DeepModels_real_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b548c0e5",
   "metadata": {},
   "source": [
    "## Repeated sampling from LAZADA real data for experiments.\n",
    "Each repeated sampling for experiments, training set is sampled by 90% randomly, and test set is keep 100%.\n",
    "\n",
    "Three paths need to be set(must be an absolute path).\n",
    "\n",
    "- `full_trainset_path`: the path of full_trainset.csv\n",
    "\n",
    "- `full_testset_path`: the path of full_testset.csv\n",
    "\n",
    "- `base_path`: the path of output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e84a44a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set:/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz\n",
      "test set:/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder \n",
    "import numpy as np \n",
    "\n",
    "# load data. path need to be set.\n",
    "full_trainset_path = \"/home/admin/uplift_data/dataset_public_md5/full_trainset.csv\"\n",
    "full_testset_path = \"/home/admin/uplift_data/dataset_public_md5/full_testset.csv\"\n",
    "\n",
    "# output dir: save as .npz file\n",
    "base_path=\"/home/admin/uplift_data/dataset_public_md5\"\n",
    "\n",
    "df_trainset = pd.read_csv(full_trainset_path)\n",
    "df_testset = pd.read_csv(full_testset_path)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "num_experiments=5\n",
    "\n",
    "\n",
    "# split and export\n",
    "train_data = {  \"yf\":[], \"t\":[], \"x\":[], \"e\":[] }\n",
    "test_data = {\"yf\":[], \"t\":[], \"x\":[], \"e\":[] }\n",
    "\n",
    "for exp_i in range(num_experiments):\n",
    "    # train set \n",
    "    _, df_sub_set = train_test_split(df_trainset, test_size=0.9 ,random_state=exp_i) \n",
    "#     df_sub_set=df_trainset\n",
    "    \n",
    "    y = df_sub_set.label.values\n",
    "    t = df_sub_set.is_treat.values\n",
    "    X = df_sub_set.iloc[:,3:].values\n",
    "    \n",
    "    train_data[\"yf\"].append(y)\n",
    "    train_data[\"x\"].append(X)\n",
    "    train_data[\"t\"].append(t)\n",
    "    train_data[\"e\"].append( np.zeros_like(t) )\n",
    "    \n",
    "    \n",
    "    # test set \n",
    "#     _, df_sub_set = train_test_split(df_testset, test_size=1 ,random_state=exp_i)\n",
    "    df_sub_set = df_testset\n",
    "    \n",
    "    y = df_sub_set.label.values\n",
    "    t = df_sub_set.is_treat.values\n",
    "    X = df_sub_set.iloc[:,3:].values\n",
    "    \n",
    "    test_data[\"yf\"].append(y)\n",
    "    test_data[\"x\"].append(X)\n",
    "    test_data[\"t\"].append(t)\n",
    "    test_data[\"e\"].append( np.ones_like(t) )\n",
    "    \n",
    "\n",
    "# format\n",
    "train_data[\"x\"] = np.swapaxes(np.swapaxes(np.array(train_data[\"x\"]), 0, 1), 1, 2)\n",
    "test_data[\"x\"] = np.swapaxes(np.swapaxes(np.array(test_data[\"x\"]), 0, 1), 1, 2)\n",
    "for col in [ \"yf\", \"t\", \"e\"]:\n",
    "    train_data[col] = np.swapaxes(train_data[col], 0, 1)\n",
    "    test_data[col] = np.swapaxes(test_data[col], 0, 1)\n",
    "    \n",
    "\n",
    "\n",
    "train_npz_path = \"{}/real_bin_set_full.{}.train\".format(base_path, num_experiments)\n",
    "test_npz_path = \"{}/real_bin_set_full.{}.test\".format(base_path, num_experiments)\n",
    "\n",
    "\n",
    "pair_list = [(train_npz_path, train_data), \n",
    "             (test_npz_path, test_data)]\n",
    "\n",
    "for (path, data_dict) in pair_list:\n",
    "    np.savez(path, yf=data_dict[\"yf\"], t=data_dict[\"t\"], x=data_dict[\"x\"], e=data_dict[\"e\"])\n",
    "\n",
    "train_npz = train_npz_path+\".npz\"\n",
    "test_npz = test_npz_path+\".npz\"\n",
    "print(\"training set:{}\".format(train_npz))\n",
    "print(\"test set:{}\".format(test_npz))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61376de",
   "metadata": {},
   "source": [
    "## X-learner / X-learner with PS \n",
    "Neural Network-based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b58a40b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 16:12:39,297 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 16:12:39,297 - DEBUG - Setting JobRuntime:name=x_learner_main\n",
      "[2022-02-15 16:12:39,466][root][INFO] - log testing ...\n",
      "[2022-02-15 16:12:39,466][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'use_ps': 0, 'model_name': 'X_learner_128_20220215_161237', 'n_experiments': 5, 'batch_size': 5000, 'base_dim': 128, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 1, 'pred_step': 1, 'optim': 'Adam', 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 16:12:39,466][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 16:12:44,803][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 16:12:44,803][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 16:12:45,948][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 16:12:45,959][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 16:12:45,960][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/X_learner_128_20220215_161237) ...\n",
      "2022-02-15 16:12:46.122329: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 16:12:46.122370: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 16:12:48,088][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:12:52,151][root][INFO] - exp_0, Train. x.shape : (834003, 83)\n",
      "[2022-02-15 16:12:52,152][root][INFO] - exp_0, Train. mean(t) : 0.22158193675562318\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 16:12:52,154][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 16:12:52,155][root][INFO] - exp_0, Train. mean(yf) : 0.01984405331875305\n",
      "[2022-02-15 16:12:52,161][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.056563852813852816\n",
      "[2022-02-15 16:12:52,169][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009391515442781379\n",
      "[2022-02-15 16:12:52,171][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 16:12:52,172][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 16:12:52,172][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 16:12:52,172][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 16:12:52,173][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 16:12:52,173][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 16:12:52,173][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 16:12:52,174][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "[2022-02-15 16:12:52,198][root][INFO] - exp_i:0,  epoch:0 ...\n",
      "[2022-02-15 16:13:10,408][root][INFO] - i_exp:0, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:13:10,409][root][INFO] - i_exp:0, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
      "[2022-02-15 16:13:10,409][root][INFO] - i_exp:0, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:13:10,409][root][INFO] - exp_i:0,  epoch:1 ...\n",
      "[2022-02-15 16:13:24,359][root][INFO] - i_exp:0, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:13:24,360][root][INFO] - i_exp:0, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:13:24,360][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 16:13:24,360][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:13:24,377][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.007054508198052645\n",
      "[2022-02-15 16:13:24,378][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.007106644567102194\n",
      "[2022-02-15 16:13:24,524][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.001685221482852672\n",
      "[2022-02-15 16:13:24,525][root][INFO] - exp_i:0,  epoch:2 ...\n",
      "[2022-02-15 16:13:43,885][root][INFO] - i_exp:0, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:13:43,885][root][INFO] - i_exp:0, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:13:43,885][root][INFO] - i_exp:0, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:13:43,885][root][INFO] - exp_i:0,  epoch:3 ...\n",
      "[2022-02-15 16:14:00,441][root][INFO] - i_exp:0, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:14:00,441][root][INFO] - i_exp:0, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:14:00,442][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 16:14:00,442][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:14:00,459][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.012275120243430138\n",
      "[2022-02-15 16:14:00,459][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.012204740196466446\n",
      "[2022-02-15 16:14:00,610][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.010332856918054365\n",
      "[2022-02-15 16:14:00,610][root][INFO] - exp_i:0,  epoch:4 ...\n",
      "[2022-02-15 16:14:16,221][root][INFO] - i_exp:0, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:14:16,221][root][INFO] - i_exp:0, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:14:16,221][root][INFO] - i_exp:0, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:14:16,221][root][INFO] - exp_i:0,  epoch:5 ...\n",
      "[2022-02-15 16:14:33,068][root][INFO] - i_exp:0, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:14:33,068][root][INFO] - i_exp:0, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:14:33,068][root][INFO] - start to predict ... i_exp:0,epochs:5, train_step:798\n",
      "[2022-02-15 16:14:33,068][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:14:33,085][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.009396297857165337\n",
      "[2022-02-15 16:14:33,086][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.009699770249426365\n",
      "[2022-02-15 16:14:33,235][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: 0.02836686052407124\n",
      "[2022-02-15 16:14:33,236][root][INFO] - exp_i:0,  epoch:6 ...\n",
      "[2022-02-15 16:14:48,909][root][INFO] - i_exp:0, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:14:48,909][root][INFO] - i_exp:0, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:14:48,909][root][INFO] - i_exp:0, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:14:48,910][root][INFO] - exp_i:0,  epoch:7 ...\n",
      "[2022-02-15 16:15:05,809][root][INFO] - i_exp:0, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:15:05,809][root][INFO] - i_exp:0, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:15:05,809][root][INFO] - start to predict ... i_exp:0,epochs:7, train_step:1064\n",
      "[2022-02-15 16:15:05,810][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:15:05,827][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.00503064040094614\n",
      "[2022-02-15 16:15:05,827][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.005241149105131626\n",
      "[2022-02-15 16:15:05,977][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.004909349268974279\n",
      "[2022-02-15 16:15:05,978][root][INFO] - exp_i:0,  epoch:8 ...\n",
      "[2022-02-15 16:15:21,724][root][INFO] - i_exp:0, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:15:21,724][root][INFO] - i_exp:0, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:15:21,724][root][INFO] - i_exp:0, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:15:21,724][root][INFO] - exp_i:0,  epoch:9 ...\n",
      "[2022-02-15 16:15:38,607][root][INFO] - i_exp:0, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:15:38,607][root][INFO] - i_exp:0, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:15:38,607][root][INFO] - start to predict ... i_exp:0,epochs:9, train_step:1330\n",
      "[2022-02-15 16:15:38,608][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:15:38,625][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.013923434540629387\n",
      "[2022-02-15 16:15:38,625][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.014140557497739792\n",
      "[2022-02-15 16:15:38,775][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.021137641221455828\n",
      "[2022-02-15 16:15:38,778][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:15:38,790][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_128_20220215_161237_test_result.test...done\n",
      "[2022-02-15 16:15:39,853][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:15:40,283][root][INFO] - exp_i:1,  epoch:0 ...\n",
      "[2022-02-15 16:15:56,170][root][INFO] - i_exp:1, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:15:56,170][root][INFO] - i_exp:1, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:15:56,171][root][INFO] - i_exp:1, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:15:56,171][root][INFO] - exp_i:1,  epoch:1 ...\n",
      "[2022-02-15 16:16:12,913][root][INFO] - i_exp:1, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:16:12,913][root][INFO] - i_exp:1, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:16:12,913][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 16:16:12,913][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:16:12,930][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.008646059781312943\n",
      "[2022-02-15 16:16:12,931][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.008858069777488708\n",
      "[2022-02-15 16:16:13,080][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.018840383786189702\n",
      "[2022-02-15 16:16:13,081][root][INFO] - exp_i:1,  epoch:2 ...\n",
      "[2022-02-15 16:16:28,889][root][INFO] - i_exp:1, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:16:28,889][root][INFO] - i_exp:1, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:16:28,889][root][INFO] - i_exp:1, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:16:28,889][root][INFO] - exp_i:1,  epoch:3 ...\n",
      "[2022-02-15 16:16:45,596][root][INFO] - i_exp:1, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:16:45,597][root][INFO] - i_exp:1, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:16:45,597][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 16:16:45,597][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:16:45,614][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.005096395965665579\n",
      "[2022-02-15 16:16:45,614][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.005267216358333826\n",
      "[2022-02-15 16:16:45,764][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.014346550201042812\n",
      "[2022-02-15 16:16:45,765][root][INFO] - exp_i:1,  epoch:4 ...\n",
      "[2022-02-15 16:17:01,580][root][INFO] - i_exp:1, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:17:01,580][root][INFO] - i_exp:1, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:17:01,580][root][INFO] - i_exp:1, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:17:01,580][root][INFO] - exp_i:1,  epoch:5 ...\n",
      "[2022-02-15 16:17:18,413][root][INFO] - i_exp:1, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:17:18,413][root][INFO] - i_exp:1, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:17:18,413][root][INFO] - start to predict ... i_exp:1,epochs:5, train_step:798\n",
      "[2022-02-15 16:17:18,413][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:17:18,430][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.005096897948533297\n",
      "[2022-02-15 16:17:18,431][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.005137651227414608\n",
      "[2022-02-15 16:17:18,581][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: -0.011962805622951395\n",
      "[2022-02-15 16:17:18,582][root][INFO] - exp_i:1,  epoch:6 ...\n",
      "[2022-02-15 16:17:34,373][root][INFO] - i_exp:1, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:17:34,373][root][INFO] - i_exp:1, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:17:34,373][root][INFO] - i_exp:1, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:17:34,373][root][INFO] - exp_i:1,  epoch:7 ...\n",
      "[2022-02-15 16:17:51,104][root][INFO] - i_exp:1, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:17:51,104][root][INFO] - i_exp:1, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:17:51,105][root][INFO] - start to predict ... i_exp:1,epochs:7, train_step:1064\n",
      "[2022-02-15 16:17:51,105][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:17:51,122][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.004105381201952696\n",
      "[2022-02-15 16:17:51,122][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.004271004348993301\n",
      "[2022-02-15 16:17:51,271][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.017582321184364005\n",
      "[2022-02-15 16:17:51,272][root][INFO] - exp_i:1,  epoch:8 ...\n",
      "[2022-02-15 16:18:07,178][root][INFO] - i_exp:1, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:18:07,178][root][INFO] - i_exp:1, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:18:07,179][root][INFO] - i_exp:1, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:18:07,179][root][INFO] - exp_i:1,  epoch:9 ...\n",
      "[2022-02-15 16:18:23,994][root][INFO] - i_exp:1, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:18:23,994][root][INFO] - i_exp:1, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:18:23,994][root][INFO] - start to predict ... i_exp:1,epochs:9, train_step:1330\n",
      "[2022-02-15 16:18:23,994][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:18:24,011][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.011044822633266449\n",
      "[2022-02-15 16:18:24,012][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.011227893643081188\n",
      "[2022-02-15 16:18:24,161][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.01247801509880937\n",
      "[2022-02-15 16:18:24,167][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:18:24,193][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_128_20220215_161237_test_result.test...done\n",
      "[2022-02-15 16:18:25,268][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:18:25,682][root][INFO] - exp_i:2,  epoch:0 ...\n",
      "[2022-02-15 16:18:41,506][root][INFO] - i_exp:2, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:18:41,506][root][INFO] - i_exp:2, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:18:41,506][root][INFO] - i_exp:2, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:18:41,506][root][INFO] - exp_i:2,  epoch:1 ...\n",
      "[2022-02-15 16:18:58,377][root][INFO] - i_exp:2, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:18:58,377][root][INFO] - i_exp:2, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:18:58,377][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 16:18:58,377][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:18:58,394][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.0084421681240201\n",
      "[2022-02-15 16:18:58,395][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.008586435578763485\n",
      "[2022-02-15 16:18:58,544][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: -0.007096803163258569\n",
      "[2022-02-15 16:18:58,545][root][INFO] - exp_i:2,  epoch:2 ...\n",
      "[2022-02-15 16:19:14,257][root][INFO] - i_exp:2, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:19:14,258][root][INFO] - i_exp:2, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:19:14,258][root][INFO] - i_exp:2, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:19:14,258][root][INFO] - exp_i:2,  epoch:3 ...\n",
      "[2022-02-15 16:19:31,180][root][INFO] - i_exp:2, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:19:31,180][root][INFO] - i_exp:2, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:19:31,181][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 16:19:31,181][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:19:31,198][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.004599671810865402\n",
      "[2022-02-15 16:19:31,198][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.0046974606812000275\n",
      "[2022-02-15 16:19:31,347][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: -0.0037437354952188145\n",
      "[2022-02-15 16:19:31,348][root][INFO] - exp_i:2,  epoch:4 ...\n",
      "[2022-02-15 16:19:46,897][root][INFO] - i_exp:2, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:19:46,897][root][INFO] - i_exp:2, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:19:46,897][root][INFO] - i_exp:2, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:19:46,897][root][INFO] - exp_i:2,  epoch:5 ...\n",
      "[2022-02-15 16:20:03,692][root][INFO] - i_exp:2, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:20:03,692][root][INFO] - i_exp:2, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:20:03,693][root][INFO] - start to predict ... i_exp:2,epochs:5, train_step:798\n",
      "[2022-02-15 16:20:03,693][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:20:03,710][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.007588026113808155\n",
      "[2022-02-15 16:20:03,710][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.0077545735985040665\n",
      "[2022-02-15 16:20:03,859][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: -0.0077296032195304076\n",
      "[2022-02-15 16:20:03,860][root][INFO] - exp_i:2,  epoch:6 ...\n",
      "[2022-02-15 16:20:19,430][root][INFO] - i_exp:2, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:20:19,430][root][INFO] - i_exp:2, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:20:19,430][root][INFO] - i_exp:2, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:20:19,430][root][INFO] - exp_i:2,  epoch:7 ...\n",
      "[2022-02-15 16:20:36,240][root][INFO] - i_exp:2, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:20:36,240][root][INFO] - i_exp:2, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:20:36,241][root][INFO] - start to predict ... i_exp:2,epochs:7, train_step:1064\n",
      "[2022-02-15 16:20:36,241][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:20:36,258][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.0035832934081554413\n",
      "[2022-02-15 16:20:36,258][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.003734604688361287\n",
      "[2022-02-15 16:20:36,407][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: -0.012050027257616943\n",
      "[2022-02-15 16:20:36,408][root][INFO] - exp_i:2,  epoch:8 ...\n",
      "[2022-02-15 16:20:51,889][root][INFO] - i_exp:2, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:20:51,890][root][INFO] - i_exp:2, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:20:51,890][root][INFO] - i_exp:2, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:20:51,890][root][INFO] - exp_i:2,  epoch:9 ...\n",
      "[2022-02-15 16:21:08,712][root][INFO] - i_exp:2, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:21:08,712][root][INFO] - i_exp:2, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:21:08,712][root][INFO] - start to predict ... i_exp:2,epochs:9, train_step:1330\n",
      "[2022-02-15 16:21:08,712][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:21:08,729][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.009372248314321041\n",
      "[2022-02-15 16:21:08,730][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.009507710114121437\n",
      "[2022-02-15 16:21:08,879][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.0014319623816855275\n",
      "[2022-02-15 16:21:08,895][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:21:08,928][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_128_20220215_161237_test_result.test...done\n",
      "[2022-02-15 16:21:09,981][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:21:10,395][root][INFO] - exp_i:3,  epoch:0 ...\n",
      "[2022-02-15 16:21:26,255][root][INFO] - i_exp:3, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:21:26,255][root][INFO] - i_exp:3, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:21:26,256][root][INFO] - i_exp:3, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:21:26,256][root][INFO] - exp_i:3,  epoch:1 ...\n",
      "[2022-02-15 16:21:43,023][root][INFO] - i_exp:3, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:21:43,023][root][INFO] - i_exp:3, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:21:43,024][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 16:21:43,024][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:21:43,041][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.008805700577795506\n",
      "[2022-02-15 16:21:43,041][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.009030221961438656\n",
      "[2022-02-15 16:21:43,190][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: -0.0028470524404437944\n",
      "[2022-02-15 16:21:43,191][root][INFO] - exp_i:3,  epoch:2 ...\n",
      "[2022-02-15 16:21:58,892][root][INFO] - i_exp:3, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:21:58,892][root][INFO] - i_exp:3, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:21:58,892][root][INFO] - i_exp:3, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:21:58,893][root][INFO] - exp_i:3,  epoch:3 ...\n",
      "[2022-02-15 16:22:15,573][root][INFO] - i_exp:3, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:22:15,573][root][INFO] - i_exp:3, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:22:15,574][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 16:22:15,574][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:22:15,591][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.009501026943325996\n",
      "[2022-02-15 16:22:15,591][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.009667111560702324\n",
      "[2022-02-15 16:22:15,742][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: -0.02370421753107672\n",
      "[2022-02-15 16:22:15,743][root][INFO] - exp_i:3,  epoch:4 ...\n",
      "[2022-02-15 16:22:31,412][root][INFO] - i_exp:3, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:22:31,412][root][INFO] - i_exp:3, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:22:31,412][root][INFO] - i_exp:3, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:22:31,412][root][INFO] - exp_i:3,  epoch:5 ...\n",
      "[2022-02-15 16:22:48,110][root][INFO] - i_exp:3, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:22:48,110][root][INFO] - i_exp:3, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:22:48,110][root][INFO] - start to predict ... i_exp:3,epochs:5, train_step:798\n",
      "[2022-02-15 16:22:48,110][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:22:48,127][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.010778797790408134\n",
      "[2022-02-15 16:22:48,128][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.010851036757230759\n",
      "[2022-02-15 16:22:48,277][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: -0.0012975515458987998\n",
      "[2022-02-15 16:22:48,278][root][INFO] - exp_i:3,  epoch:6 ...\n",
      "[2022-02-15 16:23:04,051][root][INFO] - i_exp:3, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:23:04,051][root][INFO] - i_exp:3, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:23:04,051][root][INFO] - i_exp:3, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:23:04,051][root][INFO] - exp_i:3,  epoch:7 ...\n",
      "[2022-02-15 16:23:20,654][root][INFO] - i_exp:3, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:23:20,654][root][INFO] - i_exp:3, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:23:20,655][root][INFO] - start to predict ... i_exp:3,epochs:7, train_step:1064\n",
      "[2022-02-15 16:23:20,655][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:23:20,673][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.00919579342007637\n",
      "[2022-02-15 16:23:20,673][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.00936167687177658\n",
      "[2022-02-15 16:23:20,823][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.019781616717467664\n",
      "[2022-02-15 16:23:20,823][root][INFO] - exp_i:3,  epoch:8 ...\n",
      "[2022-02-15 16:23:36,632][root][INFO] - i_exp:3, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:23:36,632][root][INFO] - i_exp:3, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:23:36,632][root][INFO] - i_exp:3, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:23:36,632][root][INFO] - exp_i:3,  epoch:9 ...\n",
      "[2022-02-15 16:23:53,323][root][INFO] - i_exp:3, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:23:53,323][root][INFO] - i_exp:3, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:23:53,324][root][INFO] - start to predict ... i_exp:3,epochs:9, train_step:1330\n",
      "[2022-02-15 16:23:53,324][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:23:53,341][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.006470902822911739\n",
      "[2022-02-15 16:23:53,341][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.006542846094816923\n",
      "[2022-02-15 16:23:53,490][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.00023218915386646145\n",
      "[2022-02-15 16:23:53,508][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:23:53,562][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_128_20220215_161237_test_result.test...done\n",
      "[2022-02-15 16:23:54,668][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:23:55,094][root][INFO] - exp_i:4,  epoch:0 ...\n",
      "[2022-02-15 16:24:10,554][root][INFO] - i_exp:4, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:24:10,554][root][INFO] - i_exp:4, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:24:10,554][root][INFO] - i_exp:4, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:24:10,554][root][INFO] - exp_i:4,  epoch:1 ...\n",
      "[2022-02-15 16:24:27,062][root][INFO] - i_exp:4, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:24:27,062][root][INFO] - i_exp:4, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:24:27,063][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 16:24:27,063][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:24:27,080][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.007331969682127237\n",
      "[2022-02-15 16:24:27,080][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.007392381317913532\n",
      "[2022-02-15 16:24:27,229][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.009165311289214434\n",
      "[2022-02-15 16:24:27,229][root][INFO] - exp_i:4,  epoch:2 ...\n",
