{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e3506e5-f32f-4ba6-b468-404125e81499",
   "metadata": {},
   "source": [
    "# Robust Shapley values - robust explanation of an aggregated radiomic model with non-optional regions\n",
    "In this jupyter notebook we provide a short example for the use of **robust Shapley values** to explain aggregated radiomic models that integrate non-optional regions (i.e. regions that will lead to invalid aggregated inputs when removed). \n",
    "\n",
    "We study the classification of the histological subtype of non-small cell lung cancer (NSCLC) from baseline 18F-FDG PET images. To do so, we use a radiomic model that aggregates features extracted from all the metastases of the patient as well as the mean SUV in a healthy region of the liver and the mean SUV in a healthy region of the spleen. For more details please refer to [this notebook](https://github.com/ncaptier/radshap/blob/main/examples/nsclc_subtype_classification.ipynb) which deals with a very similar study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "481d2726-fe73-4ff6-b0cd-b4bd02f25385",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Setup for local running - please delete this block\n",
    "import sys\n",
    "sys.path.append('C:\\\\Users\\\\ncaptier\\\\Documents\\\\GitHub\\\\radshap')\n",
    "\n",
    "import collections\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import roc_auc_score, recall_score, balanced_accuracy_score\n",
    "from sklearn.base import clone\n",
    "\n",
    "from radshap.shapley import RobustShapley\n",
    "from radshap.plot import plot_bars, plot_pet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35471de7-4dea-45b9-9b0a-e4f0ab6820b2",
   "metadata": {},
   "source": [
    "## 0. Load radiomic and clinical data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "759a4166-be86-40bc-bae2-63510cf3bd67",
   "metadata": {},
   "source": [
    "Our cohort gathers **110 NSCLC metastatic patients** with a baseline 18F-FDG PET scan (for the diagnosis of the metastatic disease) acquired before the beginning of their first line treatment. All their metastases were segmented by a 12 years experienced nuclear medicine physician. Please refer to [this notebook](https://github.com/ncaptier/radshap/blob/main/examples/nsclc_subtype_classification.ipynb) for all the necessary details about the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43a6acb6-717f-4b97-998f-5fc2fc40c856",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clinicals = pd.read_csv(\"..\\\\data\\\\data_clinicals.csv\", index_col=0)\n",
    "\n",
    "df_radiomics_all = pd.read_csv(\"..\\\\data\\\\data_allmeta_liverspleen.csv\", index_col=0)\n",
    "\n",
    "# we log-transform the volume feature to avoid linear model to deal with highly right skewed distributions\n",
    "df_radiomics_all.loc[:, \"original_shape_VoxelVolume\"] = df_radiomics_all[\"original_shape_VoxelVolume\"].apply(lambda x: np.log(x+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39adddc-c1e8-4cd9-b6fc-3777db47d941",
   "metadata": {},
   "source": [
    "## 1. Train and test the aggregated model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16c4c3a-fae8-4e74-a897-213057793a0a",
   "metadata": {},
   "source": [
    "Here we train and test a classifier which takes as input the minimum value, the maximum value, and the standard deviation of the 10 extracted radiomic features over all the metastases of the patient, as well as the SUVmean of an healthy region within the liver and the SUVmean of an healthy region within the spleen."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f4f620-7e8c-4737-9d5a-99062c80e85a",
   "metadata": {},
   "source": [
    "### 1.1 Aggregate features with min, max, and std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8208433e-7c9a-4e31-9a09-6dd8f2d988ed",
   "metadata": {},
   "source": [
    "**Note:** We reorder the features to facilitate the application of our RobustShapley function. Please refer to [this notebook](https://github.com/ncaptier/radshap/blob/main/examples/nsclc_subtype_classification.ipynb) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4c7d742-5072-4c57-9a58-b28ff8d41265",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reorder_columns(columns, function_names):\n",
    "    d = {f_name: [] for f_name in function_names}\n",
    "    for col in columns:\n",
    "        d[col[1]].append(col)    \n",
    "    return sum(d.values(), [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1eb818f-543d-4955-81ba-a35fa0a781ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_shape_Sphericity_max</th>\n",
       "      <th>original_shape_Elongation_max</th>\n",
