Decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "graphic-astronomy",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dominant-mexican",
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda install -c conda-forge mlxtend -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "trained-holiday",
   "metadata": {},
   "outputs": [],
   "source": [
    "StratifiedKFold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "academic-indian",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, learning_curve, ShuffleSplit, validation_curve\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from imblearn.pipeline import Pipeline\n",
    "from category_encoders.one_hot import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from mlxtend.evaluate import bias_variance_decomp\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "from sklearn.metrics import classification_report\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import average_precision_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southern-brick",
   "metadata": {},
   "source": [
    "## Created Packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "posted-sector",
   "metadata": {},
   "outputs": [],
   "source": [
    "parent_dir = '/mnt/c/users/17752/Documents/GeorgiaTech/machine_learning'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frank-queen",
   "metadata": {},
   "source": [
    "# Liver Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "unnecessary-direction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6291304347826087"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_df = pd.read_csv(f\"{parent_dir}/data/liver.csv\")\n",
    "liver_df = pd.read_csv(f\"{parent_dir}/data/liver.csv\")\n",
    "liver_df.drop_duplicates(inplace=True)\n",
    "impute_Albumin_and_Globulin_Ratio = liver_df[~liver_df.Albumin_and_Globulin_Ratio.isna()].Albumin_and_Globulin_Ratio.mean()\n",
    "filt_ =  liver_df.Albumin_and_Globulin_Ratio.isna()\n",
    "liver_df.loc[filt_, \"Albumin_and_Globulin_Ratio\" ]= impute_Albumin_and_Globulin_Ratio\n",
    "assert sum(liver_df.Albumin_and_Globulin_Ratio.isna()) == 0 \n",
    "# format response \n",
    "liver_df.loc[(liver_df.Dataset ==2), \"Dataset\" ] = -1\n",
    "liver_df.head(10)\n",
    "liver_df.loc[:,\"Gender\"] = pd.get_dummies(liver_df.Gender, prefix='gender').drop(\"gender_Male\", axis =1  ).gender_Female\n",
    "X = liver_df.loc[:, \"Age\":\"Albumin_and_Globulin_Ratio\"]\n",
    "y = liver_df[\"Dataset\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=62)\n",
    "\n",
    "#('over', SMOTE(sampling_strategy=\"minority\")\n",
    "#inital shot \n",
    "liver_steps = [('model',DecisionTreeClassifier())] \n",
    "liver_pipeline = Pipeline(steps=liver_steps)\n",
    "stratified_kfold = StratifiedKFold(n_splits = 10, random_state=65, shuffle=True)\n",
    "scores = cross_val_score(liver_pipeline, X_train, y_train, scoring='accuracy', cv=stratified_kfold, n_jobs=3)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fossil-doctrine",
   "metadata": {},
   "source": [
    "## Complexity-liver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "surprising-sport",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "cv = ShuffleSplit(n_splits=10, test_size=0.1, random_state=0)\n",
    "train_scores, valid_scores = validation_curve(DecisionTreeClassifier(criterion=\"entropy\", min_samples_split=10),\n",
    "                                              X_train,\n",
    "                                              y_train, \n",
    "                                              param_name =  \"max_depth\",\n",
    "                                            param_range =np.array(range(1,11)),\n",
    "                                            cv=cv)\n",
    "\n",
    "param_range = np.array(range(1,11))\n",
    "\n",
    "train_scores_mean =  np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(valid_scores, axis=1)\n",
    "test_scores_std = np.std(valid_scores, axis=1)\n",
    "\n",
    "plt.title(\"Validation Curve with Decision Tree\")\n",
    "plt.xlabel(\"Max Depth Of Trees\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.ylim(0.0, 1.1)\n",
    "lw = 2\n",
    "# semilogx\n",
    "plt.plot(param_range, train_scores_mean, label=\"Training score\",\n",
    "             color=\"darkorange\", lw=lw)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
    "                 color=\"darkorange\", lw=lw)\n",
    "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
    "             color=\"navy\", lw=lw)\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.2,\n",
    "                 color=\"navy\", lw=lw)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig(\"DECISION_LIVER_MAX_DEPTH.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rocky-suspension",
   "metadata": {},
   "source": [