      "[2022-02-15 16:24:42,483][root][INFO] - i_exp:4, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:24:42,483][root][INFO] - i_exp:4, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:24:42,483][root][INFO] - i_exp:4, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:24:42,483][root][INFO] - exp_i:4,  epoch:3 ...\n",
      "[2022-02-15 16:24:59,045][root][INFO] - i_exp:4, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:24:59,045][root][INFO] - i_exp:4, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:24:59,045][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 16:24:59,045][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:24:59,062][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.007959161885082722\n",
      "[2022-02-15 16:24:59,063][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.008072840049862862\n",
      "[2022-02-15 16:24:59,211][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.015556444252837427\n",
      "[2022-02-15 16:24:59,212][root][INFO] - exp_i:4,  epoch:4 ...\n",
      "[2022-02-15 16:25:14,454][root][INFO] - i_exp:4, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:25:14,454][root][INFO] - i_exp:4, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:25:14,454][root][INFO] - i_exp:4, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:25:14,454][root][INFO] - exp_i:4,  epoch:5 ...\n",
      "[2022-02-15 16:25:30,867][root][INFO] - i_exp:4, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:25:30,868][root][INFO] - i_exp:4, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:25:30,868][root][INFO] - start to predict ... i_exp:4,epochs:5, train_step:798\n",
      "[2022-02-15 16:25:30,868][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:25:30,885][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.006452792324125767\n",
      "[2022-02-15 16:25:30,885][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.006462301593273878\n",
      "[2022-02-15 16:25:31,034][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: 0.008613553594676021\n",
      "[2022-02-15 16:25:31,035][root][INFO] - exp_i:4,  epoch:6 ...\n",
      "[2022-02-15 16:25:46,304][root][INFO] - i_exp:4, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:25:46,305][root][INFO] - i_exp:4, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:25:46,305][root][INFO] - i_exp:4, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:25:46,305][root][INFO] - exp_i:4,  epoch:7 ...\n",
      "[2022-02-15 16:26:02,847][root][INFO] - i_exp:4, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:26:02,847][root][INFO] - i_exp:4, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:26:02,848][root][INFO] - start to predict ... i_exp:4,epochs:7, train_step:1064\n",
      "[2022-02-15 16:26:02,848][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:26:02,865][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.0064055705443024635\n",
      "[2022-02-15 16:26:02,865][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.006476120091974735\n",
      "[2022-02-15 16:26:03,014][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.025812788583844466\n",
      "[2022-02-15 16:26:03,015][root][INFO] - exp_i:4,  epoch:8 ...\n",
      "[2022-02-15 16:26:18,335][root][INFO] - i_exp:4, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:26:18,335][root][INFO] - i_exp:4, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:26:18,336][root][INFO] - i_exp:4, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:26:18,336][root][INFO] - exp_i:4,  epoch:9 ...\n",
      "[2022-02-15 16:26:34,867][root][INFO] - i_exp:4, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:26:34,867][root][INFO] - i_exp:4, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:26:34,867][root][INFO] - start to predict ... i_exp:4,epochs:9, train_step:1330\n",
      "[2022-02-15 16:26:34,867][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:26:34,884][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.01106572151184082\n",
      "[2022-02-15 16:26:34,885][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.011177636682987213\n",
      "[2022-02-15 16:26:35,033][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.02830244548057271\n",
      "[2022-02-15 16:26:35,057][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:26:35,143][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_128_20220215_161237_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.001685221482852672\n",
      "i_exp:0, AUUC:0.010332856918054365\n",
      "i_exp:0, AUUC:0.02836686052407124\n",
      "i_exp:0, AUUC:0.004909349268974279\n",
      "i_exp:0, AUUC:0.021137641221455828\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.018840383786189702\n",
      "i_exp:1, AUUC:0.014346550201042812\n",
      "i_exp:1, AUUC:-0.011962805622951395\n",
      "i_exp:1, AUUC:0.017582321184364005\n",
      "i_exp:1, AUUC:0.01247801509880937\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:-0.007096803163258569\n",
      "i_exp:2, AUUC:-0.0037437354952188145\n",
      "i_exp:2, AUUC:-0.0077296032195304076\n",
      "i_exp:2, AUUC:-0.012050027257616943\n",
      "i_exp:2, AUUC:0.0014319623816855275\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:-0.0028470524404437944\n",
      "i_exp:3, AUUC:-0.02370421753107672\n",
      "i_exp:3, AUUC:-0.0012975515458987998\n",
      "i_exp:3, AUUC:0.019781616717467664\n",
      "i_exp:3, AUUC:0.00023218915386646145\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.009165311289214434\n",
      "i_exp:4, AUUC:0.015556444252837427\n",
      "i_exp:4, AUUC:0.008613553594676021\n",
      "i_exp:4, AUUC:0.025812788583844466\n",
      "i_exp:4, AUUC:0.02830244548057271\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.02836686052407124, 0.018840383786189702, 0.0014319623816855275, 0.019781616717467664, 0.02830244548057271], 'E_att': [0.00565661148251087, 0.004906374337981051, 0.005632561008344, 0.005456107976744479, 0.007326036068508929]}\n",
      "AUUC: 0.019345 +/- 0.004395\n",
      "E_att: 0.005796 +/- 0.000363\n",
      "done.\n",
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 16:26:43,621 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 16:26:43,621 - DEBUG - Setting JobRuntime:name=x_learner_main\n",
      "[2022-02-15 16:26:43,797][root][INFO] - log testing ...\n",
      "[2022-02-15 16:26:43,797][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'use_ps': 1, 'model_name': 'X_learner_with_PS_128_20220215_162642', 'n_experiments': 5, 'batch_size': 5000, 'base_dim': 128, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 1, 'pred_step': 1, 'optim': 'Adam', 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 16:26:43,797][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 16:26:49,452][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 16:26:49,453][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 16:26:50,635][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 16:26:50,646][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 16:26:50,647][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/X_learner_with_PS_128_20220215_162642) ...\n",
      "2022-02-15 16:26:50.830825: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 16:26:50.830871: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 16:26:52,989][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:26:57,459][root][INFO] - exp_0, Train. x.shape : (834003, 83)\n",
      "[2022-02-15 16:26:57,461][root][INFO] - exp_0, Train. mean(t) : 0.22158193675562318\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 16:26:57,463][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 16:26:57,465][root][INFO] - exp_0, Train. mean(yf) : 0.01984405331875305\n",
      "[2022-02-15 16:26:57,471][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.056563852813852816\n",
      "[2022-02-15 16:26:57,480][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009391515442781379\n",
      "[2022-02-15 16:26:57,483][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 16:26:57,484][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 16:26:57,484][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 16:26:57,485][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 16:26:57,485][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 16:26:57,485][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 16:26:57,485][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 16:26:57,486][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "[2022-02-15 16:26:57,512][root][INFO] - exp_i:0,  epoch:0 ...\n",
      "[2022-02-15 16:27:13,293][root][INFO] - i_exp:0, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:27:13,293][root][INFO] - i_exp:0, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:27:13,294][root][INFO] - i_exp:0, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:27:13,294][root][INFO] - exp_i:0,  epoch:1 ...\n",
      "[2022-02-15 16:27:27,255][root][INFO] - i_exp:0, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:27:27,255][root][INFO] - i_exp:0, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:27:27,255][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 16:27:27,255][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:27:27,281][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.008928363211452961\n",
      "[2022-02-15 16:27:27,281][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.008794932626187801\n",
      "[2022-02-15 16:27:27,428][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.00808197919419736\n",
      "[2022-02-15 16:27:27,428][root][INFO] - exp_i:0,  epoch:2 ...\n",
      "[2022-02-15 16:27:42,064][root][INFO] - i_exp:0, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:27:42,064][root][INFO] - i_exp:0, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:27:42,064][root][INFO] - i_exp:0, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:27:42,064][root][INFO] - exp_i:0,  epoch:3 ...\n",
      "[2022-02-15 16:27:57,009][root][INFO] - i_exp:0, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:27:57,010][root][INFO] - i_exp:0, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:27:57,010][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 16:27:57,010][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:27:57,035][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.01280424278229475\n",
      "[2022-02-15 16:27:57,035][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.012682811357080936\n",
      "[2022-02-15 16:27:57,196][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.00920140377581005\n",
      "[2022-02-15 16:27:57,197][root][INFO] - exp_i:0,  epoch:4 ...\n",
      "[2022-02-15 16:28:12,214][root][INFO] - i_exp:0, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:28:12,214][root][INFO] - i_exp:0, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:28:12,214][root][INFO] - i_exp:0, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:28:12,214][root][INFO] - exp_i:0,  epoch:5 ...\n",
      "[2022-02-15 16:28:26,176][root][INFO] - i_exp:0, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:28:26,176][root][INFO] - i_exp:0, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:28:26,177][root][INFO] - start to predict ... i_exp:0,epochs:5, train_step:798\n",
      "[2022-02-15 16:28:26,177][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:28:26,202][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.01346665620803833\n",
      "[2022-02-15 16:28:26,202][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.013688418082892895\n",
      "[2022-02-15 16:28:26,348][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: 0.028081284786346774\n",
      "[2022-02-15 16:28:26,349][root][INFO] - exp_i:0,  epoch:6 ...\n",
      "[2022-02-15 16:28:39,426][root][INFO] - i_exp:0, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:28:39,426][root][INFO] - i_exp:0, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:28:39,426][root][INFO] - i_exp:0, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:28:39,426][root][INFO] - exp_i:0,  epoch:7 ...\n",
      "[2022-02-15 16:28:58,438][root][INFO] - i_exp:0, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:28:58,438][root][INFO] - i_exp:0, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:28:58,438][root][INFO] - start to predict ... i_exp:0,epochs:7, train_step:1064\n",
      "[2022-02-15 16:28:58,438][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:28:58,463][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.011666619218885899\n",
      "[2022-02-15 16:28:58,463][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.01184964831918478\n",
      "[2022-02-15 16:28:58,608][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.02214820277242324\n",
      "[2022-02-15 16:28:58,609][root][INFO] - exp_i:0,  epoch:8 ...\n",
      "[2022-02-15 16:29:11,713][root][INFO] - i_exp:0, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:29:11,713][root][INFO] - i_exp:0, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:29:11,713][root][INFO] - i_exp:0, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:29:11,713][root][INFO] - exp_i:0,  epoch:9 ...\n",
      "[2022-02-15 16:29:32,608][root][INFO] - i_exp:0, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:29:32,608][root][INFO] - i_exp:0, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:29:32,608][root][INFO] - start to predict ... i_exp:0,epochs:9, train_step:1330\n",
      "[2022-02-15 16:29:32,608][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:29:32,633][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.013286535628139973\n",
      "[2022-02-15 16:29:32,633][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.013382515870034695\n",
      "[2022-02-15 16:29:32,778][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.02831352010870614\n",
      "[2022-02-15 16:29:32,781][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:29:32,792][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_with_PS_128_20220215_162642_test_result.test...done\n",
      "[2022-02-15 16:29:33,947][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:29:34,329][root][INFO] - exp_i:1,  epoch:0 ...\n",
      "[2022-02-15 16:29:49,786][root][INFO] - i_exp:1, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:29:49,786][root][INFO] - i_exp:1, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:29:49,786][root][INFO] - i_exp:1, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:29:49,787][root][INFO] - exp_i:1,  epoch:1 ...\n",
      "[2022-02-15 16:30:06,112][root][INFO] - i_exp:1, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:30:06,112][root][INFO] - i_exp:1, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:30:06,113][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 16:30:06,113][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:30:06,138][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :8.418483048444614e-05\n",
      "[2022-02-15 16:30:06,138][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.0002332318399567157\n",
      "[2022-02-15 16:30:06,287][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.017156274592221382\n",
      "[2022-02-15 16:30:06,288][root][INFO] - exp_i:1,  epoch:2 ...\n",
      "[2022-02-15 16:30:22,006][root][INFO] - i_exp:1, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:30:22,006][root][INFO] - i_exp:1, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:30:22,006][root][INFO] - i_exp:1, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:30:22,006][root][INFO] - exp_i:1,  epoch:3 ...\n",
      "[2022-02-15 16:30:38,330][root][INFO] - i_exp:1, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:30:38,330][root][INFO] - i_exp:1, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:30:38,331][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 16:30:38,331][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:30:38,356][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.004372837487608194\n",
      "[2022-02-15 16:30:38,356][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.00454018684104085\n",
      "[2022-02-15 16:30:38,505][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.019167156302877297\n",
      "[2022-02-15 16:30:38,506][root][INFO] - exp_i:1,  epoch:4 ...\n",
      "[2022-02-15 16:30:54,350][root][INFO] - i_exp:1, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:30:54,350][root][INFO] - i_exp:1, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:30:54,350][root][INFO] - i_exp:1, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:30:54,350][root][INFO] - exp_i:1,  epoch:5 ...\n",
      "[2022-02-15 16:31:10,673][root][INFO] - i_exp:1, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:31:10,673][root][INFO] - i_exp:1, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:31:10,673][root][INFO] - start to predict ... i_exp:1,epochs:5, train_step:798\n",
      "[2022-02-15 16:31:10,673][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:31:10,699][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.007909473963081837\n",
      "[2022-02-15 16:31:10,699][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.00786350853741169\n",
      "[2022-02-15 16:31:10,848][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: 0.0008357042912819863\n",
      "[2022-02-15 16:31:10,849][root][INFO] - exp_i:1,  epoch:6 ...\n",
      "[2022-02-15 16:31:26,607][root][INFO] - i_exp:1, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:31:26,607][root][INFO] - i_exp:1, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:31:26,607][root][INFO] - i_exp:1, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:31:26,607][root][INFO] - exp_i:1,  epoch:7 ...\n",
      "[2022-02-15 16:31:43,023][root][INFO] - i_exp:1, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:31:43,023][root][INFO] - i_exp:1, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:31:43,024][root][INFO] - start to predict ... i_exp:1,epochs:7, train_step:1064\n",
      "[2022-02-15 16:31:43,024][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:31:43,049][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.004101663362234831\n",
      "[2022-02-15 16:31:43,049][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.004276502877473831\n",
      "[2022-02-15 16:31:43,199][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.020873798727798258\n",
      "[2022-02-15 16:31:43,200][root][INFO] - exp_i:1,  epoch:8 ...\n",
      "[2022-02-15 16:31:58,958][root][INFO] - i_exp:1, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:31:58,958][root][INFO] - i_exp:1, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:31:58,958][root][INFO] - i_exp:1, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:31:58,958][root][INFO] - exp_i:1,  epoch:9 ...\n",
      "[2022-02-15 16:32:15,399][root][INFO] - i_exp:1, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:32:15,399][root][INFO] - i_exp:1, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:32:15,399][root][INFO] - start to predict ... i_exp:1,epochs:9, train_step:1330\n",
      "[2022-02-15 16:32:15,399][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:32:15,425][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.012850751169025898\n",
      "[2022-02-15 16:32:15,425][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.012926749885082245\n",
      "[2022-02-15 16:32:15,574][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.026963992758938134\n",
      "[2022-02-15 16:32:15,581][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:32:15,606][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_with_PS_128_20220215_162642_test_result.test...done\n",
      "[2022-02-15 16:32:16,697][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:32:17,127][root][INFO] - exp_i:2,  epoch:0 ...\n",
      "[2022-02-15 16:32:33,297][root][INFO] - i_exp:2, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:32:33,298][root][INFO] - i_exp:2, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:32:33,298][root][INFO] - i_exp:2, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:32:33,298][root][INFO] - exp_i:2,  epoch:1 ...\n",
      "[2022-02-15 16:32:50,118][root][INFO] - i_exp:2, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:32:50,119][root][INFO] - i_exp:2, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:32:50,119][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 16:32:50,119][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:32:50,144][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.00516378553584218\n",
      "[2022-02-15 16:32:50,145][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.005334352608770132\n",
      "[2022-02-15 16:32:50,294][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: -0.0027328834116046603\n",
      "[2022-02-15 16:32:50,295][root][INFO] - exp_i:2,  epoch:2 ...\n",
      "[2022-02-15 16:33:06,139][root][INFO] - i_exp:2, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:33:06,139][root][INFO] - i_exp:2, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:33:06,139][root][INFO] - i_exp:2, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:33:06,139][root][INFO] - exp_i:2,  epoch:3 ...\n",
      "[2022-02-15 16:33:22,869][root][INFO] - i_exp:2, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:33:22,869][root][INFO] - i_exp:2, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:33:22,869][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 16:33:22,870][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:33:22,895][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.010010998696088791\n",
      "[2022-02-15 16:33:22,895][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.010191196575760841\n",
      "[2022-02-15 16:33:23,045][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: -0.0009233816153396557\n",
      "[2022-02-15 16:33:23,045][root][INFO] - exp_i:2,  epoch:4 ...\n",
      "[2022-02-15 16:33:38,978][root][INFO] - i_exp:2, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:33:38,978][root][INFO] - i_exp:2, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:33:38,978][root][INFO] - i_exp:2, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:33:38,978][root][INFO] - exp_i:2,  epoch:5 ...\n",
      "[2022-02-15 16:33:55,727][root][INFO] - i_exp:2, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:33:55,727][root][INFO] - i_exp:2, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:33:55,728][root][INFO] - start to predict ... i_exp:2,epochs:5, train_step:798\n",
      "[2022-02-15 16:33:55,728][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:33:55,753][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.008027725853025913\n",
      "[2022-02-15 16:33:55,753][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.008190612308681011\n",
      "[2022-02-15 16:33:55,903][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: -0.00014568998085762552\n",
      "[2022-02-15 16:33:55,903][root][INFO] - exp_i:2,  epoch:6 ...\n",
      "[2022-02-15 16:34:11,838][root][INFO] - i_exp:2, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:34:11,838][root][INFO] - i_exp:2, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:34:11,838][root][INFO] - i_exp:2, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:34:11,838][root][INFO] - exp_i:2,  epoch:7 ...\n",
      "[2022-02-15 16:34:28,559][root][INFO] - i_exp:2, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:34:28,560][root][INFO] - i_exp:2, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:34:28,560][root][INFO] - start to predict ... i_exp:2,epochs:7, train_step:1064\n",
      "[2022-02-15 16:34:28,560][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:34:28,585][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.009865105152130127\n",
      "[2022-02-15 16:34:28,586][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.010056894272565842\n",
      "[2022-02-15 16:34:28,735][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.007771679466585169\n",
      "[2022-02-15 16:34:28,736][root][INFO] - exp_i:2,  epoch:8 ...\n",
      "[2022-02-15 16:34:44,681][root][INFO] - i_exp:2, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:34:44,681][root][INFO] - i_exp:2, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:34:44,681][root][INFO] - i_exp:2, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:34:44,681][root][INFO] - exp_i:2,  epoch:9 ...\n",
      "[2022-02-15 16:35:01,476][root][INFO] - i_exp:2, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:35:01,476][root][INFO] - i_exp:2, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:35:01,476][root][INFO] - start to predict ... i_exp:2,epochs:9, train_step:1330\n",
      "[2022-02-15 16:35:01,476][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:35:01,502][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.00849828775972128\n",
      "[2022-02-15 16:35:01,502][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.008564401417970657\n",
      "[2022-02-15 16:35:01,652][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.008046295397906201\n",
      "[2022-02-15 16:35:01,667][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:35:01,718][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_with_PS_128_20220215_162642_test_result.test...done\n",
      "[2022-02-15 16:35:02,802][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:35:03,220][root][INFO] - exp_i:3,  epoch:0 ...\n",
      "[2022-02-15 16:35:19,324][root][INFO] - i_exp:3, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:35:19,325][root][INFO] - i_exp:3, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:35:19,325][root][INFO] - i_exp:3, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:35:19,325][root][INFO] - exp_i:3,  epoch:1 ...\n",
      "[2022-02-15 16:35:36,049][root][INFO] - i_exp:3, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:35:36,050][root][INFO] - i_exp:3, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:35:36,050][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 16:35:36,050][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:35:36,075][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.01206156425178051\n",
      "[2022-02-15 16:35:36,076][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.012255383655428886\n",
      "[2022-02-15 16:35:36,225][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.008418094021087067\n",
      "[2022-02-15 16:35:36,226][root][INFO] - exp_i:3,  epoch:2 ...\n",
      "[2022-02-15 16:35:52,343][root][INFO] - i_exp:3, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:35:52,343][root][INFO] - i_exp:3, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:35:52,343][root][INFO] - i_exp:3, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:35:52,343][root][INFO] - exp_i:3,  epoch:3 ...\n",
      "[2022-02-15 16:36:08,975][root][INFO] - i_exp:3, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:36:08,976][root][INFO] - i_exp:3, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:36:08,976][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 16:36:08,976][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:36:09,001][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.010597968474030495\n",
      "[2022-02-15 16:36:09,002][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.010491769760847092\n",
      "[2022-02-15 16:36:09,152][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.01699802574063836\n",
      "[2022-02-15 16:36:09,152][root][INFO] - exp_i:3,  epoch:4 ...\n",
      "[2022-02-15 16:36:25,163][root][INFO] - i_exp:3, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:36:25,163][root][INFO] - i_exp:3, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:36:25,163][root][INFO] - i_exp:3, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:36:25,163][root][INFO] - exp_i:3,  epoch:5 ...\n",
      "[2022-02-15 16:36:41,785][root][INFO] - i_exp:3, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:36:41,785][root][INFO] - i_exp:3, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:36:41,785][root][INFO] - start to predict ... i_exp:3,epochs:5, train_step:798\n",
      "[2022-02-15 16:36:41,786][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:36:41,811][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.00967731885612011\n",
      "[2022-02-15 16:36:41,811][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.009640568867325783\n",
      "[2022-02-15 16:36:41,961][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: 0.016086194959656862\n",
      "[2022-02-15 16:36:41,962][root][INFO] - exp_i:3,  epoch:6 ...\n",
      "[2022-02-15 16:36:57,983][root][INFO] - i_exp:3, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:36:57,983][root][INFO] - i_exp:3, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:36:57,983][root][INFO] - i_exp:3, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:36:57,983][root][INFO] - exp_i:3,  epoch:7 ...\n",
      "[2022-02-15 16:37:14,701][root][INFO] - i_exp:3, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:37:14,702][root][INFO] - i_exp:3, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:37:14,702][root][INFO] - start to predict ... i_exp:3,epochs:7, train_step:1064\n",
      "[2022-02-15 16:37:14,702][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:37:14,727][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.009291294030845165\n",
      "[2022-02-15 16:37:14,728][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.009417988359928131\n",
      "[2022-02-15 16:37:14,877][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.025523067986470287\n",
      "[2022-02-15 16:37:14,878][root][INFO] - exp_i:3,  epoch:8 ...\n",
      "[2022-02-15 16:37:30,934][root][INFO] - i_exp:3, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:37:30,934][root][INFO] - i_exp:3, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:37:30,934][root][INFO] - i_exp:3, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:37:30,934][root][INFO] - exp_i:3,  epoch:9 ...\n",
