mcmix/sepsisRep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8087514-1220-4e1a-afed-61d0d9151a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports \n",
    "\n",
    "\n",
    "import sys\n",
    "# setting path\n",
    "sys.path.append('../core')\n",
    "import os\n",
    "#os.environ[\"VECLIB_MAXIMUM_THREADS\"] = \"10\"\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "#bread and butter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#machine learning library\n",
    "import sklearn\n",
    "import sklearn.manifold\n",
    "import sklearn.cluster\n",
    "import sklearn.decomposition\n",
    "\n",
    "#statistics\n",
    "import scipy\n",
    "from scipy.stats import rankdata, norm\n",
    "\n",
    "import numba\n",
    "from numba import jit, njit\n",
    "\n",
    "from tqdm import tqdm\n",
    "import copy\n",
    "import cvxpy as cp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline\n",
    "#import numpy as tnp\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# import dfply\n",
    "# from dfply import *\n",
    "# import ray\n",
    "# import datetime\n",
    "\n",
    "\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "#import torch\n",
    "#torch.cuda.is_available()\n",
    "import tensorflow as tf\n",
    "print(tf.config.list_physical_devices('GPU'))\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import scipy\n",
    "\n",
    "import confound_mdp\n",
    "import confound_ope\n",
    "import confound_env\n",
    "import copy\n",
    "import pickle\n",
    "\n",
    "from subspace import *\n",
    "from clustering import *\n",
    "from emalg import *\n",
    "from helpers import *\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('matplotlibrc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8db023-6d82-402f-9571-b3d06754906a",
   "metadata": {},
   "source": [
    "Simplifications to Sepsis: (1) Rewards no longer dependent on diabetes or no diabetes (averaged across them), (2) Initial state distribution now common across diabetes/no diabetes (averaged across them).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2488600e-77b7-410a-bb8c-d159a01ff800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def idx_to_trunc(state_idx, hideGlucose=True, sa=False):\n",
    "    \"\"\"idx_to_trunc\n",
    "\n",
    "    The state index is determined by using \"bit\" arithmetic, with the\n",
    "    complication that not every state is binary\n",
    "\n",
    "    :param state_idx: Given index (720)\n",
    "    \n",
    "    Returns a truncated state where the \"action states\", \n",
    "    antibiotics, vasopressors and ventilators, are omitted\n",
    "    \"\"\"\n",
    "    term_base = 720/3 #starts with heart rate\n",
    "\n",
    "    # Start with the given state index\n",
    "    mod_idx = state_idx\n",
    "\n",
    "    hr_state = np.floor(mod_idx/term_base).astype(int)\n",
    "\n",
    "    mod_idx %= term_base\n",
    "    term_base /= 3\n",
    "    sysbp_state = np.floor(mod_idx/term_base).astype(int)\n",
    "\n",
    "    mod_idx %= term_base\n",
    "    term_base /= 2\n",
    "    percoxyg_state = np.floor(mod_idx/term_base).astype(int)\n",
    "\n",
    "    mod_idx %= term_base\n",
    "    term_base /= 5\n",
    "    glucose_state = np.floor(mod_idx/term_base).astype(int)\n",
    "    \n",
    "    categ_num = np.array([3,3,2,5])\n",
    "    state_categs = [\n",
    "            hr_state,\n",
    "            sysbp_state,\n",
    "            percoxyg_state,\n",
    "            glucose_state]\n",
    "    if hideGlucose:\n",
    "        mod_idx %= term_base\n",
    "        term_base /= 2\n",
    "        antibiotic_state = np.floor(mod_idx/term_base).astype(int)\n",
    "\n",
    "        mod_idx %= term_base\n",
    "        term_base /= 2\n",
    "        vaso_state = np.floor(mod_idx/term_base).astype(int)\n",
    "\n",
    "        mod_idx %= term_base\n",
    "        term_base /= 2\n",
    "        vent_state = np.floor(mod_idx/term_base).astype(int)\n",
