Q-SHAP Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cff0a1e6",
   "metadata": {},
   "source": [
    "# Tutorial of Using qshap for Q-SHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6f9aa6-5537-4b1c-97b0-0b3bcdb2155a",
   "metadata": {},
   "source": [
    "## Install the Package `qshap`\n",
    "`qshap` is available on PyPI. To install the package, run the following command in terminal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c8a7be1-4b8a-40c6-8f53-12e0851aad4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "python -m pip install qshap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ae8da5-110f-4087-9d33-97cafa0736fc",
   "metadata": {},
   "source": [
    "## Import Required Libraries\n",
    "Before we start, we should import the `gazer` module in `qshap`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ecef9b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qshap import gazer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f162fb-71a1-4780-a615-e00f8324d3bf",
   "metadata": {},
   "source": [
    "Then we import other important packages and modules for the usage of this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91ae1758-31cb-4c66-a24a-6760197c9fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.ensemble\n",
    "import numpy as np\n",
    "import time as time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import xgboost\n",
    "import lightgbm\n",
    "from sklearn.datasets import make_regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14029082-cb9d-4d5e-aa52-ed92b44b62de",
   "metadata": {},
   "source": [
    "## Make an Example Data\n",
    "Let's start with a toy example with 1000 samples and 1000 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57d16b9d-713a-48e3-aa15-b96200b330dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "x, y, coefficients = make_regression(n_samples=1000, n_features=1000, coef=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788ac9c0",
   "metadata": {},
   "source": [
    "## Example 1: Decision Tree from Scikit-learn\n",
    "The initial run after import will be slower because `qshap` calls `Numba`, which uses a Just-in-Time (JIT) compiler that takes time to compile the functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5244e8e",
   "metadata": {},
   "source": [
    "### Construct the decision tree\n",
    "We will use `DecisionTreeRegressor` from scikit-learn to train a decision tree on our simulated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d404625",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fitting\n",
    "# scikit learn decision tree example, the initial run after import will be slower since numba is comiling\n",
    "max_depth = 2\n",
    "tree_regressor = DecisionTreeRegressor(max_depth=max_depth)\n",
    "tree_fit = tree_regressor.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23d4ac13",
   "metadata": {},
   "source": [
    "### Calculate feature-specific $R^2$\n",
    "We instantiate the `gazer` class from `qshap` using the `tree_regressor` fitted in the previous stage. Then, we call the `rsq` method on this instance to calculate the Shapley $R^2$ using the `x` and `y` from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e8f5619",
   "metadata": {},
   "outputs": [],
   "source": [
    "gazer_rsq = gazer(tree_regressor)\n",
    "rsq_res = gazer.rsq(gazer_rsq, x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368b0f28",
   "metadata": {},
   "source": [
    "### Check with model $R^2$\n",
    "In this section, we will check the model $R^2$ as well as the sum of Shapley $R^2$ calculated from `qshap` to verify the additivity property of Shapley values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5acc2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapley R^2 sum is: 0.2759939277003993\n",
      "Model R^2 is: 0.2759939277003993\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Let's check the model R^2\n",
    "ypred = tree_regressor.predict(x)\n",
    "sst = np.sum((y - np.mean(y)) ** 2)\n",
    "sse = np.sum((y - ypred) ** 2)\n",
    "model_rsq = 1 - sse/sst\n",
    "\n",
    "print(\"Shapley R^2 sum is: \" + str(np.sum(rsq_res)))\n",
    "print(\"Model R^2 is: \" + str(model_rsq) + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5609d4",
   "metadata": {},
   "source": [
    "## Example 2: Gradient Boosted Decision Trees from Scikit-learn\n",
    "We will use the same dataset, but use Gradient Boosted Decision Trees (GBDT)s from `scikit-learn` instead. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104eeb20",
   "metadata": {},
   "source": [
    "### Construct gradient boosted decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dc63fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_depth = 2\n",
    "n_estimators = 50\n",
    "tree_regressor = GradientBoostingRegressor(n_estimators=n_estimators, max_depth=max_depth)\n",
    "tree_fit = tree_regressor.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32bcb1d",
   "metadata": {},
   "source": [
    "### Calculate feature-specific $R^2$\n",
    "Note that the usage is the same as using a decision tree as shown in the above example. We instantiate the `gazer` class from `qshap` using the `tree_regressor` fitted in the previous stage. Then, we call the `rsq` method on this instance to calculate the Shapley $R^2$ using the `x` and `y` from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f395cd04",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [00:02<00:00, 22.91it/s]\n"
