{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cox models: Introduction\n", "\n", "In this notebook we will train three Cox models with neural networks:\n", "\n", "- Cox CC: a proportional hazards model trainied with case-control sampling.\n", "- Cox DeepSurv: a proportional hazards model more or less identical to DeepSurv.\n", "- Cox-Time: a non-poportional case-control model. \n", "\n", "The Cox CC and DeepSurv models are very similar, with the Cox-Time model is a littel more flexible.\n", "\n", "We will use the METABRIC data sets as an example" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import torch\n", "from torch import nn\n", "from pycox import datasets\n", "from pycox.models import CoxCC, CoxPH, CoxTime\n", "from pycox.evaluation import EvalSurv\n", "from torchtuples import tuplefy, optim\n", "from torchtuples import callbacks as cb\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn_pandas import DataFrameMapper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`sklearn_pandas` can be installed with `! pip install sklearn-pandas`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "np.random.seed(1234)\n", "_ = torch.manual_seed(123)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataset\n", "\n", "We load the METABRIC data set and split in train, test and validation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df_train = datasets.metabric.read_df()\n", "df_test = df_train.sample(frac=0.2)\n", "df_train = df_train.drop(df_test.index)\n", "df_val = df_train.sample(frac=0.2)\n", "df_train = df_train.drop(df_val.index)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x0 x1 x2 x3 x4 x5 x6 x7 x8 \\\n", "0 5.603834 7.811392 10.797988 5.967607 1.0 1.0 0.0 1.0 56.840000 \n", "1 5.284882 9.581043 10.204620 5.664970 1.0 0.0 0.0 1.0 85.940002 \n", "3 6.654017 5.341846 8.646379 5.655888 0.0 0.0 0.0 0.0 66.910004 \n", "4 5.456747 5.339741 10.555724 6.008429 1.0 0.0 0.0 1.0 67.849998 \n", "5 5.425826 6.331182 10.455145 5.749053 1.0 1.0 0.0 1.0 70.519997 \n", "\n", " duration event \n", "0 99.333336 0 \n", "1 95.733330 1 \n", "3 239.300003 0 \n", "4 56.933334 1 \n", "5 123.533333 0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature transforms\n", "We have 9 covariates, in addition to the durations and event indicators.\n", "\n", "We will standardize the 5 numerical covariates, and leave the binary variables as is. As variables needs to be of type `'float32'`, as this is required by pytorch." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cols_standardize = ['x0', 'x1', 'x2', 'x3', 'x8']\n", "cols_leave = ['x4', 'x5', 'x6', 'x7']\n", "\n", "standardize = [([col], StandardScaler()) for col in cols_standardize]\n", "leave = [(col, None) for col in cols_leave]\n", "\n", "x_mapper = DataFrameMapper(standardize + leave)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "x_train = x_mapper.fit_transform(df_train).astype('float32')\n", "x_val = x_mapper.transform(df_val).astype('float32')\n", "x_test = x_mapper.transform(df_test).astype('float32')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The targets (durations and events) also needs to be arrays of type `'float32'`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "get_target = lambda df: (df['duration'].values.astype('float32'), df['event'].values.astype('float32'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "y_train = get_target(df_train)\n", "y_val = get_target(df_val)\n", "durations_test, events_test = get_target(df_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We collect the valiation data in a tuple with `tuplefy`. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "val = tuplefy(x_val, y_val) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This allows us to fake a larger validation data set by repeating the dataset multiple times. Here we just show how we can repeat the set 3 times" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((305, 9), ((305,), (305,)))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val.shapes()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((915, 9), ((915,), (915,)))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val.repeat(3).cat().shapes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural net\n", "\n", "We make a torch net. If this is new to you, we would recommend [the tutorials by PyTroch](https://pytorch.org/tutorials/).