    "        categ_num = np.array([3,3,2,2,2,2])\n",
    "        state_categs = [\n",
    "                hr_state,\n",
    "                sysbp_state,\n",
    "                percoxyg_state,\n",
    "                antibiotic_state,\n",
    "                vaso_state,\n",
    "                vent_state]\n",
    "    sum_idx = 0\n",
    "    prev_base = 1\n",
    "    for i in range(len(state_categs)):\n",
    "        idx = len(state_categs) - 1 - i\n",
    "        sum_idx += prev_base*state_categs[idx]\n",
    "        prev_base *= categ_num[idx]\n",
    "        \n",
    "    if sa:\n",
    "        mod_idx %= term_base\n",
    "        term_base /= 2\n",
    "        antibiotic_state = np.floor(mod_idx/term_base).astype(int)\n",
    "\n",
    "        mod_idx %= term_base\n",
    "        term_base /= 2\n",
    "        vaso_state = np.floor(mod_idx/term_base).astype(int)\n",
    "\n",
    "        mod_idx %= term_base\n",
    "        term_base /= 2\n",
    "        vent_state = np.floor(mod_idx/term_base).astype(int)\n",
    "        action = 4*antibiotic_state + 2*vaso_state + 2*vent_state\n",
    "        return sum_idx, action\n",
    "    else:\n",
    "        return sum_idx\n",
    "\n",
    "idx_to_trunc(719, sa=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce8a6700-36eb-40ae-86b0-a4cc98dfb1b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "diagnostic = False\n",
    "horizons = [20, 40, 60, 80, 100, 120, 140, 160, 180, 200]\n",
    "#horizons = [180]\n",
    "\n",
    "trials = 30\n",
    "hideGlucose = True\n",
    "import multiprocessing\n",
    "\n",
    "\n",
    "clusterAccsTrials = []\n",
    "clusterAccsNoProjsTrials = []\n",
    "classAccsTrials = []\n",
    "hardClustEMAccsTrials = []\n",
    "softClustEMAccsTrials = []\n",
    "hardEMAccsTrials = []\n",
    "softEMAccsTrials = []\n",
    "\n",
    "for trial in range(trials):\n",
    "    clusterAccs = []\n",
    "    clusterAccsNoProjs = []\n",
    "    modelEstims = []\n",
    "    classAccs = []\n",
    "    hardClustEMs = []\n",
    "    hardClustEMAccs = []\n",
    "    softClustEMs = []\n",
    "    softClustEMAccs = []\n",
    "    hardEMs = []\n",
    "    hardEMAccs = []\n",
    "    softEMs = []\n",
    "    softEMAccs = []\n",
    "    \n",
    "    start_seed = trial*multiprocessing.cpu_count()\n",
    "    print(\"Trial\", trial, \"with start seed\", start_seed)\n",
    "\n",
    "    for horizon in horizons:\n",
    "        print(\"Current Horizon is:\", horizon)\n",
    "\n",
    "        #########SETUP#########\n",
    "\n",
    "        # Get the transition and reward matrix from file\n",
    "        with open(\"./data/diab_txr_mats-replication.pkl\", \"rb\") as f:\n",
    "            mdict = pickle.load(f)\n",
    "\n",
    "        envs = []\n",
    "\n",
    "        P = mdict[\"tx_mat\"]\n",
    "        x_dist = mdict[\"p_initial_state\"].mean(0)\n",
    "        u_dist = mdict[\"p_mixture\"]\n",
    "        gamma = 1\n",
    "\n",
    "\n",
    "        #nStates = P.shape[2]\n",
    "        nActions = P.shape[1]\n",
    "\n",
    "        R = mdict[\"r_mat\"].mean(0)\n",
    "\n",
    "        pi_b = np.load('data/sepsisPol.npy')\n",
    "        pi_e = pi_b.mean(0)\n",
    "\n",
    "        term = -1\n",
    "        mdp = confound_mdp.ConfoundMDP(P, R, x_dist, u_dist, gamma)\n",
    "\n",
    "\n",
    "        dataset = getSamplesMultiProc(500, mdp, pi_b, horizon, start_seed=start_seed, iid=False)\n",
    "\n",
    "\n",
    "\n",
    "        for m in tqdm(range(dataset.shape[0])):\n",
    "            for t in range(dataset.shape[1]):\n",
    "                #s, a, u, s', r\n",
    "                dataset[m,t,0] = idx_to_trunc(dataset[m,t,0], hideGlucose=hideGlucose)\n",
    "                dataset[m,t,3] = idx_to_trunc(dataset[m,t,3], hideGlucose=hideGlucose)\n",
    "\n",
    "\n",
    "\n",
    "         # behavior value\n",
    "        print(\"value of pi_b\")\n",
    "        returns = confound_mdp.calc_returns(dataset, gamma, horizon)\n",
    "        print(returns.mean())\n",