    "## ccp_alpha-liver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "smoking-means",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `RepeatedStratifiedKFold` not found.\n"
     ]
    }
   ],
   "source": [
    "RepeatedStratifiedKFold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "unavailable-central",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# cv = ShuffleSplit(n_splits=10, test_size=0.1, random_state=0)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=45)\n",
    "train_scores, valid_scores = validation_curve(DecisionTreeClassifier( max_depth=10),\n",
    "                                              X_train,\n",
    "                                              y_train, \n",
    "                                              param_name =  \"min_samples_split\",\n",
    "                                            param_range =np.linspace(.001,1.,40),\n",
    "                                            cv=cv)\n",
    "\n",
    "param_range = np.linspace(.001,1.,40)\n",
    "\n",
    "train_scores_mean =  np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(valid_scores, axis=1)\n",
    "test_scores_std = np.std(valid_scores, axis=1)\n",
    "\n",
    "plt.title(\"Validation Curve with Decision Tree\")\n",
    "plt.xlabel(\"CCP alpha\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.ylim(0.0, 1.1)\n",
    "lw = 2\n",
    "# semilogx\n",
    "plt.plot(param_range, train_scores_mean, label=\"Training score\",\n",
    "             color=\"darkorange\", lw=lw)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
    "                 color=\"darkorange\", lw=lw)\n",
    "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
    "             color=\"navy\", lw=lw)\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.2,\n",
    "                 color=\"navy\", lw=lw)\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "\n",
    "plt.savefig(\"DECISION_LIVER_CCP_ALPHA.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "municipal-elimination",
   "metadata": {},
   "source": [
    "## Grid Search "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "devoted-medicare",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.840163 usin g {'model__ccp_alpha': 0.0, 'model__criterion': 'gini', 'model__max_depth': 4, 'model__min_samples_leaf': 18}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# example of grid searching key hyperparameters for adaboost on a classification dataset\n",
    "\n",
    "# define dataset\n",
    "# define the model with default hyperparameters\n",
    "\n",
    "# model =  AdaBoostClassifier(algorithm='SAMME')\n",
    "# define the grid of values to search\n",
    "grid = dict()\n",
    "grid['model__max_depth'] = [i for i in range(2,10,1)]\n",
    "grid['model__min_samples_leaf'] = [i for i in range(2,20,2)]\n",
    "grid['model__ccp_alpha'] = np.linspace(.0,.12,20)\n",
    "grid['model__criterion'] = [\"gini\",\"entropy\"]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# define the evaluation procedure\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=45)\n",
    "# define the grid search procedure\n",
    "grid_search = GridSearchCV(estimator=liver_pipeline, param_grid=grid, n_jobs=-1, cv=cv, scoring='average_precision')\n",
    "# execute the grid search\n",
    "grid_result = grid_search.fit(X_train, y_train)\n",
    "# summarize the best score and configuration\n",
    "print(\"Best: %f usin g %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "# summarize all scores that were evaluated\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "human-convert",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_result.best_estimator_\n",
    "#best on balanced\n",
    "# Pipeline(steps=[('model',\n",
    "#                  DecisionTreeClassifier(ccp_alpha=0.013333333333333332,\n",
    "#                                         criterion='entropy', max_depth=3,\n",
    "#                                         min_samples_split=4))])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imported-revision",
   "metadata": {},
   "source": [
    "## Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ultimate-fight",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_learn_curve(title=\"some title\", estimator=None, cv = None , X=None,y=None):\n",
    "    plt.title(title)\n",
    "    plt.ylim(0.0, 1.01)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "\n",
    "    train_sizes, train_scores, test_scores, fit_times, _ = \\\n",
    "        learning_curve(estimator, X, y, cv=cv, n_jobs=4,\n",
    "                       train_sizes=np.linspace(.1, 1.0, 10),\n",
    "                       return_times=True)\n",
    "    train_scores_mean =  1.0 - np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean =  1.0 - np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    fit_times_mean = np.mean(fit_times, axis=1)\n",
    "    fit_times_std = np.std(fit_times, axis=1)\n",
