machine_learning_explainability/04-shap-values.ipynb

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    "**[Machine Learning Explainability Home Page](https://www.kaggle.com/learn/machine-learning-explainability)**\n",
    "\n",
    "---\n",
    "\n",
    "*This exercise involves you writing code, and we check it automatically to tell you if it's right. We're having a temporary problem with out checking infrastructure, causing a bar that says `None` in some cases when you have the right answer. We're sorry. We're fixing it. In the meantime, if you see a bar saying `None` that means you've done something good.*"
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   "source": [
    "## Set Up\n",
    "\n",
    "At this point, you have enough tools to put together compelling solutions to real-world problems. You will ned to pick the right techniques for each part of the following data science scenario. Along the way, you'll use SHAP values along with your other insights tools.\n",
    "\n",
    "**The questions below give you feedback on your work by using some checking code. Run the following cell to set up our feedback system.**"
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     "text": [
      "Setup Complete\n"
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   "source": [
    "from learntools.ml_explainability.ex4 import *\n",
    "print(\"Setup Complete\")"
   ]
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    "## The Scenario\n",
    "A hospital has struggled with \"readmissions,\" where they release a patient before the patient has recovered enough, and the patient returns with health complications. \n",
    "\n",
    "The hospital wants your help identifying patients at highest risk of being readmitted. Doctors (rather than your model) will make the final decision about when to release each patient; but they hope your model will highlight issues the doctors should consider when releasing a patient.\n",
    "\n",
    "The hospital has given you relevant patient medical information.  Here is a list of columns in the data:\n"
   ]
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       "Index(['time_in_hospital', 'num_lab_procedures', 'num_procedures',\n",
       "       'num_medications', 'number_outpatient', 'number_emergency',\n",
       "       'number_inpatient', 'number_diagnoses', 'race_Caucasian',\n",
       "       'race_AfricanAmerican', 'gender_Female', 'age_[70-80)', 'age_[60-70)',\n",
       "       'age_[50-60)', 'age_[80-90)', 'age_[40-50)', 'payer_code_?',\n",
       "       'payer_code_MC', 'payer_code_HM', 'payer_code_SP', 'payer_code_BC',\n",
       "       'medical_specialty_?', 'medical_specialty_InternalMedicine',\n",
       "       'medical_specialty_Emergency/Trauma',\n",
       "       'medical_specialty_Family/GeneralPractice',\n",
       "       'medical_specialty_Cardiology', 'diag_1_428', 'diag_1_414',\n",
       "       'diag_1_786', 'diag_2_276', 'diag_2_428', 'diag_2_250', 'diag_2_427',\n",
       "       'diag_3_250', 'diag_3_401', 'diag_3_276', 'diag_3_428',\n",
       "       'max_glu_serum_None', 'A1Cresult_None', 'metformin_No',\n",
       "       'repaglinide_No', 'nateglinide_No', 'chlorpropamide_No',\n",
       "       'glimepiride_No', 'acetohexamide_No', 'glipizide_No', 'glyburide_No',\n",
       "       'tolbutamide_No', 'pioglitazone_No', 'rosiglitazone_No', 'acarbose_No',\n",
       "       'miglitol_No', 'troglitazone_No', 'tolazamide_No', 'examide_No',\n",
       "       'citoglipton_No', 'insulin_No', 'glyburide-metformin_No',\n",
       "       'glipizide-metformin_No', 'glimepiride-pioglitazone_No',\n",
       "       'metformin-rosiglitazone_No', 'metformin-pioglitazone_No', 'change_No',\n",
       "       'diabetesMed_Yes', 'readmitted'],\n",
       "      dtype='object')"
      ]
     },
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   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('../input/hospital-readmissions/train.csv')\n",
    "data.columns"
   ]
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    "Here are some quick hints at interpreting the field names:\n",
    "\n",
    "- Your prediction target is `readmitted`\n",
    "- Columns with the word `diag` indicate the diagnostic code of the illness or illnesses the patient was admitted with. For example, `diag_1_428` means the doctor said their first illness diagnosis is number \"428\".  What illness does 428 correspond to? You could look it up in a codebook, but without more medical background it wouldn't mean anything to you anyway.\n",
    "- A column names like `glimepiride_No` mean the patient did not have the medicine `glimepiride`. If this feature had a value of False, then the patient did take the drug `glimepiride`\n",
    "- Features whose names begin with `medical_specialty` describe the specialty of the doctor seeing the patient. The values in these fields are all `True` or `False`.\n"
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   "source": [
    "## Your Code Library\n",
    "As you write code to work through this scenario, these code snippets from previous tutorials may be useful. You'll still need to modify them, but we've copied them here to save you from having to look them up.\n",
    "\n",
    "**Calculate and show permutation importance:**\n",
    "```\n",