     ]
    }
   ],
   "source": [
    "gazer_rsq = gazer(tree_regressor)\n",
    "rsq_res = gazer.rsq(gazer_rsq, x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c86dc24",
   "metadata": {},
   "source": [
    "## Example 3: Tree Ensemble from XGBoost\n",
    "We will demonstrate how to use `qshap` for XGBoost in this section, on the same dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "333ba00f",
   "metadata": {},
   "source": [
    "### Construct a tree ensemble using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5c62576b",
   "metadata": {},
   "outputs": [
    {
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       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=2, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=50, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=2, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=50, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=2, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=50, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_depth = 2\n",
    "n_estimators = 50\n",
    "tree_regressor = xgboost.XGBRegressor(n_estimators=n_estimators, max_depth=max_depth)\n",
    "tree_regressor.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6acc4eec",
   "metadata": {},
   "source": [
    "### Calculate feature-specific $R^2$\n",
    "Note that the usage is the same as using a decision tree as shown in the above example. We instantiate the `gazer` class from `qshap` using the `tree_regressor` fitted in the previous stage. Then, we call the `rsq` method on this instance to calculate the Shapley $R^2$ using the `x` and `y` from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e461e927",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [00:02<00:00, 20.31it/s]\n"
     ]
    }
   ],
   "source": [
    "gazer_rsq = gazer(tree_regressor)\n",
    "rsq_res = gazer.rsq(gazer_rsq, x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e6367b-3730-4f1e-bc9c-9cf02ebdd6f9",
   "metadata": {},
   "source": [
    "## Example 4: Tree Ensemble from LightGBM\n",
    "We will demonstrate how to use `qshap` for LightGBM in this section, on the same dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3fed499-b442-437e-9c59-d9d710d18131",
   "metadata": {},
   "source": [
    "### Construct a tree ensemble using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "331088d7-1dc7-4297-82b7-19f1034c9d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(max_depth=2, n_estimators=50, verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMRegressor(max_depth=2, n_estimators=50, verbose=-1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor(max_depth=2, n_estimators=50, verbose=-1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_depth = 2\n",
    "n_estimators = 50\n",
    "tree_regressor = lightgbm.LGBMRegressor(n_estimators=n_estimators, max_depth=max_depth, verbose=-1)\n",
    "tree_regressor.fit(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830b42df-bd74-4e6b-ae32-9fec9e026bdf",
   "metadata": {},
   "source": [
    "### Calculate feature-specific $R^2$\n",
    "Note that the usage is the same as using XGBoost as shown in the above example. We instantiate the `gazer` class from `qshap` using the `tree_regressor` fitted in the previous stage. Then, we call the `rsq` method on this instance to calculate the Shapley $R^2$ using the `x` and `y` from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0d91e553-4e25-4f2f-9753-74cf9ad09fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [00:02<00:00, 22.58it/s]\n"
     ]
    }
   ],
   "source": [
    "gazer_rsq = gazer(tree_regressor)\n",
    "rsq_res = gazer.rsq(gazer_rsq, x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ed884c",
   "metadata": {},
   "source": [
    "## Use Sampling Method for Data of Large Sample Size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4faea620",
   "metadata": {},
   "source": [
    "When the sample size is very large, it would be very time-consuming to use all the samples to calculate Shapley $R^2$. Instead, users can use part of the samples by specifying the sampling number through `nsample`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "04d54782",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [00:01<00:00, 41.94it/s]\n"
     ]
    }
   ],
   "source": [
    "gazer_rsq = gazer(tree_regressor)\n",
    "rsq_res_frac = gazer.rsq(gazer_rsq, x, y, nsample=512)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7214c9",
   "metadata": {},
   "source": [
    "##  Extract Quadratic Loss for Each Sample\n",
    "`qshap` calculates Shapley $R^2$ by aggregating decomposed quadratic losses over individual samples. The package allows to check the Shapley decomposition of quadratic losses of individual samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56589ff2",
   "metadata": {},
   "source": [
    "### Output $R^2$ and quadratic loss\n",
    "Setting the argument `loss_out` to `True` will simply output both $R^2$ and quadratic losses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "610377e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [00:02<00:00, 22.78it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsq_res2 = gazer.rsq(gazer_rsq, x, y, loss_out=True)\n",
    "rsq_res2.rsq\n",
    "rsq_res2.loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95071d82",
