Predict Spectral RBF Clusters - RF ISO.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bc794da",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ceb513d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "np.set_printoptions(precision=3, suppress = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84aac384",
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_clustered = pd.read_csv(\"Data/patients_clustered_UMAP.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "585b0419",
   "metadata": {},
   "outputs": [
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       "   Unnamed: 0  veteran   age  age_group 2  sex  housing 2  education 2  \\\n",
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     "metadata": {},
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    "imp_clustered.head()"
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  {
   "cell_type": "code",
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  {
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       "   veteran   age  age_group 2  sex  housing 2  education 2  si_race 2  \\\n",
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       "\n",
       "   si_ethnicity 2  language  insured  ...  avg_ip_interval  mf_provider 2  \\\n",
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       "4             10.0     2.947368     4.222222      8.0  2.363636           2.0   \n",
       "\n",
       "   ed_visit_group  op_visit_group  \n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "PATIENT_IDS = imp_clustered[\"patient_id\"]\n",
    "imp_clustered.drop(columns=[\"Unnamed: 0\", \"patient_id\", \"KM_UMAP\"], axis=1, inplace=True, errors=\"ignore\")\n",
    "imp_clustered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aae83cd9",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectral_clusters = pd.read_csv(\"Data/Spectral_Clusters.csv\")\n",
    "spectral_clusters.drop(columns=[\"Unnamed: 0\", \"orig_RBF\", \"UMAP_KNN\", \"orig_KNN\", \"UMAP_RBF\", \"ISO_KNN\"], axis=1, inplace=True)\n",
    "spectral_clusters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a321a6ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRISO_0</th>\n",
       "      <th>SRISO_1</th>\n",
       "      <th>SRISO_2</th>\n",
       "      <th>SRISO_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SRISO_0  SRISO_1  SRISO_2  SRISO_3\n",
       "0        1        0        0        0\n",
       "1        0        0        1        0\n",
       "2        0        1        0        0\n",
       "3        0        0        1        0\n",
       "4        1        0        0        0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "umap_cluster_labels = pd.get_dummies(spectral_clusters[\"ISO_RBF\"])\n",
    "umap_cluster_labels.columns = [\"SRISO_0\", \"SRISO_1\", \"SRISO_2\", \"SRISO_3\"]\n",
    "umap_cluster_labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ad9c57",
   "metadata": {},
   "source": [
    "Determine classification proportions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80423895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of records with 0: 0.083 (187 / 2265)\n",
      "Proportion of records with 2: 0.21 (468 / 2265)\n",
      "Proportion of records with 1: 0.58 (1313 / 2265)\n",
      "Proportion of records with 3: 0.13 (297 / 2265)\n"
     ]
    }
   ],
   "source": [
    "def test_stratified(df, col):\n",
    "    classes=list(df[col].unique())\n",
    "    \n",
    "    for c in classes:\n",
    "        print(f'Proportion of records with {c}: {len(df[df[col]==c])*1./len(df):0.2} ({len(df[df[col]==c])} / {len(df)})')\n",
    "        \n",
    "        \n",
    "test_stratified(spectral_clusters, \"ISO_RBF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f81c9462",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imp_clustered.copy()\n",
    "y = spectral_clusters[\"ISO_RBF\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a70317d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 352 candidates, totalling 1760 fits\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13412/2628780624.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mdfrst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m121\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfrst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'f1_micro'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mgrid_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m# Summarize the results in a readable format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    889\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 891\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1390\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1391\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1392\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    836\u001b[0m                     )\n\u001b[0;32m    837\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 838\u001b[1;33m                 out = parallel(\n\u001b[0m\u001b[0;32m    839\u001b[0m                     delayed(_fit_and_score)(\n\u001b[0;32m    840\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1054\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1055\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1056\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1057\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1058\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    933\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    934\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 935\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    936\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    937\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    541\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    543\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    432\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 434\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "params = {\n",
    "    'n_estimators'      : range(200,1000,50),\n",
    "    'max_depth'         : [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],\n",
    "    'max_features': ['auto'],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "\n",
    "cv = StratifiedShuffleSplit(n_splits=5, test_size=0.3, random_state=121)\n",
    "dfrst = RandomForestClassifier(n_jobs=None, random_state=121)\n",
    "grid = GridSearchCV(estimator=dfrst, param_grid=params, scoring='f1_micro', n_jobs=-1, verbose=5, cv = cv)\n",
    "grid_results = grid.fit(X, y)\n",
    "\n",
    "# Summarize the results in a readable format\n",
    "print(\"Best: {0}, using {1}\".format(grid_results.cv_results_['mean_test_score'], grid_results.best_params_))\n",
    "#results_df = pd.DataFrame(grid_results.cv_results_)\n",
    "#results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c5ee098",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.9315673289183222\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(criterion='entropy', max_depth=13, max_features='auto', n_estimators=250, n_jobs=-1, random_state=121)\n",
    "\n",
    "cv = StratifiedKFold(n_splits=5, random_state=121, shuffle=True)\n",
    "results = pd.DataFrame(columns=['training_score', 'test_score'])\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "    \n",
    "for (train, test), i in zip(cv.split(X, y), range(5)):\n",
    "    clf.fit(X.iloc[train], y.iloc[train])\n",
    "    train_scores.append(clf.score(X.iloc[train], y.iloc[train]))\n",
    "    test_scores.append(clf.score(X.iloc[test], y.iloc[test]))\n",
    "\n",
    "print(np.mean(train_scores))\n",
    "print(np.mean(test_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7c545d",
   "metadata": {},
   "source": [
    "Impurity-based importances; can be misleading for high-cardinality features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "206d5f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1584x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = X.columns\n",
    "importances = clf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)\n",
    "\n",
    "forest_importances = pd.Series(importances, index=feature_names)\n",
    "forest_importances.sort_values(ascending=False, inplace=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(22,20))\n",
    "forest_importances.plot.bar(yerr=std, ax=ax)\n",
    "ax.set_title(\"Feature importances using MDI\")\n",
    "ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec3ab06",
   "metadata": {},
   "source": [
    "Permutation importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd575379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "result = permutation_importance(\n",
    "    clf, X.iloc[test], y.iloc[test], n_repeats=10, random_state=121, n_jobs=2\n",
    ")\n",
    "\n",
    "forest_importances = pd.Series(result.importances_mean, index=feature_names)\n",
    "forest_importances.sort_values(ascending=False, inplace=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(22,20))\n",
    "forest_importances.plot.bar(yerr=result.importances_std, ax=ax)\n",
    "ax.set_title(\"Feature importances using permutation on full model\")\n",
    "ax.set_ylabel(\"Mean accuracy decrease\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d94b23",
   "metadata": {},
   "source": [
    "Confusion Matrix - Multiclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1bd4e757",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.892 0.    0.054 0.054]\n",
      " [0.    0.95  0.019 0.031]\n",
      " [0.011 0.053 0.936 0.   ]\n",
      " [0.033 0.033 0.    0.933]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "\n",
    "y_pred = clf.predict(X.iloc[test])\n",
    "conf_mat = confusion_matrix(y.iloc[test], y_pred, normalize='true')\n",
    "print(conf_mat)\n",
    "\n",
    "\n",
    "disp = plot_confusion_matrix(clf, X.iloc[test], y.iloc[test], \n",
    "                             display_labels=[0,1,2,3], cmap=plt.cm.Greens, normalize='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d11f5f",
   "metadata": {},
   "source": [
    "High accuracy for these clusters, which based on feature importances, seem to be about the level of illness."
   ]
  },
  {
   "cell_type": "code",
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