Apply Binary Model To Test and Real Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Binary Model to Test and Real Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### python packages\n",
    "import os\n",
    "from os import path\n",
    "import numpy as np\n",
    "import glob as glob\n",
    "from random import random\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "### torch packages\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data.sampler import Sampler\n",
    "\n",
    "### sklearn packages\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "\n",
    "### remove these later (for notebook version only)\n",
    "'''\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.io import output_notebook, show, export_png\n",
    "from bokeh.layouts import row\n",
    "output_notebook()\n",
    "'''\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "torch.cuda.manual_seed(42)\n",
    "\n",
    "\n",
    "def GetSimData(file):\n",
    "\n",
    "    ### grab local and global views\n",
    "    data_global = np.nan_to_num(np.load(file.replace('_info2.npy','_glob.npy'),encoding='latin1'))\n",
    "    data_local = np.nan_to_num(np.load(file.replace('_info2.npy','_loc.npy'),encoding='latin1'))\n",
    "\n",
    "    ### grab centroid views\n",
    "    data_global_cen = data_global[:,1][np.newaxis,:]\n",
    "    data_local_cen = data_local[:,1][np.newaxis,:]\n",
    "\n",
    "    data_global = data_global[:,0][np.newaxis,:]\n",
    "    data_local = data_local[:,0][np.newaxis,:]\n",
    "\n",
    "    ### info file contains: [0]kic, [1]tce, [2]period, [3]epoch, [4]duration, [5]label)\n",
    "    data_info = np.load(file,encoding='latin1')\n",
    "    #np.load(self.flist_info[idx],encoding='latin1')\n",
    "\n",
    "    if data_info[6]=='PL':\n",
    "        label=1\n",
    "    else:\n",
    "        label=0\n",
    "\n",
    "    #collist=['TPERIOD','TDUR','DRRATIO','NTRANS','TSNR','TDEPTH','INDUR',\n",
    "    #         'SESMES_LOG_RATIO','PRAD_LOG_RATIO','TDUR_LOG_RATIO','RADRATIO','IMPACT',\n",
    "    #         'TESSMAG','RADIUS','PMTOTAL','LOGG','MH','TEFF']#from bls search, derived from transit model, from starpars\n",
    "    stelpars=np.nan_to_num(np.hstack((data_info[7:13].astype(float),data_info[-18:-6].astype(float))))[np.newaxis,:]\n",
    "\n",
    "    return (data_local.astype(float), data_global.astype(float), data_local_cen.astype(float), data_global_cen.astype(float), stelpars), label\n",
    "\n",
    "def GetRealData(file):\n",
    "    ### grab local and global views\n",
    "    data_global = np.nan_to_num(np.load(file.replace('_info2.npy','_glob.npy'),encoding='latin1'))\n",
    "    data_local = np.nan_to_num(np.load(file.replace('_info2.npy','_loc.npy'),encoding='latin1'))\n",
    "\n",
    "    ### grab centroid views\n",
    "    data_global_cen = data_global[:,1][np.newaxis,:]\n",
    "    data_local_cen = data_local[:,1][np.newaxis,:]\n",
    "\n",
    "    data_global = data_global[:,0][np.newaxis,:]\n",
    "    data_local = data_local[:,0][np.newaxis,:]\n",
    "\n",
    "    ### info file contains: [0]kic, [1]tce, [2]period, [3]epoch, [4]duration, [5]label)\n",
    "    data_info = np.load(file,encoding='latin1')\n",
    "    #np.load(self.flist_info[idx],encoding='latin1')\n",
    "\n",
    "    #collist=['TPERIOD','TDUR','DRRATIO','NTRANS','TSNR','TDEPTH','INDUR',\n",