\n", "\n", "We create a simple MLP with two hidden layers, relu activations, batch norm and dropout.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "class NetMLP(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " input_size = 9\n", " nodes = 32\n", " self.block0 = nn.Sequential(nn.Linear(input_size, nodes), nn.ReLU(),\n", " nn.BatchNorm1d(nodes), nn.Dropout(0.1))\n", " self.block1 = nn.Sequential(nn.Linear(nodes, nodes), nn.ReLU(),\n", " nn.BatchNorm1d(nodes), nn.Dropout(0.1))\n", " self.out = nn.Linear(32, 1, bias=False)\n", "\n", " def forward(self, x):\n", " x = self.block0(x)\n", " x = self.block1(x)\n", " x = self.out(x)\n", " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have also added a similar network structure to this in `torchtuples.practical`.\n", "Uncomment the following to get a similar network" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# from torchtuples.practical import MLPVanilla\n", "# net = MLPVanilla(9, [32, 32], 1, True, 0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cox CC\n", "\n", "Now we can fit the model. We make the network and define the optimzer.\n", "\n", "The `torchtuples.optim.Adam` optimizer is a wrapper to `torch.optim.Adam` that allows us to add some extra functionallity. You can instead just used the `torch.optim` optimizers if desired." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "net = NetMLP()\n", "# net = MLPVanilla(9, [32, 32], 1, True, 0.1)\n", "optimizer = optim.Adam(lr=0.001)\n", "cox_cc = CoxCC(net, optimizer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We make an EarlyStopping callbacks that stopps the trainig progress when the validation loss has not improved the last 10 epochs.\n", "It also checkpoints the model, and loads the best performing model after finished iterating.\n", "\n", "We can now fit the Cox CC model, where we have added 10 replicates of our validation data (this makes the validation loss a little more stable)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0:\t[0s / 0s],\t\ttrain_loss: 0.7160,\tval_loss: 0.6922\n", "1:\t[0s / 0s],\t\ttrain_loss: 0.6799,\tval_loss: 0.6894\n", "2:\t[0s / 0s],\t\ttrain_loss: 0.6702,\tval_loss: 0.6845\n", "3:\t[0s / 0s],\t\ttrain_loss: 0.6677,\tval_loss: 0.6764\n", "4:\t[0s / 0s],\t\ttrain_loss: 0.6473,\tval_loss: 0.6704\n", "5:\t[0s / 0s],\t\ttrain_loss: 0.6445,\tval_loss: 0.6637\n", "6:\t[0s / 0s],\t\ttrain_loss: 0.6469,\tval_loss: 0.6666\n", "7:\t[0s / 0s],\t\ttrain_loss: 0.6245,\tval_loss: 0.6553\n", "8:\t[0s / 0s],\t\ttrain_loss: 0.6579,\tval_loss: 0.6583\n", "9:\t[0s / 0s],\t\ttrain_loss: 0.6385,\tval_loss: 0.6475\n", "10:\t[0s / 0s],\t\ttrain_loss: 0.6379,\tval_loss: 0.6565\n", "11:\t[0s / 0s],\t\ttrain_loss: 0.6145,\tval_loss: 0.6534\n", "12:\t[0s / 0s],\t\ttrain_loss: 0.6287,\tval_loss: 0.6531\n", "13:\t[0s / 0s],\t\ttrain_loss: 0.6428,\tval_loss: 0.6540\n", "14:\t[0s / 0s],\t\ttrain_loss: 0.6255,\tval_loss: 0.6538\n", "15:\t[0s / 0s],\t\ttrain_loss: 0.6212,\tval_loss: 0.6368\n", "16:\t[0s / 1s],\t\ttrain_loss: 0.6127,\tval_loss: 0.6398\n", "17:\t[0s / 1s],\t\ttrain_loss: 0.6075,\tval_loss: 0.6374\n", "18:\t[0s / 1s],\t\ttrain_loss: 0.6271,\tval_loss: 0.6568\n", "19:\t[0s / 1s],\t\ttrain_loss: 0.6230,\tval_loss: 0.6375\n", "20:\t[0s / 1s],\t\ttrain_loss: 0.6052,\tval_loss: 0.6452\n", "21:\t[0s / 1s],\t\ttrain_loss: 0.6185,\tval_loss: 0.6554\n", "22:\t[0s / 1s],\t\ttrain_loss: 0.6026,\tval_loss: 0.6469\n", "23:\t[0s / 1s],\t\ttrain_loss: 0.6311,\tval_loss: 0.6549\n", "24:\t[0s / 1s],\t\ttrain_loss: 0.6184,\tval_loss: 0.6544\n", "25:\t[0s / 1s],\t\ttrain_loss: 0.6157,\tval_loss: 0.6554\n" ] } ], "source": [ "es = cb.EarlyStopping()\n", "log = cox_cc.fit(x_train, y_train, batch_size=128, epochs=60, callbacks=[es], val_data=val.repeat(10).cat())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = log.