       "      <th>original_shape_Flatness_max</th>\n",
       "      <th>original_shape_VoxelVolume_max</th>\n",
       "      <th>original_firstorder_Entropy_max</th>\n",
       "      <th>original_firstorder_Kurtosis_max</th>\n",
       "      <th>original_firstorder_Maximum_max</th>\n",
       "      <th>original_firstorder_Mean_max</th>\n",
       "      <th>original_firstorder_QuartileDispersion_max</th>\n",
       "      <th>original_firstorder_Skewness_max</th>\n",
       "      <th>...</th>\n",
       "      <th>original_shape_Flatness_std</th>\n",
       "      <th>original_shape_VoxelVolume_std</th>\n",
       "      <th>original_firstorder_Entropy_std</th>\n",
       "      <th>original_firstorder_Kurtosis_std</th>\n",
       "      <th>original_firstorder_Maximum_std</th>\n",
       "      <th>original_firstorder_Mean_std</th>\n",
       "      <th>original_firstorder_QuartileDispersion_std</th>\n",
       "      <th>original_firstorder_Skewness_std</th>\n",
       "      <th>liver_SUVMean</th>\n",
       "      <th>spleen_SUVMean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCTOID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient_0</th>\n",
       "      <td>0.863482</td>\n",
       "      <td>0.942119</td>\n",
       "      <td>0.830428</td>\n",
       "      <td>2.473464</td>\n",
       "      <td>4.209365</td>\n",
       "      <td>22.400791</td>\n",
       "      <td>13.207974</td>\n",
       "      <td>4.781307</td>\n",
       "      <td>0.290141</td>\n",
       "      <td>3.597876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177240</td>\n",
       "      <td>0.096137</td>\n",
       "      <td>0.726723</td>\n",
       "      <td>7.276279</td>\n",
       "      <td>3.289046</td>\n",
       "      <td>0.626308</td>\n",
       "      <td>0.071618</td>\n",
       "      <td>1.105949</td>\n",
       "      <td>2.388668</td>\n",
       "      <td>1.700440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_1</th>\n",
       "      <td>0.837041</td>\n",
       "      <td>0.900909</td>\n",
       "      <td>0.596313</td>\n",
       "      <td>2.443369</td>\n",
       "      <td>4.305888</td>\n",
       "      <td>5.855306</td>\n",
       "      <td>10.006734</td>\n",
       "      <td>4.819726</td>\n",
       "      <td>0.283288</td>\n",
       "      <td>1.707722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117702</td>\n",
       "      <td>0.144899</td>\n",
       "      <td>0.537648</td>\n",
       "      <td>1.340308</td>\n",
       "      <td>1.941857</td>\n",
       "      <td>0.531018</td>\n",
       "      <td>0.054307</td>\n",
       "      <td>0.387661</td>\n",
       "      <td>2.413486</td>\n",
       "      <td>2.199252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_2</th>\n",
       "      <td>0.780999</td>\n",
       "      <td>0.705620</td>\n",
       "      <td>0.581406</td>\n",
       "      <td>2.302411</td>\n",
       "      <td>3.800767</td>\n",
       "      <td>2.483848</td>\n",
       "      <td>7.343111</td>\n",
       "      <td>4.192277</td>\n",
       "      <td>0.211119</td>\n",
       "      <td>0.623496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.050283</td>\n",
       "      <td>2.036781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_3</th>\n",
       "      <td>0.867612</td>\n",
       "      <td>0.825841</td>\n",
       "      <td>0.696419</td>\n",
       "      <td>2.560575</td>\n",
       "      <td>4.430960</td>\n",
       "      <td>2.748872</td>\n",
       "      <td>12.187289</td>\n",
       "      <td>7.437633</td>\n",
       "      <td>0.219823</td>\n",
       "      <td>0.692009</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144040</td>\n",
       "      <td>0.184434</td>\n",
       "      <td>0.227022</td>\n",
       "      <td>0.062997</td>\n",
       "      <td>1.544987</td>\n",
       "      <td>1.066581</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.270425</td>\n",
       "      <td>1.616210</td>\n",
       "      <td>1.506132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_4</th>\n",
       "      <td>0.888275</td>\n",
       "      <td>0.899604</td>\n",
       "      <td>0.889127</td>\n",
       "      <td>2.585559</td>\n",
       "      <td>5.803551</td>\n",
       "      <td>7.975884</td>\n",
       "      <td>34.302750</td>\n",
       "      <td>9.359064</td>\n",
       "      <td>0.476792</td>\n",
       "      <td>2.106428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145913</td>\n",
       "      <td>0.149463</td>\n",
       "      <td>0.915027</td>\n",
       "      <td>1.607292</td>\n",
       "      <td>8.746587</td>\n",
       "      <td>1.971811</td>\n",