      "[2022-02-15 16:37:47,637][root][INFO] - i_exp:3, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:37:47,638][root][INFO] - i_exp:3, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:37:47,638][root][INFO] - start to predict ... i_exp:3,epochs:9, train_step:1330\n",
      "[2022-02-15 16:37:47,638][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:37:47,663][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.01116356160491705\n",
      "[2022-02-15 16:37:47,664][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.011201800778508186\n",
      "[2022-02-15 16:37:47,813][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.024847029663684874\n",
      "[2022-02-15 16:37:47,825][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:37:47,900][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_with_PS_128_20220215_162642_test_result.test...done\n",
      "[2022-02-15 16:37:48,985][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:37:49,397][root][INFO] - exp_i:4,  epoch:0 ...\n",
      "[2022-02-15 16:38:05,700][root][INFO] - i_exp:4, name:mu_c, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:38:05,700][root][INFO] - i_exp:4, name:mu_t, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:38:05,700][root][INFO] - i_exp:4, name:propensity, epoch:0, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:38:05,700][root][INFO] - exp_i:4,  epoch:1 ...\n",
      "[2022-02-15 16:38:22,447][root][INFO] - i_exp:4, name:tau_c, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:38:22,447][root][INFO] - i_exp:4, name:tau_t, epoch:1, new learning rate is: [0.00095]\n",
      "[2022-02-15 16:38:22,447][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 16:38:22,447][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:38:22,473][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.011313175782561302\n",
      "[2022-02-15 16:38:22,473][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.011207674629986286\n",
      "[2022-02-15 16:38:22,623][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: -0.0032599576308927412\n",
      "[2022-02-15 16:38:22,624][root][INFO] - exp_i:4,  epoch:2 ...\n",
      "[2022-02-15 16:38:38,689][root][INFO] - i_exp:4, name:mu_c, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:38:38,689][root][INFO] - i_exp:4, name:mu_t, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:38:38,689][root][INFO] - i_exp:4, name:propensity, epoch:2, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:38:38,689][root][INFO] - exp_i:4,  epoch:3 ...\n",
      "[2022-02-15 16:38:55,404][root][INFO] - i_exp:4, name:tau_c, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:38:55,404][root][INFO] - i_exp:4, name:tau_t, epoch:3, new learning rate is: [0.0009025]\n",
      "[2022-02-15 16:38:55,405][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 16:38:55,405][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:38:55,430][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.005922973621636629\n",
      "[2022-02-15 16:38:55,431][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.005833727307617664\n",
      "[2022-02-15 16:38:55,580][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.010504923218084796\n",
      "[2022-02-15 16:38:55,581][root][INFO] - exp_i:4,  epoch:4 ...\n",
      "[2022-02-15 16:39:11,499][root][INFO] - i_exp:4, name:mu_c, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:39:11,499][root][INFO] - i_exp:4, name:mu_t, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:39:11,500][root][INFO] - i_exp:4, name:propensity, epoch:4, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:39:11,500][root][INFO] - exp_i:4,  epoch:5 ...\n",
      "[2022-02-15 16:39:28,263][root][INFO] - i_exp:4, name:tau_c, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:39:28,263][root][INFO] - i_exp:4, name:tau_t, epoch:5, new learning rate is: [0.000857375]\n",
      "[2022-02-15 16:39:28,263][root][INFO] - start to predict ... i_exp:4,epochs:5, train_step:798\n",
      "[2022-02-15 16:39:28,263][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:39:28,289][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[t]) :0.013768121600151062\n",
      "[2022-02-15 16:39:28,289][root][INFO] - p_tau test_pred_result, epoch, 5 , mean(p_tau[~t]) :0.013650762848556042\n",
      "[2022-02-15 16:39:28,438][root][INFO] - group_name test_pred_result, epoch, 5, auuc_score: 0.018165933717675147\n",
      "[2022-02-15 16:39:28,439][root][INFO] - exp_i:4,  epoch:6 ...\n",
      "[2022-02-15 16:39:44,280][root][INFO] - i_exp:4, name:mu_c, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:39:44,280][root][INFO] - i_exp:4, name:mu_t, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:39:44,280][root][INFO] - i_exp:4, name:propensity, epoch:6, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:39:44,280][root][INFO] - exp_i:4,  epoch:7 ...\n",
      "[2022-02-15 16:40:01,065][root][INFO] - i_exp:4, name:tau_c, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:40:01,065][root][INFO] - i_exp:4, name:tau_t, epoch:7, new learning rate is: [0.0008145062499999999]\n",
      "[2022-02-15 16:40:01,065][root][INFO] - start to predict ... i_exp:4,epochs:7, train_step:1064\n",
      "[2022-02-15 16:40:01,065][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:40:01,091][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[t]) :0.0063617355190217495\n",
      "[2022-02-15 16:40:01,091][root][INFO] - p_tau test_pred_result, epoch, 7 , mean(p_tau[~t]) :0.006421648897230625\n",
      "[2022-02-15 16:40:01,241][root][INFO] - group_name test_pred_result, epoch, 7, auuc_score: 0.020645909546641892\n",
      "[2022-02-15 16:40:01,241][root][INFO] - exp_i:4,  epoch:8 ...\n",
      "[2022-02-15 16:40:17,199][root][INFO] - i_exp:4, name:mu_c, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:40:17,199][root][INFO] - i_exp:4, name:mu_t, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:40:17,199][root][INFO] - i_exp:4, name:propensity, epoch:8, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:40:17,199][root][INFO] - exp_i:4,  epoch:9 ...\n",
      "[2022-02-15 16:40:33,946][root][INFO] - i_exp:4, name:tau_c, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:40:33,946][root][INFO] - i_exp:4, name:tau_t, epoch:9, new learning rate is: [0.0007737809374999998]\n",
      "[2022-02-15 16:40:33,946][root][INFO] - start to predict ... i_exp:4,epochs:9, train_step:1330\n",
      "[2022-02-15 16:40:33,947][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:40:33,972][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[t]) :0.012528306804597378\n",
      "[2022-02-15 16:40:33,972][root][INFO] - p_tau test_pred_result, epoch, 9 , mean(p_tau[~t]) :0.012553654611110687\n",
      "[2022-02-15 16:40:34,122][root][INFO] - group_name test_pred_result, epoch, 9, auuc_score: 0.028062263987353245\n",
      "[2022-02-15 16:40:34,134][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:40:34,223][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/X_learner_with_PS_128_20220215_162642_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.00808197919419736\n",
      "i_exp:0, AUUC:0.00920140377581005\n",
      "i_exp:0, AUUC:0.028081284786346774\n",
      "i_exp:0, AUUC:0.02214820277242324\n",
      "i_exp:0, AUUC:0.02831352010870614\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.017156274592221382\n",
      "i_exp:1, AUUC:0.019167156302877297\n",
      "i_exp:1, AUUC:0.0008357042912819863\n",
      "i_exp:1, AUUC:0.020873798727798258\n",
      "i_exp:1, AUUC:0.026963992758938134\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:-0.0027328834116046603\n",
      "i_exp:2, AUUC:-0.0009233816153396557\n",
      "i_exp:2, AUUC:-0.00014568998085762552\n",
      "i_exp:2, AUUC:0.007771679466585169\n",
      "i_exp:2, AUUC:0.008046295397906201\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.008418094021087067\n",
      "i_exp:3, AUUC:0.01699802574063836\n",
      "i_exp:3, AUUC:0.016086194959656862\n",
      "i_exp:3, AUUC:0.025523067986470287\n",
      "i_exp:3, AUUC:0.024847029663684874\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:-0.0032599576308927412\n",
      "i_exp:4, AUUC:0.010504923218084796\n",
      "i_exp:4, AUUC:0.018165933717675147\n",
      "i_exp:4, AUUC:0.020645909546641892\n",
      "i_exp:4, AUUC:0.028062263987353245\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.02831352010870614, 0.026963992758938134, 0.008046295397906201, 0.025523067986470287, 0.028062263987353245], 'E_att': [0.009546851116130656, 0.009111063863048857, 0.004758601385066813, 0.0055516067248681245, 0.00878862229258806]}\n",
      "AUUC: 0.023382 +/- 0.003457\n",
      "E_att: 0.007551 +/- 0.000889\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "!python search_params.py x_learner_main.py eval4real_data.py ./conf4models/lzd_real_data/Xlearner.txt 1 {train_npz} {test_npz}\n",
    "!python search_params.py x_learner_main.py eval4real_data.py ./conf4models/lzd_real_data/Xlearner_with_PS.txt 1 {train_npz} {test_npz}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20b902c",
   "metadata": {},
   "source": [
    "## TARNet/CFRwass/CFRmmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a49676bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 16:40:43,094 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 16:40:43,095 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 16:40:43,284][root][INFO] - log testing ...\n",
      "[2022-02-15 16:40:43,285][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'TARNET_128_64_20220215_164041', 'n_experiments': 5, 'batch_size': 5000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 0, 'escvr1_w': 0, 'escvr0_w': 0, 'h1_w': 1, 'h0_w': 1, 'mu0hat_w': 0, 'mu1hat_w': 0, 'imb_dist': 'wass2', 'imb_dist_w': 0, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 16:40:43,285][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 16:40:48,892][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 16:40:48,892][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 16:40:50,098][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 16:40:50,109][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 16:40:50,110][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/TARNET_128_64_20220215_164041) ...\n",
      "2022-02-15 16:40:50.295604: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 16:40:50.295647: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 16:40:52,490][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:40:57,029][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 16:40:57,031][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 16:40:57,033][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 16:40:57,034][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 16:40:57,040][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 16:40:57,046][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 16:40:57,049][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 16:40:57,049][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 16:40:57,050][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 16:40:57,050][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 16:40:57,050][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 16:40:57,051][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 16:40:57,051][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 16:40:57,052][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:40:57,814][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 16:41:16,435][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:41:16,436][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:133\n",
      "[2022-02-15 16:41:16,436][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:41:16,561][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.030731798505876592\n",
      "[2022-02-15 16:41:16,564][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:41:16,564][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.016235968098044395\n",
      "[2022-02-15 16:41:16,565][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.016228463500738144\n",
      "[2022-02-15 16:41:35,565][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:41:35,566][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 16:41:35,566][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:41:35,689][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.02953730553745353\n",
      "[2022-02-15 16:41:35,692][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:41:35,692][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.018388167023658752\n",
      "[2022-02-15 16:41:35,693][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.018353978171944618\n",
      "[2022-02-15 16:41:54,550][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:41:54,551][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:399\n",
      "[2022-02-15 16:41:54,551][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:41:54,674][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.017885425420276546\n",
      "[2022-02-15 16:41:54,677][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:41:54,678][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.00809112936258316\n",
      "[2022-02-15 16:41:54,678][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.008100545965135098\n",
      "[2022-02-15 16:42:13,760][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:42:13,761][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 16:42:13,761][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:42:13,885][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.03891996956520522\n",
      "[2022-02-15 16:42:13,888][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:42:13,888][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.019206296652555466\n",
      "[2022-02-15 16:42:13,889][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.01919674314558506\n",
      "[2022-02-15 16:42:32,888][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:42:32,888][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:665\n",
      "[2022-02-15 16:42:32,888][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:42:33,010][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02539652334648662\n",
      "[2022-02-15 16:42:33,013][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:42:33,013][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.008140860125422478\n",
      "[2022-02-15 16:42:33,014][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.008145011030137539\n",
      "[2022-02-15 16:42:33,021][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:42:33,054][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/TARNET_128_64_20220215_164041_test_result.test...done\n",
      "[2022-02-15 16:42:34,134][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:42:53,504][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:42:53,505][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:133\n",
      "[2022-02-15 16:42:53,505][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:42:53,625][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02952241408323466\n",
      "[2022-02-15 16:42:53,628][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:42:53,628][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.01608046516776085\n",
      "[2022-02-15 16:42:53,628][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.016092907637357712\n",
      "[2022-02-15 16:43:10,792][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:43:10,792][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 16:43:10,793][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:43:10,912][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.03130202185629848\n",
      "[2022-02-15 16:43:10,914][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:43:10,915][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.012034758925437927\n",
      "[2022-02-15 16:43:10,915][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.012020322494208813\n",
      "[2022-02-15 16:43:28,236][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:43:28,237][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:399\n",
      "[2022-02-15 16:43:28,237][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:43:28,358][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.03301433927443687\n",
      "[2022-02-15 16:43:28,361][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:43:28,362][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.00955255702137947\n",
      "[2022-02-15 16:43:28,362][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.009567403234541416\n",
      "[2022-02-15 16:43:45,594][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:43:45,595][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 16:43:45,595][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:43:45,716][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.014413503347791762\n",
      "[2022-02-15 16:43:45,719][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:43:45,719][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.005878090858459473\n",
      "[2022-02-15 16:43:45,719][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.005887689534574747\n",
      "[2022-02-15 16:44:02,512][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:44:02,513][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:665\n",
      "[2022-02-15 16:44:02,513][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:44:02,631][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.00856749000757659\n",
      "[2022-02-15 16:44:02,634][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:44:02,634][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.006715286988765001\n",
      "[2022-02-15 16:44:02,634][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.00671680923551321\n",
      "[2022-02-15 16:44:02,651][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:44:02,832][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/TARNET_128_64_20220215_164041_test_result.test...done\n",
      "[2022-02-15 16:44:03,840][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:44:20,372][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:44:20,373][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:133\n",
      "[2022-02-15 16:44:20,373][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:44:20,492][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.009049208012017546\n",
      "[2022-02-15 16:44:20,494][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:44:20,495][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.012723860330879688\n",
      "[2022-02-15 16:44:20,495][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.012825998477637768\n",
      "[2022-02-15 16:44:38,249][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:44:38,250][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 16:44:38,250][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:44:38,369][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.015353615784170497\n",
      "[2022-02-15 16:44:38,372][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:44:38,372][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.013968448154628277\n",
      "[2022-02-15 16:44:38,372][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.014006276614964008\n",
      "[2022-02-15 16:44:55,795][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:44:55,795][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:399\n",
      "[2022-02-15 16:44:55,796][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:44:55,932][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.025014766374801566\n",
      "[2022-02-15 16:44:55,935][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:44:55,936][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.014272165484726429\n",
      "[2022-02-15 16:44:55,936][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.014288654550909996\n",
      "[2022-02-15 16:45:12,062][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:45:12,063][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 16:45:12,063][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:45:12,181][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.023606168816615005\n",
      "[2022-02-15 16:45:12,184][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:45:12,185][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.013678296469151974\n",
      "[2022-02-15 16:45:12,185][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.013675408437848091\n",
      "[2022-02-15 16:45:28,750][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:45:28,751][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:665\n",
      "[2022-02-15 16:45:28,751][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:45:28,891][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.01049824313046603\n",
      "[2022-02-15 16:45:28,895][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:45:28,895][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.009147332981228828\n",
      "[2022-02-15 16:45:28,896][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.009164005517959595\n",
      "[2022-02-15 16:45:28,938][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:45:29,138][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/TARNET_128_64_20220215_164041_test_result.test...done\n",
      "[2022-02-15 16:45:30,805][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:45:51,191][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:45:51,191][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:133\n",
      "[2022-02-15 16:45:51,191][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:45:51,309][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.008913028841128526\n",
      "[2022-02-15 16:45:51,311][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:45:51,312][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.014218788594007492\n",
      "[2022-02-15 16:45:51,312][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.0142894322052598\n",
      "[2022-02-15 16:46:14,353][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:46:14,354][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 16:46:14,354][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:46:14,474][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.029111282366394917\n",
      "[2022-02-15 16:46:14,477][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:46:14,478][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.011693201959133148\n",
      "[2022-02-15 16:46:14,478][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.011705315671861172\n",
      "[2022-02-15 16:46:30,341][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:46:30,342][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:399\n",
      "[2022-02-15 16:46:30,342][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:46:30,459][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: -0.006872482009611759\n",
      "[2022-02-15 16:46:30,462][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:46:30,462][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.002763344906270504\n",
      "[2022-02-15 16:46:30,462][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.00278998794965446\n",
      "[2022-02-15 16:46:46,030][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:46:46,031][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 16:46:46,031][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:46:46,149][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.022436174298413762\n",
      "[2022-02-15 16:46:46,151][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:46:46,152][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.012458393350243568\n",
      "[2022-02-15 16:46:46,152][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.012461651116609573\n",
      "[2022-02-15 16:47:01,585][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:47:01,585][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:665\n",
      "[2022-02-15 16:47:01,585][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:47:01,704][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.014871571562342988\n",
      "[2022-02-15 16:47:01,707][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:47:01,707][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.011943557299673557\n",
      "[2022-02-15 16:47:01,707][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.01193500030785799\n",
      "[2022-02-15 16:47:01,751][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:47:02,338][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/TARNET_128_64_20220215_164041_test_result.test...done\n",
      "[2022-02-15 16:47:03,301][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:47:19,295][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:47:19,295][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:133\n",
      "[2022-02-15 16:47:19,295][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:47:19,413][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02824466472220867\n",
      "[2022-02-15 16:47:19,416][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:47:19,416][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.016881626099348068\n",
      "[2022-02-15 16:47:19,417][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.0168879684060812\n",
      "[2022-02-15 16:47:35,121][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:47:35,121][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 16:47:35,121][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:47:35,239][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.019738254325597525\n",
      "[2022-02-15 16:47:35,242][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:47:35,242][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.010783175006508827\n",
      "[2022-02-15 16:47:35,242][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.010809769853949547\n",
      "[2022-02-15 16:47:50,792][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:47:50,793][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:399\n",
      "[2022-02-15 16:47:50,793][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:47:50,910][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.027338101419021505\n",
      "[2022-02-15 16:47:50,913][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:47:50,913][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.013709899969398975\n",
      "[2022-02-15 16:47:50,914][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.013721385970711708\n",
      "[2022-02-15 16:48:06,599][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:48:06,599][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 16:48:06,599][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:48:06,717][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.027976164562147906\n",
      "[2022-02-15 16:48:06,720][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:48:06,720][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.017705075442790985\n",
      "[2022-02-15 16:48:06,720][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.017721300944685936\n",
      "[2022-02-15 16:48:22,291][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:48:22,291][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:665\n",
      "[2022-02-15 16:48:22,291][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:48:22,409][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.006058608095787643\n",
      "[2022-02-15 16:48:22,412][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:48:22,412][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.008021913468837738\n",
      "[2022-02-15 16:48:22,412][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.00806095264852047\n",
      "[2022-02-15 16:48:22,459][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:48:23,046][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/TARNET_128_64_20220215_164041_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.030731798505876592\n",
      "i_exp:0, AUUC:0.02953730553745353\n",
      "i_exp:0, AUUC:0.017885425420276546\n",
      "i_exp:0, AUUC:0.03891996956520522\n",
      "i_exp:0, AUUC:0.02539652334648662\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.02952241408323466\n",
      "i_exp:1, AUUC:0.03130202185629848\n",
      "i_exp:1, AUUC:0.03301433927443687\n",
      "i_exp:1, AUUC:0.014413503347791762\n",
      "i_exp:1, AUUC:0.00856749000757659\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.009049208012017546\n",
      "i_exp:2, AUUC:0.015353615784170497\n",
      "i_exp:2, AUUC:0.025014766374801566\n",
      "i_exp:2, AUUC:0.023606168816615005\n",
      "i_exp:2, AUUC:0.01049824313046603\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.008913028841128526\n",
      "i_exp:3, AUUC:0.029111282366394917\n",
      "i_exp:3, AUUC:-0.006872482009611759\n",
      "i_exp:3, AUUC:0.022436174298413762\n",
      "i_exp:3, AUUC:0.014871571562342988\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.02824466472220867\n",
      "i_exp:4, AUUC:0.019738254325597525\n",
      "i_exp:4, AUUC:0.027338101419021505\n",
      "i_exp:4, AUUC:0.027976164562147906\n",
      "i_exp:4, AUUC:0.006058608095787643\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.03891996956520522, 0.03301433927443687, 0.025014766374801566, 0.029111282366394917, 0.02824466472220867], 'E_att': [0.015466609346578425, 0.005812871578047579, 0.010532477247426814, 0.007953516515801257, 0.013141938793371027]}\n",
      "AUUC: 0.030861 +/- 0.002132\n",
      "E_att: 0.010581 +/- 0.001550\n",
      "done.\n",
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 16:48:30,972 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 16:48:30,972 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 16:48:31,155][root][INFO] - log testing ...\n",
