FeaturedModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59c65949",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "from package import function\n",
    "import shap\n",
    "%matplotlib inline\n",
    "from package import DT\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "613239d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain = pd.read_csv(\"Xtrain_featured.csv\")\n",
    "ytrain = pd.read_csv(\"ytrain_featured.csv\").cardio\n",
    "Xtest = pd.read_csv(\"Xtest_featured.csv\")\n",
    "ytest = pd.read_csv(\"ytest_featured.csv\").cardio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "495ed754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'best'}\n",
      "Set the model with params: {'criterion': 'entropy', 'max_depth': 6}\n",
      "The score is 0.725952 for DTF\n"
     ]
    },
    {
     "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": [
    "model = DT.DecisionTree()\n",
    "model.baseline()\n",
    "model.best_model(Xtrain,ytrain)\n",
    "model.fit(Xtrain,ytrain)\n",
    "ypr,probTest = model.predict(Xtest)\n",
    "function.eval_confusion(\"DTF\",ypr,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a59ad5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(criterion = 'entropy', max_depth = 10,random_state = 100)\n",
    "model.fit(Xtrain,ytrain)\n",
    "importance = model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe8359b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x367.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(Xtest)\n",
    "shap.summary_plot(shap_values, Xtest, plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98226e34",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}