       "      <td>0.124980</td>\n",
       "      <td>0.451651</td>\n",
       "      <td>3.044990</td>\n",
       "      <td>2.701907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           original_shape_Sphericity_max  original_shape_Elongation_max  \\\n",
       "OCTOID                                                                    \n",
       "patient_0                       0.863482                       0.942119   \n",
       "patient_1                       0.837041                       0.900909   \n",
       "patient_2                       0.780999                       0.705620   \n",
       "patient_3                       0.867612                       0.825841   \n",
       "patient_4                       0.888275                       0.899604   \n",
       "\n",
       "           original_shape_Flatness_max  original_shape_VoxelVolume_max  \\\n",
       "OCTOID                                                                   \n",
       "patient_0                     0.830428                        2.473464   \n",
       "patient_1                     0.596313                        2.443369   \n",
       "patient_2                     0.581406                        2.302411   \n",
       "patient_3                     0.696419                        2.560575   \n",
       "patient_4                     0.889127                        2.585559   \n",
       "\n",
       "           original_firstorder_Entropy_max  original_firstorder_Kurtosis_max  \\\n",
       "OCTOID                                                                         \n",
       "patient_0                         4.209365                         22.400791   \n",
       "patient_1                         4.305888                          5.855306   \n",
       "patient_2                         3.800767                          2.483848   \n",
       "patient_3                         4.430960                          2.748872   \n",
       "patient_4                         5.803551                          7.975884   \n",
       "\n",
       "           original_firstorder_Maximum_max  original_firstorder_Mean_max  \\\n",
       "OCTOID                                                                     \n",
       "patient_0                        13.207974                      4.781307   \n",
       "patient_1                        10.006734                      4.819726   \n",
       "patient_2                         7.343111                      4.192277   \n",
       "patient_3                        12.187289                      7.437633   \n",
       "patient_4                        34.302750                      9.359064   \n",
       "\n",
       "           original_firstorder_QuartileDispersion_max  \\\n",
       "OCTOID                                                  \n",
       "patient_0                                    0.290141   \n",
       "patient_1                                    0.283288   \n",
       "patient_2                                    0.211119   \n",
       "patient_3                                    0.219823   \n",
       "patient_4                                    0.476792   \n",
       "\n",
       "           original_firstorder_Skewness_max  ...  original_shape_Flatness_std  \\\n",
       "OCTOID                                       ...                                \n",
       "patient_0                          3.597876  ...                     0.177240   \n",
       "patient_1                          1.707722  ...                     0.117702   \n",
       "patient_2                          0.623496  ...                     0.000000   \n",
       "patient_3                          0.692009  ...                     0.144040   \n",
       "patient_4                          2.106428  ...                     0.145913   \n",
       "\n",
       "           original_shape_VoxelVolume_std  original_firstorder_Entropy_std  \\\n",
       "OCTOID                                                                       \n",
       "patient_0                        0.096137                         0.726723   \n",
       "patient_1                        0.144899                         0.537648   \n",
       "patient_2                        0.000000                         0.000000   \n",
       "patient_3                        0.184434                         0.227022   \n",
       "patient_4                        0.149463                         0.915027   \n",
       "\n",
       "           original_firstorder_Kurtosis_std  original_firstorder_Maximum_std  \\\n",
       "OCTOID                                                                         \n",