      "[2022-02-15 16:48:31,155][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'CFR_mmd_128_64_20220215_164829', 'n_experiments': 5, 'batch_size': 5000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 0, 'escvr1_w': 0, 'escvr0_w': 0, 'h1_w': 1, 'h0_w': 1, 'mu0hat_w': 0, 'mu1hat_w': 0, 'imb_dist': 'mmd', 'imb_dist_w': 0.1, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 16:48:31,155][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 16:48:36,495][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 16:48:36,495][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 16:48:37,646][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 16:48:37,657][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 16:48:37,657][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/CFR_mmd_128_64_20220215_164829) ...\n",
      "2022-02-15 16:48:37.819626: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 16:48:37.819657: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 16:48:39,758][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:48:43,830][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 16:48:43,831][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 16:48:43,833][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 16:48:43,834][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 16:48:43,839][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 16:48:43,845][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 16:48:43,847][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 16:48:43,847][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 16:48:43,847][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 16:48:43,848][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 16:48:43,848][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 16:48:43,848][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 16:48:43,849][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 16:48:43,849][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:48:44,572][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 16:49:03,951][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:49:03,951][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:133\n",
      "[2022-02-15 16:49:03,951][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:49:04,072][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.030568192455924206\n",
      "[2022-02-15 16:49:04,075][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:49:04,075][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.03115261346101761\n",
      "[2022-02-15 16:49:04,075][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.031065143644809723\n",
      "[2022-02-15 16:49:23,632][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:49:23,633][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 16:49:23,633][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:49:23,751][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.024304934244036482\n",
      "[2022-02-15 16:49:23,754][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:49:23,754][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.0315590500831604\n",
      "[2022-02-15 16:49:23,754][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.031479693949222565\n",
      "[2022-02-15 16:49:43,262][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:49:43,262][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:399\n",
      "[2022-02-15 16:49:43,262][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:49:43,380][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.023218730401065056\n",
      "[2022-02-15 16:49:43,383][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:49:43,383][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.026878392323851585\n",
      "[2022-02-15 16:49:43,383][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.02685258910059929\n",
      "[2022-02-15 16:50:02,864][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:50:02,864][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 16:50:02,864][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:50:02,982][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.030146296636044047\n",
      "[2022-02-15 16:50:02,984][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:50:02,985][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.04090398550033569\n",
      "[2022-02-15 16:50:02,985][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.04089659824967384\n",
      "[2022-02-15 16:50:22,558][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:50:22,558][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:665\n",
      "[2022-02-15 16:50:22,558][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:50:22,678][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.027773700970701854\n",
      "[2022-02-15 16:50:22,681][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:50:22,682][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.031565021723508835\n",
      "[2022-02-15 16:50:22,682][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.031545963138341904\n",
      "[2022-02-15 16:50:22,689][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:50:22,722][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_mmd_128_64_20220215_164829_test_result.test...done\n",
      "[2022-02-15 16:50:23,692][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:50:43,584][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:50:43,584][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:133\n",
      "[2022-02-15 16:50:43,585][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:50:43,702][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.023347196611006142\n",
      "[2022-02-15 16:50:43,705][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:50:43,705][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.031238915398716927\n",
      "[2022-02-15 16:50:43,705][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.03122686594724655\n",
      "[2022-02-15 16:51:03,305][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:51:03,305][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 16:51:03,305][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:51:03,423][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.022752173254343866\n",
      "[2022-02-15 16:51:03,426][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:51:03,426][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.02985476329922676\n",
      "[2022-02-15 16:51:03,426][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.029822327196598053\n",
      "[2022-02-15 16:51:22,923][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:51:22,923][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:399\n",
      "[2022-02-15 16:51:22,923][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:51:23,042][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.02498653826004749\n",
      "[2022-02-15 16:51:23,044][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:51:23,045][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.025197457522153854\n",
      "[2022-02-15 16:51:23,045][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.02526497095823288\n",
      "[2022-02-15 16:51:42,491][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:51:42,491][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 16:51:42,491][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:51:42,611][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.025683696109545646\n",
      "[2022-02-15 16:51:42,614][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:51:42,614][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.024367500096559525\n",
      "[2022-02-15 16:51:42,615][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.024361364543437958\n",
      "[2022-02-15 16:52:01,928][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:52:01,929][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:665\n",
      "[2022-02-15 16:52:01,929][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:52:02,048][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02406546102577434\n",
      "[2022-02-15 16:52:02,051][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:52:02,051][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.028366459533572197\n",
      "[2022-02-15 16:52:02,052][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.028355712071061134\n",
      "[2022-02-15 16:52:02,066][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:52:02,134][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_mmd_128_64_20220215_164829_test_result.test...done\n",
      "[2022-02-15 16:52:03,073][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:52:22,878][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:52:22,878][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:133\n",
      "[2022-02-15 16:52:22,878][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:52:22,996][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02296603443392869\n",
      "[2022-02-15 16:52:22,999][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:52:22,999][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.024066992104053497\n",
      "[2022-02-15 16:52:22,999][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.024016041308641434\n",
      "[2022-02-15 16:52:42,332][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:52:42,332][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 16:52:42,332][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:52:42,450][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.021030770100314088\n",
      "[2022-02-15 16:52:42,453][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:52:42,453][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.03102489560842514\n",
      "[2022-02-15 16:52:42,453][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.030951661989092827\n",
      "[2022-02-15 16:53:01,860][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:53:01,861][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:399\n",
      "[2022-02-15 16:53:01,861][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:53:01,980][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.0242600361343169\n",
      "[2022-02-15 16:53:01,983][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:53:01,983][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.034609604626894\n",
      "[2022-02-15 16:53:01,983][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.034613125026226044\n",
      "[2022-02-15 16:53:21,302][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:53:21,303][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 16:53:21,303][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:53:21,422][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02617450498437523\n",
      "[2022-02-15 16:53:21,424][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:53:21,425][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.03293992951512337\n",
      "[2022-02-15 16:53:21,425][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.03292979300022125\n",
      "[2022-02-15 16:53:40,858][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:53:40,859][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:665\n",
      "[2022-02-15 16:53:40,859][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:53:40,978][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.027334340799435256\n",
      "[2022-02-15 16:53:40,980][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:53:40,981][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.034655485302209854\n",
      "[2022-02-15 16:53:40,981][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.03465912491083145\n",
      "[2022-02-15 16:53:41,011][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:53:41,116][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_mmd_128_64_20220215_164829_test_result.test...done\n",
      "[2022-02-15 16:53:42,046][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:54:01,789][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:54:01,790][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:133\n",
      "[2022-02-15 16:54:01,790][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:54:01,908][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02761305709560632\n",
      "[2022-02-15 16:54:01,911][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:54:01,911][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.030264364555478096\n",
      "[2022-02-15 16:54:01,911][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.03024088591337204\n",
      "[2022-02-15 16:54:21,390][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:54:21,391][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 16:54:21,391][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:54:21,509][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.020645303155802704\n",
      "[2022-02-15 16:54:21,512][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:54:21,512][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.028482457622885704\n",
      "[2022-02-15 16:54:21,512][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.02846635691821575\n",
      "[2022-02-15 16:54:40,926][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:54:40,926][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:399\n",
      "[2022-02-15 16:54:40,926][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:54:41,044][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.029676870494494783\n",
      "[2022-02-15 16:54:41,047][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:54:41,047][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.022038079798221588\n",
      "[2022-02-15 16:54:41,047][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.02205318585038185\n",
      "[2022-02-15 16:55:00,486][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:55:00,487][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 16:55:00,487][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:55:00,607][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.030733780809690494\n",
      "[2022-02-15 16:55:00,610][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:55:00,610][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.028189189732074738\n",
      "[2022-02-15 16:55:00,610][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.028156684711575508\n",
      "[2022-02-15 16:55:19,952][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:55:19,953][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:665\n",
      "[2022-02-15 16:55:19,953][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:55:20,072][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.04395260237474283\n",
      "[2022-02-15 16:55:20,074][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:55:20,075][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.027666183188557625\n",
      "[2022-02-15 16:55:20,075][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.027694718912243843\n",
      "[2022-02-15 16:55:20,116][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:55:20,412][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_mmd_128_64_20220215_164829_test_result.test...done\n",
      "[2022-02-15 16:55:21,340][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:55:41,103][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:55:41,103][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:133\n",
      "[2022-02-15 16:55:41,104][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:55:41,222][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.03465736253869768\n",
      "[2022-02-15 16:55:41,224][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:55:41,225][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.029818657785654068\n",
      "[2022-02-15 16:55:41,225][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.029703965410590172\n",
      "[2022-02-15 16:56:00,709][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:56:00,709][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 16:56:00,710][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:56:00,827][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.023798724137576516\n",
      "[2022-02-15 16:56:00,830][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:56:00,830][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.026388155296444893\n",
      "[2022-02-15 16:56:00,831][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.02637943997979164\n",
      "[2022-02-15 16:56:20,165][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:56:20,165][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:399\n",
      "[2022-02-15 16:56:20,165][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:56:20,283][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.031070830740444328\n",
      "[2022-02-15 16:56:20,285][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:56:20,286][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.02809755876660347\n",
      "[2022-02-15 16:56:20,286][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.02809939906001091\n",
      "[2022-02-15 16:56:39,738][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:56:39,738][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 16:56:39,738][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:56:39,856][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.03207087691075145\n",
      "[2022-02-15 16:56:39,859][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:56:39,859][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.0338725745677948\n",
      "[2022-02-15 16:56:39,860][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.03386617824435234\n",
      "[2022-02-15 16:56:59,154][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:56:59,155][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:665\n",
      "[2022-02-15 16:56:59,155][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:56:59,272][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.031227175496631003\n",
      "[2022-02-15 16:56:59,275][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:56:59,276][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.027037901803851128\n",
      "[2022-02-15 16:56:59,276][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02698039449751377\n",
      "[2022-02-15 16:56:59,322][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:56:59,714][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_mmd_128_64_20220215_164829_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.030568192455924206\n",
      "i_exp:0, AUUC:0.024304934244036482\n",
      "i_exp:0, AUUC:0.023218730401065056\n",
      "i_exp:0, AUUC:0.030146296636044047\n",
      "i_exp:0, AUUC:0.027773700970701854\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.023347196611006142\n",
      "i_exp:1, AUUC:0.022752173254343866\n",
      "i_exp:1, AUUC:0.02498653826004749\n",
      "i_exp:1, AUUC:0.025683696109545646\n",
      "i_exp:1, AUUC:0.02406546102577434\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.02296603443392869\n",
      "i_exp:2, AUUC:0.021030770100314088\n",
      "i_exp:2, AUUC:0.0242600361343169\n",
      "i_exp:2, AUUC:0.02617450498437523\n",
      "i_exp:2, AUUC:0.027334340799435256\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.02761305709560632\n",
      "i_exp:3, AUUC:0.020645303155802704\n",
      "i_exp:3, AUUC:0.029676870494494783\n",
      "i_exp:3, AUUC:0.030733780809690494\n",
      "i_exp:3, AUUC:0.04395260237474283\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.03465736253869768\n",
      "i_exp:4, AUUC:0.023798724137576516\n",
      "i_exp:4, AUUC:0.031070830740444328\n",
      "i_exp:4, AUUC:0.03207087691075145\n",
      "i_exp:4, AUUC:0.031227175496631003\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.030568192455924206, 0.025683696109545646, 0.027334340799435256, 0.04395260237474283, 0.03465736253869768], 'E_att': [0.027412926155040568, 0.020627816515872782, 0.030915799858877963, 0.023926497745225733, 0.026078974204967326]}\n",
      "AUUC: 0.032439 +/- 0.002917\n",
      "E_att: 0.025792 +/- 0.001538\n",
      "done.\n",
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 16:57:07,406 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 16:57:07,406 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 16:57:07,590][root][INFO] - log testing ...\n",
      "[2022-02-15 16:57:07,590][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'CFR_wass_128_64_20220215_165705', 'n_experiments': 5, 'batch_size': 3000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 0, 'escvr1_w': 0, 'escvr0_w': 0, 'h1_w': 1, 'h0_w': 1, 'mu0hat_w': 0, 'mu1hat_w': 0, 'imb_dist': 'wass', 'imb_dist_w': 0.1, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 16:57:07,590][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 16:57:13,005][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 16:57:13,005][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 16:57:14,148][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 16:57:14,160][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 16:57:14,160][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/CFR_wass_128_64_20220215_165705) ...\n",
      "2022-02-15 16:57:14.320317: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 16:57:14.320351: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 16:57:16,280][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 16:57:20,380][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 16:57:20,382][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 16:57:20,383][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 16:57:20,385][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 16:57:20,389][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 16:57:20,396][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 16:57:20,399][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 16:57:20,399][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 16:57:20,399][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 16:57:20,400][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 16:57:20,400][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 16:57:20,400][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 16:57:20,401][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 16:57:20,401][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:57:21,113][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 16:57:46,981][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:57:46,981][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:222\n",
      "[2022-02-15 16:57:46,981][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:57:47,101][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.022563183092324215\n",
      "[2022-02-15 16:57:47,104][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:57:47,104][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.03277147188782692\n",
      "[2022-02-15 16:57:47,104][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.03261837735772133\n",
      "[2022-02-15 16:58:13,103][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 16:58:13,103][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:444\n",
      "[2022-02-15 16:58:13,103][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:58:13,222][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.023191762684907025\n",
      "[2022-02-15 16:58:13,225][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 16:58:13,225][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.04194226488471031\n",
      "[2022-02-15 16:58:13,226][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.04180942848324776\n",
      "[2022-02-15 16:58:39,227][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 16:58:39,228][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:666\n",
      "[2022-02-15 16:58:39,228][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:58:39,346][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.0261198362440357\n",
      "[2022-02-15 16:58:39,349][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 16:58:39,349][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.0296641755849123\n",
      "[2022-02-15 16:58:39,349][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.029707062989473343\n",
      "[2022-02-15 16:59:05,249][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 16:59:05,249][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:888\n",
      "[2022-02-15 16:59:05,249][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:59:05,364][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.025951325216643357\n",
      "[2022-02-15 16:59:05,366][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 16:59:05,367][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.033961791545152664\n",
      "[2022-02-15 16:59:05,367][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.03399411588907242\n",
      "[2022-02-15 16:59:31,443][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 16:59:31,444][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:1110\n",
      "[2022-02-15 16:59:31,444][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:59:31,562][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.025894349648510055\n",
      "[2022-02-15 16:59:31,565][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 16:59:31,565][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.028896868228912354\n",
      "[2022-02-15 16:59:31,565][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02890012599527836\n",
      "[2022-02-15 16:59:31,571][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 16:59:31,602][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_wass_128_64_20220215_165705_test_result.test...done\n",
      "[2022-02-15 16:59:32,559][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 16:59:59,032][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 16:59:59,032][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:222\n",
      "[2022-02-15 16:59:59,032][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 16:59:59,149][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.020764330721004396\n",
      "[2022-02-15 16:59:59,152][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 16:59:59,152][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.025829866528511047\n",
      "[2022-02-15 16:59:59,152][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.02581503801047802\n",
      "[2022-02-15 17:00:25,117][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:00:25,118][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:444\n",
      "[2022-02-15 17:00:25,118][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:00:25,234][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.021862012889436018\n",
      "[2022-02-15 17:00:25,237][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:00:25,237][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.03919747471809387\n",
      "[2022-02-15 17:00:25,237][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.03911171108484268\n",
      "[2022-02-15 17:00:51,294][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:00:51,294][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:666\n",
      "[2022-02-15 17:00:51,294][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:00:51,410][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.026067272331799434\n",
      "[2022-02-15 17:00:51,413][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:00:51,413][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.028823966160416603\n",
      "[2022-02-15 17:00:51,414][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.02888062223792076\n",
      "[2022-02-15 17:01:17,374][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:01:17,375][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:888\n",
      "[2022-02-15 17:01:17,375][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:01:17,494][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02085523309846568\n",
      "[2022-02-15 17:01:17,497][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:01:17,497][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.021160315722227097\n",
      "[2022-02-15 17:01:17,497][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.02110319957137108\n",
      "[2022-02-15 17:01:43,338][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:01:43,339][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:1110\n",
      "[2022-02-15 17:01:43,339][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:01:43,457][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.021108842383976437\n",
      "[2022-02-15 17:01:43,460][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:01:43,460][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.028714444488286972\n",
      "[2022-02-15 17:01:43,460][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.028656573966145515\n",
      "[2022-02-15 17:01:43,475][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:01:43,555][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_wass_128_64_20220215_165705_test_result.test...done\n",
      "[2022-02-15 17:01:44,525][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:02:10,988][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:02:10,988][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:222\n",
      "[2022-02-15 17:02:10,989][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:02:11,106][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.01940575672642781\n",
      "[2022-02-15 17:02:11,109][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:02:11,109][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.02549714967608452\n",
      "[2022-02-15 17:02:11,110][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.02547265961766243\n",
      "[2022-02-15 17:02:37,061][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:02:37,061][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:444\n",
      "[2022-02-15 17:02:37,061][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:02:37,180][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.02205633089389697\n",
