Time varying prototype

docs/notebooks/introduction.ipynb

@@ -33,7 +33,7 @@
   },
   {
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-   "execution_count": 2,
+   "execution_count": 1,
    "id": "013dbcb4",
    "metadata": {},
    "outputs": [],
@@ -45,7 +45,7 @@
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@@ -72,15 +72,15 @@
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    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CUDA-enabled GPU/TPU is available.\n"
+      "No CUDA-enabled GPU found, using CPU.\n"
      ]
     }
    ],
@@ -108,7 +108,7 @@
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@@ -226,7 +226,7 @@
        "4    no   73     Post     35     II       1       26      65   772     1"
       ]
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@@ -271,7 +271,7 @@
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    "outputs": [
@@ -400,7 +400,7 @@
        "4           0.0        1.0         0.0  "
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+     "execution_count": 5,
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@@ -417,7 +417,7 @@
   },
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+   "execution_count": 6,
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@@ -447,7 +447,7 @@
   },
   {
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+   "execution_count": 7,
    "id": "326c03fc-91f1-493b-a9ba-820de17fb2f8",
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@@ -466,18 +466,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "570386fb-f0ea-4061-bae2-11b274e7f851",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "x (shape)    = torch.Size([128, 9])\n",
+      "x (shape)    = torch.Size([32, 9])\n",
       "num_features = 9\n",
-      "event        = torch.Size([128])\n",
-      "time         = torch.Size([128])\n"
+      "event        = torch.Size([32])\n",
+      "time         = torch.Size([32])\n"
      ]
     }
    ],
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "9c2bd89a-c90a-4795-aab5-b5c21906a0de",
    "metadata": {},
    "outputs": [],
@@ -535,6 +535,109 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7d97e65d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is for testing the loss function\n",
+    "x_test, (test_event, test_time) = next(iter(dataloader_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5d102dad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_hz = cox_model(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "210e6755",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x_test torch.Size([32, 9])\n",
+      "events torch.Size([32])\n",
+      "times torch.Size([32])\n",
+      "\n",
+      "time_sorted torch.Size([32])\n",
+      "log_hz_sorted torch.Size([32, 1])\n",
+      "event_sorted torch.Size([32])\n",
+      "time_unique torch.Size([30])\n",
+      "------------------------------\n",
+      "covariates torch.Size([32, 9])\n",
+      "cov_inner torch.Size([32, 32])\n",
+      "log_nom_left torch.Size([1, 32])\n",
+      "bracket torch.Size([32, 9])\n",
+      "log_nom_right torch.Size([32, 32])\n",
+      "sum_nom torch.Size([1, 32])\n",
+      "log_denom torch.Size([1, 32])\n",
+      "last_bit torch.Size([1, 32])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.4683e+04, -2.9827e+03, -2.9461e+04, -4.0582e+04, -1.7949e+04,\n",
+       "         -1.4714e+05, -1.4940e+03, -7.7085e+04, -5.3855e+04, -9.3090e+03,\n",
+       "         -9.8543e+03, -1.8929e+05, -5.1617e+03, -4.4286e+03, -9.6604e+04,\n",
+       "         -1.5469e+04, -2.7680e+04, -6.3136e+04, -1.2045e+05, -9.3347e+04,\n",
+       "         -1.7911e+05, -1.3205e+05, -1.6203e+05, -3.0884e+04, -2.3050e+03,\n",
+       "         -2.1324e+05, -1.7852e+06, -1.7429e+04, -2.9495e+05, -8.4400e+03,\n",
+       "         -5.5583e+04,  1.2975e+05]], grad_fn=<DivBackward0>)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('x_test', x_test.shape)\n",
+    "print('events', test_event.shape)\n",
+    "print('times', test_time.shape)\n",
+    "\n",
+    "time_sorted, idx = torch.sort(time)\n",
+    "log_hz_sorted = log_hz[idx]\n",
+    "event_sorted = event[idx]\n",
+    "time_unique = torch.unique(time_sorted)\n",
+    "print('')\n",
+    "print(\"time_sorted\", time_sorted.shape)\n",
+    "print('log_hz_sorted', log_hz_sorted.shape)\n",
+    "print('event_sorted', event_sorted.shape)\n",
+    "print(\"time_unique\", time_unique.shape)\n",
+    "\n",
+    "print('-'*30)\n",
+    "cov_fake = torch.clone(x_test)\n",
+    "print('covariates', cov_fake.shape)\n",
+    "covariates_sorted = cov_fake[idx, :]\n",
+    "covariate_inner_product = torch.matmul(covariates_sorted, covariates_sorted.T)\n",
+    "print('cov_inner', covariate_inner_product.shape)\n",
+    "log_nominator_left = torch.matmul(log_hz_sorted.T, covariate_inner_product)\n",
+    "print('log_nom_left', log_nominator_left.shape)\n",
+    "bracket = torch.mul(log_hz_sorted, covariates_sorted)\n",
+    "print('bracket', bracket.shape)\n",
+    "log_nominator_right = torch.matmul(bracket, bracket.T)\n",
+    "print('log_nom_right', log_nominator_right.shape)\n",
+    "sum_nominator_right = torch.sum(log_nominator_right, dim=0).unsqueeze(0)\n",
+    "print('sum_nom', sum_nominator_right.shape)\n",
+    "log_denominator = torch.logcumsumexp(log_hz_sorted.flip(0), dim=0).flip(0).T\n",
+    "print('log_denom', log_denominator.shape)\n",
+    "last_bit = torch.div(log_nominator_left - sum_nominator_right, log_denominator)\n",
+    "print('last_bit', last_bit.shape)\n",
+    "last_bit\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "97c90244",
@@ -545,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "id": "d7889dc1-1cfa-424e-a586-481cbc789581",
    "metadata": {},
    "outputs": [
@@ -1446,7 +1549,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "torchsurv_env",
+   "display_name": "conda-env2",
    "language": "python",
    "name": "python3"
   },
@@ -1460,7 +1563,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
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