Minimal_Cost_Complexity_Pruning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def underline_key(phrase_u, phrase_nu):\n",
    "    sp0, sp1 = \"\\033[1;;4m\", \"\\033[1;;2m\" \n",
    "    text_u = sp0+phrase_u+sp1+\" \"+phrase_nu\n",
    "    print(text_u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_data  = pd.read_pickle(\"./saved_variables/df_data\")\n",
    "X_train, X_test, y_train, y_test = pd.read_pickle('./saved_variables/X_y_train_test')\n",
    "df_X, df_y, df_data = pd.read_pickle('saved_variables/df_X_y_data')\n",
    "feature_names, class_names = pd.read_pickle('saved_variables/feature_class_names')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start of branch module\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(random_state=0)\n",
    "path = model.cost_complexity_pruning_path(X_train, y_train)\n",
    "#access return variables through dot notation\n",
    "ccp_alphas, impurities = path.ccp_alphas, path.impurities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.        ]\n",
      " [0.00226647 0.00453294]\n",
      " [0.00464743 0.01847522]\n",
      " [0.0046598  0.02313502]\n",
      " [0.0056338  0.02876883]\n",
      " [0.00704225 0.03581108]\n",
      " [0.00784194 0.04365302]\n",
      " [0.00911402 0.05276704]\n",
      " [0.01144366 0.0642107 ]\n",
      " [0.018988   0.0831987 ]\n",
      " [0.02314163 0.10634033]\n",
      " [0.03422475 0.14056508]\n",
      " [0.32729844 0.46786352]]\n",
      "\u001b[1;;4mLength of ccp_alphas:\u001b[1;;2m 13\n",
      "\u001b[1;;4mLength of impurities:\u001b[1;;2m 13\n"
     ]
    }
   ],
   "source": [
    "print(np.array(list(zip(ccp_alphas,impurities))))\n",
    "underline_key(\"Length of ccp_alphas:\", str(len(ccp_alphas)))\n",
    "underline_key(\"Length of impurities:\", str(len(impurities)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total Impurity vs effective alpha for training set')"
      ]
     },
     "execution_count": 16,
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle=\"steps-post\")\n",
    "ax.set_xlabel(\"effective alpha\")\n",
    "ax.set_ylabel(\"total impurity of leaves\")\n",
    "ax.set_title(\"Total Impurity vs effective alpha for training set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_to_list(list_a,new_item):\n",
    "    list_a.append(new_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes in the last tree is: 3 with ccp_alpha: 0.03422474765119576\n"
     ]
    }
   ],
   "source": [
    "clfs = []\n",
    "for ccp_alpha in ccp_alphas:\n",
    "    print(\"Training DTC with ccp alpha of %.2f\")\n",
    "    clf = DecisionTreeClassifier(random_state=0, ccp_alpha=ccp_alpha, min_impurity_split=None, min_samples_split=2)\n",
    "    clf.fit(X_train, y_train)\n",
    "    #print(clf)\n",
    "    add_to_list(clfs,clf)\n",
    "print(\"Number of nodes in the last tree is: {} with ccp_alpha: {}\".format(\n",
    "      clfs[-1].tree_.node_count, ccp_alphas[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = clfs[:-1]\n",
    "ccp_alphas = ccp_alphas[:-1]\n",
    "node_counts = [clf.tree_.node_count for clf in clfs]\n",
    "depths = [clf.tree_.max_depth for clf in clfs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots(2,1)\n",
    "#Generate two plots, number of nodes , depth vs alpha\n",
    "#First plot\n",
    "ax[0].plot(ccp_alphas, node_counts, marker='h', drawstyle='steps-post')\n",
    "ax[0].set_xlabel(\"alpha\")\n",
    "ax[0].set_ylabel(\"number of nodes\")\n",
    "ax[0].set_title(\"Number of Nodes vs alpha\")\n",
    "#Second plot\n",
    "ax[1].plot(ccp_alphas, depths, marker='d', drawstyle='steps-post')\n",
    "ax[1].set_xlabel(\"alpha\")\n",
    "ax[1].set_ylabel(\"depth of tree\")\n",
    "ax[1].set_title(\"Depth vs alpha\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scores = [clf.score(X_train, y_train) for clf in clfs]\n",
    "test_scores = [clf.score(X_test, y_test) for clf in clfs]\n",
    "score_list_index = range(len(train_scores))\n",
    "\n",
    "accuracy_gaps = []\n",
