{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results Tables\n",
    "Proportion: outcome=0/outcome=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from data_treatment import DataAtts\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier as DT\n",
    "from sklearn.tree import export_graphviz # Decision tree from sklearn\n",
    "\n",
    "import pydotplus # Decision tree plotting\n",
    "from IPython.display import Image\n",
    "\n",
    "import ipywidgets as widgets\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'original_data/diabetes.csv'\n",
    "dataAtts = DataAtts(file_name)    \n",
    "data = pd.read_csv(file_name)\n",
    "folder_name = file_name[14:-4]\n",
    "\n",
    "# Creates the training set\n",
    "training_data = [[\"original\", data.head(int(data.shape[0]*0.7))]]\n",
    "test = data.tail(int(data.shape[0]*0.3))\n",
    "for file in glob.glob(\"fake_data\\\\\" + folder_name + \"\\\\*.csv\"):\n",
    "    name = \"fake\" + str(file).split(\"\\\\\")[2][0]\n",
    "    fake_data = pd.read_csv(file)\n",
    "    fake_data.loc[getattr(fake_data, dataAtts.class_name) >= 0.5, dataAtts.class_name] = 1\n",
    "    fake_data.loc[getattr(fake_data, dataAtts.class_name) < 0.5, dataAtts.class_name] = 0\n",
    "    fake_training=fake_data.head(int(fake_data.shape[0]*0.7))\n",
    "    training_data.append([name, fake_training])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"| Database \\t| Proportion \\t| Test Error \\t|\")\n",
    "print(\"| ---------\\t| ---------: \\t| :--------- \\t|\")\n",
    "\n",
    "for episode in training_data:\n",
    "        name = episode[0]\n",
    "        train = episode[1]\n",
    "        try:\n",
    "            positive=str(round(train[dataAtts.class_name].value_counts()[0]/len(train) * 100,2))\n",
    "        except:\n",
    "            positive=\"0\"\n",
    "        try:\n",
    "            negative=str(round(train[dataAtts.class_name].value_counts()[1]/len(train) * 100,2))\n",
    "        except:\n",
    "            negative=\"0\"\n",
    "        \n",
    "    \n",
    "        trainX = train.drop(dataAtts.class_name, 1)\n",
    "        testX = test.drop(dataAtts.class_name, 1)\n",
    "        y_train = train[dataAtts.class_name]\n",
    "        y_test = test[dataAtts.class_name]\n",
    "        #trainX = pd.get_dummies(trainX)\n",
    "\n",
    "        clf1 = DT(max_depth = 3, min_samples_leaf = 1)\n",
    "        clf1 = clf1.fit(trainX,y_train)\n",
    "        export_graphviz(clf1, out_file=\"models/tree.dot\", feature_names=trainX.columns, class_names=[\"0\",\"1\"], filled=True, rounded=True)\n",
    "        g = pydotplus.graph_from_dot_file(path=\"models/tree.dot\")\n",
    "\n",
    "        pred = clf1.predict_proba(testX)\n",
    "        if pred.shape[1] > 1:\n",
    "            pred = np.argmax(pred, axis=1)\n",
    "        else:\n",
    "            pred = pred.reshape((pred.shape[0]))\n",
    "            if negative==\"0\":\n",
    "                pred = pred-1\n",
    "        \n",
    "        mse = round(((pred - y_test.values)**2).mean(axis=0), 4)\n",
    "        \n",
    "        string=\"| \" + name + \" \\t| \" + positive + \"/\" + negative + \" \\t| \" + str(mse) + \" \\t|\"\n",
    "        print(string)"
   ]
  }
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