From 78beb2ba4ed119df9085d3e3ed2acf1c04c4ef09 Mon Sep 17 00:00:00 2001
From: Chirag Nagpal <nagpalchirag1994@gmail.com>
Date: Tue, 29 Mar 2022 16:32:24 -0400
Subject: [PATCH] Delete Demo of Cox Mixtures with Heterogenous Effects.ipynb

---
 ...x Mixtures with Heterogenous Effects.ipynb | 498 ------------------
 1 file changed, 498 deletions(-)
 delete mode 100644 examples/Demo of Cox Mixtures with Heterogenous Effects.ipynb

diff --git a/examples/Demo of Cox Mixtures with Heterogenous Effects.ipynb b/examples/Demo of Cox Mixtures with Heterogenous Effects.ipynb
deleted file mode 100644
index 2a9b0e2..0000000
--- a/examples/Demo of Cox Mixtures with Heterogenous Effects.ipynb	
+++ /dev/null
@@ -1,498 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "\n",
-    "from auton_survival.datasets import load_dataset\n",
-    "from auton_survival import DeepCoxMixturesHeterogenousEffects\n",
-    "\n",
-    "outcomes, features, interventions = load_dataset(dataset='SYNTHETIC') \n",
-    "\n",
-    "model = DeepCoxMixturesHeterogenousEffects(k=1, g=2, layers=[])\n",
-    "\n",
-    "model.fit(features.values, outcomes.time.values, outcomes.event.values, interventions.values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NotImplementedError",
-     "evalue": "iLocation based boolean indexing on an integer type is not available",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/4_/4lmxprs947qfn4f1v92rnp_c0000gn/T/ipykernel_52832/3653944730.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mphi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutcomes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mplot_kaplanmeier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutcomes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moutcomes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZeta\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mphi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterventions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moutcomes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZeta\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mphi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1552\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1553\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1393\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"index\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1394\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"integer\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1395\u001b[0;31m                     raise NotImplementedError(\n\u001b[0m\u001b[1;32m   1396\u001b[0m                         \u001b[0;34m\"iLocation based boolean \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1397\u001b[0m                         \u001b[0;34m\"indexing on an integer type \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNotImplementedError\u001b[0m: iLocation based boolean indexing on an integer type is not available"
-     ]
-    }
-   ],
-   "source": [
-    "from auton_survival.reporting import plot_kaplanmeier\n",
-    "\n",
-    "for phi in set(outcomes.Zeta):\n",
-    "\tplot_kaplanmeier(outcomes[outcomes.Zeta==phi], interventions[outcomes.Zeta==phi])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>event</th>\n",
-       "      <th>time</th>\n",
-       "      <th>uncensored time treated</th>\n",
-       "      <th>uncensored time control</th>\n",
-       "      <th>Z</th>\n",
-       "      <th>Zeta</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>3.238760</td>\n",
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-       "      <td>2.558462</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <th>3</th>\n",
-       "      <td>True</td>\n",
-       "      <td>5.582874</td>\n",
-       "      <td>5.582874</td>\n",
-       "      <td>4.593198</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>True</td>\n",
-       "      <td>6.675284</td>\n",
-       "      <td>3.477266</td>\n",
-       "      <td>6.675284</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>...</th>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "      <th>4995</th>\n",
-       "      <td>True</td>\n",
-       "      <td>8.066019</td>\n",
-       "      <td>9.611860</td>\n",
-       "      <td>8.066019</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <th>4996</th>\n",
-       "      <td>True</td>\n",
-       "      <td>2.638571</td>\n",
-       "      <td>2.638571</td>\n",
-       "      <td>11.376284</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4997</th>\n",
-       "      <td>True</td>\n",
-       "      <td>6.729638</td>\n",
-       "      <td>6.729638</td>\n",
-       "      <td>1.628789</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4998</th>\n",
-       "      <td>True</td>\n",
-       "      <td>0.713366</td>\n",
-       "      <td>3.490827</td>\n",
-       "      <td>0.713366</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>4999</th>\n",
-       "      <td>False</td>\n",
-       "      <td>1.637384</td>\n",
-       "      <td>2.764871</td>\n",
-       "      <td>5.568144</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5000 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      event      time  uncensored time treated  uncensored time control    Z  \\\n",
-       "0      True  3.238760                 3.238760                 0.009182  1.0   \n",
-       "1      True  3.493217                 3.493217                 6.307030  2.0   \n",
-       "2      True  2.558462                 1.476810                 2.558462  1.0   \n",
-       "3      True  5.582874                 5.582874                 4.593198  1.0   \n",
-       "4      True  6.675284                 3.477266                 6.675284  0.0   \n",
-       "...     ...       ...                      ...                      ...  ...   \n",
-       "4995   True  8.066019                 9.611860                 8.066019  1.0   \n",
-       "4996   True  2.638571                 2.638571                11.376284  0.0   \n",