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to investigate the partial log-likelihood:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-5.000345706939697" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cox_cc.partial_log_likelihood(*val).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prediction\n", "\n", "We can call the method `predict_survival_function` to obtain survival predicitons, however we first need to fit the non-parametric baseline hazard function. \n", "As compuation of baseline hazards can be quite computational for larger data sets, you need to call this explicitly (on e.g. a subset of the training set). \n", "If no arguments are passed to the `compute_baseline_hazards` method, it will use the full training set." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Need to compute baseline_hazards_. E.g run `model.compute_baseline_hazards()`", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msurv_cc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcox_cc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_survival_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# will give an Error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/packages/pycox/pycox/models/cox/cox.py\u001b[0m in \u001b[0;36mpredict_survival_function\u001b[0;34m(self, input, max_duration, batch_size, verbose, baseline_hazards_)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m \u001b[0mSurvival\u001b[0m \u001b[0mesimates\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mOne\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meach\u001b[0m \u001b[0mindividual\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \"\"\"\n\u001b[0;32m--> 145\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_cumulative_hazards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_duration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbaseline_hazards_\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 146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m def predict_cumulative_hazards_at_times(self, times, input, batch_size=8224, return_df=True,\n", "\u001b[0;32m~/packages/pycox/pycox/models/cox/cox.py\u001b[0m in \u001b[0;36mpredict_cumulative_hazards\u001b[0;34m(self, input, max_duration, batch_size, verbose, baseline_hazards_)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbaseline_hazards_\u001b[0m \u001b[0;32mis\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 120\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'baseline_hazards_'\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--> 121\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Need to compute baseline_hazards_. E.g run `model.compute_baseline_hazards()`'\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 122\u001b[0m \u001b[0mbaseline_hazards_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbaseline_hazards_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mbaseline_hazards_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_monotonic_increasing\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;31mValueError\u001b[0m: Need to compute baseline_hazards_. E.g run `model.compute_baseline_hazards()`" ] } ], "source": [ "surv_cc = cox_cc.predict_survival_function(x_test) # will give an Error" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "_ = cox_cc.compute_baseline_hazards()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "surv_cc = cox_cc.predict_survival_function(x_test)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = surv_cc.sample(8, axis=1).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Evaluation\n", "\n", "We can use the `EvalSurv` class for evaluation the concordance, brier score and binomial log-likelihood. Setting `censor_surv='km'` means that we estimate the censoring distribution by Kaplan-Meier on the test set." