4_uid_ct' ,  'C5_uid_ct' ,  'C6_uid_ct' ,  'C7_uid_ct' ,  'C8_uid_ct' ,  'C9_uid_ct' ,  'C10_uid_ct' ,  'C11_uid_ct' ,  'C12_uid_ct' ,  'C13_uid_ct' ,  'C14_uid_ct' ,  "],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s90u_W1naJDR","executionInfo":{"status":"ok","timestamp":1617547525909,"user_tz":0,"elapsed":402854,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"fa064b62-0039-4e39-9818-fe56cedad44f"},"source":["tmp = ['DT_M','P_emaildomain','R_emaildomain','dist1','dist2','addr1','addr2']\n","aggregate_cols_cat(tmp, ['uid'], X_train, X_test)"],"execution_count":18,"outputs":[{"output_type":"stream","text":["'DT_M_uid_ct' ,  'P_emaildomain_uid_ct' ,  'R_emaildomain_uid_ct' ,  'dist1_uid_ct' ,  'dist2_uid_ct' ,  'addr1_uid_ct' ,  'addr2_uid_ct' ,  "],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wUFQzbZmjP1S","executionInfo":{"status":"ok","timestamp":1617547525909,"user_tz":0,"elapsed":399755,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"b00d3bd2-76a7-4f93-ac39-f94493bd0f34"},"source":["new_feat1 = ['uid_FE' ,  'M1_uid_mean' ,  'M1_uid_std' ,  'M2_uid_mean' ,  'M2_uid_std' ,  'M3_uid_mean' ,  'M3_uid_std' ,  'M4_uid_mean' ,  'M4_uid_std' ,  'M5_uid_mean' ,  'M5_uid_std' ,  'M6_uid_mean' ,  'M6_uid_std' ,  'M7_uid_mean' , \n","             'M7_uid_std' ,  'M8_uid_mean' ,  'M8_uid_std' ,  'M9_uid_mean' ,  'M9_uid_std', 'TransactionAmt_uid_mean' ,  'TransactionAmt_uid_std']\n","\n","new_feat2 = ['D1_uid_mean' ,  'D1_uid_std' ,  'D2_uid_mean' ,  'D2_uid_std' ,  'D3_uid_mean' ,  'D3_uid_std' ,  'D4_uid_mean' , \n","             'D4_uid_std' ,  'D5_uid_mean' ,  'D5_uid_std' ,  'D6_uid_mean' ,  'D6_uid_std' ,  'D7_uid_mean' ,  'D7_uid_std' , \n","             'D8_uid_mean' ,  'D8_uid_std' ,  'D9_uid_mean' ,  'D9_uid_std' ,  'D10_uid_mean' ,  'D10_uid_std' ,  'D11_uid_mean' , \n","             'D11_uid_std' ,  'D12_uid_mean' ,  'D12_uid_std' ,  'D13_uid_mean' ,  'D13_uid_std' ,  'D14_uid_mean' ,  'D14_uid_std','D15_uid_mean' , 'D15_uid_std']\n","\n","new_feat3 = ['C1_uid_mean' ,  'C1_uid_std' ,  'C2_uid_mean' ,  'C2_uid_std' ,  'C3_uid_mean' ,  'C3_uid_std' ,  'C4_uid_mean' , \n","             'C4_uid_std' ,  'C5_uid_mean' ,  'C5_uid_std' ,  'C6_uid_mean' ,  'C6_uid_std' ,  'C7_uid_mean' ,  'C7_uid_std' , \n","             'C8_uid_mean' ,  'C8_uid_std' ,  'C9_uid_mean' ,  'C9_uid_std' ,  'C10_uid_mean' ,  'C10_uid_std' , \n","             'C11_uid_mean' ,  'C11_uid_std' ,  'C12_uid_mean' ,  'C12_uid_std' ,  'C13_uid_mean' ,  'C13_uid_std' ,  'C14_uid_mean' ,  'C14_uid_std']\n","\n","new_feat4 = ['C1_uid_ct' ,  'C2_uid_ct' ,  'C3_uid_ct' ,  'C4_uid_ct' , \n","             'C5_uid_ct' ,  'C6_uid_ct' ,  'C7_uid_ct' ,  'C8_uid_ct' ,  'C9_uid_ct' ,  'C10_uid_ct' ,  'C11_uid_ct' ,  'C12_uid_ct' ,  'C13_uid_ct' ,  'C14_uid_ct']\n","\n","new_feat5 = ['DT_M_uid_ct' ,  'P_emaildomain_uid_ct' ,  'R_emaildomain_uid_ct' ,  'dist1_uid_ct' , \n","             'dist2_uid_ct' ,  'addr1_uid_ct' ,  'addr1_uid_ct']\n","\n","new_feats = new_feat1 + new_feat2 + new_feat3 + new_feat4 + new_feat5\n","\n","print(len(new_feat1))\n","print(len(new_feat2))\n","print(len(new_feat3))\n","print(len(new_feat4))\n","print(len(new_feat5))\n","print(len(new_feats))"],"execution_count":19,"outputs":[{"output_type":"stream","text":["21\n","30\n","28\n","14\n","7\n","100\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8gGUzEm18bAM","executionInfo":{"status":"ok","timestamp":1617547525910,"user_tz":0,"elapsed":392695,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["rem_cols = []\n","rem_cols.extend(['TransactionDT','TransactionID'])\n","norm_cols = ['D15','D4', 'D2', 'D11', 'D10']\n","rem_cols.extend(norm_cols)\n","\n","for c in ['DT_M','day','uid']:\n","    if c not in rem_cols:\n","        rem_cols.append(c)\n","\n","\n","cols = [col for col in X_train.columns if col not in rem_cols]"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"HpCFyo1zEFOX","executionInfo":{"status":"ok","timestamp":1617547538512,"user_tz":0,"elapsed":403322,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["# Scaling numeric features\n","for col in cols:\n","    if col not in cat_cols:\n","        # min max scalar\n","        dff = pd.concat([X_train[col],X_test[col]])\n","        dff = (dff - dff.min())/(dff.max() - dff.min())\n","        dff.fillna(-1,inplace=True)\n","\n","        X_train[col] = dff[:len(X_train)]\n","        X_test[col] = dff[len(X_train):]\n","\n","del dff"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"yHMp5hYoR7xJ","executionInfo":{"status":"ok","timestamp":1617547538513,"user_tz":0,"elapsed":401322,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["def model(x_train,y_train,cols):\n","    idx_train = x_train.index[:int(x_train.shape[0]*0.75)]  \n","    idx_validation = x_train.index[int(x_train.shape[0]*0.75):]\n","    \n","    print(f'fitting model on {len(cols)} columns')\n","    clf = xgb.XGBClassifier( \n","        n_estimators=2000,\n","        max_depth=12, \n","        learning_rate=0.02, \n","        subsample=0.8,\n","        colsample_bytree=0.4, \n","        missing=-1, \n","        eval_metric='auc',\n","        tree_method='gpu_hist' \n","          )\n","    model = clf.fit(x_train.loc[idx_train,cols], y_train[idx_train], \n","            eval_set=[(x_train.loc[idx_validation,cols],y_train[idx_validation])],\n","            verbose=50, early_stopping_rounds=100)\n","    del clf\n","    return model.best_score\n","\n","\n","\n","def forward_feat_selection(new_cols,old_cols,best_score):\n","    print(f'Forward feature selection on {len(new_cols)} features <-->  current best score {best_score} with {len(old_cols)} features')\n","    iteration = 1\n","    iteration_best_col = None\n","    # list of additional columns\n","    best_cols = []\n","    while iteration <= len(new_cols):\n","        for j,col in enumerate(new_cols):\n","            print(f'Model no: {iteration}_{(j+1)}')\n","            print('--'*40)\n","            if col not in best_cols:\n","                current_cols = old_cols.copy()\n","                # adding new feature\n","                best_cols.append(col) \n","                print(f'Adding new features: {best_cols}')\n","                current_cols.extend([c for c in best_cols])        \n","                current_score = model(X_train,y_train,current_cols)\n","\n","                if current_score > best_score:\n","                    print(f'Score improved on adding {col} new best score {current_score}')\n","                    iteration_best_col = col\n","                    best_score = current_score\n","                else:\n","                    print(f'Score not improved on adding {col}')\n","            \n","                best_cols.remove(col)\n","\n","            else:\n","                print(f'Skipping {col} already an selected column')\n","        \n","            print('--'*40)\n","\n","        # end of 1 iteration we add best column\n","        iteration += 1\n","        if iteration_best_col is not None:\n","            best_cols.append(iteration_best_col)\n","            print(f'Best col in this iteration {iteration_best_col}, --->   current best cols: {best_cols}')\n","            iteration_best_col = None\n","                  \n","        else:\n","            print('None of the columns improving the score in this iteration')\n","            print('###'*40)\n","            break\n","      \n","        print('###'*40)\n","\n","    print(f'Useful new features : {best_cols}')\n","    print(f'Best score : {best_score}')\n"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"H_fmK21XTOhX","executionInfo":{"status":"ok","timestamp":1617547539105,"user_tz":0,"elapsed":399867,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}}},"source":["del df_train,df_test"],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"P-DFCw3rXDwE"},"source":["### FFS on group1"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wQdxnRoHEFTj","executionInfo":{"status":"ok","timestamp":1617477782321,"user_tz":0,"elapsed":6886574,"user":{"displayName":"Arun Mohan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgjEPFlcZzC70oB37kEuyGkYj-RoCxRMydGewdImg=s64","userId":"01292347160049001009"}},"outputId":"4c87e192-8f17-402c-91f3-f4f3cb49e45c"},"source":["\n","old_cols = [col for col in X_train.columns if col not in rem_cols and col not in new_feats]\n","\n","forward_feat_selection(new_cols=new_feat1,old_cols=old_cols,best_score=0.936431)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Forward feature selection on 21 features <-->  current best score 0.936431 with 230 features\n","Model no: 1_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['uid_FE']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872855\n","[100]\tvalidation_0-auc:0.890402\n","[150]\tvalidation_0-auc:0.90463\n","[200]\tvalidation_0-auc:0.914611\n","[250]\tvalidation_0-auc:0.922935\n","[300]\tvalidation_0-auc:0.928602\n","[350]\tvalidation_0-auc:0.932582\n","[400]\tvalidation_0-auc:0.93473\n","[450]\tvalidation_0-auc:0.935976\n","[500]\tvalidation_0-auc:0.937072\n","[550]\tvalidation_0-auc:0.937852\n","[600]\tvalidation_0-auc:0.938121\n","[650]\tvalidation_0-auc:0.938356\n","[700]\tvalidation_0-auc:0.938394\n","[750]\tvalidation_0-auc:0.938256\n","[800]\tvalidation_0-auc:0.938245\n","Stopping. Best iteration:\n","[717]\tvalidation_0-auc:0.938451\n","\n","Score improved on adding uid_FE new best score 0.938451\n","--------------------------------------------------------------------------------\n","Model no: 1_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['M1_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872734\n","[100]\tvalidation_0-auc:0.888747\n","[150]\tvalidation_0-auc:0.902592\n","[200]\tvalidation_0-auc:0.913296\n","[250]\tvalidation_0-auc:0.922832\n","[300]\tvalidation_0-auc:0.928646\n","[350]\tvalidation_0-auc:0.932829\n","[400]\tvalidation_0-auc:0.934576\n","[450]\tvalidation_0-auc:0.935682\n","[500]\tvalidation_0-auc:0.936254\n","[550]\tvalidation_0-auc:0.937126\n","[600]\tvalidation_0-auc:0.937442\n","[650]\tvalidation_0-auc:0.937782\n","[700]\tvalidation_0-auc:0.937612\n","[750]\tvalidation_0-auc:0.937478\n","Stopping. Best iteration:\n","[660]\tvalidation_0-auc:0.93781\n","\n","Score not improved on adding M1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['M1_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871919\n","[100]\tvalidation_0-auc:0.889022\n","[150]\tvalidation_0-auc:0.903262\n","[200]\tvalidation_0-auc:0.913245\n","[250]\tvalidation_0-auc:0.921793\n","[300]\tvalidation_0-auc:0.927634\n","[350]\tvalidation_0-auc:0.931529\n","[400]\tvalidation_0-auc:0.933683\n","[450]\tvalidation_0-auc:0.934775\n","[500]\tvalidation_0-auc:0.935526\n","[550]\tvalidation_0-auc:0.936005\n","[600]\tvalidation_0-auc:0.93616\n","[650]\tvalidation_0-auc:0.936451\n","[700]\tvalidation_0-auc:0.936497\n","[750]\tvalidation_0-auc:0.936347\n","[800]\tvalidation_0-auc:0.93625\n","Stopping. Best iteration:\n","[717]\tvalidation_0-auc:0.936548\n","\n","Score not improved on adding M1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['M2_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873468\n","[100]\tvalidation_0-auc:0.888564\n","[150]\tvalidation_0-auc:0.90302\n","[200]\tvalidation_0-auc:0.913497\n","[250]\tvalidation_0-auc:0.922001\n","[300]\tvalidation_0-auc:0.927665\n","[350]\tvalidation_0-auc:0.931466\n","[400]\tvalidation_0-auc:0.93335\n","[450]\tvalidation_0-auc:0.934506\n","[500]\tvalidation_0-auc:0.935272\n","[550]\tvalidation_0-auc:0.935789\n","[600]\tvalidation_0-auc:0.935694\n","[650]\tvalidation_0-auc:0.936183\n","[700]\tvalidation_0-auc:0.936425\n","[750]\tvalidation_0-auc:0.93645\n","[800]\tvalidation_0-auc:0.936253\n","Stopping. Best iteration:\n","[732]\tvalidation_0-auc:0.93666\n","\n","Score not improved on adding M2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['M2_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871943\n","[100]\tvalidation_0-auc:0.889169\n","[150]\tvalidation_0-auc:0.904243\n","[200]\tvalidation_0-auc:0.914399\n","[250]\tvalidation_0-auc:0.922791\n","[300]\tvalidation_0-auc:0.928273\n","[350]\tvalidation_0-auc:0.932314\n","[400]\tvalidation_0-auc:0.934546\n","[450]\tvalidation_0-auc:0.935791\n","[500]\tvalidation_0-auc:0.936463\n","[550]\tvalidation_0-auc:0.937243\n","[600]\tvalidation_0-auc:0.937386\n","[650]\tvalidation_0-auc:0.937643\n","[700]\tvalidation_0-auc:0.937528\n","[750]\tvalidation_0-auc:0.937411\n","Stopping. Best iteration:\n","[662]\tvalidation_0-auc:0.937755\n","\n","Score not improved on adding M2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['M3_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873357\n","[100]\tvalidation_0-auc:0.889009\n","[150]\tvalidation_0-auc:0.902714\n","[200]\tvalidation_0-auc:0.913328\n","[250]\tvalidation_0-auc:0.921489\n","[300]\tvalidation_0-auc:0.927273\n","[350]\tvalidation_0-auc:0.931268\n","[400]\tvalidation_0-auc:0.933369\n","[450]\tvalidation_0-auc:0.933832\n","[500]\tvalidation_0-auc:0.934586\n","[550]\tvalidation_0-auc:0.93514\n","[600]\tvalidation_0-auc:0.935196\n","[650]\tvalidation_0-auc:0.935543\n","[700]\tvalidation_0-auc:0.935411\n","[750]\tvalidation_0-auc:0.93542\n","Stopping. Best iteration:\n","[661]\tvalidation_0-auc:0.9356\n","\n","Score not improved on adding M3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['M3_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872384\n","[100]\tvalidation_0-auc:0.889609\n","[150]\tvalidation_0-auc:0.903807\n","[200]\tvalidation_0-auc:0.914704\n","[250]\tvalidation_0-auc:0.923362\n","[300]\tvalidation_0-auc:0.928682\n","[350]\tvalidation_0-auc:0.932537\n","[400]\tvalidation_0-auc:0.934814\n","[450]\tvalidation_0-auc:0.935782\n","[500]\tvalidation_0-auc:0.936437\n","[550]\tvalidation_0-auc:0.937255\n","[600]\tvalidation_0-auc:0.93737\n","[650]\tvalidation_0-auc:0.937443\n","[700]\tvalidation_0-auc:0.937669\n","[750]\tvalidation_0-auc:0.937667\n","[800]\tvalidation_0-auc:0.93758\n","Stopping. Best iteration:\n","[741]\tvalidation_0-auc:0.93771\n","\n","Score not improved on adding M3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['M4_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872329\n","[100]\tvalidation_0-auc:0.888666\n","[150]\tvalidation_0-auc:0.903054\n","[200]\tvalidation_0-auc:0.913575\n","[250]\tvalidation_0-auc:0.92196\n","[300]\tvalidation_0-auc:0.927719\n","[350]\tvalidation_0-auc:0.9313\n","[400]\tvalidation_0-auc:0.933692\n","[450]\tvalidation_0-auc:0.934686\n","[500]\tvalidation_0-auc:0.935368\n","[550]\tvalidation_0-auc:0.936388\n","[600]\tvalidation_0-auc:0.936666\n","[650]\tvalidation_0-auc:0.937058\n","[700]\tvalidation_0-auc:0.937384\n","[750]\tvalidation_0-auc:0.937273\n","[800]\tvalidation_0-auc:0.937036\n","Stopping. Best iteration:\n","[717]\tvalidation_0-auc:0.937473\n","\n","Score not improved on adding M4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['M4_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871807\n","[100]\tvalidation_0-auc:0.888893\n","[150]\tvalidation_0-auc:0.90385\n","[200]\tvalidation_0-auc:0.914239\n","[250]\tvalidation_0-auc:0.922409\n","[300]\tvalidation_0-auc:0.927875\n","[350]\tvalidation_0-auc:0.932302\n","[400]\tvalidation_0-auc:0.934338\n","[450]\tvalidation_0-auc:0.935093\n","[500]\tvalidation_0-auc:0.93576\n","[550]\tvalidation_0-auc:0.936335\n","[600]\tvalidation_0-auc:0.93675\n","[650]\tvalidation_0-auc:0.936895\n","[700]\tvalidation_0-auc:0.937027\n","[750]\tvalidation_0-auc:0.937121\n","[800]\tvalidation_0-auc:0.936971\n","Stopping. Best iteration:\n","[741]\tvalidation_0-auc:0.937235\n","\n","Score not improved on adding M4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['M5_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871837\n","[100]\tvalidation_0-auc:0.889182\n","[150]\tvalidation_0-auc:0.904076\n","[200]\tvalidation_0-auc:0.914279\n","[250]\tvalidation_0-auc:0.923022\n","[300]\tvalidation_0-auc:0.92891\n","[350]\tvalidation_0-auc:0.932652\n","[400]\tvalidation_0-auc:0.934465\n","[450]\tvalidation_0-auc:0.935508\n","[500]\tvalidation_0-auc:0.93655\n","[550]\tvalidation_0-auc:0.93724\n","[600]\tvalidation_0-auc:0.93742\n","[650]\tvalidation_0-auc:0.937733\n","[700]\tvalidation_0-auc:0.937796\n","[750]\tvalidation_0-auc:0.937968\n","[800]\tvalidation_0-auc:0.937986\n","Stopping. Best iteration:\n","[733]\tvalidation_0-auc:0.938081\n","\n","Score not improved on adding M5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['M5_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872661\n","[100]\tvalidation_0-auc:0.889764\n","[150]\tvalidation_0-auc:0.904244\n","[200]\tvalidation_0-auc:0.914861\n","[250]\tvalidation_0-auc:0.922342\n","[300]\tvalidation_0-auc:0.928049\n","[350]\tvalidation_0-auc:0.932584\n","[400]\tvalidation_0-auc:0.934759\n","[450]\tvalidation_0-auc:0.93578\n","[500]\tvalidation_0-auc:0.936682\n","[550]\tvalidation_0-auc:0.937351\n","[600]\tvalidation_0-auc:0.937555\n","[650]\tvalidation_0-auc:0.937775\n","[700]\tvalidation_0-auc:0.937869\n","[750]\tvalidation_0-auc:0.937928\n","[800]\tvalidation_0-auc:0.937896\n","Stopping. Best iteration:\n","[741]\tvalidation_0-auc:0.938046\n","\n","Score not improved on adding M5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['M6_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872342\n","[100]\tvalidation_0-auc:0.888507\n","[150]\tvalidation_0-auc:0.903027\n","[200]\tvalidation_0-auc:0.913605\n","[250]\tvalidation_0-auc:0.922167\n","[300]\tvalidation_0-auc:0.927604\n","[350]\tvalidation_0-auc:0.931614\n","[400]\tvalidation_0-auc:0.933623\n","[450]\tvalidation_0-auc:0.934427\n","[500]\tvalidation_0-auc:0.935213\n","[550]\tvalidation_0-auc:0.93585\n","[600]\tvalidation_0-auc:0.936021\n","[650]\tvalidation_0-auc:0.935972\n","Stopping. Best iteration:\n","[585]\tvalidation_0-auc:0.936171\n","\n","Score not improved on adding M6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['M6_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872611\n","[100]\tvalidation_0-auc:0.8891\n","[150]\tvalidation_0-auc:0.903828\n","[200]\tvalidation_0-auc:0.913885\n","[250]\tvalidation_0-auc:0.922088\n","[300]\tvalidation_0-auc:0.927542\n","[350]\tvalidation_0-auc:0.931576\n","[400]\tvalidation_0-auc:0.933811\n","[450]\tvalidation_0-auc:0.935074\n","[500]\tvalidation_0-auc:0.935991\n","[550]\tvalidation_0-auc:0.936652\n","[600]\tvalidation_0-auc:0.937093\n","[650]\tvalidation_0-auc:0.937155\n","[700]\tvalidation_0-auc:0.937228\n","[750]\tvalidation_0-auc:0.937304\n","[800]\tvalidation_0-auc:0.937404\n","[850]\tvalidation_0-auc:0.937121\n","[900]\tvalidation_0-auc:0.937036\n","Stopping. Best iteration:\n","[800]\tvalidation_0-auc:0.937404\n","\n","Score not improved on adding M6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['M7_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871534\n","[100]\tvalidation_0-auc:0.888865\n","[150]\tvalidation_0-auc:0.902544\n","[200]\tvalidation_0-auc:0.913108\n","[250]\tvalidation_0-auc:0.921988\n","[300]\tvalidation_0-auc:0.928316\n","[350]\tvalidation_0-auc:0.932122\n","[400]\tvalidation_0-auc:0.934362\n","[450]\tvalidation_0-auc:0.935696\n","[500]\tvalidation_0-auc:0.936441\n","[550]\tvalidation_0-auc:0.937201\n","[600]\tvalidation_0-auc:0.93755\n","[650]\tvalidation_0-auc:0.937855\n","[700]\tvalidation_0-auc:0.93775\n","[750]\tvalidation_0-auc:0.93765\n","[800]\tvalidation_0-auc:0.937366\n","Stopping. Best iteration:\n","[717]\tvalidation_0-auc:0.937923\n","\n","Score not improved on adding M7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['M7_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872469\n","[100]\tvalidation_0-auc:0.889174\n","[150]\tvalidation_0-auc:0.903599\n","[200]\tvalidation_0-auc:0.91391\n","[250]\tvalidation_0-auc:0.92245\n","[300]\tvalidation_0-auc:0.928226\n","[350]\tvalidation_0-auc:0.932447\n","[400]\tvalidation_0-auc:0.93477\n","[450]\tvalidation_0-auc:0.935982\n","[500]\tvalidation_0-auc:0.936802\n","[550]\tvalidation_0-auc:0.937445\n","[600]\tvalidation_0-auc:0.937489\n","[650]\tvalidation_0-auc:0.937576\n","[700]\tvalidation_0-auc:0.937556\n","[750]\tvalidation_0-auc:0.937553\n","[800]\tvalidation_0-auc:0.937266\n","Stopping. Best iteration:\n","[717]\tvalidation_0-auc:0.937655\n","\n","Score not improved on adding M7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['M8_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872271\n","[100]\tvalidation_0-auc:0.888564\n","[150]\tvalidation_0-auc:0.90274\n","[200]\tvalidation_0-auc:0.913313\n","[250]\tvalidation_0-auc:0.922007\n","[300]\tvalidation_0-auc:0.928061\n","[350]\tvalidation_0-auc:0.932259\n","[400]\tvalidation_0-auc:0.93452\n","[450]\tvalidation_0-auc:0.935749\n","[500]\tvalidation_0-auc:0.936334\n","[550]\tvalidation_0-auc:0.937139\n","[600]\tvalidation_0-auc:0.937442\n","[650]\tvalidation_0-auc:0.938049\n","[700]\tvalidation_0-auc:0.938018\n","[750]\tvalidation_0-auc:0.937947\n","Stopping. Best iteration:\n","[658]\tvalidation_0-auc:0.938096\n","\n","Score not improved on adding M8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['M8_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872793\n","[100]\tvalidation_0-auc:0.889036\n","[150]\tvalidation_0-auc:0.903575\n","[200]\tvalidation_0-auc:0.913384\n","[250]\tvalidation_0-auc:0.922411\n","[300]\tvalidation_0-auc:0.927953\n","[350]\tvalidation_0-auc:0.932201\n","[400]\tvalidation_0-auc:0.934418\n","[450]\tvalidation_0-auc:0.935794\n","[500]\tvalidation_0-auc:0.936674\n","[550]\tvalidation_0-auc:0.937219\n","[600]\tvalidation_0-auc:0.937589\n","[650]\tvalidation_0-auc:0.937622\n","[700]\tvalidation_0-auc:0.93769\n","[750]\tvalidation_0-auc:0.937745\n","[800]\tvalidation_0-auc:0.937487\n","Stopping. Best iteration:\n","[716]\tvalidation_0-auc:0.937836\n","\n","Score not improved on adding M8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872264\n","[100]\tvalidation_0-auc:0.888975\n","[150]\tvalidation_0-auc:0.90341\n","[200]\tvalidation_0-auc:0.913649\n","[250]\tvalidation_0-auc:0.922103\n","[300]\tvalidation_0-auc:0.928446\n","[350]\tvalidation_0-auc:0.932592\n","[400]\tvalidation_0-auc:0.934887\n","[450]\tvalidation_0-auc:0.935892\n","[500]\tvalidation_0-auc:0.936684\n","[550]\tvalidation_0-auc:0.937537\n","[600]\tvalidation_0-auc:0.937738\n","[650]\tvalidation_0-auc:0.938166\n","[700]\tvalidation_0-auc:0.938299\n","[750]\tvalidation_0-auc:0.938177\n","[800]\tvalidation_0-auc:0.937935\n","Stopping. Best iteration:\n","[735]\tvalidation_0-auc:0.938454\n","\n","Score improved on adding M9_uid_mean new best score 0.938454\n","--------------------------------------------------------------------------------\n","Model no: 1_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872328\n","[100]\tvalidation_0-auc:0.88949\n","[150]\tvalidation_0-auc:0.904469\n","[200]\tvalidation_0-auc:0.914376\n","[250]\tvalidation_0-auc:0.92277\n","[300]\tvalidation_0-auc:0.928288\n","[350]\tvalidation_0-auc:0.932262\n","[400]\tvalidation_0-auc:0.934382\n","[450]\tvalidation_0-auc:0.935645\n","[500]\tvalidation_0-auc:0.936195\n","[550]\tvalidation_0-auc:0.936774\n","[600]\tvalidation_0-auc:0.936972\n","[650]\tvalidation_0-auc:0.937363\n","[700]\tvalidation_0-auc:0.937395\n","[750]\tvalidation_0-auc:0.93755\n","[800]\tvalidation_0-auc:0.937351\n","Stopping. Best iteration:\n","[742]\tvalidation_0-auc:0.937616\n","\n","Score not improved on adding M9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['TransactionAmt_uid_mean']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872456\n","[100]\tvalidation_0-auc:0.889896\n","[150]\tvalidation_0-auc:0.904131\n","[200]\tvalidation_0-auc:0.915103\n","[250]\tvalidation_0-auc:0.923199\n","[300]\tvalidation_0-auc:0.928907\n","[350]\tvalidation_0-auc:0.932466\n","[400]\tvalidation_0-auc:0.934787\n","[450]\tvalidation_0-auc:0.935903\n","[500]\tvalidation_0-auc:0.936601\n","[550]\tvalidation_0-auc:0.937265\n","[600]\tvalidation_0-auc:0.937798\n","[650]\tvalidation_0-auc:0.937912\n","[700]\tvalidation_0-auc:0.937739\n","Stopping. Best iteration:\n","[647]\tvalidation_0-auc:0.937952\n","\n","Score not improved on adding TransactionAmt_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['TransactionAmt_uid_std']\n","fitting model on 231 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871917\n","[100]\tvalidation_0-auc:0.88991\n","[150]\tvalidation_0-auc:0.905344\n","[200]\tvalidation_0-auc:0.915844\n","[250]\tvalidation_0-auc:0.923277\n","[300]\tvalidation_0-auc:0.928711\n","[350]\tvalidation_0-auc:0.932765\n","[400]\tvalidation_0-auc:0.934451\n","[450]\tvalidation_0-auc:0.935663\n","[500]\tvalidation_0-auc:0.936259\n","[550]\tvalidation_0-auc:0.937049\n","[600]\tvalidation_0-auc:0.937267\n","[650]\tvalidation_0-auc:0.937528\n","[700]\tvalidation_0-auc:0.937505\n","[750]\tvalidation_0-auc:0.937302\n","Stopping. Best iteration:\n","[676]\tvalidation_0-auc:0.937623\n","\n","Score not improved on adding TransactionAmt_uid_std\n","--------------------------------------------------------------------------------\n","Best col in this iteration M9_uid_mean, --->   current best cols: ['M9_uid_mean']\n","########################################################################################################################\n","Model no: 2_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'uid_FE']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877679\n","[100]\tvalidation_0-auc:0.892555\n","[150]\tvalidation_0-auc:0.90593\n","[200]\tvalidation_0-auc:0.915921\n","[250]\tvalidation_0-auc:0.922786\n","[300]\tvalidation_0-auc:0.928659\n","[350]\tvalidation_0-auc:0.93257\n","[400]\tvalidation_0-auc:0.935037\n","[450]\tvalidation_0-auc:0.936731\n","[500]\tvalidation_0-auc:0.937693\n","[550]\tvalidation_0-auc:0.938028\n","[600]\tvalidation_0-auc:0.938311\n","[650]\tvalidation_0-auc:0.937964\n","[700]\tvalidation_0-auc:0.937819\n","Stopping. Best iteration:\n","[600]\tvalidation_0-auc:0.938311\n","\n","Score not improved on adding uid_FE\n","--------------------------------------------------------------------------------\n","Model no: 2_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M1_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.874668\n","[100]\tvalidation_0-auc:0.889424\n","[150]\tvalidation_0-auc:0.903294\n","[200]\tvalidation_0-auc:0.91467\n","[250]\tvalidation_0-auc:0.922489\n","[300]\tvalidation_0-auc:0.928332\n","[350]\tvalidation_0-auc:0.932261\n","[400]\tvalidation_0-auc:0.934397\n","[450]\tvalidation_0-auc:0.935619\n","[500]\tvalidation_0-auc:0.936484\n","[550]\tvalidation_0-auc:0.93701\n","[600]\tvalidation_0-auc:0.937324\n","[650]\tvalidation_0-auc:0.937556\n","[700]\tvalidation_0-auc:0.937337\n","Stopping. Best iteration:\n","[631]\tvalidation_0-auc:0.937582\n","\n","Score not improved on adding M1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M1_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875418\n","[100]\tvalidation_0-auc:0.890122\n","[150]\tvalidation_0-auc:0.904813\n","[200]\tvalidation_0-auc:0.915092\n","[250]\tvalidation_0-auc:0.922819\n","[300]\tvalidation_0-auc:0.92888\n","[350]\tvalidation_0-auc:0.932719\n","[400]\tvalidation_0-auc:0.934683\n","[450]\tvalidation_0-auc:0.936418\n","[500]\tvalidation_0-auc:0.937406\n","[550]\tvalidation_0-auc:0.937887\n","[600]\tvalidation_0-auc:0.938275\n","[650]\tvalidation_0-auc:0.938208\n","[700]\tvalidation_0-auc:0.938198\n","Stopping. Best iteration:\n","[605]\tvalidation_0-auc:0.938308\n","\n","Score not improved on adding M1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M2_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875614\n","[100]\tvalidation_0-auc:0.890806\n","[150]\tvalidation_0-auc:0.904513\n","[200]\tvalidation_0-auc:0.915056\n","[250]\tvalidation_0-auc:0.922257\n","[300]\tvalidation_0-auc:0.928185\n","[350]\tvalidation_0-auc:0.932366\n","[400]\tvalidation_0-auc:0.934421\n","[450]\tvalidation_0-auc:0.935696\n","[500]\tvalidation_0-auc:0.936536\n","[550]\tvalidation_0-auc:0.936882\n","[600]\tvalidation_0-auc:0.937314\n","[650]\tvalidation_0-auc:0.937297\n","[700]\tvalidation_0-auc:0.936967\n","Stopping. Best iteration:\n","[613]\tvalidation_0-auc:0.937423\n","\n","Score not improved on adding M2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M2_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875387\n","[100]\tvalidation_0-auc:0.890251\n","[150]\tvalidation_0-auc:0.904495\n","[200]\tvalidation_0-auc:0.914786\n","[250]\tvalidation_0-auc:0.921925\n","[300]\tvalidation_0-auc:0.928348\n","[350]\tvalidation_0-auc:0.932301\n","[400]\tvalidation_0-auc:0.934495\n","[450]\tvalidation_0-auc:0.935943\n","[500]\tvalidation_0-auc:0.937049\n","[550]\tvalidation_0-auc:0.937447\n","[600]\tvalidation_0-auc:0.937834\n","[650]\tvalidation_0-auc:0.93794\n","[700]\tvalidation_0-auc:0.938262\n","[750]\tvalidation_0-auc:0.938233\n","[800]\tvalidation_0-auc:0.938095\n","Stopping. Best iteration:\n","[725]\tvalidation_0-auc:0.938365\n","\n","Score not improved on adding M2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M3_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876345\n","[100]\tvalidation_0-auc:0.890904\n","[150]\tvalidation_0-auc:0.904369\n","[200]\tvalidation_0-auc:0.915065\n","[250]\tvalidation_0-auc:0.922754\n","[300]\tvalidation_0-auc:0.928748\n","[350]\tvalidation_0-auc:0.932738\n","[400]\tvalidation_0-auc:0.934355\n","[450]\tvalidation_0-auc:0.935711\n","[500]\tvalidation_0-auc:0.936262\n","[550]\tvalidation_0-auc:0.936817\n","[600]\tvalidation_0-auc:0.93699\n","[650]\tvalidation_0-auc:0.936993\n","[700]\tvalidation_0-auc:0.93681\n","Stopping. Best iteration:\n","[615]\tvalidation_0-auc:0.937126\n","\n","Score not improved on adding M3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M3_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876182\n","[100]\tvalidation_0-auc:0.891112\n","[150]\tvalidation_0-auc:0.904982\n","[200]\tvalidation_0-auc:0.915769\n","[250]\tvalidation_0-auc:0.923163\n","[300]\tvalidation_0-auc:0.929467\n","[350]\tvalidation_0-auc:0.933514\n","[400]\tvalidation_0-auc:0.935688\n","[450]\tvalidation_0-auc:0.93734\n","[500]\tvalidation_0-auc:0.938242\n","[550]\tvalidation_0-auc:0.938542\n","[600]\tvalidation_0-auc:0.938689\n","[650]\tvalidation_0-auc:0.938688\n","[700]\tvalidation_0-auc:0.938649\n","Stopping. Best iteration:\n","[616]\tvalidation_0-auc:0.93885\n","\n","Score improved on adding M3_uid_std new best score 0.93885\n","--------------------------------------------------------------------------------\n","Model no: 2_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M4_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875873\n","[100]\tvalidation_0-auc:0.890367\n","[150]\tvalidation_0-auc:0.90422\n","[200]\tvalidation_0-auc:0.914863\n","[250]\tvalidation_0-auc:0.922666\n","[300]\tvalidation_0-auc:0.928856\n","[350]\tvalidation_0-auc:0.932723\n","[400]\tvalidation_0-auc:0.934754\n","[450]\tvalidation_0-auc:0.936067\n","[500]\tvalidation_0-auc:0.936895\n","[550]\tvalidation_0-auc:0.937576\n","[600]\tvalidation_0-auc:0.937821\n","[650]\tvalidation_0-auc:0.937763\n","[700]\tvalidation_0-auc:0.937726\n","Stopping. Best iteration:\n","[607]\tvalidation_0-auc:0.937877\n","\n","Score not improved on adding M4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M4_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.874845\n","[100]\tvalidation_0-auc:0.890521\n","[150]\tvalidation_0-auc:0.905229\n","[200]\tvalidation_0-auc:0.915993\n","[250]\tvalidation_0-auc:0.922776\n","[300]\tvalidation_0-auc:0.928758\n","[350]\tvalidation_0-auc:0.932528\n","[400]\tvalidation_0-auc:0.934875\n","[450]\tvalidation_0-auc:0.936434\n","[500]\tvalidation_0-auc:0.937327\n","[550]\tvalidation_0-auc:0.937986\n","[600]\tvalidation_0-auc:0.938359\n","[650]\tvalidation_0-auc:0.938411\n","[700]\tvalidation_0-auc:0.938508\n","[750]\tvalidation_0-auc:0.938488\n","[800]\tvalidation_0-auc:0.938152\n","Stopping. Best iteration:\n","[716]\tvalidation_0-auc:0.938598\n","\n","Score not improved on adding M4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M5_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873711\n","[100]\tvalidation_0-auc:0.890622\n","[150]\tvalidation_0-auc:0.904199\n","[200]\tvalidation_0-auc:0.914869\n","[250]\tvalidation_0-auc:0.922573\n","[300]\tvalidation_0-auc:0.929272\n","[350]\tvalidation_0-auc:0.932871\n","[400]\tvalidation_0-auc:0.934791\n","[450]\tvalidation_0-auc:0.936082\n","[500]\tvalidation_0-auc:0.936863\n","[550]\tvalidation_0-auc:0.9375\n","[600]\tvalidation_0-auc:0.93781\n","[650]\tvalidation_0-auc:0.937734\n","[700]\tvalidation_0-auc:0.93793\n","[750]\tvalidation_0-auc:0.937706\n","Stopping. Best iteration:\n","[693]\tvalidation_0-auc:0.937982\n","\n","Score not improved on adding M5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M5_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875401\n","[100]\tvalidation_0-auc:0.890188\n","[150]\tvalidation_0-auc:0.904786\n","[200]\tvalidation_0-auc:0.914892\n","[250]\tvalidation_0-auc:0.921885\n","[300]\tvalidation_0-auc:0.927799\n","[350]\tvalidation_0-auc:0.931765\n","[400]\tvalidation_0-auc:0.934036\n","[450]\tvalidation_0-auc:0.935554\n","[500]\tvalidation_0-auc:0.936635\n","[550]\tvalidation_0-auc:0.937216\n","[600]\tvalidation_0-auc:0.937504\n","[650]\tvalidation_0-auc:0.937582\n","[700]\tvalidation_0-auc:0.93804\n","[750]\tvalidation_0-auc:0.938135\n","[800]\tvalidation_0-auc:0.93785\n","Stopping. Best iteration:\n","[718]\tvalidation_0-auc:0.93817\n","\n","Score not improved on adding M5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M6_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875182\n","[100]\tvalidation_0-auc:0.890281\n","[150]\tvalidation_0-auc:0.904356\n","[200]\tvalidation_0-auc:0.915057\n","[250]\tvalidation_0-auc:0.922417\n","[300]\tvalidation_0-auc:0.92807\n","[350]\tvalidation_0-auc:0.93221\n","[400]\tvalidation_0-auc:0.933922\n","[450]\tvalidation_0-auc:0.935206\n","[500]\tvalidation_0-auc:0.936\n","[550]\tvalidation_0-auc:0.936377\n","[600]\tvalidation_0-auc:0.936593\n","[650]\tvalidation_0-auc:0.93613\n","Stopping. Best iteration:\n","[597]\tvalidation_0-auc:0.93663\n","\n","Score not improved on adding M6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M6_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875212\n","[100]\tvalidation_0-auc:0.890219\n","[150]\tvalidation_0-auc:0.904616\n","[200]\tvalidation_0-auc:0.91478\n","[250]\tvalidation_0-auc:0.921612\n","[300]\tvalidation_0-auc:0.927898\n","[350]\tvalidation_0-auc:0.931907\n","[400]\tvalidation_0-auc:0.934042\n","[450]\tvalidation_0-auc:0.935901\n","[500]\tvalidation_0-auc:0.93719\n","[550]\tvalidation_0-auc:0.937487\n","[600]\tvalidation_0-auc:0.937851\n","[650]\tvalidation_0-auc:0.938021\n","[700]\tvalidation_0-auc:0.938219\n","[750]\tvalidation_0-auc:0.938226\n","[800]\tvalidation_0-auc:0.937947\n","Stopping. Best iteration:\n","[717]\tvalidation_0-auc:0.938345\n","\n","Score not improved on adding M6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M7_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875207\n","[100]\tvalidation_0-auc:0.890187\n","[150]\tvalidation_0-auc:0.904319\n","[200]\tvalidation_0-auc:0.914336\n","[250]\tvalidation_0-auc:0.921629\n","[300]\tvalidation_0-auc:0.927082\n","[350]\tvalidation_0-auc:0.930885\n","[400]\tvalidation_0-auc:0.932954\n","[450]\tvalidation_0-auc:0.934298\n","[500]\tvalidation_0-auc:0.935498\n","[550]\tvalidation_0-auc:0.935831\n","[600]\tvalidation_0-auc:0.935987\n","[650]\tvalidation_0-auc:0.936178\n","[700]\tvalidation_0-auc:0.935853\n","Stopping. Best iteration:\n","[623]\tvalidation_0-auc:0.936224\n","\n","Score not improved on adding M7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M7_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875025\n","[100]\tvalidation_0-auc:0.890346\n","[150]\tvalidation_0-auc:0.904601\n","[200]\tvalidation_0-auc:0.915193\n","[250]\tvalidation_0-auc:0.922235\n","[300]\tvalidation_0-auc:0.928323\n","[350]\tvalidation_0-auc:0.932166\n","[400]\tvalidation_0-auc:0.934483\n","[450]\tvalidation_0-auc:0.936068\n","[500]\tvalidation_0-auc:0.936969\n","[550]\tvalidation_0-auc:0.937271\n","[600]\tvalidation_0-auc:0.937695\n","[650]\tvalidation_0-auc:0.93791\n","[700]\tvalidation_0-auc:0.937947\n","[750]\tvalidation_0-auc:0.937832\n","Stopping. Best iteration:\n","[677]\tvalidation_0-auc:0.938082\n","\n","Score not improved on adding M7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M8_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875006\n","[100]\tvalidation_0-auc:0.88986\n","[150]\tvalidation_0-auc:0.904234\n","[200]\tvalidation_0-auc:0.914859\n","[250]\tvalidation_0-auc:0.922568\n","[300]\tvalidation_0-auc:0.928501\n","[350]\tvalidation_0-auc:0.932663\n","[400]\tvalidation_0-auc:0.93459\n","[450]\tvalidation_0-auc:0.935937\n","[500]\tvalidation_0-auc:0.936854\n","[550]\tvalidation_0-auc:0.937349\n","[600]\tvalidation_0-auc:0.937786\n","[650]\tvalidation_0-auc:0.937717\n","[700]\tvalidation_0-auc:0.937808\n","[750]\tvalidation_0-auc:0.937864\n","[800]\tvalidation_0-auc:0.937791\n","[850]\tvalidation_0-auc:0.937519\n","Stopping. Best iteration:\n","[756]\tvalidation_0-auc:0.937918\n","\n","Score not improved on adding M8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M8_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.874927\n","[100]\tvalidation_0-auc:0.890513\n","[150]\tvalidation_0-auc:0.904654\n","[200]\tvalidation_0-auc:0.915624\n","[250]\tvalidation_0-auc:0.923089\n","[300]\tvalidation_0-auc:0.929197\n","[350]\tvalidation_0-auc:0.933136\n","[400]\tvalidation_0-auc:0.935384\n","[450]\tvalidation_0-auc:0.936985\n","[500]\tvalidation_0-auc:0.937967\n","[550]\tvalidation_0-auc:0.938405\n","[600]\tvalidation_0-auc:0.938653\n","[650]\tvalidation_0-auc:0.938577\n","[700]\tvalidation_0-auc:0.938795\n","[750]\tvalidation_0-auc:0.938754\n","Stopping. Best iteration:\n","[696]\tvalidation_0-auc:0.938903\n","\n","Score improved on adding M8_uid_std new best score 0.938903\n","--------------------------------------------------------------------------------\n","Model no: 2_18\n","--------------------------------------------------------------------------------\n","Skipping M9_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 2_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'M9_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875015\n","[100]\tvalidation_0-auc:0.890495\n","[150]\tvalidation_0-auc:0.904618\n","[200]\tvalidation_0-auc:0.915177\n","[250]\tvalidation_0-auc:0.92262\n","[300]\tvalidation_0-auc:0.928978\n","[350]\tvalidation_0-auc:0.932987\n","[400]\tvalidation_0-auc:0.935023\n","[450]\tvalidation_0-auc:0.936527\n","[500]\tvalidation_0-auc:0.937443\n","[550]\tvalidation_0-auc:0.937963\n","[600]\tvalidation_0-auc:0.938231\n","[650]\tvalidation_0-auc:0.938327\n","[700]\tvalidation_0-auc:0.938159\n","[750]\tvalidation_0-auc:0.938335\n","Stopping. Best iteration:\n","[654]\tvalidation_0-auc:0.938412\n","\n","Score not improved on adding M9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_mean']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875437\n","[100]\tvalidation_0-auc:0.890498\n","[150]\tvalidation_0-auc:0.90487\n","[200]\tvalidation_0-auc:0.915353\n","[250]\tvalidation_0-auc:0.923006\n","[300]\tvalidation_0-auc:0.92929\n","[350]\tvalidation_0-auc:0.932794\n","[400]\tvalidation_0-auc:0.9351\n","[450]\tvalidation_0-auc:0.936509\n","[500]\tvalidation_0-auc:0.937433\n","[550]\tvalidation_0-auc:0.93805\n","[600]\tvalidation_0-auc:0.93834\n","[650]\tvalidation_0-auc:0.938238\n","[700]\tvalidation_0-auc:0.938076\n","Stopping. Best iteration:\n","[607]\tvalidation_0-auc:0.938447\n","\n","Score not improved on adding TransactionAmt_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std']\n","fitting model on 232 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875992\n","[100]\tvalidation_0-auc:0.890722\n","[150]\tvalidation_0-auc:0.90579\n","[200]\tvalidation_0-auc:0.916075\n","[250]\tvalidation_0-auc:0.9233\n","[300]\tvalidation_0-auc:0.929345\n","[350]\tvalidation_0-auc:0.933174\n","[400]\tvalidation_0-auc:0.935405\n","[450]\tvalidation_0-auc:0.936918\n","[500]\tvalidation_0-auc:0.938012\n","[550]\tvalidation_0-auc:0.938517\n","[600]\tvalidation_0-auc:0.938965\n","[650]\tvalidation_0-auc:0.938897\n","[700]\tvalidation_0-auc:0.938934\n","Stopping. Best iteration:\n","[612]\tvalidation_0-auc:0.939007\n","\n","Score improved on adding TransactionAmt_uid_std new best score 0.939007\n","--------------------------------------------------------------------------------\n","Best col in this iteration TransactionAmt_uid_std, --->   current best cols: ['M9_uid_mean', 'TransactionAmt_uid_std']\n","########################################################################################################################\n","Model no: 3_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'uid_FE']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.874597\n","[100]\tvalidation_0-auc:0.893101\n","[150]\tvalidation_0-auc:0.906579\n","[200]\tvalidation_0-auc:0.91711\n","[250]\tvalidation_0-auc:0.925805\n","[300]\tvalidation_0-auc:0.930944\n","[350]\tvalidation_0-auc:0.934832\n","[400]\tvalidation_0-auc:0.937213\n","[450]\tvalidation_0-auc:0.938569\n","[500]\tvalidation_0-auc:0.939467\n","[550]\tvalidation_0-auc:0.939922\n","[600]\tvalidation_0-auc:0.939999\n","[650]\tvalidation_0-auc:0.940021\n","[700]\tvalidation_0-auc:0.93997\n","Stopping. Best iteration:\n","[613]\tvalidation_0-auc:0.940144\n","\n","Score improved on adding uid_FE new best score 0.940144\n","--------------------------------------------------------------------------------\n","Model no: 3_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M1_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87362\n","[100]\tvalidation_0-auc:0.891587\n","[150]\tvalidation_0-auc:0.905258\n","[200]\tvalidation_0-auc:0.916556\n","[250]\tvalidation_0-auc:0.92513\n","[300]\tvalidation_0-auc:0.930653\n","[350]\tvalidation_0-auc:0.933996\n","[400]\tvalidation_0-auc:0.936422\n","[450]\tvalidation_0-auc:0.937831\n","[500]\tvalidation_0-auc:0.938635\n","[550]\tvalidation_0-auc:0.938967\n","[600]\tvalidation_0-auc:0.939296\n","[650]\tvalidation_0-auc:0.939581\n","[700]\tvalidation_0-auc:0.939371\n","[750]\tvalidation_0-auc:0.939208\n","Stopping. Best iteration:\n","[653]\tvalidation_0-auc:0.939597\n","\n","Score not improved on adding M1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M1_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873143\n","[100]\tvalidation_0-auc:0.891726\n","[150]\tvalidation_0-auc:0.90517\n","[200]\tvalidation_0-auc:0.916513\n","[250]\tvalidation_0-auc:0.925515\n","[300]\tvalidation_0-auc:0.930908\n","[350]\tvalidation_0-auc:0.934207\n","[400]\tvalidation_0-auc:0.936788\n","[450]\tvalidation_0-auc:0.938462\n","[500]\tvalidation_0-auc:0.939415\n","[550]\tvalidation_0-auc:0.939737\n","[600]\tvalidation_0-auc:0.940051\n","[650]\tvalidation_0-auc:0.940054\n","[700]\tvalidation_0-auc:0.939844\n","Stopping. Best iteration:\n","[615]\tvalidation_0-auc:0.940133\n","\n","Score not improved on adding M1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M2_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873169\n","[100]\tvalidation_0-auc:0.892198\n","[150]\tvalidation_0-auc:0.905704\n","[200]\tvalidation_0-auc:0.916718\n","[250]\tvalidation_0-auc:0.925231\n","[300]\tvalidation_0-auc:0.930331\n","[350]\tvalidation_0-auc:0.933643\n","[400]\tvalidation_0-auc:0.936246\n","[450]\tvalidation_0-auc:0.937783\n","[500]\tvalidation_0-auc:0.938504\n","[550]\tvalidation_0-auc:0.938871\n","[600]\tvalidation_0-auc:0.939153\n","[650]\tvalidation_0-auc:0.939213\n","[700]\tvalidation_0-auc:0.938994\n","Stopping. Best iteration:\n","[613]\tvalidation_0-auc:0.939298\n","\n","Score not improved on adding M2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M2_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873178\n","[100]\tvalidation_0-auc:0.892755\n","[150]\tvalidation_0-auc:0.905673\n","[200]\tvalidation_0-auc:0.91673\n","[250]\tvalidation_0-auc:0.925421\n","[300]\tvalidation_0-auc:0.930216\n","[350]\tvalidation_0-auc:0.933703\n","[400]\tvalidation_0-auc:0.936482\n","[450]\tvalidation_0-auc:0.938147\n","[500]\tvalidation_0-auc:0.939242\n","[550]\tvalidation_0-auc:0.939743\n","[600]\tvalidation_0-auc:0.939837\n","[650]\tvalidation_0-auc:0.939946\n","[700]\tvalidation_0-auc:0.939985\n","[750]\tvalidation_0-auc:0.939914\n","Stopping. Best iteration:\n","[681]\tvalidation_0-auc:0.94019\n","\n","Score improved on adding M2_uid_std new best score 0.94019\n","--------------------------------------------------------------------------------\n","Model no: 3_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M3_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873829\n","[100]\tvalidation_0-auc:0.892269\n","[150]\tvalidation_0-auc:0.905653\n","[200]\tvalidation_0-auc:0.916979\n","[250]\tvalidation_0-auc:0.926027\n","[300]\tvalidation_0-auc:0.930953\n","[350]\tvalidation_0-auc:0.934188\n","[400]\tvalidation_0-auc:0.936719\n","[450]\tvalidation_0-auc:0.93819\n","[500]\tvalidation_0-auc:0.938818\n","[550]\tvalidation_0-auc:0.93908\n","[600]\tvalidation_0-auc:0.93912\n","[650]\tvalidation_0-auc:0.939187\n","[700]\tvalidation_0-auc:0.938725\n","Stopping. Best iteration:\n","[646]\tvalidation_0-auc:0.939273\n","\n","Score not improved on adding M3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M3_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873169\n","[100]\tvalidation_0-auc:0.892585\n","[150]\tvalidation_0-auc:0.906109\n","[200]\tvalidation_0-auc:0.91715\n","[250]\tvalidation_0-auc:0.925283\n","[300]\tvalidation_0-auc:0.930724\n","[350]\tvalidation_0-auc:0.934469\n","[400]\tvalidation_0-auc:0.936918\n","[450]\tvalidation_0-auc:0.938115\n","[500]\tvalidation_0-auc:0.938764\n","[550]\tvalidation_0-auc:0.939014\n","[600]\tvalidation_0-auc:0.939057\n","[650]\tvalidation_0-auc:0.939227\n","[700]\tvalidation_0-auc:0.939256\n","[750]\tvalidation_0-auc:0.939268\n","Stopping. Best iteration:\n","[685]\tvalidation_0-auc:0.939413\n","\n","Score not improved on adding M3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M4_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872796\n","[100]\tvalidation_0-auc:0.892233\n","[150]\tvalidation_0-auc:0.906398\n","[200]\tvalidation_0-auc:0.916722\n","[250]\tvalidation_0-auc:0.925048\n","[300]\tvalidation_0-auc:0.930273\n","[350]\tvalidation_0-auc:0.933585\n","[400]\tvalidation_0-auc:0.93597\n","[450]\tvalidation_0-auc:0.936948\n","[500]\tvalidation_0-auc:0.93766\n","[550]\tvalidation_0-auc:0.938261\n","[600]\tvalidation_0-auc:0.938542\n","[650]\tvalidation_0-auc:0.938775\n","[700]\tvalidation_0-auc:0.93865\n","[750]\tvalidation_0-auc:0.938325\n","Stopping. Best iteration:\n","[657]\tvalidation_0-auc:0.938837\n","\n","Score not improved on adding M4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M4_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872916\n","[100]\tvalidation_0-auc:0.892709\n","[150]\tvalidation_0-auc:0.906122\n","[200]\tvalidation_0-auc:0.917108\n","[250]\tvalidation_0-auc:0.926108\n","[300]\tvalidation_0-auc:0.930938\n","[350]\tvalidation_0-auc:0.934266\n","[400]\tvalidation_0-auc:0.936729\n","[450]\tvalidation_0-auc:0.9383\n","[500]\tvalidation_0-auc:0.939041\n","[550]\tvalidation_0-auc:0.939585\n","[600]\tvalidation_0-auc:0.93965\n","[650]\tvalidation_0-auc:0.939608\n","[700]\tvalidation_0-auc:0.93971\n","[750]\tvalidation_0-auc:0.939573\n","[800]\tvalidation_0-auc:0.939315\n","Stopping. Best iteration:\n","[717]\tvalidation_0-auc:0.939782\n","\n","Score not improved on adding M4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872782\n","[100]\tvalidation_0-auc:0.892801\n","[150]\tvalidation_0-auc:0.906368\n","[200]\tvalidation_0-auc:0.916976\n","[250]\tvalidation_0-auc:0.926194\n","[300]\tvalidation_0-auc:0.931197\n","[350]\tvalidation_0-auc:0.934447\n","[400]\tvalidation_0-auc:0.936933\n","[450]\tvalidation_0-auc:0.93843\n","[500]\tvalidation_0-auc:0.939106\n","[550]\tvalidation_0-auc:0.939828\n","[600]\tvalidation_0-auc:0.940327\n","[650]\tvalidation_0-auc:0.940798\n","[700]\tvalidation_0-auc:0.940779\n","[750]\tvalidation_0-auc:0.940635\n","Stopping. Best iteration:\n","[675]\tvalidation_0-auc:0.940981\n","\n","Score improved on adding M5_uid_mean new best score 0.940981\n","--------------------------------------------------------------------------------\n","Model no: 3_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873426\n","[100]\tvalidation_0-auc:0.892559\n","[150]\tvalidation_0-auc:0.906247\n","[200]\tvalidation_0-auc:0.916869\n","[250]\tvalidation_0-auc:0.925601\n","[300]\tvalidation_0-auc:0.930957\n","[350]\tvalidation_0-auc:0.934372\n","[400]\tvalidation_0-auc:0.936661\n","[450]\tvalidation_0-auc:0.938073\n","[500]\tvalidation_0-auc:0.938777\n","[550]\tvalidation_0-auc:0.939309\n","[600]\tvalidation_0-auc:0.93959\n","[650]\tvalidation_0-auc:0.939744\n","[700]\tvalidation_0-auc:0.939663\n","[750]\tvalidation_0-auc:0.939566\n","Stopping. Best iteration:\n","[684]\tvalidation_0-auc:0.939865\n","\n","Score not improved on adding M5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M6_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873608\n","[100]\tvalidation_0-auc:0.891996\n","[150]\tvalidation_0-auc:0.905358\n","[200]\tvalidation_0-auc:0.916319\n","[250]\tvalidation_0-auc:0.924928\n","[300]\tvalidation_0-auc:0.930908\n","[350]\tvalidation_0-auc:0.934126\n","[400]\tvalidation_0-auc:0.936511\n","[450]\tvalidation_0-auc:0.937977\n","[500]\tvalidation_0-auc:0.938665\n","[550]\tvalidation_0-auc:0.938874\n","[600]\tvalidation_0-auc:0.938927\n","[650]\tvalidation_0-auc:0.938799\n","[700]\tvalidation_0-auc:0.938677\n","Stopping. Best iteration:\n","[614]\tvalidation_0-auc:0.939086\n","\n","Score not improved on adding M6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M6_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872932\n","[100]\tvalidation_0-auc:0.892664\n","[150]\tvalidation_0-auc:0.90607\n","[200]\tvalidation_0-auc:0.916978\n","[250]\tvalidation_0-auc:0.92565\n","[300]\tvalidation_0-auc:0.930782\n","[350]\tvalidation_0-auc:0.93392\n","[400]\tvalidation_0-auc:0.936239\n","[450]\tvalidation_0-auc:0.937676\n","[500]\tvalidation_0-auc:0.938408\n","[550]\tvalidation_0-auc:0.938751\n","[600]\tvalidation_0-auc:0.938985\n","[650]\tvalidation_0-auc:0.939054\n","[700]\tvalidation_0-auc:0.938905\n","[750]\tvalidation_0-auc:0.939049\n","Stopping. Best iteration:\n","[681]\tvalidation_0-auc:0.939195\n","\n","Score not improved on adding M6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M7_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873009\n","[100]\tvalidation_0-auc:0.891384\n","[150]\tvalidation_0-auc:0.905249\n","[200]\tvalidation_0-auc:0.916338\n","[250]\tvalidation_0-auc:0.925266\n","[300]\tvalidation_0-auc:0.93028\n","[350]\tvalidation_0-auc:0.933617\n","[400]\tvalidation_0-auc:0.936284\n","[450]\tvalidation_0-auc:0.937895\n","[500]\tvalidation_0-auc:0.938785\n","[550]\tvalidation_0-auc:0.939291\n","[600]\tvalidation_0-auc:0.939634\n","[650]\tvalidation_0-auc:0.939819\n","[700]\tvalidation_0-auc:0.939622\n","Stopping. Best iteration:\n","[646]\tvalidation_0-auc:0.939824\n","\n","Score not improved on adding M7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M7_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873776\n","[100]\tvalidation_0-auc:0.892214\n","[150]\tvalidation_0-auc:0.905745\n","[200]\tvalidation_0-auc:0.916292\n","[250]\tvalidation_0-auc:0.925147\n","[300]\tvalidation_0-auc:0.930234\n","[350]\tvalidation_0-auc:0.933698\n","[400]\tvalidation_0-auc:0.936152\n","[450]\tvalidation_0-auc:0.937809\n","[500]\tvalidation_0-auc:0.938556\n","[550]\tvalidation_0-auc:0.938968\n","[600]\tvalidation_0-auc:0.939183\n","[650]\tvalidation_0-auc:0.939171\n","[700]\tvalidation_0-auc:0.938933\n","Stopping. Best iteration:\n","[615]\tvalidation_0-auc:0.939328\n","\n","Score not improved on adding M7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M8_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873363\n","[100]\tvalidation_0-auc:0.891477\n","[150]\tvalidation_0-auc:0.905172\n","[200]\tvalidation_0-auc:0.916337\n","[250]\tvalidation_0-auc:0.925375\n","[300]\tvalidation_0-auc:0.930867\n","[350]\tvalidation_0-auc:0.934128\n","[400]\tvalidation_0-auc:0.936708\n","[450]\tvalidation_0-auc:0.937915\n","[500]\tvalidation_0-auc:0.938573\n","[550]\tvalidation_0-auc:0.939265\n","[600]\tvalidation_0-auc:0.939448\n","[650]\tvalidation_0-auc:0.939223\n","[700]\tvalidation_0-auc:0.939127\n","Stopping. Best iteration:\n","[606]\tvalidation_0-auc:0.939493\n","\n","Score not improved on adding M8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M8_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873561\n","[100]\tvalidation_0-auc:0.891677\n","[150]\tvalidation_0-auc:0.905633\n","[200]\tvalidation_0-auc:0.916549\n","[250]\tvalidation_0-auc:0.925398\n","[300]\tvalidation_0-auc:0.930984\n","[350]\tvalidation_0-auc:0.934336\n","[400]\tvalidation_0-auc:0.936907\n","[450]\tvalidation_0-auc:0.938351\n","[500]\tvalidation_0-auc:0.939318\n","[550]\tvalidation_0-auc:0.939424\n","[600]\tvalidation_0-auc:0.939547\n","[650]\tvalidation_0-auc:0.939725\n","[700]\tvalidation_0-auc:0.939479\n","[750]\tvalidation_0-auc:0.939303\n","Stopping. Best iteration:\n","[675]\tvalidation_0-auc:0.939763\n","\n","Score not improved on adding M8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_18\n","--------------------------------------------------------------------------------\n","Skipping M9_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 3_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M9_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.874225\n","[100]\tvalidation_0-auc:0.892245\n","[150]\tvalidation_0-auc:0.906092\n","[200]\tvalidation_0-auc:0.917056\n","[250]\tvalidation_0-auc:0.925701\n","[300]\tvalidation_0-auc:0.930303\n","[350]\tvalidation_0-auc:0.933959\n","[400]\tvalidation_0-auc:0.936246\n","[450]\tvalidation_0-auc:0.937919\n","[500]\tvalidation_0-auc:0.939017\n","[550]\tvalidation_0-auc:0.939674\n","[600]\tvalidation_0-auc:0.939882\n","[650]\tvalidation_0-auc:0.939954\n","[700]\tvalidation_0-auc:0.939933\n","Stopping. Best iteration:\n","[647]\tvalidation_0-auc:0.940072\n","\n","Score not improved on adding M9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'TransactionAmt_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809854\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873866\n","[100]\tvalidation_0-auc:0.8924\n","[150]\tvalidation_0-auc:0.906521\n","[200]\tvalidation_0-auc:0.9173\n","[250]\tvalidation_0-auc:0.926539\n","[300]\tvalidation_0-auc:0.93095\n","[350]\tvalidation_0-auc:0.934684\n","[400]\tvalidation_0-auc:0.937088\n","[450]\tvalidation_0-auc:0.938595\n","[500]\tvalidation_0-auc:0.939537\n","[550]\tvalidation_0-auc:0.939959\n","[600]\tvalidation_0-auc:0.940342\n","[650]\tvalidation_0-auc:0.940337\n","[700]\tvalidation_0-auc:0.940504\n","[750]\tvalidation_0-auc:0.94029\n","[800]\tvalidation_0-auc:0.940158\n","Stopping. Best iteration:\n","[720]\tvalidation_0-auc:0.940589\n","\n","Score not improved on adding TransactionAmt_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_21\n","--------------------------------------------------------------------------------\n","Skipping TransactionAmt_uid_std already an selected column\n","--------------------------------------------------------------------------------\n","Best col in this iteration M5_uid_mean, --->   current best cols: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean']\n","########################################################################################################################\n","Model no: 4_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'uid_FE']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.79674\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877999\n","[100]\tvalidation_0-auc:0.892769\n","[150]\tvalidation_0-auc:0.905097\n","[200]\tvalidation_0-auc:0.917107\n","[250]\tvalidation_0-auc:0.924706\n","[300]\tvalidation_0-auc:0.931266\n","[350]\tvalidation_0-auc:0.934906\n","[400]\tvalidation_0-auc:0.937493\n","[450]\tvalidation_0-auc:0.938761\n","[500]\tvalidation_0-auc:0.939421\n","[550]\tvalidation_0-auc:0.939972\n","[600]\tvalidation_0-auc:0.939975\n","[650]\tvalidation_0-auc:0.940271\n","[700]\tvalidation_0-auc:0.940441\n","[750]\tvalidation_0-auc:0.940406\n","[800]\tvalidation_0-auc:0.940652\n","[850]\tvalidation_0-auc:0.940759\n","[900]\tvalidation_0-auc:0.940756\n","Stopping. Best iteration:\n","[834]\tvalidation_0-auc:0.940843\n","\n","Score not improved on adding uid_FE\n","--------------------------------------------------------------------------------\n","Model no: 4_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M1_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803448\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876474\n","[100]\tvalidation_0-auc:0.890979\n","[150]\tvalidation_0-auc:0.903436\n","[200]\tvalidation_0-auc:0.915479\n","[250]\tvalidation_0-auc:0.923361\n","[300]\tvalidation_0-auc:0.929652\n","[350]\tvalidation_0-auc:0.933203\n","[400]\tvalidation_0-auc:0.935849\n","[450]\tvalidation_0-auc:0.937158\n","[500]\tvalidation_0-auc:0.937968\n","[550]\tvalidation_0-auc:0.93849\n","[600]\tvalidation_0-auc:0.939048\n","[650]\tvalidation_0-auc:0.939104\n","[700]\tvalidation_0-auc:0.939063\n","[750]\tvalidation_0-auc:0.938767\n","Stopping. Best iteration:\n","[684]\tvalidation_0-auc:0.939241\n","\n","Score not improved on adding M1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M1_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804115\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876566\n","[100]\tvalidation_0-auc:0.891284\n","[150]\tvalidation_0-auc:0.903943\n","[200]\tvalidation_0-auc:0.916871\n","[250]\tvalidation_0-auc:0.924312\n","[300]\tvalidation_0-auc:0.930764\n","[350]\tvalidation_0-auc:0.934308\n","[400]\tvalidation_0-auc:0.936865\n","[450]\tvalidation_0-auc:0.937967\n","[500]\tvalidation_0-auc:0.938958\n","[550]\tvalidation_0-auc:0.939527\n","[600]\tvalidation_0-auc:0.939736\n","[650]\tvalidation_0-auc:0.939784\n","[700]\tvalidation_0-auc:0.940029\n","[750]\tvalidation_0-auc:0.94005\n","[800]\tvalidation_0-auc:0.940047\n","[850]\tvalidation_0-auc:0.939875\n","[900]\tvalidation_0-auc:0.939519\n","Stopping. Best iteration:\n","[813]\tvalidation_0-auc:0.940172\n","\n","Score not improved on adding M1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M2_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804253\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876392\n","[100]\tvalidation_0-auc:0.891514\n","[150]\tvalidation_0-auc:0.904397\n","[200]\tvalidation_0-auc:0.916425\n","[250]\tvalidation_0-auc:0.924054\n","[300]\tvalidation_0-auc:0.930058\n","[350]\tvalidation_0-auc:0.933151\n","[400]\tvalidation_0-auc:0.935434\n","[450]\tvalidation_0-auc:0.936934\n","[500]\tvalidation_0-auc:0.937657\n","[550]\tvalidation_0-auc:0.938041\n","[600]\tvalidation_0-auc:0.938171\n","[650]\tvalidation_0-auc:0.9382\n","[700]\tvalidation_0-auc:0.938154\n","Stopping. Best iteration:\n","[644]\tvalidation_0-auc:0.938271\n","\n","Score not improved on adding M2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M2_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803564\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876908\n","[100]\tvalidation_0-auc:0.891845\n","[150]\tvalidation_0-auc:0.904274\n","[200]\tvalidation_0-auc:0.916634\n","[250]\tvalidation_0-auc:0.924175\n","[300]\tvalidation_0-auc:0.93085\n","[350]\tvalidation_0-auc:0.934284\n","[400]\tvalidation_0-auc:0.936815\n","[450]\tvalidation_0-auc:0.938133\n","[500]\tvalidation_0-auc:0.938936\n","[550]\tvalidation_0-auc:0.939263\n","[600]\tvalidation_0-auc:0.939478\n","[650]\tvalidation_0-auc:0.939386\n","[700]\tvalidation_0-auc:0.939572\n","[750]\tvalidation_0-auc:0.939448\n","[800]\tvalidation_0-auc:0.939423\n","Stopping. Best iteration:\n","[709]\tvalidation_0-auc:0.939643\n","\n","Score not improved on adding M2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M3_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.80414\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87667\n","[100]\tvalidation_0-auc:0.891285\n","[150]\tvalidation_0-auc:0.904469\n","[200]\tvalidation_0-auc:0.91658\n","[250]\tvalidation_0-auc:0.92432\n","[300]\tvalidation_0-auc:0.929991\n","[350]\tvalidation_0-auc:0.933444\n","[400]\tvalidation_0-auc:0.935661\n","[450]\tvalidation_0-auc:0.936967\n","[500]\tvalidation_0-auc:0.937862\n","[550]\tvalidation_0-auc:0.938618\n","[600]\tvalidation_0-auc:0.938657\n","[650]\tvalidation_0-auc:0.938914\n","[700]\tvalidation_0-auc:0.93918\n","[750]\tvalidation_0-auc:0.938997\n","[800]\tvalidation_0-auc:0.938998\n","Stopping. Best iteration:\n","[703]\tvalidation_0-auc:0.939205\n","\n","Score not improved on adding M3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M3_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.80352\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877988\n","[100]\tvalidation_0-auc:0.891246\n","[150]\tvalidation_0-auc:0.904136\n","[200]\tvalidation_0-auc:0.916513\n","[250]\tvalidation_0-auc:0.924347\n","[300]\tvalidation_0-auc:0.930812\n","[350]\tvalidation_0-auc:0.934147\n","[400]\tvalidation_0-auc:0.936586\n","[450]\tvalidation_0-auc:0.937937\n","[500]\tvalidation_0-auc:0.938842\n","[550]\tvalidation_0-auc:0.939449\n","[600]\tvalidation_0-auc:0.939843\n","[650]\tvalidation_0-auc:0.940161\n","[700]\tvalidation_0-auc:0.940292\n","[750]\tvalidation_0-auc:0.940374\n","[800]\tvalidation_0-auc:0.940188\n","Stopping. Best iteration:\n","[738]\tvalidation_0-auc:0.940402\n","\n","Score not improved on adding M3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M4_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.796737\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876427\n","[100]\tvalidation_0-auc:0.891335\n","[150]\tvalidation_0-auc:0.904522\n","[200]\tvalidation_0-auc:0.916585\n","[250]\tvalidation_0-auc:0.924558\n","[300]\tvalidation_0-auc:0.930631\n","[350]\tvalidation_0-auc:0.933858\n","[400]\tvalidation_0-auc:0.93625\n","[450]\tvalidation_0-auc:0.937604\n","[500]\tvalidation_0-auc:0.938456\n","[550]\tvalidation_0-auc:0.939106\n","[600]\tvalidation_0-auc:0.939178\n","[650]\tvalidation_0-auc:0.939558\n","[700]\tvalidation_0-auc:0.939933\n","[750]\tvalidation_0-auc:0.939896\n","[800]\tvalidation_0-auc:0.939802\n","Stopping. Best iteration:\n","[702]\tvalidation_0-auc:0.939992\n","\n","Score not improved on adding M4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M4_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803127\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87706\n","[100]\tvalidation_0-auc:0.891879\n","[150]\tvalidation_0-auc:0.904086\n","[200]\tvalidation_0-auc:0.916039\n","[250]\tvalidation_0-auc:0.923845\n","[300]\tvalidation_0-auc:0.930281\n","[350]\tvalidation_0-auc:0.933471\n","[400]\tvalidation_0-auc:0.936121\n","[450]\tvalidation_0-auc:0.937744\n","[500]\tvalidation_0-auc:0.938724\n","[550]\tvalidation_0-auc:0.939283\n","[600]\tvalidation_0-auc:0.939192\n","[650]\tvalidation_0-auc:0.939571\n","[700]\tvalidation_0-auc:0.939441\n","[750]\tvalidation_0-auc:0.939543\n","Stopping. Best iteration:\n","[653]\tvalidation_0-auc:0.939632\n","\n","Score not improved on adding M4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_10\n","--------------------------------------------------------------------------------\n","Skipping M5_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 4_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M5_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803818\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876437\n","[100]\tvalidation_0-auc:0.891132\n","[150]\tvalidation_0-auc:0.903812\n","[200]\tvalidation_0-auc:0.916197\n","[250]\tvalidation_0-auc:0.924295\n","[300]\tvalidation_0-auc:0.930914\n","[350]\tvalidation_0-auc:0.934383\n","[400]\tvalidation_0-auc:0.93706\n","[450]\tvalidation_0-auc:0.938304\n","[500]\tvalidation_0-auc:0.939317\n","[550]\tvalidation_0-auc:0.939826\n","[600]\tvalidation_0-auc:0.939782\n","[650]\tvalidation_0-auc:0.939966\n","[700]\tvalidation_0-auc:0.940191\n","[750]\tvalidation_0-auc:0.940409\n","[800]\tvalidation_0-auc:0.940382\n","[850]\tvalidation_0-auc:0.940224\n","Stopping. Best iteration:\n","[762]\tvalidation_0-auc:0.940511\n","\n","Score not improved on adding M5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M6_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804023\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87655\n","[100]\tvalidation_0-auc:0.891653\n","[150]\tvalidation_0-auc:0.904073\n","[200]\tvalidation_0-auc:0.91607\n","[250]\tvalidation_0-auc:0.924214\n","[300]\tvalidation_0-auc:0.930573\n","[350]\tvalidation_0-auc:0.933632\n","[400]\tvalidation_0-auc:0.936348\n","[450]\tvalidation_0-auc:0.937636\n","[500]\tvalidation_0-auc:0.93863\n","[550]\tvalidation_0-auc:0.939125\n","[600]\tvalidation_0-auc:0.93911\n","[650]\tvalidation_0-auc:0.93932\n","[700]\tvalidation_0-auc:0.939677\n","[750]\tvalidation_0-auc:0.939903\n","[800]\tvalidation_0-auc:0.939744\n","[850]\tvalidation_0-auc:0.939764\n","Stopping. Best iteration:\n","[762]\tvalidation_0-auc:0.939938\n","\n","Score not improved on adding M6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M6_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803826\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876454\n","[100]\tvalidation_0-auc:0.891295\n","[150]\tvalidation_0-auc:0.90376\n","[200]\tvalidation_0-auc:0.916103\n","[250]\tvalidation_0-auc:0.923593\n","[300]\tvalidation_0-auc:0.929831\n","[350]\tvalidation_0-auc:0.933199\n","[400]\tvalidation_0-auc:0.9359\n","[450]\tvalidation_0-auc:0.937333\n","[500]\tvalidation_0-auc:0.938306\n","[550]\tvalidation_0-auc:0.938967\n","[600]\tvalidation_0-auc:0.939216\n","[650]\tvalidation_0-auc:0.939426\n","[700]\tvalidation_0-auc:0.939745\n","[750]\tvalidation_0-auc:0.939898\n","[800]\tvalidation_0-auc:0.939784\n","[850]\tvalidation_0-auc:0.939866\n","[900]\tvalidation_0-auc:0.939508\n","Stopping. Best iteration:\n","[821]\tvalidation_0-auc:0.940025\n","\n","Score not improved on adding M6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M7_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804199\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876127\n","[100]\tvalidation_0-auc:0.891439\n","[150]\tvalidation_0-auc:0.903695\n","[200]\tvalidation_0-auc:0.916052\n","[250]\tvalidation_0-auc:0.923197\n","[300]\tvalidation_0-auc:0.929846\n","[350]\tvalidation_0-auc:0.933545\n","[400]\tvalidation_0-auc:0.935863\n","[450]\tvalidation_0-auc:0.937496\n","[500]\tvalidation_0-auc:0.938513\n","[550]\tvalidation_0-auc:0.938968\n","[600]\tvalidation_0-auc:0.939112\n","[650]\tvalidation_0-auc:0.93914\n","[700]\tvalidation_0-auc:0.93917\n","[750]\tvalidation_0-auc:0.938951\n","[800]\tvalidation_0-auc:0.938991\n","Stopping. Best iteration:\n","[709]\tvalidation_0-auc:0.939224\n","\n","Score not improved on adding M7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M7_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803677\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876536\n","[100]\tvalidation_0-auc:0.891378\n","[150]\tvalidation_0-auc:0.903944\n","[200]\tvalidation_0-auc:0.916779\n","[250]\tvalidation_0-auc:0.924799\n","[300]\tvalidation_0-auc:0.930833\n","[350]\tvalidation_0-auc:0.933933\n","[400]\tvalidation_0-auc:0.936363\n","[450]\tvalidation_0-auc:0.937684\n","[500]\tvalidation_0-auc:0.938646\n","[550]\tvalidation_0-auc:0.938999\n","[600]\tvalidation_0-auc:0.939122\n","[650]\tvalidation_0-auc:0.93934\n","[700]\tvalidation_0-auc:0.939377\n","[750]\tvalidation_0-auc:0.939164\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.939466\n","\n","Score not improved on adding M7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M8_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803922\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876169\n","[100]\tvalidation_0-auc:0.891371\n","[150]\tvalidation_0-auc:0.903726\n","[200]\tvalidation_0-auc:0.915959\n","[250]\tvalidation_0-auc:0.923263\n","[300]\tvalidation_0-auc:0.929487\n","[350]\tvalidation_0-auc:0.932977\n","[400]\tvalidation_0-auc:0.935723\n","[450]\tvalidation_0-auc:0.93728\n","[500]\tvalidation_0-auc:0.93833\n","[550]\tvalidation_0-auc:0.938973\n","[600]\tvalidation_0-auc:0.93904\n","[650]\tvalidation_0-auc:0.939087\n","Stopping. Best iteration:\n","[581]\tvalidation_0-auc:0.939158\n","\n","Score not improved on adding M8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M8_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803714\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877119\n","[100]\tvalidation_0-auc:0.8916\n","[150]\tvalidation_0-auc:0.904369\n","[200]\tvalidation_0-auc:0.916946\n","[250]\tvalidation_0-auc:0.924268\n","[300]\tvalidation_0-auc:0.930437\n","[350]\tvalidation_0-auc:0.933344\n","[400]\tvalidation_0-auc:0.935865\n","[450]\tvalidation_0-auc:0.937063\n","[500]\tvalidation_0-auc:0.937817\n","[550]\tvalidation_0-auc:0.938386\n","[600]\tvalidation_0-auc:0.938474\n","[650]\tvalidation_0-auc:0.938686\n","[700]\tvalidation_0-auc:0.939007\n","[750]\tvalidation_0-auc:0.938904\n","[800]\tvalidation_0-auc:0.938934\n","Stopping. Best iteration:\n","[704]\tvalidation_0-auc:0.939026\n","\n","Score not improved on adding M8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_18\n","--------------------------------------------------------------------------------\n","Skipping M9_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 4_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'M9_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803789\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.876819\n","[100]\tvalidation_0-auc:0.891751\n","[150]\tvalidation_0-auc:0.904444\n","[200]\tvalidation_0-auc:0.916795\n","[250]\tvalidation_0-auc:0.924332\n","[300]\tvalidation_0-auc:0.931262\n","[350]\tvalidation_0-auc:0.934602\n","[400]\tvalidation_0-auc:0.937165\n","[450]\tvalidation_0-auc:0.938372\n","[500]\tvalidation_0-auc:0.939231\n","[550]\tvalidation_0-auc:0.939582\n","[600]\tvalidation_0-auc:0.939827\n","[650]\tvalidation_0-auc:0.940018\n","[700]\tvalidation_0-auc:0.940199\n","[750]\tvalidation_0-auc:0.94018\n","Stopping. Best iteration:\n","[696]\tvalidation_0-auc:0.940259\n","\n","Score not improved on adding M9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean', 'TransactionAmt_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.790886\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877701\n","[100]\tvalidation_0-auc:0.892352\n","[150]\tvalidation_0-auc:0.904587\n","[200]\tvalidation_0-auc:0.91759\n","[250]\tvalidation_0-auc:0.926028\n","[300]\tvalidation_0-auc:0.932098\n","[350]\tvalidation_0-auc:0.935157\n","[400]\tvalidation_0-auc:0.937524\n","[450]\tvalidation_0-auc:0.938814\n","[500]\tvalidation_0-auc:0.93972\n","[550]\tvalidation_0-auc:0.940121\n","[600]\tvalidation_0-auc:0.940358\n","[650]\tvalidation_0-auc:0.940349\n","[700]\tvalidation_0-auc:0.940566\n","[750]\tvalidation_0-auc:0.940611\n","[800]\tvalidation_0-auc:0.940628\n","[850]\tvalidation_0-auc:0.940509\n","Stopping. Best iteration:\n","[792]\tvalidation_0-auc:0.94074\n","\n","Score not improved on adding