survshap-heart_failure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SurvSHAP(t): Time-Dependent Explanations Of Machine Learning Survival Models\n",
    "### M. Krzyziński, M. Spytek, H. Baniecki, P. Biecek\n",
    "## Experiment 3: Real-world use case: predicting survival of patients with heart failure\n",
    "(https://github.com/MI2DataLab/survshap/blob/main/exp3.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn_pandas import DataFrameMapper\n",
    "from abc import ABC\n",
    "import torchtuples as tt\n",
    "from plotly.figure_factory import np\n",
    "from pycox.models import DeepHitSingle\n",
    "from sksurv.functions import StepFunction\n",
    "from survshap import SurvivalModelExplainer, PredictSurvSHAP\n",
    "import timeit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preparing data and models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_data = pd.read_csv(\"data/exp3_heart_failure_dataset.csv\")\n",
    "real_data.rename(columns={\"DEATH_EVENT\": \"event\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "df_train = real_data\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)\n",
    "train_column_names = [\"age\", \"creatinine_phosphokinase\", \"ejection_fraction\", \"platelets\", \"serum_creatinine\",\n",
    "                      \"serum_sodium\", \"sex\", \"smoking\"]\n",
    "max_time = real_data.time.max()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "cols_standardize = [\"age\", \"creatinine_phosphokinase\", \"ejection_fraction\", \"platelets\", \"serum_creatinine\", \"serum_sodium\"]\n",
    "cols_leave = ['sex', 'smoking']\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)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "y_train = (df_train['time'].values, df_train['event'].values)\n",
    "y_val = (df_val['time'].values, df_val['event'].values)\n",
    "\n",
    "train = (x_train, y_train)\n",
    "val = (x_val, y_val)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "in_features = x_train.shape[1]\n",
    "num_nodes = [32, 32]\n",
    "out_features = max_time + 1\n",
    "batch_norm = True\n",
    "dropout = 0.1\n",
    "\n",
    "network = tt.practical.MLPVanilla(in_features, num_nodes, out_features, batch_norm, dropout)\n",
    "\n",
    "class CustomDeepHitSingle(DeepHitSingle, ABC):\n",
    "    def __init__(self, net, optimizer=None, device=None, duration_index=None, alpha=0.2, sigma=0.1, loss=None):\n",
    "        super().__init__(net, optimizer=optimizer, device=device, duration_index=duration_index, alpha=alpha,\n",
    "                         sigma=sigma, loss=loss)\n",
    "\n",
    "\n",
    "    def predict_survival_function(self, X, return_array=False):\n",
    "        preds = self.predict_surv_df(X.values)\n",
    "        ret = [StepFunction(x=np.arange(0, preds.shape[0]), y=preds[column].values) for i, column in enumerate(preds)]\n",
    "        return ret\n",
    "\n",
    "model = CustomDeepHitSingle(network, optimizer=tt.optim.Adam, alpha=0.2, sigma=0.1, duration_index=np.arange(0, max_time + 1))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "lr_finder = model.lr_finder(x_train, y_train, batch_size, tolerance=3)\n",
    "_ = lr_finder.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "model.optimizer.set_lr(lr_finder.get_best_lr() * 0.7)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\t[0s / 0s],\t\ttrain_loss: 0.6304,\tval_loss: 0.7851\n",
      "1:\t[0s / 0s],\t\ttrain_loss: 0.6072,\tval_loss: 0.7791\n",
      "2:\t[0s / 0s],\t\ttrain_loss: 0.5864,\tval_loss: 0.7746\n",
      "3:\t[0s / 0s],\t\ttrain_loss: 0.5662,\tval_loss: 0.7705\n",
      "4:\t[0s / 0s],\t\ttrain_loss: 0.5460,\tval_loss: 0.7675\n",
      "5:\t[0s / 0s],\t\ttrain_loss: 0.5268,\tval_loss: 0.7644\n",
      "6:\t[0s / 0s],\t\ttrain_loss: 0.5161,\tval_loss: 0.7614\n",
      "7:\t[0s / 0s],\t\ttrain_loss: 0.4994,\tval_loss: 0.7589\n",
      "8:\t[0s / 0s],\t\ttrain_loss: 0.4815,\tval_loss: 0.7572\n",
      "9:\t[0s / 0s],\t\ttrain_loss: 0.4742,\tval_loss: 0.7555\n",
      "10:\t[0s / 0s],\t\ttrain_loss: 0.4617,\tval_loss: 0.7544\n",
      "11:\t[0s / 0s],\t\ttrain_loss: 0.4510,\tval_loss: 0.7537\n",
      "12:\t[0s / 0s],\t\ttrain_loss: 0.4291,\tval_loss: 0.7532\n",