    "    #         'SESMES_LOG_RATIO','PRAD_LOG_RATIO','TDUR_LOG_RATIO','RADRATIO','IMPACT',\n",
    "    #         'TESSMAG','RADIUS','PMTOTAL','LOGG','MH','TEFF']#from bls search, derived from transit model, from starpars\n",
    "    newixs=np.array([16, 24, 17, 30, 31, 21])\n",
    "    stelpars=np.nan_to_num(np.hstack((data_info[newixs].astype(float),data_info[-18:-6].astype(float))))[np.newaxis,:]\n",
    "\n",
    "    return (data_local.astype(float), data_global.astype(float), data_local_cen.astype(float), data_global_cen.astype(float), stelpars), np.nan\n",
    "\n",
    "\n",
    "class Model(nn.Module):\n",
    "\n",
    "    '''\n",
    "    \n",
    "    PURPOSE: DEFINE EXTRANET MODEL ARCHITECTURE\n",
    "    INPUT: GLOBAL + LOCAL LIGHT CURVES AND CENTROID CURVES, STELLAR PARAMETERS\n",
    "    OUTPUT: BINARY CLASSIFIER\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    def __init__(self):\n",
    "\n",
    "        ### initialize model\n",
    "        super(Model, self).__init__()\n",
    "\n",
    "        ### define global convolutional lalyer\n",
    "        self.fc_global = nn.Sequential(\n",
    "            nn.Conv1d(2, 16, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(16, 16, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(5, stride=2),\n",
    "            nn.Conv1d(16, 32, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 32, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(5, stride=2),\n",
    "            nn.Conv1d(32, 64, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(64, 64, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(5, stride=2),\n",
    "            nn.Conv1d(64, 128, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(128, 128, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(5, stride=2),\n",
    "            nn.Conv1d(128, 256, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(256, 256, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(5, stride=2),\n",
    "        )\n",
    "\n",
    "        ### define local convolutional lalyer\n",
    "        self.fc_local = nn.Sequential(\n",
    "            nn.Conv1d(2, 16, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(16, 16, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(7, stride=2),\n",
    "            nn.Conv1d(16, 32, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 32, 5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(7, stride=2),\n",
    "        )\n",
    "\n",
    "        ### define fully connected layer that combines both views\n",
    "        self.final_layer = nn.Sequential(\n",
    "            nn.Linear(7858, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 1),\n",
    "            nn.Sigmoid())\n",
    "\n",
    "    def forward(self, x_local, x_local_cen, x_global, x_global_cen, x_star):\n",
    "        \n",
    "        x_local_all = torch.cat([x_local, x_local_cen], dim=1)\n",
    "        x_global_all = torch.cat([x_global, x_global_cen], dim=1)\n",
    "\n",
    "        ### get outputs of global and local convolutional layers\n",
    "        out_global = self.fc_global(x_global_all)\n",
    "        out_local = self.fc_local(x_local_all)\n",
    "        \n",
    "        ### flattening outputs (multi-dim tensor) from convolutional layers into vector\n",
    "        out_global = out_global.view(out_global.shape[0], -1)\n",
    "        out_local = out_local.view(out_local.shape[0], -1)\n",