TransactionAmt_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_21\n","--------------------------------------------------------------------------------\n","Skipping TransactionAmt_uid_std already an selected column\n","--------------------------------------------------------------------------------\n","None of the columns improving the score in this iteration\n","########################################################################################################################\n","Useful new features : ['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean']\n","Best score : 0.940981\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0zfuVoASMPIT"},"source":["## FSS on group2"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MAynjZHBEFWL","executionInfo":{"status":"ok","timestamp":1617513967241,"user_tz":0,"elapsed":14099,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"58cbe839-222b-4bb6-f979-dcf10e8a9a62"},"source":["old_cols2 = [col for col in X_train.columns if col not in rem_cols and col not in new_feats]\n","old_cols2.extend(['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean'])\n","print(len(old_cols2))"],"execution_count":23,"outputs":[{"output_type":"stream","text":["232\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xxjNFWB6EFde","executionInfo":{"status":"ok","timestamp":1617521785733,"user_tz":0,"elapsed":7459156,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"5d9b4e5e-21b4-44c0-e42e-76f8ebe7820a"},"source":["forward_feat_selection(new_cols=new_feat2,old_cols=old_cols2,best_score=0.940981)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Forward feature selection on 30 features <-->  current best score 0.940981 with 232 features\n","Model no: 1_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['D1_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872521\n","[100]\tvalidation_0-auc:0.893938\n","[150]\tvalidation_0-auc:0.908054\n","[200]\tvalidation_0-auc:0.919011\n","[250]\tvalidation_0-auc:0.928352\n","[300]\tvalidation_0-auc:0.933794\n","[350]\tvalidation_0-auc:0.937339\n","[400]\tvalidation_0-auc:0.939436\n","[450]\tvalidation_0-auc:0.940756\n","[500]\tvalidation_0-auc:0.941295\n","[550]\tvalidation_0-auc:0.941738\n","[600]\tvalidation_0-auc:0.941949\n","[650]\tvalidation_0-auc:0.942078\n","[700]\tvalidation_0-auc:0.942096\n","[750]\tvalidation_0-auc:0.942042\n","Stopping. Best iteration:\n","[678]\tvalidation_0-auc:0.942245\n","\n","Score improved on adding D1_uid_mean new best score 0.942245\n","--------------------------------------------------------------------------------\n","Model no: 1_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['D1_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.875399\n","[100]\tvalidation_0-auc:0.895368\n","[150]\tvalidation_0-auc:0.909543\n","[200]\tvalidation_0-auc:0.921029\n","[250]\tvalidation_0-auc:0.92935\n","[300]\tvalidation_0-auc:0.935412\n","[350]\tvalidation_0-auc:0.938711\n","[400]\tvalidation_0-auc:0.94079\n","[450]\tvalidation_0-auc:0.941592\n","[500]\tvalidation_0-auc:0.942484\n","[550]\tvalidation_0-auc:0.942877\n","[600]\tvalidation_0-auc:0.942828\n","[650]\tvalidation_0-auc:0.942659\n","Stopping. Best iteration:\n","[569]\tvalidation_0-auc:0.942946\n","\n","Score improved on adding D1_uid_std new best score 0.942946\n","--------------------------------------------------------------------------------\n","Model no: 1_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873703\n","[100]\tvalidation_0-auc:0.89626\n","[150]\tvalidation_0-auc:0.911366\n","[200]\tvalidation_0-auc:0.922491\n","[250]\tvalidation_0-auc:0.931019\n","[300]\tvalidation_0-auc:0.936668\n","[350]\tvalidation_0-auc:0.93969\n","[400]\tvalidation_0-auc:0.941363\n","[450]\tvalidation_0-auc:0.942386\n","[500]\tvalidation_0-auc:0.942979\n","[550]\tvalidation_0-auc:0.943455\n","[600]\tvalidation_0-auc:0.9435\n","[650]\tvalidation_0-auc:0.943563\n","[700]\tvalidation_0-auc:0.943571\n","[750]\tvalidation_0-auc:0.94333\n","Stopping. Best iteration:\n","[684]\tvalidation_0-auc:0.94365\n","\n","Score improved on adding D2_uid_mean new best score 0.94365\n","--------------------------------------------------------------------------------\n","Model no: 1_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.874802\n","[100]\tvalidation_0-auc:0.896391\n","[150]\tvalidation_0-auc:0.910124\n","[200]\tvalidation_0-auc:0.921394\n","[250]\tvalidation_0-auc:0.929021\n","[300]\tvalidation_0-auc:0.934362\n","[350]\tvalidation_0-auc:0.937711\n","[400]\tvalidation_0-auc:0.93956\n","[450]\tvalidation_0-auc:0.940604\n","[500]\tvalidation_0-auc:0.941298\n","[550]\tvalidation_0-auc:0.941775\n","[600]\tvalidation_0-auc:0.941796\n","[650]\tvalidation_0-auc:0.941656\n","Stopping. Best iteration:\n","[591]\tvalidation_0-auc:0.941813\n","\n","Score not improved on adding D2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['D3_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877778\n","[100]\tvalidation_0-auc:0.898987\n","[150]\tvalidation_0-auc:0.912691\n","[200]\tvalidation_0-auc:0.922409\n","[250]\tvalidation_0-auc:0.929779\n","[300]\tvalidation_0-auc:0.935187\n","[350]\tvalidation_0-auc:0.938216\n","[400]\tvalidation_0-auc:0.940202\n","[450]\tvalidation_0-auc:0.941819\n","[500]\tvalidation_0-auc:0.942377\n","[550]\tvalidation_0-auc:0.942687\n","[600]\tvalidation_0-auc:0.942962\n","[650]\tvalidation_0-auc:0.942947\n","[700]\tvalidation_0-auc:0.942964\n","Stopping. Best iteration:\n","[613]\tvalidation_0-auc:0.94307\n","\n","Score not improved on adding D3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['D3_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872078\n","[100]\tvalidation_0-auc:0.89392\n","[150]\tvalidation_0-auc:0.90777\n","[200]\tvalidation_0-auc:0.918177\n","[250]\tvalidation_0-auc:0.927085\n","[300]\tvalidation_0-auc:0.931954\n","[350]\tvalidation_0-auc:0.93568\n","[400]\tvalidation_0-auc:0.937861\n","[450]\tvalidation_0-auc:0.939059\n","[500]\tvalidation_0-auc:0.939572\n","[550]\tvalidation_0-auc:0.939878\n","[600]\tvalidation_0-auc:0.940124\n","[650]\tvalidation_0-auc:0.940348\n","[700]\tvalidation_0-auc:0.940441\n","[750]\tvalidation_0-auc:0.940368\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.940461\n","\n","Score not improved on adding D3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['D4_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.873016\n","[100]\tvalidation_0-auc:0.892661\n","[150]\tvalidation_0-auc:0.906766\n","[200]\tvalidation_0-auc:0.917417\n","[250]\tvalidation_0-auc:0.926451\n","[300]\tvalidation_0-auc:0.931863\n","[350]\tvalidation_0-auc:0.935456\n","[400]\tvalidation_0-auc:0.937763\n","[450]\tvalidation_0-auc:0.939106\n","[500]\tvalidation_0-auc:0.939872\n","[550]\tvalidation_0-auc:0.940372\n","[600]\tvalidation_0-auc:0.940363\n","Stopping. Best iteration:\n","[540]\tvalidation_0-auc:0.940456\n","\n","Score not improved on adding D4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['D4_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872321\n","[100]\tvalidation_0-auc:0.892521\n","[150]\tvalidation_0-auc:0.906676\n","[200]\tvalidation_0-auc:0.918159\n","[250]\tvalidation_0-auc:0.926563\n","[300]\tvalidation_0-auc:0.931955\n","[350]\tvalidation_0-auc:0.934961\n","[400]\tvalidation_0-auc:0.93738\n","[450]\tvalidation_0-auc:0.938596\n","[500]\tvalidation_0-auc:0.939188\n","[550]\tvalidation_0-auc:0.939517\n","[600]\tvalidation_0-auc:0.939673\n","[650]\tvalidation_0-auc:0.939801\n","[700]\tvalidation_0-auc:0.939931\n","[750]\tvalidation_0-auc:0.939695\n","[800]\tvalidation_0-auc:0.939626\n","Stopping. Best iteration:\n","[702]\tvalidation_0-auc:0.939948\n","\n","Score not improved on adding D4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['D5_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.874318\n","[100]\tvalidation_0-auc:0.893804\n","[150]\tvalidation_0-auc:0.907199\n","[200]\tvalidation_0-auc:0.918031\n","[250]\tvalidation_0-auc:0.926224\n","[300]\tvalidation_0-auc:0.931689\n","[350]\tvalidation_0-auc:0.934894\n","[400]\tvalidation_0-auc:0.937362\n","[450]\tvalidation_0-auc:0.938519\n","[500]\tvalidation_0-auc:0.938949\n","[550]\tvalidation_0-auc:0.939601\n","[600]\tvalidation_0-auc:0.939558\n","[650]\tvalidation_0-auc:0.939495\n","Stopping. Best iteration:\n","[576]\tvalidation_0-auc:0.939721\n","\n","Score not improved on adding D5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['D5_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871955\n","[100]\tvalidation_0-auc:0.892965\n","[150]\tvalidation_0-auc:0.90645\n","[200]\tvalidation_0-auc:0.917594\n","[250]\tvalidation_0-auc:0.926747\n","[300]\tvalidation_0-auc:0.932155\n","[350]\tvalidation_0-auc:0.935893\n","[400]\tvalidation_0-auc:0.938101\n","[450]\tvalidation_0-auc:0.93919\n","[500]\tvalidation_0-auc:0.939955\n","[550]\tvalidation_0-auc:0.940534\n","[600]\tvalidation_0-auc:0.940702\n","[650]\tvalidation_0-auc:0.940856\n","[700]\tvalidation_0-auc:0.940871\n","[750]\tvalidation_0-auc:0.940678\n","Stopping. Best iteration:\n","[677]\tvalidation_0-auc:0.941009\n","\n","Score not improved on adding D5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['D6_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871724\n","[100]\tvalidation_0-auc:0.891531\n","[150]\tvalidation_0-auc:0.905383\n","[200]\tvalidation_0-auc:0.916494\n","[250]\tvalidation_0-auc:0.925586\n","[300]\tvalidation_0-auc:0.930848\n","[350]\tvalidation_0-auc:0.934351\n","[400]\tvalidation_0-auc:0.936686\n","[450]\tvalidation_0-auc:0.938402\n","[500]\tvalidation_0-auc:0.939066\n","[550]\tvalidation_0-auc:0.939353\n","[600]\tvalidation_0-auc:0.939773\n","[650]\tvalidation_0-auc:0.939912\n","[700]\tvalidation_0-auc:0.94001\n","[750]\tvalidation_0-auc:0.939857\n","Stopping. Best iteration:\n","[682]\tvalidation_0-auc:0.940131\n","\n","Score not improved on adding D6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['D6_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871833\n","[100]\tvalidation_0-auc:0.891917\n","[150]\tvalidation_0-auc:0.905484\n","[200]\tvalidation_0-auc:0.916344\n","[250]\tvalidation_0-auc:0.925318\n","[300]\tvalidation_0-auc:0.930709\n","[350]\tvalidation_0-auc:0.934314\n","[400]\tvalidation_0-auc:0.936498\n","[450]\tvalidation_0-auc:0.938255\n","[500]\tvalidation_0-auc:0.939262\n","[550]\tvalidation_0-auc:0.939684\n","[600]\tvalidation_0-auc:0.939722\n","[650]\tvalidation_0-auc:0.940028\n","[700]\tvalidation_0-auc:0.939721\n","[750]\tvalidation_0-auc:0.939531\n","Stopping. Best iteration:\n","[653]\tvalidation_0-auc:0.940032\n","\n","Score not improved on adding D6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['D7_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871983\n","[100]\tvalidation_0-auc:0.89152\n","[150]\tvalidation_0-auc:0.905478\n","[200]\tvalidation_0-auc:0.916862\n","[250]\tvalidation_0-auc:0.925685\n","[300]\tvalidation_0-auc:0.931171\n","[350]\tvalidation_0-auc:0.934454\n","[400]\tvalidation_0-auc:0.936552\n","[450]\tvalidation_0-auc:0.938045\n","[500]\tvalidation_0-auc:0.939322\n","[550]\tvalidation_0-auc:0.93977\n","[600]\tvalidation_0-auc:0.940004\n","[650]\tvalidation_0-auc:0.940056\n","[700]\tvalidation_0-auc:0.939899\n","[750]\tvalidation_0-auc:0.9399\n","Stopping. Best iteration:\n","[677]\tvalidation_0-auc:0.940213\n","\n","Score not improved on adding D7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['D7_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872071\n","[100]\tvalidation_0-auc:0.891839\n","[150]\tvalidation_0-auc:0.905676\n","[200]\tvalidation_0-auc:0.916631\n","[250]\tvalidation_0-auc:0.92599\n","[300]\tvalidation_0-auc:0.931656\n","[350]\tvalidation_0-auc:0.935001\n","[400]\tvalidation_0-auc:0.937438\n","[450]\tvalidation_0-auc:0.93887\n","[500]\tvalidation_0-auc:0.939649\n","[550]\tvalidation_0-auc:0.940053\n","[600]\tvalidation_0-auc:0.940189\n","[650]\tvalidation_0-auc:0.940392\n","[700]\tvalidation_0-auc:0.940573\n","[750]\tvalidation_0-auc:0.940455\n","Stopping. Best iteration:\n","[677]\tvalidation_0-auc:0.940613\n","\n","Score not improved on adding D7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['D8_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871941\n","[100]\tvalidation_0-auc:0.890919\n","[150]\tvalidation_0-auc:0.905326\n","[200]\tvalidation_0-auc:0.915911\n","[250]\tvalidation_0-auc:0.924936\n","[300]\tvalidation_0-auc:0.930736\n","[350]\tvalidation_0-auc:0.93405\n","[400]\tvalidation_0-auc:0.936457\n","[450]\tvalidation_0-auc:0.938076\n","[500]\tvalidation_0-auc:0.93887\n","[550]\tvalidation_0-auc:0.939527\n","[600]\tvalidation_0-auc:0.939533\n","[650]\tvalidation_0-auc:0.939319\n","Stopping. Best iteration:\n","[592]\tvalidation_0-auc:0.939564\n","\n","Score not improved on adding D8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['D8_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871708\n","[100]\tvalidation_0-auc:0.891786\n","[150]\tvalidation_0-auc:0.905631\n","[200]\tvalidation_0-auc:0.916743\n","[250]\tvalidation_0-auc:0.9256\n","[300]\tvalidation_0-auc:0.931165\n","[350]\tvalidation_0-auc:0.934614\n","[400]\tvalidation_0-auc:0.937075\n","[450]\tvalidation_0-auc:0.938506\n","[500]\tvalidation_0-auc:0.939275\n","[550]\tvalidation_0-auc:0.939837\n","[600]\tvalidation_0-auc:0.940326\n","[650]\tvalidation_0-auc:0.940633\n","[700]\tvalidation_0-auc:0.940612\n","[750]\tvalidation_0-auc:0.940389\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.940765\n","\n","Score not improved on adding D8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['D9_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872166\n","[100]\tvalidation_0-auc:0.89179\n","[150]\tvalidation_0-auc:0.905473\n","[200]\tvalidation_0-auc:0.916496\n","[250]\tvalidation_0-auc:0.925414\n","[300]\tvalidation_0-auc:0.931278\n","[350]\tvalidation_0-auc:0.934976\n","[400]\tvalidation_0-auc:0.937479\n","[450]\tvalidation_0-auc:0.938871\n","[500]\tvalidation_0-auc:0.939602\n","[550]\tvalidation_0-auc:0.94016\n","[600]\tvalidation_0-auc:0.94042\n","[650]\tvalidation_0-auc:0.940799\n","[700]\tvalidation_0-auc:0.94091\n","[750]\tvalidation_0-auc:0.940931\n","[800]\tvalidation_0-auc:0.94062\n","Stopping. Best iteration:\n","[746]\tvalidation_0-auc:0.940997\n","\n","Score not improved on adding D9_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['D9_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871589\n","[100]\tvalidation_0-auc:0.890953\n","[150]\tvalidation_0-auc:0.904988\n","[200]\tvalidation_0-auc:0.916166\n","[250]\tvalidation_0-auc:0.924934\n","[300]\tvalidation_0-auc:0.930389\n","[350]\tvalidation_0-auc:0.934052\n","[400]\tvalidation_0-auc:0.936247\n","[450]\tvalidation_0-auc:0.937592\n","[500]\tvalidation_0-auc:0.938258\n","[550]\tvalidation_0-auc:0.93887\n","[600]\tvalidation_0-auc:0.939188\n","[650]\tvalidation_0-auc:0.939212\n","[700]\tvalidation_0-auc:0.939052\n","Stopping. Best iteration:\n","[639]\tvalidation_0-auc:0.939283\n","\n","Score not improved on adding D9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['D10_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872758\n","[100]\tvalidation_0-auc:0.89309\n","[150]\tvalidation_0-auc:0.907073\n","[200]\tvalidation_0-auc:0.918372\n","[250]\tvalidation_0-auc:0.926882\n","[300]\tvalidation_0-auc:0.932442\n","[350]\tvalidation_0-auc:0.935704\n","[400]\tvalidation_0-auc:0.938102\n","[450]\tvalidation_0-auc:0.939288\n","[500]\tvalidation_0-auc:0.940002\n","[550]\tvalidation_0-auc:0.94048\n","[600]\tvalidation_0-auc:0.940852\n","[650]\tvalidation_0-auc:0.941132\n","[700]\tvalidation_0-auc:0.940929\n","Stopping. Best iteration:\n","[648]\tvalidation_0-auc:0.941196\n","\n","Score not improved on adding D10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['D10_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872398\n","[100]\tvalidation_0-auc:0.89317\n","[150]\tvalidation_0-auc:0.908671\n","[200]\tvalidation_0-auc:0.920127\n","[250]\tvalidation_0-auc:0.928946\n","[300]\tvalidation_0-auc:0.934222\n","[350]\tvalidation_0-auc:0.937596\n","[400]\tvalidation_0-auc:0.939564\n","[450]\tvalidation_0-auc:0.940425\n","[500]\tvalidation_0-auc:0.941014\n","[550]\tvalidation_0-auc:0.94125\n","[600]\tvalidation_0-auc:0.941227\n","[650]\tvalidation_0-auc:0.941351\n","[700]\tvalidation_0-auc:0.941239\n","[750]\tvalidation_0-auc:0.940974\n","Stopping. Best iteration:\n","[683]\tvalidation_0-auc:0.941405\n","\n","Score not improved on adding D10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['D11_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872618\n","[100]\tvalidation_0-auc:0.893658\n","[150]\tvalidation_0-auc:0.907939\n","[200]\tvalidation_0-auc:0.919512\n","[250]\tvalidation_0-auc:0.927816\n","[300]\tvalidation_0-auc:0.933284\n","[350]\tvalidation_0-auc:0.936752\n","[400]\tvalidation_0-auc:0.939271\n","[450]\tvalidation_0-auc:0.940714\n","[500]\tvalidation_0-auc:0.941202\n","[550]\tvalidation_0-auc:0.941505\n","[600]\tvalidation_0-auc:0.941838\n","[650]\tvalidation_0-auc:0.94197\n","[700]\tvalidation_0-auc:0.941879\n","Stopping. Best iteration:\n","[616]\tvalidation_0-auc:0.942046\n","\n","Score not improved on adding D11_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['D11_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871929\n","[100]\tvalidation_0-auc:0.894656\n","[150]\tvalidation_0-auc:0.910102\n","[200]\tvalidation_0-auc:0.921458\n","[250]\tvalidation_0-auc:0.929896\n","[300]\tvalidation_0-auc:0.935474\n","[350]\tvalidation_0-auc:0.938637\n","[400]\tvalidation_0-auc:0.940755\n","[450]\tvalidation_0-auc:0.94168\n","[500]\tvalidation_0-auc:0.942099\n","[550]\tvalidation_0-auc:0.942365\n","[600]\tvalidation_0-auc:0.94243\n","[650]\tvalidation_0-auc:0.942355\n","[700]\tvalidation_0-auc:0.942077\n","Stopping. Best iteration:\n","[603]\tvalidation_0-auc:0.942483\n","\n","Score not improved on adding D11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['D12_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871897\n","[100]\tvalidation_0-auc:0.891829\n","[150]\tvalidation_0-auc:0.90521\n","[200]\tvalidation_0-auc:0.916095\n","[250]\tvalidation_0-auc:0.925107\n","[300]\tvalidation_0-auc:0.930777\n","[350]\tvalidation_0-auc:0.934614\n","[400]\tvalidation_0-auc:0.937062\n","[450]\tvalidation_0-auc:0.938564\n","[500]\tvalidation_0-auc:0.939169\n","[550]\tvalidation_0-auc:0.939698\n","[600]\tvalidation_0-auc:0.939937\n","[650]\tvalidation_0-auc:0.940113\n","[700]\tvalidation_0-auc:0.940136\n","[750]\tvalidation_0-auc:0.939963\n","Stopping. Best iteration:\n","[678]\tvalidation_0-auc:0.940401\n","\n","Score not improved on adding D12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['D12_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871644\n","[100]\tvalidation_0-auc:0.891761\n","[150]\tvalidation_0-auc:0.905495\n","[200]\tvalidation_0-auc:0.91659\n","[250]\tvalidation_0-auc:0.925811\n","[300]\tvalidation_0-auc:0.931311\n","[350]\tvalidation_0-auc:0.9346\n","[400]\tvalidation_0-auc:0.937074\n","[450]\tvalidation_0-auc:0.938556\n","[500]\tvalidation_0-auc:0.939445\n","[550]\tvalidation_0-auc:0.939875\n","[600]\tvalidation_0-auc:0.940107\n","[650]\tvalidation_0-auc:0.940181\n","[700]\tvalidation_0-auc:0.940319\n","[750]\tvalidation_0-auc:0.940311\n","[800]\tvalidation_0-auc:0.940106\n","Stopping. Best iteration:\n","[719]\tvalidation_0-auc:0.940451\n","\n","Score not improved on adding D12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_25\n","--------------------------------------------------------------------------------\n","Adding new features: ['D13_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871658\n","[100]\tvalidation_0-auc:0.891123\n","[150]\tvalidation_0-auc:0.905347\n","[200]\tvalidation_0-auc:0.916665\n","[250]\tvalidation_0-auc:0.92541\n","[300]\tvalidation_0-auc:0.931162\n","[350]\tvalidation_0-auc:0.93453\n","[400]\tvalidation_0-auc:0.936705\n","[450]\tvalidation_0-auc:0.938294\n","[500]\tvalidation_0-auc:0.938867\n","[550]\tvalidation_0-auc:0.93933\n","[600]\tvalidation_0-auc:0.939298\n","Stopping. Best iteration:\n","[544]\tvalidation_0-auc:0.939361\n","\n","Score not improved on adding D13_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['D13_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871183\n","[100]\tvalidation_0-auc:0.89143\n","[150]\tvalidation_0-auc:0.905677\n","[200]\tvalidation_0-auc:0.916829\n","[250]\tvalidation_0-auc:0.925819\n","[300]\tvalidation_0-auc:0.931595\n","[350]\tvalidation_0-auc:0.934786\n","[400]\tvalidation_0-auc:0.937198\n","[450]\tvalidation_0-auc:0.938757\n","[500]\tvalidation_0-auc:0.939345\n","[550]\tvalidation_0-auc:0.939805\n","[600]\tvalidation_0-auc:0.940191\n","[650]\tvalidation_0-auc:0.940254\n","[700]\tvalidation_0-auc:0.940183\n","Stopping. Best iteration:\n","[618]\tvalidation_0-auc:0.940355\n","\n","Score not improved on adding D13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['D14_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.871686\n","[100]\tvalidation_0-auc:0.891247\n","[150]\tvalidation_0-auc:0.905577\n","[200]\tvalidation_0-auc:0.916741\n","[250]\tvalidation_0-auc:0.925699\n","[300]\tvalidation_0-auc:0.930904\n","[350]\tvalidation_0-auc:0.934302\n","[400]\tvalidation_0-auc:0.93665\n","[450]\tvalidation_0-auc:0.938106\n","[500]\tvalidation_0-auc:0.938958\n","[550]\tvalidation_0-auc:0.939527\n","[600]\tvalidation_0-auc:0.939904\n","[650]\tvalidation_0-auc:0.94008\n","[700]\tvalidation_0-auc:0.94034\n","[750]\tvalidation_0-auc:0.940062\n","Stopping. Best iteration:\n","[680]\tvalidation_0-auc:0.940495\n","\n","Score not improved on adding D14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['D14_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872248\n","[100]\tvalidation_0-auc:0.891373\n","[150]\tvalidation_0-auc:0.905731\n","[200]\tvalidation_0-auc:0.916815\n","[250]\tvalidation_0-auc:0.925713\n","[300]\tvalidation_0-auc:0.931033\n","[350]\tvalidation_0-auc:0.934452\n","[400]\tvalidation_0-auc:0.936836\n","[450]\tvalidation_0-auc:0.93869\n","[500]\tvalidation_0-auc:0.939308\n","[550]\tvalidation_0-auc:0.939763\n","[600]\tvalidation_0-auc:0.939968\n","[650]\tvalidation_0-auc:0.940257\n","[700]\tvalidation_0-auc:0.940134\n","[750]\tvalidation_0-auc:0.939904\n","Stopping. Best iteration:\n","[665]\tvalidation_0-auc:0.940358\n","\n","Score not improved on adding D14_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_29\n","--------------------------------------------------------------------------------\n","Adding new features: ['D15_uid_mean']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872274\n","[100]\tvalidation_0-auc:0.89437\n","[150]\tvalidation_0-auc:0.908782\n","[200]\tvalidation_0-auc:0.919807\n","[250]\tvalidation_0-auc:0.927641\n","[300]\tvalidation_0-auc:0.933118\n","[350]\tvalidation_0-auc:0.936801\n","[400]\tvalidation_0-auc:0.938963\n","[450]\tvalidation_0-auc:0.94003\n","[500]\tvalidation_0-auc:0.940532\n","[550]\tvalidation_0-auc:0.940852\n","[600]\tvalidation_0-auc:0.941086\n","[650]\tvalidation_0-auc:0.941319\n","[700]\tvalidation_0-auc:0.941538\n","[750]\tvalidation_0-auc:0.941308\n","Stopping. Best iteration:\n","[699]\tvalidation_0-auc:0.941556\n","\n","Score not improved on adding D15_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_30\n","--------------------------------------------------------------------------------\n","Adding new features: ['D15_uid_std']\n","fitting model on 233 columns\n","[0]\tvalidation_0-auc:0.809897\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.872725\n","[100]\tvalidation_0-auc:0.892945\n","[150]\tvalidation_0-auc:0.907345\n","[200]\tvalidation_0-auc:0.918826\n","[250]\tvalidation_0-auc:0.92769\n","[300]\tvalidation_0-auc:0.933559\n","[350]\tvalidation_0-auc:0.937168\n","[400]\tvalidation_0-auc:0.939165\n","[450]\tvalidation_0-auc:0.940257\n","[500]\tvalidation_0-auc:0.940905\n","[550]\tvalidation_0-auc:0.940841\n","Stopping. Best iteration:\n","[498]\tvalidation_0-auc:0.940952\n","\n","Score not improved on adding D15_uid_std\n","--------------------------------------------------------------------------------\n","Best col in this iteration D2_uid_mean, --->   current best cols: ['D2_uid_mean']\n","########################################################################################################################\n","Model no: 2_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D1_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804709\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879226\n","[100]\tvalidation_0-auc:0.896696\n","[150]\tvalidation_0-auc:0.909784\n","[200]\tvalidation_0-auc:0.921723\n","[250]\tvalidation_0-auc:0.929425\n","[300]\tvalidation_0-auc:0.935482\n","[350]\tvalidation_0-auc:0.938594\n","[400]\tvalidation_0-auc:0.940312\n","[450]\tvalidation_0-auc:0.941344\n","[500]\tvalidation_0-auc:0.941887\n","[550]\tvalidation_0-auc:0.941978\n","[600]\tvalidation_0-auc:0.942155\n","[650]\tvalidation_0-auc:0.942326\n","[700]\tvalidation_0-auc:0.942371\n","[750]\tvalidation_0-auc:0.942181\n","Stopping. Best iteration:\n","[689]\tvalidation_0-auc:0.942464\n","\n","Score not improved on adding D1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D1_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.805494\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879831\n","[100]\tvalidation_0-auc:0.89705\n","[150]\tvalidation_0-auc:0.910152\n","[200]\tvalidation_0-auc:0.922206\n","[250]\tvalidation_0-auc:0.930822\n","[300]\tvalidation_0-auc:0.936523\n","[350]\tvalidation_0-auc:0.939297\n","[400]\tvalidation_0-auc:0.941346\n","[450]\tvalidation_0-auc:0.942269\n","[500]\tvalidation_0-auc:0.943063\n","[550]\tvalidation_0-auc:0.943403\n","[600]\tvalidation_0-auc:0.943452\n","[650]\tvalidation_0-auc:0.943787\n","[700]\tvalidation_0-auc:0.943913\n","[750]\tvalidation_0-auc:0.943492\n","Stopping. Best iteration:\n","[695]\tvalidation_0-auc:0.943946\n","\n","Score improved on adding D1_uid_std new best score 0.943946\n","--------------------------------------------------------------------------------\n","Model no: 2_3\n","--------------------------------------------------------------------------------\n","Skipping D2_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 2_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D2_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.801426\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.88041\n","[100]\tvalidation_0-auc:0.897608\n","[150]\tvalidation_0-auc:0.910787\n","[200]\tvalidation_0-auc:0.922987\n","[250]\tvalidation_0-auc:0.93144\n","[300]\tvalidation_0-auc:0.937718\n","[350]\tvalidation_0-auc:0.940553\n","[400]\tvalidation_0-auc:0.942493\n","[450]\tvalidation_0-auc:0.943658\n","[500]\tvalidation_0-auc:0.944141\n","[550]\tvalidation_0-auc:0.944592\n","[600]\tvalidation_0-auc:0.944942\n","[650]\tvalidation_0-auc:0.944896\n","[700]\tvalidation_0-auc:0.944985\n","Stopping. Best iteration:\n","[612]\tvalidation_0-auc:0.945071\n","\n","Score improved on adding D2_uid_std new best score 0.945071\n","--------------------------------------------------------------------------------\n","Model no: 2_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D3_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.816609\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.884025\n","[100]\tvalidation_0-auc:0.899477\n","[150]\tvalidation_0-auc:0.912951\n","[200]\tvalidation_0-auc:0.924019\n","[250]\tvalidation_0-auc:0.931504\n","[300]\tvalidation_0-auc:0.937074\n","[350]\tvalidation_0-auc:0.939592\n","[400]\tvalidation_0-auc:0.941611\n","[450]\tvalidation_0-auc:0.942643\n","[500]\tvalidation_0-auc:0.943589\n","[550]\tvalidation_0-auc:0.9437\n","[600]\tvalidation_0-auc:0.943959\n","[650]\tvalidation_0-auc:0.944024\n","[700]\tvalidation_0-auc:0.943937\n","Stopping. Best iteration:\n","[645]\tvalidation_0-auc:0.944067\n","\n","Score not improved on adding D3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D3_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.805131\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878128\n","[100]\tvalidation_0-auc:0.896375\n","[150]\tvalidation_0-auc:0.909549\n","[200]\tvalidation_0-auc:0.921401\n","[250]\tvalidation_0-auc:0.930255\n","[300]\tvalidation_0-auc:0.936453\n","[350]\tvalidation_0-auc:0.939804\n","[400]\tvalidation_0-auc:0.941745\n","[450]\tvalidation_0-auc:0.942741\n","[500]\tvalidation_0-auc:0.943376\n","[550]\tvalidation_0-auc:0.943671\n","[600]\tvalidation_0-auc:0.943768\n","[650]\tvalidation_0-auc:0.943986\n","[700]\tvalidation_0-auc:0.944029\n","[750]\tvalidation_0-auc:0.943904\n","[800]\tvalidation_0-auc:0.943904\n","Stopping. Best iteration:\n","[705]\tvalidation_0-auc:0.944094\n","\n","Score not improved on adding D3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D4_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.80542\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878316\n","[100]\tvalidation_0-auc:0.896622\n","[150]\tvalidation_0-auc:0.909113\n","[200]\tvalidation_0-auc:0.920388\n","[250]\tvalidation_0-auc:0.929276\n","[300]\tvalidation_0-auc:0.935663\n","[350]\tvalidation_0-auc:0.938572\n","[400]\tvalidation_0-auc:0.940234\n","[450]\tvalidation_0-auc:0.941454\n","[500]\tvalidation_0-auc:0.942186\n","[550]\tvalidation_0-auc:0.942378\n","[600]\tvalidation_0-auc:0.942243\n","Stopping. Best iteration:\n","[549]\tvalidation_0-auc:0.942383\n","\n","Score not improved on adding D4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D4_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804097\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878089\n","[100]\tvalidation_0-auc:0.897119\n","[150]\tvalidation_0-auc:0.909739\n","[200]\tvalidation_0-auc:0.921426\n","[250]\tvalidation_0-auc:0.930401\n","[300]\tvalidation_0-auc:0.936588\n","[350]\tvalidation_0-auc:0.939989\n","[400]\tvalidation_0-auc:0.941789\n","[450]\tvalidation_0-auc:0.942343\n","[500]\tvalidation_0-auc:0.943252\n","[550]\tvalidation_0-auc:0.943304\n","[600]\tvalidation_0-auc:0.943724\n","[650]\tvalidation_0-auc:0.943878\n","[700]\tvalidation_0-auc:0.943924\n","Stopping. Best iteration:\n","[644]\tvalidation_0-auc:0.943942\n","\n","Score not improved on adding D4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D5_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.791801\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879183\n","[100]\tvalidation_0-auc:0.897723\n","[150]\tvalidation_0-auc:0.910063\n","[200]\tvalidation_0-auc:0.921801\n","[250]\tvalidation_0-auc:0.92982\n","[300]\tvalidation_0-auc:0.936269\n","[350]\tvalidation_0-auc:0.939318\n","[400]\tvalidation_0-auc:0.940952\n","[450]\tvalidation_0-auc:0.941699\n","[500]\tvalidation_0-auc:0.942532\n","[550]\tvalidation_0-auc:0.942936\n","[600]\tvalidation_0-auc:0.94297\n","[650]\tvalidation_0-auc:0.943182\n","[700]\tvalidation_0-auc:0.943267\n","[750]\tvalidation_0-auc:0.943202\n","[800]\tvalidation_0-auc:0.94318\n","Stopping. Best iteration:\n","[702]\tvalidation_0-auc:0.943303\n","\n","Score not improved on adding D5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D5_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804363\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878044\n","[100]\tvalidation_0-auc:0.896657\n","[150]\tvalidation_0-auc:0.909916\n","[200]\tvalidation_0-auc:0.922283\n","[250]\tvalidation_0-auc:0.930853\n","[300]\tvalidation_0-auc:0.936744\n","[350]\tvalidation_0-auc:0.939719\n","[400]\tvalidation_0-auc:0.941793\n","[450]\tvalidation_0-auc:0.942775\n","[500]\tvalidation_0-auc:0.943534\n","[550]\tvalidation_0-auc:0.943571\n","[600]\tvalidation_0-auc:0.943766\n","[650]\tvalidation_0-auc:0.944013\n","[700]\tvalidation_0-auc:0.94382\n","Stopping. Best iteration:\n","[644]\tvalidation_0-auc:0.944041\n","\n","Score not improved on adding D5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D6_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.805036\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877704\n","[100]\tvalidation_0-auc:0.896034\n","[150]\tvalidation_0-auc:0.90893\n","[200]\tvalidation_0-auc:0.920453\n","[250]\tvalidation_0-auc:0.929183\n","[300]\tvalidation_0-auc:0.935574\n","[350]\tvalidation_0-auc:0.938724\n","[400]\tvalidation_0-auc:0.940646\n","[450]\tvalidation_0-auc:0.941847\n","[500]\tvalidation_0-auc:0.942645\n","[550]\tvalidation_0-auc:0.942837\n","[600]\tvalidation_0-auc:0.942809\n","[650]\tvalidation_0-auc:0.942927\n","[700]\tvalidation_0-auc:0.942914\n","[750]\tvalidation_0-auc:0.942689\n","Stopping. Best iteration:\n","[688]\tvalidation_0-auc:0.942991\n","\n","Score not improved on adding D6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D6_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804248\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877607\n","[100]\tvalidation_0-auc:0.896524\n","[150]\tvalidation_0-auc:0.909255\n","[200]\tvalidation_0-auc:0.920719\n","[250]\tvalidation_0-auc:0.929352\n","[300]\tvalidation_0-auc:0.935854\n","[350]\tvalidation_0-auc:0.938999\n","[400]\tvalidation_0-auc:0.940742\n","[450]\tvalidation_0-auc:0.941828\n","[500]\tvalidation_0-auc:0.942716\n","[550]\tvalidation_0-auc:0.942958\n","[600]\tvalidation_0-auc:0.942976\n","[650]\tvalidation_0-auc:0.943152\n","[700]\tvalidation_0-auc:0.943063\n","[750]\tvalidation_0-auc:0.94309\n","Stopping. Best iteration:\n","[654]\tvalidation_0-auc:0.943201\n","\n","Score not improved on adding D6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D7_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804312\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877806\n","[100]\tvalidation_0-auc:0.895787\n","[150]\tvalidation_0-auc:0.908768\n","[200]\tvalidation_0-auc:0.920638\n","[250]\tvalidation_0-auc:0.929477\n","[300]\tvalidation_0-auc:0.935812\n","[350]\tvalidation_0-auc:0.939407\n","[400]\tvalidation_0-auc:0.941288\n","[450]\tvalidation_0-auc:0.942403\n","[500]\tvalidation_0-auc:0.943181\n","[550]\tvalidation_0-auc:0.94369\n","[600]\tvalidation_0-auc:0.943586\n","[650]\tvalidation_0-auc:0.943957\n","[700]\tvalidation_0-auc:0.943713\n","[750]\tvalidation_0-auc:0.943474\n","Stopping. Best iteration:\n","[654]\tvalidation_0-auc:0.94397\n","\n","Score not improved on adding D7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D7_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804183\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877571\n","[100]\tvalidation_0-auc:0.896062\n","[150]\tvalidation_0-auc:0.90911\n","[200]\tvalidation_0-auc:0.92096\n","[250]\tvalidation_0-auc:0.929471\n","[300]\tvalidation_0-auc:0.935825\n","[350]\tvalidation_0-auc:0.939473\n","[400]\tvalidation_0-auc:0.941255\n","[450]\tvalidation_0-auc:0.942266\n","[500]\tvalidation_0-auc:0.942905\n","[550]\tvalidation_0-auc:0.943249\n","[600]\tvalidation_0-auc:0.943529\n","[650]\tvalidation_0-auc:0.94347\n","[700]\tvalidation_0-auc:0.943416\n","Stopping. Best iteration:\n","[604]\tvalidation_0-auc:0.943638\n","\n","Score not improved on adding D7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D8_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803899\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877996\n","[100]\tvalidation_0-auc:0.895606\n","[150]\tvalidation_0-auc:0.908385\n","[200]\tvalidation_0-auc:0.920586\n","[250]\tvalidation_0-auc:0.929183\n","[300]\tvalidation_0-auc:0.93571\n","[350]\tvalidation_0-auc:0.939264\n","[400]\tvalidation_0-auc:0.94091\n","[450]\tvalidation_0-auc:0.941813\n","[500]\tvalidation_0-auc:0.942629\n","[550]\tvalidation_0-auc:0.943015\n","[600]\tvalidation_0-auc:0.943158\n","[650]\tvalidation_0-auc:0.943336\n","[700]\tvalidation_0-auc:0.943498\n","[750]\tvalidation_0-auc:0.943378\n","[800]\tvalidation_0-auc:0.943391\n","Stopping. Best iteration:\n","[705]\tvalidation_0-auc:0.943529\n","\n","Score not improved on adding