COX nnet.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`\n",
      "F:\\Anaconda3\\lib\\site-packages\\theano\\configdefaults.py:560: UserWarning: DeprecationWarning: there is no c++ compiler.This is deprecated and with Theano 0.11 a c++ compiler will be mandatory\n",
      "  warnings.warn(\"DeprecationWarning: there is no c++ compiler.\"\n",
      "WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n",
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "from cox_nnet import *\n",
    "import numpy\n",
    "import sklearn\n",
    "from sklearn import model_selection as cross_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_folds'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-53b00df1dd38>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;31m#cross validate training set to determine lambda parameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m cv_likelihoods, L2_reg_params, mean_cvpl = L2CVProfile(x_train,ytime_train,ystatus_train,\n\u001b[0m\u001b[0;32m     21\u001b[0m     model_params,search_params,cv_params, verbose=False)\n",
      "\u001b[1;32m~\\cox_nnet.py\u001b[0m in \u001b[0;36mL2CVProfile\u001b[1;34m(x_train, ytime_train, ystatus_train, model_params, search_params, cv_params, verbose)\u001b[0m\n\u001b[0;32m    522\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL2_range\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m         \u001b[0mmodel_params\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'L2_reg'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL2_range\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 524\u001b[1;33m         \u001b[0mcvpl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcrossValidate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mytime_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mystatus_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msearch_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m         \u001b[0mcv_likelihoods\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcvpl\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\cox_nnet.py\u001b[0m in \u001b[0;36mcrossValidate\u001b[1;34m(x_train, ytime_train, ystatus_train, model_params, search_params, cv_params, verbose)\u001b[0m\n\u001b[0;32m    438\u001b[0m     \u001b[0mN_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mytime_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m     \u001b[0mcv_likelihoods\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn_folds\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"float64\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m     \u001b[0mcv_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcross_validation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_folds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_folds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcv_seed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m     \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mtraincv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtestcv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcv_folds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_folds'"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "x = numpy.loadtxt(fname=\"E:/中山三院/CKD/usa/MDRD_NIDDK_DEVELOPEMENT_output.csv\",delimiter=\",\",skiprows=0)\n",
    "ytime = numpy.loadtxt(fname=\"E:/中山三院/CKD/usa/MDRD_NIDDK_DEVELOPEMENT -time.csv\",delimiter=\",\",skiprows=0)\n",
    "ystatus = numpy.loadtxt(fname=\"E:/中山三院/CKD/usa/MDRD_NIDDK_DEVELOPEMENT - event.csv\",delimiter=\",\",skiprows=0)\n",
    "\n",
    "# split into test/train sets\n",
    "x_train, x_test, ytime_train, ytime_test, ystatus_train, ystatus_test = \\\n",
    "    cross_validation.train_test_split(x, ytime, ystatus, \n",
    "    test_size = 0.2, random_state = 100)\n",
    "\n",
    "#Define parameters\n",
    "model_params = dict(node_map = None, input_split = None)\n",
    "search_params = dict(method = \"nesterov\", learning_rate=0.01, momentum=0.9,\n",
    "    max_iter=2000, stop_threshold=0.995, patience=1000, patience_incr=2, rand_seed = 123,\n",
    "    eval_step=23, lr_decay = 0.9, lr_growth = 1.0)\n",
    "cv_params = dict(cv_seed=1, n_folds=5, cv_metric = \"loglikelihood\",\n",
    "    L2_range = numpy.arange(-4.5,1,0.5))\n",
    "\n",
    "#cross validate training set to determine lambda parameters\n",
    "cv_likelihoods, L2_reg_params, mean_cvpl = L2CVProfile(x_train,ytime_train,ystatus_train,\n",
    "    model_params,search_params,cv_params, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build the model\n",
    "L2_reg = L2_reg_params[numpy.argmax(mean_cvpl)]\n",
    "model_params = dict(node_map = None, input_split = None, L2_reg=numpy.exp(L2_reg))\n",
    "model, cost_iter = trainCoxMlp(x_train, ytime_train, ystatus_train, \n",
    "    model_params, search_params, verbose=True)\n",
    "theta = model.predictNewData(x_test)"
   ]
  }
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