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "\n",
    "perm = PermutationImportance(my_model, random_state=1).fit(val_X, val_y)\n",
    "eli5.show_weights(perm, feature_names = val_X.columns.tolist())\n",
    "```\n",
    "\n",
    "**Calculate and show partial dependence plot:**\n",
    "```\n",
    "from matplotlib import pyplot as plt\n",
    "from pdpbox import pdp, get_dataset, info_plots\n",
    "\n",
    "# Create the data that we will plot\n",
    "pdp_goals = pdp.pdp_isolate(model=my_model, dataset=val_X, model_features=feature_names, feature='Goal Scored')\n",
    "\n",
    "# plot it\n",
    "pdp.pdp_plot(pdp_goals, 'Goal Scored')\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "**Calculate and show Shap Values for One Prediction:**\n",
    "```\n",
    "import shap  # package used to calculate Shap values\n",
    "\n",
    "data_for_prediction = val_X.iloc[0,:]  # use 1 row of data here. Could use multiple rows if desired\n",
    "\n",
    "# Create object that can calculate shap values\n",
    "explainer = shap.TreeExplainer(my_model)\n",
    "shap_values = explainer.shap_values(data_for_prediction)\n",
    "shap.initjs()\n",
    "shap.force_plot(explainer.expected_value[0], shap_values[0], data_for_prediction)\n",
    "```\n",
    "\n",
    "## Step 1:\n",
    "You have built a simple model, but the doctors say they don't know how to evaluate a model, and they'd like you to show them some evidence the model is doing something in line with their medical intuition. Create any graphics or tables that will show them a quick overview of what the model is doing?\n",
    "\n",
    "They are very busy. So they want you to condense your model overview into just 1 or 2 graphics, rather than a long string of graphics.\n",
    "\n",
    "We'll start after the point where you've built a basic model. Just run the following cell to build the model called `my_model`."
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   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "data = pd.read_csv('../input/hospital-readmissions/train.csv')\n",
    "\n",
    "y = data.readmitted\n",
    "\n",
    "base_features = [c for c in data.columns if c != \"readmitted\"]\n",
    "\n",
    "X = data[base_features]\n",
    "\n",
    "\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)\n",
    "my_model = RandomForestClassifier(n_estimators=30, random_state=1).fit(train_X, train_y)\n"
   ]
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   "source": [
    "Now use the following cell to create the materials for the doctors."
   ]
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       "                medical_specialty_Emergency/Trauma\n",
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       "                0.0014\n",
       "                \n",
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       "                \n",
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       "                \n",
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       "                medical_specialty_Family/GeneralPractice\n",
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       "                0.0013\n",
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       "                diag_2_427\n",
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       "                0.0013\n",
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       "                \n",
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       "                diag_2_276\n",
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       "                0.0011\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
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       "                age_[50-60)\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.62%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0010\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                age_[80-90)\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.88%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0006\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                repaglinide_No\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.02%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0010\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                diag_1_428\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.05%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                payer_code_SP\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.17%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0005\n",
       "                \n",
       "                    &plusmn; 0.0030\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                insulin_No\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.25%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0028\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                diabetesMed_Yes\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.33%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0021\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                diag_3_250\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.37%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0018\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                diag_2_250\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.46%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0015\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                glipizide_No\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 99.46%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 44 more &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "\n",