      "13:\t[0s / 0s],\t\ttrain_loss: 0.4047,\tval_loss: 0.7531\n",
      "14:\t[0s / 0s],\t\ttrain_loss: 0.3987,\tval_loss: 0.7539\n",
      "15:\t[0s / 0s],\t\ttrain_loss: 0.4064,\tval_loss: 0.7552\n",
      "16:\t[0s / 0s],\t\ttrain_loss: 0.3749,\tval_loss: 0.7569\n",
      "17:\t[0s / 0s],\t\ttrain_loss: 0.3446,\tval_loss: 0.7592\n",
      "18:\t[0s / 0s],\t\ttrain_loss: 0.3406,\tval_loss: 0.7620\n",
      "19:\t[0s / 0s],\t\ttrain_loss: 0.3279,\tval_loss: 0.7649\n",
      "20:\t[0s / 0s],\t\ttrain_loss: 0.3147,\tval_loss: 0.7693\n",
      "21:\t[0s / 0s],\t\ttrain_loss: 0.3066,\tval_loss: 0.7743\n",
      "22:\t[0s / 0s],\t\ttrain_loss: 0.2934,\tval_loss: 0.7792\n",
      "23:\t[0s / 0s],\t\ttrain_loss: 0.2809,\tval_loss: 0.7849\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "callbacks = [tt.callbacks.EarlyStopping()]\n",
    "log = model.fit(x_train, y_train, batch_size, epochs, callbacks, val_data=val)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = log.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00134177399922919\n"
     ]
    },
    {
     "data": {
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         "mode": "lines",
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     "text": [
      "                         variable_str             variable_name  \\\n",
      "0                         age = 0.831                       age   \n",
      "1  creatinine_phosphokinase = -0.4892  creatinine_phosphokinase   \n",
      "2           ejection_fraction = 1.771         ejection_fraction   \n",
      "3                 platelets = -0.1016                 platelets   \n",
      "4          serum_creatinine = -0.2915          serum_creatinine   \n",
      "\n",
      "   variable_value  B  aggregated_change     t = 0     t = 1     t = 2  \\\n",
      "0        0.830985  0           5.228015 -0.000975 -0.001807 -0.002434   \n",
      "1       -0.489152  0           1.181423 -0.000247 -0.000517 -0.000969   \n",
      "2        1.770741  0           5.531200  0.000693 -0.000120 -0.001084   \n",
      "3       -0.101610  0           0.492245  0.000094 -0.000204 -0.000202   \n",
      "4       -0.291505  0           1.498599 -0.000010  0.000114 -0.000018   \n",
      "\n",
      "      t = 3     t = 4  ...   t = 276   t = 277   t = 278   t = 279   t = 280  \\\n",
      "0 -0.003586 -0.004798  ... -0.007730 -0.006172 -0.005810 -0.006106 -0.005964   \n",
      "1 -0.001356 -0.001519  ...  0.001315  0.001073  0.000664  0.000358  0.000614   \n",
      "2 -0.000692 -0.002310  ... -0.002407 -0.003288 -0.001008  0.000469  0.001069   \n",
      "3 -0.000267  0.000036  ... -0.001892 -0.001978 -0.002587 -0.002470 -0.002435   \n",
      "4 -0.000082 -0.000306  ...  0.000921  0.000868  0.000433  0.000328  0.000204   \n",
      "\n",
      "    t = 281   t = 282   t = 283   t = 284   t = 285  \n",
      "0 -0.004366 -0.003175 -0.002414 -0.000871  0.000165  \n",
      "1  0.000711  0.000114  0.000548 -0.000093  0.000074  \n",
      "2  0.001480  0.003117  0.002936 -0.000602 -0.000275  \n",
      "3 -0.002367 -0.001119 -0.000855 -0.000062  0.000084  \n",
      "4  0.000246  0.000336  0.000210  0.000101  0.000003  \n",
      "\n",
      "[5 rows x 291 columns]\n"
     ]
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    "y_dtype = np.dtype({'names':['event', 'time'], 'formats':[np.uint8, np.uint8]})\n",
    "y = np.array([tuple(row) for row in np.stack((y_train[1], y_train[0])).transpose(1, 0)], dtype=y_dtype)\n",
    "model_exp = SurvivalModelExplainer(model, pd.DataFrame(x_train, columns=train_column_names), y)\n",
    "n_obs = len(x_test)\n",
    "survshaps = [None for i in range(n_obs)]\n",
    "time_array = []\n",
    "for i, obs in enumerate(x_test[:1]):\n",
    "    xx = pd.DataFrame(np.atleast_2d(obs), columns=train_column_names)\n",
    "    survshap = PredictSurvSHAP()\n",
    "    start = timeit.timeit()\n",
    "    survshap.fit(model_exp, xx)\n",
    "    end = timeit.timeit()\n",
    "    print(end - start)\n",
    "    time_array.append(end - start)\n",
    "    survshaps[i] = survshap\n",
    "    survshap.plot()\n",
    "    plt.show()\n",
    "    print(survshap.result.head())"
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