-       "4997   True  6.729638                 6.729638                 1.628789  0.0   \n",
-       "4998   True  0.713366                 3.490827                 0.713366  1.0   \n",
-       "4999  False  1.637384                 2.764871                 5.568144  0.0   \n",
-       "\n",
-       "      Zeta  \n",
-       "0      0.0  \n",
-       "1      0.0  \n",
-       "2      0.0  \n",
-       "3      0.0  \n",
-       "4      1.0  \n",
-       "...    ...  \n",
-       "4995   1.0  \n",
-       "4996   1.0  \n",
-       "4997   0.0  \n",
-       "4998   0.0  \n",
-       "4999   1.0  \n",
-       "\n",
-       "[5000 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outcomes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/1 [00:00<?, ?it/s]\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "size mismatch, m1: [100 x 8], m2: [2 x 8] at /Users/distiller/project/conda/conda-bld/pytorch_1595629449223/work/aten/src/TH/generic/THTensorMath.cpp:41",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/4_/4lmxprs947qfn4f1v92rnp_c0000gn/T/ipykernel_52832/1988741118.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDeepCoxMixturesHeterogenousEffects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutcomes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutcomes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterventions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Research/auton-survival/examples/../auton_survival/models/cmhe/cmhe_api.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, t, e, a, vsize, val_data, iters, learning_rate, batch_size, optimizer, random_state)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gen_torch_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m     model, _ = train_cmhe(model,\n\u001b[0m\u001b[1;32m    154\u001b[0m                           \u001b[0;34m(\u001b[0m\u001b[0mx_tr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_tr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me_tr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_tr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                           \u001b[0;34m(\u001b[0m\u001b[0mx_vl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_vl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me_vl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_vl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Research/auton-survival/examples/../auton_survival/models/cmhe/cmhe_utilities.py\u001b[0m in \u001b[0;36mtrain_cmhe\u001b[0;34m(model, train_data, val_data, epochs, patience, vloss, bs, typ, lr, use_posteriors, debug, random_state, return_losses, update_splines_after, smoothing_factor)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;31m# train_step_start = time.time()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m     breslow_splines = train_step(model, xt, tt, et, at, breslow_splines,\n\u001b[0m\u001b[1;32m    269\u001b[0m                                  \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m                                  \u001b[0muse_posteriors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_posteriors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Research/auton-survival/examples/../auton_survival/models/cmhe/cmhe_utilities.py\u001b[0m in \u001b[0;36mtrain_step\u001b[0;34m(model, x, t, e, a, breslow_splines, optimizer, bs, seed, typ, use_posteriors, update_splines_after, smoothing_factor)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_likelihoods\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m     \u001b[0mepoch_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Research/auton-survival/examples/../auton_survival/models/cmhe/cmhe_utilities.py\u001b[0m in \u001b[0;36mm_step\u001b[0;34m(model, optimizer, x, t, e, a, log_likelihoods, typ)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m   \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m   \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_likelihoods\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m   \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m   \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Research/auton-survival/examples/../auton_survival/models/cmhe/cmhe_utilities.py\u001b[0m in \u001b[0;36mq_function\u001b[0;34m(model, x, t, e, a, log_likelihoods, typ)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0mzeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_hard_z\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzeta_posteriors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m   \u001b[0mgates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlrisks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m   \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    720\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    724\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Research/auton-survival/examples/../auton_survival/models/cmhe/cmhe_torch.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, a)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m     \u001b[0mlog_hrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mlogp_z_gate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogSoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz_gate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    720\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    724\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m   1674\u001b[0m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1675\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1676\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1677\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1678\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: size mismatch, m1: [100 x 8], m2: [2 x 8] at /Users/distiller/project/conda/conda-bld/pytorch_1595629449223/work/aten/src/TH/generic/THTensorMath.cpp:41"
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-       "\n",
-       "            X8  \n",
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-       "4995  0.207052  \n",
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