    "\n",
    "\n",
    "    # Plot learning curve\n",
    "    plt.grid()\n",
    "    plt.fill_between(train_sizes,  train_scores_mean - train_scores_std,\n",
    "                         train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                         color=\"r\")\n",
    "    plt.fill_between(train_sizes,  test_scores_mean - test_scores_std,\n",
    "                         test_scores_mean + test_scores_std, alpha=0.1,\n",
    "                         color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"orange\",\n",
    "                 label=\"Training Error\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'x-', color=\"b\",\n",
    "                 label=\"Cross-validation Error\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.ylabel(\"Error\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "utility-bradley",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "my_learn_curve(\"Biased Default Decision Tree\", DecisionTreeClassifier(max_depth=1), cv=cv , X=X_train, y=y_train )\n",
    "#all 1s\n",
    "plt.savefig(\"DECISION_TREE_BIASED_LIVER.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "consistent-mileage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f3a3a433130>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(estimator=  DecisionTreeClassifier(max_depth=1).fit(X_train,y_train), X=X_train, y_true= y_train  , \n",
    "                      cmap=plt.cm.Blues,\n",
    "                       normalize=\"true\",\n",
    "                      display_labels = [\"Not Patient\" ,\"Patient\"]\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "violent-leave",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "\n",
    "my_learn_curve(\"Grid Search Decision Tree\", grid_result.best_estimator_, cv=cv ,X=X_train, y=y_train )\n",
    "plt.savefig(\"DECISION_TREE_final_learning_curve_liver.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "gothic-maria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7eff32fc3400>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(estimator= grid_result.best_estimator_, X=X_train, y_true= y_train  , \n",
    "                      cmap=plt.cm.Blues,\n",
    "                       normalize=\"true\",\n",
    "                      display_labels = [\"Not Patient\" ,\"Patient\"]\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "czech-animal",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "my_learn_curve(\"Overfit Default Decision Tree\", DecisionTreeClassifier(), cv=cv, X=X_train, y=y_train  )\n",
    "plt.savefig(\"DECISION_TREE_overfit_LIVER.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "operational-estate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f3a3bbc3250>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEKCAYAAAAhEP83AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAb40lEQVR4nO3dfbiVdZ3v8fdnb0Axn0UZRRQz8qlRQhQfRocyHeycfLZMrNS8jCnjXJ608epyirRz5WiOpWKE5uFYJmk6isYRyplSQxoeRBIcC7UU9BQo+ZCkgt/zx31vXCz3Xuvem7X2+i3uz8vrvlz30/f+sRd8+fFbv993KSIwM7N0dLS6AWZmtjEnZjOzxDgxm5klxonZzCwxTsxmZolxYjYzS4wTs5nZJpB0s6Q/SXq8h/OSdK2k5ZKWSBpdL6YTs5nZppkOjK9x/nhgZL6dD3y3XkAnZjOzTRARDwIv1bjkROCWyMwDtpe0a62YAxrZwLLTgMGhQdu0uhnWCx/cb49WN8F6adGihasjYudNidG57Z4R69YWujbWrloK/LXi0LSImNaLxw0DnqvYX5Efe6GnG5yYG0iDtmGLfT7e6mZYL/zq19e3ugnWS4MH6g+bGiPWrS38Z/Wvi6f8NSLGbMLj1F0Tat3gxGxmJSRQv43krgCGV+zvDjxf6waPMZtZ+Qjo6Cy2bbqZwKfz2RmHAS9HRI/DGOAes5mVlbobYehLGN0GjAOGSFoBfA0YCBARU4FZwEeB5cDrwDn1Yjoxm1kJNW4oIyI+Wed8AF/oTUwnZjMrpwb1mJvBidnMykf054d/vebEbGYlJPeYzcyS05gZF03hxGxmJdSv85h7zYnZzMpHeCjDzCw57jGbmaXEQxlmZmkR0OkP/8zM0uIxZjOzlHgow8wsPe4xm5klxj1mM7OEyEuyzczS4yXZZmYp8Yd/Zmbp8VCGmVlCXI/ZzCw1HsowM0uPP/wzM0uMx5jNzBIiD2WYmaXHPWYzs7TIidnMLB3ZN0s5MZuZpUNCHU7MZmZJcY/ZzCwxTsxmZolxYjYzS4nyLVFOzGZWOkLuMZuZpaajwyv/zMyS4h6zmVlKPMZsZpaelHvM6Q6ymJk1SdeHf0W2urGk8ZKelLRc0iXdnN9O0r2SHpO0VNI59WK6x2xmpdSIJdmSOoEpwLHACmC+pJkRsazisi8AyyLiY5J2Bp6UdGtEvNlTXPeYzax8RKN6zIcCyyPi6TzRzgBOrLomgG2UBdsaeAlYVyuoe8xmVkq9GGMeImlBxf60iJiWvx4GPFdxbgUwtur+64GZwPPANsAnIuLtWg90YjazUupFYl4dEWN6CtPNsaja/wdgMfBhYG/gZ5IeiohXenqghzLMrHQa+OHfCmB4xf7uZD3jSucAd0VmOfAMsG+toE7MZlZOKrjVNh8YKWkvSYOAM8iGLSo9CxwDIGkosA/wdK2gHsows/JRY5ZkR8Q6SRcAs4FO4OaIWCppYn5+KnA5MF3Sb7In808RsbpWXCdmMyulRi0wiYhZwKyqY1MrXj8PHNebmE7MZlZO6S788xiz1XbdP0/gt7O/ydwZX2l1U6ygn89dxiGnXsbokydzzfQ5rW5Oshq18q8ZmpaYJYWkqyv2L5I0uc49J0nav4dzkyWtlLRY0uOSTqgT6ytV+3N70fzqWGdL2q2v97ez2+6bx2mTprS6GVbQ+vVvc/GVt3PHdz7PvNsv5c45C/mvp19odbOSUzQpb3aJGXgDOEXSkF7ccxLQbWLOXRMRo4DTgZsl1Wr/Rok5Io7oRTuqnQ2UMjHPffQp1rzyequbYQUtXPp73jt8CCN2H8KggQM45djRzPrlklY3K0llTczrgGnAhdUnJO0p6QFJS/L/7yHpCOAE4Kq8V7x3T4Ej4ok8/hBJd0tamBcHOT+PfwUwOI9za37stYrnXyxpfv78r+fHRkh6QtKNeaw5kgZLOg0YA9yaxxvcsJ+QWYO9sOplhg3dYcP+bkN34IVVL7ewRelShwptrdDsMeYpwARJ21Udvx64JSIOBG4Fro2IuWTz/y6OiFER8VRPQSWNBd4GVgHnRsTBZMlzkqSdIuISYG0eZ0LVvccBI8nWuI8CDpZ0dH56JDAlIg4A/gycGhE/ARYAE/J4a6vinS9pgaQFsW6jU2b9LqJ60RkkXN2ypVLuMTd1VkZEvCLpFmASUJm1DgdOyV//ALiyYMgLJZ0FvEq23jwkTZJ0cn5+OFlyfbFGjOPy7dF8f+v8nmeBZyJicX58ITCiXoPyNfPTADq22uXdfyrM+tFuu2zPyj+u2bD//B/X8DdDqvtF1lXEKFX9MSvj28BngffUuKZoQrsm77UeFREPSRoHfAQ4PCIOIku2W9aJIeCbeZxREfG+iPh+fu6NiuvW4+mE1mZG778nTz27ij+sXM2bb63jrp8t4vijD2x1s5Ijsn9JFNlaoemJOSJeAm4nS85d5pItXQSYADycv36VrPpSUdsBayLidUn7AodVnHtL0sBu7pkNnCtpawBJwyTtUuc5vW3XZuOmb5zNnJu/xPv2HMrj913OWScc3uomWQ0DBnRy5Zc/zqmTpjD29G9w0kc+yH5779rqZiUo7VkZ/dUjvBq4oGJ/EtmsiovJxom7KvrPAG6UNAk4rdY4c+5+YKKkJcCTwLyKc9OAJZIWVY4zR8QcSfsBj+Q/9NeAs8h6yD2ZDkyVtJasd16aweTzLp3e6iZYLx135AEcd+QBrW5G8jpa9MFeEeruwwLrm46tdokt9vl4q5thvbBm/vWtboL10uCBWlijDGchW+76/hjxmesKXfvkv4zf5Of1lsdQzax0RNo9ZidmMyulhCdlODGbWTmlPF3OidnMyqeFU+GKcGI2s9IRakih/GZxYjazUnKP2cwsMR5jNjNLiceYzczSktXKSDczOzGbWSklnJedmM2snLzyz8wsJYnXY3ZiNrPS6arHnConZjMrodbVWi7CidnMSinhvOzEbGYlJH/4Z2aWFM9jNjNLkBOzmVliEs7LTsxmVk7uMZuZpcRFjMzM0pIVyk83Mzsxm1kpdSTcZU73u1XMzJpIKrbVj6Pxkp6UtFzSJT1cM07SYklLJf2yXkz3mM2sdNSgIkaSOoEpwLHACmC+pJkRsazimu2BG4DxEfGspF3qxXWP2cxKqUPFtjoOBZZHxNMR8SYwAzix6pozgbsi4lmAiPhTvaA99pglXQdET+cjYlLdJpuZJaoXH/4NkbSgYn9aREzLXw8Dnqs4twIYW3X/+4GBkn4BbAN8JyJuqfXAWkMZC2qcMzNrWyKbmVHQ6ogYUyNUteoO7QDgYOAYYDDwiKR5EfHbnh7YY2KOiP+z0dOl90TEX3q63sysnTRottwKYHjF/u7A891cszrPn3+R9CBwENBjYq47xizpcEnLgCfy/YMk3dDLxpuZpUNZPeYiWx3zgZGS9pI0CDgDmFl1zT3AUZIGSNqKbKjjiVpBi8zK+DbwD10Pi4jHJB1d4D4zs2Q1YhpzRKyTdAEwG+gEbo6IpZIm5uenRsQTku4HlgBvAzdFxOO14haaLhcRz1X9zbG+L78IM7MUiMYtMImIWcCsqmNTq/avAq4qGrNIYn5O0hFA5F31SdTphpuZpS7lJdlF5jFPBL5ANi1kJTAq3zcza0tFV/21atV23R5zRKwGJvRDW8zM+k1b18qQ9F5J90paJelPku6R9N7+aJyZWbOo4NYKRYYyfgTcDuwK7AbcAdzWzEaZmTVbg6bLNUWRxKyI+EFErMu3H1JjqbaZWeqyWRkNqZXRFLVqZeyYv/yPvJTdDLKE/Angp/3QNjOz5lD7FspfSJaIu1r/uYpzAVzerEaZmTVbW37nX0Ts1Z8NMTPrL11DGakqtPJP0geA/YEtu47VK1tnZpaytuwxd5H0NWAcWWKeBRwPPAw4MZtZ20o3LReblXEaWR3R/xcR55CVq9uiqa0yM2siCTo7VGhrhSJDGWsj4m1J6yRtC/wJ8AITM2trbT2UASzIv0zwRrKZGq8B/9nMRpmZNVvCeblQrYzP5y+n5jVFt42IJc1tlplZ8wglXSuj1gKT0bXORcSi5jTJzKzJWlg5rohaPeara5wL4MMNbkvb++B+e/CrX1/f6mZYL+xwyAWtboK1SFuOMUfEh/qzIWZm/UVAZzsmZjOzzVnbr/wzM9vcODGbmSUk+9qodDNzkW8wkaSzJH01399D0qHNb5qZWfOkXI+5yJLsG4DDgU/m+68CU5rWIjOzftDWX8YKjI2I0ZIeBYiINZIGNbldZmZNI2BAwkMZRRLzW5I6yb9OStLOwNtNbZWZWZMlnJcLJeZrgX8DdpH0v8iqzV3a1FaZmTWR1KZLsrtExK2SFpKV/hRwUkQ80fSWmZk1UcJ5uVCh/D2A14F7K49FxLPNbJiZWTO1+zzmn/LOl7JuCewFPAkc0MR2mZk1jaBlRfCLKDKU8beV+3nVuc/1cLmZWfpaOEe5iF6v/IuIRZIOaUZjzMz6ixL+1r8iY8z/s2K3AxgNrGpai8zMmky0f495m4rX68jGnO9sTnPMzPpH2ybmfGHJ1hFxcT+1x8ysX6RcxKjWV0sNiIh1tb5iysysHUnQWaRSUIvUalrXN2EvljRT0qckndK19UfjzMyapSNf/Vdvq0fSeElPSlou6ZIa1x0iab2k0+rFLDLGvCPwItl3/HXNZw7grgL3mpklp1Ef/uXDvVOAY4EVwHxJMyNiWTfX/Qswu0jcWol5l3xGxuO8k5C7RC/abmaWnAYNMR8KLI+Ip7OYmgGcCCyruu6LZJMmCk01rpWYO4GtodvJfk7MZtbGREfxecxDJC2o2J8WEdPy18OA5yrOrQDGbvQkaRhwMtmowyYn5hci4rIiQczM2onoVY95dUSMqRGqWnXH9dvAP0XE+qIzQWol5nTnkpiZbQrBgMZMZF4BDK/Y3x14vuqaMcCMPCkPAT4qaV1E3N1T0FqJ+Zi+tdPMLG297DHXMh8YKWkvYCVwBnBm5QURsdeG50rTgftqJWWokZgj4qVNaKyZWdIaUSg/X+txAdlsi07g5ohYKmlifn5qX+L2uoiRmdnmoFEL/yJiFjCr6li3CTkizi4S04nZzEpH1F5d12pOzGZWPmrMUEazODGbWelkK/+cmM3MkpJuWnZiNrOSSrjD7MRsZmWk9qzHbGa2ufKsDDOzBPnDPzOzlKhNv1rKzGxz5aEMM7MEucdsZpaYdNOyE7OZlZCATveYzczSknBedmI2szISSngww4nZzErJPWYzs4Rk0+XSzcxOzGZWPnKP2cwsOV6SbWaWkKxQfqtb0TMnZjMrJc/KMDNLTMIjGUnX8bAE/HzuMg459TJGnzyZa6bPaXVzrI7r/nkCv539TebO+Eqrm5I8FfyvFdoqMUtaL2mxpMcl3SFpqxrXjpN0RMX+REmf7uNzR0g6sy/3trP169/m4itv547vfJ55t1/KnXMW8l9Pv9DqZlkNt903j9MmTWl1M5LXNcZcZGuFtkrMwNqIGBURHwDeBCbWuHYcsCExR8TUiLilj88dAZQuMS9c+nveO3wII3YfwqCBAzjl2NHM+uWSVjfLapj76FOseeX1VjcjfRIdBbdWaLfEXOkh4H2SPibp15IelfRzSUMljSBL2hfmPeyjJE2WdBGApL0l3S9poaSHJO2bH58u6VpJcyU9Lem0/FlXAEflsS5sxS+2FV5Y9TLDhu6wYX+3oTvwwqqXW9gis8ZRwa0V2vLDP0kDgOOB+4GHgcMiIiSdB3w5Ir4kaSrwWkR8K7/nmIoQ04CJEfE7SWOBG4AP5+d2Bf4O2BeYCfwEuAS4KCL+ezdtOR84H2D4Hns0/hfbQhHxrmMpf2BiVlQ2lJHub+Z2S8yDJS3OXz8EfB/YB/ixpF2BQcAztQJI2ppsiOOOikLZW1RccndEvA0skzS0XoMiYhpZoufgg8e8O5O1sd122Z6Vf1yzYf/5P67hb4Zs18IWmTVOumm5/RLz2ogYVXlA0nXAv0bETEnjgMl1YnQAf66OU+GNyvB9auVmYvT+e/LUs6v4w8rV7LrL9tz1s0XcePnZrW6WWWMk/Ke73RJzd7YDVuavP1Nx/FVg2+qLI+IVSc9IOj0i7lDWbT4wIh6r8YxXgW0a1uI2MWBAJ1d++eOcOmkK69cHE044jP323rXVzbIabvrG2Rx58Eh22n5rHr/vcq6YNosfznyk1c1Kkocymmsy2bDESmAesFd+/F7gJ5JOBL5Ydc8E4LuSLgUGAjOAWol5CbBO0mPA9Ii4poHtT9pxRx7AcUce0OpmWEHnXTq91U1oG+mm5TZLzBGxdTfH7gHu6eb4b4EDKw49VHHuGWB8N/ec3d3zIuIt4Jjq682sjSWcmdsqMZuZNUI2FS7dzOzEbGblk3g95nZeYGJm1meNWmAiabykJyUtl3RJN+cnSFqSb3MlHVQvpnvMZlZCQg3oMkvqBKYAxwIrgPmSZkbEsorLngH+PiLWSDqebN3D2FpxnZjNrJQaNJRxKLA8Ip7OYmoGcCKwITFHxNyK6+cBu9cL6qEMMyudosMYee4eImlBxXZ+RahhwHMV+yvyYz35LPB/67XPPWYzK6fiPebVETGmF1G6Lc0g6UNkifnv6j3QidnMSqlB0+VWAMMr9ncHnn/Xs6QDgZuA4yPixXpBPZRhZqUkFdvqmA+MlLSXpEHAGWRVKSueoz2Au4BP5Qvf6nKP2czKp0HzmCNinaQLgNlAJ3BzRCyVNDE/PxX4KrATcEM+E2RdjaERwInZzEqqUSv/ImIWMKvq2NSK1+cB5/UmphOzmZWOSHvlnxOzmZVSwnnZidnMSirhzOzEbGal5EL5ZmaJSTctOzGbWVklnJmdmM2sdFwo38wsNYkXyndiNrNSSjgvOzGbWRk1plB+szgxm1kpJZyXnZjNrHyKfp9fqzgxm1k5JZyZnZjNrJQ8Xc7MLDEeYzYzS4mgw4nZzCw16WZmJ2YzKx0XyjczS1DCedmJ2czKyT1mM7PEeEm2mVli0k3LTsxmVkJy2U8zs/R45Z+ZWWrSzctOzGZWTgnnZSdmMysj0ZHwILMTs5mVTuor/zpa3QAzM9uYe8xmVkop95idmM2slDxdzswsJV5gYmaWltQ//HNiNrNS8lCGmVliUu4xe7qcmZWSCm5140jjJT0pabmkS7o5L0nX5ueXSBpdL6YTs5mVUwMys6ROYApwPLA/8ElJ+1dddjwwMt/OB75br2lOzGZWOgI6pEJbHYcCyyPi6Yh4E5gBnFh1zYnALZGZB2wvaddaQT3G3ECLFi1cPXig/tDqdjTJEGB1qxthvbK5vmd7bmqARYsWzh48UEMKXr6lpAUV+9MiYlr+ehjwXMW5FcDYqvu7u2YY8EJPD3RibqCI2LnVbWgWSQsiYkyr22HF+T3rWUSMb1Co7rrU0YdrNuKhDDOzvlsBDK/Y3x14vg/XbMSJ2cys7+YDIyXtJWkQcAYws+qamcCn89kZhwEvR0SPwxjgoQwrblr9Sywxfs+aLCLWSboAmA10AjdHxFJJE/PzU4FZwEeB5cDrwDn14iqi5lCHmZn1Mw9lmJklxonZzCwxTsxtTFJIurpi/yJJk+vcc1I3K5O6zk2WtFLSYkmPSzqhTqyvVO3P7UXzq2OdLWm3vt6/uZG0vuJ9uEPSVjWuHSfpiIr9iZI+3cfnjpB0Zl/utcZxYm5vbwCnSIUnygOcRLZ0tCfXRMQo4HTgZkm1fo9slJgj4oieLizgbMCJ+R1rI2JURHwAeBOYWOPaccCGn31ETI2IW/r43BGAE3OLOTG3t3Vkn7xfWH1C0p6SHsiLpjwgaY+8V3UCcFXeG9u7p8AR8UQef4ikuyUtlLRU0vl5/CuAwXmcW/Njr1U8/2JJ8/Pnfz0/NkLSE5JuzGPNkTRY0mnAGODWPN7ghv2ENg8PAe+T9DFJv5b0qKSfSxoqaQRZ0r4w/9kdlf/L5yIASXtLuj9//x6StG9+fHpeWGeupKfz9wDgCuCoPNa7fl9ZP4kIb226Aa8B2wK/B7YDLgIm5+fuBT6Tvz4XuDt/PR04rYd4k4GL8tdjySbBC9gxPzYYeBzYqev51e3J/38c2V8YIvvL/z7gaLLe2DpgVH7d7cBZ+etfAGNa/TNNZav4WQ4A7gH+EdiBd2ZSnQdcXf2+dfM+PgCMrHhP/73i98Ed+fuzP1m9B8h63/e1+tdf9s3zmNtcRLwi6RZgErC24tThwCn56x8AVxYMeaGks4BXgU9EREiaJOnk/PxwsipZL9aIcVy+PZrvb53f8yzwTEQszo8vJEvW9m6DJS3OXz8EfB/YB/hxXgBnEPBMrQCStiYb4rhD7xTj2aLikrsj4m1gmaShDWy7bSIn5s3Dt4FFwP+ucU3RCevXRMS3unYkjQM+AhweEa9L+gWwZZ0YAr4ZEd/b6GD2z+43Kg6tJ+uF27utjWysfwNJ1wH/GhEz8/dlcp0YHcCfq+NUqHwvEi4bXz4eY94MRMRLZMMCn604PJdseSjABODh/PWrwDa9CL8dsCZPyvsCh1Wce0vSwG7umQ2cm/fYkDRM0i51ntPbdpXRdsDK/PVnKo53+7OLiFeAZySdDhsKth9U5xl+HxLgxLz5uJqszGOXScA5kpYAnwL+R358BnBx/gFSjx/+VbgfGJDHuRyYV3FuGrCk68O/LhExB/gR8Iik3wA/of4f9unAVH/4V9NksmGJh9i4nOe9wMldH/5V3TMB+Kykx4ClvLtWcLUlwDpJj/nDv9bxkmwzs8S4x2xmlhgnZjOzxDgxm5klxonZzCwxTsxmZolxYrZ+1ZuqaQViTe+q8SDppp6q5uXnN6rA1otn/L67IlE9Ha+65rVa57u5fkONCys3J2brbzWrpknq7EvQiDgvIpbVuGQcFRXYzFLmxGyt1FU1bZyk/5D0I+A3kjolXVVRne5zsGHl2vWSlkn6KbBhNaGkX0gak78eL2lRvkjigR4qsO0s6c78GfMlHZnfu1Ne9e5RSd+jwFJldVN9r+Lc1XlbHpC0c36s24pvZl1cK8NaQtIA4HiylYUAhwIfiIhn8uT2ckQcImkL4FeS5gAfJCvk87fAUGAZcHNV3J2BG4Gj81g7RsRLkqaSVWz7Vn7dj8jqgjwsaQ+yZeT7AV8DHo6IyyT9N2CjRNuDc/NnDAbmS7ozIl4E3gMsiogvSfpqHvsCshWTEyPid5LGAjcAH+7Dj9E2U07M1t+6q5p2BPCfEdFVLe044MCKGsHbkVWnOxq4LSLWA89L+vdu4h8GPNgVK68j0p2PAPtXVF3bVtI2+TNOye/9qaQ1BX5NPVXfexv4cX78h8BdBSq+mTkxW7/rrmoawF8qDwFfjIjZVdd9lPpV8lTgGsiG8Q6PiMpSqV1tKVynoJfV94L6Fd/MPMZsSZoN/GNX5TpJ75f0HuBB4Ix8DHpX4EPd3PsI8PeS9srv3TE/Xl01bQ7ZsAL5daPylw+SFf5B0vFkxelrqVV9rwPo6vWfSTZE0peKb1YyTsyWopvIxo8XSXoc+B7Zv+7+Dfgd8Bvgu8Avq2+MiFVk48J35RXVuoYSqiuwTQLG5B8uLuOd2SFfB46WtIhsSOXZOm2tVX3vL8ABkhaSjSFflh/vbcU3KxlXlzMzS4x7zGZmiXFiNjNLjBOzmVlinJjNzBLjxGxmlhgnZjOzxDgxm5kl5v8DHw93yQgaXtIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(estimator= DecisionTreeClassifier().fit(X_train,y_train), X=X_train, y_true= y_train  , \n",
    "                      cmap=plt.cm.Blues,\n",
    "                       normalize=\"true\",\n",
    "                      display_labels = [\"Not Patient\" ,\"Patient\"]\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "respiratory-queue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f3a3a062e50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(estimator= grid_result.best_estimator_, X=X_test, y_true= y_test  , \n",
    "                      cmap=plt.cm.Blues,\n",
    "                       normalize=\"true\",\n",
    "                      display_labels = [\"Not Patient\" ,\"Patient\"]\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "integral-georgia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(average_precision_score(y_train, liver_pipeline.fit(X_train, y_train).predict_proba(X_train)[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "burning-suspect",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8974406735092382\n",
      "0.8580906204038183\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(average_precision_score(y_train, grid_result.predict_proba(X_train)[:,1]))\n",
    "print(average_precision_score(y_test, grid_result.predict_proba(X_test)[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "valid-dubai",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'memory': None, 'steps': [('model', DecisionTreeClassifier(max_depth=4, min_samples_leaf=18))], 'verbose': False, 'model': DecisionTreeClassifier(max_depth=4, min_samples_leaf=18), 'model__ccp_alpha': 0.0, 'model__class_weight': None, 'model__criterion': 'gini', 'model__max_depth': 4, 'model__max_features': None, 'model__max_leaf_nodes': None, 'model__min_impurity_decrease': 0.0, 'model__min_impurity_split': None, 'model__min_samples_leaf': 18, 'model__min_samples_split': 2, 'model__min_weight_fraction_leaf': 0.0, 'model__presort': 'deprecated', 'model__random_state': None, 'model__splitter': 'best'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.62      0.40      0.49       138\n",
      "           1       0.77      0.90      0.83       318\n",
      "\n",