    "\n",
    "        ### join two outputs together\n",
    "        out = torch.cat([out_global, out_local, x_star.squeeze(1)], dim=1)\n",
    "        out = self.final_layer(out)\n",
    "\n",
    "        return out\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "allmodels={}\n",
    "for k in range(8):\n",
    "    foldname    = \"/home/hosborn/TESS/final_runs/exonet_binary_CV_globcents3b_k8_2\"\n",
    "    savename    = \"exonet_CV_\"+str(k)+\".8_101_all_Big\"\n",
    "    savedicname = \"exonet_binary_CV_globcents3b_k8_2_dic\"\n",
    "    mod         = \"Big\"\n",
    "    aug         = \"all\"\n",
    "    fpath       = \"101\"\n",
    "\n",
    "    allmodels[k]=Model().cuda()\n",
    "    lr = 1.5e-5\n",
    "\n",
    "    optimizer = torch.optim.Adam(allmodels[k].parameters(), lr=lr)\n",
    "    criterion = nn.BCELoss()\n",
    "    batch_size = 64\n",
    "    n_epochs = 200\n",
    "    \n",
    "    #LOADING MODEL STATE:\n",
    "    allmodels[k].load_state_dict(torch.load(path.join(foldname,savename.replace('_comp','')+'_best.pth')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NEED TO PROCESS pickle FILES INTO info2 FILES\n",
    "### NEED TO WHITEN TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "real_nan\n",
      "test\n",
      "AP: 0.97277438364112\n",
      "UNK  AV accuracy: 0.9763819095477387\n",
      "UNK  AV recall: 0.984794728839331\n",
      "UNK  AV A.P.: 0.9935775502451326\n",
      "PL  AV accuracy: 0.9184782608695652\n",
      "PL  AV recall: 0.8779220779220779\n",
      "PL  AV A.P.: 0.9519712170371073\n",
      "real\n"
     ]
    }
   ],
   "source": [
    "dataloaders={'real':glob.glob('/home/hosborn/TESS/Processed_RealDat_2/*info2.npy'),\n",
    "             'real_nan':glob.glob('/home/hosborn/TESS/Processed_RealDat_2/nans/*info2.npy'),\n",
    "             'test':glob.glob('/home/hosborn/TESS/processed_dv_101_centfixed2/test/*info2.npy')}#,'nan':nan_data_loader\n",
    "model_output={}\n",
    "\n",
    "for data_loader in dataloaders:\n",
    "    print(data_loader)\n",
    "    model_output[data_loader]=pd.DataFrame()\n",
    "    \n",
    "    for file in dataloaders[data_loader]:\n",
    "        if data_loader=='test':\n",
    "            x_data, ys = GetSimData(file)\n",
    "        else:\n",
    "            x_data, ys = GetRealData(file)\n",
    "\n",
    "        ### get local view, global view, and label for training\n",
    "        x_local, x_global, x_local_cent,x_global_cent, x_star = x_data\n",
    "\n",
    "        x_local = Variable(torch.from_numpy(x_local)).type(torch.FloatTensor).cuda().unsqueeze(1)\n",
    "        x_local_cent = Variable(torch.from_numpy(x_local_cent)).type(torch.FloatTensor).cuda().unsqueeze(1)\n",
    "        x_global = Variable(torch.from_numpy(x_global)).type(torch.FloatTensor).cuda().unsqueeze(1)\n",
    "        x_global_cent = Variable(torch.from_numpy(x_global_cent)).type(torch.FloatTensor).cuda().unsqueeze(1)\n",
    "        x_star = Variable(torch.from_numpy(x_star)).type(torch.FloatTensor).cuda().unsqueeze(1)\n",
    "        \n",
    "        preds={}\n",
    "        for k in allmodels:\n",
    "            ### Looping through each model to predict:            \n",
    "            preds[str(k)]=allmodels[k](x_local, x_local_cent, x_global, x_global_cent, x_star).cpu().detach().numpy().ravel()[0]\n",