      "[2022-02-15 17:02:37,183][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:02:37,183][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.02254360541701317\n",
      "[2022-02-15 17:02:37,183][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.022498907521367073\n",
      "[2022-02-15 17:03:03,325][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:03:03,325][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:666\n",
      "[2022-02-15 17:03:03,325][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:03:03,444][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.024054184332753647\n",
      "[2022-02-15 17:03:03,447][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:03:03,447][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.02670382335782051\n",
      "[2022-02-15 17:03:03,447][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.026707198470830917\n",
      "[2022-02-15 17:03:29,481][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:03:29,481][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:888\n",
      "[2022-02-15 17:03:29,481][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:03:29,600][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.023597541712814148\n",
      "[2022-02-15 17:03:29,603][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:03:29,603][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.03234650194644928\n",
      "[2022-02-15 17:03:29,603][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.03232767432928085\n",
      "[2022-02-15 17:03:55,579][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:03:55,580][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:1110\n",
      "[2022-02-15 17:03:55,580][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:03:55,698][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02547181811474896\n",
      "[2022-02-15 17:03:55,701][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:03:55,701][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.03604310750961304\n",
      "[2022-02-15 17:03:55,702][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.03604112192988396\n",
      "[2022-02-15 17:03:55,731][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:03:55,869][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_wass_128_64_20220215_165705_test_result.test...done\n",
      "[2022-02-15 17:03:56,828][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:04:23,279][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:04:23,280][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:222\n",
      "[2022-02-15 17:04:23,280][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:04:23,396][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.022661982585527124\n",
      "[2022-02-15 17:04:23,399][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:04:23,399][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.04044691473245621\n",
      "[2022-02-15 17:04:23,399][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.040398381650447845\n",
      "[2022-02-15 17:04:49,325][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:04:49,325][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:444\n",
      "[2022-02-15 17:04:49,325][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:04:49,443][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.025477590712442967\n",
      "[2022-02-15 17:04:49,446][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:04:49,446][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.033918336033821106\n",
      "[2022-02-15 17:04:49,446][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.03394276276230812\n",
      "[2022-02-15 17:05:15,408][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:05:15,408][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:666\n",
      "[2022-02-15 17:05:15,408][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:05:15,526][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.024655221440874474\n",
      "[2022-02-15 17:05:15,528][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:05:15,529][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.030480559915304184\n",
      "[2022-02-15 17:05:15,529][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.03044440783560276\n",
      "[2022-02-15 17:05:41,484][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:05:41,484][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:888\n",
      "[2022-02-15 17:05:41,485][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:05:41,600][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02206340695996724\n",
      "[2022-02-15 17:05:41,603][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:05:41,603][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.033153846859931946\n",
      "[2022-02-15 17:05:41,604][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.03310320898890495\n",
      "[2022-02-15 17:06:07,517][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:06:07,517][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:1110\n",
      "[2022-02-15 17:06:07,517][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:06:07,636][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.026106790986360692\n",
      "[2022-02-15 17:06:07,639][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:06:07,639][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.02851273864507675\n",
      "[2022-02-15 17:06:07,639][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02850935608148575\n",
      "[2022-02-15 17:06:07,680][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:06:07,915][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_wass_128_64_20220215_165705_test_result.test...done\n",
      "[2022-02-15 17:06:08,870][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:06:35,466][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:06:35,466][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:222\n",
      "[2022-02-15 17:06:35,466][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:06:35,584][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02349974660115121\n",
      "[2022-02-15 17:06:35,586][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:06:35,587][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.03918778896331787\n",
      "[2022-02-15 17:06:35,587][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.03917328640818596\n",
      "[2022-02-15 17:07:01,603][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:07:01,603][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:444\n",
      "[2022-02-15 17:07:01,603][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:07:01,720][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.021298447250445236\n",
      "[2022-02-15 17:07:01,722][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:07:01,723][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.026764782145619392\n",
      "[2022-02-15 17:07:01,723][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.026771122589707375\n",
      "[2022-02-15 17:07:27,688][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:07:27,689][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:666\n",
      "[2022-02-15 17:07:27,689][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:07:27,807][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.025505636617480357\n",
      "[2022-02-15 17:07:27,809][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:07:27,810][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.03157082200050354\n",
      "[2022-02-15 17:07:27,810][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.031674861907958984\n",
      "[2022-02-15 17:07:53,822][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:07:53,822][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:888\n",
      "[2022-02-15 17:07:53,822][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:07:53,940][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.022913199595410765\n",
      "[2022-02-15 17:07:53,943][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:07:53,943][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.027911726385354996\n",
      "[2022-02-15 17:07:53,943][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.02788061648607254\n",
      "[2022-02-15 17:08:20,083][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:08:20,084][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:1110\n",
      "[2022-02-15 17:08:20,084][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:08:20,201][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.026664433229481924\n",
      "[2022-02-15 17:08:20,204][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:08:20,204][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.028870970010757446\n",
      "[2022-02-15 17:08:20,205][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02887693978846073\n",
      "[2022-02-15 17:08:20,249][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:08:20,527][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/CFR_wass_128_64_20220215_165705_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.022563183092324215\n",
      "i_exp:0, AUUC:0.023191762684907025\n",
      "i_exp:0, AUUC:0.0261198362440357\n",
      "i_exp:0, AUUC:0.025951325216643357\n",
      "i_exp:0, AUUC:0.025894349648510055\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.020764330721004396\n",
      "i_exp:1, AUUC:0.021862012889436018\n",
      "i_exp:1, AUUC:0.026067272331799434\n",
      "i_exp:1, AUUC:0.02085523309846568\n",
      "i_exp:1, AUUC:0.021108842383976437\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.01940575672642781\n",
      "i_exp:2, AUUC:0.02205633089389697\n",
      "i_exp:2, AUUC:0.024054184332753647\n",
      "i_exp:2, AUUC:0.023597541712814148\n",
      "i_exp:2, AUUC:0.02547181811474896\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.022661982585527124\n",
      "i_exp:3, AUUC:0.025477590712442967\n",
      "i_exp:3, AUUC:0.024655221440874474\n",
      "i_exp:3, AUUC:0.02206340695996724\n",
      "i_exp:3, AUUC:0.026106790986360692\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.02349974660115121\n",
      "i_exp:4, AUUC:0.021298447250445236\n",
      "i_exp:4, AUUC:0.025505636617480357\n",
      "i_exp:4, AUUC:0.022913199595410765\n",
      "i_exp:4, AUUC:0.026664433229481924\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.0261198362440357, 0.026067272331799434, 0.02547181811474896, 0.026106790986360692, 0.026664433229481924], 'E_att': [0.025924492004225558, 0.02508428071708471, 0.03230341834099085, 0.02477305506439001, 0.025131282704780405]}\n",
      "AUUC: 0.026086 +/- 0.000169\n",
      "E_att: 0.026643 +/- 0.001277\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "!python search_params.py main.py eval4real_data.py ./conf4models/lzd_real_data/TARNet.txt 1 {train_npz} {test_npz}\n",
    "!python search_params.py main.py eval4real_data.py ./conf4models/lzd_real_data/CFRmmd.txt 1 {train_npz} {test_npz}\n",
    "!python search_params.py main.py eval4real_data.py ./conf4models/lzd_real_data/CFRwass.txt 1 {train_npz} {test_npz}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be238f9",
   "metadata": {},
   "source": [
    "## ES + TARNet/CFRwass/CFRmmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46c92f93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 17:08:28,146 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 17:08:28,146 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 17:08:28,330][root][INFO] - log testing ...\n",
      "[2022-02-15 17:08:28,330][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'ES_TARNet128_64_20220215_170826', 'n_experiments': 5, 'batch_size': 5000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 0.5, 'escvr1_w': 2, 'escvr0_w': 1, 'h1_w': 0, 'h0_w': 0, 'mu0hat_w': 0, 'mu1hat_w': 0, 'imb_dist': 'wass', 'imb_dist_w': 0, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 17:08:28,330][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 17:08:33,652][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 17:08:33,652][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 17:08:34,799][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 17:08:34,811][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 17:08:34,811][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/ES_TARNet128_64_20220215_170826) ...\n",
      "2022-02-15 17:08:34.974083: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 17:08:34.974121: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 17:08:36,910][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 17:08:40,925][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 17:08:40,926][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 17:08:40,928][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 17:08:40,930][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 17:08:40,934][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 17:08:40,940][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 17:08:40,942][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 17:08:40,942][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 17:08:40,943][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 17:08:40,943][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 17:08:40,944][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 17:08:40,944][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 17:08:40,944][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 17:08:40,945][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:08:41,635][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 17:08:57,581][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:08:57,582][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:133\n",
      "[2022-02-15 17:08:57,582][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:08:57,702][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.03204164868036981\n",
      "[2022-02-15 17:08:57,704][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:08:57,705][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.021181857213377953\n",
      "[2022-02-15 17:08:57,705][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.021162306889891624\n",
      "[2022-02-15 17:09:13,631][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:09:13,632][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 17:09:13,632][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:09:13,750][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.019334681514238852\n",
      "[2022-02-15 17:09:13,752][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:09:13,753][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.016626304015517235\n",
      "[2022-02-15 17:09:13,753][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.016589704900979996\n",
      "[2022-02-15 17:09:29,213][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:09:29,213][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:399\n",
      "[2022-02-15 17:09:29,213][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:09:29,332][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.022871236202313315\n",
      "[2022-02-15 17:09:29,335][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:09:29,335][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.013034991919994354\n",
      "[2022-02-15 17:09:29,335][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.013045134954154491\n",
      "[2022-02-15 17:09:44,884][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:09:44,885][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 17:09:44,885][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:09:45,003][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.035710260923564115\n",
      "[2022-02-15 17:09:45,006][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:09:45,006][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.026936214417219162\n",
      "[2022-02-15 17:09:45,006][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.026892678812146187\n",
      "[2022-02-15 17:10:00,404][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:10:00,404][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:665\n",
      "[2022-02-15 17:10:00,404][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:10:00,521][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.025706149364587465\n",
      "[2022-02-15 17:10:00,523][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:10:00,524][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.017463484779000282\n",
      "[2022-02-15 17:10:00,524][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.017426133155822754\n",
      "[2022-02-15 17:10:00,530][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:10:00,560][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_TARNet128_64_20220215_170826_test_result.test...done\n",
      "[2022-02-15 17:10:01,528][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:10:17,504][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:10:17,504][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:133\n",
      "[2022-02-15 17:10:17,505][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:10:17,623][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.013825241337589115\n",
      "[2022-02-15 17:10:17,625][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:10:17,626][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.020765244960784912\n",
      "[2022-02-15 17:10:17,626][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.020726913586258888\n",
      "[2022-02-15 17:10:33,118][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:10:33,118][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 17:10:33,118][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:10:33,237][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.019172826710387007\n",
      "[2022-02-15 17:10:33,239][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:10:33,240][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.012631578370928764\n",
      "[2022-02-15 17:10:33,240][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.012647637166082859\n",
      "[2022-02-15 17:10:48,936][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:10:48,937][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:399\n",
      "[2022-02-15 17:10:48,937][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:10:49,055][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.03078175048574597\n",
      "[2022-02-15 17:10:49,058][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:10:49,058][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.015017926692962646\n",
      "[2022-02-15 17:10:49,058][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.015013633295893669\n",
      "[2022-02-15 17:11:04,683][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:11:04,684][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 17:11:04,684][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:11:04,803][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.01966964512536004\n",
      "[2022-02-15 17:11:04,806][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:11:04,807][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.011406186036765575\n",
      "[2022-02-15 17:11:04,807][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.011420810595154762\n",
      "[2022-02-15 17:11:20,240][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:11:20,240][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:665\n",
      "[2022-02-15 17:11:20,240][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:11:20,359][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02611947126666953\n",
      "[2022-02-15 17:11:20,361][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:11:20,362][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.01961086131632328\n",
      "[2022-02-15 17:11:20,362][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.019609689712524414\n",
      "[2022-02-15 17:11:20,381][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:11:20,450][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_TARNet128_64_20220215_170826_test_result.test...done\n",
      "[2022-02-15 17:11:21,375][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:11:37,227][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:11:37,227][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:133\n",
      "[2022-02-15 17:11:37,227][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:11:37,345][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.01832261226680977\n",
      "[2022-02-15 17:11:37,348][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:11:37,348][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.01757373847067356\n",
      "[2022-02-15 17:11:37,348][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.017599603161215782\n",
      "[2022-02-15 17:12:01,357][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:12:01,358][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 17:12:01,370][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:12:01,591][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.025710940606163808\n",
      "[2022-02-15 17:12:01,594][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:12:01,594][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.015024705789983273\n",
      "[2022-02-15 17:12:01,594][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.015015754848718643\n",
      "[2022-02-15 17:12:30,085][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:12:30,086][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:399\n",
      "[2022-02-15 17:12:30,086][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:12:30,231][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.030120449188141902\n",
      "[2022-02-15 17:12:30,234][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:12:30,235][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.022359097376465797\n",
      "[2022-02-15 17:12:30,235][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.022307226434350014\n",
      "[2022-02-15 17:12:57,767][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:12:57,768][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 17:12:57,768][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:12:57,885][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02994093393131541\n",
      "[2022-02-15 17:12:57,888][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:12:57,888][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.01710149459540844\n",
      "[2022-02-15 17:12:57,888][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.017054561525583267\n",
      "[2022-02-15 17:13:13,558][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:13:13,559][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:665\n",
      "[2022-02-15 17:13:13,559][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:13:13,676][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.03479475398107758\n",
      "[2022-02-15 17:13:13,679][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:13:13,679][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.020884128287434578\n",
      "[2022-02-15 17:13:13,679][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.020807595923542976\n",
      "[2022-02-15 17:13:13,707][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:13:14,019][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_TARNet128_64_20220215_170826_test_result.test...done\n",
      "[2022-02-15 17:13:14,923][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:13:30,771][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:13:30,772][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:133\n",
      "[2022-02-15 17:13:30,772][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:13:30,889][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02556318160890907\n",
      "[2022-02-15 17:13:30,892][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:13:30,892][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.022035915404558182\n",
      "[2022-02-15 17:13:30,892][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.02205265499651432\n",
      "[2022-02-15 17:13:46,764][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:13:46,765][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 17:13:46,765][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:13:46,883][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.010881216529014933\n",
      "[2022-02-15 17:13:46,886][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:13:46,886][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.016743898391723633\n",
      "[2022-02-15 17:13:46,886][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.016770925372838974\n",
      "[2022-02-15 17:14:02,511][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:14:02,511][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:399\n",
      "[2022-02-15 17:14:02,511][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:14:02,629][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.009628750250054946\n",
      "[2022-02-15 17:14:02,631][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:14:02,632][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.012574367225170135\n",
      "[2022-02-15 17:14:02,632][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.012588205747306347\n",
      "[2022-02-15 17:14:18,419][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:14:18,419][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 17:14:18,419][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:14:18,536][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.03543364362132731\n",
      "[2022-02-15 17:14:18,539][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:14:18,539][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.020254291594028473\n",
      "[2022-02-15 17:14:18,540][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.020219270139932632\n",
      "[2022-02-15 17:14:34,191][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:14:34,191][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:665\n",
      "[2022-02-15 17:14:34,191][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:14:34,308][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.028526773777281102\n",
      "[2022-02-15 17:14:34,311][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:14:34,311][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.02039809711277485\n",
      "[2022-02-15 17:14:34,312][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.020380062982439995\n",
      "[2022-02-15 17:14:34,353][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:14:34,825][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_TARNet128_64_20220215_170826_test_result.test...done\n",
      "[2022-02-15 17:14:35,729][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:14:51,759][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:14:51,760][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:133\n",
      "[2022-02-15 17:14:51,760][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:14:51,877][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.00813939731552754\n",
      "[2022-02-15 17:14:51,880][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:14:51,880][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.02402070350944996\n",
      "[2022-02-15 17:14:51,880][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.024019619449973106\n",
      "[2022-02-15 17:15:07,717][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:15:07,717][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 17:15:07,717][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:15:07,835][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.009153111588741148\n",
      "[2022-02-15 17:15:07,837][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:15:07,838][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.016664613038301468\n",
      "[2022-02-15 17:15:07,838][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.016670163720846176\n",
      "[2022-02-15 17:15:23,328][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:15:23,328][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:399\n",
      "[2022-02-15 17:15:23,328][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:15:23,446][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.013819692914684838\n",
      "[2022-02-15 17:15:23,449][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:15:23,450][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.016909437254071236\n",
      "[2022-02-15 17:15:23,450][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.016925765201449394\n",
      "[2022-02-15 17:15:39,008][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:15:39,008][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 17:15:39,008][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:15:39,126][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.020533559628462265\n",
      "[2022-02-15 17:15:39,129][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:15:39,129][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.019187945872545242\n",
      "[2022-02-15 17:15:39,129][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.01917984150350094\n",
      "[2022-02-15 17:15:54,571][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:15:54,571][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:665\n",
      "[2022-02-15 17:15:54,571][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:15:54,688][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.033071943380833094\n",
      "[2022-02-15 17:15:54,691][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:15:54,691][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.01826714538037777\n",
      "[2022-02-15 17:15:54,692][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.018241291865706444\n",