      "    accuracy                           0.75       456\n",
      "   macro avg       0.70      0.65      0.66       456\n",
      "weighted avg       0.73      0.75      0.73       456\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.38      0.42      0.40        26\n",
      "           1       0.82      0.80      0.81        88\n",
      "\n",
      "    accuracy                           0.71       114\n",
      "   macro avg       0.60      0.61      0.60       114\n",
      "weighted avg       0.72      0.71      0.72       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(grid_result.best_estimator_.get_params())\n",
    "print(classification_report(y_train, grid_result.best_estimator_.predict(X_train) ))\n",
    "print(classification_report(y_test, grid_result.best_estimator_.predict(X_test) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nearby-shuttle",
   "metadata": {},
   "source": [
    "## Census Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "alone-syria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape is (26029, 15)\n",
      "Test data shape is (6508, 15)\n",
      "Gradient Boosting  AUC imputing missing values: 0.753\n"
     ]
    }
   ],
   "source": [
    "census_df = pd.read_csv(f\"{parent_dir}/data/adult.csv\")\n",
    "census_df.drop_duplicates(inplace=True)\n",
    "# # Deleting duplicates \n",
    "census_df.duplicated().sum()\n",
    "#Replacing '?' for nans\n",
    "census_df = census_df.replace('?', np.NaN)\n",
    "# Mapping already our target variable in  1 and 0.\n",
    "census_df.income = census_df.income.map({'<=50K':-1, '>50K':1})\n",
    "\n",
    "train_data, test_data = train_test_split(census_df,\n",
    "                                         test_size=0.2,\n",
    "                                         stratify=census_df['income'],\n",
    "                                         random_state=65)\n",
    "print('Train data shape is {}'.format(train_data.shape))\n",
    "print('Test data shape is {}'.format(test_data.shape))\n",
    "X_c_train = train_data.drop('income', axis=1)\n",
    "y_c_train = train_data['income']\n",
    "\n",
    "#Transforming categorical columns\n",
    "categorical_columns= X_c_train.select_dtypes(object).columns\n",
    "\n",
    "\n",
    "#         ('over', SMOTE()),\n",
    "# Using pipeline\n",
    "census_steps = [('encoding', OneHotEncoder(cols=categorical_columns)),\n",
    "         ('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),\n",
    "         ('model',DecisionTreeClassifier())]\n",
    "census_pipeline = Pipeline(steps=census_steps)\n",
    "\n",
    "#inital shot \n",
    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=65, shuffle=True)\n",
    "scores = cross_val_score(census_pipeline, X_c_train, y_c_train, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
    "print(f'Gradient Boosting  AUC imputing missing values: {round(np.mean(scores),3)}')\n",
    "X_c_test = test_data.drop('income', axis=1)\n",
    "y_c_test = test_data['income']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polish-drove",
   "metadata": {},
   "source": [
    "## Complexity -census "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fundamental-gross",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19758"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_c_train==-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "intermediate-watson",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "broke-template",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "\n",
    "train_scores, valid_scores = validation_curve(census_pipeline,\n",
    "                                              X_c_train,\n",
    "                                              y_c_train, \n",
    "                                              param_name =  \"model__max_depth\",\n",
    "                                            param_range =np.array(range(1,15)),\n",
    "                                            cv=stratified_kfold)\n",
    "\n",
    "param_range = np.array(range(1,15))\n",
    "\n",
    "train_scores_mean =  np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(valid_scores, axis=1)\n",
    "test_scores_std = np.std(valid_scores, axis=1)\n",
    "\n",
    "plt.title(\"Validation Curve with Decision Tree\")\n",
    "plt.xlabel(\"Max Depth Of Trees\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.ylim(0.0, 1.1)\n",
    "lw = 2\n",
    "# semilogx\n",
    "plt.plot(param_range, train_scores_mean, label=\"Training score\",\n",
    "             color=\"darkorange\", lw=lw)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
    "                 color=\"darkorange\", lw=lw)\n",
    "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
    "             color=\"navy\", lw=lw)\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.2,\n",