   "metadata": {},
   "source": [
    "### Output the quadratic loss only\n",
    "If you are only intersted in the quadratic loss, its decomposition could be directly calculated using the `loss` funciton of the class `gazer`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7a327de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [00:02<00:00, 20.66it/s]\n"
     ]
    }
   ],
   "source": [
    "loss_res = gazer.loss(gazer_rsq, x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4eef0f0",
   "metadata": {},
   "source": [
    "### Calculate the quadratic loss for limited samples\n",
    "If you are only interested in a small set of samples for their quadratic losses attached to different features, you don't need go through all samples. All you have to do is to add the original mean of y, in addition to the data of interest. This makes the calculation very fast, especially when the sample size is large. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9b15665d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 50/50 [00:00<00:00, 2728.93it/s]\n",
      "100%|██████████████████████████████████████████| 50/50 [00:00<00:00, 208.74it/s]\n"
     ]
    }
   ],
   "source": [
    "# loss decomposition for the first sample.\n",
    "loss_sample1 = gazer.loss(gazer_rsq, x[0:1, :], y[0], y_mean_ori=np.mean(y))\n",
    "# loss decomposition for the first 100 samples.\n",
    "loss_multiple = gazer.loss(gazer_rsq, x[0:99], y[0:99], y_mean_ori=np.mean(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e65829",
   "metadata": {},
   "source": [
    "## Visualization of Feature-Specific $R^2$\n",
    "This section will demonstrate how to use the visualization module in `qshap`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aac8e5a",
   "metadata": {},
   "source": [
    "### Import the module\n",
    "First, we import the `vis` module from `qshap`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4720a1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qshap import vis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b95fe4",
   "metadata": {},
   "source": [
    "### Quick plot\n",
    "Simply call the `rsq` function in the `vis` class on the result from `gazer.rsq` to visualize.  \n",
    "(`vis` is a general function that can work on numpy arrays)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c8b73743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# default\n",
    "vis.rsq(rsq_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af995726",
   "metadata": {},
   "source": [
    "### Different color schemes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2f8f69",
   "metadata": {},
   "source": [
    "You can change the color map of the histogram by changing `color_map_name`, taking arguments from `matplotlib`, with some examples [here](https://matplotlib.org/stable/users/explain/colors/colormaps.html). In addition, you can focus on more influential by setting a cutoff of $R^2$ by changing the `cutoff` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7320660e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change color \n",
    "vis.rsq(rsq_res, cutoff=0.05, color_map_name=\"Pastel2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11137f6f",
   "metadata": {},
   "source": [
    "### More options\n",
    "There are many other options for the visualization function. Below is an example that outputs a horizontal plot, hides \"Model $R^2$\" label, changes the number of features to show, and saves the plot in pdf format with a user-specified name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "774aa780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis.rsq(rsq_res, color_map_name=\"PuBu\", horizontal=True, model_rsq=False, max_feature=15, save_name=\"rsq_eg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a105f5",
   "metadata": {},
   "source": [
    "### Visualization of quadratic losses of individual samples\n",
    "We can use the function `loss` to visualize the Shapley decomposition of individual quadratic losses.\n",
    "The plot is interactive and you can explore the sample of interest by changing the value in the box next to \"Sample Index\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "99fd5888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "016eeefc943549a88757c2d3aac728c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(BoundedIntText(value=0, continuous_update=True, description='Sample Index', layout=Layout(width…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9668eb3bdc04abfae3f2d36fd37b792",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vidualize individual loss\n",
    "# The interactive effect works with jupyter notebook\n",
    "vis.loss(loss_res)\n",
    "\n",
    "# Find a lovely plot for one sample and save it, say for the 10-th sample\n",
    "vis.loss(loss_res, save_ind=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4e6197",
   "metadata": {},
   "source": [
    "### Generalized correlation of individual features to the outcome\n",
    "For each feature, its generalized correlation to the outcome is defined as the square root of its Shapley $R^2$. You can visualize it by simply calling `gcorr` on the output of `gazer.rsq`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1a7532bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis.gcorr(rsq_res, color_map_name=\"viridis\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}