      "[2022-02-15 17:15:54,759][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:15:55,150][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_TARNet128_64_20220215_170826_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.03204164868036981\n",
      "i_exp:0, AUUC:0.019334681514238852\n",
      "i_exp:0, AUUC:0.022871236202313315\n",
      "i_exp:0, AUUC:0.035710260923564115\n",
      "i_exp:0, AUUC:0.025706149364587465\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.013825241337589115\n",
      "i_exp:1, AUUC:0.019172826710387007\n",
      "i_exp:1, AUUC:0.03078175048574597\n",
      "i_exp:1, AUUC:0.01966964512536004\n",
      "i_exp:1, AUUC:0.02611947126666953\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.01832261226680977\n",
      "i_exp:2, AUUC:0.025710940606163808\n",
      "i_exp:2, AUUC:0.030120449188141902\n",
      "i_exp:2, AUUC:0.02994093393131541\n",
      "i_exp:2, AUUC:0.03479475398107758\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.02556318160890907\n",
      "i_exp:3, AUUC:0.010881216529014933\n",
      "i_exp:3, AUUC:0.009628750250054946\n",
      "i_exp:3, AUUC:0.03543364362132731\n",
      "i_exp:3, AUUC:0.028526773777281102\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.00813939731552754\n",
      "i_exp:4, AUUC:0.009153111588741148\n",
      "i_exp:4, AUUC:0.013819692914684838\n",
      "i_exp:4, AUUC:0.020533559628462265\n",
      "i_exp:4, AUUC:0.033071943380833094\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.035710260923564115, 0.03078175048574597, 0.03479475398107758, 0.03543364362132731, 0.033071943380833094], 'E_att': [0.02319652711124212, 0.01127824031830818, 0.017144440981457537, 0.01651460615069658, 0.014527459937045878]}\n",
      "AUUC: 0.033958 +/- 0.000820\n",
      "E_att: 0.016532 +/- 0.001748\n",
      "done.\n",
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 17:16:02,505 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 17:16:02,505 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 17:16:02,687][root][INFO] - log testing ...\n",
      "[2022-02-15 17:16:02,687][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'ES_CFR_mmd_128_64_20220215_171601', 'n_experiments': 5, 'batch_size': 5000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 1, 'escvr1_w': 1, 'escvr0_w': 1, 'h1_w': 0, 'h0_w': 0, 'mu0hat_w': 0, 'mu1hat_w': 0, 'imb_dist': 'mmd', 'imb_dist_w': 0.1, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 17:16:02,687][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 17:16:07,950][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 17:16:07,950][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 17:16:09,076][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 17:16:09,087][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 17:16:09,088][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/ES_CFR_mmd_128_64_20220215_171601) ...\n",
      "2022-02-15 17:16:09.250157: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 17:16:09.250196: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 17:16:11,175][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 17:16:15,184][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 17:16:15,186][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 17:16:15,188][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 17:16:15,190][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 17:16:15,195][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 17:16:15,201][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 17:16:15,204][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 17:16:15,204][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 17:16:15,204][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 17:16:15,205][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 17:16:15,205][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 17:16:15,205][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 17:16:15,205][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 17:16:15,206][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:16:15,944][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 17:16:35,284][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:16:35,284][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:133\n",
      "[2022-02-15 17:16:35,284][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:16:35,404][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.026051727761434183\n",
      "[2022-02-15 17:16:35,406][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:16:35,407][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.029089557006955147\n",
      "[2022-02-15 17:16:35,407][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.029098430648446083\n",
      "[2022-02-15 17:16:55,178][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:16:55,179][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 17:16:55,179][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:16:55,297][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.0318754793722367\n",
      "[2022-02-15 17:16:55,299][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:16:55,300][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.025302721187472343\n",
      "[2022-02-15 17:16:55,300][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.02523679845035076\n",
      "[2022-02-15 17:17:15,158][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:17:15,158][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:399\n",
      "[2022-02-15 17:17:15,158][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:17:15,276][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.034429137482047054\n",
      "[2022-02-15 17:17:15,279][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:17:15,279][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.023631449788808823\n",
      "[2022-02-15 17:17:15,280][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.023578457534313202\n",
      "[2022-02-15 17:17:35,198][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:17:35,198][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 17:17:35,198][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:17:35,318][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.03174067056074363\n",
      "[2022-02-15 17:17:35,320][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:17:35,321][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.03434448689222336\n",
      "[2022-02-15 17:17:35,321][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.034253355115652084\n",
      "[2022-02-15 17:17:55,094][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:17:55,094][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:665\n",
      "[2022-02-15 17:17:55,094][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:17:55,212][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.023583285319043072\n",
      "[2022-02-15 17:17:55,215][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:17:55,215][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.02658754028379917\n",
      "[2022-02-15 17:17:55,215][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02651837468147278\n",
      "[2022-02-15 17:17:55,223][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:17:55,254][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_mmd_128_64_20220215_171601_test_result.test...done\n",
      "[2022-02-15 17:17:56,193][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:18:16,515][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:18:16,515][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:133\n",
      "[2022-02-15 17:18:16,515][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:18:16,632][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.024480063498325138\n",
      "[2022-02-15 17:18:16,635][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:18:16,635][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.025001903995871544\n",
      "[2022-02-15 17:18:16,636][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.024914473295211792\n",
      "[2022-02-15 17:18:36,460][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:18:36,460][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 17:18:36,461][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:18:36,578][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.03022549347949284\n",
      "[2022-02-15 17:18:36,581][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:18:36,582][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.019367756322026253\n",
      "[2022-02-15 17:18:36,582][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.019258834421634674\n",
      "[2022-02-15 17:18:56,550][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:18:56,550][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:399\n",
      "[2022-02-15 17:18:56,550][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:18:56,668][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.033750895767209375\n",
      "[2022-02-15 17:18:56,671][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:18:56,671][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.023328488692641258\n",
      "[2022-02-15 17:18:56,671][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.023233966901898384\n",
      "[2022-02-15 17:19:16,588][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:19:16,588][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 17:19:16,588][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:19:16,707][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.033248125279895714\n",
      "[2022-02-15 17:19:16,710][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:19:16,710][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.023360475897789\n",
      "[2022-02-15 17:19:16,710][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.02324194647371769\n",
      "[2022-02-15 17:19:36,543][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:19:36,543][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:665\n",
      "[2022-02-15 17:19:36,543][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:19:36,662][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02046772595802351\n",
      "[2022-02-15 17:19:36,665][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:19:36,665][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.02781130187213421\n",
      "[2022-02-15 17:19:36,666][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.027763040736317635\n",
      "[2022-02-15 17:19:36,681][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:19:36,749][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_mmd_128_64_20220215_171601_test_result.test...done\n",
      "[2022-02-15 17:19:37,656][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:19:57,913][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:19:57,913][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:133\n",
      "[2022-02-15 17:19:57,913][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:19:58,032][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.01201708084141604\n",
      "[2022-02-15 17:19:58,035][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:19:58,035][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.01941010355949402\n",
      "[2022-02-15 17:19:58,035][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.019446926191449165\n",
      "[2022-02-15 17:20:17,836][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:20:17,837][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 17:20:17,837][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:20:17,956][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.02956943971893748\n",
      "[2022-02-15 17:20:17,959][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:20:17,959][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.025608640164136887\n",
      "[2022-02-15 17:20:17,959][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.025521311908960342\n",
      "[2022-02-15 17:20:37,873][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:20:37,874][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:399\n",
      "[2022-02-15 17:20:37,874][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:20:37,991][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.030479849568300584\n",
      "[2022-02-15 17:20:37,994][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:20:37,995][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.03179366886615753\n",
      "[2022-02-15 17:20:37,995][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.03163893148303032\n",
      "[2022-02-15 17:20:57,781][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:20:57,781][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 17:20:57,781][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:20:57,900][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.030948723000461302\n",
      "[2022-02-15 17:20:57,903][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:20:57,903][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.027595046907663345\n",
      "[2022-02-15 17:20:57,903][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.02749747596681118\n",
      "[2022-02-15 17:21:17,791][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:21:17,791][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:665\n",
      "[2022-02-15 17:21:17,792][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:21:17,910][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.023130242299992682\n",
      "[2022-02-15 17:21:17,913][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:21:17,914][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.03338588774204254\n",
      "[2022-02-15 17:21:17,914][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.033265370875597\n",
      "[2022-02-15 17:21:17,943][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:21:18,043][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_mmd_128_64_20220215_171601_test_result.test...done\n",
      "[2022-02-15 17:21:18,949][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:21:39,157][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:21:39,158][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:133\n",
      "[2022-02-15 17:21:39,158][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:21:39,275][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.026760910956488075\n",
      "[2022-02-15 17:21:39,278][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:21:39,278][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.02677583508193493\n",
      "[2022-02-15 17:21:39,278][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.026786986738443375\n",
      "[2022-02-15 17:21:59,238][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:21:59,238][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 17:21:59,238][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:21:59,355][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.03028248626160301\n",
      "[2022-02-15 17:21:59,358][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:21:59,359][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.023778891190886497\n",
      "[2022-02-15 17:21:59,359][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.023766344413161278\n",
      "[2022-02-15 17:22:21,033][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:22:21,034][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:399\n",
      "[2022-02-15 17:22:21,034][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:22:21,152][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.033124797720678105\n",
      "[2022-02-15 17:22:21,155][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:22:21,155][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.02143579162657261\n",
      "[2022-02-15 17:22:21,155][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.021403953433036804\n",
      "[2022-02-15 17:22:42,843][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:22:42,844][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 17:22:42,844][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:22:42,964][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.030646236000029893\n",
      "[2022-02-15 17:22:42,967][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:22:42,967][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.0268892589956522\n",
      "[2022-02-15 17:22:42,967][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.026800211519002914\n",
      "[2022-02-15 17:23:04,612][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:23:04,612][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:665\n",
      "[2022-02-15 17:23:04,613][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:23:04,732][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.024255616439842434\n",
      "[2022-02-15 17:23:04,734][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:23:04,735][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.030067360028624535\n",
      "[2022-02-15 17:23:04,735][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.030001843348145485\n",
      "[2022-02-15 17:23:04,775][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:23:05,025][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_mmd_128_64_20220215_171601_test_result.test...done\n",
      "[2022-02-15 17:23:05,922][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:23:25,543][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:23:25,543][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:133\n",
      "[2022-02-15 17:23:25,543][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:23:25,660][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.010850491054464338\n",
      "[2022-02-15 17:23:25,663][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:23:25,663][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.029936227947473526\n",
      "[2022-02-15 17:23:25,663][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.029919352382421494\n",
      "[2022-02-15 17:23:45,057][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:23:45,057][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 17:23:45,057][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:23:45,175][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.026168883177013044\n",
      "[2022-02-15 17:23:45,177][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:23:45,178][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.026841148734092712\n",
      "[2022-02-15 17:23:45,178][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.026751622557640076\n",
      "[2022-02-15 17:24:04,445][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:24:04,445][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:399\n",
      "[2022-02-15 17:24:04,445][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:24:04,562][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.03338168176809211\n",
      "[2022-02-15 17:24:04,565][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:24:04,565][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.026042591780424118\n",
      "[2022-02-15 17:24:04,565][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.02596251480281353\n",
      "[2022-02-15 17:24:23,932][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:24:23,933][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 17:24:23,933][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:24:24,050][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.03279392069598872\n",
      "[2022-02-15 17:24:24,053][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:24:24,053][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.029176022857427597\n",
      "[2022-02-15 17:24:24,053][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.029094021767377853\n",
      "[2022-02-15 17:24:43,304][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:24:43,304][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:665\n",
      "[2022-02-15 17:24:43,304][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:24:43,422][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02965462549588874\n",
      "[2022-02-15 17:24:43,424][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:24:43,425][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.030918478965759277\n",
      "[2022-02-15 17:24:43,425][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.03079713135957718\n",
      "[2022-02-15 17:24:43,470][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:24:43,655][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_mmd_128_64_20220215_171601_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.026051727761434183\n",
      "i_exp:0, AUUC:0.0318754793722367\n",
      "i_exp:0, AUUC:0.034429137482047054\n",
      "i_exp:0, AUUC:0.03174067056074363\n",
      "i_exp:0, AUUC:0.023583285319043072\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.024480063498325138\n",
      "i_exp:1, AUUC:0.03022549347949284\n",
      "i_exp:1, AUUC:0.033750895767209375\n",
      "i_exp:1, AUUC:0.033248125279895714\n",
      "i_exp:1, AUUC:0.02046772595802351\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.01201708084141604\n",
      "i_exp:2, AUUC:0.02956943971893748\n",
      "i_exp:2, AUUC:0.030479849568300584\n",
      "i_exp:2, AUUC:0.030948723000461302\n",
      "i_exp:2, AUUC:0.023130242299992682\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.026760910956488075\n",
      "i_exp:3, AUUC:0.03028248626160301\n",
      "i_exp:3, AUUC:0.033124797720678105\n",
      "i_exp:3, AUUC:0.030646236000029893\n",
      "i_exp:3, AUUC:0.024255616439842434\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.010850491054464338\n",
      "i_exp:4, AUUC:0.026168883177013044\n",
      "i_exp:4, AUUC:0.03338168176809211\n",
      "i_exp:4, AUUC:0.03279392069598872\n",
      "i_exp:4, AUUC:0.02965462549588874\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.034429137482047054, 0.033750895767209375, 0.030948723000461302, 0.033124797720678105, 0.03338168176809211], 'E_att': [0.01989176620812208, 0.019588801386664217, 0.023855361464331454, 0.017696108045885867, 0.02230290074915678]}\n",
      "AUUC: 0.033127 +/- 0.000525\n",
      "E_att: 0.020667 +/- 0.000968\n",
      "done.\n",
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 17:24:51,028 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 17:24:51,028 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 17:24:51,213][root][INFO] - log testing ...\n",
      "[2022-02-15 17:24:51,213][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'ES_CFR_wass_128_64_20220215_172449', 'n_experiments': 5, 'batch_size': 5000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 2, 'escvr1_w': 2, 'escvr0_w': 2, 'h1_w': 0, 'h0_w': 0, 'mu0hat_w': 0, 'mu1hat_w': 0, 'imb_dist': 'wass', 'imb_dist_w': 0.1, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 17:24:51,213][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 17:24:56,518][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 17:24:56,518][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 17:24:57,645][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 17:24:57,657][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 17:24:57,657][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/ES_CFR_wass_128_64_20220215_172449) ...\n",
      "2022-02-15 17:24:57.819053: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 17:24:57.819090: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 17:24:59,739][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 17:25:03,728][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 17:25:03,729][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 17:25:03,731][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 17:25:03,732][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 17:25:03,737][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 17:25:03,743][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 17:25:03,745][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 17:25:03,745][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 17:25:03,746][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 17:25:03,746][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 17:25:03,747][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 17:25:03,747][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 17:25:03,747][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 17:25:03,748][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:25:04,521][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 17:25:29,449][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:25:29,450][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:133\n",
      "[2022-02-15 17:25:29,450][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:25:29,563][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.024873140199172503\n",
      "[2022-02-15 17:25:29,566][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:25:29,566][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.029149523004889488\n",
      "[2022-02-15 17:25:29,566][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.02909911796450615\n",
      "[2022-02-15 17:25:54,530][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:25:54,531][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 17:25:54,531][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:25:54,642][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.023482886952540288\n",
      "[2022-02-15 17:25:54,645][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:25:54,645][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.028916258364915848\n",
      "[2022-02-15 17:25:54,646][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.028852948918938637\n",
      "[2022-02-15 17:26:19,244][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:26:19,244][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:399\n",
      "[2022-02-15 17:26:19,244][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:26:19,356][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.024072126537506493\n",
      "[2022-02-15 17:26:19,358][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:26:19,359][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.025314489379525185\n",
      "[2022-02-15 17:26:19,359][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.025301963090896606\n",
      "[2022-02-15 17:26:43,868][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:26:43,868][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 17:26:43,868][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:26:43,980][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.0236012252631136\n",
      "[2022-02-15 17:26:43,983][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:26:43,983][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.037113796919584274\n",
      "[2022-02-15 17:26:43,983][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.037048134952783585\n",
      "[2022-02-15 17:27:08,388][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:27:08,388][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:665\n",
      "[2022-02-15 17:27:08,388][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:27:08,499][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02247270405642323\n",
      "[2022-02-15 17:27:08,502][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:27:08,502][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.028933342546224594\n",
      "[2022-02-15 17:27:08,502][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02888033539056778\n",
      "[2022-02-15 17:27:08,508][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:27:08,539][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_wass_128_64_20220215_172449_test_result.test...done\n",
      "[2022-02-15 17:27:09,465][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:27:34,873][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:27:34,874][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:133\n",
      "[2022-02-15 17:27:34,874][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:27:34,984][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.017712968452384025\n",
      "[2022-02-15 17:27:34,987][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:27:34,987][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.026797432452440262\n",
      "[2022-02-15 17:27:34,987][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.026699475944042206\n",
      "[2022-02-15 17:27:59,939][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:27:59,939][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 17:27:59,939][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:28:00,049][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.020787786788065196\n",
      "[2022-02-15 17:28:00,052][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:28:00,052][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.021448709070682526\n",
      "[2022-02-15 17:28:00,053][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.021334366872906685\n",
      "[2022-02-15 17:28:29,276][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:28:29,277][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:399\n",
      "[2022-02-15 17:28:29,277][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:28:29,499][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.02517974672883403\n",
      "[2022-02-15 17:28:29,502][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:28:29,502][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.02183692902326584\n",
      "[2022-02-15 17:28:29,502][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.021743617951869965\n",
      "[2022-02-15 17:29:15,347][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:29:15,347][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 17:29:15,348][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:29:15,481][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.033298158502041535\n",