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "ev_cox_cc = EvalSurv(surv_cc, durations_test, events_test, censor_surv='km')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6666807933548059" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev_cox_cc.concordance_td()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/havard/packages/pycox/pycox/evaluation/ipcw.py:52: RuntimeWarning: invalid value encountered in true_divide\n", " return np.sum(scores, axis=1) / np.sum(weights, axis=1)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "time_grid = np.linspace(durations_test.min(), durations_test.max(), 100)\n", "_ = ev_cox_cc.brier_score(time_grid).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that there are some instatilities at the end. This is because there are very few individuals alive here. Hence we make a new `time_grid` that exclude the last part." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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tzC3lH1l7+e7cUQoCCWmBXvKJjgzn8impXD4llbz9h8grPUS/3j3pHxfFz97awuJluVwxNZVhycfvlvt06XxdOozP53jgzc0MjO/J7bOHeV2OSJeTnhTDnNH9GDcojr6xUdx30Vh6RoTzk6XZtPdVHYWBdJhX1uaTVVDJ/50/5vD1URE5fcm9o/jeBaP492f7eSd7b7t+tsJAOkRVXSO/emcbZ6QlcNkZg7wuR6TbuGHWEMYM6M2iv2+mur6x3T5XYSDtrriylusfW0FJVR0/vnSsJ935inRXEeFhPPCVCRRW1PK//8ppt89VGEi72lJUyVce+pjt+6pYfGMG04Ykel2SSLczPT2RqzPSWLxsBxv2HGiXz1QYSLtZtr2EKx/5BJ+Dv37jTOaN6+91SSLd1v2XjqV/XE/u+euGdun2QmEg7WJzYSV3PLOGwX1j+NtdZ6vfIZEOFtezBw9eMYnPiqv43Xuftfnz9JyBtNn+qjq+/nQmcdER/OXW6fTT8wQineLcUcmHLxe1lc4MpE3qG33c+exa9lfV8dhNGQoCkU72+eWitlIYyGlzzvGTpVmsyivjl1dOYlJqgtcliYSczy8XtZUuE8lpaWzycf9rWbyYuYc75wxXl9QiHjp3VHKbP0NhIK12qK6Rhc+v5YNtJXzryyP47rxRXpckIm2kMJBWKa2q49anVpNVUMHPL5/IdTMHe12SiLQDhYG0yg9f28S2vQdZfGMGc/UcgUi3oQZkCdh7m/fxTvY+vjN3lIJApJtRGEhAqusb+cnSbEb1j+W/vjTU63JEpJ3pMpEE5A/v51BwoIaX7jiTHhq2UqTb0W+1nNL2fQd5bFkuX52Wyoyh6nhOpDvSmYGcUGlVHUs3FPLUJ3nE9ozgvovHel2SiHQQhYEcI2//IX7+1hbe31pMo88xflAcDyyYQGJMpNeliUgHURjIYc45XllbwE9ezyI8zLjtnKFcPjWFMQPivC5NRDqYwkAAqKxt4EevZbF0QyEzhibyu6snMygh2uuyRKSTKAyE/PJqbnlyNTv3H+J780Zx53kjCNdQlSIhRWEQ4rIKKrj1qdXUNjTxzG0zOGt4ktcliYgHFAYhprahiQPVDVTUNLClqJIfvraJPr0iee6/ZjKqf2+vyxMRjwQUBmY2H/g9EA782Tn34FHzZwO/AyYB1zjnXm4xrwnY5H+72zl3WXsULq1T29DEg//YytOf5uFzR6aPHxTHk7dodDKRUHfKMDCzcOAhYB6QD6w2s6XOuc0tFtsN3ALcc5yPqHHOTW6HWuU05RRX8X9eWMeWokqumZ7GxNR44qN7kBAdSUZ6H3r2CPe6RBHxWCBnBjOAHOdcLoCZLQEWAIfDwDmX55/n64AapQ1eXZvP/a9lER0ZzuM3Z3D+WHUwJyLHCqQ7ihRgT4v3+f5pgeppZplmtsLMvnK8Bczsdv8ymSUlJa34aDkRn8/xq3e2cvdLGzgjLZ5/fPtLCgIROaHOaEAe4pwrMLNhwPtmtsk5t6PlAs65xcBigIyMDHe8D5HA1TY0cc9fN/DGxiKunZHGogUT1LmciJxUIGFQAKS1eJ/qnxYQ51yB//9cM/sQmALsOOlKctoqqhu49alVrN19gPsuGsPts4dhpmcGROTkAvlzcTUw0syGmlkkcA2wNJAPN7M+Zhblf50EnE2LtgZpXwdrG7jpyVVsKqjg4euncse5wxUEIhKQU4aBc64RWAi8A2wBXnLOZZvZIjO7DMDMpptZPvBV4E9mlu1ffSyQaWYbgA+AB4+6C0nayaG6Rm55cjXZBRU8dN1ULp440OuSRKQLMeeC6xJ9RkaGy8zM9LqMLqW6vjkI1uwq54/XTuEiBYFIyDGzNc65jNNdX62KXZxzjh+8vJHMvDJ+c9UZCgIROS0Kgy7ur2vyeWNjEd+7YDQLJrfmjl8RkSMUBl1YbkkVP12azaxhiXzj3OFelyMiXZjCoIuqa2ziW0vWERkRxu+unqIup0WkTdRraRfk8zl+/uYWsgoqWXzjNAbEq5M5EWkbhUEXU3Cghnte2sCnuaXcclY6F4wf4HVJItINKAy6COccr64t4KdLs/E5xy+umMhVGWmnXlFEJAAKgy7g0x2l/OqdrazdfYAZ6Yn8+qozSEvs5XVZItKNKAyC2Gf7DrLojc38+7P9DIjryf/8Z/PZgBqLRaS9KQyC1M79h7h68Qqcc/zokrHcMGuIBqERkQ6jMAhCJQfruPmJVRjw6p1nk54U43VJItLNKQyCzKG6Rr721GpKDtbxwu2zFAQi0ikUBkHEOcddz69lc1Elj900jclpCV6XJCIhQk8gB5H3txbz4bYSfnTJWL48RkNUikjnURgEkUc+3EFKQjQ3zBridSkiEmIUBkFidV4ZmbvKuX32MI1XLCKdTt86QeLhD3LoGxOpp4pFxBMKgyCwpaiSD7aVcOvZ6URH6lkCEel8CoMg8MiHO4iJDOfGWelelyIiIUph4LHdpdW8sbGQ62cNIb5XD6/LEZEQpTDw2FOf5BEeZtx2zlCvSxGREKYw8FBtQxMvr9nDheMH0D9OA9SIiHcUBh56Y2MRlbWNXD9TzxWIiLcUBh56buUuhiXHMGtYoteliEiIUxh4JLuwgnW7D3D9zCGYaXwCEfGWwsAjz6/cTVREGFdMTfG6FBERhYEXquoa+du6Ai6dNIiEXpFelyMiojDwwuvrCzhU38T1swZ7XYqICKAw6HT1jT6eWL6TsQPjmKLxCkQkSCgMOtmfl+eyo+QQ35s3Sg3HIhI0FAadaHdpNb9/7zPmjx/A3HEavEZEgofCoJM45/jR61lEhBk/uWyc1+WIiHyBwqCTvLGxiGXbS7jnwtEMjI/2uhwRkS9QGHSCnOKDLHpjMxNT4rnpzHSvyxEROUaE1wV0Z/WNPh79aAd/fD+H6MhwHrxiIuFhajQWkeAT0JmBmc03s21mlmNm9x5n/mwzW2tmjWZ25VHzbjazz/z/bm6vwoPd9n0HufQP/+Y3727nwgkDeO/ucxk/KN7rskREjuuUZwZmFg48BMwD8oHVZrbUObe5xWK7gVuAe45aNxH4CZABOGCNf93y9ik/eN3/2ib2V9Xz+M0ZnD9Wdw6JSHAL5MxgBpDjnMt1ztUDS4AFLRdwzuU55zYCvqPWvRB41zlX5g+Ad4H57VB3UFu7u5zVeeUsPG+EgkBEuoRAwiAF2NPifb5/WiDasm6X9ed/5xLXM4Krp6d5XYqISECC4m4iM7vdzDLNLLOkpMTrctpkV+kh3s7ayw2zhhATpfZ5EekaAgmDAqDln7ip/mmBCGhd59xi51yGcy4jOTk5wI8OTk8s30l4mHHLWelelyIiErBAwmA1MNLMhppZJHANsDTAz38HuMDM+phZH+AC/7RuqfxQPS9l5vOVySn005jGItKFnDIMnHONwEKav8S3AC8557LNbJGZXQZgZtPNLB/4KvAnM8v2r1sGPEBzoKwGFvmndUvPrthFTUMTX589zOtSRERaJaCL2s65t4C3jpr24xavV9N8Ceh46z4BPNGGGruEg7UNPPVJHnNGJzOqf2+vyxERaZWgaEDuDv7wfg5l1fV8d+4or0sREWk1hUE7yCmu4onlO7lqWhpnaMAaEemCFAZt5Jxj0Rubie4Rzvfnj/a6HBGR06IwaKP3thSzbHsJ35k3iqTYKK/LERE5LQqDNqhtaOKBNzYzsl8sN505xOtyREROmx6RPU1NPscPXt7I7rJqnr1tJj3Clasi0nXpG+w0OOe4/7VNLN1QyA/mj+ackUlelyQi0iYKg1ZyzvHfb25hyeo93HXecO6cM8LrkkRE2kxh0Eq/fe8zHl++k1vOSueeC3T3kIh0DwqDVnhsWS7/+6/P+Oq0VH586TjMNISliHQPCoMAvbBqNz97awuXTBzIg1dMIkxjGYtIN6IwCMDSDYX88LVNzBmdzG+vnqxB7UWk21EYnMIHW4u5+8X1TE9P5JHrpxEZoV0mIt2PvtlOIjOvjG8+t4YxA3vz+M0ZREeGe12SiEiHUBicwNa9lXztqdUMio/mqVtn0LtnD69LEhHpMAqD49hTVs1Nj6+iV2QET982Q30OiUi3pzA4jt++u51DdY08fdsMUvv08rocEZEOpzA4yv6qOt7YWMSV01I1YpmIhAyFwVFeXL2H+iYfN56Z7nUpIiKdRmHQQmOTj2dX7OKcEUmM6BfrdTkiIp1GYdDCe1uKKaqo5UaNTSAiIUZh0MLTn+aRkhDN+WP6eV2KiEinUhj4fbbvIJ/sKOW6mYOJ0EA1IhJi9K3n98yKXUSGh3HN9DSvSxER6XQKA5oHrHlrUxEXjO9PXz1gJiIhSGEA7K2sZX9VPdPTE70uRUTEEwoDIKugEoAJKXEeVyIi4g2FAZBVUEGYwdiBCgMRCU0KA5rDYHhyLL0iI7wuRUTEEwoDIKuwggkp8V6XISLimZAPg+KDteyrrGP8IF0iEpHQFfJhkF3Y3Hg8UWcGIhLCQj4MsvIrABinMwMRCWEKg8IKhibFaFhLEQlpCoOCSrUXiEjIC+kwKD9UT8GBGrUXiEjICygMzGy+mW0zsxwzu/c486PM7EX//JVmlu6fnm5mNWa23v/v0fYtv22yCpvbC3RbqYiEulM+ZWVm4cBDwDwgH1htZkudc5tbLHYbUO6cG2Fm1wC/AK72z9vhnJvcznW3i8+7odBlIhEJdYGcGcwAcpxzuc65emAJsOCoZRYAf/G/fhk438ys/crsGFkFFaQlRpPQK9LrUkREPBVIGKQAe1q8z/dPO+4yzrlGoALo65831MzWmdlHZval4/0AM7vdzDLNLLOkpKRVG9AWWYUVTBikS0QiIh3dgFwEDHbOTQHuBp43s2OuyTjnFjvnMpxzGcnJyR1cUrOKmgZ2lVarvUBEhMDCoABoOfxXqn/acZcxswggHih1ztU550oBnHNrgB3AqLYW3R4y88oANR6LiEBgYbAaGGlmQ80sErgGWHrUMkuBm/2vrwTed845M0v2N0BjZsOAkUBu+5TeNi9l7iExJpJZwzSgjYjIKe8mcs41mtlC4B0gHHjCOZdtZouATOfcUuBx4BkzywHKaA4MgNnAIjNrAHzAN5xzZR2xIa1RfLCWf20p5taz04mKCPe6HBERzwXUgb9z7i3graOm/bjF61rgq8dZ7xXglTbW2O5eXpNPo89x9fTBXpciIhIUQu4JZJ/P8eLqPcxIT2REv1ivyxERCQohFwYrckvZVVrNNTPSTr2wiEiICLkweGH1HuJ6RnDxxIFelyIiEjRCKgzKDtXzTtZeLp+SQs8eajgWEflcSIXBa+sKqG/ycc0MNRyLiLQUUmGwbHsJo/rHMnagOqYTEWkpZMLAOUd2YQUTUxK8LkVEJOiETBgUH6xjf1U9E1J0ViAicrSQCYNs/0A249VLqYjIMUImDD4fyGacBrIRETlGyIRBdmEFQ5NiiI0KqAcOEZGQEkJhUKmzAhGREwiJMDhQXU9+eY1GNRMROYGQCIPNhRr4XkTkZEIiDLIVBiIiJxUSYZBVWMHA+J70jY3yuhQRkaAUEmGQXVipswIRkZPo9mFQXd9IbkmVHjYTETmJbh8GW4oO4nNqLxAROZluHwab/d1QTEjRmYGIyIl0+zDIKqikT68eDIzv6XUpIiJBq9uHQXZRBeMHxWNmXpciIhK0unUYHKiuZ/veKsar22oRkZPq1mHw+PKd1Df5uHxKiteliIgEtW4bBgeq63ny4zwumjCAMQN0ZiAicjLdNgyeWL6TqrpGvnX+SK9LEREJet0yDCqqG3jy4zzmjx/A2IE6KxAROZVuGQaPf7yTgzorEBEJWJcd9itv/yGq6hoxgzAzmnyOusYmquubePLjnVw4vr8GsxERCVCXC4OqukYe+PtmXszcc8JleoSbzgpERFqhS4XBqp1l3P3SegoP1HDH7GFkpCficw6fzxEeZkT1CCcyPIzUPtGkJfbyulwRkS6jy4TBK2vyueflDaT16cVLd5xJRnqi1yWJiHQbXSIM6hqb+OU7W5mclsCzt80kJqpLlC0i0mV0ibuJXllTwL7KOu6eN0pBICLSAYI+DBqbfDzyUQ5npMZzzogkr8sREemWgj4Mlm4oZEIyjJwAAAV5SURBVE9ZDXedN0I9j4qIdJCAwsDM5pvZNjPLMbN7jzM/ysxe9M9faWbpLebd55++zcwubE1xPp/j4Q93MGZAb+aO7d+aVUVEpBVOGQZmFg48BFwEjAOuNbNxRy12G1DunBsB/Bb4hX/dccA1wHhgPvCw//MC8k72XnKKq7jzvBGEhemsQESkowTSGjsDyHHO5QKY2RJgAbC5xTILgJ/6X78M/NGar+ksAJY45+qAnWaW4/+8T0/0w7bvO8jc33wEwL7KWoYmxXDJxIGt2igREWmdQMIgBWj5uG8+MPNEyzjnGs2sAujrn77iqHWPGVzAzG4HbgeIGzSM0f17AzB6QG+unzGYcJ0ViIh0qKC4T9M5txhYDJCRkeEeun6qxxWJiISWQBqQC4C0Fu9T/dOOu4yZRQDxQGmA64qIiMcCCYPVwEgzG2pmkTQ3CC89apmlwM3+11cC7zvnnH/6Nf67jYYCI4FV7VO6iIi0l1NeJvK3ASwE3gHCgSecc9lmtgjIdM4tBR4HnvE3EJfRHBj4l3uJ5sbmRuAu51xTB22LiIicJmv+Az54ZGRkuMzMTK/LEBHpUsxsjXMu43TXD/onkEVEpOMpDERERGEgIiIKAxERIQgbkM3sILDN6zqCRBKw3+sigoT2xRHaF0doXxwx2jnX+3RXDoonkI+yrS0t4t2JmWVqXzTTvjhC++II7YsjzKxNt2HqMpGIiCgMREQkOMNgsdcFBBHtiyO0L47QvjhC++KINu2LoGtAFhGRzheMZwYiItLJFAYiIhJcYWBm881sm5nlmNm9XtfT2cwsz8w2mdn6z28TM7NEM3vXzD7z/9/H6zo7gpk9YWbFZpbVYtpxt92a/a//ONloZt1qNKQT7IufmlmB/9hYb2YXt5h3n39fbDOzC72pumOYWZqZfWBmm80s28y+7Z8ecsfGSfZF+xwbzrmg+Edz99g7gGFAJLABGOd1XZ28D/KApKOm/RK41//6XuAXXtfZQds+G5gKZJ1q24GLgX8ABswCVnpdfyfsi58C9xxn2XH+35UoYKj/dyjc621ox30xEJjqf90b2O7f5pA7Nk6yL9rl2AimM4MZQI5zLtc5Vw8sARZ4XFMwWAD8xf/6L8BXPKylwzjnltE8FkZLJ9r2BcDTrtkKIMHMBnZOpR3vBPviRBYAS5xzdc65nUAOzb9L3YJzrsg5t9b/+iCwheZx1EPu2DjJvjiRVh0bwRQGKcCeFu/zOfmGdkcO+KeZrTGz2/3T+jvnivyv9wL9vSnNEyfa9lA9Vhb6L3080eJyYcjsCzNLB6YAKwnxY+OofQHtcGwEUxgInOOcmwpcBNxlZrNbznTN534heS9wKG+73yPAcGAyUAT82ttyOpeZxQKvAN9xzlW2nBdqx8Zx9kW7HBvBFAYFQFqL96n+aSHDOVfg/78YeI3mU7p9n5/m+v8v9q7CTneibQ+5Y8U5t8851+Sc8wGPceR0v9vvCzPrQfOX33POuVf9k0Py2DjevmivYyOYwmA1MNLMhppZJM3jKC/1uKZOY2YxZtb789fABUAWzfvgZv9iNwOve1OhJ0607UuBm/x3jswCKlpcMuiWjrrufTnNxwY074trzCzKzIYCI4FVnV1fRzEzo3mM9S3Oud+0mBVyx8aJ9kW7HRtet5Af1fp9Mc0t5DuA+72up5O3fRjNLf8bgOzPtx/oC/wL+Ax4D0j0utYO2v4XaD7FbaD52uZtJ9p2mu8Uech/nGwCMryuvxP2xTP+bd3o/yUf2GL5+/37Yhtwkdf1t/O+OIfmS0AbgfX+fxeH4rFxkn3RLseGuqMQEZGgukwkIiIeURiIiIjCQEREFAYiIoLCQEREUBiIiAgKAxERAf4/vWKjJHREAaAAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "time_grid = np.linspace(durations_test.min(), 250, 100)\n", "_ = ev_cox_cc.brier_score(time_grid).plot()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.16877665320905055, -0.5025647259210938)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev_cox_cc.integrated_brier_score(time_grid), ev_cox_cc.integrated_mbll(time_grid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cox DeepSurv\n", "\n", "We now fit a DeepSurv model that is very similar to CoxCC, so we basically just repeat the steps.\n", "\n", "Note that as we now optimize the partial log-likelihood, we have no need to repeat the valiation data set as the score is deterministic. \n", "\n", "Also note that because the trainig set and validation set are not of equal size (train is 4 times larger), the partial log-likelihood is not comparable between the data sets (larger data sets gives smaller partial log-likelihood). Hence, we set the `val_batch_size` equal to the trining batch size, to obtain comparble training curves." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0:\t[0s / 0s],\t\ttrain_loss: 4.0632,\tval_loss: 3.8893\n", "1:\t[0s / 0s],\t\ttrain_loss: 3.9958,\tval_loss: 3.8668\n", "2:\t[0s / 0s],\t\ttrain_loss: 3.9852,\tval_loss: 3.8285\n", "3:\t[0s / 0s],\t\ttrain_loss: 3.9562,\tval_loss: 3.7940\n", "4:\t[0s / 0s],\t\ttrain_loss: 3.9294,\tval_loss: 3.7805\n", "5:\t[0s / 0s],\t\ttrain_loss: 3.9309,\tval_loss: 3.7761\n", "6:\t[0s / 0s],\t\ttrain_loss: 3.9393,\tval_loss: 3.7764\n", "7:\t[0s / 0s],\t\ttrain_loss: 3.9142,\tval_loss: 3.7769\n", "8:\t[0s / 0s],\t\ttrain_loss: 3.9198,\tval_loss: 3.7760\n", "9:\t[0s / 0s],\t\ttrain_loss: 3.9284,\tval_loss: 3.7744\n", "10:\t[0s / 0s],\t\ttrain_loss: 3.9042,\tval_loss: 3.7707\n", "11:\t[0s / 0s],\t\ttrain_loss: 3.9173,\tval_loss: 3.7744\n", "12:\t[0s / 0s],\t\ttrain_loss: 3.9165,\tval_loss: 3.7745\n", "13:\t[0s / 0s],\t\ttrain_loss: 3.9142,\tval_loss: 3.7739\n", "14:\t[0s / 0s],\t\ttrain_loss: 3.9018,\tval_loss: 3.7709\n", "15:\t[0s / 0s],\t\ttrain_loss: 3.9044,\tval_loss: 3.7710\n", "16:\t[0s / 1s],\t\ttrain_loss: 3.8831,\tval_loss: 3.7690\n", "17:\t[0s / 1s],\t\ttrain_loss: 3.8941,\tval_loss: 3.7762\n", "18:\t[0s / 1s],\t\ttrain_loss: 3.8886,\tval_loss: 3.7751\n", "19:\t[0s / 1s],\t\ttrain_loss: 3.8913,\tval_loss: 3.7776\n", "20:\t[0s / 1s],\t\ttrain_loss: 3.9058,\tval_loss: 3.7793\n", "21:\t[0s / 1s],\t\ttrain_loss: 3.8815,\tval_loss: 3.7760\n", "22:\t[0s / 1s],\t\ttrain_loss: 3.8844,\tval_loss: 3.7788\n", "23:\t[0s / 1s],\t\ttrain_loss: 3.8839,\tval_loss: 3.7817\n", "24:\t[0s / 1s],\t\ttrain_loss: 3.8782,\tval_loss: 3.7832\n", "25:\t[0s / 1s],\t\ttrain_loss: 3.8722,\tval_loss: 3.7808\n", "26:\t[0s / 1s],\t\ttrain_loss: 3.8803,\tval_loss: 3.7797\n" ] } ], "source": [ "net = NetMLP()\n", "cox_ph = CoxPH(net, optim.Adam(lr=0.001))\n", "es = cb.EarlyStopping()\n", "log = cox_ph.fit(x_train, y_train, batch_size=128, epochs=60, callbacks=[es], val_data=val, val_batch_size=128)\n", "cox_ph.compute_baseline_hazards()\n", "surv_ph = cox_ph.predict_survival_function(x_test)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = log.plot()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6647948804882183" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev_cox_ph = EvalSurv(surv_ph, durations_test, events_test, censor_surv='km')\n", "ev_cox_ph.concordance_td()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cox-Time\n", "\n", "Next, we fit a Cox-Time model. This is a little different than the Cox CC and Cox DeepSurv, so we need to make some small alterations to our neural net." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "class NetMLPCoxTime(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " input_size = 9 + 1 # Need one more for time\n", " nodes = 32\n", " self.block0 = nn.Sequential(nn.Linear(input_size, nodes), nn.ReLU(),\n", " nn.BatchNorm1d(nodes), nn.Dropout(0.1))\n", " self.block1 = nn.Sequential(nn.Linear(nodes, nodes), nn.ReLU(),\n", " nn.BatchNorm1d(nodes), nn.Dropout(0.1))\n", " self.out = nn.Linear(32, 1, bias=False)\n", "\n", " def forward(self, x, t): # need time in put\n", " x = torch.cat([x, t], dim=1) # concatenate time with other covariates\n", " x = self.block0(x)\n", " x = self.block1(x)\n", " x = self.out(x)\n", " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we could again base our net on the `torchtuples.practical.MLPVanilla`:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# class NetMLPCoxTime(torch.nn.Module):\n", "# def __init__(self, in_features, num_nodes, out_features, batch_norm=True, dropout=None):\n", "# super().__init__()\n", "# self.net = MLPVanilla(in_features+1, num_nodes, out_features, batch_norm, dropout, output_bias=False)\n", "\n", "# def forward(self, x, t):\n", "# x = torch.cat([x, t], dim=1)\n", "# return self.net(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocessing\n", "\n", "As we are now passing time as a covariate to our network, we tupically need to standardize it.\n", "We have added `LabTransCoxTime` which can do a log-transform and standardize the duration, while keeping track of the original durations (which is useful for predictions)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "from pycox.preprocessing.label_transforms import LabTransCoxTime\n", "labtrans = LabTransCoxTime()\n", "y_train_cox_time = labtrans.fit_transform(*get_target(df_train))\n", "y_val_cox_time = labtrans.transform(*get_target(df_val))\n", "val = tuplefy(x_val, y_val_cox_time)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "net = NetMLPCoxTime()\n", "cox_time = CoxTime(net, optim.Adam(lr=0.001))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0:\t[0s / 0s],\t\ttrain_loss: 0.7155,\tval_loss: 0.6927\n", "1:\t[0s / 0s],\t\ttrain_loss: 0.7086,\tval_loss: 0.6913\n", "2:\t[0s / 0s],\t\ttrain_loss: 0.6998,\tval_loss: 0.6901\n", "3:\t[0s / 0s],\t\ttrain_loss: 0.6875,\tval_loss: 0.6876\n", "4:\t[0s / 0s],\t\ttrain_loss: 0.6638,\tval_loss: 0.6855\n", "5:\t[0s / 0s],\t\ttrain_loss: 0.6649,\tval_loss: 0.6828\n", "6:\t[0s / 0s],\t\ttrain_loss: 0.6628,\tval_loss: 0.6805\n", "7:\t[0s / 0s],\t\ttrain_loss: 0.6553,\tval_loss: 0.6728\n", "8:\t[0s / 0s],\t\ttrain_loss: 0.6510,\tval_loss: 0.6714\n", "9:\t[0s / 0s],\t\ttrain_loss: 0.6409,\tval_loss: 0.6636\n", "10:\t[0s / 0s],\t\ttrain_loss: 0.6408,\tval_loss: 0.6557\n", "11:\t[0s / 0s],\t\ttrain_loss: 0.6226,\tval_loss: 0.6569\n", "12:\t[0s / 0s],\t\ttrain_loss: 0.6330,\tval_loss: 0.6521\n", "13:\t[0s / 0s],\t\ttrain_loss: 0.6365,\tval_loss: 0.6533\n", "14:\t[0s / 0s],\t\ttrain_loss: 0.6299,\tval_loss: 0.6399\n", "15:\t[0s / 1s],\t\ttrain_loss: 0.6358,\tval_loss: 0.6422\n", "16:\t[0s / 1s],\t\ttrain_loss: 0.6280,\tval_loss: 0.6343\n", "17:\t[0s / 1s],\t\ttrain_loss: 0.6223,\tval_loss: 0.6362\n", "18:\t[0s / 1s],\t\ttrain_loss: 0.6364,\tval_loss: 0.6340\n", "19:\t[0s / 1s],\t\ttrain_loss: 0.6090,\tval_loss: 0.6364\n", "20:\t[0s / 1s],\t\ttrain_loss: 0.6111,\tval_loss: 0.6268\n", "21:\t[0s / 1s],\t\ttrain_loss: 0.6174,\tval_loss: 0.6214\n", "22:\t[0s / 1s],\t\ttrain_loss: 0.6059,\tval_loss: 0.6282\n", "23:\t[0s / 1s],\t\ttrain_loss: 0.6027,\tval_loss: 0.6141\n", "24:\t[0s / 1s],\t\ttrain_loss: 0.5848,\tval_loss: 0.6136\n", "25:\t[0s / 1s],\t\ttrain_loss: 0.6079,\tval_loss: 0.6221\n", "26:\t[0s / 1s],\t\ttrain_loss: 0.6054,\tval_loss: 0.6323\n", "27:\t[0s / 1s],\t\ttrain_loss: 0.6057,\tval_loss: 0.6168\n", "28:\t[0s / 1s],\t\ttrain_loss: 0.6078,\tval_loss: 0.6192\n", "29:\t[0s / 1s],\t\ttrain_loss: 