D8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D8_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804246\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877563\n","[100]\tvalidation_0-auc:0.895586\n","[150]\tvalidation_0-auc:0.908622\n","[200]\tvalidation_0-auc:0.9207\n","[250]\tvalidation_0-auc:0.929785\n","[300]\tvalidation_0-auc:0.936034\n","[350]\tvalidation_0-auc:0.939504\n","[400]\tvalidation_0-auc:0.941164\n","[450]\tvalidation_0-auc:0.942321\n","[500]\tvalidation_0-auc:0.943134\n","[550]\tvalidation_0-auc:0.943592\n","[600]\tvalidation_0-auc:0.943753\n","[650]\tvalidation_0-auc:0.943843\n","[700]\tvalidation_0-auc:0.943717\n","Stopping. Best iteration:\n","[644]\tvalidation_0-auc:0.943993\n","\n","Score not improved on adding D8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D9_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803796\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878213\n","[100]\tvalidation_0-auc:0.896638\n","[150]\tvalidation_0-auc:0.908463\n","[200]\tvalidation_0-auc:0.920148\n","[250]\tvalidation_0-auc:0.929209\n","[300]\tvalidation_0-auc:0.935985\n","[350]\tvalidation_0-auc:0.939454\n","[400]\tvalidation_0-auc:0.94121\n","[450]\tvalidation_0-auc:0.942533\n","[500]\tvalidation_0-auc:0.943182\n","[550]\tvalidation_0-auc:0.943495\n","[600]\tvalidation_0-auc:0.94352\n","[650]\tvalidation_0-auc:0.943614\n","[700]\tvalidation_0-auc:0.943571\n","Stopping. Best iteration:\n","[643]\tvalidation_0-auc:0.943692\n","\n","Score not improved on adding D9_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D9_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804406\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877596\n","[100]\tvalidation_0-auc:0.896136\n","[150]\tvalidation_0-auc:0.909181\n","[200]\tvalidation_0-auc:0.920765\n","[250]\tvalidation_0-auc:0.92946\n","[300]\tvalidation_0-auc:0.93563\n","[350]\tvalidation_0-auc:0.939228\n","[400]\tvalidation_0-auc:0.940961\n","[450]\tvalidation_0-auc:0.942104\n","[500]\tvalidation_0-auc:0.942974\n","[550]\tvalidation_0-auc:0.943324\n","[600]\tvalidation_0-auc:0.943256\n","[650]\tvalidation_0-auc:0.943575\n","[700]\tvalidation_0-auc:0.943455\n","Stopping. Best iteration:\n","[644]\tvalidation_0-auc:0.943606\n","\n","Score not improved on adding D9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D10_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804406\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877984\n","[100]\tvalidation_0-auc:0.896421\n","[150]\tvalidation_0-auc:0.909703\n","[200]\tvalidation_0-auc:0.92181\n","[250]\tvalidation_0-auc:0.930362\n","[300]\tvalidation_0-auc:0.936358\n","[350]\tvalidation_0-auc:0.939279\n","[400]\tvalidation_0-auc:0.941171\n","[450]\tvalidation_0-auc:0.942227\n","[500]\tvalidation_0-auc:0.943091\n","[550]\tvalidation_0-auc:0.943338\n","[600]\tvalidation_0-auc:0.943403\n","[650]\tvalidation_0-auc:0.943585\n","[700]\tvalidation_0-auc:0.943647\n","[750]\tvalidation_0-auc:0.943428\n","Stopping. Best iteration:\n","[695]\tvalidation_0-auc:0.943767\n","\n","Score not improved on adding D10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D10_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804531\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878164\n","[100]\tvalidation_0-auc:0.896699\n","[150]\tvalidation_0-auc:0.910064\n","[200]\tvalidation_0-auc:0.922516\n","[250]\tvalidation_0-auc:0.931201\n","[300]\tvalidation_0-auc:0.937166\n","[350]\tvalidation_0-auc:0.939968\n","[400]\tvalidation_0-auc:0.941946\n","[450]\tvalidation_0-auc:0.943095\n","[500]\tvalidation_0-auc:0.943893\n","[550]\tvalidation_0-auc:0.944252\n","[600]\tvalidation_0-auc:0.944399\n","[650]\tvalidation_0-auc:0.944634\n","[700]\tvalidation_0-auc:0.944712\n","[750]\tvalidation_0-auc:0.944554\n","[800]\tvalidation_0-auc:0.944257\n","Stopping. Best iteration:\n","[704]\tvalidation_0-auc:0.944728\n","\n","Score not improved on adding D10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D11_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804482\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879142\n","[100]\tvalidation_0-auc:0.89711\n","[150]\tvalidation_0-auc:0.910128\n","[200]\tvalidation_0-auc:0.922263\n","[250]\tvalidation_0-auc:0.931243\n","[300]\tvalidation_0-auc:0.937358\n","[350]\tvalidation_0-auc:0.940397\n","[400]\tvalidation_0-auc:0.942099\n","[450]\tvalidation_0-auc:0.942951\n","[500]\tvalidation_0-auc:0.943929\n","[550]\tvalidation_0-auc:0.944375\n","[600]\tvalidation_0-auc:0.944508\n","[650]\tvalidation_0-auc:0.944738\n","[700]\tvalidation_0-auc:0.944885\n","[750]\tvalidation_0-auc:0.944689\n","Stopping. Best iteration:\n","[695]\tvalidation_0-auc:0.944938\n","\n","Score not improved on adding D11_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D11_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804242\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878959\n","[100]\tvalidation_0-auc:0.897325\n","[150]\tvalidation_0-auc:0.910941\n","[200]\tvalidation_0-auc:0.923587\n","[250]\tvalidation_0-auc:0.932289\n","[300]\tvalidation_0-auc:0.938247\n","[350]\tvalidation_0-auc:0.941211\n","[400]\tvalidation_0-auc:0.942731\n","[450]\tvalidation_0-auc:0.943594\n","[500]\tvalidation_0-auc:0.944452\n","[550]\tvalidation_0-auc:0.944636\n","[600]\tvalidation_0-auc:0.944603\n","[650]\tvalidation_0-auc:0.944643\n","[700]\tvalidation_0-auc:0.944677\n","Stopping. Best iteration:\n","[615]\tvalidation_0-auc:0.944813\n","\n","Score not improved on adding D11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D12_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.805231\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877618\n","[100]\tvalidation_0-auc:0.896186\n","[150]\tvalidation_0-auc:0.908931\n","[200]\tvalidation_0-auc:0.920976\n","[250]\tvalidation_0-auc:0.929865\n","[300]\tvalidation_0-auc:0.936035\n","[350]\tvalidation_0-auc:0.939324\n","[400]\tvalidation_0-auc:0.941229\n","[450]\tvalidation_0-auc:0.942143\n","[500]\tvalidation_0-auc:0.943083\n","[550]\tvalidation_0-auc:0.943371\n","[600]\tvalidation_0-auc:0.943526\n","[650]\tvalidation_0-auc:0.943597\n","[700]\tvalidation_0-auc:0.943647\n","[750]\tvalidation_0-auc:0.943543\n","[800]\tvalidation_0-auc:0.943516\n","Stopping. Best iteration:\n","[704]\tvalidation_0-auc:0.943693\n","\n","Score not improved on adding D12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D12_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804242\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877727\n","[100]\tvalidation_0-auc:0.895776\n","[150]\tvalidation_0-auc:0.9086\n","[200]\tvalidation_0-auc:0.920261\n","[250]\tvalidation_0-auc:0.929414\n","[300]\tvalidation_0-auc:0.935809\n","[350]\tvalidation_0-auc:0.939134\n","[400]\tvalidation_0-auc:0.940735\n","[450]\tvalidation_0-auc:0.941893\n","[500]\tvalidation_0-auc:0.94269\n","[550]\tvalidation_0-auc:0.943178\n","[600]\tvalidation_0-auc:0.943403\n","[650]\tvalidation_0-auc:0.943594\n","[700]\tvalidation_0-auc:0.943288\n","[750]\tvalidation_0-auc:0.943027\n","Stopping. Best iteration:\n","[653]\tvalidation_0-auc:0.943608\n","\n","Score not improved on adding D12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_25\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D13_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.80438\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877496\n","[100]\tvalidation_0-auc:0.895551\n","[150]\tvalidation_0-auc:0.90882\n","[200]\tvalidation_0-auc:0.920519\n","[250]\tvalidation_0-auc:0.929172\n","[300]\tvalidation_0-auc:0.935548\n","[350]\tvalidation_0-auc:0.938619\n","[400]\tvalidation_0-auc:0.940479\n","[450]\tvalidation_0-auc:0.941524\n","[500]\tvalidation_0-auc:0.942386\n","[550]\tvalidation_0-auc:0.94262\n","[600]\tvalidation_0-auc:0.942748\n","[650]\tvalidation_0-auc:0.942955\n","[700]\tvalidation_0-auc:0.943048\n","[750]\tvalidation_0-auc:0.943068\n","[800]\tvalidation_0-auc:0.942992\n","[850]\tvalidation_0-auc:0.942957\n","Stopping. Best iteration:\n","[764]\tvalidation_0-auc:0.943131\n","\n","Score not improved on adding D13_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D13_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804247\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877611\n","[100]\tvalidation_0-auc:0.8962\n","[150]\tvalidation_0-auc:0.909166\n","[200]\tvalidation_0-auc:0.920871\n","[250]\tvalidation_0-auc:0.929722\n","[300]\tvalidation_0-auc:0.936073\n","[350]\tvalidation_0-auc:0.939321\n","[400]\tvalidation_0-auc:0.941059\n","[450]\tvalidation_0-auc:0.942267\n","[500]\tvalidation_0-auc:0.943112\n","[550]\tvalidation_0-auc:0.94335\n","[600]\tvalidation_0-auc:0.943512\n","[650]\tvalidation_0-auc:0.943775\n","[700]\tvalidation_0-auc:0.943833\n","[750]\tvalidation_0-auc:0.943699\n","Stopping. Best iteration:\n","[696]\tvalidation_0-auc:0.943912\n","\n","Score not improved on adding D13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D14_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.804238\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877662\n","[100]\tvalidation_0-auc:0.896291\n","[150]\tvalidation_0-auc:0.909026\n","[200]\tvalidation_0-auc:0.920377\n","[250]\tvalidation_0-auc:0.929011\n","[300]\tvalidation_0-auc:0.934897\n","[350]\tvalidation_0-auc:0.938368\n","[400]\tvalidation_0-auc:0.940138\n","[450]\tvalidation_0-auc:0.941307\n","[500]\tvalidation_0-auc:0.942038\n","[550]\tvalidation_0-auc:0.942379\n","[600]\tvalidation_0-auc:0.94242\n","[650]\tvalidation_0-auc:0.942726\n","[700]\tvalidation_0-auc:0.942748\n","[750]\tvalidation_0-auc:0.942476\n","Stopping. Best iteration:\n","[695]\tvalidation_0-auc:0.942798\n","\n","Score not improved on adding D14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D14_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803998\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878353\n","[100]\tvalidation_0-auc:0.895841\n","[150]\tvalidation_0-auc:0.909356\n","[200]\tvalidation_0-auc:0.921173\n","[250]\tvalidation_0-auc:0.929483\n","[300]\tvalidation_0-auc:0.935595\n","[350]\tvalidation_0-auc:0.939098\n","[400]\tvalidation_0-auc:0.94107\n","[450]\tvalidation_0-auc:0.942141\n","[500]\tvalidation_0-auc:0.943054\n","[550]\tvalidation_0-auc:0.943512\n","[600]\tvalidation_0-auc:0.943545\n","[650]\tvalidation_0-auc:0.943837\n","[700]\tvalidation_0-auc:0.943773\n","[750]\tvalidation_0-auc:0.943632\n","Stopping. Best iteration:\n","[654]\tvalidation_0-auc:0.943898\n","\n","Score not improved on adding D14_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_29\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.805976\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880038\n","[100]\tvalidation_0-auc:0.897293\n","[150]\tvalidation_0-auc:0.910702\n","[200]\tvalidation_0-auc:0.92254\n","[250]\tvalidation_0-auc:0.931461\n","[300]\tvalidation_0-auc:0.937685\n","[350]\tvalidation_0-auc:0.940732\n","[400]\tvalidation_0-auc:0.942482\n","[450]\tvalidation_0-auc:0.943482\n","[500]\tvalidation_0-auc:0.944377\n","[550]\tvalidation_0-auc:0.944673\n","[600]\tvalidation_0-auc:0.944907\n","[650]\tvalidation_0-auc:0.945045\n","[700]\tvalidation_0-auc:0.945188\n","[750]\tvalidation_0-auc:0.94522\n","[800]\tvalidation_0-auc:0.945093\n","Stopping. Best iteration:\n","[746]\tvalidation_0-auc:0.945277\n","\n","Score improved on adding D15_uid_mean new best score 0.945277\n","--------------------------------------------------------------------------------\n","Model no: 2_30\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_std']\n","fitting model on 234 columns\n","[0]\tvalidation_0-auc:0.803992\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878406\n","[100]\tvalidation_0-auc:0.89637\n","[150]\tvalidation_0-auc:0.91009\n","[200]\tvalidation_0-auc:0.922266\n","[250]\tvalidation_0-auc:0.929866\n","[300]\tvalidation_0-auc:0.936366\n","[350]\tvalidation_0-auc:0.939409\n","[400]\tvalidation_0-auc:0.941359\n","[450]\tvalidation_0-auc:0.942221\n","[500]\tvalidation_0-auc:0.942899\n","[550]\tvalidation_0-auc:0.94329\n","[600]\tvalidation_0-auc:0.943381\n","[650]\tvalidation_0-auc:0.943599\n","[700]\tvalidation_0-auc:0.943561\n","Stopping. Best iteration:\n","[645]\tvalidation_0-auc:0.943666\n","\n","Score not improved on adding D15_uid_std\n","--------------------------------------------------------------------------------\n","Best col in this iteration D15_uid_mean, --->   current best cols: ['D2_uid_mean', 'D15_uid_mean']\n","########################################################################################################################\n","Model no: 3_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D1_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805982\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880379\n","[100]\tvalidation_0-auc:0.899113\n","[150]\tvalidation_0-auc:0.911594\n","[200]\tvalidation_0-auc:0.922211\n","[250]\tvalidation_0-auc:0.930488\n","[300]\tvalidation_0-auc:0.936368\n","[350]\tvalidation_0-auc:0.939422\n","[400]\tvalidation_0-auc:0.941286\n","[450]\tvalidation_0-auc:0.942175\n","[500]\tvalidation_0-auc:0.942609\n","[550]\tvalidation_0-auc:0.942858\n","[600]\tvalidation_0-auc:0.94297\n","[650]\tvalidation_0-auc:0.943157\n","[700]\tvalidation_0-auc:0.943131\n","[750]\tvalidation_0-auc:0.943073\n","Stopping. Best iteration:\n","[683]\tvalidation_0-auc:0.943357\n","\n","Score not improved on adding D1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D1_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.806705\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879996\n","[100]\tvalidation_0-auc:0.89911\n","[150]\tvalidation_0-auc:0.91252\n","[200]\tvalidation_0-auc:0.923624\n","[250]\tvalidation_0-auc:0.931608\n","[300]\tvalidation_0-auc:0.937239\n","[350]\tvalidation_0-auc:0.940511\n","[400]\tvalidation_0-auc:0.942646\n","[450]\tvalidation_0-auc:0.943465\n","[500]\tvalidation_0-auc:0.943816\n","[550]\tvalidation_0-auc:0.944265\n","[600]\tvalidation_0-auc:0.944663\n","[650]\tvalidation_0-auc:0.944684\n","[700]\tvalidation_0-auc:0.94446\n","Stopping. Best iteration:\n","[638]\tvalidation_0-auc:0.944785\n","\n","Score not improved on adding D1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_3\n","--------------------------------------------------------------------------------\n","Skipping D2_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 3_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D2_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.802536\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.881248\n","[100]\tvalidation_0-auc:0.900277\n","[150]\tvalidation_0-auc:0.913554\n","[200]\tvalidation_0-auc:0.923987\n","[250]\tvalidation_0-auc:0.931506\n","[300]\tvalidation_0-auc:0.93675\n","[350]\tvalidation_0-auc:0.940516\n","[400]\tvalidation_0-auc:0.942203\n","[450]\tvalidation_0-auc:0.942868\n","[500]\tvalidation_0-auc:0.943152\n","[550]\tvalidation_0-auc:0.943503\n","[600]\tvalidation_0-auc:0.943412\n","[650]\tvalidation_0-auc:0.943461\n","[700]\tvalidation_0-auc:0.943385\n","Stopping. Best iteration:\n","[636]\tvalidation_0-auc:0.943646\n","\n","Score not improved on adding D2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D3_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.817385\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.884109\n","[100]\tvalidation_0-auc:0.902136\n","[150]\tvalidation_0-auc:0.914469\n","[200]\tvalidation_0-auc:0.92481\n","[250]\tvalidation_0-auc:0.931923\n","[300]\tvalidation_0-auc:0.93766\n","[350]\tvalidation_0-auc:0.940769\n","[400]\tvalidation_0-auc:0.942361\n","[450]\tvalidation_0-auc:0.943427\n","[500]\tvalidation_0-auc:0.943993\n","[550]\tvalidation_0-auc:0.944377\n","[600]\tvalidation_0-auc:0.944463\n","[650]\tvalidation_0-auc:0.944535\n","[700]\tvalidation_0-auc:0.944693\n","[750]\tvalidation_0-auc:0.944773\n","[800]\tvalidation_0-auc:0.944717\n","[850]\tvalidation_0-auc:0.94477\n","[900]\tvalidation_0-auc:0.944708\n","Stopping. Best iteration:\n","[836]\tvalidation_0-auc:0.944824\n","\n","Score not improved on adding D3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D3_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805625\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880009\n","[100]\tvalidation_0-auc:0.899521\n","[150]\tvalidation_0-auc:0.91255\n","[200]\tvalidation_0-auc:0.923324\n","[250]\tvalidation_0-auc:0.931284\n","[300]\tvalidation_0-auc:0.93721\n","[350]\tvalidation_0-auc:0.94089\n","[400]\tvalidation_0-auc:0.942412\n","[450]\tvalidation_0-auc:0.943368\n","[500]\tvalidation_0-auc:0.944032\n","[550]\tvalidation_0-auc:0.944245\n","[600]\tvalidation_0-auc:0.944382\n","[650]\tvalidation_0-auc:0.944533\n","[700]\tvalidation_0-auc:0.944556\n","[750]\tvalidation_0-auc:0.94448\n","Stopping. Best iteration:\n","[680]\tvalidation_0-auc:0.944726\n","\n","Score not improved on adding D3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D4_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805895\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880377\n","[100]\tvalidation_0-auc:0.898818\n","[150]\tvalidation_0-auc:0.912019\n","[200]\tvalidation_0-auc:0.923002\n","[250]\tvalidation_0-auc:0.930819\n","[300]\tvalidation_0-auc:0.936689\n","[350]\tvalidation_0-auc:0.940502\n","[400]\tvalidation_0-auc:0.941956\n","[450]\tvalidation_0-auc:0.942865\n","[500]\tvalidation_0-auc:0.94319\n","[550]\tvalidation_0-auc:0.943514\n","[600]\tvalidation_0-auc:0.94343\n","[650]\tvalidation_0-auc:0.943276\n","Stopping. Best iteration:\n","[582]\tvalidation_0-auc:0.943597\n","\n","Score not improved on adding D4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D4_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.80593\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880389\n","[100]\tvalidation_0-auc:0.899016\n","[150]\tvalidation_0-auc:0.911985\n","[200]\tvalidation_0-auc:0.922714\n","[250]\tvalidation_0-auc:0.93053\n","[300]\tvalidation_0-auc:0.936618\n","[350]\tvalidation_0-auc:0.940194\n","[400]\tvalidation_0-auc:0.941588\n","[450]\tvalidation_0-auc:0.942398\n","[500]\tvalidation_0-auc:0.943008\n","[550]\tvalidation_0-auc:0.94333\n","[600]\tvalidation_0-auc:0.943521\n","[650]\tvalidation_0-auc:0.94353\n","[700]\tvalidation_0-auc:0.943422\n","Stopping. Best iteration:\n","[609]\tvalidation_0-auc:0.943594\n","\n","Score not improved on adding D4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D5_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.79224\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.881248\n","[100]\tvalidation_0-auc:0.899838\n","[150]\tvalidation_0-auc:0.912891\n","[200]\tvalidation_0-auc:0.923221\n","[250]\tvalidation_0-auc:0.931058\n","[300]\tvalidation_0-auc:0.937001\n","[350]\tvalidation_0-auc:0.94074\n","[400]\tvalidation_0-auc:0.942383\n","[450]\tvalidation_0-auc:0.943252\n","[500]\tvalidation_0-auc:0.943653\n","[550]\tvalidation_0-auc:0.944077\n","[600]\tvalidation_0-auc:0.944067\n","[650]\tvalidation_0-auc:0.943943\n","Stopping. Best iteration:\n","[571]\tvalidation_0-auc:0.944208\n","\n","Score not improved on adding D5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D5_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805749\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.88018\n","[100]\tvalidation_0-auc:0.898748\n","[150]\tvalidation_0-auc:0.912271\n","[200]\tvalidation_0-auc:0.922864\n","[250]\tvalidation_0-auc:0.930783\n","[300]\tvalidation_0-auc:0.936567\n","[350]\tvalidation_0-auc:0.940128\n","[400]\tvalidation_0-auc:0.941747\n","[450]\tvalidation_0-auc:0.942553\n","[500]\tvalidation_0-auc:0.942884\n","[550]\tvalidation_0-auc:0.943074\n","[600]\tvalidation_0-auc:0.943095\n","[650]\tvalidation_0-auc:0.943104\n","[700]\tvalidation_0-auc:0.943249\n","[750]\tvalidation_0-auc:0.943017\n","Stopping. Best iteration:\n","[692]\tvalidation_0-auc:0.9433\n","\n","Score not improved on adding D5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D6_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805926\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879549\n","[100]\tvalidation_0-auc:0.898418\n","[150]\tvalidation_0-auc:0.911292\n","[200]\tvalidation_0-auc:0.922256\n","[250]\tvalidation_0-auc:0.930604\n","[300]\tvalidation_0-auc:0.936713\n","[350]\tvalidation_0-auc:0.940153\n","[400]\tvalidation_0-auc:0.941824\n","[450]\tvalidation_0-auc:0.942791\n","[500]\tvalidation_0-auc:0.943345\n","[550]\tvalidation_0-auc:0.943738\n","[600]\tvalidation_0-auc:0.943915\n","[650]\tvalidation_0-auc:0.94406\n","[700]\tvalidation_0-auc:0.944243\n","[750]\tvalidation_0-auc:0.943976\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.944385\n","\n","Score not improved on adding D6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D6_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805939\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879674\n","[100]\tvalidation_0-auc:0.89892\n","[150]\tvalidation_0-auc:0.912031\n","[200]\tvalidation_0-auc:0.922817\n","[250]\tvalidation_0-auc:0.930926\n","[300]\tvalidation_0-auc:0.937022\n","[350]\tvalidation_0-auc:0.94058\n","[400]\tvalidation_0-auc:0.942219\n","[450]\tvalidation_0-auc:0.942981\n","[500]\tvalidation_0-auc:0.943302\n","[550]\tvalidation_0-auc:0.943492\n","[600]\tvalidation_0-auc:0.943436\n","[650]\tvalidation_0-auc:0.943327\n","Stopping. Best iteration:\n","[570]\tvalidation_0-auc:0.943594\n","\n","Score not improved on adding D6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D7_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805883\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879476\n","[100]\tvalidation_0-auc:0.898302\n","[150]\tvalidation_0-auc:0.911014\n","[200]\tvalidation_0-auc:0.922211\n","[250]\tvalidation_0-auc:0.930624\n","[300]\tvalidation_0-auc:0.936644\n","[350]\tvalidation_0-auc:0.940134\n","[400]\tvalidation_0-auc:0.941909\n","[450]\tvalidation_0-auc:0.94276\n","[500]\tvalidation_0-auc:0.943063\n","[550]\tvalidation_0-auc:0.943392\n","[600]\tvalidation_0-auc:0.943566\n","[650]\tvalidation_0-auc:0.943595\n","[700]\tvalidation_0-auc:0.943623\n","[750]\tvalidation_0-auc:0.94337\n","Stopping. Best iteration:\n","[685]\tvalidation_0-auc:0.943755\n","\n","Score not improved on adding D7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D7_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805661\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879516\n","[100]\tvalidation_0-auc:0.898794\n","[150]\tvalidation_0-auc:0.911613\n","[200]\tvalidation_0-auc:0.922463\n","[250]\tvalidation_0-auc:0.930499\n","[300]\tvalidation_0-auc:0.936526\n","[350]\tvalidation_0-auc:0.93991\n","[400]\tvalidation_0-auc:0.941567\n","[450]\tvalidation_0-auc:0.942308\n","[500]\tvalidation_0-auc:0.942525\n","[550]\tvalidation_0-auc:0.942555\n","[600]\tvalidation_0-auc:0.942744\n","[650]\tvalidation_0-auc:0.942895\n","[700]\tvalidation_0-auc:0.942878\n","[750]\tvalidation_0-auc:0.942689\n","Stopping. Best iteration:\n","[692]\tvalidation_0-auc:0.943034\n","\n","Score not improved on adding D7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D8_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805545\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880215\n","[100]\tvalidation_0-auc:0.898041\n","[150]\tvalidation_0-auc:0.910653\n","[200]\tvalidation_0-auc:0.92249\n","[250]\tvalidation_0-auc:0.929987\n","[300]\tvalidation_0-auc:0.936111\n","[350]\tvalidation_0-auc:0.940042\n","[400]\tvalidation_0-auc:0.941581\n","[450]\tvalidation_0-auc:0.942273\n","[500]\tvalidation_0-auc:0.942824\n","[550]\tvalidation_0-auc:0.942998\n","[600]\tvalidation_0-auc:0.942979\n","[650]\tvalidation_0-auc:0.942826\n","Stopping. Best iteration:\n","[567]\tvalidation_0-auc:0.943153\n","\n","Score not improved on adding D8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D8_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805868\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879011\n","[100]\tvalidation_0-auc:0.89843\n","[150]\tvalidation_0-auc:0.91101\n","[200]\tvalidation_0-auc:0.922491\n","[250]\tvalidation_0-auc:0.930791\n","[300]\tvalidation_0-auc:0.937091\n","[350]\tvalidation_0-auc:0.94071\n","[400]\tvalidation_0-auc:0.942175\n","[450]\tvalidation_0-auc:0.942924\n","[500]\tvalidation_0-auc:0.943302\n","[550]\tvalidation_0-auc:0.943791\n","[600]\tvalidation_0-auc:0.943954\n","[650]\tvalidation_0-auc:0.944055\n","[700]\tvalidation_0-auc:0.944023\n","[750]\tvalidation_0-auc:0.944122\n","Stopping. Best iteration:\n","[684]\tvalidation_0-auc:0.944232\n","\n","Score not improved on adding D8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D9_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805345\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879426\n","[100]\tvalidation_0-auc:0.898245\n","[150]\tvalidation_0-auc:0.911389\n","[200]\tvalidation_0-auc:0.92268\n","[250]\tvalidation_0-auc:0.931312\n","[300]\tvalidation_0-auc:0.936956\n","[350]\tvalidation_0-auc:0.94042\n","[400]\tvalidation_0-auc:0.941946\n","[450]\tvalidation_0-auc:0.942539\n","[500]\tvalidation_0-auc:0.94299\n","[550]\tvalidation_0-auc:0.943357\n","[600]\tvalidation_0-auc:0.943535\n","[650]\tvalidation_0-auc:0.943524\n","[700]\tvalidation_0-auc:0.943356\n","Stopping. Best iteration:\n","[607]\tvalidation_0-auc:0.943635\n","\n","Score not improved on adding D9_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D9_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805898\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878702\n","[100]\tvalidation_0-auc:0.898401\n","[150]\tvalidation_0-auc:0.911343\n","[200]\tvalidation_0-auc:0.922303\n","[250]\tvalidation_0-auc:0.930434\n","[300]\tvalidation_0-auc:0.936301\n","[350]\tvalidation_0-auc:0.939541\n","[400]\tvalidation_0-auc:0.941286\n","[450]\tvalidation_0-auc:0.942206\n","[500]\tvalidation_0-auc:0.942569\n","[550]\tvalidation_0-auc:0.94288\n","[600]\tvalidation_0-auc:0.942752\n","[650]\tvalidation_0-auc:0.942964\n","[700]\tvalidation_0-auc:0.942831\n","[750]\tvalidation_0-auc:0.942789\n","Stopping. Best iteration:\n","[685]\tvalidation_0-auc:0.943016\n","\n","Score not improved on adding D9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D10_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805963\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880074\n","[100]\tvalidation_0-auc:0.899078\n","[150]\tvalidation_0-auc:0.912316\n","[200]\tvalidation_0-auc:0.923251\n","[250]\tvalidation_0-auc:0.93113\n","[300]\tvalidation_0-auc:0.936903\n","[350]\tvalidation_0-auc:0.940429\n","[400]\tvalidation_0-auc:0.941876\n","[450]\tvalidation_0-auc:0.942979\n","[500]\tvalidation_0-auc:0.943149\n","[550]\tvalidation_0-auc:0.943521\n","[600]\tvalidation_0-auc:0.943466\n","[650]\tvalidation_0-auc:0.943544\n","[700]\tvalidation_0-auc:0.943632\n","[750]\tvalidation_0-auc:0.943334\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.943747\n","\n","Score not improved on adding D10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D10_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805923\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880517\n","[100]\tvalidation_0-auc:0.899225\n","[150]\tvalidation_0-auc:0.912103\n","[200]\tvalidation_0-auc:0.922948\n","[250]\tvalidation_0-auc:0.931001\n","[300]\tvalidation_0-auc:0.936811\n","[350]\tvalidation_0-auc:0.94001\n","[400]\tvalidation_0-auc:0.941465\n","[450]\tvalidation_0-auc:0.942234\n","[500]\tvalidation_0-auc:0.942537\n","[550]\tvalidation_0-auc:0.942843\n","[600]\tvalidation_0-auc:0.943103\n","[650]\tvalidation_0-auc:0.943135\n","[700]\tvalidation_0-auc:0.943123\n","[750]\tvalidation_0-auc:0.942852\n","Stopping. Best iteration:\n","[681]\tvalidation_0-auc:0.943316\n","\n","Score not improved on adding D10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D11_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805795\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880335\n","[100]\tvalidation_0-auc:0.899671\n","[150]\tvalidation_0-auc:0.913073\n","[200]\tvalidation_0-auc:0.924071\n","[250]\tvalidation_0-auc:0.93134\n","[300]\tvalidation_0-auc:0.937323\n","[350]\tvalidation_0-auc:0.940842\n","[400]\tvalidation_0-auc:0.942536\n","[450]\tvalidation_0-auc:0.943419\n","[500]\tvalidation_0-auc:0.943817\n","[550]\tvalidation_0-auc:0.944113\n","[600]\tvalidation_0-auc:0.944118\n","[650]\tvalidation_0-auc:0.944085\n","[700]\tvalidation_0-auc:0.944196\n","[750]\tvalidation_0-auc:0.944225\n","Stopping. Best iteration:\n","[690]\tvalidation_0-auc:0.944248\n","\n","Score not improved on adding D11_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D11_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805918\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880982\n","[100]\tvalidation_0-auc:0.899594\n","[150]\tvalidation_0-auc:0.913548\n","[200]\tvalidation_0-auc:0.924135\n","[250]\tvalidation_0-auc:0.931902\n","[300]\tvalidation_0-auc:0.93759\n","[350]\tvalidation_0-auc:0.941104\n","[400]\tvalidation_0-auc:0.942713\n","[450]\tvalidation_0-auc:0.943586\n","[500]\tvalidation_0-auc:0.943976\n","[550]\tvalidation_0-auc:0.944354\n","[600]\tvalidation_0-auc:0.944371\n","[650]\tvalidation_0-auc:0.944497\n","[700]\tvalidation_0-auc:0.944319\n","Stopping. Best iteration:\n","[647]\tvalidation_0-auc:0.944562\n","\n","Score not improved on adding D11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D12_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.806115\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879199\n","[100]\tvalidation_0-auc:0.898589\n","[150]\tvalidation_0-auc:0.911031\n","[200]\tvalidation_0-auc:0.92228\n","[250]\tvalidation_0-auc:0.93027\n","[300]\tvalidation_0-auc:0.936922\n","[350]\tvalidation_0-auc:0.940281\n","[400]\tvalidation_0-auc:0.941899\n","[450]\tvalidation_0-auc:0.942692\n","[500]\tvalidation_0-auc:0.943214\n","[550]\tvalidation_0-auc:0.943491\n","[600]\tvalidation_0-auc:0.943511\n","[650]\tvalidation_0-auc:0.943629\n","[700]\tvalidation_0-auc:0.943922\n","[750]\tvalidation_0-auc:0.943872\n","Stopping. Best iteration:\n","[697]\tvalidation_0-auc:0.943997\n","\n","Score not improved on adding D12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D12_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805931\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878534\n","[100]\tvalidation_0-auc:0.898421\n","[150]\tvalidation_0-auc:0.911457\n","[200]\tvalidation_0-auc:0.922536\n","[250]\tvalidation_0-auc:0.930614\n","[300]\tvalidation_0-auc:0.936684\n","[350]\tvalidation_0-auc:0.940185\n","[400]\tvalidation_0-auc:0.942033\n","[450]\tvalidation_0-auc:0.94279\n","[500]\tvalidation_0-auc:0.94329\n","[550]\tvalidation_0-auc:0.943589\n","[600]\tvalidation_0-auc:0.943674\n","[650]\tvalidation_0-auc:0.943588\n","Stopping. Best iteration:\n","[582]\tvalidation_0-auc:0.943761\n","\n","Score not improved on adding D12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_25\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D13_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805969\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878825\n","[100]\tvalidation_0-auc:0.898132\n","[150]\tvalidation_0-auc:0.911065\n","[200]\tvalidation_0-auc:0.922063\n","[250]\tvalidation_0-auc:0.930343\n","[300]\tvalidation_0-auc:0.936266\n","[350]\tvalidation_0-auc:0.93969\n","[400]\tvalidation_0-auc:0.941105\n","[450]\tvalidation_0-auc:0.941727\n","[500]\tvalidation_0-auc:0.94184\n","[550]\tvalidation_0-auc:0.942315\n","[600]\tvalidation_0-auc:0.942562\n","[650]\tvalidation_0-auc:0.942624\n","[700]\tvalidation_0-auc:0.942632\n","[750]\tvalidation_0-auc:0.942319\n","Stopping. Best iteration:\n","[692]\tvalidation_0-auc:0.942787\n","\n","Score not improved on adding D13_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D13_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805841\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879208\n","[100]\tvalidation_0-auc:0.898687\n","[150]\tvalidation_0-auc:0.911243\n","[200]\tvalidation_0-auc:0.922185\n","[250]\tvalidation_0-auc:0.930373\n","[300]\tvalidation_0-auc:0.936159\n","[350]\tvalidation_0-auc:0.93948\n","[400]\tvalidation_0-auc:0.941383\n","[450]\tvalidation_0-auc:0.942355\n","[500]\tvalidation_0-auc:0.942871\n","[550]\tvalidation_0-auc:0.943355\n","[600]\tvalidation_0-auc:0.943734\n","[650]\tvalidation_0-auc:0.943877\n","[700]\tvalidation_0-auc:0.943819\n","[750]\tvalidation_0-auc:0.943686\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.943982\n","\n","Score not improved on adding D13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D14_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805926\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878968\n","[100]\tvalidation_0-auc:0.898113\n","[150]\tvalidation_0-auc:0.911003\n","[200]\tvalidation_0-auc:0.922055\n","[250]\tvalidation_0-auc:0.93032\n","[300]\tvalidation_0-auc:0.936451\n","[350]\tvalidation_0-auc:0.939814\n","[400]\tvalidation_0-auc:0.941428\n","[450]\tvalidation_0-auc:0.942428\n","[500]\tvalidation_0-auc:0.942889\n","[550]\tvalidation_0-auc:0.943395\n","[600]\tvalidation_0-auc:0.943489\n","[650]\tvalidation_0-auc:0.943347\n","Stopping. Best iteration:\n","[581]\tvalidation_0-auc:0.94364\n","\n","Score not improved on adding D14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D14_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805409\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879132\n","[100]\tvalidation_0-auc:0.898518\n","[150]\tvalidation_0-auc:0.911332\n","[200]\tvalidation_0-auc:0.922527\n","[250]\tvalidation_0-auc:0.930874\n","[300]\tvalidation_0-auc:0.936752\n","[350]\tvalidation_0-auc:0.940275\n","[400]\tvalidation_0-auc:0.941855\n","[450]\tvalidation_0-auc:0.942704\n","[500]\tvalidation_0-auc:0.94306\n","[550]\tvalidation_0-auc:0.943249\n","[600]\tvalidation_0-auc:0.943401\n","[650]\tvalidation_0-auc:0.943332\n","[700]\tvalidation_0-auc:0.94327\n","[750]\tvalidation_0-auc:0.943244\n","Stopping. Best iteration:\n","[684]\tvalidation_0-auc:0.943498\n","\n","Score not improved on adding D14_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_29\n","--------------------------------------------------------------------------------\n","Skipping D15_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 3_30\n","--------------------------------------------------------------------------------\n","Adding new features: ['D2_uid_mean', 'D15_uid_mean', 'D15_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805942\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87953\n","[100]\tvalidation_0-auc:0.898486\n","[150]\tvalidation_0-auc:0.912203\n","[200]\tvalidation_0-auc:0.922967\n","[250]\tvalidation_0-auc:0.93082\n","[300]\tvalidation_0-auc:0.93691\n","[350]\tvalidation_0-auc:0.940463\n","[400]\tvalidation_0-auc:0.941934\n","[450]\tvalidation_0-auc:0.942918\n","[500]\tvalidation_0-auc:0.943628\n","[550]\tvalidation_0-auc:0.943933\n","[600]\tvalidation_0-auc:0.944304\n","[650]\tvalidation_0-auc:0.944316\n","[700]\tvalidation_0-auc:0.944521\n","[750]\tvalidation_0-auc:0.944489\n","Stopping. Best iteration:\n","[691]\tvalidation_0-auc:0.944641\n","\n","Score not improved on adding D15_uid_std\n","--------------------------------------------------------------------------------\n","None of the columns improving the score in this iteration\n","########################################################################################################################\n","Useful new features : ['D2_uid_mean', 'D15_uid_mean']\n","Best score : 0.945277\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"snb6bYQGhvww"},"source":["## FFS on group3"]},{"cell_type":"code","metadata":{"id":"Ac0kt6zMEFbJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617531684529,"user_tz":0,"elapsed":328820,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"967c3889-7c7f-4f18-c73f-7ede07e5184b"},"source":["\n","\n","\n","old_cols3 = [col for col in X_train.columns if col not in rem_cols and col not in new_feats]\n","old_cols3.extend(['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean'])\n","old_cols3.extend(['D2_uid_mean', 'D15_uid_mean'])\n","\n","\n","print(len(old_cols3))\n"],"execution_count":24,"outputs":[{"output_type":"stream","text":["234\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"R5OlGDNThRu4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617541083054,"user_tz":0,"elapsed":9727336,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"16026277-7bee-434f-f9ce-116492da36cf"},"source":["forward_feat_selection(new_cols=new_feat3,old_cols=old_cols3,best_score=0.945277)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Forward feature selection on 28 features <-->  current best score 0.945277 with 234 features\n","Model no: 1_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['C1_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.806333\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880006\n","[100]\tvalidation_0-auc:0.90027\n","[150]\tvalidation_0-auc:0.912564\n","[200]\tvalidation_0-auc:0.923378\n","[250]\tvalidation_0-auc:0.931524\n","[300]\tvalidation_0-auc:0.937548\n","[350]\tvalidation_0-auc:0.941233\n","[400]\tvalidation_0-auc:0.942866\n","[450]\tvalidation_0-auc:0.943837\n","[500]\tvalidation_0-auc:0.94454\n","[550]\tvalidation_0-auc:0.944789\n","[600]\tvalidation_0-auc:0.944815\n","[650]\tvalidation_0-auc:0.944802\n","Stopping. Best iteration:\n","[575]\tvalidation_0-auc:0.944919\n","\n","Score not improved on adding C1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['C1_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805558\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879413\n","[100]\tvalidation_0-auc:0.898606\n","[150]\tvalidation_0-auc:0.912439\n","[200]\tvalidation_0-auc:0.923049\n","[250]\tvalidation_0-auc:0.931177\n","[300]\tvalidation_0-auc:0.937234\n","[350]\tvalidation_0-auc:0.940504\n","[400]\tvalidation_0-auc:0.941863\n","[450]\tvalidation_0-auc:0.942861\n","[500]\tvalidation_0-auc:0.94335\n","[550]\tvalidation_0-auc:0.943644\n","[600]\tvalidation_0-auc:0.943795\n","[650]\tvalidation_0-auc:0.94367\n","Stopping. Best iteration:\n","[579]\tvalidation_0-auc:0.943892\n","\n","Score not improved on adding C1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['C2_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805954\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880718\n","[100]\tvalidation_0-auc:0.899159\n","[150]\tvalidation_0-auc:0.912691\n","[200]\tvalidation_0-auc:0.923479\n","[250]\tvalidation_0-auc:0.930835\n","[300]\tvalidation_0-auc:0.936243\n","[350]\tvalidation_0-auc:0.939346\n","[400]\tvalidation_0-auc:0.940608\n","[450]\tvalidation_0-auc:0.941486\n","[500]\tvalidation_0-auc:0.94202\n","[550]\tvalidation_0-auc:0.942514\n","[600]\tvalidation_0-auc:0.942659\n","[650]\tvalidation_0-auc:0.942592\n","Stopping. Best iteration:\n","[571]\tvalidation_0-auc:0.942771\n","\n","Score not improved on adding C2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['C2_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805176\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87884\n","[100]\tvalidation_0-auc:0.899379\n","[150]\tvalidation_0-auc:0.912402\n","[200]\tvalidation_0-auc:0.922845\n","[250]\tvalidation_0-auc:0.930908\n","[300]\tvalidation_0-auc:0.937095\n","[350]\tvalidation_0-auc:0.94064\n","[400]\tvalidation_0-auc:0.942183\n","[450]\tvalidation_0-auc:0.943082\n","[500]\tvalidation_0-auc:0.943432\n","[550]\tvalidation_0-auc:0.943734\n","[600]\tvalidation_0-auc:0.944059\n","[650]\tvalidation_0-auc:0.944014\n","[700]\tvalidation_0-auc:0.944227\n","[750]\tvalidation_0-auc:0.944111\n","Stopping. Best iteration:\n","[692]\tvalidation_0-auc:0.944307\n","\n","Score not improved on adding C2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['C3_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805936\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879288\n","[100]\tvalidation_0-auc:0.898493\n","[150]\tvalidation_0-auc:0.911657\n","[200]\tvalidation_0-auc:0.922593\n","[250]\tvalidation_0-auc:0.93089\n","[300]\tvalidation_0-auc:0.936829\n","[350]\tvalidation_0-auc:0.940496\n","[400]\tvalidation_0-auc:0.942394\n","[450]\tvalidation_0-auc:0.942877\n","[500]\tvalidation_0-auc:0.943252\n","[550]\tvalidation_0-auc:0.943587\n","[600]\tvalidation_0-auc:0.943842\n","[650]\tvalidation_0-auc:0.943629\n","Stopping. Best iteration:\n","[591]\tvalidation_0-auc:0.943894\n","\n","Score not improved on adding C3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['C3_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805722\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878672\n","[100]\tvalidation_0-auc:0.89781\n","[150]\tvalidation_0-auc:0.910599\n","[200]\tvalidation_0-auc:0.921856\n","[250]\tvalidation_0-auc:0.930098\n","[300]\tvalidation_0-auc:0.936124\n","[350]\tvalidation_0-auc:0.939487\n","[400]\tvalidation_0-auc:0.941095\n","[450]\tvalidation_0-auc:0.942183\n","[500]\tvalidation_0-auc:0.942723\n","[550]\tvalidation_0-auc:0.943083\n","[600]\tvalidation_0-auc:0.943044\n","[650]\tvalidation_0-auc:0.943113\n","[700]\tvalidation_0-auc:0.943258\n","[750]\tvalidation_0-auc:0.943154\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.943377\n","\n","Score not improved on adding C3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['C4_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.809406\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879343\n","[100]\tvalidation_0-auc:0.898092\n","[150]\tvalidation_0-auc:0.910928\n","[200]\tvalidation_0-auc:0.922149\n","[250]\tvalidation_0-auc:0.9301\n","[300]\tvalidation_0-auc:0.93611\n","[350]\tvalidation_0-auc:0.93975\n","[400]\tvalidation_0-auc:0.941274\n","[450]\tvalidation_0-auc:0.942203\n","[500]\tvalidation_0-auc:0.942898\n","[550]\tvalidation_0-auc:0.94337\n","[600]\tvalidation_0-auc:0.943519\n","[650]\tvalidation_0-auc:0.943594\n","[700]\tvalidation_0-auc:0.943853\n","[750]\tvalidation_0-auc:0.943687\n","Stopping. Best iteration:\n","[693]\tvalidation_0-auc:0.943967\n","\n","Score not improved on adding C4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['C4_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.813146\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879355\n","[100]\tvalidation_0-auc:0.898575\n","[150]\tvalidation_0-auc:0.911779\n","[200]\tvalidation_0-auc:0.922405\n","[250]\tvalidation_0-auc:0.929998\n","[300]\tvalidation_0-auc:0.936354\n","[350]\tvalidation_0-auc:0.94013\n","[400]\tvalidation_0-auc:0.941696\n","[450]\tvalidation_0-auc:0.942613\n","[500]\tvalidation_0-auc:0.943221\n","[550]\tvalidation_0-auc:0.943654\n","[600]\tvalidation_0-auc:0.943353\n","[650]\tvalidation_0-auc:0.943417\n","Stopping. Best iteration:\n","[551]\tvalidation_0-auc:0.943669\n","\n","Score not improved on adding C4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['C5_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.788683\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879829\n","[100]\tvalidation_0-auc:0.898885\n","[150]\tvalidation_0-auc:0.911771\n","[200]\tvalidation_0-auc:0.922664\n","[250]\tvalidation_0-auc:0.930509\n","[300]\tvalidation_0-auc:0.936628\n","[350]\tvalidation_0-auc:0.939805\n","[400]\tvalidation_0-auc:0.941211\n","[450]\tvalidation_0-auc:0.942087\n","[500]\tvalidation_0-auc:0.942428\n","[550]\tvalidation_0-auc:0.942692\n","[600]\tvalidation_0-auc:0.942812\n","[650]\tvalidation_0-auc:0.942808\n","[700]\tvalidation_0-auc:0.942658\n","Stopping. Best iteration:\n","[608]\tvalidation_0-auc:0.942872\n","\n","Score not improved on adding C5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['C5_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.80572\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879054\n","[100]\tvalidation_0-auc:0.89825\n","[150]\tvalidation_0-auc:0.911639\n","[200]\tvalidation_0-auc:0.92241\n","[250]\tvalidation_0-auc:0.930225\n","[300]\tvalidation_0-auc:0.936211\n","[350]\tvalidation_0-auc:0.939707\n","[400]\tvalidation_0-auc:0.941266\n","[450]\tvalidation_0-auc:0.942011\n","[500]\tvalidation_0-auc:0.942525\n","[550]\tvalidation_0-auc:0.943039\n","[600]\tvalidation_0-auc:0.943064\n","[650]\tvalidation_0-auc:0.943118\n","Stopping. Best iteration:\n","[575]\tvalidation_0-auc:0.943143\n","\n","Score not improved on adding C5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['C6_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805807\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.881095\n","[100]\tvalidation_0-auc:0.900049\n","[150]\tvalidation_0-auc:0.913673\n","[200]\tvalidation_0-auc:0.924005\n","[250]\tvalidation_0-auc:0.931982\n","[300]\tvalidation_0-auc:0.937802\n","[350]\tvalidation_0-auc:0.941145\n","[400]\tvalidation_0-auc:0.942536\n","[450]\tvalidation_0-auc:0.943326\n","[500]\tvalidation_0-auc:0.943635\n","[550]\tvalidation_0-auc:0.943799\n","[600]\tvalidation_0-auc:0.943931\n","[650]\tvalidation_0-auc:0.943945\n","[700]\tvalidation_0-auc:0.94394\n","Stopping. Best iteration:\n","[634]\tvalidation_0-auc:0.944081\n","\n","Score not improved on adding C6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['C6_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805786\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880125\n","[100]\tvalidation_0-auc:0.898844\n","[150]\tvalidation_0-auc:0.912435\n","[200]\tvalidation_0-auc:0.922959\n","[250]\tvalidation_0-auc:0.930918\n","[300]\tvalidation_0-auc:0.937003\n","[350]\tvalidation_0-auc:0.940771\n","[400]\tvalidation_0-auc:0.941763\n","[450]\tvalidation_0-auc:0.9427\n","[500]\tvalidation_0-auc:0.942954\n","[550]\tvalidation_0-auc:0.943189\n","[600]\tvalidation_0-auc:0.943263\n","[650]\tvalidation_0-auc:0.943174\n","Stopping. Best iteration:\n","[571]\tvalidation_0-auc:0.943358\n","\n","Score not improved on adding C6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['C7_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.814136\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879678\n","[100]\tvalidation_0-auc:0.898285\n","[150]\tvalidation_0-auc:0.911514\n","[200]\tvalidation_0-auc:0.922015\n","[250]\tvalidation_0-auc:0.929985\n","[300]\tvalidation_0-auc:0.936031\n","[350]\tvalidation_0-auc:0.939835\n","[400]\tvalidation_0-auc:0.941693\n","[450]\tvalidation_0-auc:0.94258\n","[500]\tvalidation_0-auc:0.943083\n","[550]\tvalidation_0-auc:0.943082\n","[600]\tvalidation_0-auc:0.943212\n","[650]\tvalidation_0-auc:0.943533\n","[700]\tvalidation_0-auc:0.943657\n","[750]\tvalidation_0-auc:0.943217\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.943801\n","\n","Score not improved on adding C7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['C7_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.809975\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879643\n","[100]\tvalidation_0-auc:0.898283\n","[150]\tvalidation_0-auc:0.911552\n","[200]\tvalidation_0-auc:0.922343\n","[250]\tvalidation_0-auc:0.929979\n","[300]\tvalidation_0-auc:0.93619\n","[350]\tvalidation_0-auc:0.939781\n","[400]\tvalidation_0-auc:0.941324\n","[450]\tvalidation_0-auc:0.942321\n","[500]\tvalidation_0-auc:0.94278\n","[550]\tvalidation_0-auc:0.943182\n","[600]\tvalidation_0-auc:0.943094\n","[650]\tvalidation_0-auc:0.943227\n","[700]\tvalidation_0-auc:0.943445\n","[750]\tvalidation_0-auc:0.94339\n","Stopping. Best iteration:\n","[698]\tvalidation_0-auc:0.943506\n","\n","Score not improved on adding C7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['C8_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.808951\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878712\n","[100]\tvalidation_0-auc:0.898289\n","[150]\tvalidation_0-auc:0.911651\n","[200]\tvalidation_0-auc:0.92242\n","[250]\tvalidation_0-auc:0.93078\n","[300]\tvalidation_0-auc:0.936488\n","[350]\tvalidation_0-auc:0.939947\n","[400]\tvalidation_0-auc:0.941334\n","[450]\tvalidation_0-auc:0.942236\n","[500]\tvalidation_0-auc:0.942884\n","[550]\tvalidation_0-auc:0.943242\n","[600]\tvalidation_0-auc:0.943416\n","[650]\tvalidation_0-auc:0.943333\n","[700]\tvalidation_0-auc:0.943064\n","Stopping. Best iteration:\n","[600]\tvalidation_0-auc:0.943416\n","\n","Score not improved on adding C8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['C8_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.809944\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879173\n","[100]\tvalidation_0-auc:0.898205\n","[150]\tvalidation_0-auc:0.911544\n","[200]\tvalidation_0-auc:0.921782\n","[250]\tvalidation_0-auc:0.930165\n","[300]\tvalidation_0-auc:0.936033\n","[350]\tvalidation_0-auc:0.939764\n","[400]\tvalidation_0-auc:0.941323\n","[450]\tvalidation_0-auc:0.941899\n","[500]\tvalidation_0-auc:0.942361\n","[550]\tvalidation_0-auc:0.942765\n","[600]\tvalidation_0-auc:0.942704\n","[650]\tvalidation_0-auc:0.942716\n","Stopping. Best iteration:\n","[570]\tvalidation_0-auc:0.942863\n","\n","Score not improved on adding C8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['C9_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805952\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879773\n","[100]\tvalidation_0-auc:0.898816\n","[150]\tvalidation_0-auc:0.91259\n","[200]\tvalidation_0-auc:0.923398\n","[250]\tvalidation_0-auc:0.931118\n","[300]\tvalidation_0-auc:0.93693\n","[350]\tvalidation_0-auc:0.940467\n","[400]\tvalidation_0-auc:0.941718\n","[450]\tvalidation_0-auc:0.942736\n","[500]\tvalidation_0-auc:0.943127\n","[550]\tvalidation_0-auc:0.943342\n","[600]\tvalidation_0-auc:0.943499\n","[650]\tvalidation_0-auc:0.943874\n","[700]\tvalidation_0-auc:0.943663\n","Stopping. Best iteration:\n","[634]\tvalidation_0-auc:0.943936\n","\n","Score not improved on adding C9_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['C9_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805713\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879391\n","[100]\tvalidation_0-auc:0.898897\n","[150]\tvalidation_0-auc:0.912311\n","[200]\tvalidation_0-auc:0.92285\n","[250]\tvalidation_0-auc:0.930599\n","[300]\tvalidation_0-auc:0.937007\n","[350]\tvalidation_0-auc:0.940553\n","[400]\tvalidation_0-auc:0.942205\n","[450]\tvalidation_0-auc:0.942921\n","[500]\tvalidation_0-auc:0.943332\n","[550]\tvalidation_0-auc:0.943646\n","[600]\tvalidation_0-auc:0.943498\n","[650]\tvalidation_0-auc:0.943566\n","Stopping. Best iteration:\n","[570]\tvalidation_0-auc:0.943733\n","\n","Score not improved on adding C9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['C10_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.809354\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879681\n","[100]\tvalidation_0-auc:0.898744\n","[150]\tvalidation_0-auc:0.911994\n","[200]\tvalidation_0-auc:0.92309\n","[250]\tvalidation_0-auc:0.930503\n","[300]\tvalidation_0-auc:0.936767\n","[350]\tvalidation_0-auc:0.940107\n","[400]\tvalidation_0-auc:0.941643\n","[450]\tvalidation_0-auc:0.942465\n","[500]\tvalidation_0-auc:0.942825\n","[550]\tvalidation_0-auc:0.943121\n","[600]\tvalidation_0-auc:0.943495\n","[650]\tvalidation_0-auc:0.943485\n","[700]\tvalidation_0-auc:0.943394\n","Stopping. Best iteration:\n","[636]\tvalidation_0-auc:0.943566\n","\n","Score not improved on adding C10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['C10_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.810467\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879171\n","[100]\tvalidation_0-auc:0.898433\n","[150]\tvalidation_0-auc:0.911646\n","[200]\tvalidation_0-auc:0.922709\n","[250]\tvalidation_0-auc:0.930761\n","[300]\tvalidation_0-auc:0.93662\n","[350]\tvalidation_0-auc:0.940058\n","[400]\tvalidation_0-auc:0.941696\n","[450]\tvalidation_0-auc:0.942667\n","[500]\tvalidation_0-auc:0.943067\n","[550]\tvalidation_0-auc:0.943304\n","[600]\tvalidation_0-auc:0.943312\n","[650]\tvalidation_0-auc:0.943341\n","[700]\tvalidation_0-auc:0.943438\n","[750]\tvalidation_0-auc:0.943273\n","Stopping. Best iteration:\n","[692]\tvalidation_0-auc:0.94351\n","\n","Score not improved on adding C10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['C11_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805682\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880073\n","[100]\tvalidation_0-auc:0.899561\n","[150]\tvalidation_0-auc:0.912612\n","[200]\tvalidation_0-auc:0.923331\n","[250]\tvalidation_0-auc:0.931379\n","[300]\tvalidation_0-auc:0.937268\n","[350]\tvalidation_0-auc:0.940822\n","[400]\tvalidation_0-auc:0.94233\n","[450]\tvalidation_0-auc:0.943301\n","[500]\tvalidation_0-auc:0.943511\n","[550]\tvalidation_0-auc:0.943828\n","[600]\tvalidation_0-auc:0.943842\n","[650]\tvalidation_0-auc:0.943925\n","Stopping. Best iteration:\n","[583]\tvalidation_0-auc:0.944064\n","\n","Score not improved on adding C11_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['C11_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.805549\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878745\n","[100]\tvalidation_0-auc:0.898507\n","[150]\tvalidation_0-auc:0.911444\n","[200]\tvalidation_0-auc:0.922285\n","[250]\tvalidation_0-auc:0.930662\n","[300]\tvalidation_0-auc:0.936888\n","[350]\tvalidation_0-auc:0.940593\n","[400]\tvalidation_0-auc:0.942168\n","[450]\tvalidation_0-auc:0.942939\n","[500]\tvalidation_0-auc:0.943514\n","[550]\tvalidation_0-auc:0.943708\n","[600]\tvalidation_0-auc:0.943579\n","Stopping. Best iteration:\n","[535]\tvalidation_0-auc:0.943727\n","\n","Score not improved on adding C11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['C12_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.810856\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879708\n","[100]\tvalidation_0-auc:0.899185\n","[150]\tvalidation_0-auc:0.911684\n","[200]\tvalidation_0-auc:0.922958\n","[250]\tvalidation_0-auc:0.930764\n","[300]\tvalidation_0-auc:0.93708\n","[350]\tvalidation_0-auc:0.940323\n","[400]\tvalidation_0-auc:0.941899\n","[450]\tvalidation_0-auc:0.942776\n","[500]\tvalidation_0-auc:0.943402\n","[550]\tvalidation_0-auc:0.943829\n","[600]\tvalidation_0-auc:0.943908\n","[650]\tvalidation_0-auc:0.9437\n","Stopping. Best iteration:\n","[581]\tvalidation_0-auc:0.94402\n","\n","Score not improved on adding C12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['C12_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.81383\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879605\n","[100]\tvalidation_0-auc:0.898607\n","[150]\tvalidation_0-auc:0.91184\n","[200]\tvalidation_0-auc:0.922455\n","[250]\tvalidation_0-auc:0.930596\n","[300]\tvalidation_0-auc:0.936585\n","[350]\tvalidation_0-auc:0.94018\n","[400]\tvalidation_0-auc:0.941876\n","[450]\tvalidation_0-auc:0.942607\n","[500]\tvalidation_0-auc:0.943024\n","[550]\tvalidation_0-auc:0.943272\n","[600]\tvalidation_0-auc:0.94344\n","[650]\tvalidation_0-auc:0.943636\n","[700]\tvalidation_0-auc:0.94392\n","[750]\tvalidation_0-auc:0.943965\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.944054\n","\n","Score not improved on adding C12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_25\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879478\n","[100]\tvalidation_0-auc:0.897972\n","[150]\tvalidation_0-auc:0.911656\n","[200]\tvalidation_0-auc:0.923737\n","[250]\tvalidation_0-auc:0.932232\n","[300]\tvalidation_0-auc:0.93832\n","[350]\tvalidation_0-auc:0.941558\n","[400]\tvalidation_0-auc:0.943215\n","[450]\tvalidation_0-auc:0.94437\n","[500]\tvalidation_0-auc:0.944816\n","[550]\tvalidation_0-auc:0.945058\n","[600]\tvalidation_0-auc:0.945323\n","[650]\tvalidation_0-auc:0.945105\n","[700]\tvalidation_0-auc:0.945181\n","[750]\tvalidation_0-auc:0.94523\n","Stopping. Best iteration:\n","[684]\tvalidation_0-auc:0.945399\n","\n","Score improved on adding C13_uid_mean new best score 0.945399\n","--------------------------------------------------------------------------------\n","Model no: 1_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.801853\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880238\n","[100]\tvalidation_0-auc:0.899738\n","[150]\tvalidation_0-auc:0.912753\n","[200]\tvalidation_0-auc:0.923599\n","[250]\tvalidation_0-auc:0.931591\n","[300]\tvalidation_0-auc:0.937768\n","[350]\tvalidation_0-auc:0.941105\n","[400]\tvalidation_0-auc:0.942718\n","[450]\tvalidation_0-auc:0.943508\n","[500]\tvalidation_0-auc:0.943933\n","[550]\tvalidation_0-auc:0.944101\n","[600]\tvalidation_0-auc:0.944062\n","[650]\tvalidation_0-auc:0.943909\n","Stopping. Best iteration:\n","[585]\tvalidation_0-auc:0.944182\n","\n","Score not improved on adding C13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 1_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['C14_uid_mean']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.807693\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879088\n","[100]\tvalidation_0-auc:0.897182\n","[150]\tvalidation_0-auc:0.910869\n","[200]\tvalidation_0-auc:0.922994\n","[250]\tvalidation_0-auc:0.932199\n","[300]\tvalidation_0-auc:0.93807\n","[350]\tvalidation_0-auc:0.941209\n","[400]\tvalidation_0-auc:0.942486\n","[450]\tvalidation_0-auc:0.943159\n","[500]\tvalidation_0-auc:0.943676\n","[550]\tvalidation_0-auc:0.943886\n","[600]\tvalidation_0-auc:0.943766\n","Stopping. Best iteration:\n","[529]\tvalidation_0-auc:0.943949\n","\n","Score not improved on adding C14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 1_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['C14_uid_std']\n","fitting model on 235 columns\n","[0]\tvalidation_0-auc:0.802804\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879328\n","[100]\tvalidation_0-auc:0.898327\n","[150]\tvalidation_0-auc:0.911831\n","[200]\tvalidation_0-auc:0.922517\n","[250]\tvalidation_0-auc:0.931036\n","[300]\tvalidation_0-auc:0.93723\n","[350]\tvalidation_0-auc:0.940891\n","[400]\tvalidation_0-auc:0.942332\n","[450]\tvalidation_0-auc:0.943207\n","[500]\tvalidation_0-auc:0.943779\n","[550]\tvalidation_0-auc:0.944313\n","[600]\tvalidation_0-auc:0.944444\n","[650]\tvalidation_0-auc:0.944337\n","Stopping. Best iteration:\n","[583]\tvalidation_0-auc:0.944549\n","\n","Score not improved on adding C14_uid_std\n","--------------------------------------------------------------------------------\n","Best col in this iteration C13_uid_mean, --->   current best cols: ['C13_uid_mean']\n","########################################################################################################################\n","Model no: 2_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C1_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878621\n","[100]\tvalidation_0-auc:0.896856\n","[150]\tvalidation_0-auc:0.912765\n","[200]\tvalidation_0-auc:0.925082\n","[250]\tvalidation_0-auc:0.933511\n","[300]\tvalidation_0-auc:0.938486\n","[350]\tvalidation_0-auc:0.941168\n","[400]\tvalidation_0-auc:0.942482\n","[450]\tvalidation_0-auc:0.94369\n","[500]\tvalidation_0-auc:0.944495\n","[550]\tvalidation_0-auc:0.944751\n","[600]\tvalidation_0-auc:0.944817\n","[650]\tvalidation_0-auc:0.944681\n","[700]\tvalidation_0-auc:0.944846\n","Stopping. Best iteration:\n","[617]\tvalidation_0-auc:0.944979\n","\n","Score not improved on adding C1_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C1_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878136\n","[100]\tvalidation_0-auc:0.896139\n","[150]\tvalidation_0-auc:0.911888\n","[200]\tvalidation_0-auc:0.924059\n","[250]\tvalidation_0-auc:0.932352\n","[300]\tvalidation_0-auc:0.937724\n","[350]\tvalidation_0-auc:0.940849\n","[400]\tvalidation_0-auc:0.942641\n","[450]\tvalidation_0-auc:0.943391\n","[500]\tvalidation_0-auc:0.944112\n","[550]\tvalidation_0-auc:0.944099\n","[600]\tvalidation_0-auc:0.944111\n","[650]\tvalidation_0-auc:0.944512\n","[700]\tvalidation_0-auc:0.944558\n","[750]\tvalidation_0-auc:0.944829\n","[800]\tvalidation_0-auc:0.944874\n","[850]\tvalidation_0-auc:0.944771\n","Stopping. Best iteration:\n","[777]\tvalidation_0-auc:0.944988\n","\n","Score not improved on adding C1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C2_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879042\n","[100]\tvalidation_0-auc:0.897105\n","[150]\tvalidation_0-auc:0.912596\n","[200]\tvalidation_0-auc:0.924932\n","[250]\tvalidation_0-auc:0.933355\n","[300]\tvalidation_0-auc:0.938489\n","[350]\tvalidation_0-auc:0.941886\n","[400]\tvalidation_0-auc:0.943398\n","[450]\tvalidation_0-auc:0.944338\n","[500]\tvalidation_0-auc:0.944943\n","[550]\tvalidation_0-auc:0.945085\n","[600]\tvalidation_0-auc:0.945162\n","[650]\tvalidation_0-auc:0.945211\n","[700]\tvalidation_0-auc:0.945334\n","[750]\tvalidation_0-auc:0.945389\n","[800]\tvalidation_0-auc:0.945295\n","Stopping. Best iteration:\n","[749]\tvalidation_0-auc:0.94544\n","\n","Score improved on adding C2_uid_mean new best score 0.94544\n","--------------------------------------------------------------------------------\n","Model no: 2_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C2_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878278\n","[100]\tvalidation_0-auc:0.896759\n","[150]\tvalidation_0-auc:0.912624\n","[200]\tvalidation_0-auc:0.924955\n","[250]\tvalidation_0-auc:0.933228\n","[300]\tvalidation_0-auc:0.93854\n","[350]\tvalidation_0-auc:0.941636\n","[400]\tvalidation_0-auc:0.943061\n","[450]\tvalidation_0-auc:0.943585\n","[500]\tvalidation_0-auc:0.944209\n","[550]\tvalidation_0-auc:0.944401\n","[600]\tvalidation_0-auc:0.94447\n","[650]\tvalidation_0-auc:0.944709\n","[700]\tvalidation_0-auc:0.945073\n","[750]\tvalidation_0-auc:0.94537\n","[800]\tvalidation_0-auc:0.945404\n","[850]\tvalidation_0-auc:0.945293\n","[900]\tvalidation_0-auc:0.945386\n","Stopping. Best iteration:\n","[810]\tvalidation_0-auc:0.945427\n","\n","Score not improved on adding C2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C3_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878787\n","[100]\tvalidation_0-auc:0.896383\n","[150]\tvalidation_0-auc:0.911581\n","[200]\tvalidation_0-auc:0.923922\n","[250]\tvalidation_0-auc:0.932779\n","[300]\tvalidation_0-auc:0.938039\n","[350]\tvalidation_0-auc:0.941021\n","[400]\tvalidation_0-auc:0.942513\n","[450]\tvalidation_0-auc:0.943443\n","[500]\tvalidation_0-auc:0.944201\n","[550]\tvalidation_0-auc:0.944725\n","[600]\tvalidation_0-auc:0.944694\n","[650]\tvalidation_0-auc:0.944682\n","Stopping. Best iteration:\n","[582]\tvalidation_0-auc:0.944803\n","\n","Score not improved on adding C3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C3_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878698\n","[100]\tvalidation_0-auc:0.896968\n","[150]\tvalidation_0-auc:0.911845\n","[200]\tvalidation_0-auc:0.923833\n","[250]\tvalidation_0-auc:0.932608\n","[300]\tvalidation_0-auc:0.938107\n","[350]\tvalidation_0-auc:0.941176\n","[400]\tvalidation_0-auc:0.942609\n","[450]\tvalidation_0-auc:0.943638\n","[500]\tvalidation_0-auc:0.944341\n","[550]\tvalidation_0-auc:0.944619\n","[600]\tvalidation_0-auc:0.944581\n","[650]\tvalidation_0-auc:0.944704\n","[700]\tvalidation_0-auc:0.94481\n","[750]\tvalidation_0-auc:0.944871\n","[800]\tvalidation_0-auc:0.944814\n","Stopping. Best iteration:\n","[747]\tvalidation_0-auc:0.944904\n","\n","Score not improved on adding C3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C4_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87854\n","[100]\tvalidation_0-auc:0.895615\n","[150]\tvalidation_0-auc:0.911526\n","[200]\tvalidation_0-auc:0.923928\n","[250]\tvalidation_0-auc:0.932416\n","[300]\tvalidation_0-auc:0.93799\n","[350]\tvalidation_0-auc:0.941098\n","[400]\tvalidation_0-auc:0.942536\n","[450]\tvalidation_0-auc:0.943664\n","[500]\tvalidation_0-auc:0.944295\n","[550]\tvalidation_0-auc:0.944701\n","[600]\tvalidation_0-auc:0.945032\n","[650]\tvalidation_0-auc:0.945108\n","[700]\tvalidation_0-auc:0.945042\n","Stopping. Best iteration:\n","[617]\tvalidation_0-auc:0.945274\n","\n","Score not improved on adding C4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C4_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878793\n","[100]\tvalidation_0-auc:0.895681\n","[150]\tvalidation_0-auc:0.911786\n","[200]\tvalidation_0-auc:0.923312\n","[250]\tvalidation_0-auc:0.931778\n","[300]\tvalidation_0-auc:0.937131\n","[350]\tvalidation_0-auc:0.940094\n","[400]\tvalidation_0-auc:0.941619\n","[450]\tvalidation_0-auc:0.942604\n","[500]\tvalidation_0-auc:0.943269\n","[550]\tvalidation_0-auc:0.943552\n","[600]\tvalidation_0-auc:0.943793\n","[650]\tvalidation_0-auc:0.944012\n","[700]\tvalidation_0-auc:0.944141\n","[750]\tvalidation_0-auc:0.944201\n","[800]\tvalidation_0-auc:0.944105\n","Stopping. Best iteration:\n","[735]\tvalidation_0-auc:0.944307\n","\n","Score not improved on adding C4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C5_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878802\n","[100]\tvalidation_0-auc:0.896352\n","[150]\tvalidation_0-auc:0.911955\n","[200]\tvalidation_0-auc:0.92396\n","[250]\tvalidation_0-auc:0.932386\n","[300]\tvalidation_0-auc:0.938026\n","[350]\tvalidation_0-auc:0.940763\n","[400]\tvalidation_0-auc:0.942268\n","[450]\tvalidation_0-auc:0.943192\n","[500]\tvalidation_0-auc:0.943883\n","[550]\tvalidation_0-auc:0.94424\n","[600]\tvalidation_0-auc:0.944248\n","Stopping. Best iteration:\n","[533]\tvalidation_0-auc:0.944343\n","\n","Score not improved on adding C5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C5_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878725\n","[100]\tvalidation_0-auc:0.897461\n","[150]\tvalidation_0-auc:0.912053\n","[200]\tvalidation_0-auc:0.924157\n","[250]\tvalidation_0-auc:0.932369\n","[300]\tvalidation_0-auc:0.937571\n","[350]\tvalidation_0-auc:0.940888\n","[400]\tvalidation_0-auc:0.942343\n","[450]\tvalidation_0-auc:0.943012\n","[500]\tvalidation_0-auc:0.943793\n","[550]\tvalidation_0-auc:0.944071\n","[600]\tvalidation_0-auc:0.94431\n","[650]\tvalidation_0-auc:0.944424\n","[700]\tvalidation_0-auc:0.944568\n","[750]\tvalidation_0-auc:0.94473\n","[800]\tvalidation_0-auc:0.944588\n","Stopping. Best iteration:\n","[747]\tvalidation_0-auc:0.94479\n","\n","Score not improved on adding C5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C6_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880076\n","[100]\tvalidation_0-auc:0.896829\n","[150]\tvalidation_0-auc:0.912848\n","[200]\tvalidation_0-auc:0.92589\n","[250]\tvalidation_0-auc:0.933204\n","[300]\tvalidation_0-auc:0.938563\n","[350]\tvalidation_0-auc:0.94173\n","[400]\tvalidation_0-auc:0.943369\n","[450]\tvalidation_0-auc:0.944495\n","[500]\tvalidation_0-auc:0.945101\n","[550]\tvalidation_0-auc:0.94538\n","[600]\tvalidation_0-auc:0.945605\n","[650]\tvalidation_0-auc:0.945527\n","[700]\tvalidation_0-auc:0.945365\n","Stopping. Best iteration:\n","[612]\tvalidation_0-auc:0.9457\n","\n","Score improved on adding C6_uid_mean new best score 0.9457\n","--------------------------------------------------------------------------------\n","Model no: 2_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C6_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878488\n","[100]\tvalidation_0-auc:0.897062\n","[150]\tvalidation_0-auc:0.91196\n","[200]\tvalidation_0-auc:0.924377\n","[250]\tvalidation_0-auc:0.932207\n","[300]\tvalidation_0-auc:0.937796\n","[350]\tvalidation_0-auc:0.940664\n","[400]\tvalidation_0-auc:0.942271\n","[450]\tvalidation_0-auc:0.943557\n","[500]\tvalidation_0-auc:0.944388\n","[550]\tvalidation_0-auc:0.944599\n","[600]\tvalidation_0-auc:0.944658\n","[650]\tvalidation_0-auc:0.944641\n","[700]\tvalidation_0-auc:0.944679\n","[750]\tvalidation_0-auc:0.945013\n","[800]\tvalidation_0-auc:0.945064\n","[850]\tvalidation_0-auc:0.944907\n","Stopping. Best iteration:\n","[784]\tvalidation_0-auc:0.945087\n","\n","Score not improved on adding C6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C7_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878348\n","[100]\tvalidation_0-auc:0.895388\n","[150]\tvalidation_0-auc:0.9113\n","[200]\tvalidation_0-auc:0.924273\n","[250]\tvalidation_0-auc:0.932621\n","[300]\tvalidation_0-auc:0.937787\n","[350]\tvalidation_0-auc:0.940599\n","[400]\tvalidation_0-auc:0.941993\n","[450]\tvalidation_0-auc:0.943139\n","[500]\tvalidation_0-auc:0.943992\n","[550]\tvalidation_0-auc:0.944182\n","[600]\tvalidation_0-auc:0.944391\n","[650]\tvalidation_0-auc:0.944537\n","[700]\tvalidation_0-auc:0.944473\n","[750]\tvalidation_0-auc:0.944665\n","[800]\tvalidation_0-auc:0.944603\n","Stopping. Best iteration:\n","[739]\tvalidation_0-auc:0.94473\n","\n","Score not improved on adding C7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C7_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878255\n","[100]\tvalidation_0-auc:0.895635\n","[150]\tvalidation_0-auc:0.911295\n","[200]\tvalidation_0-auc:0.923713\n","[250]\tvalidation_0-auc:0.932248\n","[300]\tvalidation_0-auc:0.937667\n","[350]\tvalidation_0-auc:0.940544\n","[400]\tvalidation_0-auc:0.942162\n","[450]\tvalidation_0-auc:0.942914\n","[500]\tvalidation_0-auc:0.943855\n","[550]\tvalidation_0-auc:0.944068\n","[600]\tvalidation_0-auc:0.944409\n","[650]\tvalidation_0-auc:0.944627\n","[700]\tvalidation_0-auc:0.944519\n","[750]\tvalidation_0-auc:0.944732\n","[800]\tvalidation_0-auc:0.944561\n","Stopping. Best iteration:\n","[746]\tvalidation_0-auc:0.944761\n","\n","Score not improved on adding C7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C8_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878269\n","[100]\tvalidation_0-auc:0.894941\n","[150]\tvalidation_0-auc:0.910784\n","[200]\tvalidation_0-auc:0.92355\n","[250]\tvalidation_0-auc:0.932172\n","[300]\tvalidation_0-auc:0.937527\n","[350]\tvalidation_0-auc:0.940417\n","[400]\tvalidation_0-auc:0.941946\n","[450]\tvalidation_0-auc:0.942899\n","[500]\tvalidation_0-auc:0.943585\n","[550]\tvalidation_0-auc:0.943629\n","[600]\tvalidation_0-auc:0.94352\n","Stopping. Best iteration:\n","[535]\tvalidation_0-auc:0.943758\n","\n","Score not improved on adding C8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C8_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878377\n","[100]\tvalidation_0-auc:0.895331\n","[150]\tvalidation_0-auc:0.910442\n","[200]\tvalidation_0-auc:0.922716\n","[250]\tvalidation_0-auc:0.931648\n","[300]\tvalidation_0-auc:0.937309\n","[350]\tvalidation_0-auc:0.940532\n","[400]\tvalidation_0-auc:0.942367\n","[450]\tvalidation_0-auc:0.9434\n","[500]\tvalidation_0-auc:0.944261\n","[550]\tvalidation_0-auc:0.944525\n","[600]\tvalidation_0-auc:0.944465\n","Stopping. Best iteration:\n","[537]\tvalidation_0-auc:0.944629\n","\n","Score not improved on adding C8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_17\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879423\n","[100]\tvalidation_0-auc:0.896789\n","[150]\tvalidation_0-auc:0.912618\n","[200]\tvalidation_0-auc:0.924904\n","[250]\tvalidation_0-auc:0.933173\n","[300]\tvalidation_0-auc:0.938781\n","[350]\tvalidation_0-auc:0.941889\n","[400]\tvalidation_0-auc:0.943664\n","[450]\tvalidation_0-auc:0.944691\n","[500]\tvalidation_0-auc:0.945767\n","[550]\tvalidation_0-auc:0.945786\n","[600]\tvalidation_0-auc:0.945799\n","[650]\tvalidation_0-auc:0.945969\n","[700]\tvalidation_0-auc:0.946245\n","[750]\tvalidation_0-auc:0.946218\n","[800]\tvalidation_0-auc:0.946207\n","Stopping. Best iteration:\n","[732]\tvalidation_0-auc:0.946307\n","\n","Score improved on adding C9_uid_mean new best score 0.946307\n","--------------------------------------------------------------------------------\n","Model no: 