    "perm = PermutationImportance(my_model, random_state=1).fit(val_X, val_y)\n",
    "eli5.show_weights(perm, feature_names = val_X.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08c548e",
   "metadata": {
    "papermill": {
     "duration": 0.024725,
     "end_time": "2021-08-18T01:11:41.905370",
     "exception": false,
     "start_time": "2021-08-18T01:11:41.880645",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For our idea of what to show, run the cell below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ffe99bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:41.962097Z",
     "iopub.status.busy": "2021-08-18T01:11:41.961179Z",
     "iopub.status.idle": "2021-08-18T01:11:41.965744Z",
     "shell.execute_reply": "2021-08-18T01:11:41.966295Z",
     "shell.execute_reply.started": "2021-08-18T01:10:17.069521Z"
    },
    "papermill": {
     "duration": 0.034324,
     "end_time": "2021-08-18T01:11:41.966523",
     "exception": false,
     "start_time": "2021-08-18T01:11:41.932199",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# q_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534b77c9",
   "metadata": {
    "papermill": {
     "duration": 0.02479,
     "end_time": "2021-08-18T01:11:42.016324",
     "exception": false,
     "start_time": "2021-08-18T01:11:41.991534",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If you'd like to discuss your approach or see what others have done, we have a discussion forum [here](https://www.kaggle.com/learn-forum/66267#latest-390149).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1613fd3",
   "metadata": {
    "papermill": {
     "duration": 0.025257,
     "end_time": "2021-08-18T01:11:42.067738",
     "exception": false,
     "start_time": "2021-08-18T01:11:42.042481",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 2\n",
    "\n",
    "It appears `number_inpatient` is a really important feature. The doctors would like to know more about that. Create a graph for them that shows how `num_inpatient` affects the model's predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "563c2ae4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:42.124843Z",
     "iopub.status.busy": "2021-08-18T01:11:42.124109Z",
     "iopub.status.idle": "2021-08-18T01:11:43.064731Z",
     "shell.execute_reply": "2021-08-18T01:11:43.063899Z",
     "shell.execute_reply.started": "2021-08-18T01:10:17.075313Z"
    },
    "papermill": {
     "duration": 0.971176,
     "end_time": "2021-08-18T01:11:43.064948",
     "exception": false,
     "start_time": "2021-08-18T01:11:42.093772",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your Code Here\n",
    "from matplotlib import pyplot as plt\n",
    "from pdpbox import pdp, get_dataset, info_plots\n",
    "\n",
    "# Create the data that we will plot\n",
    "pdp_n_in = pdp.pdp_isolate(model=my_model, dataset=val_X, model_features=base_features, feature='number_inpatient')\n",
    "\n",
    "# plot it\n",
    "pdp.pdp_plot(pdp_n_in, 'number_inpatient')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d076af",
   "metadata": {
    "papermill": {
     "duration": 0.026431,
     "end_time": "2021-08-18T01:11:43.119512",
     "exception": false,
     "start_time": "2021-08-18T01:11:43.093081",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For our solution, uncomment the line below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cd46382",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:43.178897Z",
     "iopub.status.busy": "2021-08-18T01:11:43.178147Z",
     "iopub.status.idle": "2021-08-18T01:11:43.181759Z",
     "shell.execute_reply": "2021-08-18T01:11:43.181038Z",
     "shell.execute_reply.started": "2021-08-18T01:10:17.817798Z"
    },
    "papermill": {
     "duration": 0.035868,
     "end_time": "2021-08-18T01:11:43.181922",
     "exception": false,
     "start_time": "2021-08-18T01:11:43.146054",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# q_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e3bd39",
   "metadata": {
    "papermill": {
     "duration": 0.025431,
     "end_time": "2021-08-18T01:11:43.233598",
     "exception": false,
     "start_time": "2021-08-18T01:11:43.208167",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 3\n",
    "\n",
    "The doctors think it's a good sign that increasing the number of inpatient procedures leads to increased predictions.  But they can't tell from this plot whether that change in the plot is big or small. They'd like you to create something similar for `time_in_hospital` to see how that compares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd504b5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:43.291310Z",
     "iopub.status.busy": "2021-08-18T01:11:43.290566Z",
     "iopub.status.idle": "2021-08-18T01:11:44.443936Z",
     "shell.execute_reply": "2021-08-18T01:11:44.442918Z",
     "shell.execute_reply.started": "2021-08-18T01:10:17.824408Z"
    },