    "\n",
    "\n",
    "\n",
    "        nStates = int(720/8) if not hideGlucose else int(720/(5))\n",
    "\n",
    "        stateactions = dataset[:, :, [0, 1]]\n",
    "        memorder = 'C'\n",
    "\n",
    "        if memorder == 'F':\n",
    "            stateactions = (dataset[:,:,0] + dataset[:,:,1]*nStates).astype(int) #we code (S x A) as s + a|S|, 0,16,32,48 1,17,33,49\n",
    "        else:\n",
    "            stateactions = (dataset[:,:,0]*nActions + dataset[:,:,1]).astype(int)\n",
    "        states = (dataset[:,:,0]).astype(int)\n",
    "        actions = (dataset[:,:,1]).astype(int)\n",
    "        currstates = dataset[:,:,[0,1]].astype(int)\n",
    "        confounders = dataset[:,:,2].astype(int)\n",
    "        labels = confounders[:,0].astype(int)\n",
    "        nextstates = dataset[:,:,3].astype(int)\n",
    "\n",
    "        L = 2\n",
    "        K = 2\n",
    "        #S = nStates*nActions\n",
    "        #N = len(threes)\n",
    "\n",
    "\n",
    "        ########SUBSPACE ESTIMATION########\n",
    "\n",
    "        sadim = nStates*nActions\n",
    "        spdim = nStates\n",
    "        omegaone = np.array([i for i in range(int(horizon/4), 2*int(horizon/4))])\n",
    "        omegatwo = np.array([i for i in range(3*int(horizon/4), horizon)])\n",
    "        onehotsa = np.eye(sadim)[stateactions]\n",
    "        #onehotsaflat = copy.deepcopy(onehotsa)\n",
    "        onehotsa = onehotsa.reshape((onehotsa.shape[0], onehotsa.shape[1], nStates, nActions), order=memorder)\n",
    "        onehotsp = np.eye(np.max(nextstates)+1)[nextstates]\n",
    "        sz = int(onehotsa.shape[0]/3)\n",
    "\n",
    "        eigvalsa, eigvecsa = getEig(onehotsa[:sz], onehotsp[:sz], omegaone, omegatwo, K, wt=True, smalldata=False, device='/GPU:0')\n",
    "\n",
    "        #########CLUSTERING#########\n",
    "\n",
    "        device = \"/GPU:0\"\n",
    "\n",
    "        hs = np.array([geth(onehotsa[sz:,omegaone,:,:], \n",
    "                                         onehotsp[sz:,omegaone,:]), \n",
    "                                  geth(onehotsa[sz:,omegatwo,:,:], \n",
    "                                       onehotsp[sz:,omegatwo,:])], dtype=np.float32)\n",
    "\n",
    "        statmns = computeStat(hs, \n",
    "                          eigvecsa, numpy=False, smalldata=False, device=device, proj=True)\n",
    "        \n",
    "        statNoProjs = computeStat(hs, \n",
    "                          eigvecsa, numpy=False, smalldata=False, device=device, proj=False)\n",
    "        if diagnostic:\n",
    "            hi = 0.00005 if hideGlucose else 0.00012\n",
    "            clusterDiagnostics(statmns, K=K, labels=confounders[sz:, 0],\n",
    "                           lo=0, hi=hi, step=0.02*hi) #0,0.01, 0.0001\n",
    "            plt.show()\n",
    "            clusterDiagnostics(statNoProjs, K=K, labels=confounders[sz:, 0],\n",
    "                       lo=0, hi=hi, step=0.02*hi) #0,0.01, 0.0001\n",
    "            plt.show()\n",
    "\n",
    "\n",
    "        thresh = 2e-5 if hideGlucose else 0.00003 #0.0005\n",
    "        clusterlabs = sklearn.cluster.spectral_clustering((statmns < thresh).astype(int), n_clusters=2,\n",
    "                                                         assign_labels='kmeans')\n",
    "\n",
    "        clusterlabsNoProjs = sklearn.cluster.spectral_clustering((statNoProjs < thresh).astype(int), n_clusters=2,\n",
    "                                                         assign_labels='kmeans')\n",
    "\n",
    "        permutation = np.argmax([np.mean(clusterlabs == confounders[sz:, 0]), \n",
    "                        np.mean(clusterlabs != confounders[sz:, 0])])\n",
    "        if diagnostic:\n",
    "            print(permutation, np.max([np.mean(clusterlabs == confounders[sz:, 0]), \n",
    "                        np.mean(clusterlabs != confounders[sz:, 0])]))\n",
    "        clusterAccs.append(np.max([np.mean(clusterlabs == confounders[sz:, 0]), \n",