2_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87841\n","[100]\tvalidation_0-auc:0.896307\n","[150]\tvalidation_0-auc:0.911895\n","[200]\tvalidation_0-auc:0.92359\n","[250]\tvalidation_0-auc:0.931853\n","[300]\tvalidation_0-auc:0.937151\n","[350]\tvalidation_0-auc:0.940305\n","[400]\tvalidation_0-auc:0.94176\n","[450]\tvalidation_0-auc:0.942762\n","[500]\tvalidation_0-auc:0.943645\n","[550]\tvalidation_0-auc:0.943955\n","[600]\tvalidation_0-auc:0.944137\n","[650]\tvalidation_0-auc:0.944247\n","[700]\tvalidation_0-auc:0.944447\n","[750]\tvalidation_0-auc:0.944686\n","[800]\tvalidation_0-auc:0.944593\n","Stopping. Best iteration:\n","[733]\tvalidation_0-auc:0.944794\n","\n","Score not improved on adding C9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C10_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878671\n","[100]\tvalidation_0-auc:0.895376\n","[150]\tvalidation_0-auc:0.910924\n","[200]\tvalidation_0-auc:0.923405\n","[250]\tvalidation_0-auc:0.93216\n","[300]\tvalidation_0-auc:0.937762\n","[350]\tvalidation_0-auc:0.940665\n","[400]\tvalidation_0-auc:0.942133\n","[450]\tvalidation_0-auc:0.942879\n","[500]\tvalidation_0-auc:0.943678\n","[550]\tvalidation_0-auc:0.943939\n","[600]\tvalidation_0-auc:0.944064\n","[650]\tvalidation_0-auc:0.944152\n","[700]\tvalidation_0-auc:0.944227\n","[750]\tvalidation_0-auc:0.944237\n","[800]\tvalidation_0-auc:0.944063\n","Stopping. Best iteration:\n","[736]\tvalidation_0-auc:0.944366\n","\n","Score not improved on adding C10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C10_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878951\n","[100]\tvalidation_0-auc:0.89609\n","[150]\tvalidation_0-auc:0.911097\n","[200]\tvalidation_0-auc:0.923492\n","[250]\tvalidation_0-auc:0.932152\n","[300]\tvalidation_0-auc:0.93746\n","[350]\tvalidation_0-auc:0.940645\n","[400]\tvalidation_0-auc:0.942207\n","[450]\tvalidation_0-auc:0.943263\n","[500]\tvalidation_0-auc:0.943995\n","[550]\tvalidation_0-auc:0.944362\n","[600]\tvalidation_0-auc:0.944429\n","[650]\tvalidation_0-auc:0.94453\n","[700]\tvalidation_0-auc:0.944597\n","[750]\tvalidation_0-auc:0.944577\n","Stopping. Best iteration:\n","[679]\tvalidation_0-auc:0.944623\n","\n","Score not improved on adding C10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C11_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87894\n","[100]\tvalidation_0-auc:0.897427\n","[150]\tvalidation_0-auc:0.913497\n","[200]\tvalidation_0-auc:0.92613\n","[250]\tvalidation_0-auc:0.933616\n","[300]\tvalidation_0-auc:0.939117\n","[350]\tvalidation_0-auc:0.942249\n","[400]\tvalidation_0-auc:0.943726\n","[450]\tvalidation_0-auc:0.944566\n","[500]\tvalidation_0-auc:0.944973\n","[550]\tvalidation_0-auc:0.945221\n","[600]\tvalidation_0-auc:0.945367\n","[650]\tvalidation_0-auc:0.945357\n","[700]\tvalidation_0-auc:0.945593\n","[750]\tvalidation_0-auc:0.945812\n","[800]\tvalidation_0-auc:0.945825\n","[850]\tvalidation_0-auc:0.945793\n","[900]\tvalidation_0-auc:0.945494\n","Stopping. Best iteration:\n","[835]\tvalidation_0-auc:0.945929\n","\n","Score not improved on adding C11_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C11_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878798\n","[100]\tvalidation_0-auc:0.896384\n","[150]\tvalidation_0-auc:0.911744\n","[200]\tvalidation_0-auc:0.923532\n","[250]\tvalidation_0-auc:0.932048\n","[300]\tvalidation_0-auc:0.93771\n","[350]\tvalidation_0-auc:0.940703\n","[400]\tvalidation_0-auc:0.941934\n","[450]\tvalidation_0-auc:0.942907\n","[500]\tvalidation_0-auc:0.943554\n","[550]\tvalidation_0-auc:0.944037\n","[600]\tvalidation_0-auc:0.944057\n","[650]\tvalidation_0-auc:0.944222\n","[700]\tvalidation_0-auc:0.94452\n","[750]\tvalidation_0-auc:0.944527\n","[800]\tvalidation_0-auc:0.944423\n","Stopping. Best iteration:\n","[713]\tvalidation_0-auc:0.944691\n","\n","Score not improved on adding C11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C12_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879457\n","[100]\tvalidation_0-auc:0.896618\n","[150]\tvalidation_0-auc:0.912733\n","[200]\tvalidation_0-auc:0.924973\n","[250]\tvalidation_0-auc:0.932856\n","[300]\tvalidation_0-auc:0.938272\n","[350]\tvalidation_0-auc:0.940937\n","[400]\tvalidation_0-auc:0.942499\n","[450]\tvalidation_0-auc:0.943391\n","[500]\tvalidation_0-auc:0.943981\n","[550]\tvalidation_0-auc:0.944094\n","[600]\tvalidation_0-auc:0.943878\n","Stopping. Best iteration:\n","[525]\tvalidation_0-auc:0.944125\n","\n","Score not improved on adding C12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C12_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878478\n","[100]\tvalidation_0-auc:0.895133\n","[150]\tvalidation_0-auc:0.911196\n","[200]\tvalidation_0-auc:0.923827\n","[250]\tvalidation_0-auc:0.932027\n","[300]\tvalidation_0-auc:0.937603\n","[350]\tvalidation_0-auc:0.940373\n","[400]\tvalidation_0-auc:0.941911\n","[450]\tvalidation_0-auc:0.942996\n","[500]\tvalidation_0-auc:0.943726\n","[550]\tvalidation_0-auc:0.944415\n","[600]\tvalidation_0-auc:0.944661\n","[650]\tvalidation_0-auc:0.944634\n","[700]\tvalidation_0-auc:0.944701\n","[750]\tvalidation_0-auc:0.944892\n","[800]\tvalidation_0-auc:0.944797\n","[850]\tvalidation_0-auc:0.944724\n","Stopping. Best iteration:\n","[750]\tvalidation_0-auc:0.944892\n","\n","Score not improved on adding C12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_25\n","--------------------------------------------------------------------------------\n","Skipping C13_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 2_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C13_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879631\n","[100]\tvalidation_0-auc:0.897351\n","[150]\tvalidation_0-auc:0.911975\n","[200]\tvalidation_0-auc:0.923688\n","[250]\tvalidation_0-auc:0.932819\n","[300]\tvalidation_0-auc:0.938021\n","[350]\tvalidation_0-auc:0.941091\n","[400]\tvalidation_0-auc:0.942745\n","[450]\tvalidation_0-auc:0.943588\n","[500]\tvalidation_0-auc:0.944242\n","[550]\tvalidation_0-auc:0.944456\n","[600]\tvalidation_0-auc:0.944851\n","[650]\tvalidation_0-auc:0.944934\n","[700]\tvalidation_0-auc:0.944885\n","[750]\tvalidation_0-auc:0.94491\n","Stopping. Best iteration:\n","[686]\tvalidation_0-auc:0.945011\n","\n","Score not improved on adding C13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 2_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C14_uid_mean']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877366\n","[100]\tvalidation_0-auc:0.894843\n","[150]\tvalidation_0-auc:0.911862\n","[200]\tvalidation_0-auc:0.923916\n","[250]\tvalidation_0-auc:0.932449\n","[300]\tvalidation_0-auc:0.937548\n","[350]\tvalidation_0-auc:0.94056\n","[400]\tvalidation_0-auc:0.942263\n","[450]\tvalidation_0-auc:0.943262\n","[500]\tvalidation_0-auc:0.94404\n","[550]\tvalidation_0-auc:0.944206\n","[600]\tvalidation_0-auc:0.944213\n","[650]\tvalidation_0-auc:0.944209\n","[700]\tvalidation_0-auc:0.944287\n","[750]\tvalidation_0-auc:0.944733\n","[800]\tvalidation_0-auc:0.944607\n","Stopping. Best iteration:\n","[746]\tvalidation_0-auc:0.944786\n","\n","Score not improved on adding C14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 2_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C14_uid_std']\n","fitting model on 236 columns\n","[0]\tvalidation_0-auc:0.808461\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878432\n","[100]\tvalidation_0-auc:0.896345\n","[150]\tvalidation_0-auc:0.911238\n","[200]\tvalidation_0-auc:0.923503\n","[250]\tvalidation_0-auc:0.93223\n","[300]\tvalidation_0-auc:0.937383\n","[350]\tvalidation_0-auc:0.940446\n","[400]\tvalidation_0-auc:0.941969\n","[450]\tvalidation_0-auc:0.943322\n","[500]\tvalidation_0-auc:0.944041\n","[550]\tvalidation_0-auc:0.944273\n","[600]\tvalidation_0-auc:0.944391\n","[650]\tvalidation_0-auc:0.944506\n","[700]\tvalidation_0-auc:0.944526\n","[750]\tvalidation_0-auc:0.94462\n","[800]\tvalidation_0-auc:0.944384\n","[850]\tvalidation_0-auc:0.944558\n","Stopping. Best iteration:\n","[751]\tvalidation_0-auc:0.944661\n","\n","Score not improved on adding C14_uid_std\n","--------------------------------------------------------------------------------\n","Best col in this iteration C9_uid_mean, --->   current best cols: ['C13_uid_mean', 'C9_uid_mean']\n","########################################################################################################################\n","Model no: 3_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808144\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878363\n","[100]\tvalidation_0-auc:0.898567\n","[150]\tvalidation_0-auc:0.913589\n","[200]\tvalidation_0-auc:0.925957\n","[250]\tvalidation_0-auc:0.934006\n","[300]\tvalidation_0-auc:0.939227\n","[350]\tvalidation_0-auc:0.942116\n","[400]\tvalidation_0-auc:0.944085\n","[450]\tvalidation_0-auc:0.945107\n","[500]\tvalidation_0-auc:0.945951\n","[550]\tvalidation_0-auc:0.946556\n","[600]\tvalidation_0-auc:0.946689\n","[650]\tvalidation_0-auc:0.947018\n","[700]\tvalidation_0-auc:0.947251\n","[750]\tvalidation_0-auc:0.946998\n","[800]\tvalidation_0-auc:0.946959\n","Stopping. Best iteration:\n","[705]\tvalidation_0-auc:0.947257\n","\n","Score improved on adding C1_uid_mean new best score 0.947257\n","--------------------------------------------------------------------------------\n","Model no: 3_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808264\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877181\n","[100]\tvalidation_0-auc:0.8967\n","[150]\tvalidation_0-auc:0.912338\n","[200]\tvalidation_0-auc:0.925005\n","[250]\tvalidation_0-auc:0.933093\n","[300]\tvalidation_0-auc:0.938416\n","[350]\tvalidation_0-auc:0.941621\n","[400]\tvalidation_0-auc:0.943219\n","[450]\tvalidation_0-auc:0.944404\n","[500]\tvalidation_0-auc:0.944969\n","[550]\tvalidation_0-auc:0.945155\n","[600]\tvalidation_0-auc:0.945373\n","[650]\tvalidation_0-auc:0.945451\n","[700]\tvalidation_0-auc:0.945814\n","[750]\tvalidation_0-auc:0.945912\n","[800]\tvalidation_0-auc:0.945862\n","[850]\tvalidation_0-auc:0.945849\n","Stopping. Best iteration:\n","[781]\tvalidation_0-auc:0.946085\n","\n","Score not improved on adding C1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C2_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808282\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879133\n","[100]\tvalidation_0-auc:0.898589\n","[150]\tvalidation_0-auc:0.913975\n","[200]\tvalidation_0-auc:0.926175\n","[250]\tvalidation_0-auc:0.933243\n","[300]\tvalidation_0-auc:0.938899\n","[350]\tvalidation_0-auc:0.941628\n","[400]\tvalidation_0-auc:0.943423\n","[450]\tvalidation_0-auc:0.944413\n","[500]\tvalidation_0-auc:0.945292\n","[550]\tvalidation_0-auc:0.945992\n","[600]\tvalidation_0-auc:0.946016\n","[650]\tvalidation_0-auc:0.946145\n","[700]\tvalidation_0-auc:0.945943\n","Stopping. Best iteration:\n","[646]\tvalidation_0-auc:0.946202\n","\n","Score not improved on adding C2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C2_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808185\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877327\n","[100]\tvalidation_0-auc:0.897353\n","[150]\tvalidation_0-auc:0.912814\n","[200]\tvalidation_0-auc:0.925188\n","[250]\tvalidation_0-auc:0.933105\n","[300]\tvalidation_0-auc:0.938017\n","[350]\tvalidation_0-auc:0.941274\n","[400]\tvalidation_0-auc:0.943128\n","[450]\tvalidation_0-auc:0.943946\n","[500]\tvalidation_0-auc:0.944775\n","[550]\tvalidation_0-auc:0.945184\n","[600]\tvalidation_0-auc:0.945603\n","[650]\tvalidation_0-auc:0.945734\n","[700]\tvalidation_0-auc:0.94572\n","Stopping. Best iteration:\n","[636]\tvalidation_0-auc:0.945871\n","\n","Score not improved on adding C2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C3_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808452\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877347\n","[100]\tvalidation_0-auc:0.897417\n","[150]\tvalidation_0-auc:0.91187\n","[200]\tvalidation_0-auc:0.924893\n","[250]\tvalidation_0-auc:0.932666\n","[300]\tvalidation_0-auc:0.937852\n","[350]\tvalidation_0-auc:0.94091\n","[400]\tvalidation_0-auc:0.942578\n","[450]\tvalidation_0-auc:0.943593\n","[500]\tvalidation_0-auc:0.944342\n","[550]\tvalidation_0-auc:0.944769\n","[600]\tvalidation_0-auc:0.944868\n","[650]\tvalidation_0-auc:0.945064\n","[700]\tvalidation_0-auc:0.945109\n","[750]\tvalidation_0-auc:0.945038\n","[800]\tvalidation_0-auc:0.945083\n","[850]\tvalidation_0-auc:0.944888\n","Stopping. Best iteration:\n","[786]\tvalidation_0-auc:0.945184\n","\n","Score not improved on adding C3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C3_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.80858\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87694\n","[100]\tvalidation_0-auc:0.897272\n","[150]\tvalidation_0-auc:0.91264\n","[200]\tvalidation_0-auc:0.924937\n","[250]\tvalidation_0-auc:0.932653\n","[300]\tvalidation_0-auc:0.938221\n","[350]\tvalidation_0-auc:0.941115\n","[400]\tvalidation_0-auc:0.942437\n","[450]\tvalidation_0-auc:0.943334\n","[500]\tvalidation_0-auc:0.944258\n","[550]\tvalidation_0-auc:0.944715\n","[600]\tvalidation_0-auc:0.94513\n","[650]\tvalidation_0-auc:0.945365\n","[700]\tvalidation_0-auc:0.945483\n","[750]\tvalidation_0-auc:0.945411\n","[800]\tvalidation_0-auc:0.945339\n","Stopping. Best iteration:\n","[707]\tvalidation_0-auc:0.945546\n","\n","Score not improved on adding C3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C4_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.815272\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878632\n","[100]\tvalidation_0-auc:0.897021\n","[150]\tvalidation_0-auc:0.91186\n","[200]\tvalidation_0-auc:0.924494\n","[250]\tvalidation_0-auc:0.932474\n","[300]\tvalidation_0-auc:0.937839\n","[350]\tvalidation_0-auc:0.9409\n","[400]\tvalidation_0-auc:0.942643\n","[450]\tvalidation_0-auc:0.943726\n","[500]\tvalidation_0-auc:0.944193\n","[550]\tvalidation_0-auc:0.944719\n","[600]\tvalidation_0-auc:0.94499\n","[650]\tvalidation_0-auc:0.945224\n","[700]\tvalidation_0-auc:0.945437\n","[750]\tvalidation_0-auc:0.945543\n","[800]\tvalidation_0-auc:0.945515\n","Stopping. Best iteration:\n","[740]\tvalidation_0-auc:0.945641\n","\n","Score not improved on adding C4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C4_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.81659\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877638\n","[100]\tvalidation_0-auc:0.89692\n","[150]\tvalidation_0-auc:0.911408\n","[200]\tvalidation_0-auc:0.923984\n","[250]\tvalidation_0-auc:0.932244\n","[300]\tvalidation_0-auc:0.937442\n","[350]\tvalidation_0-auc:0.940504\n","[400]\tvalidation_0-auc:0.942225\n","[450]\tvalidation_0-auc:0.943289\n","[500]\tvalidation_0-auc:0.944113\n","[550]\tvalidation_0-auc:0.944574\n","[600]\tvalidation_0-auc:0.944885\n","[650]\tvalidation_0-auc:0.945096\n","[700]\tvalidation_0-auc:0.94516\n","[750]\tvalidation_0-auc:0.945121\n","Stopping. Best iteration:\n","[687]\tvalidation_0-auc:0.945267\n","\n","Score not improved on adding C4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C5_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808464\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877869\n","[100]\tvalidation_0-auc:0.897377\n","[150]\tvalidation_0-auc:0.912616\n","[200]\tvalidation_0-auc:0.925465\n","[250]\tvalidation_0-auc:0.933098\n","[300]\tvalidation_0-auc:0.938516\n","[350]\tvalidation_0-auc:0.941318\n","[400]\tvalidation_0-auc:0.943238\n","[450]\tvalidation_0-auc:0.944179\n","[500]\tvalidation_0-auc:0.944873\n","[550]\tvalidation_0-auc:0.945246\n","[600]\tvalidation_0-auc:0.945583\n","[650]\tvalidation_0-auc:0.945673\n","[700]\tvalidation_0-auc:0.945749\n","[750]\tvalidation_0-auc:0.945554\n","Stopping. Best iteration:\n","[679]\tvalidation_0-auc:0.945848\n","\n","Score not improved on adding C5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C5_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808578\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877027\n","[100]\tvalidation_0-auc:0.897358\n","[150]\tvalidation_0-auc:0.912937\n","[200]\tvalidation_0-auc:0.925263\n","[250]\tvalidation_0-auc:0.932847\n","[300]\tvalidation_0-auc:0.938315\n","[350]\tvalidation_0-auc:0.941147\n","[400]\tvalidation_0-auc:0.94273\n","[450]\tvalidation_0-auc:0.943795\n","[500]\tvalidation_0-auc:0.944689\n","[550]\tvalidation_0-auc:0.945039\n","[600]\tvalidation_0-auc:0.945032\n","[650]\tvalidation_0-auc:0.945456\n","[700]\tvalidation_0-auc:0.945482\n","[750]\tvalidation_0-auc:0.945478\n","[800]\tvalidation_0-auc:0.94576\n","[850]\tvalidation_0-auc:0.945658\n","[900]\tvalidation_0-auc:0.945604\n","[950]\tvalidation_0-auc:0.94545\n","Stopping. Best iteration:\n","[879]\tvalidation_0-auc:0.945789\n","\n","Score not improved on adding C5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C6_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.807951\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879586\n","[100]\tvalidation_0-auc:0.898331\n","[150]\tvalidation_0-auc:0.914169\n","[200]\tvalidation_0-auc:0.926266\n","[250]\tvalidation_0-auc:0.933975\n","[300]\tvalidation_0-auc:0.939283\n","[350]\tvalidation_0-auc:0.942115\n","[400]\tvalidation_0-auc:0.943674\n","[450]\tvalidation_0-auc:0.944477\n","[500]\tvalidation_0-auc:0.94509\n","[550]\tvalidation_0-auc:0.945588\n","[600]\tvalidation_0-auc:0.945545\n","[650]\tvalidation_0-auc:0.945599\n","[700]\tvalidation_0-auc:0.945366\n","Stopping. Best iteration:\n","[636]\tvalidation_0-auc:0.945699\n","\n","Score not improved on adding C6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C6_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.807937\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878994\n","[100]\tvalidation_0-auc:0.897708\n","[150]\tvalidation_0-auc:0.912484\n","[200]\tvalidation_0-auc:0.925126\n","[250]\tvalidation_0-auc:0.933222\n","[300]\tvalidation_0-auc:0.938574\n","[350]\tvalidation_0-auc:0.941421\n","[400]\tvalidation_0-auc:0.943185\n","[450]\tvalidation_0-auc:0.944418\n","[500]\tvalidation_0-auc:0.945106\n","[550]\tvalidation_0-auc:0.945681\n","[600]\tvalidation_0-auc:0.945665\n","[650]\tvalidation_0-auc:0.945831\n","[700]\tvalidation_0-auc:0.945816\n","Stopping. Best iteration:\n","[636]\tvalidation_0-auc:0.945881\n","\n","Score not improved on adding C6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C7_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.816859\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877869\n","[100]\tvalidation_0-auc:0.897186\n","[150]\tvalidation_0-auc:0.912168\n","[200]\tvalidation_0-auc:0.924686\n","[250]\tvalidation_0-auc:0.932819\n","[300]\tvalidation_0-auc:0.938163\n","[350]\tvalidation_0-auc:0.94077\n","[400]\tvalidation_0-auc:0.942448\n","[450]\tvalidation_0-auc:0.943591\n","[500]\tvalidation_0-auc:0.944427\n","[550]\tvalidation_0-auc:0.94478\n","[600]\tvalidation_0-auc:0.945113\n","[650]\tvalidation_0-auc:0.945332\n","[700]\tvalidation_0-auc:0.945375\n","[750]\tvalidation_0-auc:0.945265\n","Stopping. Best iteration:\n","[671]\tvalidation_0-auc:0.945449\n","\n","Score not improved on adding C7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C7_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.816368\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877556\n","[100]\tvalidation_0-auc:0.897224\n","[150]\tvalidation_0-auc:0.911958\n","[200]\tvalidation_0-auc:0.924285\n","[250]\tvalidation_0-auc:0.932417\n","[300]\tvalidation_0-auc:0.937835\n","[350]\tvalidation_0-auc:0.94085\n","[400]\tvalidation_0-auc:0.942775\n","[450]\tvalidation_0-auc:0.943868\n","[500]\tvalidation_0-auc:0.944629\n","[550]\tvalidation_0-auc:0.94519\n","[600]\tvalidation_0-auc:0.945378\n","[650]\tvalidation_0-auc:0.945526\n","[700]\tvalidation_0-auc:0.945655\n","[750]\tvalidation_0-auc:0.945601\n","[800]\tvalidation_0-auc:0.945557\n","Stopping. Best iteration:\n","[710]\tvalidation_0-auc:0.945732\n","\n","Score not improved on adding C7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C8_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.814387\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877005\n","[100]\tvalidation_0-auc:0.896686\n","[150]\tvalidation_0-auc:0.912231\n","[200]\tvalidation_0-auc:0.924562\n","[250]\tvalidation_0-auc:0.93245\n","[300]\tvalidation_0-auc:0.937829\n","[350]\tvalidation_0-auc:0.94097\n","[400]\tvalidation_0-auc:0.942622\n","[450]\tvalidation_0-auc:0.943644\n","[500]\tvalidation_0-auc:0.944234\n","[550]\tvalidation_0-auc:0.944775\n","[600]\tvalidation_0-auc:0.945218\n","[650]\tvalidation_0-auc:0.945482\n","[700]\tvalidation_0-auc:0.945539\n","[750]\tvalidation_0-auc:0.945651\n","[800]\tvalidation_0-auc:0.945648\n","[850]\tvalidation_0-auc:0.945583\n","Stopping. Best iteration:\n","[787]\tvalidation_0-auc:0.945792\n","\n","Score not improved on adding C8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C8_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.813408\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877585\n","[100]\tvalidation_0-auc:0.896448\n","[150]\tvalidation_0-auc:0.912008\n","[200]\tvalidation_0-auc:0.924419\n","[250]\tvalidation_0-auc:0.932793\n","[300]\tvalidation_0-auc:0.937714\n","[350]\tvalidation_0-auc:0.940423\n","[400]\tvalidation_0-auc:0.942174\n","[450]\tvalidation_0-auc:0.943058\n","[500]\tvalidation_0-auc:0.943767\n","[550]\tvalidation_0-auc:0.944093\n","[600]\tvalidation_0-auc:0.944311\n","[650]\tvalidation_0-auc:0.94456\n","[700]\tvalidation_0-auc:0.944927\n","[750]\tvalidation_0-auc:0.944697\n","Stopping. Best iteration:\n","[699]\tvalidation_0-auc:0.944946\n","\n","Score not improved on adding C8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_17\n","--------------------------------------------------------------------------------\n","Skipping C9_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 3_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C9_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808537\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877124\n","[100]\tvalidation_0-auc:0.897521\n","[150]\tvalidation_0-auc:0.91191\n","[200]\tvalidation_0-auc:0.924659\n","[250]\tvalidation_0-auc:0.932443\n","[300]\tvalidation_0-auc:0.937922\n","[350]\tvalidation_0-auc:0.941107\n","[400]\tvalidation_0-auc:0.942727\n","[450]\tvalidation_0-auc:0.943591\n","[500]\tvalidation_0-auc:0.944271\n","[550]\tvalidation_0-auc:0.944795\n","[600]\tvalidation_0-auc:0.945023\n","[650]\tvalidation_0-auc:0.945239\n","[700]\tvalidation_0-auc:0.945273\n","[750]\tvalidation_0-auc:0.945082\n","Stopping. Best iteration:\n","[676]\tvalidation_0-auc:0.945362\n","\n","Score not improved on adding C9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C10_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.81827\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87763\n","[100]\tvalidation_0-auc:0.897453\n","[150]\tvalidation_0-auc:0.912111\n","[200]\tvalidation_0-auc:0.924424\n","[250]\tvalidation_0-auc:0.932429\n","[300]\tvalidation_0-auc:0.937717\n","[350]\tvalidation_0-auc:0.940529\n","[400]\tvalidation_0-auc:0.942341\n","[450]\tvalidation_0-auc:0.943408\n","[500]\tvalidation_0-auc:0.943875\n","[550]\tvalidation_0-auc:0.944316\n","[600]\tvalidation_0-auc:0.944525\n","[650]\tvalidation_0-auc:0.944993\n","[700]\tvalidation_0-auc:0.945001\n","[750]\tvalidation_0-auc:0.944783\n","Stopping. Best iteration:\n","[670]\tvalidation_0-auc:0.945037\n","\n","Score not improved on adding C10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C10_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.819649\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878011\n","[100]\tvalidation_0-auc:0.897031\n","[150]\tvalidation_0-auc:0.911428\n","[200]\tvalidation_0-auc:0.924429\n","[250]\tvalidation_0-auc:0.932505\n","[300]\tvalidation_0-auc:0.938086\n","[350]\tvalidation_0-auc:0.941014\n","[400]\tvalidation_0-auc:0.942712\n","[450]\tvalidation_0-auc:0.943711\n","[500]\tvalidation_0-auc:0.944378\n","[550]\tvalidation_0-auc:0.944867\n","[600]\tvalidation_0-auc:0.944922\n","[650]\tvalidation_0-auc:0.944929\n","[700]\tvalidation_0-auc:0.945039\n","[750]\tvalidation_0-auc:0.945046\n","[800]\tvalidation_0-auc:0.944863\n","Stopping. Best iteration:\n","[736]\tvalidation_0-auc:0.945096\n","\n","Score not improved on adding C10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C11_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.807747\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878748\n","[100]\tvalidation_0-auc:0.898532\n","[150]\tvalidation_0-auc:0.914125\n","[200]\tvalidation_0-auc:0.926053\n","[250]\tvalidation_0-auc:0.933399\n","[300]\tvalidation_0-auc:0.93881\n","[350]\tvalidation_0-auc:0.941757\n","[400]\tvalidation_0-auc:0.943341\n","[450]\tvalidation_0-auc:0.944112\n","[500]\tvalidation_0-auc:0.944678\n","[550]\tvalidation_0-auc:0.945166\n","[600]\tvalidation_0-auc:0.945273\n","[650]\tvalidation_0-auc:0.945593\n","[700]\tvalidation_0-auc:0.945746\n","[750]\tvalidation_0-auc:0.945597\n","[800]\tvalidation_0-auc:0.945665\n","[850]\tvalidation_0-auc:0.945617\n","Stopping. Best iteration:\n","[788]\tvalidation_0-auc:0.945798\n","\n","Score not improved on adding C11_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C11_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.8083\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878098\n","[100]\tvalidation_0-auc:0.897563\n","[150]\tvalidation_0-auc:0.912355\n","[200]\tvalidation_0-auc:0.924642\n","[250]\tvalidation_0-auc:0.932649\n","[300]\tvalidation_0-auc:0.93817\n","[350]\tvalidation_0-auc:0.941268\n","[400]\tvalidation_0-auc:0.942927\n","[450]\tvalidation_0-auc:0.944003\n","[500]\tvalidation_0-auc:0.944933\n","[550]\tvalidation_0-auc:0.945336\n","[600]\tvalidation_0-auc:0.945496\n","[650]\tvalidation_0-auc:0.945347\n","[700]\tvalidation_0-auc:0.945536\n","[750]\tvalidation_0-auc:0.945425\n","[800]\tvalidation_0-auc:0.945289\n","Stopping. Best iteration:\n","[707]\tvalidation_0-auc:0.94557\n","\n","Score not improved on adding C11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C12_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.817446\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878097\n","[100]\tvalidation_0-auc:0.89815\n","[150]\tvalidation_0-auc:0.912832\n","[200]\tvalidation_0-auc:0.925536\n","[250]\tvalidation_0-auc:0.933646\n","[300]\tvalidation_0-auc:0.938785\n","[350]\tvalidation_0-auc:0.941581\n","[400]\tvalidation_0-auc:0.943408\n","[450]\tvalidation_0-auc:0.944467\n","[500]\tvalidation_0-auc:0.9453\n","[550]\tvalidation_0-auc:0.94594\n","[600]\tvalidation_0-auc:0.946039\n","[650]\tvalidation_0-auc:0.946194\n","[700]\tvalidation_0-auc:0.946329\n","[750]\tvalidation_0-auc:0.946174\n","[800]\tvalidation_0-auc:0.946237\n","Stopping. Best iteration:\n","[705]\tvalidation_0-auc:0.946407\n","\n","Score not improved on adding C12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C12_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.815656\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877956\n","[100]\tvalidation_0-auc:0.897586\n","[150]\tvalidation_0-auc:0.912395\n","[200]\tvalidation_0-auc:0.924618\n","[250]\tvalidation_0-auc:0.9326\n","[300]\tvalidation_0-auc:0.937908\n","[350]\tvalidation_0-auc:0.940798\n","[400]\tvalidation_0-auc:0.942775\n","[450]\tvalidation_0-auc:0.943923\n","[500]\tvalidation_0-auc:0.94469\n","[550]\tvalidation_0-auc:0.945286\n","[600]\tvalidation_0-auc:0.94545\n","[650]\tvalidation_0-auc:0.945749\n","[700]\tvalidation_0-auc:0.945961\n","[750]\tvalidation_0-auc:0.945624\n","[800]\tvalidation_0-auc:0.945542\n","Stopping. Best iteration:\n","[707]\tvalidation_0-auc:0.945998\n","\n","Score not improved on adding C12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_25\n","--------------------------------------------------------------------------------\n","Skipping C13_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 3_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C13_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.80922\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877895\n","[100]\tvalidation_0-auc:0.897692\n","[150]\tvalidation_0-auc:0.913085\n","[200]\tvalidation_0-auc:0.925161\n","[250]\tvalidation_0-auc:0.933025\n","[300]\tvalidation_0-auc:0.938281\n","[350]\tvalidation_0-auc:0.941004\n","[400]\tvalidation_0-auc:0.942591\n","[450]\tvalidation_0-auc:0.943631\n","[500]\tvalidation_0-auc:0.944173\n","[550]\tvalidation_0-auc:0.944538\n","[600]\tvalidation_0-auc:0.944719\n","[650]\tvalidation_0-auc:0.944781\n","[700]\tvalidation_0-auc:0.944718\n","Stopping. Best iteration:\n","[607]\tvalidation_0-auc:0.94487\n","\n","Score not improved on adding C13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 3_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C14_uid_mean']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.807816\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877129\n","[100]\tvalidation_0-auc:0.896336\n","[150]\tvalidation_0-auc:0.91228\n","[200]\tvalidation_0-auc:0.925038\n","[250]\tvalidation_0-auc:0.932403\n","[300]\tvalidation_0-auc:0.937584\n","[350]\tvalidation_0-auc:0.940776\n","[400]\tvalidation_0-auc:0.942148\n","[450]\tvalidation_0-auc:0.943395\n","[500]\tvalidation_0-auc:0.944184\n","[550]\tvalidation_0-auc:0.944688\n","[600]\tvalidation_0-auc:0.945055\n","[650]\tvalidation_0-auc:0.94505\n","[700]\tvalidation_0-auc:0.944967\n","Stopping. Best iteration:\n","[609]\tvalidation_0-auc:0.945091\n","\n","Score not improved on adding C14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 3_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C14_uid_std']\n","fitting model on 237 columns\n","[0]\tvalidation_0-auc:0.808436\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877551\n","[100]\tvalidation_0-auc:0.897865\n","[150]\tvalidation_0-auc:0.913345\n","[200]\tvalidation_0-auc:0.925119\n","[250]\tvalidation_0-auc:0.933387\n","[300]\tvalidation_0-auc:0.938156\n","[350]\tvalidation_0-auc:0.941034\n","[400]\tvalidation_0-auc:0.942983\n","[450]\tvalidation_0-auc:0.943939\n","[500]\tvalidation_0-auc:0.944573\n","[550]\tvalidation_0-auc:0.945016\n","[600]\tvalidation_0-auc:0.945195\n","[650]\tvalidation_0-auc:0.945091\n","[700]\tvalidation_0-auc:0.945204\n","Stopping. Best iteration:\n","[613]\tvalidation_0-auc:0.945229\n","\n","Score not improved on adding C14_uid_std\n","--------------------------------------------------------------------------------\n","Best col in this iteration C1_uid_mean, --->   current best cols: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean']\n","########################################################################################################################\n","Model no: 4_1\n","--------------------------------------------------------------------------------\n","Skipping C1_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 4_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C1_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808721\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878215\n","[100]\tvalidation_0-auc:0.89655\n","[150]\tvalidation_0-auc:0.915143\n","[200]\tvalidation_0-auc:0.92743\n","[250]\tvalidation_0-auc:0.935204\n","[300]\tvalidation_0-auc:0.940413\n","[350]\tvalidation_0-auc:0.943144\n","[400]\tvalidation_0-auc:0.945069\n","[450]\tvalidation_0-auc:0.946146\n","[500]\tvalidation_0-auc:0.946637\n","[550]\tvalidation_0-auc:0.946745\n","[600]\tvalidation_0-auc:0.946943\n","[650]\tvalidation_0-auc:0.946899\n","[700]\tvalidation_0-auc:0.94701\n","[750]\tvalidation_0-auc:0.946942\n","[800]\tvalidation_0-auc:0.946596\n","Stopping. Best iteration:\n","[741]\tvalidation_0-auc:0.947081\n","\n","Score not improved on adding C1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C2_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808536\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879241\n","[100]\tvalidation_0-auc:0.897225\n","[150]\tvalidation_0-auc:0.914922\n","[200]\tvalidation_0-auc:0.926707\n","[250]\tvalidation_0-auc:0.935018\n","[300]\tvalidation_0-auc:0.940141\n","[350]\tvalidation_0-auc:0.943107\n","[400]\tvalidation_0-auc:0.944497\n","[450]\tvalidation_0-auc:0.945327\n","[500]\tvalidation_0-auc:0.945908\n","[550]\tvalidation_0-auc:0.946432\n","[600]\tvalidation_0-auc:0.946582\n","[650]\tvalidation_0-auc:0.946561\n","[700]\tvalidation_0-auc:0.946507\n","[750]\tvalidation_0-auc:0.946383\n","Stopping. Best iteration:\n","[659]\tvalidation_0-auc:0.946651\n","\n","Score not improved on adding C2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C2_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808594\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87847\n","[100]\tvalidation_0-auc:0.897495\n","[150]\tvalidation_0-auc:0.915333\n","[200]\tvalidation_0-auc:0.92785\n","[250]\tvalidation_0-auc:0.935664\n","[300]\tvalidation_0-auc:0.940751\n","[350]\tvalidation_0-auc:0.943243\n","[400]\tvalidation_0-auc:0.945006\n","[450]\tvalidation_0-auc:0.945987\n","[500]\tvalidation_0-auc:0.946422\n","[550]\tvalidation_0-auc:0.946699\n","[600]\tvalidation_0-auc:0.946971\n","[650]\tvalidation_0-auc:0.946946\n","[700]\tvalidation_0-auc:0.946903\n","[750]\tvalidation_0-auc:0.94668\n","Stopping. Best iteration:\n","[660]\tvalidation_0-auc:0.947078\n","\n","Score not improved on adding C2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C3_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808538\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878535\n","[100]\tvalidation_0-auc:0.89624\n","[150]\tvalidation_0-auc:0.914527\n","[200]\tvalidation_0-auc:0.926633\n","[250]\tvalidation_0-auc:0.934759\n","[300]\tvalidation_0-auc:0.939812\n","[350]\tvalidation_0-auc:0.942404\n","[400]\tvalidation_0-auc:0.944337\n","[450]\tvalidation_0-auc:0.945551\n","[500]\tvalidation_0-auc:0.946154\n","[550]\tvalidation_0-auc:0.946563\n","[600]\tvalidation_0-auc:0.946856\n","[650]\tvalidation_0-auc:0.946983\n","[700]\tvalidation_0-auc:0.947192\n","[750]\tvalidation_0-auc:0.946994\n","Stopping. Best iteration:\n","[697]\tvalidation_0-auc:0.947195\n","\n","Score not improved on adding C3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C3_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808588\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878733\n","[100]\tvalidation_0-auc:0.896774\n","[150]\tvalidation_0-auc:0.914173\n","[200]\tvalidation_0-auc:0.926585\n","[250]\tvalidation_0-auc:0.934548\n","[300]\tvalidation_0-auc:0.939678\n","[350]\tvalidation_0-auc:0.942527\n","[400]\tvalidation_0-auc:0.944448\n","[450]\tvalidation_0-auc:0.945491\n","[500]\tvalidation_0-auc:0.945949\n","[550]\tvalidation_0-auc:0.946565\n","[600]\tvalidation_0-auc:0.946825\n","[650]\tvalidation_0-auc:0.946829\n","[700]\tvalidation_0-auc:0.947102\n","[750]\tvalidation_0-auc:0.946849\n","[800]\tvalidation_0-auc:0.946514\n","Stopping. Best iteration:\n","[703]\tvalidation_0-auc:0.947107\n","\n","Score not improved on adding C3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C4_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.816055\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87859\n","[100]\tvalidation_0-auc:0.897181\n","[150]\tvalidation_0-auc:0.914824\n","[200]\tvalidation_0-auc:0.926977\n","[250]\tvalidation_0-auc:0.935012\n","[300]\tvalidation_0-auc:0.940261\n","[350]\tvalidation_0-auc:0.943585\n","[400]\tvalidation_0-auc:0.945116\n","[450]\tvalidation_0-auc:0.946016\n","[500]\tvalidation_0-auc:0.946483\n","[550]\tvalidation_0-auc:0.946949\n","[600]\tvalidation_0-auc:0.947045\n","[650]\tvalidation_0-auc:0.947066\n","[700]\tvalidation_0-auc:0.947091\n","[750]\tvalidation_0-auc:0.946802\n","Stopping. Best iteration:\n","[683]\tvalidation_0-auc:0.947148\n","\n","Score not improved on adding C4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C4_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.81796\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878712\n","[100]\tvalidation_0-auc:0.896224\n","[150]\tvalidation_0-auc:0.91425\n","[200]\tvalidation_0-auc:0.926883\n","[250]\tvalidation_0-auc:0.935242\n","[300]\tvalidation_0-auc:0.940306\n","[350]\tvalidation_0-auc:0.943286\n","[400]\tvalidation_0-auc:0.94514\n","[450]\tvalidation_0-auc:0.94632\n","[500]\tvalidation_0-auc:0.946587\n","[550]\tvalidation_0-auc:0.946853\n","[600]\tvalidation_0-auc:0.947146\n","[650]\tvalidation_0-auc:0.947016\n","Stopping. Best iteration:\n","[598]\tvalidation_0-auc:0.947157\n","\n","Score not improved on adding C4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C5_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808551\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87832\n","[100]\tvalidation_0-auc:0.897428\n","[150]\tvalidation_0-auc:0.914493\n","[200]\tvalidation_0-auc:0.926873\n","[250]\tvalidation_0-auc:0.935136\n","[300]\tvalidation_0-auc:0.93978\n","[350]\tvalidation_0-auc:0.942598\n","[400]\tvalidation_0-auc:0.944276\n","[450]\tvalidation_0-auc:0.945188\n","[500]\tvalidation_0-auc:0.945664\n","[550]\tvalidation_0-auc:0.945991\n","[600]\tvalidation_0-auc:0.946122\n","[650]\tvalidation_0-auc:0.945999\n","Stopping. Best iteration:\n","[588]\tvalidation_0-auc:0.946204\n","\n","Score not improved on adding C5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C5_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808587\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878836\n","[100]\tvalidation_0-auc:0.896996\n","[150]\tvalidation_0-auc:0.914535\n","[200]\tvalidation_0-auc:0.927136\n","[250]\tvalidation_0-auc:0.935232\n","[300]\tvalidation_0-auc:0.940306\n","[350]\tvalidation_0-auc:0.942796\n","[400]\tvalidation_0-auc:0.944529\n","[450]\tvalidation_0-auc:0.945403\n","[500]\tvalidation_0-auc:0.945925\n","[550]\tvalidation_0-auc:0.946427\n","[600]\tvalidation_0-auc:0.946807\n","[650]\tvalidation_0-auc:0.946773\n","[700]\tvalidation_0-auc:0.947118\n","[750]\tvalidation_0-auc:0.946925\n","[800]\tvalidation_0-auc:0.946751\n","Stopping. Best iteration:\n","[700]\tvalidation_0-auc:0.947118\n","\n","Score not improved on adding C5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C6_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808203\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880821\n","[100]\tvalidation_0-auc:0.898133\n","[150]\tvalidation_0-auc:0.916067\n","[200]\tvalidation_0-auc:0.927637\n","[250]\tvalidation_0-auc:0.935783\n","[300]\tvalidation_0-auc:0.940598\n","[350]\tvalidation_0-auc:0.943475\n","[400]\tvalidation_0-auc:0.945112\n","[450]\tvalidation_0-auc:0.946129\n","[500]\tvalidation_0-auc:0.946557\n","[550]\tvalidation_0-auc:0.946674\n","[600]\tvalidation_0-auc:0.946921\n","[650]\tvalidation_0-auc:0.946963\n","[700]\tvalidation_0-auc:0.94701\n","[750]\tvalidation_0-auc:0.947012\n","[800]\tvalidation_0-auc:0.946872\n","Stopping. Best iteration:\n","[710]\tvalidation_0-auc:0.947056\n","\n","Score not improved on adding C6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C6_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808616\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878147\n","[100]\tvalidation_0-auc:0.896989\n","[150]\tvalidation_0-auc:0.915146\n","[200]\tvalidation_0-auc:0.926748\n","[250]\tvalidation_0-auc:0.934859\n","[300]\tvalidation_0-auc:0.939782\n","[350]\tvalidation_0-auc:0.942758\n","[400]\tvalidation_0-auc:0.944648\n","[450]\tvalidation_0-auc:0.945671\n","[500]\tvalidation_0-auc:0.946105\n","[550]\tvalidation_0-auc:0.946458\n","[600]\tvalidation_0-auc:0.946565\n","[650]\tvalidation_0-auc:0.94655\n","[700]\tvalidation_0-auc:0.946763\n","[750]\tvalidation_0-auc:0.946477\n","[800]\tvalidation_0-auc:0.946173\n","Stopping. Best iteration:\n","[700]\tvalidation_0-auc:0.946763\n","\n","Score not improved on adding C6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C7_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.817671\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877894\n","[100]\tvalidation_0-auc:0.89667\n","[150]\tvalidation_0-auc:0.914862\n","[200]\tvalidation_0-auc:0.927023\n","[250]\tvalidation_0-auc:0.934908\n","[300]\tvalidation_0-auc:0.940161\n","[350]\tvalidation_0-auc:0.942675\n","[400]\tvalidation_0-auc:0.944675\n","[450]\tvalidation_0-auc:0.946058\n","[500]\tvalidation_0-auc:0.946566\n","[550]\tvalidation_0-auc:0.947142\n","[600]\tvalidation_0-auc:0.947345\n","[650]\tvalidation_0-auc:0.947278\n","[700]\tvalidation_0-auc:0.947432\n","[750]\tvalidation_0-auc:0.947278\n","[800]\tvalidation_0-auc:0.94718\n","Stopping. Best iteration:\n","[701]\tvalidation_0-auc:0.947444\n","\n","Score improved on adding C7_uid_mean new best score 0.947444\n","--------------------------------------------------------------------------------\n","Model no: 4_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C7_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.817776\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878358\n","[100]\tvalidation_0-auc:0.896731\n","[150]\tvalidation_0-auc:0.914654\n","[200]\tvalidation_0-auc:0.926967\n","[250]\tvalidation_0-auc:0.934941\n","[300]\tvalidation_0-auc:0.939983\n","[350]\tvalidation_0-auc:0.943127\n","[400]\tvalidation_0-auc:0.944817\n","[450]\tvalidation_0-auc:0.945991\n","[500]\tvalidation_0-auc:0.946238\n","[550]\tvalidation_0-auc:0.946512\n","[600]\tvalidation_0-auc:0.946667\n","[650]\tvalidation_0-auc:0.94681\n","[700]\tvalidation_0-auc:0.946878\n","[750]\tvalidation_0-auc:0.94656\n","Stopping. Best iteration:\n","[699]\tvalidation_0-auc:0.946886\n","\n","Score not improved on adding C7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C8_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.815561\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878767\n","[100]\tvalidation_0-auc:0.896597\n","[150]\tvalidation_0-auc:0.914176\n","[200]\tvalidation_0-auc:0.926044\n","[250]\tvalidation_0-auc:0.934485\n","[300]\tvalidation_0-auc:0.939569\n","[350]\tvalidation_0-auc:0.942632\n","[400]\tvalidation_0-auc:0.944696\n","[450]\tvalidation_0-auc:0.945643\n","[500]\tvalidation_0-auc:0.946162\n","[550]\tvalidation_0-auc:0.946645\n","[600]\tvalidation_0-auc:0.946825\n","[650]\tvalidation_0-auc:0.946787\n","[700]\tvalidation_0-auc:0.946892\n","[750]\tvalidation_0-auc:0.946629\n","[800]\tvalidation_0-auc:0.946555\n","Stopping. Best iteration:\n","[702]\tvalidation_0-auc:0.946911\n","\n","Score not improved on adding C8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C8_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.814721\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877908\n","[100]\tvalidation_0-auc:0.896385\n","[150]\tvalidation_0-auc:0.914477\n","[200]\tvalidation_0-auc:0.926262\n","[250]\tvalidation_0-auc:0.934691\n","[300]\tvalidation_0-auc:0.939199\n","[350]\tvalidation_0-auc:0.942439\n","[400]\tvalidation_0-auc:0.944349\n","[450]\tvalidation_0-auc:0.945092\n","[500]\tvalidation_0-auc:0.945506\n","[550]\tvalidation_0-auc:0.945721\n","[600]\tvalidation_0-auc:0.946094\n","[650]\tvalidation_0-auc:0.946056\n","[700]\tvalidation_0-auc:0.94634\n","[750]\tvalidation_0-auc:0.946376\n","[800]\tvalidation_0-auc:0.946213\n","Stopping. Best iteration:\n","[701]\tvalidation_0-auc:0.94638\n","\n","Score not improved on adding C8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_17\n","--------------------------------------------------------------------------------\n","Skipping C9_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 4_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C9_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808592\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87847\n","[100]\tvalidation_0-auc:0.896497\n","[150]\tvalidation_0-auc:0.914316\n","[200]\tvalidation_0-auc:0.927352\n","[250]\tvalidation_0-auc:0.935664\n","[300]\tvalidation_0-auc:0.940584\n","[350]\tvalidation_0-auc:0.943268\n","[400]\tvalidation_0-auc:0.94502\n","[450]\tvalidation_0-auc:0.945862\n","[500]\tvalidation_0-auc:0.94658\n","[550]\tvalidation_0-auc:0.947246\n","[600]\tvalidation_0-auc:0.947414\n","[650]\tvalidation_0-auc:0.947364\n","Stopping. Best iteration:\n","[585]\tvalidation_0-auc:0.947521\n","\n","Score improved on adding C9_uid_std new best score 0.947521\n","--------------------------------------------------------------------------------\n","Model no: 4_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C10_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.818949\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878437\n","[100]\tvalidation_0-auc:0.896233\n","[150]\tvalidation_0-auc:0.914617\n","[200]\tvalidation_0-auc:0.92723\n","[250]\tvalidation_0-auc:0.935163\n","[300]\tvalidation_0-auc:0.940186\n","[350]\tvalidation_0-auc:0.943026\n","[400]\tvalidation_0-auc:0.944883\n","[450]\tvalidation_0-auc:0.945686\n","[500]\tvalidation_0-auc:0.946249\n","[550]\tvalidation_0-auc:0.946815\n","[600]\tvalidation_0-auc:0.947172\n","[650]\tvalidation_0-auc:0.947047\n","[700]\tvalidation_0-auc:0.947076\n","Stopping. Best iteration:\n","[628]\tvalidation_0-auc:0.947228\n","\n","Score not improved on adding C10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C10_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.820129\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878071\n","[100]\tvalidation_0-auc:0.896341\n","[150]\tvalidation_0-auc:0.914867\n","[200]\tvalidation_0-auc:0.927008\n","[250]\tvalidation_0-auc:0.935157\n","[300]\tvalidation_0-auc:0.940082\n","[350]\tvalidation_0-auc:0.943214\n","[400]\tvalidation_0-auc:0.944847\n","[450]\tvalidation_0-auc:0.945967\n","[500]\tvalidation_0-auc:0.946456\n","[550]\tvalidation_0-auc:0.946844\n","[600]\tvalidation_0-auc:0.947049\n","[650]\tvalidation_0-auc:0.946985\n","[700]\tvalidation_0-auc:0.947165\n","[750]\tvalidation_0-auc:0.946978\n","Stopping. Best iteration:\n","[681]\tvalidation_0-auc:0.94721\n","\n","Score not improved on adding C10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_21\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87913\n","[100]\tvalidation_0-auc:0.896664\n","[150]\tvalidation_0-auc:0.915863\n","[200]\tvalidation_0-auc:0.927402\n","[250]\tvalidation_0-auc:0.934999\n","[300]\tvalidation_0-auc:0.940228\n","[350]\tvalidation_0-auc:0.943592\n","[400]\tvalidation_0-auc:0.945446\n","[450]\tvalidation_0-auc:0.946316\n","[500]\tvalidation_0-auc:0.946743\n","[550]\tvalidation_0-auc:0.946973\n","[600]\tvalidation_0-auc:0.947158\n","[650]\tvalidation_0-auc:0.94734\n","[700]\tvalidation_0-auc:0.947541\n","[750]\tvalidation_0-auc:0.947262\n","[800]\tvalidation_0-auc:0.94737\n","Stopping. Best iteration:\n","[700]\tvalidation_0-auc:0.947541\n","\n","Score improved on adding C11_uid_mean new best score 0.947541\n","--------------------------------------------------------------------------------\n","Model no: 4_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808648\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879165\n","[100]\tvalidation_0-auc:0.897084\n","[150]\tvalidation_0-auc:0.915544\n","[200]\tvalidation_0-auc:0.927691\n","[250]\tvalidation_0-auc:0.935657\n","[300]\tvalidation_0-auc:0.94049\n","[350]\tvalidation_0-auc:0.94331\n","[400]\tvalidation_0-auc:0.944978\n","[450]\tvalidation_0-auc:0.946119\n","[500]\tvalidation_0-auc:0.94668\n","[550]\tvalidation_0-auc:0.946863\n","[600]\tvalidation_0-auc:0.947082\n","[650]\tvalidation_0-auc:0.947146\n","[700]\tvalidation_0-auc:0.9471\n","Stopping. Best iteration:\n","[629]\tvalidation_0-auc:0.947302\n","\n","Score not improved on adding C11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C12_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.818849\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878811\n","[100]\tvalidation_0-auc:0.897269\n","[150]\tvalidation_0-auc:0.915362\n","[200]\tvalidation_0-auc:0.926908\n","[250]\tvalidation_0-auc:0.934713\n","[300]\tvalidation_0-auc:0.939933\n","[350]\tvalidation_0-auc:0.943146\n","[400]\tvalidation_0-auc:0.944959\n","[450]\tvalidation_0-auc:0.945704\n","[500]\tvalidation_0-auc:0.946182\n","[550]\tvalidation_0-auc:0.946523\n","[600]\tvalidation_0-auc:0.946512\n","[650]\tvalidation_0-auc:0.946497\n","[700]\tvalidation_0-auc:0.946609\n","Stopping. Best iteration:\n","[631]\tvalidation_0-auc:0.946672\n","\n","Score not improved on adding C12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C12_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.816695\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879384\n","[100]\tvalidation_0-auc:0.896667\n","[150]\tvalidation_0-auc:0.91484\n","[200]\tvalidation_0-auc:0.926788\n","[250]\tvalidation_0-auc:0.934249\n","[300]\tvalidation_0-auc:0.939394\n","[350]\tvalidation_0-auc:0.942647\n","[400]\tvalidation_0-auc:0.944215\n","[450]\tvalidation_0-auc:0.945239\n","[500]\tvalidation_0-auc:0.945547\n","[550]\tvalidation_0-auc:0.945946\n","[600]\tvalidation_0-auc:0.946185\n","[650]\tvalidation_0-auc:0.94606\n","[700]\tvalidation_0-auc:0.946376\n","[750]\tvalidation_0-auc:0.946143\n","[800]\tvalidation_0-auc:0.946069\n","Stopping. Best iteration:\n","[702]\tvalidation_0-auc:0.946398\n","\n","Score not improved on adding C12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_25\n","--------------------------------------------------------------------------------\n","Skipping C13_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 4_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C13_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808716\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879254\n","[100]\tvalidation_0-auc:0.896585\n","[150]\tvalidation_0-auc:0.913822\n","[200]\tvalidation_0-auc:0.926231\n","[250]\tvalidation_0-auc:0.934109\n","[300]\tvalidation_0-auc:0.939657\n","[350]\tvalidation_0-auc:0.942468\n","[400]\tvalidation_0-auc:0.94417\n","[450]\tvalidation_0-auc:0.945049\n","[500]\tvalidation_0-auc:0.9457\n","[550]\tvalidation_0-auc:0.946181\n","[600]\tvalidation_0-auc:0.946346\n","[650]\tvalidation_0-auc:0.946341\n","[700]\tvalidation_0-auc:0.946651\n","[750]\tvalidation_0-auc:0.946356\n","[800]\tvalidation_0-auc:0.94607\n","Stopping. Best iteration:\n","[700]\tvalidation_0-auc:0.946651\n","\n","Score not improved on adding C13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 4_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C14_uid_mean']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808128\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877701\n","[100]\tvalidation_0-auc:0.895567\n","[150]\tvalidation_0-auc:0.914373\n","[200]\tvalidation_0-auc:0.926482\n","[250]\tvalidation_0-auc:0.934836\n","[300]\tvalidation_0-auc:0.940095\n","[350]\tvalidation_0-auc:0.942681\n","[400]\tvalidation_0-auc:0.944506\n","[450]\tvalidation_0-auc:0.945437\n","[500]\tvalidation_0-auc:0.946051\n","[550]\tvalidation_0-auc:0.946422\n","[600]\tvalidation_0-auc:0.946483\n","[650]\tvalidation_0-auc:0.946421\n","[700]\tvalidation_0-auc:0.946695\n","[750]\tvalidation_0-auc:0.946512\n","[800]\tvalidation_0-auc:0.94652\n","Stopping. Best iteration:\n","[703]\tvalidation_0-auc:0.946737\n","\n","Score not improved on adding C14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 4_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C14_uid_std']\n","fitting model on 238 columns\n","[0]\tvalidation_0-auc:0.808575\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879156\n","[100]\tvalidation_0-auc:0.896205\n","[150]\tvalidation_0-auc:0.914596\n","[200]\tvalidation_0-auc:0.927221\n","[250]\tvalidation_0-auc:0.935012\n","[300]\tvalidation_0-auc:0.940448\n","[350]\tvalidation_0-auc:0.943106\n","[400]\tvalidation_0-auc:0.945073\n","[450]\tvalidation_0-auc:0.946048\n","[500]\tvalidation_0-auc:0.9466\n","[550]\tvalidation_0-auc:0.946992\n","[600]\tvalidation_0-auc:0.947068\n","[650]\tvalidation_0-auc:0.94705\n","[700]\tvalidation_0-auc:0.947215\n","[750]\tvalidation_0-auc:0.947264\n","[800]\tvalidation_0-auc:0.946992\n","Stopping. Best iteration:\n","[711]\tvalidation_0-auc:0.947312\n","\n","Score not improved on adding C14_uid_std\n","--------------------------------------------------------------------------------\n","Best col in this iteration C11_uid_mean, --->   current best cols: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean']\n","########################################################################################################################\n","Model no: 5_1\n","--------------------------------------------------------------------------------\n","Skipping C1_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 5_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C1_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879058\n","[100]\tvalidation_0-auc:0.898319\n","[150]\tvalidation_0-auc:0.915444\n","[200]\tvalidation_0-auc:0.92764\n","[250]\tvalidation_0-auc:0.935497\n","[300]\tvalidation_0-auc:0.940701\n","[350]\tvalidation_0-auc:0.943419\n","[400]\tvalidation_0-auc:0.944817\n","[450]\tvalidation_0-auc:0.945616\n","[500]\tvalidation_0-auc:0.946083\n","[550]\tvalidation_0-auc:0.946481\n","[600]\tvalidation_0-auc:0.946496\n","[650]\tvalidation_0-auc:0.946322\n","Stopping. Best iteration:\n","[571]\tvalidation_0-auc:0.946603\n","\n","Score not improved on adding C1_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C2_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880102\n","[100]\tvalidation_0-auc:0.898271\n","[150]\tvalidation_0-auc:0.915849\n","[200]\tvalidation_0-auc:0.927446\n","[250]\tvalidation_0-auc:0.935575\n","[300]\tvalidation_0-auc:0.940985\n","[350]\tvalidation_0-auc:0.943199\n","[400]\tvalidation_0-auc:0.94483\n","[450]\tvalidation_0-auc:0.945713\n","[500]\tvalidation_0-auc:0.946052\n","[550]\tvalidation_0-auc:0.946575\n","[600]\tvalidation_0-auc:0.946707\n","[650]\tvalidation_0-auc:0.94654\n","[700]\tvalidation_0-auc:0.946479\n","Stopping. Best iteration:\n","[616]\tvalidation_0-auc:0.946747\n","\n","Score not improved on adding C2_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C2_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879207\n","[100]\tvalidation_0-auc:0.898316\n","[150]\tvalidation_0-auc:0.915723\n","[200]\tvalidation_0-auc:0.927199\n","[250]\tvalidation_0-auc:0.93563\n","[300]\tvalidation_0-auc:0.940707\n","[350]\tvalidation_0-auc:0.943105\n","[400]\tvalidation_0-auc:0.944594\n","[450]\tvalidation_0-auc:0.945306\n","[500]\tvalidation_0-auc:0.945707\n","[550]\tvalidation_0-auc:0.946059\n","[600]\tvalidation_0-auc:0.946356\n","[650]\tvalidation_0-auc:0.946327\n","[700]\tvalidation_0-auc:0.946212\n","Stopping. Best iteration:\n","[640]\tvalidation_0-auc:0.946418\n","\n","Score not improved on adding C2_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C3_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878683\n","[100]\tvalidation_0-auc:0.898084\n","[150]\tvalidation_0-auc:0.915127\n","[200]\tvalidation_0-auc:0.927076\n","[250]\tvalidation_0-auc:0.935135\n","[300]\tvalidation_0-auc:0.940425\n","[350]\tvalidation_0-auc:0.943263\n","[400]\tvalidation_0-auc:0.94477\n","[450]\tvalidation_0-auc:0.945727\n","[500]\tvalidation_0-auc:0.946272\n","[550]\tvalidation_0-auc:0.946594\n","[600]\tvalidation_0-auc:0.946727\n","[650]\tvalidation_0-auc:0.946556\n","[700]\tvalidation_0-auc:0.946335\n","Stopping. Best iteration:\n","[608]\tvalidation_0-auc:0.946811\n","\n","Score not improved on adding C3_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C3_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878783\n","[100]\tvalidation_0-auc:0.898262\n","[150]\tvalidation_0-auc:0.915067\n","[200]\tvalidation_0-auc:0.927343\n","[250]\tvalidation_0-auc:0.935732\n","[300]\tvalidation_0-auc:0.941176\n","[350]\tvalidation_0-auc:0.943733\n","[400]\tvalidation_0-auc:0.945279\n","[450]\tvalidation_0-auc:0.946207\n","[500]\tvalidation_0-auc:0.946601\n","[550]\tvalidation_0-auc:0.947123\n","[600]\tvalidation_0-auc:0.94737\n","[650]\tvalidation_0-auc:0.947133\n","[700]\tvalidation_0-auc:0.947019\n","Stopping. Best iteration:\n","[600]\tvalidation_0-auc:0.94737\n","\n","Score not improved on adding C3_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C4_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879044\n","[100]\tvalidation_0-auc:0.897885\n","[150]\tvalidation_0-auc:0.914447\n","[200]\tvalidation_0-auc:0.925855\n","[250]\tvalidation_0-auc:0.93469\n","[300]\tvalidation_0-auc:0.940204\n","[350]\tvalidation_0-auc:0.943182\n","[400]\tvalidation_0-auc:0.944593\n","[450]\tvalidation_0-auc:0.945673\n","[500]\tvalidation_0-auc:0.946257\n","[550]\tvalidation_0-auc:0.946545\n","[600]\tvalidation_0-auc:0.946605\n","[650]\tvalidation_0-auc:0.94654\n","Stopping. Best iteration:\n","[584]\tvalidation_0-auc:0.946807\n","\n","Score not improved on adding C4_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C4_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878923\n","[100]\tvalidation_0-auc:0.898267\n","[150]\tvalidation_0-auc:0.914979\n","[200]\tvalidation_0-auc:0.926959\n","[250]\tvalidation_0-auc:0.935275\n","[300]\tvalidation_0-auc:0.940429\n","[350]\tvalidation_0-auc:0.943135\n","[400]\tvalidation_0-auc:0.944781\n","[450]\tvalidation_0-auc:0.945611\n","[500]\tvalidation_0-auc:0.945971\n","[550]\tvalidation_0-auc:0.946434\n","[600]\tvalidation_0-auc:0.94653\n","[650]\tvalidation_0-auc:0.946363\n","Stopping. Best iteration:\n","[590]\tvalidation_0-auc:0.946599\n","\n","Score not improved on adding C4_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C5_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878831\n","[100]\tvalidation_0-auc:0.897663\n","[150]\tvalidation_0-auc:0.915295\n","[200]\tvalidation_0-auc:0.927335\n","[250]\tvalidation_0-auc:0.935411\n","[300]\tvalidation_0-auc:0.94075\n","[350]\tvalidation_0-auc:0.943335\n","[400]\tvalidation_0-auc:0.945173\n","[450]\tvalidation_0-auc:0.946152\n","[500]\tvalidation_0-auc:0.946648\n","[550]\tvalidation_0-auc:0.946856\n","[600]\tvalidation_0-auc:0.94697\n","[650]\tvalidation_0-auc:0.946741\n","Stopping. Best iteration:\n","[577]\tvalidation_0-auc:0.947023\n","\n","Score not improved on adding C5_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C5_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878816\n","[100]\tvalidation_0-auc:0.898548\n","[150]\tvalidation_0-auc:0.915763\n","[200]\tvalidation_0-auc:0.927228\n","[250]\tvalidation_0-auc:0.935494\n","[300]\tvalidation_0-auc:0.940706\n","[350]\tvalidation_0-auc:0.943511\n","[400]\tvalidation_0-auc:0.945018\n","[450]\tvalidation_0-auc:0.946108\n","[500]\tvalidation_0-auc:0.946609\n","[550]\tvalidation_0-auc:0.947143\n","[600]\tvalidation_0-auc:0.9472\n","[650]\tvalidation_0-auc:0.946985\n","Stopping. Best iteration:\n","[577]\tvalidation_0-auc:0.947279\n","\n","Score not improved on adding C5_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C6_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.881553\n","[100]\tvalidation_0-auc:0.899283\n","[150]\tvalidation_0-auc:0.916257\n","[200]\tvalidation_0-auc:0.927882\n","[250]\tvalidation_0-auc:0.936077\n","[300]\tvalidation_0-auc:0.941372\n","[350]\tvalidation_0-auc:0.943581\n","[400]\tvalidation_0-auc:0.945107\n","[450]\tvalidation_0-auc:0.945883\n","[500]\tvalidation_0-auc:0.946514\n","[550]\tvalidation_0-auc:0.946889\n","[600]\tvalidation_0-auc:0.947065\n","[650]\tvalidation_0-auc:0.947014\n","[700]\tvalidation_0-auc:0.946918\n","Stopping. Best iteration:\n","[633]\tvalidation_0-auc:0.947145\n","\n","Score not improved on adding C6_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C6_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879047\n","[100]\tvalidation_0-auc:0.898279\n","[150]\tvalidation_0-auc:0.915563\n","[200]\tvalidation_0-auc:0.927984\n","[250]\tvalidation_0-auc:0.935938\n","[300]\tvalidation_0-auc:0.941258\n","[350]\tvalidation_0-auc:0.943799\n","[400]\tvalidation_0-auc:0.945416\n","[450]\tvalidation_0-auc:0.946166\n","[500]\tvalidation_0-auc:0.946627\n","[550]\tvalidation_0-auc:0.946985\n","[600]\tvalidation_0-auc:0.947094\n","[650]\tvalidation_0-auc:0.94694\n","Stopping. Best iteration:\n","[584]\tvalidation_0-auc:0.947174\n","\n","Score not improved on adding C6_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C7_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879092\n","[100]\tvalidation_0-auc:0.898309\n","[150]\tvalidation_0-auc:0.915129\n","[200]\tvalidation_0-auc:0.926536\n","[250]\tvalidation_0-auc:0.935034\n","[300]\tvalidation_0-auc:0.940391\n","[350]\tvalidation_0-auc:0.943014\n","[400]\tvalidation_0-auc:0.944471\n","[450]\tvalidation_0-auc:0.944817\n","[500]\tvalidation_0-auc:0.945036\n","[550]\tvalidation_0-auc:0.945214\n","[600]\tvalidation_0-auc:0.945341\n","[650]\tvalidation_0-auc:0.945433\n","[700]\tvalidation_0-auc:0.945286\n","[750]\tvalidation_0-auc:0.945223\n","Stopping. Best iteration:\n","[659]\tvalidation_0-auc:0.945545\n","\n","Score not improved on adding C7_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C7_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878438\n","[100]\tvalidation_0-auc:0.897637\n","[150]\tvalidation_0-auc:0.91487\n","[200]\tvalidation_0-auc:0.926563\n","[250]\tvalidation_0-auc:0.935082\n","[300]\tvalidation_0-auc:0.940457\n","[350]\tvalidation_0-auc:0.943217\n","[400]\tvalidation_0-auc:0.944808\n","[450]\tvalidation_0-auc:0.945892\n","[500]\tvalidation_0-auc:0.946357\n","[550]\tvalidation_0-auc:0.946658\n","[600]\tvalidation_0-auc:0.946823\n","[650]\tvalidation_0-auc:0.946489\n","[700]\tvalidation_0-auc:0.946406\n","Stopping. Best iteration:\n","[613]\tvalidation_0-auc:0.946856\n","\n","Score not improved on adding C7_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_15\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C8_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880022\n","[100]\tvalidation_0-auc:0.898167\n","[150]\tvalidation_0-auc:0.914703\n","[200]\tvalidation_0-auc:0.926239\n","[250]\tvalidation_0-auc:0.935036\n","[300]\tvalidation_0-auc:0.940304\n","[350]\tvalidation_0-auc:0.942998\n","[400]\tvalidation_0-auc:0.944178\n","[450]\tvalidation_0-auc:0.945144\n","[500]\tvalidation_0-auc:0.945696\n","[550]\tvalidation_0-auc:0.946045\n","[600]\tvalidation_0-auc:0.946231\n","[650]\tvalidation_0-auc:0.945802\n","[700]\tvalidation_0-auc:0.945657\n","Stopping. Best iteration:\n","[601]\tvalidation_0-auc:0.946236\n","\n","Score not improved on adding C8_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_16\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C8_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877797\n","[100]\tvalidation_0-auc:0.898178\n","[150]\tvalidation_0-auc:0.915039\n","[200]\tvalidation_0-auc:0.926915\n","[250]\tvalidation_0-auc:0.934945\n","[300]\tvalidation_0-auc:0.940313\n","[350]\tvalidation_0-auc:0.942966\n","[400]\tvalidation_0-auc:0.944359\n","[450]\tvalidation_0-auc:0.945172\n","[500]\tvalidation_0-auc:0.945809\n","[550]\tvalidation_0-auc:0.946182\n","[600]\tvalidation_0-auc:0.946175\n","[650]\tvalidation_0-auc:0.946003\n","Stopping. Best iteration:\n","[553]\tvalidation_0-auc:0.946259\n","\n","Score not improved on adding C8_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_17\n","--------------------------------------------------------------------------------\n","Skipping C9_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 5_18\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C9_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878791\n","[100]\tvalidation_0-auc:0.898528\n","[150]\tvalidation_0-auc:0.915884\n","[200]\tvalidation_0-auc:0.927358\n","[250]\tvalidation_0-auc:0.935876\n","[300]\tvalidation_0-auc:0.940813\n","[350]\tvalidation_0-auc:0.943552\n","[400]\tvalidation_0-auc:0.945021\n","[450]\tvalidation_0-auc:0.946077\n","[500]\tvalidation_0-auc:0.946418\n","[550]\tvalidation_0-auc:0.946622\n","[600]\tvalidation_0-auc:0.94677\n","[650]\tvalidation_0-auc:0.946756\n","[700]\tvalidation_0-auc:0.946694\n","Stopping. Best iteration:\n","[639]\tvalidation_0-auc:0.946867\n","\n","Score not improved on adding C9_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_19\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C10_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878683\n","[100]\tvalidation_0-auc:0.897693\n","[150]\tvalidation_0-auc:0.915008\n","[200]\tvalidation_0-auc:0.926132\n","[250]\tvalidation_0-auc:0.934994\n","[300]\tvalidation_0-auc:0.940527\n","[350]\tvalidation_0-auc:0.943271\n","[400]\tvalidation_0-auc:0.944602\n","[450]\tvalidation_0-auc:0.945354\n","[500]\tvalidation_0-auc:0.94585\n","[550]\tvalidation_0-auc:0.946252\n","[600]\tvalidation_0-auc:0.946687\n","[650]\tvalidation_0-auc:0.946383\n","[700]\tvalidation_0-auc:0.946093\n","Stopping. Best iteration:\n","[600]\tvalidation_0-auc:0.946687\n","\n","Score not improved on adding C10_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_20\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C10_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878953\n","[100]\tvalidation_0-auc:0.897512\n","[150]\tvalidation_0-auc:0.914956\n","[200]\tvalidation_0-auc:0.926896\n","[250]\tvalidation_0-auc:0.935406\n","[300]\tvalidation_0-auc:0.940875\n","[350]\tvalidation_0-auc:0.943626\n","[400]\tvalidation_0-auc:0.944993\n","[450]\tvalidation_0-auc:0.945886\n","[500]\tvalidation_0-auc:0.946372\n","[550]\tvalidation_0-auc:0.946839\n","[600]\tvalidation_0-auc:0.946901\n","[650]\tvalidation_0-auc:0.946571\n","Stopping. Best iteration:\n","[590]\tvalidation_0-auc:0.947002\n","\n","Score not improved on adding C10_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_21\n","--------------------------------------------------------------------------------\n","Skipping C11_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 5_22\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C11_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878401\n","[100]\tvalidation_0-auc:0.897191\n","[150]\tvalidation_0-auc:0.914969\n","[200]\tvalidation_0-auc:0.926692\n","[250]\tvalidation_0-auc:0.935139\n","[300]\tvalidation_0-auc:0.940433\n","[350]\tvalidation_0-auc:0.943056\n","[400]\tvalidation_0-auc:0.944569\n","[450]\tvalidation_0-auc:0.945586\n","[500]\tvalidation_0-auc:0.945778\n","[550]\tvalidation_0-auc:0.946211\n","[600]\tvalidation_0-auc:0.946496\n","[650]\tvalidation_0-auc:0.946162\n","[700]\tvalidation_0-auc:0.945932\n","Stopping. Best iteration:\n","[612]\tvalidation_0-auc:0.94655\n","\n","Score not improved on adding C11_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_23\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C12_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878502\n","[100]\tvalidation_0-auc:0.898597\n","[150]\tvalidation_0-auc:0.915073\n","[200]\tvalidation_0-auc:0.926842\n","[250]\tvalidation_0-auc:0.935601\n","[300]\tvalidation_0-auc:0.940673\n","[350]\tvalidation_0-auc:0.943071\n","[400]\tvalidation_0-auc:0.94464\n","[450]\tvalidation_0-auc:0.945439\n","[500]\tvalidation_0-auc:0.945774\n","[550]\tvalidation_0-auc:0.946064\n","[600]\tvalidation_0-auc:0.946125\n","[650]\tvalidation_0-auc:0.945934\n","[700]\tvalidation_0-auc:0.946022\n","Stopping. Best iteration:\n","[609]\tvalidation_0-auc:0.946248\n","\n","Score not improved on adding C12_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_24\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C12_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879157\n","[100]\tvalidation_0-auc:0.898322\n","[150]\tvalidation_0-auc:0.915771\n","[200]\tvalidation_0-auc:0.92715\n","[250]\tvalidation_0-auc:0.935606\n","[300]\tvalidation_0-auc:0.940994\n","[350]\tvalidation_0-auc:0.943514\n","[400]\tvalidation_0-auc:0.944835\n","[450]\tvalidation_0-auc:0.945747\n","[500]\tvalidation_0-auc:0.946357\n","[550]\tvalidation_0-auc:0.946424\n","[600]\tvalidation_0-auc:0.946447\n","[650]\tvalidation_0-auc:0.94635\n","[700]\tvalidation_0-auc:0.94614\n","Stopping. Best iteration:\n","[608]\tvalidation_0-auc:0.946525\n","\n","Score not improved on adding C12_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_25\n","--------------------------------------------------------------------------------\n","Skipping C13_uid_mean already an selected column\n","--------------------------------------------------------------------------------\n","Model no: 5_26\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C13_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880178\n","[100]\tvalidation_0-auc:0.898806\n","[150]\tvalidation_0-auc:0.914835\n","[200]\tvalidation_0-auc:0.92736\n","[250]\tvalidation_0-auc:0.935587\n","[300]\tvalidation_0-auc:0.940854\n","[350]\tvalidation_0-auc:0.943366\n","[400]\tvalidation_0-auc:0.944956\n","[450]\tvalidation_0-auc:0.945638\n","[500]\tvalidation_0-auc:0.94615\n","[550]\tvalidation_0-auc:0.946664\n","[600]\tvalidation_0-auc:0.947148\n","[650]\tvalidation_0-auc:0.947263\n","[700]\tvalidation_0-auc:0.94714\n","Stopping. Best iteration:\n","[646]\tvalidation_0-auc:0.947309\n","\n","Score not improved on adding C13_uid_std\n","--------------------------------------------------------------------------------\n","Model no: 5_27\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C14_uid_mean']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.877975\n","[100]\tvalidation_0-auc:0.898579\n","[150]\tvalidation_0-auc:0.916256\n","[200]\tvalidation_0-auc:0.927269\n","[250]\tvalidation_0-auc:0.936195\n","[300]\tvalidation_0-auc:0.941202\n","[350]\tvalidation_0-auc:0.94314\n","[400]\tvalidation_0-auc:0.944329\n","[450]\tvalidation_0-auc:0.945427\n","[500]\tvalidation_0-auc:0.945863\n","[550]\tvalidation_0-auc:0.946211\n","[600]\tvalidation_0-auc:0.946451\n","[650]\tvalidation_0-auc:0.946299\n","Stopping. Best iteration:\n","[577]\tvalidation_0-auc:0.946483\n","\n","Score not improved on adding C14_uid_mean\n","--------------------------------------------------------------------------------\n","Model no: 5_28\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean', 'C14_uid_std']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878644\n","[100]\tvalidation_0-auc:0.897991\n","[150]\tvalidation_0-auc:0.914865\n","[200]\tvalidation_0-auc:0.927183\n","[250]\tvalidation_0-auc:0.935492\n","[300]\tvalidation_0-auc:0.94067\n","[350]\tvalidation_0-auc:0.943101\n","[400]\tvalidation_0-auc:0.944691\n","[450]\tvalidation_0-auc:0.945435\n","[500]\tvalidation_0-auc:0.946011\n","[550]\tvalidation_0-auc:0.946368\n","[600]\tvalidation_0-auc:0.946693\n","[650]\tvalidation_0-auc:0.946548\n","[700]\tvalidation_0-auc:0.946531\n","Stopping. Best iteration:\n","[612]\tvalidation_0-auc:0.946741\n","\n","Score not improved on adding C14_uid_std\n","--------------------------------------------------------------------------------\n","None of the columns improving the score in this iteration\n","########################################################################################################################\n","Useful new features : ['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean']\n","Best score : 0.947541\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"1iW5bKcEwOCF"},"source":["### FFS on group 4"]},{"cell_type":"code","metadata":{"id":"s8APCDb6hRw5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617547539105,"user_tz":0,"elapsed":388200,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"370f43fc-fc06-4348-d3ad-0ff8f1df277d"},"source":["old_cols4 = [col for col in X_train.columns if col not in rem_cols and col not in new_feats]\n","old_cols4.extend(['M9_uid_mean', 'TransactionAmt_uid_std', 'M5_uid_mean'])\n","old_cols4.extend(['D2_uid_mean', 'D15_uid_mean'])\n","old_cols4.extend(['C13_uid_mean', 'C9_uid_mean', 'C1_uid_mean', 