    "papermill": {
     "duration": 1.185105,
     "end_time": "2021-08-18T01:11:44.444208",
     "exception": false,
     "start_time": "2021-08-18T01:11:43.259103",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your Code Here\n",
    "from matplotlib import pyplot as plt\n",
    "from pdpbox import pdp, get_dataset, info_plots\n",
    "\n",
    "# Create the data that we will plot\n",
    "pdp_time = pdp.pdp_isolate(model=my_model, dataset=val_X, model_features=base_features, feature='time_in_hospital')\n",
    "\n",
    "# plot it\n",
    "pdp.pdp_plot(pdp_time, 'time_in_hospital')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046c0486",
   "metadata": {
    "papermill": {
     "duration": 0.027697,
     "end_time": "2021-08-18T01:11:44.500908",
     "exception": false,
     "start_time": "2021-08-18T01:11:44.473211",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Uncomment the relevant line below to see one solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78a7d275",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:44.560049Z",
     "iopub.status.busy": "2021-08-18T01:11:44.558812Z",
     "iopub.status.idle": "2021-08-18T01:11:44.564551Z",
     "shell.execute_reply": "2021-08-18T01:11:44.563972Z",
     "shell.execute_reply.started": "2021-08-18T01:10:18.996739Z"
    },
    "papermill": {
     "duration": 0.037116,
     "end_time": "2021-08-18T01:11:44.564708",
     "exception": false,
     "start_time": "2021-08-18T01:11:44.527592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# q_3.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136b9935",
   "metadata": {
    "papermill": {
     "duration": 0.028846,
     "end_time": "2021-08-18T01:11:44.622109",
     "exception": false,
     "start_time": "2021-08-18T01:11:44.593263",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 4\n",
    "\n",
    "Woah!  It seems like `time_in_hospital` doesn't matter at all.  The difference between the lowest value on the partial dependence plot and the highest value is about 5%.\n",
    "\n",
    "If that is what your model concluded, the doctors will believe it. But it seems so low. Could  the data be wrong, or is your model doing something more complex than they expect?  \n",
    "\n",
    "They'd like you to show them the raw readmission rate for each value of `time_in_hospital` to see how it compares to the partial dependence plot.\n",
    "\n",
    "- Make that plot. \n",
    "- Are the results similar or different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "478c57df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:44.687772Z",
     "iopub.status.busy": "2021-08-18T01:11:44.686435Z",
     "iopub.status.idle": "2021-08-18T01:11:44.941902Z",
     "shell.execute_reply": "2021-08-18T01:11:44.941293Z",
     "shell.execute_reply.started": "2021-08-18T01:10:19.001816Z"
    },
    "papermill": {
     "duration": 0.291793,
     "end_time": "2021-08-18T01:11:44.942065",
     "exception": false,
     "start_time": "2021-08-18T01:11:44.650272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your Code Here\n",
    "train_data = pd.concat([train_X, train_y], axis=1)\n",
    "\n",
    "train_data.groupby(['time_in_hospital']).mean().readmitted.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08236627",
   "metadata": {
    "papermill": {
     "duration": 0.029213,
     "end_time": "2021-08-18T01:11:45.001797",
     "exception": false,
     "start_time": "2021-08-18T01:11:44.972584",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For a hint or solution, uncomment the relevant line below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f80bb7da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:45.066254Z",
     "iopub.status.busy": "2021-08-18T01:11:45.065556Z",
     "iopub.status.idle": "2021-08-18T01:11:45.071857Z",
     "shell.execute_reply": "2021-08-18T01:11:45.072450Z",
     "shell.execute_reply.started": "2021-08-18T01:10:19.213869Z"
    },
    "papermill": {
     "duration": 0.04198,
     "end_time": "2021-08-18T01:11:45.072643",
     "exception": false,
     "start_time": "2021-08-18T01:11:45.030663",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 2, \"questionType\": 4, \"questionId\": \"4_RawActualsInsteadOfPDP\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#3366cc\">Hint:</span> This requires a groupby (from pandas) on the raw data, rather than using a model"
      ],
      "text/plain": [
       "Hint: This requires a groupby (from pandas) on the raw data, rather than using a model"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q_4.hint()\n",
    "# q_4.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04580a70",
   "metadata": {
    "papermill": {
     "duration": 0.030871,
     "end_time": "2021-08-18T01:11:45.133284",
     "exception": false,
     "start_time": "2021-08-18T01:11:45.102413",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 5\n",
    "Now the doctors are convinced you have the right data, and the model overview looked reasonable.  It's time to turn this into a finished product they can use. Specifically, the hospital wants you to create a function `patient_risk_factors` that does the following\n",
    "- Takes a single row with patient data (of the same format you as your raw data)\n",