    "                        np.mean(clusterlabs != confounders[sz:, 0])]))\n",
    "\n",
    "        clusterAccsNoProjs.append(np.max([np.mean(clusterlabsNoProjs == confounders[sz:, 0]), \n",
    "                        np.mean(clusterlabsNoProjs != confounders[sz:, 0])]))\n",
    "\n",
    "        ########MODEL ESTIMATION#########\n",
    "        Phat_ksa = getModelEstim(clusterlabs.astype(int), \n",
    "                             states[sz:,:], \n",
    "                             actions[sz:,:], \n",
    "                             nextstates[sz:,:],\n",
    "                            K=K, nStates=nStates, nActions=nActions, hard=True)\n",
    "        modelEstims.append(Phat_ksa)\n",
    "\n",
    "\n",
    "        ########CLASSIFICATION#########\n",
    "        priorclass = np.bincount(clusterlabs)/len(clusterlabs) \n",
    "        piclust = getPolicyEstim(states[sz:], actions[sz:], \n",
    "                                 K, nStates, nActions, preds=clusterlabs, hard=True)\n",
    "        startweights = getStartWeights(states[sz:], clusterlabs, K, nStates)\n",
    "        subspaceClass = classify(Phat_ksa, states[:sz,:], actions[:sz,:], \n",
    "                               nextstates[:sz,:], piclust, reg=1, \n",
    "                           prior=priorclass, startweights = startweights, labs=True)\n",
    "        if diagnostic:\n",
    "            print('Subspace Classification accuracy:', [np.mean(labels[:sz] == subspaceClass), \n",
    "                                                    np.mean(labels[:sz] != subspaceClass)][permutation])\n",
    "        classAccs.append([np.mean(labels[:sz] == subspaceClass), np.mean(labels[:sz] != subspaceClass)][permutation])\n",
    "\n",
    "\n",
    "        ########EM ALGORITHM########\n",
    "        priorclass = np.bincount(clusterlabs)/len(clusterlabs) \n",
    "        piclust = getPolicyEstim(states[sz:], actions[sz:], \n",
    "                                 K, nStates, nActions, preds=clusterlabs, hard=True)\n",
    "\n",
    "        startweights = getStartWeights(states[sz:], clusterlabs, K, nStates)\n",
    "        maxapos = classify(Phat_ksa, states, actions, \n",
    "                               nextstates, piclust, reg=1, \n",
    "                           prior=priorclass, startweights = startweights, labs=True)\n",
    "        mleprobs = classify(Phat_ksa, states, actions, \n",
    "                               nextstates, piclust, reg=1, \n",
    "                            prior=priorclass, startweights = startweights, labs=False)\n",
    "        if diagnostic:\n",
    "            print('MAP estimate overall accuracy:', [np.mean(labels == maxapos), np.mean(labels != maxapos)][permutation])\n",
    "\n",
    "        # HARD EM\n",
    "        expectclass, modelestimclass, loglikclass = em(maxapos, \n",
    "                                Phat_ksa, states, actions, \n",
    "                            nextstates, labels=labels, \n",
    "                            K=K, nStates=nStates, nActions=nActions,\n",
    "                            prior = priorclass, reg = 1, max_iter=100,\n",
    "                                                   permute=False, permutation=permutation,\n",
    "                                                   checkin=1, hard=True, verbose=False)\n",
    "        hardClustEMs.append([expectclass, modelestimclass, loglikclass])\n",
    "        hardClustEMAccs.append(np.max([np.mean(labels == expectclass), np.mean(labels != expectclass)]))\n",
    "\n",
    "        # SOFT EM\n",
    "        normprobs = np.exp(mleprobs-np.max(mleprobs))\n",
    "        regprobs = 0.8*(normprobs)/np.nansum(np.abs(normprobs), 0) + 0.1*np.ones(mleprobs.shape)\n",
    "        expectclasssoft, modelestimclasssoft, loglikclasssoft = em(regprobs, \n",
    "                                    Phat_ksa, states, actions, \n",
    "                                nextstates, labels, \n",
    "                                K=2, nStates=nStates, nActions=nActions,\n",