0.6024,\tval_loss: 0.6204\n", "30:\t[0s / 1s],\t\ttrain_loss: 0.6058,\tval_loss: 0.6092\n", "31:\t[0s / 1s],\t\ttrain_loss: 0.5729,\tval_loss: 0.6103\n", "32:\t[0s / 1s],\t\ttrain_loss: 0.5918,\tval_loss: 0.6151\n", "33:\t[0s / 1s],\t\ttrain_loss: 0.6039,\tval_loss: 0.6194\n", "34:\t[0s / 2s],\t\ttrain_loss: 0.5770,\tval_loss: 0.6130\n", "35:\t[0s / 2s],\t\ttrain_loss: 0.6019,\tval_loss: 0.6134\n", "36:\t[0s / 2s],\t\ttrain_loss: 0.5988,\tval_loss: 0.6023\n", "37:\t[0s / 2s],\t\ttrain_loss: 0.5828,\tval_loss: 0.6098\n", "38:\t[0s / 2s],\t\ttrain_loss: 0.5961,\tval_loss: 0.6112\n", "39:\t[0s / 2s],\t\ttrain_loss: 0.5757,\tval_loss: 0.5950\n", "40:\t[0s / 2s],\t\ttrain_loss: 0.5808,\tval_loss: 0.5966\n", "41:\t[0s / 2s],\t\ttrain_loss: 0.5719,\tval_loss: 0.6030\n", "42:\t[0s / 2s],\t\ttrain_loss: 0.5813,\tval_loss: 0.6000\n", "43:\t[0s / 2s],\t\ttrain_loss: 0.5752,\tval_loss: 0.5994\n", "44:\t[0s / 2s],\t\ttrain_loss: 0.5808,\tval_loss: 0.6148\n", "45:\t[0s / 2s],\t\ttrain_loss: 0.5836,\tval_loss: 0.6015\n", "46:\t[0s / 2s],\t\ttrain_loss: 0.5786,\tval_loss: 0.6096\n", "47:\t[0s / 2s],\t\ttrain_loss: 0.5739,\tval_loss: 0.5950\n", "48:\t[0s / 2s],\t\ttrain_loss: 0.5923,\tval_loss: 0.6136\n", "49:\t[0s / 2s],\t\ttrain_loss: 0.5967,\tval_loss: 0.6042\n", "50:\t[0s / 2s],\t\ttrain_loss: 0.5727,\tval_loss: 0.6077\n", "51:\t[0s / 2s],\t\ttrain_loss: 0.5771,\tval_loss: 0.6100\n", "52:\t[0s / 3s],\t\ttrain_loss: 0.5534,\tval_loss: 0.6074\n", "53:\t[0s / 3s],\t\ttrain_loss: 0.5560,\tval_loss: 0.6029\n", "54:\t[0s / 3s],\t\ttrain_loss: 0.5744,\tval_loss: 0.6180\n", "55:\t[0s / 3s],\t\ttrain_loss: 0.5903,\tval_loss: 0.6032\n", "56:\t[0s / 3s],\t\ttrain_loss: 0.5954,\tval_loss: 0.6113\n", "57:\t[0s / 3s],\t\ttrain_loss: 0.5767,\tval_loss: 0.6157\n" ] } ], "source": [ "es = cb.EarlyStopping()\n", "log = cox_time.fit(x_train, y_train_cox_time, epochs=60, callbacks=[es], val_data=tuplefy(val).repeat(10).cat())" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = log.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predictions\n", "\n", "If we just use predict in the same manner as before, we see that the durations (on the x-axis fo the plot) are still scaled." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "_ = cox_time.compute_baseline_hazards()\n", "surv_cox_time = cox_time.predict_survival_function(x_test)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = surv_cox_time.sample(8, axis=1).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To obtain the original duration scale, we can use the `labtrans` object:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "surv_cox_time.index = labtrans.map_scaled_to_orig(surv_cox_time.index)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = surv_cox_time.sample(8, axis=1).plot()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "ev_cox_time = EvalSurv(surv_cox_time, durations_test, events_test, censor_surv='km')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.6666807933548059, 0.6647948804882183, 0.6732708933717579)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev_cox_cc.concordance_td(), ev_cox_ph.concordance_td(), ev_cox_time.concordance_td()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ev_cox_cc.brier_score(time_grid).rename('Cox CC').plot()\n", "ev_cox_ph.brier_score(time_grid).rename('Cox DeepSurv').plot()\n", "ev_cox_time.brier_score(time_grid).rename('Cox-Time').plot()\n", "plt.legend()\n", "plt.grid()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.16877665320905055, 0.16848124177129836, 0.16671147729705982)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev_cox_cc.integrated_brier_score(time_grid), ev_cox_ph.integrated_brier_score(time_grid), ev_cox_time.integrated_brier_score(time_grid)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.5025647259210938, -0.5009060947736069, -0.4943029028366378)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev_cox_cc.integrated_mbll(time_grid), ev_cox_ph.integrated_mbll(time_grid), ev_cox_time.integrated_mbll(time_grid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }