diabete_rf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7255abf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2945d52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_parquet(r\"./data/processedData_first.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d88de71b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data[['systolic','diastolic','hdl','ldl','bmi','age','diabetic']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a01eb3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>patientid</th>\n",
       "      <th>dateofobservation</th>\n",
       "      <th>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>hdl</th>\n",
       "      <th>ldl</th>\n",
       "      <th>bmi</th>\n",
       "      <th>age</th>\n",
       "      <th>start</th>\n",
       "      <th>diabetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02314aa6-55ab-e606-7089-e3185adee368</td>\n",
       "      <td>2015-02-18T14:33:45Z</td>\n",
       "      <td>145.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>66.1</td>\n",
       "      <td>84.5</td>\n",
       "      <td>29.2</td>\n",
       "      <td>31.221918</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>032391df-fed2-6b2d-f882-d0328ef23cbe</td>\n",
       "      <td>2013-03-22T18:42:57Z</td>\n",
       "      <td>111.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>83.0</td>\n",
       "      <td>29.3</td>\n",
       "      <td>31.221918</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              patientid     dateofobservation systolic  \\\n",
       "0  02314aa6-55ab-e606-7089-e3185adee368  2015-02-18T14:33:45Z    145.0   \n",
       "1  032391df-fed2-6b2d-f882-d0328ef23cbe  2013-03-22T18:42:57Z    111.0   \n",
       "\n",
       "  diastolic   hdl   ldl   bmi        age       start  diabetic  \n",
       "0     111.0  66.1  84.5  29.2  31.221918  2015-02-18         1  \n",
       "1      84.0  71.1  83.0  29.3  31.221918        None         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b5fb5a1",
   "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>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>hdl</th>\n",
       "      <th>ldl</th>\n",
       "      <th>bmi</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>145.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>66.1</td>\n",
       "      <td>84.5</td>\n",
       "      <td>29.2</td>\n",
       "      <td>31.221918</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>83.0</td>\n",
       "      <td>29.3</td>\n",
       "      <td>31.221918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  systolic diastolic   hdl   ldl   bmi        age  diabetic\n",
       "0    145.0     111.0  66.1  84.5  29.2  31.221918         1\n",
       "1    111.0      84.0  71.1  83.0  29.3  31.221918         0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2fc04ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"diabetic\"].value_counts().plot(kind=\"bar\",color=[\"orange\",\"crimson\"])\n",
    "plt.xticks(np.arange(2), ('No Diabetes', 'Diabetes'),rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b55d9f0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 230.05x180 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "sns.pairplot(df,hue='diabetic',palette='gnuplot');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a2644477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,col in enumerate(['systolic','diastolic','hdl','ldl','bmi']):\n",
    "    plt.figure(i)\n",
    "    sns.histplot(data=df[df['diabetic']==1],x=col).set(xscale=\"linear\",title=\"{} distribution\".format(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfd5c13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "df.sample(frac=1)\n",
    "\n",
    "\n",
    "X = df.drop(\"diabetic\",axis=1)\n",
    "y = df[\"diabetic\"]\n",
    "X_train,X_test,y_train,y_test =  train_test_split(X,\n",
    "                                              y,\n",
    "                                              test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "abbe6284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18188, 4548)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(X_train),len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "956ae1de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9311785400175902"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression(random_state=0)\n",
    "log_reg.fit(X_train,y_train)\n",
    "log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f14857e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9423922603342129"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train,y_train);\n",
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "643f9587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9481090589270009"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf= RandomForestClassifier()\n",
    "clf.fit(X_train,y_train);\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7f39cd2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9397537379067722"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC()\n",
    "svm.fit(X_train,y_train)\n",
    "svm.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f87a224a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a814cd26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
      "[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:  5.9min\n",
      "[Parallel(n_jobs=3)]: Done 300 out of 300 | elapsed:  9.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9478891820580475"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "rfc=RandomForestClassifier(random_state=42)\n",
    "gs_clf = GridSearchCV(rfc,\n",
    "                          param_grid=param_grid,\n",
    "                          cv=5,\n",
    "                          n_jobs=3,\n",
    "                          verbose=True)\n",
    "\n",
    "#fit grid search cv\n",
    "gs_clf.fit(X_train,y_train)\n",
    "gs_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aca10447",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
      "[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:  2.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9476693051890941"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "rs_clf = RandomizedSearchCV(rfc,\n",
    "                                param_distributions=param_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                n_jobs=3,\n",
    "                                verbose=True)\n",
    "\n",
    "\n",
    "rs_clf.fit(X_train, y_train)\n",
    "rs_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3de46f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds = gs_clf.predict(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a0010726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36c398ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x250a7f7cb50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_roc_curve\n",
    "plot_roc_curve(gs_clf,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b281d4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(confusion_matrix(y_test,y_preds), annot=True,cbar=False, fmt='g')\n",
    "plt.xlabel(\"True label\")\n",
    "plt.ylabel(\"Predicted label\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc12f11a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97      4071\n",
      "           1       0.91      0.56      0.69       477\n",
      "\n",
      "    accuracy                           0.95      4548\n",
      "   macro avg       0.93      0.78      0.83      4548\n",
      "weighted avg       0.95      0.95      0.94      4548\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a58f4d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save trained model to file\n",
    "pickle.dump(gs_clf, open(\"diabetes0712.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dadbd17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pickle.load(open(\"diabetes0712.pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "157d9c32",
   "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>systolic</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>hdl</th>\n",
       "      <th>ldl</th>\n",
       "      <th>bmi</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>145.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>66.1</td>\n",
       "      <td>84.5</td>\n",
       "      <td>29.2</td>\n",
       "      <td>31.221918</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>113.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>69.4</td>\n",
       "      <td>88.3</td>\n",
       "      <td>30.2</td>\n",
       "      <td>85.476712</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>156.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>130.1</td>\n",
       "      <td>25.6</td>\n",
       "      <td>23.205479</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>104.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>52.3</td>\n",
       "      <td>117.9</td>\n",
       "      <td>27.4</td>\n",
       "      <td>68.427397</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>105.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>69.7</td>\n",
       "      <td>100.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>91.153425</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   systolic diastolic   hdl    ldl   bmi        age  diabetic\n",
       "0     145.0     111.0  66.1   84.5  29.2  31.221918         1\n",
       "12    113.0      83.0  69.4   88.3  30.2  85.476712         1\n",
       "25    156.0     102.0  45.0  130.1  25.6  23.205479         1\n",
       "29    104.0      84.0  52.3  117.9  27.4  68.427397         1\n",
       "41    105.0      80.0  69.7  100.5  28.6  91.153425         1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['diabetic']==1].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4edbf1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_df = pd.DataFrame([pd.Series([113,82,61.9,102.6,27.3,90])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "70eee1b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05703446498273414"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(row_df)[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46180de1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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