    "                                prior = priorclass, reg = 1, permute=False,\n",
    "                                permutation=permutation, checkin=1, hard=False, verbose=False)\n",
    "        softClustEMs.append([expectclasssoft, modelestimclasssoft, loglikclasssoft])\n",
    "        softClustEMAccs.append(np.max([np.mean(labels == expectclasssoft.argmax(0)),\n",
    "                                np.mean(labels != expectclasssoft.argmax(0))]))\n",
    "\n",
    "        #############BASE EM###############\n",
    "\n",
    "        labelsem = []\n",
    "        logliksem = []\n",
    "        for i in tqdm(range(30)):\n",
    "            randlabs = np.random.binomial(1, 0.5, size=len(dataset)).astype(int)\n",
    "            randmodel = getModelEstim(randlabs, states, actions, nextstates,\n",
    "                                      K, nStates, nActions, hard=True)\n",
    "            randlabs, randmodel, loglikrand = em(randlabs, randmodel,\n",
    "               states, actions, nextstates, labels, \n",
    "                                K=K, nStates=nStates, nActions=nActions,\n",
    "                                prior = priorclass, reg = 0, permute=True, permutation=permutation, checkin=1, verbose=False, hard=True)\n",
    "            labelsem.append(randlabs)\n",
    "            logliksem.append(loglikrand)\n",
    "        if diagnostic:\n",
    "            plt.figure(figsize=(12,7))\n",
    "            plt.scatter([max(np.mean(i == labels), np.mean(i != labels)) for i in labelsem], logliksem)\n",
    "            print(np.mean([max(np.mean(i == labels), np.mean(i != labels)) for i in labelsem]))\n",
    "            print(np.median([max(np.mean(i == labels), np.mean(i != labels)) for i in labelsem]))\n",
    "            print(np.max([max(np.mean(i == labels), np.mean(i != labels)) for i in labelsem]))\n",
    "            plt.xlabel('Accuracy')\n",
    "            plt.ylabel('Log-likelihood')\n",
    "            plt.title('Randomly Initialized Hard EM Algorithm')\n",
    "            plt.show()\n",
    "            plt.hist([max(np.mean(i == labels), np.mean(i != labels)) for i in labelsem])\n",
    "            plt.show()\n",
    "        hardEMAccs.append(np.mean([max(np.mean(i == labels), np.mean(i != labels)) for i in labelsem]))\n",
    "        hardEMs.append([labelsem, logliksem])\n",
    "\n",
    "        labelsemsoft = []\n",
    "        logliksemsoft = []\n",
    "        for i in tqdm(range(30)):\n",
    "            unifs = np.random.uniform(size=len(dataset))\n",
    "            randlabsoft = np.vstack([unifs, 1-unifs])\n",
    "            randmodelsoft = getModelEstim(randlabsoft, states, actions, nextstates,\n",
    "                                      K, nStates, nActions, hard=False)\n",
    "            randlabsoft, randmodelsoft, loglikrand = em(randlabsoft, randmodelsoft,\n",
    "           states, actions, nextstates, labels, \n",
    "                                K=K, nStates=nStates, nActions=nActions,\n",
    "                                prior = priorclass, reg = 0, permute=True, permutation=permutation, checkin=1, verbose=False, hard=False)\n",
    "            labelsemsoft.append(randlabsoft)\n",
    "            logliksemsoft.append(loglikrand)\n",
    "        if diagnostic:\n",
    "            plt.figure(figsize=(12,7))\n",
    "            plt.scatter([max(np.mean(i == labels), np.mean(i != labels)) for i in np.array(labelsemsoft).argmax(axis=1)], \n",
    "                        logliksemsoft)\n",
    "            print(np.mean([max(np.mean(i == labels), np.mean(i != labels)) for i in np.array(labelsemsoft).argmax(axis=1)]))\n",
    "            print(np.median([max(np.mean(i == labels), np.mean(i != labels)) for i in np.array(labelsemsoft).argmax(axis=1)]))\n",
    "            print(np.max([max(np.mean(i == labels), np.mean(i != labels)) for i in np.array(labelsemsoft).argmax(axis=1)]))\n",
    "            plt.ticklabel_format(style='plain',useOffset=False)\n",