    "- Creates a visualization showing what features of that patient increased their risk of readmission, what features decreased it, and how much those features mattered.\n",
    "\n",
    "It's not important to show every feature with every miniscule impact on the readmission risk.  It's fine to focus on only the most important features for that patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a27bbcd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:45.198156Z",
     "iopub.status.busy": "2021-08-18T01:11:45.197028Z",
     "iopub.status.idle": "2021-08-18T01:11:48.483352Z",
     "shell.execute_reply": "2021-08-18T01:11:48.482609Z",
     "shell.execute_reply.started": "2021-08-18T01:10:19.225699Z"
    },
    "papermill": {
     "duration": 3.319258,
     "end_time": "2021-08-18T01:11:48.483538",
     "exception": false,
     "start_time": "2021-08-18T01:11:45.164280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_in_hospital                   2\n",
      "num_lab_procedures                23\n",
      "num_procedures                     1\n",
      "num_medications                    8\n",
      "number_outpatient                  2\n",
      "                               ...  \n",
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p=this.xlabel.selectAll(\"option\").data(f);p.enter().append(\"option\").merge(p).attr(\"value\",function(t){return t}).text(function(t){return t}),p.exit().remove();var h=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";f=(0,y.map)(this.usedFeatures,function(e){return[t.props.featureNames[e],t.props.featureNames[e]+\" effects\"]}),f.unshift([\"model output value\",h]);var d=this.ylabel.selectAll(\"option\").data(f);d.enter().append(\"option\").merge(d).attr(\"value\",function(t){return t[0]}).text(function(t){return t[1]}),d.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var t=this,e=!0,n=!1,r=void 0;try{for(var i,o=this.props.explanations[Symbol.iterator]();!(e=(i=o.next()).done);e=!0){var a=i.value,c=!0,s=!1,l=void 0;try{for(var 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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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-3.8851380290158926e-05, \"value\": 1.0}, \"59\": {\"effect\": -9.642930565150113e-07, \"value\": 1.0}, \"62\": {\"effect\": 0.020239573659819297, \"value\": 0.0}, \"63\": {\"effect\": 0.0011643662381777312, \"value\": 1.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iE8BZIDOY7RXTK19WFBI3')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x7fe9aebe9e50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your Code Here\n",
    "import shap  # package used to calculate Shap values\n",
    "\n",
    "data_for_prediction = val_X.iloc[1,:]  # use 1 row of data here. Could use multiple rows if desired\n",
    "\n",
    "def patient_risk_factors(model, patient_features):\n",
    "    # Create object that can calculate shap values\n",
    "    print(patient_features)\n",
    "    explainer = shap.TreeExplainer(model)\n",
    "    shap_values = explainer.shap_values(patient_features)\n",
    "    print(shap_values)\n",
    "    shap.initjs()\n",
    "    return shap.force_plot(explainer.expected_value[1], shap_values[1], patient_features)\n",
    "\n",
    "patient_risk_factors(my_model, data_for_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b48652",
   "metadata": {
    "papermill": {
     "duration": 0.041061,
     "end_time": "2021-08-18T01:11:48.566896",
     "exception": false,
     "start_time": "2021-08-18T01:11:48.525835",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Uncomment the relevant line below for a hint or solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2ed00fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-18T01:11:48.671741Z",
     "iopub.status.busy": "2021-08-18T01:11:48.671011Z",
     "iopub.status.idle": "2021-08-18T01:11:48.677475Z",
     "shell.execute_reply": "2021-08-18T01:11:48.676803Z",
     "shell.execute_reply.started": "2021-08-18T01:10:22.490055Z"
    },
    "papermill": {
     "duration": 0.060576,
     "end_time": "2021-08-18T01:11:48.677656",
     "exception": false,
     "start_time": "2021-08-18T01:11:48.617080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# q_5.hint()\n",
    "# q_5.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545db600",
   "metadata": {
    "papermill": {
     "duration": 0.042185,
     "end_time": "2021-08-18T01:11:48.766365",
     "exception": false,
     "start_time": "2021-08-18T01:11:48.724180",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Keep Going\n",
    "You have some powerful tools to get insights about both models and individual predictions. Next you will look at **[Aggregations of SHAP Values](https://www.kaggle.com/dansbecker/advanced-uses-of-shap-values)** to link the model-level and prediction-level insights.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9c542a",
   "metadata": {
    "papermill": {
     "duration": 0.047347,
     "end_time": "2021-08-18T01:11:48.860052",
     "exception": false,
     "start_time": "2021-08-18T01:11:48.812705",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "**[Machine Learning Explainability Home Page](https://www.kaggle.com/learn/machine-learning-explainability)**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum) to chat with other Learners.*"
   ]
  }
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