       "patient_0                          7.276279                         3.289046   \n",
       "patient_1                          1.340308                         1.941857   \n",
       "patient_2                          0.000000                         0.000000   \n",
       "patient_3                          0.062997                         1.544987   \n",
       "patient_4                          1.607292                         8.746587   \n",
       "\n",
       "           original_firstorder_Mean_std  \\\n",
       "OCTOID                                    \n",
       "patient_0                      0.626308   \n",
       "patient_1                      0.531018   \n",
       "patient_2                      0.000000   \n",
       "patient_3                      1.066581   \n",
       "patient_4                      1.971811   \n",
       "\n",
       "           original_firstorder_QuartileDispersion_std  \\\n",
       "OCTOID                                                  \n",
       "patient_0                                    0.071618   \n",
       "patient_1                                    0.054307   \n",
       "patient_2                                    0.000000   \n",
       "patient_3                                    0.027920   \n",
       "patient_4                                    0.124980   \n",
       "\n",
       "           original_firstorder_Skewness_std  liver_SUVMean  spleen_SUVMean  \n",
       "OCTOID                                                                      \n",
       "patient_0                          1.105949       2.388668        1.700440  \n",
       "patient_1                          0.387661       2.413486        2.199252  \n",
       "patient_2                          0.000000       3.050283        2.036781  \n",
       "patient_3                          0.270425       1.616210        1.506132  \n",
       "patient_4                          0.451651       3.044990        2.701907  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fun_std = lambda x: x.std(ddof=0) #ddof is set to 0 to normalize by N and not N-1 and thus handle the cases where there is only one lesion\n",
    "fun_std.__name__ = \"std\"\n",
    "\n",
    "mask_liverspleen = df_radiomics_all[\"localization\"].isin([\"Liver\", \"Spleen\"])\n",
    "df_agg = df_radiomics_all[~mask_liverspleen].iloc[:, 2:].groupby(level=0).agg((\"max\", \"min\", fun_std)).loc[df_clinicals.index]\n",
    "\n",
    "#reorder columns\n",
    "df_agg = df_agg[reorder_columns(df_agg.columns, function_names = (\"max\", \"min\", \"std\"))]\n",
    "df_agg.columns = df_agg.columns.map('_'.join)\n",
    "\n",
    "df_agg[\"liver_SUVMean\"] = df_radiomics_all[df_radiomics_all[\"localization\"]== \"Liver\"][\"original_firstorder_Mean\"]\n",
    "df_agg[\"spleen_SUVMean\"] = df_radiomics_all[df_radiomics_all[\"localization\"]== \"Spleen\"][\"original_firstorder_Mean\"]\n",
    "\n",
    "df_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437bbf53-8e34-40bd-8c23-013958aad1aa",
   "metadata": {},
   "source": [
    "### 1.2 Train and test logistic regression mode with repeated cross-validation schemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c8ecd15-40c5-4df9-a249-eea3545ea822",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define specificity scorer\n",
    "def specificity_score(y_true, y_pred):\n",
    "    TN = ((y_true == 0) & (y_pred == 0)).sum()\n",
    "    N = (1-y_true).sum()\n",
    "    return TN/N\n",
    "\n",
    "def fit_and_score(clf, X, y, train, test, results_dict):\n",
    "    Xtrain, Xtest, ytrain,  ytest = X[train, :], X[test, :], y[train], y[test]\n",
    "    clf.fit(Xtrain, ytrain)\n",
    "    preds = clf.predict(Xtest)\n",
    "    probas = clf.predict_proba(Xtest)[:,1]\n",
    "    results_dict[\"roc_auc\"].append(roc_auc_score(ytest, probas))\n",
    "    results_dict[\"balanced_accuracy\"].append(balanced_accuracy_score(ytest, preds))\n",
    "    results_dict[\"sensitivity\"].append(recall_score(ytest, preds))\n",
    "    results_dict[\"specificity\"].append(specificity_score(ytest, preds))\n",
    "    return probas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae40c7d0-c424-4e12-8608-04a1f44e0bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "bmask = ~df_agg[\"spleen_SUVMean\"].isnull() #remove one patient with no spleen (surgically removed before the PET scan)\n",
    "\n",
    "X_all = df_agg[bmask].values\n",
    "y = y = 1*(df_clinicals[\"Histology\"] == 'adenocarcinoma')[bmask].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3a510c5-28db-4d32-889e-cf1fad14d702",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(steps = [(\"scaling\", StandardScaler()),\n",