    "            plt.xlabel('Accuracy')\n",
    "            plt.ylabel('Log-likelihood')\n",
    "            plt.title('Randomly Initialized Soft EM Algorithm')\n",
    "            plt.show()\n",
    "            plt.hist([max(np.mean(i == labels), np.mean(i != labels)) for i in np.array(labelsemsoft).argmax(axis=1)])\n",
    "            plt.show()\n",
    "        softEMAccs.append(np.mean([max(np.mean(i == labels), np.mean(i != labels)) for i in np.array(labelsemsoft).argmax(axis=1)]))\n",
    "        softEMs.append([labelsemsoft, logliksemsoft])\n",
    "        \n",
    "    clusterAccsTrials.append(clusterAccs)\n",
    "    clusterAccsNoProjsTrials.append(clusterAccsNoProjs)\n",
    "    classAccsTrials.append(classAccs)\n",
    "    hardClustEMAccsTrials.append(hardClustEMAccs)\n",
    "    softClustEMAccsTrials.append(softClustEMAccs)\n",
    "    hardEMAccsTrials.append(hardEMAccs)\n",
    "    softEMAccsTrials.append(softEMAccs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19124c6f-833f-403d-94bb-634655de813d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#try hidden glucose and actions in state space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "602e92de-4831-4c37-9d00-5d72bc52c1ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.771875, 0.984375])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(clusterAccs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d7b90646-803d-4306-820b-9057fc204939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(16,9))\n",
    "plt.plot(horizons, 1-np.mean(np.array(clusterAccsTrials),0), label='With Subspace')\n",
    "plt.plot(horizons, 1-np.mean(np.array(clusterAccsNoProjsTrials),0), label='Clustering Error No Subspace')\n",
    "plt.ylabel(\"Clustering Error\")\n",
    "plt.xlabel(\"Trajectory Length\")\n",
    "plt.title(\"Clustering Error vs Trajectory Length, Sepsis\")\n",
    "plt.legend()\n",
    "plt.savefig('figs/clustering.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "9ea41fcd-386c-4968-833b-718aaa669fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(16,9))\n",
    "'''\n",
    "plt.plot(horizons, 1-np.median(np.array(hardClustEMAccsTrials),0),\n",
    "         label='Hard EM Cluster Init')\n",
    "plt.plot(horizons, 1-np.median(np.array(hardEMAccsTrials),0),\n",
    "        label='Hard EM')\n",
    "'''       \n",
    "plt.plot(horizons, 1-np.mean(np.array(softClustEMAccsTrials),0),\n",
    "         label='Soft EM Cluster Init')\n",
    "plt.plot(horizons, 1-np.mean(np.array(softEMAccsTrials),0),\n",
    "        label='Soft EM Random Init')\n",
    "plt.ylabel(\"Clustering Error\")\n",
    "plt.xlabel(\"Trajectory Length\")\n",
    "plt.title(\"Clustering Performance Comparison\")\n",
    "plt.legend()\n",
    "plt.savefig('figs/emcomp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd35dac1-855f-45d7-a66b-67ffd407f4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7febc7d9f790>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.plot(horizons, 1-np.mean(np.array(clusterAccsTrials),0), label='With Subspace')\n",
    "plt.plot(horizons, 1-np.mean(np.array(clusterAccsNoProjsTrials),0), label='Clustering Error No Subspace')\n",
    "'''\n",
    "plt.plot(horizons, 1-np.array(classAccs), label='Subspace Classification')\n",
    "plt.plot(horizons, 1-np.array([np.max([np.mean(i[0]==labels), \n",
    "                  np.mean(i[0]!=labels)]) for i in hardClustEMs]),\n",
    "         label='Hard EM Cluster Init')\n",
    "plt.plot(horizons, 1-np.array([np.mean([np.max([np.mean(j.argmax(0)==labels), np.mean(j.argmax(0)!=labels)]) \n",
    "  for j in hardEMs[i][0]]) for i in range(len(hardEMs))]),\n",
    "        label='Hard EM')\n",
    "'''\n",
    "plt.plot(horizons, 1-np.mean(np.array(softClustEMAccsTrials),0),\n",
    "         label='Soft EM Cluster Init')\n",
    "plt.plot(horizons, 1-np.mean(np.array(softEMAccsTrials),0),\n",
    "        label='Soft EM')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "base"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}