    "                 color=\"navy\", lw=lw)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig(\"DECSION_TREE_MAX_DEPTH_CENSUS.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "royal-translation",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# cv = ShuffleSplit(n_splits=10, test_size=0.1, random_state=0)\n",
    "\n",
    "# # Using pipeline\n",
    "# census_steps_depth = [('encoding', OneHotEncoder(cols=categorical_columns)),\n",
    "#          ('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),\n",
    "#          ('model',DecisionTreeClassifier(max_depth=40))]\n",
    "# census_pipeline_depth = Pipeline(steps=census_steps_depth)\n",
    "\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=45)\n",
    "train_scores, valid_scores = validation_curve(census_pipeline,\n",
    "                                              X_c_train,\n",
    "                                              y_c_train, \n",
    "                                              param_name =  \"model__min_samples_leaf\",\n",
    "                                            param_range = np.array(range(1,40)),\n",
    "                                            cv=stratified_kfold)\n",
    "\n",
    "param_range =  np.array(range(1,40))\n",
    "\n",
    "train_scores_mean =  np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(valid_scores, axis=1)\n",
    "test_scores_std = np.std(valid_scores, axis=1)\n",
    "\n",
    "plt.title(\"Validation Curve with Decision Tree\")\n",
    "plt.xlabel(\"Minimum Leaf Size\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.ylim(0.0, 1.1)\n",
    "lw = 2\n",
    "# semilogx\n",
    "plt.plot(param_range, train_scores_mean, label=\"Training score\",\n",
    "             color=\"darkorange\", lw=lw)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
    "                 color=\"darkorange\", lw=lw)\n",
    "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
    "             color=\"navy\", lw=lw)\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.2,\n",
    "                 color=\"navy\", lw=lw)\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "\n",
    "plt.savefig(\"DECSION_TREE_MININMUM_LEAF_SIZE_CENSUS.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "informational-front",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.770337 usin g {'model__ccp_alpha': 0.0, 'model__max_depth': 11, 'model__min_samples_leaf': 18}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# example of grid searching key hyperparameters for adaboost on a classification dataset\n",
    "\n",
    "# define dataset\n",
    "# define the model with default hyperparameters\n",
    "\n",
    "# model =  AdaBoostClassifier(algorithm='SAMME')\n",
    "# define the grid of values to search\n",
    "grid = dict()\n",
    "# grid['model__n_estimators'] = [i for i in range(1,25,5)]\n",
    "# grid['model__learning_rate'] = [0.01, 0.1]\n",
    "# grid['model__base_estimator'] = [DecisionTreeClassifier(max_depth=max_depth_,\n",
    "#                                                  ccp_alpha=ccp, \n",
    "#                                                  criterion=crit,\n",
    "#                                                  min_samples_split = min_samples_split_)  \n",
    "#                           for max_depth_ in range(1,4) \n",
    "#                           for ccp in np.arange(0.0001, 0.051, 0.005)\n",
    "#                           for crit in [\"entropy\"]\n",
    "#                           for min_samples_split_ in range(2,10,2)\n",
    "#                            ]\n",
    "\n",
    "grid['model__max_depth'] = [i for i in range(2,12,1)]\n",
    "grid['model__min_samples_leaf'] = [i for i in range(2,20,2)]\n",
    "grid['model__ccp_alpha'] = np.linspace(.0,.12,10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# define the evaluation procedure\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=45)\n",
    "# define the grid search procedure\n",
    "grid_search_census = GridSearchCV(estimator=census_pipeline, param_grid=grid, n_jobs=-1, cv=cv, scoring='average_precision')\n",
    "# execute the grid search\n",
    "grid_result_census = grid_search_census.fit(X_c_train, y_c_train)\n",
    "# summarize the best score and configuration\n",
    "print(\"Best: %f usin g %s\" % (grid_result_census.best_score_, grid_result_census.best_params_))\n",
    "# summarize all scores that were evaluated\n",
    "means = grid_result_census.cv_results_['mean_test_score']\n",
    "stds = grid_result_census.cv_results_['std_test_score']\n",
    "params = grid_result_census.cv_results_['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "athletic-novel",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "my_learn_curve(\"Default Decision Tree\",census_pipeline, cv=cv ,X=X_c_train, y=y_c_train )\n",