    "        if data_loader is 'test':\n",
    "            preds['gt']=ys\n",
    "        model_output[data_loader] = model_output[data_loader].append(pd.Series(preds,name=file.split('/')[-1][:18]))\n",
    "    allks=model_output[data_loader][[str(k) for k in range(8)]].values\n",
    "    model_output[data_loader]['med'] = np.median(allks,axis=1)\n",
    "    model_output[data_loader]['av'] = np.average(allks,axis=1)\n",
    "    if data_loader is 'test':\n",
    "        labels=['UNK','PL']\n",
    "        onehot=make_one_hot(model_output['test']['gt'].values.astype(int),2)\n",
    "        avvals=np.column_stack((1.0-model_output['test']['av'].values,model_output['test']['av'].values))\n",
    "        print(\"AP:\",average_precision_score(onehot,avvals))\n",
    "        for n in range(2):\n",
    "            print(labels[n],\" AV accuracy:\",np.sum((np.argmax(avvals,axis=1)==n)*onehot[:,n].astype(bool))/np.sum(np.argmax(avvals,axis=1)==n))\n",
    "            print(labels[n],\" AV recall:\",np.sum((np.argmax(avvals,axis=1)==n)*onehot[:,n].astype(bool))/np.sum(onehot[:,n]))\n",
    "            print(labels[n],\" AV A.P.:\", average_precision_score(onehot,avvals,average=None)[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ap: 0.9557892461233882"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model_output,open('binarymod.pickle','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_one_hot(labels, C=2):\n",
    "    '''\n",
    "    Converts an integer label torch.autograd.Variable to a one-hot Variable.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    labels : torch.autograd.Variable of torch.cuda.LongTensor\n",
    "        N x 1 x H x W, where N is batch size. \n",
    "        Each value is an integer representing correct classification.\n",
    "    C : integer. \n",
    "        number of classes in labels.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    target : torch.autograd.Variable of torch.cuda.FloatTensor\n",
    "        N x C x H x W, where C is class number. One-hot encoded.\n",
    "    '''\n",
    "    if type(labels)==list or type(labels)==np.ndarray:\n",
    "        target=np.zeros((len(labels),C))\n",
    "        target[np.arange(len(labels)), np.array(labels).astype(int)] = 1\n",
    "    else:\n",
    "        one_hot = torch.cuda.FloatTensor(labels.size(0), C, labels.size(2), labels.size(3)).zero_()\n",
    "        target = one_hot.scatter_(1, labels.data, 1)\n",
    "\n",
    "        target = Variable(target)\n",
    "        \n",
    "    return target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AP: 0.9755840287549226\n",
      "UNK  AV accuracy: 0.9803229061553986\n",
      "UNK  AV recall: 0.984794728839331\n",
      "UNK  AV A.P.: 0.9953788113864569\n",
      "PL  AV accuracy: 0.9202127659574468\n",
      "PL  AV recall: 0.8987012987012987\n",
      "PL  AV A.P.: 0.9557892461233882\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2358, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avallks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_output['test']['gt'].values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('realdat_binarymod.npy',model_output['real'])\n",
    "np.save('realdat_binarymod_nans.npy',model_output['real_nan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model_output,open('binarymod.pickle','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_precision_score?\n",