    "                         (\"logistic\", LogisticRegression(class_weight = \"balanced\"))\n",
    "                        ]\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41b6f1c3-6e48-46f3-891c-6952a205abb2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:05<00:00, 17.27it/s]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0) #fix seed for reproducibility\n",
    "\n",
    "results_all =  collections.defaultdict(list)\n",
    "\n",
    "for i in tqdm(range(100)):\n",
    "\n",
    "    all_dict = collections.defaultdict(list)\n",
    "    \n",
    "    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=i)\n",
    "    \n",
    "    # Iterate over the 5 folds of the cross-validation scheme\n",
    "    for train, test in cv.split(np.zeros(len(y)), y):\n",
    "\n",
    "        probas_all = fit_and_score(clone(pipe), X_all, y, train, test, results_dict=all_dict)\n",
    "     \n",
    "    # Aggregate the results of the 5 folds\n",
    "    for metric in all_dict.keys():\n",
    "        results_all[metric].append(np.mean(all_dict[metric]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8cc54c-12d6-48e0-a66c-11007c8cfa9e",
   "metadata": {},
   "source": [
    "### 1.3 Display the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef9e9721-82e6-4366-83b1-58dbed110a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.746055</td>\n",
       "      <td>0.678546</td>\n",
       "      <td>0.741625</td>\n",
       "      <td>0.615467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.037063</td>\n",
       "      <td>0.044101</td>\n",
       "      <td>0.033859</td>\n",
       "      <td>0.073097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       roc_auc  balanced_accuracy  sensitivity  specificity\n",
       "mean  0.746055           0.678546     0.741625     0.615467\n",
       "std   0.037063           0.044101     0.033859     0.073097"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_all = pd.DataFrame(results_all)\n",
    "\n",
    "results_all.apply(['mean', 'std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd26c04-6a7c-450a-a480-2af23c76992b",
   "metadata": {},
   "source": [
    "## 2. Interpretation of the aggregated model with Robust Shapley values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5709c93-9cca-4421-a419-bda6c669962f",
   "metadata": {},
   "source": [
    "Here we use the **radshap package** to interpret our aggregated radiomic model and highlight the importance of each region for the prediction of the histological subtype. In particular we show how to deal with the liver and the spleen and compute Shapley values with our robust approach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39eddf51-424d-466b-aaa5-840210bfe603",
   "metadata": {},
   "source": [
    "### 2.1 Train and predict on the whole data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3c38de9-21b3-4dcd-8b88-d27c8d156509",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_all = clone(pipe).fit(X_all, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7244b731-1696-407b-9c42-41cfba3fbfde",
   "metadata": {},
   "source": [
    "### 2.2 Compute and display Shapley values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74cf7a96-084d-4d4a-bb20-62cb37097efa",
   "metadata": {},
   "source": [
    "Since we want to compute the Shapley values for both all the lesions of one patients and the liver and the spleen we need to consider the following setting:  \n",
    "\n",
    "\"The lesions, the region in the spleen, and the region in the liver are all comparable regions (meaning characterized by the same features) and there exists a function that aggregates all these regions together into a valid input for the predictive model (i.e. the min, max and std of the features across all the lesions + the SUVmean of the spleen and the SUVmean of the liver).\"\n",
    "\n",
    "In other words we need to represent each patient by a matrix of shape (n_lesions + 2, n_features) that contains all the lesions, the spleen, and the liver, and we need to define an aggregation function which can transform such a matrix into a valid input vector for the logistic regression model. The following figure describes one way of doing it.\n",
    "\n",
    " <img src=\"robust_shapley_example.png\" alt=\"\" class=\"center\"> \n",
    " \n",
    "* Two new features are added to each region, namely the \"liver_SUVmean\" and the \"spleen_SUVmean\" but they only take values for the liver and the spleen respectively. The spleen and the liver are also associated with the other radiomic features but their values are always NaN.\n",