    "plt.savefig(\"DECISION_TREE_CENSUS_base_LEARNING_CURVE.PDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "comic-participation",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "my_learn_curve(\"Grid Search Decision Tree\", grid_result_census.best_estimator_, cv=cv ,X=X_c_train, y=y_c_train )\n",
    "plt.savefig(\"DECISION_TREE_CENSUS_FINAL_LEARNING_CURVE.PDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "leading-manchester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/flufylobster/anaconda3/envs/ML39/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 74,
     "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": [
    "my_learn_curve(\"Grid Search Decision Tree\",census_pipeline, cv=cv ,X=X_c_train, y=y_c_train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "turkish-suggestion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7eff32d926a0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(estimator= grid_result_census.best_estimator_, X=X_c_train, y_true= y_c_train  , \n",
    "                      cmap=plt.cm.Blues,\n",
    "                       normalize=\"true\",\n",
    "                      display_labels = [\"< 50 K\" ,\"> 50K\"]\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "loved-collect",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/flufylobster/anaconda3/envs/ML39/lib/python3.9/site-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  elif pd.api.types.is_categorical(cols):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(average_precision_score(y_c_train, census_pipeline.fit(X_c_train, y_c_train).predict_proba(X_c_train)[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "limiting-secondary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8065813714965082\n",
      "0.774824260419377\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(average_precision_score(y_c_train, grid_result_census.predict_proba(X_c_train)[:,1]))\n",
    "print(average_precision_score(y_c_test, grid_result_census.predict_proba(X_c_test)[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "alternate-posting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'memory': None, 'steps': [('encoding', OneHotEncoder(cols=Index(['workclass', 'education', 'marital.status', 'occupation',\n",
      "       'relationship', 'race', 'sex', 'native.country'],\n",
      "      dtype='object'))), ('imputer', SimpleImputer(strategy='most_frequent')), ('model', DecisionTreeClassifier(max_depth=11, min_samples_leaf=18))], 'verbose': False, 'encoding': OneHotEncoder(cols=Index(['workclass', 'education', 'marital.status', 'occupation',\n",
      "       'relationship', 'race', 'sex', 'native.country'],\n",
      "      dtype='object')), 'imputer': SimpleImputer(strategy='most_frequent'), 'model': DecisionTreeClassifier(max_depth=11, min_samples_leaf=18), 'encoding__cols': Index(['workclass', 'education', 'marital.status', 'occupation',\n",
      "       'relationship', 'race', 'sex', 'native.country'],\n",
      "      dtype='object'), 'encoding__drop_invariant': False, 'encoding__handle_missing': 'value', 'encoding__handle_unknown': 'value', 'encoding__return_df': True, 'encoding__use_cat_names': False, 'encoding__verbose': 0, 'imputer__add_indicator': False, 'imputer__copy': True, 'imputer__fill_value': None, 'imputer__missing_values': nan, 'imputer__strategy': 'most_frequent', 'imputer__verbose': 0, 'model__ccp_alpha': 0.0, 'model__class_weight': None, 'model__criterion': 'gini', 'model__max_depth': 11, 'model__max_features': None, 'model__max_leaf_nodes': None, 'model__min_impurity_decrease': 0.0, 'model__min_impurity_split': None, 'model__min_samples_leaf': 18, 'model__min_samples_split': 2, 'model__min_weight_fraction_leaf': 0.0, 'model__presort': 'deprecated', 'model__random_state': None, 'model__splitter': 'best'}\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.89      0.95      0.91     19758\n",
      "           1       0.78      0.62      0.69      6271\n",
      "\n",
      "    accuracy                           0.87     26029\n",
      "   macro avg       0.83      0.78      0.80     26029\n",
      "weighted avg       0.86      0.87      0.86     26029\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.88      0.94      0.91      4940\n",
      "           1       0.75      0.59      0.66      1568\n",
      "\n",
      "    accuracy                           0.85      6508\n",
      "   macro avg       0.81      0.77      0.78      6508\n",
      "weighted avg       0.85      0.85      0.85      6508\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(grid_result_census.best_estimator_.get_params())\n",
    "print(classification_report(y_c_train, grid_result_census.best_estimator_.predict(X_c_train) ))\n",
    "print(classification_report(y_c_test, grid_result_census.best_estimator_.predict(X_c_test) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interpreted-national",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}