      "[2022-02-15 17:29:15,486][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:29:15,487][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.02079697698354721\n",
      "[2022-02-15 17:29:15,487][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.020697372034192085\n",
      "[2022-02-15 17:30:04,743][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:30:04,743][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:665\n",
      "[2022-02-15 17:30:04,743][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:30:04,989][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.0229979682450453\n",
      "[2022-02-15 17:30:05,005][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:30:05,005][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.02523687854409218\n",
      "[2022-02-15 17:30:05,006][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02516762539744377\n",
      "[2022-02-15 17:30:05,038][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:30:05,218][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_wass_128_64_20220215_172449_test_result.test...done\n",
      "[2022-02-15 17:30:06,337][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:30:47,109][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:30:47,109][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:133\n",
      "[2022-02-15 17:30:47,109][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:30:47,219][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.021161278196969998\n",
      "[2022-02-15 17:30:47,222][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:30:47,222][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.020930854603648186\n",
      "[2022-02-15 17:30:47,222][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.020934998989105225\n",
      "[2022-02-15 17:31:12,170][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:31:12,171][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 17:31:12,171][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:31:12,281][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.020779569569051387\n",
      "[2022-02-15 17:31:12,284][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:31:12,284][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.027441194280982018\n",
      "[2022-02-15 17:31:12,285][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.027380889281630516\n",
      "[2022-02-15 17:31:37,046][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:31:37,046][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:399\n",
      "[2022-02-15 17:31:37,046][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:31:37,158][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.01972919473909511\n",
      "[2022-02-15 17:31:37,160][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:31:37,161][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.03286639228463173\n",
      "[2022-02-15 17:31:37,161][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.03275151550769806\n",
      "[2022-02-15 17:32:02,126][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:32:02,126][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 17:32:02,127][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:32:02,244][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02015926023348156\n",
      "[2022-02-15 17:32:02,247][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:32:02,247][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.028848381713032722\n",
      "[2022-02-15 17:32:02,247][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.028784943744540215\n",
      "[2022-02-15 17:32:27,784][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:32:27,785][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:665\n",
      "[2022-02-15 17:32:27,785][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:32:27,897][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02090514800651656\n",
      "[2022-02-15 17:32:27,899][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:32:27,900][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.033921558409929276\n",
      "[2022-02-15 17:32:27,900][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.0338049940764904\n",
      "[2022-02-15 17:32:27,923][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:32:28,032][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_wass_128_64_20220215_172449_test_result.test...done\n",
      "[2022-02-15 17:32:28,943][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:32:54,276][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:32:54,277][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:133\n",
      "[2022-02-15 17:32:54,277][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:32:54,387][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.026616373153858258\n",
      "[2022-02-15 17:32:54,390][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:32:54,390][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.025902042165398598\n",
      "[2022-02-15 17:32:54,390][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.025867532938718796\n",
      "[2022-02-15 17:33:19,961][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:33:19,962][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 17:33:19,962][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:33:20,072][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.023947949941090098\n",
      "[2022-02-15 17:33:20,075][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:33:20,075][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.022510727867484093\n",
      "[2022-02-15 17:33:20,075][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.022466065362095833\n",
      "[2022-02-15 17:33:45,351][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:33:45,351][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:399\n",
      "[2022-02-15 17:33:45,351][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:33:45,462][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.023360799725663097\n",
      "[2022-02-15 17:33:45,465][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:33:45,465][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.019208401441574097\n",
      "[2022-02-15 17:33:45,466][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.019173655658960342\n",
      "[2022-02-15 17:34:10,534][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:34:10,534][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 17:34:10,534][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:34:10,646][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02282284625488438\n",
      "[2022-02-15 17:34:10,649][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:34:10,649][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.026725733652710915\n",
      "[2022-02-15 17:34:10,649][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.026645531877875328\n",
      "[2022-02-15 17:34:35,580][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:34:35,581][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:665\n",
      "[2022-02-15 17:34:35,581][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:34:35,692][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.023875118777801702\n",
      "[2022-02-15 17:34:35,695][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:34:35,695][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.02566857635974884\n",
      "[2022-02-15 17:34:35,695][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.025600016117095947\n",
      "[2022-02-15 17:34:35,739][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:34:35,982][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_wass_128_64_20220215_172449_test_result.test...done\n",
      "[2022-02-15 17:34:36,889][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:35:02,769][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:35:02,770][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:133\n",
      "[2022-02-15 17:35:02,770][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:35:02,880][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.018477185816649658\n",
      "[2022-02-15 17:35:02,883][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:35:02,883][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.029326768592000008\n",
      "[2022-02-15 17:35:02,883][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.02929476834833622\n",
      "[2022-02-15 17:35:28,543][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:35:28,543][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 17:35:28,543][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:35:28,654][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.02120194936295869\n",
      "[2022-02-15 17:35:28,656][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:35:28,657][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.026683157309889793\n",
      "[2022-02-15 17:35:28,657][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.026612311601638794\n",
      "[2022-02-15 17:35:53,659][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:35:53,659][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:399\n",
      "[2022-02-15 17:35:53,659][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:35:53,769][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.023622652164790134\n",
      "[2022-02-15 17:35:53,772][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:35:53,772][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.026653679087758064\n",
      "[2022-02-15 17:35:53,772][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.026589954271912575\n",
      "[2022-02-15 17:36:18,879][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:36:18,880][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 17:36:18,880][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:36:18,994][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.023229819108759087\n",
      "[2022-02-15 17:36:18,996][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:36:18,997][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.029130198061466217\n",
      "[2022-02-15 17:36:18,997][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.029036331921815872\n",
      "[2022-02-15 17:36:45,755][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:36:45,756][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:665\n",
      "[2022-02-15 17:36:45,756][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:36:45,867][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.02614772497451162\n",
      "[2022-02-15 17:36:45,870][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:36:45,871][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.027780119329690933\n",
      "[2022-02-15 17:36:45,871][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.02766251191496849\n",
      "[2022-02-15 17:36:45,931][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:36:46,128][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/ES_CFR_wass_128_64_20220215_172449_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.024873140199172503\n",
      "i_exp:0, AUUC:0.023482886952540288\n",
      "i_exp:0, AUUC:0.024072126537506493\n",
      "i_exp:0, AUUC:0.0236012252631136\n",
      "i_exp:0, AUUC:0.02247270405642323\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.017712968452384025\n",
      "i_exp:1, AUUC:0.020787786788065196\n",
      "i_exp:1, AUUC:0.02517974672883403\n",
      "i_exp:1, AUUC:0.033298158502041535\n",
      "i_exp:1, AUUC:0.0229979682450453\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.021161278196969998\n",
      "i_exp:2, AUUC:0.020779569569051387\n",
      "i_exp:2, AUUC:0.01972919473909511\n",
      "i_exp:2, AUUC:0.02015926023348156\n",
      "i_exp:2, AUUC:0.02090514800651656\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.026616373153858258\n",
      "i_exp:3, AUUC:0.023947949941090098\n",
      "i_exp:3, AUUC:0.023360799725663097\n",
      "i_exp:3, AUUC:0.02282284625488438\n",
      "i_exp:3, AUUC:0.023875118777801702\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.018477185816649658\n",
      "i_exp:4, AUUC:0.02120194936295869\n",
      "i_exp:4, AUUC:0.023622652164790134\n",
      "i_exp:4, AUUC:0.023229819108759087\n",
      "i_exp:4, AUUC:0.02614772497451162\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.024873140199172503, 0.033298158502041535, 0.021161278196969998, 0.026616373153858258, 0.02614772497451162], 'E_att': [0.025409837561557597, 0.01705728967757017, 0.017191169160316294, 0.022162358584711855, 0.024040430161068743]}\n",
      "AUUC: 0.026419 +/- 0.001760\n",
      "E_att: 0.021172 +/- 0.001548\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "!python search_params.py main.py eval4real_data.py ./conf4models/lzd_real_data/ES_TARNet.txt 1 {train_npz} {test_npz}\n",
    "!python search_params.py main.py eval4real_data.py ./conf4models/lzd_real_data/ES_CFRmmd.txt 1 {train_npz} {test_npz}\n",
    "!python search_params.py main.py eval4real_data.py ./conf4models/lzd_real_data/ES_CFRwass.txt 1 {train_npz} {test_npz}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4394c788",
   "metadata": {},
   "source": [
    "## X-network/ESCN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84b0ab98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 17:52:24,184 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 17:52:24,185 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 17:52:24,367][root][INFO] - log testing ...\n",
      "[2022-02-15 17:52:24,367][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'Xnetwork_128_64_20220215_175222', 'n_experiments': 5, 'batch_size': 5000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 0, 'escvr1_w': 0, 'escvr0_w': 0, 'h1_w': 2, 'h0_w': 2, 'mu0hat_w': 1, 'mu1hat_w': 2, 'imb_dist': '', 'imb_dist_w': 0, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 17:52:24,367][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 17:52:29,624][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 17:52:29,624][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 17:52:30,747][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 17:52:30,758][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 17:52:30,758][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/Xnetwork_128_64_20220215_175222) ...\n",
      "2022-02-15 17:52:30.919662: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 17:52:30.919703: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 17:52:32,840][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 17:52:36,836][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 17:52:36,838][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 17:52:36,840][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 17:52:36,841][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 17:52:36,846][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 17:52:36,852][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 17:52:36,855][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 17:52:36,855][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 17:52:36,855][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 17:52:36,856][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 17:52:36,856][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 17:52:36,856][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 17:52:36,856][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 17:52:36,857][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:52:37,550][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 17:52:53,119][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:52:53,120][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:133\n",
      "[2022-02-15 17:52:53,120][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:52:53,239][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02784165597517691\n",
      "[2022-02-15 17:52:53,241][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:52:53,242][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.013929354958236217\n",
      "[2022-02-15 17:52:53,242][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.013871180824935436\n",
      "[2022-02-15 17:53:08,887][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:53:08,888][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 17:53:08,888][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:53:09,006][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.02828404227877681\n",
      "[2022-02-15 17:53:09,008][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:53:09,009][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.01032053492963314\n",
      "[2022-02-15 17:53:09,009][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.01029293518513441\n",
      "[2022-02-15 17:53:24,609][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:53:24,609][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:399\n",
      "[2022-02-15 17:53:24,610][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:53:24,728][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.03800783210040422\n",
      "[2022-02-15 17:53:24,732][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:53:24,732][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.008081098087131977\n",
      "[2022-02-15 17:53:24,732][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.008070982061326504\n",
      "[2022-02-15 17:53:40,673][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:53:40,674][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 17:53:40,674][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:53:40,791][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.033947077533565684\n",
      "[2022-02-15 17:53:40,794][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:53:40,795][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.009526466950774193\n",
      "[2022-02-15 17:53:40,795][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.009518333710730076\n",
      "[2022-02-15 17:53:56,536][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:53:56,536][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:665\n",
      "[2022-02-15 17:53:56,536][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:53:56,655][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.032337170873947094\n",
      "[2022-02-15 17:53:56,657][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:53:56,658][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.008654710836708546\n",
      "[2022-02-15 17:53:56,658][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.008643308654427528\n",
      "[2022-02-15 17:53:56,664][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:53:56,696][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/Xnetwork_128_64_20220215_175222_test_result.test...done\n",
      "[2022-02-15 17:53:57,618][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:54:13,843][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:54:13,843][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:133\n",
      "[2022-02-15 17:54:13,843][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:54:13,960][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.018626944031393585\n",
      "[2022-02-15 17:54:13,963][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:54:13,963][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.010627849027514458\n",
      "[2022-02-15 17:54:13,963][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.010640936903655529\n",
      "[2022-02-15 17:54:29,705][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:54:29,706][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 17:54:29,706][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:54:29,825][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.01142761761525663\n",
      "[2022-02-15 17:54:29,827][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:54:29,828][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.0047000702470541\n",
      "[2022-02-15 17:54:29,828][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.004703764338046312\n",
      "[2022-02-15 17:54:45,690][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:54:45,690][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:399\n",
      "[2022-02-15 17:54:45,690][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:54:45,808][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.03285568856880953\n",
      "[2022-02-15 17:54:45,811][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:54:45,811][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.005640763323754072\n",
      "[2022-02-15 17:54:45,811][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.005635072011500597\n",
      "[2022-02-15 17:55:01,669][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:55:01,669][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 17:55:01,669][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:55:01,788][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.033550645050346724\n",
      "[2022-02-15 17:55:01,791][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:55:01,791][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.0064651938155293465\n",
      "[2022-02-15 17:55:01,792][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.006447171326726675\n",
      "[2022-02-15 17:55:17,585][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:55:17,586][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:665\n",
      "[2022-02-15 17:55:17,586][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:55:17,704][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.03263368966811541\n",
      "[2022-02-15 17:55:17,707][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:55:17,707][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.005730994511395693\n",
      "[2022-02-15 17:55:17,708][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.005714454222470522\n",
      "[2022-02-15 17:55:17,725][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:55:17,794][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/Xnetwork_128_64_20220215_175222_test_result.test...done\n",
      "[2022-02-15 17:55:18,701][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:55:34,940][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:55:34,940][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:133\n",
      "[2022-02-15 17:55:34,940][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:55:35,057][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.010010061820506136\n",
      "[2022-02-15 17:55:35,060][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:55:35,060][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.010290325619280338\n",
      "[2022-02-15 17:55:35,061][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.010324395261704922\n",
      "[2022-02-15 17:55:50,804][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:55:50,805][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 17:55:50,805][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:55:50,922][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.01406738053197633\n",
      "[2022-02-15 17:55:50,925][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:55:50,925][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.009433473460376263\n",
      "[2022-02-15 17:55:50,925][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.009459227323532104\n",
      "[2022-02-15 17:56:06,818][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:56:06,818][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:399\n",
      "[2022-02-15 17:56:06,819][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:56:06,936][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.0028123780151065366\n",
      "[2022-02-15 17:56:06,938][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:56:06,939][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.005544464103877544\n",
      "[2022-02-15 17:56:06,939][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.005585377104580402\n",
      "[2022-02-15 17:56:22,676][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:56:22,677][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 17:56:22,677][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:56:22,794][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02371079238610348\n",
      "[2022-02-15 17:56:22,797][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:56:22,797][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.008696594275534153\n",
      "[2022-02-15 17:56:22,798][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.008701497688889503\n",
      "[2022-02-15 17:56:38,816][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:56:38,816][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:665\n",
      "[2022-02-15 17:56:38,816][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:56:38,935][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.027205544268587973\n",
      "[2022-02-15 17:56:38,938][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:56:38,938][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.007493883837014437\n",
      "[2022-02-15 17:56:38,938][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.007505317218601704\n",
      "[2022-02-15 17:56:38,965][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:56:39,074][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/Xnetwork_128_64_20220215_175222_test_result.test...done\n",
      "[2022-02-15 17:56:39,977][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:56:56,122][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:56:56,122][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:133\n",
      "[2022-02-15 17:56:56,122][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:56:56,241][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: -0.015183888795503465\n",
      "[2022-02-15 17:56:56,243][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:56:56,244][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.01022470835596323\n",
      "[2022-02-15 17:56:56,244][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.010298961773514748\n",
      "[2022-02-15 17:57:11,898][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:57:11,899][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 17:57:11,899][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:57:12,017][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.007524062852206308\n",
      "[2022-02-15 17:57:12,019][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:57:12,020][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.008335904218256474\n",
      "[2022-02-15 17:57:12,020][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.00835938286036253\n",
      "[2022-02-15 17:57:27,771][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:57:27,771][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:399\n",
      "[2022-02-15 17:57:27,771][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:57:27,889][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: -0.004769279959485572\n",
      "[2022-02-15 17:57:27,892][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:57:27,892][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.00405201455578208\n",
      "[2022-02-15 17:57:27,892][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.004065722227096558\n",
      "[2022-02-15 17:57:43,723][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:57:43,723][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 17:57:43,724][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:57:43,841][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.020399280794506654\n",
      "[2022-02-15 17:57:43,844][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:57:43,844][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.0074082668870687485\n",
      "[2022-02-15 17:57:43,844][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.007414855994284153\n",
      "[2022-02-15 17:57:59,528][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:57:59,528][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:665\n",
      "[2022-02-15 17:57:59,528][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:57:59,646][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.033023044868850694\n",
      "[2022-02-15 17:57:59,649][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:57:59,649][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.007792848162353039\n",
      "[2022-02-15 17:57:59,650][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.007792599964886904\n",
      "[2022-02-15 17:57:59,698][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:58:00,152][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/Xnetwork_128_64_20220215_175222_test_result.test...done\n",
      "[2022-02-15 17:58:01,077][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:58:17,118][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:58:17,119][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:133\n",
      "[2022-02-15 17:58:17,119][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:58:17,236][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.03043112603471502\n",
      "[2022-02-15 17:58:17,239][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:58:17,239][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.013024444691836834\n",
      "[2022-02-15 17:58:17,239][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.013001472689211369\n",
      "[2022-02-15 17:58:33,069][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 17:58:33,070][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 17:58:33,070][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:58:33,187][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.020825595250673574\n",
      "[2022-02-15 17:58:33,190][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 17:58:33,190][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.008243952877819538\n",