'C11_uid_mean'])\n","\n","\n","print(len(old_cols4))"],"execution_count":24,"outputs":[{"output_type":"stream","text":["238\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"elZ21TuskWVw","executionInfo":{"status":"ok","timestamp":1617548692052,"user_tz":0,"elapsed":1540535,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"af1276e0-aeb2-4bb1-fa02-d9010b2cde90"},"source":["forward_feat_selection(new_cols=new_feat4,old_cols=old_cols4,best_score=0.947541)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Forward feature selection on 14 features <-->  current best score 0.947541 with 238 features\n","Model no: 1_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['C1_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87893\n","[100]\tvalidation_0-auc:0.898312\n","[150]\tvalidation_0-auc:0.915803\n","[200]\tvalidation_0-auc:0.927774\n","[250]\tvalidation_0-auc:0.936234\n","[300]\tvalidation_0-auc:0.941219\n","[350]\tvalidation_0-auc:0.943746\n","[400]\tvalidation_0-auc:0.945342\n","[450]\tvalidation_0-auc:0.946174\n","[500]\tvalidation_0-auc:0.946427\n","[550]\tvalidation_0-auc:0.947002\n","[600]\tvalidation_0-auc:0.94698\n","[650]\tvalidation_0-auc:0.946888\n","Stopping. Best iteration:\n","[590]\tvalidation_0-auc:0.947048\n","\n","Score not improved on adding C1_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['C2_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878855\n","[100]\tvalidation_0-auc:0.898108\n","[150]\tvalidation_0-auc:0.915049\n","[200]\tvalidation_0-auc:0.927157\n","[250]\tvalidation_0-auc:0.935667\n","[300]\tvalidation_0-auc:0.940797\n","[350]\tvalidation_0-auc:0.943027\n","[400]\tvalidation_0-auc:0.944498\n","[450]\tvalidation_0-auc:0.945273\n","[500]\tvalidation_0-auc:0.945413\n","[550]\tvalidation_0-auc:0.945853\n","[600]\tvalidation_0-auc:0.945906\n","[650]\tvalidation_0-auc:0.945684\n","Stopping. Best iteration:\n","[577]\tvalidation_0-auc:0.94599\n","\n","Score not improved on adding C2_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['C3_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878556\n","[100]\tvalidation_0-auc:0.897954\n","[150]\tvalidation_0-auc:0.915011\n","[200]\tvalidation_0-auc:0.926828\n","[250]\tvalidation_0-auc:0.935292\n","[300]\tvalidation_0-auc:0.940796\n","[350]\tvalidation_0-auc:0.943199\n","[400]\tvalidation_0-auc:0.944768\n","[450]\tvalidation_0-auc:0.945726\n","[500]\tvalidation_0-auc:0.946128\n","[550]\tvalidation_0-auc:0.946233\n","[600]\tvalidation_0-auc:0.946218\n","[650]\tvalidation_0-auc:0.945963\n","Stopping. Best iteration:\n","[576]\tvalidation_0-auc:0.946362\n","\n","Score not improved on adding C3_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['C4_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878686\n","[100]\tvalidation_0-auc:0.89832\n","[150]\tvalidation_0-auc:0.915395\n","[200]\tvalidation_0-auc:0.927179\n","[250]\tvalidation_0-auc:0.935718\n","[300]\tvalidation_0-auc:0.940665\n","[350]\tvalidation_0-auc:0.943367\n","[400]\tvalidation_0-auc:0.944767\n","[450]\tvalidation_0-auc:0.945555\n","[500]\tvalidation_0-auc:0.946036\n","[550]\tvalidation_0-auc:0.946558\n","[600]\tvalidation_0-auc:0.946567\n","[650]\tvalidation_0-auc:0.946409\n","Stopping. Best iteration:\n","[572]\tvalidation_0-auc:0.946666\n","\n","Score not improved on adding C4_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['C5_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879248\n","[100]\tvalidation_0-auc:0.89831\n","[150]\tvalidation_0-auc:0.915134\n","[200]\tvalidation_0-auc:0.926465\n","[250]\tvalidation_0-auc:0.935383\n","[300]\tvalidation_0-auc:0.940791\n","[350]\tvalidation_0-auc:0.943217\n","[400]\tvalidation_0-auc:0.944991\n","[450]\tvalidation_0-auc:0.945661\n","[500]\tvalidation_0-auc:0.946086\n","[550]\tvalidation_0-auc:0.946258\n","[600]\tvalidation_0-auc:0.946232\n","[650]\tvalidation_0-auc:0.945945\n","Stopping. Best iteration:\n","[582]\tvalidation_0-auc:0.946354\n","\n","Score not improved on adding C5_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['C6_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879444\n","[100]\tvalidation_0-auc:0.898297\n","[150]\tvalidation_0-auc:0.915637\n","[200]\tvalidation_0-auc:0.927847\n","[250]\tvalidation_0-auc:0.935855\n","[300]\tvalidation_0-auc:0.941056\n","[350]\tvalidation_0-auc:0.943337\n","[400]\tvalidation_0-auc:0.944987\n","[450]\tvalidation_0-auc:0.945788\n","[500]\tvalidation_0-auc:0.946228\n","[550]\tvalidation_0-auc:0.9467\n","[600]\tvalidation_0-auc:0.946852\n","[650]\tvalidation_0-auc:0.946722\n","[700]\tvalidation_0-auc:0.946746\n","Stopping. Best iteration:\n","[600]\tvalidation_0-auc:0.946852\n","\n","Score not improved on adding C6_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['C7_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878737\n","[100]\tvalidation_0-auc:0.898072\n","[150]\tvalidation_0-auc:0.914959\n","[200]\tvalidation_0-auc:0.926664\n","[250]\tvalidation_0-auc:0.935398\n","[300]\tvalidation_0-auc:0.940949\n","[350]\tvalidation_0-auc:0.943333\n","[400]\tvalidation_0-auc:0.944681\n","[450]\tvalidation_0-auc:0.945861\n","[500]\tvalidation_0-auc:0.946168\n","[550]\tvalidation_0-auc:0.946814\n","[600]\tvalidation_0-auc:0.946789\n","[650]\tvalidation_0-auc:0.946552\n","Stopping. Best iteration:\n","[577]\tvalidation_0-auc:0.946926\n","\n","Score not improved on adding C7_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_8\n","--------------------------------------------------------------------------------\n","Adding new features: ['C8_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878842\n","[100]\tvalidation_0-auc:0.897472\n","[150]\tvalidation_0-auc:0.914721\n","[200]\tvalidation_0-auc:0.92651\n","[250]\tvalidation_0-auc:0.935623\n","[300]\tvalidation_0-auc:0.940579\n","[350]\tvalidation_0-auc:0.943358\n","[400]\tvalidation_0-auc:0.944843\n","[450]\tvalidation_0-auc:0.945783\n","[500]\tvalidation_0-auc:0.946086\n","[550]\tvalidation_0-auc:0.946482\n","[600]\tvalidation_0-auc:0.94657\n","[650]\tvalidation_0-auc:0.946308\n","Stopping. Best iteration:\n","[577]\tvalidation_0-auc:0.946607\n","\n","Score not improved on adding C8_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_9\n","--------------------------------------------------------------------------------\n","Adding new features: ['C9_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879259\n","[100]\tvalidation_0-auc:0.898057\n","[150]\tvalidation_0-auc:0.915337\n","[200]\tvalidation_0-auc:0.92747\n","[250]\tvalidation_0-auc:0.935461\n","[300]\tvalidation_0-auc:0.940526\n","[350]\tvalidation_0-auc:0.943122\n","[400]\tvalidation_0-auc:0.944665\n","[450]\tvalidation_0-auc:0.945369\n","[500]\tvalidation_0-auc:0.945708\n","[550]\tvalidation_0-auc:0.946267\n","[600]\tvalidation_0-auc:0.946338\n","[650]\tvalidation_0-auc:0.946253\n","[700]\tvalidation_0-auc:0.946085\n","Stopping. Best iteration:\n","[612]\tvalidation_0-auc:0.946456\n","\n","Score not improved on adding C9_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_10\n","--------------------------------------------------------------------------------\n","Adding new features: ['C10_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.87861\n","[100]\tvalidation_0-auc:0.897571\n","[150]\tvalidation_0-auc:0.914879\n","[200]\tvalidation_0-auc:0.927004\n","[250]\tvalidation_0-auc:0.93608\n","[300]\tvalidation_0-auc:0.940977\n","[350]\tvalidation_0-auc:0.943376\n","[400]\tvalidation_0-auc:0.944917\n","[450]\tvalidation_0-auc:0.94567\n","[500]\tvalidation_0-auc:0.945824\n","[550]\tvalidation_0-auc:0.945956\n","[600]\tvalidation_0-auc:0.945891\n","Stopping. Best iteration:\n","[545]\tvalidation_0-auc:0.946013\n","\n","Score not improved on adding C10_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_11\n","--------------------------------------------------------------------------------\n","Adding new features: ['C11_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878755\n","[100]\tvalidation_0-auc:0.897867\n","[150]\tvalidation_0-auc:0.915136\n","[200]\tvalidation_0-auc:0.927131\n","[250]\tvalidation_0-auc:0.935397\n","[300]\tvalidation_0-auc:0.940469\n","[350]\tvalidation_0-auc:0.943321\n","[400]\tvalidation_0-auc:0.944984\n","[450]\tvalidation_0-auc:0.945722\n","[500]\tvalidation_0-auc:0.946091\n","[550]\tvalidation_0-auc:0.946346\n","[600]\tvalidation_0-auc:0.946589\n","[650]\tvalidation_0-auc:0.946324\n","[700]\tvalidation_0-auc:0.946128\n","Stopping. Best iteration:\n","[613]\tvalidation_0-auc:0.946614\n","\n","Score not improved on adding C11_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_12\n","--------------------------------------------------------------------------------\n","Adding new features: ['C12_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879238\n","[100]\tvalidation_0-auc:0.898252\n","[150]\tvalidation_0-auc:0.91518\n","[200]\tvalidation_0-auc:0.92699\n","[250]\tvalidation_0-auc:0.935422\n","[300]\tvalidation_0-auc:0.940818\n","[350]\tvalidation_0-auc:0.943142\n","[400]\tvalidation_0-auc:0.94499\n","[450]\tvalidation_0-auc:0.945779\n","[500]\tvalidation_0-auc:0.946145\n","[550]\tvalidation_0-auc:0.946494\n","[600]\tvalidation_0-auc:0.946729\n","[650]\tvalidation_0-auc:0.946544\n","[700]\tvalidation_0-auc:0.946357\n","Stopping. Best iteration:\n","[616]\tvalidation_0-auc:0.946804\n","\n","Score not improved on adding C12_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_13\n","--------------------------------------------------------------------------------\n","Adding new features: ['C13_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.880002\n","[100]\tvalidation_0-auc:0.899144\n","[150]\tvalidation_0-auc:0.91526\n","[200]\tvalidation_0-auc:0.92652\n","[250]\tvalidation_0-auc:0.935398\n","[300]\tvalidation_0-auc:0.940245\n","[350]\tvalidation_0-auc:0.942905\n","[400]\tvalidation_0-auc:0.944334\n","[450]\tvalidation_0-auc:0.945136\n","[500]\tvalidation_0-auc:0.945439\n","[550]\tvalidation_0-auc:0.945944\n","[600]\tvalidation_0-auc:0.94624\n","[650]\tvalidation_0-auc:0.946172\n","[700]\tvalidation_0-auc:0.946224\n","Stopping. Best iteration:\n","[608]\tvalidation_0-auc:0.946309\n","\n","Score not improved on adding C13_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_14\n","--------------------------------------------------------------------------------\n","Adding new features: ['C14_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878846\n","[100]\tvalidation_0-auc:0.898215\n","[150]\tvalidation_0-auc:0.914996\n","[200]\tvalidation_0-auc:0.927095\n","[250]\tvalidation_0-auc:0.935358\n","[300]\tvalidation_0-auc:0.940618\n","[350]\tvalidation_0-auc:0.943311\n","[400]\tvalidation_0-auc:0.94504\n","[450]\tvalidation_0-auc:0.946082\n","[500]\tvalidation_0-auc:0.946393\n","[550]\tvalidation_0-auc:0.946814\n","[600]\tvalidation_0-auc:0.946886\n","[650]\tvalidation_0-auc:0.946711\n","[700]\tvalidation_0-auc:0.946755\n","Stopping. Best iteration:\n","[612]\tvalidation_0-auc:0.946985\n","\n","Score not improved on adding C14_uid_ct\n","--------------------------------------------------------------------------------\n","None of the columns improving the score in this iteration\n","########################################################################################################################\n","Useful new features : []\n","Best score : 0.947541\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"d24xK_-xUt9a"},"source":["## ffs on group5"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H4yjonjzkWaO","executionInfo":{"status":"ok","timestamp":1617550422773,"user_tz":0,"elapsed":578428,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"86173d45-b21a-46fe-c33b-d6b02eec09c9"},"source":["forward_feat_selection(new_cols=new_feat5,old_cols=old_cols4,best_score=0.947541)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Forward feature selection on 7 features <-->  current best score 0.947541 with 238 features\n","Model no: 1_1\n","--------------------------------------------------------------------------------\n","Adding new features: ['DT_M_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878865\n","[100]\tvalidation_0-auc:0.899489\n","[150]\tvalidation_0-auc:0.915675\n","[200]\tvalidation_0-auc:0.927581\n","[250]\tvalidation_0-auc:0.936277\n","[300]\tvalidation_0-auc:0.941534\n","[350]\tvalidation_0-auc:0.944166\n","[400]\tvalidation_0-auc:0.945468\n","[450]\tvalidation_0-auc:0.946243\n","[500]\tvalidation_0-auc:0.946557\n","[550]\tvalidation_0-auc:0.946593\n","[600]\tvalidation_0-auc:0.946733\n","[650]\tvalidation_0-auc:0.946457\n","[700]\tvalidation_0-auc:0.946387\n","Stopping. Best iteration:\n","[609]\tvalidation_0-auc:0.946741\n","\n","Score not improved on adding DT_M_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_2\n","--------------------------------------------------------------------------------\n","Adding new features: ['P_emaildomain_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879621\n","[100]\tvalidation_0-auc:0.898096\n","[150]\tvalidation_0-auc:0.915467\n","[200]\tvalidation_0-auc:0.926879\n","[250]\tvalidation_0-auc:0.935418\n","[300]\tvalidation_0-auc:0.940749\n","[350]\tvalidation_0-auc:0.943327\n","[400]\tvalidation_0-auc:0.945153\n","[450]\tvalidation_0-auc:0.946047\n","[500]\tvalidation_0-auc:0.946419\n","[550]\tvalidation_0-auc:0.946839\n","[600]\tvalidation_0-auc:0.946913\n","[650]\tvalidation_0-auc:0.94675\n","Stopping. Best iteration:\n","[576]\tvalidation_0-auc:0.946944\n","\n","Score not improved on adding P_emaildomain_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_3\n","--------------------------------------------------------------------------------\n","Adding new features: ['R_emaildomain_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878924\n","[100]\tvalidation_0-auc:0.898344\n","[150]\tvalidation_0-auc:0.915137\n","[200]\tvalidation_0-auc:0.926945\n","[250]\tvalidation_0-auc:0.935406\n","[300]\tvalidation_0-auc:0.94047\n","[350]\tvalidation_0-auc:0.942865\n","[400]\tvalidation_0-auc:0.944453\n","[450]\tvalidation_0-auc:0.945323\n","[500]\tvalidation_0-auc:0.945816\n","[550]\tvalidation_0-auc:0.946064\n","[600]\tvalidation_0-auc:0.94611\n","[650]\tvalidation_0-auc:0.94609\n","Stopping. Best iteration:\n","[577]\tvalidation_0-auc:0.946236\n","\n","Score not improved on adding R_emaildomain_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_4\n","--------------------------------------------------------------------------------\n","Adding new features: ['dist1_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879529\n","[100]\tvalidation_0-auc:0.898839\n","[150]\tvalidation_0-auc:0.915628\n","[200]\tvalidation_0-auc:0.926772\n","[250]\tvalidation_0-auc:0.935717\n","[300]\tvalidation_0-auc:0.940624\n","[350]\tvalidation_0-auc:0.94302\n","[400]\tvalidation_0-auc:0.9446\n","[450]\tvalidation_0-auc:0.945361\n","[500]\tvalidation_0-auc:0.945826\n","[550]\tvalidation_0-auc:0.946286\n","[600]\tvalidation_0-auc:0.946359\n","[650]\tvalidation_0-auc:0.94621\n","[700]\tvalidation_0-auc:0.946063\n","Stopping. Best iteration:\n","[602]\tvalidation_0-auc:0.946386\n","\n","Score not improved on adding dist1_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_5\n","--------------------------------------------------------------------------------\n","Adding new features: ['dist2_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878727\n","[100]\tvalidation_0-auc:0.897895\n","[150]\tvalidation_0-auc:0.915035\n","[200]\tvalidation_0-auc:0.927004\n","[250]\tvalidation_0-auc:0.935679\n","[300]\tvalidation_0-auc:0.940869\n","[350]\tvalidation_0-auc:0.943272\n","[400]\tvalidation_0-auc:0.944845\n","[450]\tvalidation_0-auc:0.945593\n","[500]\tvalidation_0-auc:0.945921\n","[550]\tvalidation_0-auc:0.946298\n","[600]\tvalidation_0-auc:0.946289\n","[650]\tvalidation_0-auc:0.945955\n","[700]\tvalidation_0-auc:0.945954\n","Stopping. Best iteration:\n","[609]\tvalidation_0-auc:0.946377\n","\n","Score not improved on adding dist2_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_6\n","--------------------------------------------------------------------------------\n","Adding new features: ['addr1_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878601\n","[100]\tvalidation_0-auc:0.897964\n","[150]\tvalidation_0-auc:0.914995\n","[200]\tvalidation_0-auc:0.92709\n","[250]\tvalidation_0-auc:0.935494\n","[300]\tvalidation_0-auc:0.940645\n","[350]\tvalidation_0-auc:0.943353\n","[400]\tvalidation_0-auc:0.944912\n","[450]\tvalidation_0-auc:0.94591\n","[500]\tvalidation_0-auc:0.946138\n","[550]\tvalidation_0-auc:0.946369\n","[600]\tvalidation_0-auc:0.946574\n","[650]\tvalidation_0-auc:0.946153\n","Stopping. Best iteration:\n","[576]\tvalidation_0-auc:0.946633\n","\n","Score not improved on adding addr1_uid_ct\n","--------------------------------------------------------------------------------\n","Model no: 1_7\n","--------------------------------------------------------------------------------\n","Adding new features: ['addr1_uid_ct']\n","fitting model on 239 columns\n","[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.878656\n","[100]\tvalidation_0-auc:0.897945\n","[150]\tvalidation_0-auc:0.915064\n","[200]\tvalidation_0-auc:0.927194\n","[250]\tvalidation_0-auc:0.935756\n","[300]\tvalidation_0-auc:0.940915\n","[350]\tvalidation_0-auc:0.943101\n","[400]\tvalidation_0-auc:0.944715\n","[450]\tvalidation_0-auc:0.94568\n","[500]\tvalidation_0-auc:0.946109\n","[550]\tvalidation_0-auc:0.946457\n","[600]\tvalidation_0-auc:0.946721\n","[650]\tvalidation_0-auc:0.946538\n","[700]\tvalidation_0-auc:0.946396\n","Stopping. Best iteration:\n","[609]\tvalidation_0-auc:0.946724\n","\n","Score not improved on adding addr1_uid_ct\n","--------------------------------------------------------------------------------\n","None of the columns improving the score in this iteration\n","########################################################################################################################\n","Useful new features : []\n","Best score : 0.947541\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"4DPveLGCiQbk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617551804858,"user_tz":0,"elapsed":94331,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"616b2261-fc74-452b-91e3-0e9cf99de743"},"source":["cols = old_cols4.copy()\n","\n","\n","\n","# 75-25 split\n","#0.934\n","idx_train = X_train.index[:int(X_train.shape[0]*0.75)]  \n","idx_validation = X_train.index[int(X_train.shape[0]*0.75):]\n","\n","clf = xgb.XGBClassifier( \n","        n_estimators=2000,\n","        max_depth=12, \n","        learning_rate=0.02, \n","        subsample=0.8,\n","        colsample_bytree=0.4,\n","        missing=-1, \n","        eval_metric='auc',\n","        tree_method='gpu_hist' \n","    )\n","model = clf.fit(X_train.loc[idx_train,cols], y_train[idx_train], \n","    eval_set=[(X_train.loc[idx_validation,cols],y_train[idx_validation])],\n","    verbose=50, early_stopping_rounds=100)"],"execution_count":27,"outputs":[{"output_type":"stream","text":["[0]\tvalidation_0-auc:0.80812\n","Will train until validation_0-auc hasn't improved in 100 rounds.\n","[50]\tvalidation_0-auc:0.879138\n","[100]\tvalidation_0-auc:0.896654\n","[150]\tvalidation_0-auc:0.915402\n","[200]\tvalidation_0-auc:0.926922\n","[250]\tvalidation_0-auc:0.934718\n","[300]\tvalidation_0-auc:0.939816\n","[350]\tvalidation_0-auc:0.942864\n","[400]\tvalidation_0-auc:0.944606\n","[450]\tvalidation_0-auc:0.945828\n","[500]\tvalidation_0-auc:0.946269\n","[550]\tvalidation_0-auc:0.946748\n","[600]\tvalidation_0-auc:0.946992\n","[650]\tvalidation_0-auc:0.946962\n","[700]\tvalidation_0-auc:0.947049\n","[750]\tvalidation_0-auc:0.946916\n","[800]\tvalidation_0-auc:0.946644\n","Stopping. Best iteration:\n","[711]\tvalidation_0-auc:0.947057\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":680},"id":"AH4melg9mJwy","executionInfo":{"status":"ok","timestamp":1617551840965,"user_tz":0,"elapsed":36095,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"edd381fd-ac7a-490f-f086-fb3760fa8c74"},"source":["\n","x_train = X_train[cols]\n","y_train_pred = model.predict(x_train.iloc[idx_train])\n","y_test_pred = model.predict(x_train.iloc[idx_validation])\n","\n","train_fpr, train_tpr, thresholds = roc_curve(y_train.iloc[idx_train], model.predict_proba(x_train.iloc[idx_train])[:,1])\n","test_fpr, test_tpr, thresholds = roc_curve(y_train.iloc[idx_validation], model.predict_proba(x_train.iloc[idx_validation])[:,1])\n","\n","#Area under ROC curve\n","print('Area under train roc {}'.format(auc(train_fpr, train_tpr)))\n","print('Area under test roc {}'.format(auc(test_fpr, test_tpr)))\n","\n","\n","train_cf = confusion_matrix(y_train.iloc[idx_train],y_train_pred)\n","plt.figure(figsize=(7,5))\n","sns.heatmap(train_cf,annot=True,annot_kws={\"size\": 16},fmt=\"0\")\n","plt.title('Train confusion matrix')\n","plt.show()\n","\n","\n","cv_cf = confusion_matrix(y_train.iloc[idx_validation],y_test_pred)\n","plt.figure(figsize=(7,5))\n","sns.heatmap(cv_cf,annot=True,annot_kws={\"size\": 16},fmt=\"0\")\n","plt.title('Test confusion matrix')\n","plt.show()"],"execution_count":28,"outputs":[{"output_type":"stream","text":["Area under train roc 0.9994531152174883\n","Area under test roc 0.9470569258187016\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 504x360 with 2 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6OhofH196d69O9u3b6dv375YrVamT58OQGxsLOPHj8fhcBAcHExISAgAkZGRREVFYbFYmDhxIh4eFectFuOPBteqoMh+0NVNipSrRsMwd3dBqpniwnSXtndq9aRKX1PjoQl/XOkiooxGRMSdqsHOAAo0IiLupEAjIiKm0u7NIiJiKmU0IiJiKtevx7roKNCIiLiTMhoRETGVAo2IiJhKiwFERMRMhkNzNCIiYiYNnYmIiKk0dCYiIqaqBkNnek2AiIiYSoGmEhwOB68tSaBVp+4seGtZhfWO/pzFrV3v47HBL5Qqz8vPZ9rchXR78DFu7XofkU8MIfHjT0vV+elIBkPHTOG2Hn0JiejNU8Ni+XZ/6XdxJ3/xFVGDYmgffj9dekUxNm4O9l+OO88bhsHK//uA+x55hls69+S2Hn2ZNnchefn5LvhdkItVnTp+vDJnMod++IKCvEN89+3nxI4ZisViAX59Pe/UKaM5fCiF/JMH+ffOTXS587fdrx99JJLiwvRyjwP7d7jrti5/Jr6P5mKhQPMnHc85wcARY9n44TY8PCznrRs353WKiorLlD8/fjrbPtvB+OeHsO7t1+h4axtixr3EjpSvADiRe5LHh4wiv6CAN2ZN5u3XXsbL05Onhr3IseO/vuXuq93/4ZmYcbS64TpWLZnHtHHPk/rNXkaOe8n5PcvXrWfGq4t4tM99vLdsIdPGjWTLJ9t5ceocF/6OyMVm5fI3uOuuOxjw5HBatrqdV+ctZuKEkQwb+jQAL8WN4akno3hu2FhuufUuPvxwG+vfj6dlyxYArFm7nkaNbypzfPJJMlu3fe7OW7u8KdDIWRsSP8bL05NVS17F09OzwnofJX3O13u+5faQdqXKDxw8zKc7UnjhuacJbX8LQY0bMfyZx2nSuBEbNm8FYOOH27Af+4VZk8fQ8vq/0KL5tUweM5ycE7l8tuPXN9wlrH6X5k2DGDV0INcGXU27W4IZ/OQjpHy9h6M/ZwHwwaYt3BbSjvvuuYvGja4itP0t9O9zP1s/20FBwSmTfofEna6+uiG33noTMTET+HjrZxw69BOvv7GULVs+5YH778HXtzbRzz5O3Etzef/9Tezbd4AXx07nP99+T8yIZwA4ffo0mZnZpY6bb/orbdq0ZvyEl918h5cxw6j8cYnRYoA/qXNYRx7u3fO8b5ErKDjFtLkLiYkeQMpXuzlx8qTzXNMmjdm2fjl16viVuibAvx45ObkAPPi3CLrcFkLd39WpV7cOFouFnBO/1pn6YgynT58+p426ABw/kctVV9qwWCx4epbup9XbGwDLH2Rjcmk6ciSDwAYtyz1XXFxMaMit1KhRgw8/Sip1bsuWT+jX9/5yr/Pw8GDWrInMeWUhR49murzP8l+XYIZSWX8qo8nPz+fw4cMcPnyYgoICs/t0Ubq64ZXnDTIA8xe/Q+OGV9Kre3iZcx4eHtQP8Mfb67fYbj/2C3v3fU+r/w5deHt7YwsMKHXdJ8n/xjAMWt34a52aNXzwr1e3VJ1tn+2kdq2aNG3SGIA+9/Xg0+QUkr/4ipKSEo5mZrPq3Q30uKszNXx8Kn/zcsnx8vKi/6ORdOrUjtmvLKRZsyYA/PhjWql6Bw/+RMOGV1KzZo0ybURG/o1GDa/k1XlLLkSXqy+HUfnjEnPejGb37t3ExcWRm5tLvXr1MAyDrKwsGjRowPjx42nRosWF6udF79v9B1j7/r9Y/fd5zsnX8ykuLmH05FnUrl2TPvf1KLdOtv0Xpsx8jU4d2tKmdfk/re5I+Yrl69YzdGB/fK64AoBe94Rj/+U4Tw9/EU8PD4pLSujcqQMTRz1X9RuUS8anSe/Trt3N2O2/0C/qWT74YDOjRw3B4XBw6lTpbPjsAhE/P98yw6ojY57l739fyYn/ZtNikur+HM1LL71EXFwczZo1K1W+d+9eJk+ezPLly03t3KXC4XAw6eX5PNrnPpo1ueYP6xcWFjJi7Et8s3cfb86No46fb5k6R3/OYsDQMdSuVZNp40aW207yF1/x3OhJdL09hCce7u0s/yDxY96MX8XooQO5JfivHDn6M3Ne/zvjp7/K9PHPV/1G5ZLQ9+FnqF+/Hn+7txsrl7/BUwPL//tzPp1C23FTcEse6vO0CT2UUi7BDKWyzhtoDMMoE2QAWrZsSUlJiWmdutSs+scG8vLyGdi/7x/WPXX6NENGTebb/QdYPDeO4L/eUKbOT0cyGPDcGOr41WbhnKnUq1unTJ1tn+1gxLiX6HbnbUwZM9yZRRmGwcvzFhHZqzsP9+4JwPXXNaNWzZo8NSyWx/rcz/XXlf0zlcvHkSMZHDmSwddf76V2rVq8+soUJkyciYeHB7Vr1yIv77dl7md/yDk3a+nV826+3fc9P/zw44XserVkVIM5mvMGmuDgYAYNGkTXrl3x9/cHwG63k5iYSLt27c53abXy4bbP+Sn9KO3C73OWORwGhmEQfNs9TB4znJ53d6WkpISYcdP44dBh3nl9Js2uDSrTlv2X4zw1LJarGgTy+qxJ1K5Vq0ydlK93M3xsHA/1uodRQweWGqo7nnOC4zm5XBvUuNQ111zdEIDDRzIUaC5D11zTiNDQdqxZs77UD4F79n6Hv389Tv43uDRr1oRdu/Y6zzdv3pTDh4+UGVK7555wNmzYfGE6X91V94xmzJgxfPHFFyQnJ/PNN98AYLPZGDx4MDfffPMF6eClYErscE6dsxJs/pvvkJltZ+qLI2gQWP/XssUJfL37PyS8MavcIONwOBj+4lTq+PmycPaUcidos+2/MHTMFHp1v4vRwwaVOV+3jh8+V1zB4bQjpcoPHf51EriBrX6V71MuXn9p3pSE+Nf4+WhWqWdeWv31egoKTvH++5vIy8un2113lAo0ERGd+demj0u11bDhlfzlL9eSvPPLC9b/aq26z9EA3Hrrrdx6660Xoi8XtRO5JykqKnJ+Ljh1CvuxXwC4qkFgmWdrfGvXIjcvj780bQJAxs+ZLF2xjugnH6GOn6/zWvh1RZp/vbps/HAbX+/+lrfmTafg1CkKTv02OXvFFVfgW7sWry1JwOrtzdOPPlSqDYDatWvhc8UVPHT/Paz6xz9pdm0QbVq35GhmNi/Pf5NmTa7hr9df5+rfGrkIbN32OV+kfM0br89g6LCxfH/gELff1pGBAx/l7aWrOHkyj1mzX+eF56P5z3/2s2fvPp4b8iSNGl7J7DlvlGrrL82vBeDHQz+541aqn+qe0chvhsZOIeWr3c7P8Sv/QfzKfwCQuG4pja5qcN7rv0j9huKSEl5dtJRXFy0tda7hlTY2/188O1K+xjAMnhgyqsz1Pe/uStzYGHakfEX2sV8If6B/mTpTY0fQ655whg16nLp+fixYkkBW9jF8fWsR0u4WRkY/iZdXxQ+byqXL4XDQs1d/4qaO4e9vzcXPrzaHfkxjytQ5vDL3TQDiXnoVDw8PFrw2jYCAeuza9R/u7t6PQ+cElLr1fp0TzD2Zd8Hvo1qqBnM0FsNw/WOmRfaDrm5SpFw1Gob9cSURFyouTHdpe/nj+1T6mlqTV7m0D2ZTRiMi4k6aoxEREVNpjkZERMxUHZ6j0e7NIiKXif3799O1a1eWLfv1fVlHjx7lkUceoV+/fgwdOpTCwkIA1q9fzwMPPEDv3r1Zu3YtAEVFRcTExNC3b1+ioqJIS/v1kYh9+/bRp08f+vTpw4QJE5zftWTJEh588EF69+5NUlIS56NAIyLiTi7aVLOgoIApU6bQsWNHZ9m8efPo168fK1asICgoiHXr1lFQUMCCBQtYunQpCQkJxMfHk5OTw4YNG/Dz82PlypUMGjSI2bNnAxAXF0dsbCyrVq0iLy+PpKQk0tLS2LhxIytWrGDRokVMmzbtvLvFKNCIiLiTiwKN1Wpl8eLF2Gw2Z9nOnTvp0qULAJ07dyY5OZldu3bRqlUrfH198fHxoU2bNqSmppKcnEx4+K87z4eEhJCamkphYSHp6em0bt26VBs7d+4kLCwMq9WKv78/jRo14sCBA2U79V8KNCIi7mQ4Kn+Uw8vLC59zXgNy6tQprFYrAAEBAWRnZ2O3251bigH4+/uXKffw8MBisWC32/Hz++39WH/URkW0GEBExJ0u0Kqzih6ZrEx5Zds4SxmNiIgbGQ6j0sefVbNmTecbeTMzM7HZbNhsNux2u7NOVlaWs/xsVlJUVIRhGAQGBpKTk+OsW1EbZ8srokAjIuJOJr5hMyQkhMTERAA2b95MWFgYwcHB7N69m9zcXPLz80lNTaVt27aEhoayadMmALZu3Ur79u3x9vamadOmpKSklGqjQ4cObNu2jcLCQjIzM8nKyqJ58+YV9kNDZyIi7uSi52j27NnDjBkzSE9Px8vLi8TERGbNmsXo0aNZvXo1DRs2pFevXnh7exMTE8OAAQOwWCxER0fj6+tL9+7d2b59O3379sVqtTJ9+nQAYmNjGT9+PA6Hg+DgYEJCQgCIjIwkKioKi8XCxIkTz/uqe+11Jpc07XUmF5qr9zo7+ezdlb7G9/V/ubQPZlNGIyLiTtqCRkREzGTCoNJFR4FGRMSdlNGIiIipFGhERMRMlXku5lKlQCMi4k4KNCIiYqrL/3U0CjQiIu6koTMRETFXNQg02utMRERMpYxGRMSdNEcjIiJm0hyNiIiYSxmNiIiYSRmNiIiYSxmNiIiYyVCgERERUynQiIiImZTRiIiIuRRoRETETMpoRETEVAo0IiJiKgUaERExl2Fxdw9Mp0AjIuJGymhERMRUhkMZjYiImKg6ZDR68ZmIiJhKGY2IiBsZWgwgIiJmqg5DZwo0IiJu5IrFAPn5+YwaNYoTJ05QVFREdHQ0gYGBTJw4EYAWLVowadIkAJYsWcKmTZuwWCwMHjyY22+/nZMnTxITE8PJkyepWbMms2fPpm7dumzfvp05c+bg6enJbbfdRnR0dJX6p0AjIuJGhgvee/buu+9y7bXXEhMTQ2ZmJv379ycwMJDY2Fhat25NTEwMSUlJNG3alI0bN7Jq1Sry8vLo168fnTp1Ij4+nnbt2vHkk0+yevVqFi9ezPPPP8/UqVN56623aNCgAVFRUXTr1o3mzZtXun9aDCAi4kaGw1Lp41z16tUjJycHgNzcXOrWrUt6ejqtW7cGoHPnziQnJ7Nz507CwsKwWq34+/vTqFEjDhw4QHJyMuHh4aXqpqWlUadOHa666io8PDy4/fbbSU5OrtI9KtCIiLiRKwLNPffcQ0ZGBuHh4URFRfHCCy/g5+fnPB8QEEB2djZ2ux1/f39nub+/f5nygIAAsrKyyM7OLrduVWjoTETEjVwxdPb+++/TsGFD3nrrLfbt20d0dDS+vr6/+47yv6S88orq/i8UaERE3MgViwFSU1Pp1KkTANdffz1nzpyhuLjYeT4zMxObzYbNZuPQoUPllmdnZ+Pr61uqzG63l6lbFRo6ExFxI8OwVPo4V1BQELt27QIgPT2dWrVq0axZM1JSUgDYvHkzYWFhdOjQgW3btlFYWEhmZiZZWVk0b96c0NBQNm3aVKru1VdfTV5eHkeOHKG4uJitW7cSGhpapXu0GCbkSUX2g65uUqRcNRqGubsLUs0UF6a7tL0DN3ar9DXN/5NY6nN+fj6xsbEcO3aM4uJihg4dSmBgIOPHj8fhcBAcHMyYMWMASEhI4IMPPsBisTBs2DA6duxIfn4+zz//PDk5Ofj5+TFz5kx8fX354osvmDVrFgB33XUXAwYMqNI9KtDIJU2BRi40Vwea/TdEVPqa677d5NI+mE1zNCIibqQtaERExFR6TYCIiJjKhNXEFx0FGhERN1JGIyIipnJUgzkaPUcjIiKmUkYjIuJGWnUmIiKm0mIAERExVXWYo1GgERFxIw2diYiIqTR0VkV+jTub0axIGZ4eWjgplzYNnYmIiKk0dCYiIqZSRiMiIqaqBlM0CjQiIu6kjEZEREylORoRETGVw90duAAUaERE3MhAGY2IiJjIUQ1WAyjQiIi4kUMZjYiImKk6DJ1p/w4RETGVMhoRETfSqjMRETFVdRg6U6AREXEjZTQiImIqBRoRETGVhs5ERMRUjss/zijQiIi4kx7YFBERU7lqB5r169ezZMkSvLy8eO6552jRogUvvPACJSUlBAYGMnPmTKxWK+vXryc+Ph4PDw8iIyPp3bs3RUVFjB49moyMDDw9PZk2bRqNGzdm3759TA5Yfj8AABLqSURBVJw4EYAWLVowadKkKvVND2yKiLiRowrHuY4fP86CBQtYsWIFCxcuZMuWLcybN49+/fqxYsUKgoKCWLduHQUFBSxYsIClS5eSkJBAfHw8OTk5bNiwAT8/P1auXMmgQYOYPXs2AHFxccTGxrJq1Sry8vJISkqq0j0q0IiIuJHDYqn0ca7k5GQ6duxI7dq1sdlsTJkyhZ07d9KlSxcAOnfuTHJyMrt27aJVq1b4+vri4+NDmzZtSE1NJTk5mfDwcABCQkJITU2lsLCQ9PR0WrduXaqNqtDQmYiIG7li6OzIkSOcPn2aQYMGkZuby5AhQzh16hRWqxWAgIAAsrOzsdvt+Pv7O6/z9/cvU+7h4YHFYsFut+Pn5+ese7aNqlCgERFxI1c9R5OTk8Nrr71GRkYGjz76KIbxWwj7/a9/rzLlFdX9MzR0JiLiRg5L5Y9zBQQEcPPNN+Pl5cU111xDrVq1qFWrFqdPnwYgMzMTm82GzWbDbrc7r8vKynKWn81WioqKMAyDwMBAcnJynHXPtlEVCjQiIm7kwFLp41ydOnVix44dOBwOjh8/TkFBASEhISQmJgKwefNmwsLCCA4OZvfu3eTm5pKfn09qaipt27YlNDSUTZs2AbB161bat2+Pt7c3TZs2JSUlpVQbVaGhMxERN3LFHE2DBg3o1q0bkZGRAIwdO5ZWrVoxatQoVq9eTcOGDenVqxfe3t7ExMQwYMAALBYL0dHR+Pr60r17d7Zv307fvn2xWq1Mnz4dgNjYWMaPH4/D4SA4OJiQkJAq9c9i/C8DbxWoUSPI1U2KlMthVIedouRicuZ0mkvbe6dRVKWveTR9mUv7YDYNnYmIiKk0dCYi4kbVISdXoBERcSOXz11chBRoRETcSLs3i4iIqTR0JiIiplKgERERUxkaOhMRETMpoxEREVMp0IiIiKm0vFlEREyl5c0iImIqDZ2JiIipFGhERMRUmqMRERFTaY5GRERMpaEzERExlYbORETEVI5qEGr0hk0RETGVMhoRETfSHI2IiJjq8h84U6AREXErZTQiImIqPUcjIiKmqg6rzhRoRETc6PIPMwo0IiJupTkaERExlYbORETEVJd/mFGgERFxKw2diYiIqVw5dHb69Gl69OjBs88+S8eOHXnhhRcoKSkhMDCQmTNnYrVaWb9+PfHx8Xh4eBAZGUnv3r0pKipi9OjRZGRk4OnpybRp02jcuDH79u1j4sSJALRo0YJJkyZVqV/a60xExI2MKhwVeeONN6hTpw4A8+bNo1+/fqxYsYKgoCDWrVtHQUEBCxYsYOnSpSQkJBAfH09OTg4bNmzAz8+PlStXMmjQIGbPng1AXFwcsbGxrFq1iry8PJKSkqp0jwo0IiJu5KjCUZ4ffviBAwcOcMcddwCwc+dOunTpAkDnzp1JTk5m165dtGrVCl9fX3x8fGjTpg2pqakkJycTHh4OQEhICKmpqRQWFpKenk7r1q1LtVEVCjQiIm5kVOG/8syYMYPRo0c7P586dQqr1QpAQEAA2dnZ2O12/P39nXX8/f3LlHt4eGCxWLDb7fj5+Tnrnm2jKjRHIyLiRq5YDPDee+9x00030bhx43LPG0b5waky5RXV/TOU0bhQnTp+zJ49ke+/T+bEie/Zu/cTRo0agsXy22ZGVquV116bxqlTh4mKerBMGzZbfd58cxYHDuwgM3MPH3/8f9x+e4jzfFTUg5w6dbjcIyCg3gW5T7l4WCwWxo0bwamCw4wdO7zUuZo1azD3lSkc/vFLco5/z7at73LLLcHO84880pszp9PKPb77bjsAt93WocI6bdq0vqD3erlyYFT6ONe2bdvYsmULkZGRrF27ltdff52aNWty+vRpADIzM7HZbNhsNux2u/O6rKwsZ/nZbKWoqAjDMAgMDCQnJ8dZ92wbVaGMxoUSEl4jKOhqnnpqJD/++BMREXcye/ZETp8+zauvLiYoqDErVrxBzZo1yr3e29ub999/B4BHHx2M3X6ckSOf4b333qZDh3v47rsDzrpNmrQtc/2xY8fNuTG5KAUE1OOd+Ndo0qQxDkfZn4sT3llAhw63ED14NLu/+ZYxsc+x8Z/LublNVzIyfmbt2g/YvHlbmeuWLXudgwcPlyoLCe3BkSMZpcrs9l9cej9SdXPnznX+ev78+TRq1IivvvqKxMREevbsyebNmwkLCyM4OJixY8eSm5uLp6cnqampxMbGkpeXx6ZNmwgLC2Pr1q20b98eb29vmjZtSkpKCm3btmXz5s088sgjVeqfMhoXufrqq2jb9iaef34y27Z9zo8/prFwYTwff/wZ993XHYDBgx9n9+5v6dnz0XLb6NIljJtuasnAgSPZvj2F/ft/IDp6DEVFxdx/f/dSdTMzs8scUr3063s/xcXFhIT2oLi4uNS5pk2D6NEjnIkTZ/Lee//ih4M/8tRTMZzIPckzz/QHfl0Ke+7foZuCW9Lm5lZMnDizVHt2+7EydUtKSi7YvV7OXLnq7PeGDBnCe++9R79+/cjJyaFXr174+PgQExPDgAEDePzxx4mOjsbX15fu3bvjcDjo27cvy5cvJyYmBoDY2FjmzJlDnz59uOaaawgJCfmDby2fMhoXOXLkKA0blj+UcPYfgddfX8qhQz/RoEFgufU+/DCJZs3akZGR6SwrKioiJ+cE9ev7l3uNVF8fbNjMawv+Xu7Y+Q3X/wWAzz7/t7PMMAwSN23lzs5hjGNGmWs8PDyYOXMCr8xdxNGjmWXOizlcvQXNkCFDnL9+++23y5yPiIggIiKiVNnZZ2fO1bx5c1asWPE/96nKgSY3N7fUigQpzcvLi7597yM0tB2PPjoYgEOHfjrvNSUlJaWCDECrVjfQuHEjvvjia9P6KpemH39Mq/Ccp6cnACXnZDrZ9mM0bRpU7jW9e/+Nhg2vZP78t1zXSflD1WFngCoPnQ0ePNiV/bisbN36D3Jy9jNlyigefXQwGzZ8WKV2ateuxVtvvcKePftYu/aDUuemTBlFaupHpKV9xT//uZybb/6rK7oul4n9+38A4OZzJuxbt7oRX99a5V4TM2IQb7+9ihMncsucGzJ4AF/8O5H0I7tI2vYeXbqEub7T1ZSrljdfzM6b0SxfvrzCc5mZSq0r8sgj0QQE+NOjRzgJCa/xzDOjWLXqvUq1UbeuH++/H0/9+gGEh/d2joefPn2ajIyfOXHiJP37D6Fu3TqMGjWYLVv+jw4dujv/gZHqbd93B0j6JJmJE0byzTf/4fvvD9K/fyQhIbdSVFRcpn5oaDuCg1vSt++gUuWFhUUcPZpJYWERTw8cyRVXWHnmmcfY8MEyIu7uQ1JS1R7gk99Uh4zmvIFm6dKldOzYsdwlbedOPspvjhw5ypEjR9m1ay+1a9di9uxJrF79/p9eh16/vj8bNiyjbl0/7rorkh9++NF5bt26Daxbt6FU/dTUb9i/P5lhw57m2WdHufJW5BLWv/8QEhIW8PVXWygqKuJf//qYOa8s5Omny64c6vm3CPbt+54fDv5YqnzHji9pcm3pFY7//vdX3BTcktGjhijQuMClmKFU1nkDzYIFC5g6dSpjx451PmF61s6dO03t2KXmmmsaERJyK2vXflBqNc7evd/h718Xm63+n1oZVrNmDd57Lx6r1Urnzvdz9GjWH16Tn1/AoUOHufLKqq1xl8vT0aOZdO36IDZbfU6fPkNu7klmvjyBPbu/LVP3nnu6/ukhXofDwd6933HDDX9xdZerpeqQ0Zx3jua6665j0aJFeHmVjUe/3+pAoFmzJrz99qt06tS+VPlf/3o9BQWn+OWXnAquLO3112dQv3497r67b7lBZvjwgYwc+Uypslq1atKsWRMOHDhY9RuQy4qXlxeRkT25vkVzsrLs5OaexNvbm569Ivhgw+ZSdRs2vJLmza9lx84vy7Tz6KORTJ82tlSZh4cHrVvfyPffHzL1HqoLh2FU+rjU/OGqsxo1yn+4sGXLli7vzKUsKSmZL7/cxYIF0xg+fDwHDhzitts68tRTUbzzzhqKioqoX98fT09P51LlOnV8nUudMzOz6dixLQ891JPHHx8GUGoZdGFhIcePnyA/v4A5cyZRUuJg/fpE6tTxZezYEXh6erJw4TsX/sbFberVq4vV6u38XLtWLeffmezsY4wYPhCAZ6NHU5BfwLhxI8jPy2f58n+Uaqd58yZA+avYsrPsDF04E8MwiI9fjaeXF0OHPsW1117D0wNHmnRn1culFzYqz2L8LxvYVKBGjfKXT17ubLb6TJ48ioiIzvj5+fLjj2ksX76OV19dQnFxMfv2fUZQUPl7EdWoEURs7FDGjRtR7vlPPkmmW7c+ADzxRF8GDepP06ZBlJSUsGPHl0yYMJOvv95j2r1drBxGdRh4KN/mzWu4/baO5Z67rkVHDMPg1blTnVl2YuI2Rj4/kZ9/Lp0p/+1vEaxds5iWLcM48Lv5wLN69ozg+ZHRtGjRDE9PT776eg9xca/w8cefufyeLgVnTle8rLwq+gXdV+lrVhx+16V9MJsCjVzSqnOgEfdwdaDpG9Sr0tesPFy5Vazupp0BRETcqDr8qKRAIyLiRq7eguZipEAjIuJG1f45GhERMZeGzkRExFQmrMe66Oh9NCIiYiplNCIibqTFACIiYirN0YiIiKm06kxEREyloTMRETFVdVh1pkAjIuJGmqMRERFTaY5GRERMpTkaERExleZoRETEVMpoRETEVJqjERERUzk0dCYiIma6/MOMAo2IiFtpjkZEREzlqkDz8ssv8+WXX1JcXMzAgQNp1aoVL7zwAiUlJQQGBjJz5kysVivr168nPj4eDw8PIiMj6d27N0VFRYwePZqMjAw8PT2ZNm0ajRs3Zt++fUycOBGAFi1aMGnSpCr1Te+jERFxI8MwKn2ca8eOHXz//fesXr2aJUuW8NJLLzFv3jz69evHihUrCAoKYt26dRQUFLBgwQKWLl1KQkIC8fHx5OTksGHDBvz8/Fi5ciWDBg1i9uzZAMTFxREbG8uqVavIy8sjKSmpSveoQCMicom79dZbefXVVwHw8/Pj1KlT7Ny5ky5dugDQuXNnkpOT2bVrF61atcLX1xcfHx/atGlDamoqycnJhIeHAxASEkJqaiqFhYWkp6fTunXrUm1UhQKNiIgbOTAqfZzL09OTmjVrArBu3Tpuu+02Tp06hdVqBSAgIIDs7Gzsdjv+/v7O6/z9/cuUe3h4YLFYsNvt+Pn5OeuebaMqFGhERNzIqMJ/Ffnoo49Yt24d48ePL/0dFSyhrkz5/7KDgQKNiIgbuWKOBuDTTz9l4cKFLF68GF9fX2rWrMnp06cByMzMxGazYbPZsNvtzmuysrKc5WezlaKiIgzDIDAwkJycHGfds21UhQKNiIgbuWLo7OTJk7z88sssWrSIunXrAr/OtSQmJgKwefNmwsLCCA4OZvfu3eTm5pKfn09qaipt27YlNDSUTZs2AbB161bat2+Pt7c3TZs2JSUlpVQbVaHlzSIibuSKTTU3btzI8ePHGTZsmLNs+vTpjB07ltWrV9OwYUN69eqFt7c3MTExDBgwAIvFQnR0NL6+vnTv3p3t27fTt29frFYr06dPByA2Npbx48fjcDgIDg4mJCSkSv2zGCZsHVqjRpCrmxQpl8OoDq+NkovJmdNpLm0v+MrK/+O96+ftLu2D2ZTRiIi4kTbVFBERU2lTTRERMZUyGhERMZUyGhERMZUyGhERMZUyGhERMZUyGhERMZUyGhERMZUyGhERMZVRDXa30KaaIiJiKmU0IiJuVN5uzJcbBRoRETcyYV/ji44CjYiIGymjERERUymjERERU+k5GhERMZWeoxEREVNp6ExEREylxQAiImIqZTQiImIqLQYQERFTKaMRERFTaY5GRERMpYxGRERMpTkaERExlR7YFBERUymjERERU1WHORq9YVNEREyljEZExI00RyMiIqaqDkNnCjQiIm5UHQKNxagOdykiIm6jxQAiImIqBRoRETGVAo2IiJhKgeYi8NJLL/HQQw/Rp08fvvnmG3d3R6qB/fv307VrV5YtW+burkg1oFVnbvbvf/+bw4cPs3r1an744QdiY2NZvXq1u7sll7GCggKmTJlCx44d3d0VqSaU0bhZcnIyXbt2BaBZs2acOHGCvLw8N/dKLmdWq5XFixdjs9nc3RWpJhRo3Mxut1OvXj3nZ39/f7Kzs93YI7nceXl54ePj4+5uSDWiQHOR0WNNInK5UaBxM5vNht1ud37OysoiMDDQjT0SEXEtBRo3Cw0NJTExEYC9e/dis9moXbu2m3slIuI62oLmIjBr1ixSUlKwWCxMmDCB66+/3t1dksvYnj17mDFjBunp6Xh5edGgQQPmz59P3bp13d01uUwp0IiIiKk0dCYiIqZSoBEREVMp0IiIiKkUaERExFQKNCIiYioFGhERMZUCjYiImEqBRkRETPX/3Y8o/rR5j3YAAAAASUVORK5CYII=\n","text/plain":["<Figure size 504x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":202},"id":"hP7-eebCmJzl","executionInfo":{"status":"ok","timestamp":1617551859791,"user_tz":0,"elapsed":18823,"user":{"displayName":"Arun Mohan","photoUrl":"","userId":"08546098418817162842"}},"outputId":"a38e530d-4a72-481e-bd9a-a55d0f76711d"},"source":["x_test = X_test[cols]\n","y_pred_test = model.predict_proba(x_test)[:,1]\n","submission = pd.read_csv('data/sample_submission.csv')\n","submission['isFraud'] = y_pred_test\n","submission.to_csv('submission.csv',index=False)\n","submission.head()"],"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>TransactionID</th>\n","      <th>isFraud</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3663549</td>\n","      <td>0.000755</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3663550</td>\n","      <td>0.001230</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3663551</td>\n","      <td>0.002938</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3663552</td>\n","      <td>0.001546</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3663553</td>\n","      <td>0.001915</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   TransactionID   isFraud\n","0        3663549  0.000755\n","1        3663550  0.001230\n","2        3663551  0.002938\n","3        3663552  0.001546\n","4        3663553  0.001915"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"markdown","metadata":{"id":"SrxIJZMSrCl9"},"source":["# LocalCv: 9470\n","# PrivateLB: 0.915889\n","# PublicLB: 0.942315\n","\n"]},{"cell_type":"code","metadata":{"id":"8oXCv5PsmJ2T"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m0QX_v4QmJ40"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"PZwFeqm8lrEy"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Jz9Fa8w-lrTD"},"source":[""],"execution_count":null,"outputs":[]}]}