    "#(gt,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9557892461233882"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_precision_score(gt,model_output[data_loader]['av'],average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall: 0.8987012987012987\n",
      "accuracy: 0.9202127659574468\n",
      "recall: 0.984794728839331\n",
      "accuracy: 0.9803229061553986\n"
     ]
    }
   ],
   "source": [
    "print(\"recall:\",np.sum((model_output['test']['av']>0.5)*(gt==1))/np.sum(gt==1))\n",
    "print(\"accuracy:\",np.sum((model_output['test']['av']>0.5)*(gt==1))/np.sum(model_output['test']['av']>0.5))\n",
    "\n",
    "print(\"recall:\",np.sum((model_output['test']['av']<0.5)*(gt==0))/np.sum(gt==0))\n",
    "print(\"accuracy:\",np.sum((model_output['test']['av']<0.5)*(gt==0))/np.sum(model_output['test']['av']<0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall: 0.984794728839331\n",
      "accuracy: 0.9803229061553986\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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39HWJhqpfz6ZeXM8u/74tuVaTqbwCPkepfEn/9uKEumJBvfZAn4baI5LKrcuePDOzHG4cHV3Q0dF5HRxIKhJ05C5WYGQLrl4cL7/5IBr0y++Y5XCjFq0O1/5OyhR6qgpMtmJZqV/5xyeoIAIAADVBYAIAQIu4pq9N1/S1NXoaW6rkeiq65XfJDs9mNZ8raj5X0rHRBZU8K8cYPXBsXJK0syOqsyvW19ioqfSFy7SVhCXAetojlS/47i2ujSGVw43JVPn7dTpd0Oh8TslIQOdnszo+ntKuzqhm0gU9fmZGiXBAvW0hvTBWvijbFQut+l6vxMr1fyQthyVLc6tEPOzX1aGYciVPuaKrlblIoeQp4HMU9DuLQcOFQEKSgn5neZ2QSvW2tc472rfiorckuVb61vFJ7euJqz+5/pon8dD6f9J6i+8LmEzl9NJkZrnS4rnhecXDfhVKno6MlNej6owG9Mz5eU2nC7q2v03HxhZ0fiarXZ1RZYueJha/j7viQU2lymFIwOfISmqP+NWbWH/OK31hscXUH77lBp2ZTuufv3P2ZdtcLlTPFEqXfKxWLhU4bpQxZvU6O3Xy8UdO62fu2F3XYwAAgCsfgQkAALgifPGZEX3xmRH1J8IaraAipZKwBNgKz5yfk5UUD/k0ny3p+dF5hfw+RYM+fev4pIqup0ODST23WOlkZCpqv/f84gVjqVwtsrISrdqwpJ6MMYoEfMvrjaA+wv7aPb8rr5svVVPIlKswPn94WF98xugX7t6j8YW8+pMRjc5l9cJYSru7osoXPX3n9LRCfp/298b1tedHNT6fV9DvqFDyLn1QSa/9swc2Nc/h2ezy52emV/+OWApLpAuVmm4VecD7P/NM065V5F7+ad0Qn2PkVfMEbcDnDg8TmAAAgKoRmAAAgCtKJWEJ0MyePje73MbqUpbCEkkNWasIV75QwJHfMcvry1TqyPD8utt0x4P6nw+fftn9pyYvVCUVXU9PnZ1Zvr1eWLId/K9vn1YyWnlFWb24NagMqbTtHQAAwFYjMAEAAAA2oCse0tRiS6uBZERtYb+OjS1ob3dcBwbadHR0Qd2xoK7tT2ghV5Qx0q7OmAI+o3zJ02B7RL7F9ShanPMBAAAgAElEQVQ6Y0H9wt8/1uAzAjbOMUYD7WGdnc6uv3GVuluo1dkqRsoXPYUCtV8PrBqeZ5UrugpXUcXl26K85Kf+9tvLn3fGgvrzn7xZoRpWRwEAgCsfgQkAAACuCHu7Yzo1mZYk3bSzXZm8q4mFvK4fSqg3EdbpqbR2d8U01B7RfLaozlhQnbGgrKS2sF/RYPmfxuGAsyUX2AI+Z7mVD7AdtIUDGmwvV/J5VVaaXEoiEqjrgvbNzKjcDm8ja7fUUsmz5XVKrFRY0TYrX3LlWelrz49JKlf17OuN64FjE6vC45+4bafu2td12WP4nK3/mk6nC3rHRx7VH77lBu3vjW/58QEAwPZEYAIAAIAt0dMWXl4w+dBgUqNzOWUKJd2yq0NtYb+G53La3xtXfyKs2WxR/YmwOqIBeba8wPLSu5sjAV9DLr4BrcTvOHIcI8dIuaIrSQr6fbptT1ylkqfpTFHz2aLOz12oOMkXPZU8q7aQXwGfkUx5TR1jyi2zAj5HQb8jU35I0oXF5AM+R0ba1mHJ0qLm1lrlSt6m19UxRvKsJHuhDZa1i2ukmPLi9fmSK7/jqOR5yhZc+X3lapR0viRrpUjQp7lsUdZaRYI+ZQtuTc7tsZemZIzR4TXtAUfmsvrLrx/TX35d+ug7b1ckePFzbuTX9f2feUaf+MU7aQsGAAA2hMAEAAAAL9MWDmghV5Qk7eiIqi3s16nJtA4MJLS/J66zMxkNtke0uzOqfMlTZyyo9mhAxhh1xYJVtW4BcGkrK5N8jtG1fQkdGZlTJODTHVd16eREStZKN+xIyhijscUgsj0a0GymqL5EWF3xoBxj5HOMfMbIcaRo0K9E2C+fYzZ9YfmeR8/oM0+dr8fprquZAhbHGFlrNTKXUyzk33RgIhnNZgqazRTW3/QS8qULAUmtwhJpY2uQvOtjj+qed9910ccaHXLf89hZvf32XQ2dAwAA2B4ITAAAQFPrioU0xkLum3JwIKkjI3OKhwJ69f4uTaULCvgcXdPXppDfkYy0syOqoM9RNORTVyy4vC/vwN068ZBfM1VcGEXzGGqP6vxsRpJ02+5OvTC2IGutbtndIccYDc9mdXVfm3rbQhqZzao/GVFPW1C5oqe2sF/tkaCsrCIBn2Kh8p9ofp/ZNmsvBHyNW3Mj3EzrfVhpZC6nuWxRPRWsw9Ksr77RS1SNXMz9L4zrNdf2vuz+RgdbX3p2lMAEAABsCIEJAABoalf1xHRkZK7R09gS0aBfOzoiOja2oOv6E7p+KKmR2awG2iPa0xWVZ6XueFDt0XLAkQj7l9uxYPvZ0x3TzBkCk3roiAaXw6jetrDiYb9OTqS0tzuma/vadHR0Qd3xkA4MJDSfK8pnjHZ1RRXwGeVLnobaI3KMkd9n1BENLreN2i4Bxlbb1RVt2LHDAV/TrAe09D3XlwhVFCI1Y14d9DubOpd/fOTMRQOTX37NPrmelWPMheqmxWGjQb9iIf+qiiff4naOY/QXXzumh09OVXUehVLtqm0AAMCVjcAEAAA0te8/1K+vPT+23EO/WSUjAbmelMoXdf1gUru7ohqbz2tfb0w7OqJyPauh9ohiIb8cU96eao7W9l37u/XkmZlGT6MhBpIRjSyufXFtX5tSeVdj8zndtDOpgWREx8fL4caurqhmMwW1R4PqawvLs1axkF/JSEBS+Z3vtH9rvGt62xTy+1a1g9oqjjEabA/r9FRmy499Mb1tIXWuqNrbDGMkxzEyWgpPyp971sr1ymuaxEP+8howi78/Sp6nfNFTLORXeLGCcGndmJLryTFGoYBPSx2xlsaVpKDvQvu11evKVP67aamV41qHBpOSyuu7ZIuurC2f18RCXtmiq7lsUWemM4oEfCqUPB2fSMkxUnskqIdPTumF0QXFQ36l8iW5nr1kSDbUEVl+fQAAAKgEgQkAAGhqPW0h/cKr9+pvHjipklf/dxAPJCPqiAY1mcrr4GBCe7tjSudL2t0VU1csqKDfUX8iLIdFx1Glu/Z16eFT03r0VHXvnK6HWNCvdKEkSdrbHdfwbFb5kqubdrSrPRrUS5NpXd0X167OqCZSefUnwuptC6vkeWqPBtUWLv+Z0Rb2U5XRApLRgH76jl366LdONeT4sZBfu7uiOj+bU2kLKk36k2GNzpVbRe7tiWk+W9Jctqjb93To7qt79MLovHZ3xrS7q/zz4TNmQ2u8RIN+XdffVu/p191P/e23taMjqnMztQmxMoVySDKXvRDGXKqi6PxMVuPzeV3dF191//7e+EW3BwAAWIvABAAANL3vurpHuztj+odHTuvY2MKGqk06okEdGEjo3ExG+3vjurY/IdfztLMjqvZoUD7HqCNKlQcaxxij97x2vz77VFSffvJ8xYHgyndad8VDSoT9OjWZ1jV9bbq2v00nJ9IaSIa1vzeubMFVIhJQXyIsqVzptBRu+B1DizdU7PsO9ilf8vTJ75zdkvZYyUhg+QL6UHtUM5mCIkGfOqPlYPv4eErd8ZCG2iN6aSqjaNCnQ4MJlTyrguvp0EBSIb+jfMnTtf3xciWG39Hurqgcp7Y/B4WSp88+NSwrW9Nxm1mtwhKpHCQlowHNZS5evbJW0fU0sZBftY7MQDJSs/lsN9ZaTaUL6o5vfl0dAABaEYEJAADYFnZ1RfU7bzyw/Ie/Z8sXnozKwQcXerEd+X2O3nrrDr3xhgG9NJXW6HxOS9dU8yVPnrXa1RlVOOBTyO+opy1UbttjTEMX2gbWMsboTTcN6vY9nTp8blanp9LKFS8EJ8+PzMta6Zbd7XKM0fhCXvt74+qJhzSRymuovdxKqeh66ogGFQ2VK5OSkcC2r1IK+h0NtIc1PJtt9FS2rf5EWLJaVWVyOSsDk6Dfp2ATv166ntVUOq+Q31dVO7FHTk7pz792bN3t/uxtN2lHR+PWHQIAoNkRmAAAgG3FGMO7JHHFiQR9OjCQ0IGBRKOnAlSlPxlWf7K/0dNoOjfvbCcwqYLPMRrqiKg9GlC64Cq/uA6KVM6Y0/lyC8FEJCC/YxQKOPqjH71BV/VU34orVyjp/GILttlMUacmUgr4HR0bW9C3T0xpT3dMuzqjmlms4jBGeuTUtKyVDg4m9PS5WUmrq6Iu5u593frV111d8Tw/9G8ba4n3m588rF/6nn16zbW9FR8LAIArGYEJAAAAAAB19LZbd+qRU9OaSuUbPZVtLRbyKxba2GWMB45N6Ph4SvGwX5Opgo6PLagvGdb4fE5ff35cs9mi+hIhvTiWklRu5TmTKWx6TsfHU8ufX9UTVzhwoZplKSyRNl4dU4lHTk4pld/4+H/zwAndubdLkeD2rt6Syi3HaK8KAKglAhMAAAAAAOooEvTp/T90QB+8/4ReHF+o+/FWrm0UDpQr2J48M6PueEiv3NOps9MZ+XxGB/oTMkaaz5V0VXdMkaBP2YKrgWRYQX/5wn9XLKQ/+uLzWzLvWvrKc6Mvu28mXdDYfH65ref8ihCjkrBkrZMTKe3vjS8/d1vlHx89s+l9/vabJ/Xe11de0VIrJdfTmemMnh2el7VWx8YWdGI8rUODCY3O53RiIqWAr9yScqlKK+j3qVB6+Xp2v/emQ7qun0pNAEB1CEwAAAAAAKizgWRE/+lNh/TUuVmdnEhrfCG3/Fgm7+ro6Lyu7mvT/p64Tk9n1BMPandXTLmiq3DAp/5kWEblNVHao0EZIwUcZ0uqBHZ3RbddYLLWfLaokbmcoiGf2iMBDc/m1t+pAsfHUzo4uHUX7RdyRe3vietHbhzU3z14csP7ffvkpN6r1YHJ2ZmMplN5FT2rh45PKlf0dPPOds1mi8oWXHXGgnpueF7Ds1ldP5TQ0dEFDc9m1RUL6V1379Ftezo3Nfe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      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "%matplotlib inline\n",
    "%config IPython.matplotlib.backend = \"retina\"\n",
    "from matplotlib import rcParams\n",
    "rcParams[\"savefig.dpi\"] = 300\n",
    "rcParams[\"figure.dpi\"] = 300\n",
    "\n",
    "savefile='Binary_ensemble_testdat_avmedcomp.png'\n",
    "\n",
    "gt=model_output['test']['gt']\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "labels=['PL']\n",
    "P, R, _ =precision_recall_curve(gt,model_output['test']['med'])\n",
    "steps=plt.step(R, P,linewidth=2.5,label='median',alpha=0.75)\n",
    "P, R, _ =precision_recall_curve(gt,model_output['test']['av'])\n",