      "[2022-02-15 17:58:33,190][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.008235644549131393\n",
      "[2022-02-15 17:58:48,933][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 17:58:48,933][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:399\n",
      "[2022-02-15 17:58:48,933][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:58:49,050][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.025944477167843014\n",
      "[2022-02-15 17:58:49,053][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 17:58:49,053][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.008613232523202896\n",
      "[2022-02-15 17:58:49,054][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.008610360324382782\n",
      "[2022-02-15 17:59:04,911][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 17:59:04,912][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 17:59:04,912][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:59:05,030][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.022312388710096873\n",
      "[2022-02-15 17:59:05,032][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 17:59:05,033][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.007482069544494152\n",
      "[2022-02-15 17:59:05,033][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.0074682701379060745\n",
      "[2022-02-15 17:59:20,773][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 17:59:20,773][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:665\n",
      "[2022-02-15 17:59:20,773][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:59:20,891][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.024271425787707447\n",
      "[2022-02-15 17:59:20,894][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 17:59:20,894][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.00842939130961895\n",
      "[2022-02-15 17:59:20,894][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.008422378450632095\n",
      "[2022-02-15 17:59:20,946][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 17:59:21,472][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/Xnetwork_128_64_20220215_175222_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.02784165597517691\n",
      "i_exp:0, AUUC:0.02828404227877681\n",
      "i_exp:0, AUUC:0.03800783210040422\n",
      "i_exp:0, AUUC:0.033947077533565684\n",
      "i_exp:0, AUUC:0.032337170873947094\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.018626944031393585\n",
      "i_exp:1, AUUC:0.01142761761525663\n",
      "i_exp:1, AUUC:0.03285568856880953\n",
      "i_exp:1, AUUC:0.033550645050346724\n",
      "i_exp:1, AUUC:0.03263368966811541\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.010010061820506136\n",
      "i_exp:2, AUUC:0.01406738053197633\n",
      "i_exp:2, AUUC:0.0028123780151065366\n",
      "i_exp:2, AUUC:0.02371079238610348\n",
      "i_exp:2, AUUC:0.027205544268587973\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:-0.015183888795503465\n",
      "i_exp:3, AUUC:0.007524062852206308\n",
      "i_exp:3, AUUC:-0.004769279959485572\n",
      "i_exp:3, AUUC:0.020399280794506654\n",
      "i_exp:3, AUUC:0.033023044868850694\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.03043112603471502\n",
      "i_exp:4, AUUC:0.020825595250673574\n",
      "i_exp:4, AUUC:0.025944477167843014\n",
      "i_exp:4, AUUC:0.022312388710096873\n",
      "i_exp:4, AUUC:0.024271425787707447\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.03800783210040422, 0.033550645050346724, 0.027205544268587973, 0.033023044868850694, 0.03043112603471502], 'E_att': [0.00434141357512266, 0.0027255079065361676, 0.003754198393682545, 0.00405316225335986, 0.009284758317182368]}\n",
      "AUUC: 0.032444 +/- 0.001600\n",
      "E_att: 0.004832 +/- 0.001025\n",
      "done.\n",
      "------------------------------\n",
      "Run 1 of 1:\n",
      "------------------------------\n",
      "\n",
      "2022-02-15 17:59:28,908 - DEBUG - Setting JobRuntime:name=UNKNOWN_NAME\n",
      "2022-02-15 17:59:28,908 - DEBUG - Setting JobRuntime:name=main\n",
      "[2022-02-15 17:59:29,091][root][INFO] - log testing ...\n",
      "[2022-02-15 17:59:29,091][root][INFO] - cfg:{'lr': 0.001, 'decay_rate': 0.95, 'decay_step_size': 1, 'l2': 0.001, 'model_name': 'DESCN_128_64_20220215_175927', 'n_experiments': 5, 'batch_size': 5000, 'share_dim': 128, 'base_dim': 64, 'reweight_sample': 1, 'val_rate': 0.2, 'do_rate': 0.1, 'normalization': 'divide', 'epochs': 5, 'log_step': 50, 'pred_step': 1, 'optim': 'Adam', 'BatchNorm1d': 'true', 'prpsy_w': 0.5, 'escvr1_w': 0.5, 'escvr0_w': 1, 'h1_w': 0, 'h0_w': 0, 'mu0hat_w': 0.5, 'mu1hat_w': 1, 'imb_dist': 'wass', 'imb_dist_w': 0, 'device': 'cuda:1', 'verbose': 0, 'pred_output_dir': '/home/admin/dufeng/ESX_Model/results/lzd_real', 'data_train_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.train.npz', 'data_test_path': '/home/admin/uplift_data/dataset_public_md5/real_bin_set_full.5.test.npz', 'summary_base_dir': '/home/admin/dufeng/ESX_Model/runs', 'loss': 'log', 'overwrite': 1, 'sample_alpha': 0, 'total_size': 0}\n",
      "[2022-02-15 17:59:29,091][root][INFO] - training dataset loading ...\n",
      "[2022-02-15 17:59:34,399][root][INFO] - training dataset loading ...done.\n",
      "[2022-02-15 17:59:34,399][root][INFO] - test dataset loading ....\n",
      "[2022-02-15 17:59:35,533][root][INFO] - test dataset loading ...done.\n",
      "[2022-02-15 17:59:35,545][root][INFO] - Use GPU cuda:1.\n",
      "[2022-02-15 17:59:35,545][root][INFO] -  os.mkdir(/home/admin/dufeng/ESX_Model/runs/DESCN_128_64_20220215_175927) ...\n",
      "2022-02-15 17:59:35.707782: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib:/usr/lib:/usr/local/lib:/usr/local/lib64:/usr/local/hadoop/lib/native:/usr/local/jdk/jre/lib/amd64/server:/usr/local/cuda/lib64:/usr/local/gcc-4.9.2/lib\n",
      "2022-02-15 17:59:35.707822: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "[2022-02-15 17:59:37,639][root][INFO] - training set: x.shape:(667203, 83)\n",
      "[2022-02-15 17:59:41,634][root][INFO] - exp_0, Train. x.shape : (667203, 83)\n",
      "[2022-02-15 17:59:41,636][root][INFO] - exp_0, Train. mean(t) : 0.22156075437310682\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/admin/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "[2022-02-15 17:59:41,638][root][INFO] - exp_0, Train. mean(t) when e=1: nan\n",
      "[2022-02-15 17:59:41,639][root][INFO] - exp_0, Train. mean(yf) : 0.01974961143759845\n",
      "[2022-02-15 17:59:41,644][root][INFO] - exp_0, Train. mean(yf) when t=1: 0.05663415096126527\n",
      "[2022-02-15 17:59:41,651][root][INFO] - exp_0, Train. mean(yf) when t=0: 0.009251468586402556\n",
      "[2022-02-15 17:59:41,654][root][INFO] - exp_0, Train. mean(yf) when t=0 and e=1: nan\n",
      "[2022-02-15 17:59:41,654][root][INFO] - exp_0, Test. x.shape : torch.Size([181669, 83])\n",
      "[2022-02-15 17:59:41,654][root][INFO] - exp_0, Test. mean(t): 0.521178662776947\n",
      "[2022-02-15 17:59:41,655][root][INFO] - exp_0, Test. mean(t) when e=1: 0.521178662776947\n",
      "[2022-02-15 17:59:41,655][root][INFO] - exp_0, Test. mean(yf): 0.03520688787102699\n",
      "[2022-02-15 17:59:41,655][root][INFO] - exp_0, Test. mean(yf) when t=1: 0.03699753060936928\n",
      "[2022-02-15 17:59:41,655][root][INFO] - exp_0, Test. mean(yf) when t=0: 0.03325784206390381\n",
      "[2022-02-15 17:59:41,656][root][INFO] - exp_0, Test. mean(yf) when t=0 and e=1: 0.03325784206390381\n",
      "use BatchNorm1d\n",
      "[2022-02-15 17:59:42,355][root][INFO] - model saved. pytorch_total_params is 85866\n",
      "[2022-02-15 17:59:57,856][root][INFO] - i_exp:0, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 17:59:57,856][root][INFO] - start to predict ... i_exp:0,epochs:0, train_step:133\n",
      "[2022-02-15 17:59:57,856][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 17:59:57,975][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.02081917987933289\n",
      "[2022-02-15 17:59:57,978][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 17:59:57,978][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.015400214120745659\n",
      "[2022-02-15 17:59:57,978][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.015271979384124279\n",
      "[2022-02-15 18:00:13,953][root][INFO] - i_exp:0, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 18:00:13,954][root][INFO] - start to predict ... i_exp:0,epochs:1, train_step:266\n",
      "[2022-02-15 18:00:13,954][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:00:14,072][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.020882460635448876\n",
      "[2022-02-15 18:00:14,074][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 18:00:14,075][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.011504030786454678\n",
      "[2022-02-15 18:00:14,075][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.01144379936158657\n",
      "[2022-02-15 18:00:29,928][root][INFO] - i_exp:0, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 18:00:29,928][root][INFO] - start to predict ... i_exp:0,epochs:2, train_step:399\n",
      "[2022-02-15 18:00:29,929][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:00:30,046][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.036122072721496416\n",
      "[2022-02-15 18:00:30,049][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 18:00:30,049][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.010462196543812752\n",
      "[2022-02-15 18:00:30,049][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.01043002214282751\n",
      "[2022-02-15 18:00:45,995][root][INFO] - i_exp:0, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 18:00:45,996][root][INFO] - start to predict ... i_exp:0,epochs:3, train_step:532\n",
      "[2022-02-15 18:00:45,996][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:00:46,113][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.02838930826008761\n",
      "[2022-02-15 18:00:46,116][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 18:00:46,116][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.005945480894297361\n",
      "[2022-02-15 18:00:46,117][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.005923583637923002\n",
      "[2022-02-15 18:01:01,833][root][INFO] - i_exp:0, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 18:01:01,833][root][INFO] - start to predict ... i_exp:0,epochs:4, train_step:665\n",
      "[2022-02-15 18:01:01,833][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:01:01,954][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.023587798973501228\n",
      "[2022-02-15 18:01:01,957][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 18:01:01,957][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.009981891140341759\n",
      "[2022-02-15 18:01:01,958][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.009927093982696533\n",
      "[2022-02-15 18:01:01,967][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 18:01:01,997][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/DESCN_128_64_20220215_175927_test_result.test...done\n",
      "[2022-02-15 18:01:02,925][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 18:01:19,085][root][INFO] - i_exp:1, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 18:01:19,086][root][INFO] - start to predict ... i_exp:1,epochs:0, train_step:133\n",
      "[2022-02-15 18:01:19,086][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:01:19,203][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.016793404236812588\n",
      "[2022-02-15 18:01:19,205][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 18:01:19,206][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.010342061519622803\n",
      "[2022-02-15 18:01:19,206][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.010294251143932343\n",
      "[2022-02-15 18:01:34,780][root][INFO] - i_exp:1, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 18:01:34,781][root][INFO] - start to predict ... i_exp:1,epochs:1, train_step:266\n",
      "[2022-02-15 18:01:34,781][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:01:34,898][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.011997476153423121\n",
      "[2022-02-15 18:01:34,900][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 18:01:34,901][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.006865122355520725\n",
      "[2022-02-15 18:01:34,901][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.00683580944314599\n",
      "[2022-02-15 18:01:50,644][root][INFO] - i_exp:1, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 18:01:50,644][root][INFO] - start to predict ... i_exp:1,epochs:2, train_step:399\n",
      "[2022-02-15 18:01:50,644][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:01:50,761][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.030330560884726906\n",
      "[2022-02-15 18:01:50,764][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 18:01:50,765][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.006836557760834694\n",
      "[2022-02-15 18:01:50,765][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.006806784775108099\n",
      "[2022-02-15 18:02:06,448][root][INFO] - i_exp:1, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 18:02:06,448][root][INFO] - start to predict ... i_exp:1,epochs:3, train_step:532\n",
      "[2022-02-15 18:02:06,449][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:02:06,568][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.03299001459332371\n",
      "[2022-02-15 18:02:06,571][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 18:02:06,571][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.008108440786600113\n",
      "[2022-02-15 18:02:06,571][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.0080575468018651\n",
      "[2022-02-15 18:02:22,198][root][INFO] - i_exp:1, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 18:02:22,199][root][INFO] - start to predict ... i_exp:1,epochs:4, train_step:665\n",
      "[2022-02-15 18:02:22,199][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:02:22,317][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.03278398905131761\n",
      "[2022-02-15 18:02:22,320][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 18:02:22,320][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.005378163885325193\n",
      "[2022-02-15 18:02:22,321][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.0053454614244401455\n",
      "[2022-02-15 18:02:22,336][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 18:02:22,402][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/DESCN_128_64_20220215_175927_test_result.test...done\n",
      "[2022-02-15 18:02:23,316][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 18:02:39,352][root][INFO] - i_exp:2, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 18:02:39,353][root][INFO] - start to predict ... i_exp:2,epochs:0, train_step:133\n",
      "[2022-02-15 18:02:39,353][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:02:39,470][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.025942761618117274\n",
      "[2022-02-15 18:02:39,473][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 18:02:39,473][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.011077025905251503\n",
      "[2022-02-15 18:02:39,474][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.011029406450688839\n",
      "[2022-02-15 18:02:55,080][root][INFO] - i_exp:2, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 18:02:55,081][root][INFO] - start to predict ... i_exp:2,epochs:1, train_step:266\n",
      "[2022-02-15 18:02:55,081][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:02:55,198][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.03154551102757849\n",
      "[2022-02-15 18:02:55,201][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 18:02:55,202][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.00952359288930893\n",
      "[2022-02-15 18:02:55,202][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.00948510505259037\n",
      "[2022-02-15 18:03:10,954][root][INFO] - i_exp:2, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 18:03:10,954][root][INFO] - start to predict ... i_exp:2,epochs:2, train_step:399\n",
      "[2022-02-15 18:03:10,954][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:03:11,072][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.011460839692966136\n",
      "[2022-02-15 18:03:11,075][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 18:03:11,075][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.0028135310858488083\n",
      "[2022-02-15 18:03:11,076][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.002801836933940649\n",
      "[2022-02-15 18:03:26,704][root][INFO] - i_exp:2, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 18:03:26,705][root][INFO] - start to predict ... i_exp:2,epochs:3, train_step:532\n",
      "[2022-02-15 18:03:26,705][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:03:26,824][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.031107750426074988\n",
      "[2022-02-15 18:03:26,826][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 18:03:26,827][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.005711373873054981\n",
      "[2022-02-15 18:03:26,827][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.0056703342124819756\n",
      "[2022-02-15 18:03:42,560][root][INFO] - i_exp:2, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 18:03:42,560][root][INFO] - start to predict ... i_exp:2,epochs:4, train_step:665\n",
      "[2022-02-15 18:03:42,560][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:03:42,679][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.034447157970156295\n",
      "[2022-02-15 18:03:42,682][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 18:03:42,683][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.007278358098119497\n",
      "[2022-02-15 18:03:42,683][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.007224279921501875\n",
      "[2022-02-15 18:03:42,712][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 18:03:42,813][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/DESCN_128_64_20220215_175927_test_result.test...done\n",
      "[2022-02-15 18:03:43,720][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 18:03:59,627][root][INFO] - i_exp:3, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 18:03:59,627][root][INFO] - start to predict ... i_exp:3,epochs:0, train_step:133\n",
      "[2022-02-15 18:03:59,627][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:03:59,745][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.019101163701129735\n",
      "[2022-02-15 18:03:59,747][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 18:03:59,748][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.0130149619653821\n",
      "[2022-02-15 18:03:59,748][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.012988731265068054\n",
      "[2022-02-15 18:04:15,668][root][INFO] - i_exp:3, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 18:04:15,668][root][INFO] - start to predict ... i_exp:3,epochs:1, train_step:266\n",
      "[2022-02-15 18:04:15,668][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:04:15,785][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.034135801955321336\n",
      "[2022-02-15 18:04:15,788][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 18:04:15,788][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.010134085081517696\n",
      "[2022-02-15 18:04:15,789][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.010122990235686302\n",
      "[2022-02-15 18:04:31,634][root][INFO] - i_exp:3, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 18:04:31,635][root][INFO] - start to predict ... i_exp:3,epochs:2, train_step:399\n",
      "[2022-02-15 18:04:31,635][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:04:31,752][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.03439806209546604\n",
      "[2022-02-15 18:04:31,755][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 18:04:31,755][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.008321081288158894\n",
      "[2022-02-15 18:04:31,756][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.00830649584531784\n",
      "[2022-02-15 18:04:47,655][root][INFO] - i_exp:3, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 18:04:47,656][root][INFO] - start to predict ... i_exp:3,epochs:3, train_step:532\n",
      "[2022-02-15 18:04:47,656][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:04:47,775][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.034441847238110015\n",
      "[2022-02-15 18:04:47,778][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 18:04:47,778][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.006695345975458622\n",
      "[2022-02-15 18:04:47,778][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.006672292947769165\n",
      "[2022-02-15 18:05:03,583][root][INFO] - i_exp:3, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 18:05:03,583][root][INFO] - start to predict ... i_exp:3,epochs:4, train_step:665\n",
      "[2022-02-15 18:05:03,583][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:05:03,702][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.023583947659258378\n",
      "[2022-02-15 18:05:03,705][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 18:05:03,705][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.008975351229310036\n",
      "[2022-02-15 18:05:03,705][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.008922896347939968\n",
      "[2022-02-15 18:05:03,745][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 18:05:04,051][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/DESCN_128_64_20220215_175927_test_result.test...done\n",
      "[2022-02-15 18:05:04,958][root][INFO] - training set: x.shape:(667203, 83)\n",
      "use BatchNorm1d\n",
      "[2022-02-15 18:05:21,185][root][INFO] - i_exp:4, epoch:0 ,lr_scheduler.step() and new learning rate is : [0.00095]\n",
      "[2022-02-15 18:05:21,185][root][INFO] - start to predict ... i_exp:4,epochs:0, train_step:133\n",
      "[2022-02-15 18:05:21,185][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:05:21,303][root][INFO] - group_name test_pred_result, epoch, 0, auuc_score: 0.014056891075291495\n",
      "[2022-02-15 18:05:21,306][root][INFO] - group_name test_pred_result, epoch 0, total_loss nan\n",
      "[2022-02-15 18:05:21,306][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[t]) :0.012608731165528297\n",
      "[2022-02-15 18:05:21,306][root][INFO] - p_tau test_pred_result, epoch, 0 , mean(p_tau[~t]) :0.01254239585250616\n",
      "[2022-02-15 18:05:37,238][root][INFO] - i_exp:4, epoch:1 ,lr_scheduler.step() and new learning rate is : [0.0009025]\n",
      "[2022-02-15 18:05:37,239][root][INFO] - start to predict ... i_exp:4,epochs:1, train_step:266\n",
      "[2022-02-15 18:05:37,239][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:05:37,356][root][INFO] - group_name test_pred_result, epoch, 1, auuc_score: 0.023498577746984824\n",
      "[2022-02-15 18:05:37,359][root][INFO] - group_name test_pred_result, epoch 1, total_loss nan\n",
      "[2022-02-15 18:05:37,359][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[t]) :0.009819801896810532\n",
      "[2022-02-15 18:05:37,360][root][INFO] - p_tau test_pred_result, epoch, 1 , mean(p_tau[~t]) :0.009789267554879189\n",
      "[2022-02-15 18:05:53,173][root][INFO] - i_exp:4, epoch:2 ,lr_scheduler.step() and new learning rate is : [0.000857375]\n",
      "[2022-02-15 18:05:53,173][root][INFO] - start to predict ... i_exp:4,epochs:2, train_step:399\n",
      "[2022-02-15 18:05:53,173][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:05:53,291][root][INFO] - group_name test_pred_result, epoch, 2, auuc_score: 0.031519251191528326\n",
      "[2022-02-15 18:05:53,294][root][INFO] - group_name test_pred_result, epoch 2, total_loss nan\n",
      "[2022-02-15 18:05:53,294][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[t]) :0.008136396296322346\n",
      "[2022-02-15 18:05:53,294][root][INFO] - p_tau test_pred_result, epoch, 2 , mean(p_tau[~t]) :0.008117380551993847\n",
      "[2022-02-15 18:06:10,026][root][INFO] - i_exp:4, epoch:3 ,lr_scheduler.step() and new learning rate is : [0.0008145062499999999]\n",
      "[2022-02-15 18:06:10,027][root][INFO] - start to predict ... i_exp:4,epochs:3, train_step:532\n",
      "[2022-02-15 18:06:10,027][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:06:10,145][root][INFO] - group_name test_pred_result, epoch, 3, auuc_score: 0.03188044168659739\n",
      "[2022-02-15 18:06:10,148][root][INFO] - group_name test_pred_result, epoch 3, total_loss nan\n",
      "[2022-02-15 18:06:10,148][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[t]) :0.00562302814796567\n",
      "[2022-02-15 18:06:10,149][root][INFO] - p_tau test_pred_result, epoch, 3 , mean(p_tau[~t]) :0.005598931107670069\n",
      "[2022-02-15 18:06:27,664][root][INFO] - i_exp:4, epoch:4 ,lr_scheduler.step() and new learning rate is : [0.0007737809374999998]\n",
      "[2022-02-15 18:06:27,664][root][INFO] - start to predict ... i_exp:4,epochs:4, train_step:665\n",
      "[2022-02-15 18:06:27,664][root][INFO] - group_name:test_pred_result, evalWithData... -----------------------------------\n",
      "[2022-02-15 18:06:27,782][root][INFO] - group_name test_pred_result, epoch, 4, auuc_score: 0.019010289994232838\n",
      "[2022-02-15 18:06:27,785][root][INFO] - group_name test_pred_result, epoch 4, total_loss nan\n",
      "[2022-02-15 18:06:27,786][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[t]) :0.007100799586623907\n",
      "[2022-02-15 18:06:27,786][root][INFO] - p_tau test_pred_result, epoch, 4 , mean(p_tau[~t]) :0.007080599199980497\n",
      "[2022-02-15 18:06:27,832][root][INFO] - saving predict result as a file...\n",
      "[2022-02-15 18:06:28,370][root][INFO] - saving predict result as a file: /home/admin/dufeng/ESX_Model/results/lzd_real/DESCN_128_64_20220215_175927_test_result.test...done\n",
      "i_exp:1/5\n",
      "i_sel: 4\n",
      "i_exp:0, att:0.0037396854433318916\n",
      "i_exp:0, AUUC:0.02081917987933289\n",
      "i_exp:0, AUUC:0.020882460635448876\n",
      "i_exp:0, AUUC:0.036122072721496416\n",
      "i_exp:0, AUUC:0.02838930826008761\n",
      "i_exp:0, AUUC:0.023587798973501228\n",
      "i_exp:2/5\n",
      "i_sel: 4\n",
      "i_exp:1, att:0.0037396854433318916\n",
      "i_exp:1, AUUC:0.016793404236812588\n",
      "i_exp:1, AUUC:0.011997476153423121\n",
      "i_exp:1, AUUC:0.030330560884726906\n",
      "i_exp:1, AUUC:0.03299001459332371\n",
      "i_exp:1, AUUC:0.03278398905131761\n",
      "i_exp:3/5\n",
      "i_sel: 4\n",
      "i_exp:2, att:0.0037396854433318916\n",
      "i_exp:2, AUUC:0.025942761618117274\n",
      "i_exp:2, AUUC:0.03154551102757849\n",
      "i_exp:2, AUUC:0.011460839692966136\n",
      "i_exp:2, AUUC:0.031107750426074988\n",
      "i_exp:2, AUUC:0.034447157970156295\n",
      "i_exp:4/5\n",
      "i_sel: 4\n",
      "i_exp:3, att:0.0037396854433318916\n",
      "i_exp:3, AUUC:0.019101163701129735\n",
      "i_exp:3, AUUC:0.034135801955321336\n",
      "i_exp:3, AUUC:0.03439806209546604\n",
      "i_exp:3, AUUC:0.034441847238110015\n",
      "i_exp:3, AUUC:0.023583947659258378\n",
      "i_exp:5/5\n",
      "i_sel: 4\n",
      "i_exp:4, att:0.0037396854433318916\n",
      "i_exp:4, AUUC:0.014056891075291495\n",
      "i_exp:4, AUUC:0.023498577746984824\n",
      "i_exp:4, AUUC:0.031519251191528326\n",
      "i_exp:4, AUUC:0.03188044168659739\n",
      "i_exp:4, AUUC:0.019010289994232838\n",
      "--------------------------------------------test set. split line --------------------------------------------\n",
      "{'AUUC': [0.036122072721496416, 0.03299001459332371, 0.034447157970156295, 0.034441847238110015, 0.03188044168659739], 'E_att': [0.006722510169158286, 0.004368755343268221, 0.003538671723465031, 0.002955660066465443, 0.001883342704633778]}\n",
      "AUUC: 0.033976 +/- 0.000645\n",
      "E_att: 0.003894 +/- 0.000729\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "!python search_params.py main.py eval4real_data.py  ./conf4models/lzd_real_data/Xnetwork.txt 1 {train_npz} {test_npz}\n",
    "!python search_params.py main.py eval4real_data.py  ./conf4models/lzd_real_data/DESCN.txt 1 {train_npz} {test_npz}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd260b3a",
   "metadata": {},
   "source": [
    "## Print all results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "038047bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_learner_128_20220215_161237,auuc: 0.019345 +/- 0.004395,e_att: 0.005796 +/- 0.000363\r\n",
      "X_learner_with_PS_128_20220215_162642,auuc: 0.023382 +/- 0.003457,e_att: 0.007551 +/- 0.000889\r\n",
      "TARNET_128_64_20220215_164041,auuc: 0.030861 +/- 0.002132,e_att: 0.010581 +/- 0.001550\r\n",
      "CFR_mmd_128_64_20220215_164829,auuc: 0.032439 +/- 0.002917,e_att: 0.025792 +/- 0.001538\r\n",
      "CFR_wass_128_64_20220215_165705,auuc: 0.026086 +/- 0.000169,e_att: 0.026643 +/- 0.001277\r\n",
      "ES_TARNet128_64_20220215_170826,auuc: 0.033958 +/- 0.000820,e_att: 0.016532 +/- 0.001748\r\n",
      "ES_CFR_mmd_128_64_20220215_171601,auuc: 0.033127 +/- 0.000525,e_att: 0.020667 +/- 0.000968\r\n",
      "ES_CFR_wass_128_64_20220215_172449,auuc: 0.026419 +/- 0.001760,e_att: 0.021172 +/- 0.001548\r\n",
      "Xnetwork_128_64_20220215_175222,auuc: 0.032444 +/- 0.001600,e_att: 0.004832 +/- 0.001025\r\n",
      "DESCN_128_64_20220215_175927,auuc: 0.033976 +/- 0.000645,e_att: 0.003894 +/- 0.000729\r\n"
     ]
    }
   ],
   "source": [
    "# print all results.\n",
    "!cat /home/admin/dufeng/ESX_Model/results/lzd_real/eval_result.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "441ae86b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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