challenge_eval_folds_subject_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "spectacular-prevention",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set_context('paper')\n",
    "sns.set()\n",
    "import random\n",
    "from sklearn.manifold import TSNE\n",
    "from utils.utils import find_norm_params\n",
    "\n",
    "legend_size = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "yellow-rough",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_path:  data/challenge/s5/results_Mechanistic/\n"
     ]
    }
   ],
   "source": [
    "s = '5' # most from 4, 5 for 1,1, 2\n",
    "models = ['results_Mechanistic','results_MechanisticGauss']\n",
    "selected = models[0]\n",
    "data_path = \"data/challenge/s{}/{}/\".format(s, selected)\n",
    "\n",
    "shedding = np.load(data_path + 'shedding.npy')\n",
    "symptoms = np.load(data_path + 'symptoms.npy')\n",
    "y_bf = np.load(data_path + 'observations.npy')\n",
    "times = np.load(data_path + 'times.npy')\n",
    "\n",
    "\n",
    "mu_50_post_bf = np.load(data_path + 'mu_50_post.npy')\n",
    "mu_25_post_bf = np.load(data_path + 'mu_25_post.npy')\n",
    "mu_75_post_bf = np.load(data_path + 'mu_75_post.npy')\n",
    "\n",
    "x_post = np.load(data_path + 'solution_xt_post.npy')\n",
    "x_post = np.swapaxes(x_post,1,2)\n",
    "\n",
    "mu_50_prior_bf = np.load(data_path + 'mu_50_prior.npy')\n",
    "mu_25_prior_bf = np.load(data_path + 'mu_25_prior.npy')\n",
    "mu_75_prior_bf = np.load(data_path + 'mu_75_prior.npy')\n",
    "x_prior = np.load(data_path + 'solution_xt_prior.npy')\n",
    "x_prior = np.swapaxes(x_prior,1,2)\n",
    "\n",
    "print(\"data_path: \", data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "accepting-outreach",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 1)\n",
      "(7, 1)\n",
      "(7, 4, 142)\n",
      "(142,)\n",
      "(7, 4, 142)\n",
      "(7, 5, 142)\n",
      "(7, 2)\n"
     ]
    }
   ],
   "source": [
    "print(shedding.shape)\n",
    "print(symptoms.shape)\n",
    "print(y_bf.shape)\n",
    "print(times.shape)\n",
    "print(mu_75_prior_bf.shape)\n",
    "print(x_post.shape)\n",
    "outcomes = np.concatenate((shedding,symptoms), axis=1)\n",
    "print(outcomes.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fleet-embassy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find_norm_params:  (7, 142, 4)\n",
      "max_val:  [0.89978111 0.80684257 2.12642002 0.97729176] min_val:  [0.01899851 0.06201001 0.         0.10918851]\n",
      "(7, 4, 142)\n",
      "1.0 0.0\n",
      "0.8562329411506653 -0.0004997701616957784\n",
      "[[0. 0.]\n",
      " [1. 1.]\n",
      " [1. 1.]\n",
      " [1. 1.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "## Renormalize data\n",
    "\n",
    "data_norm_params = find_norm_params(np.swapaxes(y_bf,1,2)) \n",
    "min_val = data_norm_params[\"min\"]\n",
    "max_val = data_norm_params[\"max\"]\n",
    "\n",
    "\n",
    "def re_normalize(data):\n",
    "    sample = np.swapaxes(data,1,2)\n",
    "   # print(\"sample: \", sample.shape)\n",
    "    new_sample = np.zeros(sample.shape)\n",
    "    #print(\"before: \", new_sample.shape)\n",
    "    for feature in range(min_val.shape[0]):\n",
    "        new_sample[:,:, feature] = (sample[:, :, feature] - min_val[feature]) / (\n",
    "                    max_val[feature] - min_val[feature])\n",
    "        #print( new_sample[:, :, feature])\n",
    "    new_sample = np.swapaxes(new_sample,1,2)\n",
    "    #print(\"after: \", new_sample.shape)\n",
    "    return new_sample\n",
    "\n",
    "mu_50_post =  re_normalize(mu_50_post_bf)\n",
    "mu_25_post =  re_normalize(mu_25_post_bf)\n",
    "mu_75_post =  re_normalize(mu_75_post_bf)\n",
    "\n",
    "\n",
    "mu_50_prior = re_normalize(mu_50_prior_bf)\n",
    "mu_25_prior =  re_normalize(mu_25_prior_bf)\n",
    "mu_75_prior =  re_normalize(mu_75_prior_bf)\n",
    "\n",
    "y =  re_normalize(y_bf)\n",
    "\n",
    "\n",
    "print(mu_50_post.shape)\n",
    "print(np.max(y), np.min(y))\n",
    "print(np.max(mu_50_post), np.min(mu_50_post))\n",
    "\n",
    "print(outcomes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "apparent-cabinet",
   "metadata": {},
   "source": [
    "## Plot Shedding/Symptoms at all times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "parallel-scenario",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "from matplotlib import cm\n",
    "mu_50 = mu_50_post\n",
    "mu_25 = mu_25_post\n",
    "mu_75 = mu_75_post\n",
    "\n",
    "\n",
    "colors = [cm.rainbow(x) for x in np.linspace(0, 1, 4)] \n",
    "for pid in range(mu_50.shape[0]):\n",
    "    if pid +1 in [2, 3, 5, 6] and s=='4':\n",
    "        continue\n",
    "    elif pid +1 != 4 and s=='5':\n",
    "        continue\n",
    "        \n",
    "    fig, axs = plt.subplots(4, 1, sharex=True, sharey=True, figsize=(4, 8))\n",
    "    fs = 14\n",
    "    for r in range(4):\n",
    "        label =outcomes[pid]\n",
    "        axs[r].plot(times, mu_50[pid, :, :][r], '-', color=colors[r],  lw=2, alpha=0.75)\n",
    "        axs[r].plot(times, y[pid, :, :][r], '.', color=colors[r],\n",
    "                                   markersize=2, alpha=1)\n",
    "        axs[r].set_xlim(0.0, max(times) + 0.01)\n",
    "        axs[r].set_ylim(-0.05, 1.05)\n",
    "         \n",
    "        \n",
    "    rows = ['HR', 'TEMP', 'EDA', 'ACC']\n",
    "    cols = 'sh={}'.format(int(label[0])) + ', sx={}'.format(int(label[1]))\n",
    "#     for ax, col in zip(axs[0], cols):\n",
    "   \n",
    "\n",
    "\n",
    "    for ax, row in zip(axs[:], rows):\n",
    "        ax.set_ylabel(row, fontsize=fs)\n",
    "\n",
    "    fig.text(0, 0.5, \"Normalized output\", ha=\"center\", va=\"center\", rotation=90, fontsize=fs, fontweight='bold')\n",
    "    fig.text(0.5, 0, \"Time (hrs)\", ha=\"center\", va=\"bottom\", fontsize=fs, fontweight='bold')\n",
    "    fig.text(0.5, 1, cols, ha=\"center\", va=\"bottom\", fontsize=fs, fontweight='bold')\n",
    "    #fig.legend(bbox_to_anchor=(1.02,1), fontsize=fs-2)\n",
    "    fig.tight_layout()\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "instructional-alert",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "impressive-backing",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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