    "* For aggregating the input matrix into a valid input vector we first take the max, min and std of every radiomic feature ignoring the NaNs (i.e. ignoring the spleen and liver regions). We then take the maximum values of the \"liver_SUVmean\" and the \"spleen_SUVmean\" features across all the regions ignoring the NaNs again.\n",
    "\n",
    "The Shapley values computation implies the computation of a prediction for every possible subset of regions. Unfortunately here several subsets of regions will lead to invalid inputs that the logistic regression model cannot predict from. For instance, in the example above, the subset {lesion_1, liver} will lead to a NaN value for the spleen_SUVmean in the input vector, a NaN value that the logistic regression model cannot deal with. We thus need to find a way to still predict something while ignoring the NaNs value for invalid input vectors. The **RobustShapley** class uses the strategy of the [SHAP method](https://shap.readthedocs.io/en/latest/) to deal with these cases. It replaces NaN values by every possible value of a background data set (usually the training set), predict for every replacement, and take the average prediction at the end.\n",
    "\n",
    "**Note:** Here the background data set should correpond to multiple valid aggregated inputs, the simplest solution is to take the aggregated training data set (i.e. *X_all*) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc33e4b-196f-41b5-b6f8-419c88dbb41a",
   "metadata": {},
   "source": [
    "##### **A. Define the appropriate aggregation function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da2fedcf-c3c3-4534-9301-16f2e7e71d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "shap = RobustShapley(predictor = lambda x: model_all.predict_proba(x)[:, 1], \n",
    "                     aggregation = [('nanmax', np.arange(10)), ('nanmin', np.arange(10)), ('nanstd', np.arange(10)), ('nanmax', [10, 11])],\n",
    "                     background_data = X_all\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "291070ca-5232-4a58-8ad5-44d2b3a9e2b4",
   "metadata": {},
   "source": [
    "##### **B. Define the input multi-region matrix for the patient of interest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c756d904-2ca8-42a0-9c78-c61540f9995b",
   "metadata": {},
   "outputs": [],
   "source": [
    "patient = \"patient_31\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a822bea-e3aa-45e5-bdad-3c062579b751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_shape_Sphericity</th>\n",
       "      <th>original_shape_Elongation</th>\n",
       "      <th>original_shape_Flatness</th>\n",
       "      <th>original_shape_VoxelVolume</th>\n",
       "      <th>original_firstorder_Entropy</th>\n",
       "      <th>original_firstorder_Kurtosis</th>\n",
       "      <th>original_firstorder_Maximum</th>\n",
       "      <th>original_firstorder_Mean</th>\n",
       "      <th>original_firstorder_QuartileDispersion</th>\n",
       "      <th>original_firstorder_Skewness</th>\n",
       "      <th>liver_SUVMean</th>\n",
       "      <th>spleen_SUVMean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>localization</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>META_PULM_CL</th>\n",
       "      <td>0.664706</td>\n",
       "      <td>0.640411</td>\n",
       "      <td>0.403067</td>\n",
       "      <td>2.458746</td>\n",
       "      <td>3.537173</td>\n",
       "      <td>2.576803</td>\n",
       "      <td>7.027557</td>\n",
       "      <td>3.804840</td>\n",
       "      <td>0.202877</td>\n",
       "      <td>0.708567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRIMITIF_PULM</th>\n",
       "      <td>0.631080</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>0.556270</td>\n",
       "      <td>2.323567</td>\n",
       "      <td>3.463295</td>\n",
       "      <td>5.036077</td>\n",
       "      <td>7.845982</td>\n",
       "      <td>3.673207</td>\n",
       "      <td>0.179143</td>\n",
       "      <td>1.424637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liver</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.456892</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spleen</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.853674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               original_shape_Sphericity  original_shape_Elongation  \\\n",
       "localization                                                          \n",
       "META_PULM_CL                    0.664706                   0.640411   \n",
       "PRIMITIF_PULM                   0.631080                   0.851270   \n",