    "plt.step(R, P,linewidth=2.5,label='average',linestyle='--',color=steps[0].get_color(),alpha=0.75)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlim(0.0,1.05)\n",
    "plt.ylim(0.2,1.05)\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "savefile='Binary_ensemble_testdat_avmedcomp'\n",
    "plt.savefig(path.join('/home/hosborn/TESS/PaperFigures',savefile+'_PR.png'))\n",
    "plt.savefig(path.join('/home/hosborn/TESS/PaperFigures',savefile+'_PR.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VzveWJI8euL8uyevTO2Nlt9baYa21u7bWDk+yS3r/c3hWkjNy09IKAAAAAACm1ky+kmtYa+276b02KlW1LsneSXZL7zVWV6R3cPqPW2ubJhZySFXtkeQvhoZf0Fq70eHz/QPU31NVZyb5bJID+r/aN8mfpfeqrNXI97gkjx8YujDJMa21r29ufj/nWf3Pa6tqt9XIBQAAAAAAq2GbKEwGtdauTe9pjPMWmjthz03vqYw5n05ywpYmt9YuqKqnJPmPgeFnV9X/aa1dOspgVbVnkr8dGPpFknu31r632D1aaz8fZSYAAAAAAFhNM/1KrlnVP7vkSUPDL2mtbfU1Vq21TyQZPKB+lyTHjjhe0nvyZc+B+xcupSwBAAAAAIBZozCZjLund3j7nB8kOW2Ra98ydP/wUQSaU1XrkzxhYOgnSf5+lN8BAAAAAADTRmEyGQ8euv/4Qk+XDM4duj+6qnYaQaY5/yO9g+bnnNJau36E+wMAAAAAwNRRmEzGnYbuz1jswtbahUl+ODC0LsnhI8g0Z7jMOXWEewMAAAAAwFSamkPfq+qtQ0OttfbkRcwblc1+3yo5bOj+20tc/+0kBwzt98WVBBrwW0P3X0uSqlqT5AFJ/iDJEUn2TbIpycVJvpLkw0ne2Vq7akQ5AAAAAABgbKamMEnyxCRzr6Wq/vXmCozBeaOyte8b7RdV7Zhk/6Hh85e4zfD8Wy8/0byq2jXJoQND17fWzq2qg5KcnORum1m2a5JDkjw6ycur6vmttbePIg8AAAAAAIyLV3KN357pFTRzNiW5aIl7XDB0v9eKEs07KDfO9suqOjzJl7P5smTYrZL8U1W9akR5AAAAAABgLKbpCZPkxn+sH8W8abTz0P1VSzjwfc6VC+y5XBuH7luSD6b3FEmSXJXkX5J8OsmlSfZIcu8kj0my48C651XVBa21vxtFqKraK8nNl7js4FF8NwAAAAAA3TBNhcmBI543rYbLjauXscevFthzuYYLk936nyT5UpJHtNbOG5rz9qp6eZL3JbnDwPirq+qjrbXvjiDXHyV58Qj2AQAAAACAzZqawqS1du4o502xHYbur13GHtcM3e+42VlLt6Xi5UdJfru19vPN/bK19sOqul+SbyTZuz+8PslzkjxtRNkAAAAAAGDVOMNk/IafKFm3jD3WL7Dncm1pn+O3VJbMaa1dkuT5Q8OP7x9yDwAAAAAAU21qnjDpkCuG7oefOFmM4RJieM/l2tw+P0vy7kWuf2eS12b+zJMdkhyV5FMrzPWGJP+6xDUHp/eaMAAAAAAAWJDCZPyGS4kNVVVLPPh9pwX2XK7N7fO51tqmxSxurV1dVWcm+e2B4btkhYVJa+2iJBctZU1VreQrAQAAAADoGK/kGr9LkgyWI9sn2WuJe+wzdL+kMmErfrqZsaUe2v5fQ/dL/bcBAAAAAMDYdaIwqaqjquodVXV+Vf2qqi6sqg9U1UPGnaW19qsk5w0N77/EbYbnn7X8RDfy/dz0EPrLl7jH8Pzdlh8HAAAAAADGY+YKk6p6QFV9euBz6ALzn5XkjCTHpvdkxvokeyd5UJL3VtXJVTXu/w7DBcfhS1x/2AL7LUtr7frc9ImS4QPmFzJ8JstVy08EAAAAADB6J697RW78IiCYwcIkyR8muUeS/57kZq21Lb4yqqrun+Rv0vt3Vnr/FzD3SX/suCSvX83Am/HVofu7L3ZhVd0yyQEDQ5uSfHsEmeZ8eej+FktcP/wKrktXkAUAAAAAYOR2ryvywx0em92W/IIdtmWzWJjcd+D6lAXm/r+ZL0rSv74oydUD45XkaVW16NJiBD44dH//Wvwp5Q8Yuj+1tTaqQ9+T5P1D93de4vrh+cNnmgAAAAAATIWv7PCMSUdgisxUYVJVByXZc2Dow1uZe58kt8t8WfLFJL/ZWrtlko1J/rw/Pvf7Z4827Vadkd7h73MOSnL0Itc+eej+faMINOAj6RVKc+5QVb+5mIVVddvc9HVhp40oFwAAAADAyN25/P980zNThUmSwfNKNiX55lbm/n7/Z/XnPqq19v0kaa1taq39bZI39n9fSR5cVTuOPvJNtdZuSHLS0PCLF3rKpKrul+SeA0O/TPKuEWe7MsnJQ8N/ucjlfzV0/6nW2kUrTwUAAAAAsDqesPbjk47AlJi1wuQ3+j9bknP7h5RvyQMyf17J+1tr529mzmsHrtcnueNIUi7OCUkGX6V17yTP29LkqtonyZuHhl/bWrtkc/MH1rWhz9GLyPbS3PgpkydU1R8u8D1/lOTYoeFXLuK7AAAAAAAm5n7bDR/rTFfNWmGyy8D1L7Y0qar2z3y5kiTv3dy81trZSS4eGLrNitItQb/oeMXQ8Cur6g1Vdau5gararqoent5rvA4YmHthegfar0a2H6VX6Ax6c1W9rqr2Gxysqv2r6o1JXjc0/x2ttY+uRj4AAAAAgJtYf7NlLdu5rs7BdcGIwzCLZq0wWT9wvbWnS+YOcJ97xdUntzJ38MmT3ZYTagVOyE0PgH9mkvOq6vtV9eUklyZ5T5L9B+b8KsmxrbXLVjHby4ayVZL/meTcfrYzq+r7Sc5N8ozM/7dOki8nedoqZgMAAAAAuLE9D03W7rCspZ9Yf3yevObfRxyIWTNrhcmVA9e7bmXevQeuf9Ba+8lW5l47cL1hWamWqX+WyaOTnDL0qzXpHQR/RHoH1A+6NMmDWmunr3K265M8Ksnbhn5V/Wy/1f857P1J7t1au2IzvwMAAAAAWB1r1iaHPXTZy1+0/cnZc8svNqIDZq0wubT/s5IcUFVrtzDvd/o/W5JPL7DnYCFx1QqyLUtr7erW2nHplRNf3crUK5O8IcnhrbXTxpTtmtbaE5Mck2RrBU1L8oUkD2mtPUxZAgAAAABMxDEnJDvttezl/7ruJaPLwszZUuEwrb45cL0+yf2TfGRwQlXdLb2zPlp/6LQF9tx74PpnK4u3fK21dyd5d1UdkuSuSfZJsi7JZUm+k+T01trVW9liS/vWwrMW3OMjST7SP3j+bumdD7NDkp8n+XE/20Ur/R4AAAAAgBXZsHvytFOT9/9p8v1PLHn5gdv9dBVCMStmrTD5Rnp/pN+Y3lMmL6+qU1tr1yRJVa1J7+yN9H9/XZKPbWmz/uHwg0+Y/GA1Qi9Fa+17Sb436Ryb01q7IMm/TToHAAAAAMAW7bpv8rh3Jy8dPu0Atm6mXsnVWrsuyTvSK0Naemd8fLmqjq+qZyX5TJL79H/Xkny0tba1SvDuQ/ffHn1qAAAAAADGqirZ+BuTTsGMmbUnTJLeEySPTXKz/v1hSV41NKeS3JDkfy+w1yMHrr/XWrt0izMBAAAAAJgdt7xjctm5k07BDJmpJ0ySpP/EyLFJrs38kyaD5s7s+KvW2he3tE9VbUzvMPO5p1FOG3lYAAAAAAAm406PWfKSy9uOqxCEWTFzhUmStNY+nuQuST6c5Pr0SpK5z9lJHttae8UC2zwjyYbMFywfWJ20AAAAAACM3a2PSe543JKWnN32XaUwzIJZfCVXkqS19q0kD66qmyU5MMmOSS5srZ23yC3OTfLsgfuPjzgiAAAAAACT9LDXJ7e4bfKxv5x0EmbAzBYmc1prlyf52jLWvWMV4gAAAAAAMC22W5Pc/U+S87+QfMdLhti6mXwlFwAAAAAALNraHSadgBmgMAEAAAAAADpPYQIAAAAAAHSewgQAAAAAAOi8qTn0vaqeMDzWWvunxcwblc19HwAAAAAAsO2bmsIkyUlJ2tDY5gqMzc0bFYUJAAAAAAB00DQVJoMqC5ciNaLvaov8PgAAAAAAYBs1bYXJYkuQUZUlo94LAAAAAACYQdNUmDxpxPMAAAAAAAAWZWoKk9ba20Y5DwAAAAAAYLG2m3QAAAAAAACASVOYAAAAAAAAnacwAQAAAACAJHfe7uzsVz+ddAwmRGECAAAAAAB9n1n/7Dx/7TsmHYMJmMnCpKr2H/jsssw9dhncZ9QZAQAAAACYTc9Y+4EcWd+ddAzGbOYKk6o6Jsk5A59DlrnVoUl+OLdPVR09inwAAAAAAMy+/2/9SyYdgTGbucIkyVOTVP/zydbaV5azSWvtS0k+NbDXU0aWEAAAAAAAmCkzVZhU1fZJfjtJ639W+iK5kweuj6mqWuF+AAAAAABsI+qXP550BMZopgqTJHdMslN6T4QkyUdXuN9HBq43JrndCvcDAAAAAGDarN1h0gmYAbNWmBw2cH1xa+2ClWzWX3/xwNDhK9kPAAAAAIAptPftl7Wsbb/jiIMwzWatMLl5/2dL8pMR7Tn4TNXeI9oTAAAAAIBpcdB9ku3WTjoFU27WCpPB56auHtGe1wxc7zSiPQEAAAAAmBY3PzS553MmnYIpN2uFyc8Grvcc0Z57DFz/ckR7AgAAAAAwTe51fHLfF006BVNs1gqTS/o/K8n+VbXrSjarqo1JfiO9V3wlNz7PBAAAAACAbcWatcm9npPvHPani15yzxM/ma//6LJVDMU0mbXC5Ov9ny3JmiQPWeF+D+3vU/3776xwPwAAAAAAptiVO+23pPkPfd3p+er5SpMumKnCpLX23STn928ryYuravvl7FVV65K8KPNPl/y0tfa1lacEAAAAAGBb8vDXnz7pCIzBTBUmfaekV5a0JAclefsy93l7koMH9nrnSNIBAAAAALDN+c6PL590BFbZLBYmJya5on9dSR5dVadV1UGLWVxVB1fVaUkelfmnS65K8spRBwUAAAAAYNtwypnnTToCq2ztpAMsVWvt0qo6Pskb0ys8Ksm9kvxXVX04yb8n+c8kF6VXrOycZK8kd0nyoCTHpFcUzZ1b0pIc31q7aJz/DgAAAAAAZscPLrly0hFYZTNXmCRJa+3vq+qwJH+a+adE1iR5cP+zNXOv4JorW/62tfam1coKAAAAAMD02GOndctat9/uG0achGkzi6/kSpK01p6V5NlJrst8CZL+9ZY+yXxRcm2SP2mtPWeMsQEAAAAAmKB9Ni6v+Dj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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "%matplotlib inline\n",
    "%config IPython.matplotlib.backend = \"retina\"\n",
    "from matplotlib import rcParams\n",
    "rcParams[\"savefig.dpi\"] = 300\n",
    "rcParams[\"figure.dpi\"] = 300\n",
    "\n",
    "savefile='Binary_ensemble_testdat_avmedcomp.png'\n",
    "\n",
    "gt=model_output['test']['gt']\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "labels=['PL']\n",
    "P, R, _ =precision_recall_curve(gt,model_output['test']['med'])\n",
    "steps=plt.step(R, P,linewidth=2.5,label='median',alpha=0.75)\n",
    "P, R, _ =precision_recall_curve(gt,model_output['test']['av'])\n",
    "plt.step(R, P,linewidth=2.5,label='average',linestyle='--',color=steps[0].get_color(),alpha=0.75)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlim(0.0,1.05)\n",
    "plt.ylim(0.2,1.05)\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "savefile='Binary_ensemble_testdat_avmedcomp_2'\n",
    "plt.savefig(path.join('/home/hosborn/TESS/PaperFigures',savefile+'_PR_2.png'))\n",
    "plt.savefig(path.join('/home/hosborn/TESS/PaperFigures',savefile+'_PR_2.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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