       "Liver                                NaN                        NaN   \n",
       "Spleen                               NaN                        NaN   \n",
       "\n",
       "               original_shape_Flatness  original_shape_VoxelVolume  \\\n",
       "localization                                                         \n",
       "META_PULM_CL                  0.403067                    2.458746   \n",
       "PRIMITIF_PULM                 0.556270                    2.323567   \n",
       "Liver                              NaN                         NaN   \n",
       "Spleen                             NaN                         NaN   \n",
       "\n",
       "               original_firstorder_Entropy  original_firstorder_Kurtosis  \\\n",
       "localization                                                               \n",
       "META_PULM_CL                      3.537173                      2.576803   \n",
       "PRIMITIF_PULM                     3.463295                      5.036077   \n",
       "Liver                                  NaN                           NaN   \n",
       "Spleen                                 NaN                           NaN   \n",
       "\n",
       "               original_firstorder_Maximum  original_firstorder_Mean  \\\n",
       "localization                                                           \n",
       "META_PULM_CL                      7.027557                  3.804840   \n",
       "PRIMITIF_PULM                     7.845982                  3.673207   \n",
       "Liver                                  NaN                       NaN   \n",
       "Spleen                                 NaN                       NaN   \n",
       "\n",
       "               original_firstorder_QuartileDispersion  \\\n",
       "localization                                            \n",
       "META_PULM_CL                                 0.202877   \n",
       "PRIMITIF_PULM                                0.179143   \n",
       "Liver                                             NaN   \n",
       "Spleen                                            NaN   \n",
       "\n",
       "               original_firstorder_Skewness  liver_SUVMean  spleen_SUVMean  \n",
       "localization                                                                \n",
       "META_PULM_CL                       0.708567            NaN             NaN  \n",
       "PRIMITIF_PULM                      1.424637            NaN             NaN  \n",
       "Liver                                   NaN       2.456892             NaN  \n",
       "Spleen                                  NaN            NaN        1.853674  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df_radiomics_all.loc[patient].reset_index().set_index(\"localization\")\n",
    "temp.loc[[\"Liver\", \"Spleen\"]] = np.nan\n",
    "temp.loc[\"Liver\", \"liver_SUVMean\"] = df_agg.loc[patient, \"liver_SUVMean\"]\n",
    "temp.loc[\"Spleen\", \"spleen_SUVMean\"] = df_agg.loc[patient, \"spleen_SUVMean\"]\n",
    "temp.iloc[:, 2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8db3a59-19fc-498e-8adb-68354b3d7f8e",
   "metadata": {},
   "source": [
    "##### **C. Compute the Shapley values with RobustShapley**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd54e6c6-ca95-4c88-87ae-36abe8adba9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filtering RuntimeWarning is needed when using nanmin, nanmax or nanstd and inputs with only NaNs.\n",
    "\n",
    "import warnings\n",
    "\n",
    "x_explain = temp.iloc[:, 2:].values\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", RuntimeWarning)\n",
    "    shap_values = shap.explain(x_explain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2c632fe3-9660-4195-866e-c6fb831cd662",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bars(shap_values, names=[\"META_LUNG_CL\", \"PRIMARY_LUNG\", \"LIVER\", \"SPLEEN\"], nbest=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d11054b-5743-4c33-b18f-b5d840d9399b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"E:\\\\Images\\\\\" + patient + \"\\\\baseline_PET\\\\PET.nii.gz\"\n",
    "mask_path = \"E:\\\\Images\\\\\" + patient + \"\\\\baseline_PET\\\\segmentations\\\\\"\n",
    "mask_paths = []\n",
    "for name in df_radiomics_all.loc[patient, \"localization\"].values:\n",
    "    mask_paths.append(mask_path + name + '.uint16.nii.gz')\n",
    "    \n",
    "plot_pet(shap_values, image_path=image_path, masks_paths=mask_paths, cmap_lim = 0.2)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python [conda env:dicom_env]",
   "language": "python",
   "name": "conda-env-dicom_env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}