    "for i in score_list_index:\n",
    "    accuracy_gap = train_scores[i] - test_scores[i]\n",
    "    accuracy_gaps.append(accuracy_gap)\n",
    "    \n",
    "#wont be ploted\n",
    "accuracy_ratios = []\n",
    "for i in score_list_index:\n",
    "    accuracy_ratio = train_scores[i] / test_scores[i]\n",
    "    accuracy_ratios.append(accuracy_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>ccp_alphas</th>\n",
       "      <th>train_scores</th>\n",
       "      <th>test_scores</th>\n",
       "      <th>accuracy_gaps</th>\n",
       "      <th>accuracy_ratios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.881119</td>\n",
       "      <td>0.118881</td>\n",
       "      <td>1.134921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002266</td>\n",
       "      <td>0.997653</td>\n",
       "      <td>0.888112</td>\n",
       "      <td>0.109541</td>\n",
       "      <td>1.123341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004647</td>\n",
       "      <td>0.990610</td>\n",
       "      <td>0.916084</td>\n",
       "      <td>0.074526</td>\n",
       "      <td>1.081353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004660</td>\n",
       "      <td>0.988263</td>\n",
       "      <td>0.916084</td>\n",
       "      <td>0.072179</td>\n",
       "      <td>1.078791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005634</td>\n",
       "      <td>0.983568</td>\n",
       "      <td>0.916084</td>\n",
       "      <td>0.067484</td>\n",
       "      <td>1.073666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ccp_alphas  train_scores  test_scores  accuracy_gaps  accuracy_ratios\n",
       "0    0.000000      1.000000     0.881119       0.118881         1.134921\n",
       "1    0.002266      0.997653     0.888112       0.109541         1.123341\n",
       "2    0.004647      0.990610     0.916084       0.074526         1.081353\n",
       "3    0.004660      0.988263     0.916084       0.072179         1.078791\n",
       "4    0.005634      0.983568     0.916084       0.067484         1.073666"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating data frame from list for easier access\n",
    "dataframe_data = [ccp_alphas, train_scores, test_scores, accuracy_gaps, accuracy_ratios]\n",
    "scoring_columns = [\"ccp_alphas\",\"train_scores\", \"test_scores\", \"accuracy_gaps\", \"accuracy_ratios\"]\n",
    "df1 = pd.DataFrame(data = dataframe_data)\n",
    "df2 = df1.T\n",
    "df2.columns = scoring_columns\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select minimum alpha as best alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining variables for print output\n",
    "best_alpha_index = test_scores.index(max(test_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accuracy double plot\n",
    "fig, ax1 = plt.subplots(figsize=(9,6))\n",
    "\n",
    "\n",
    "ax1.set_ylim(0.88, 1)\n",
    "\n",
    "ax1.set_xlabel('alpha')\n",
    "ax1.set_ylabel('accuracy', color='blue')\n",
    "ax1.set_title(\"Accuracy, Accuracy Gap vs alpha for training and testing sets\")\n",
    "ax1.plot(ccp_alphas, train_scores, marker='o', label='train', color='orange')\n",
    "ax1.plot(ccp_alphas, test_scores, marker='o', label='test', color='blue')\n",
    "plt.axvline(x=ccp_alphas[best_alpha_index], linestyle='--', color='red')\n",
    "ax1.legend()\n",
    "#fig.text(text='This is the figure title', fontsize=12)\n",
    "ax1.tick_params(axis='y', labelcolor='blue')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax2.set_ylabel('accuracy gap', color='green')\n",
    "ax2.plot(ccp_alphas, accuracy_gaps, marker='x', label='accuracy gap',  color='green')\n",
    "ax2.legend(loc='upper center')\n",
    "ax2.tick_params(axis='y', labelcolor='green')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selected Tree Information - final product?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;;4mBest alpha:\u001b[1;;2m 0.011443661971830986\n",
      "\u001b[1;;4mBest alpha's test score\u001b[1;;2m 0.9370629370629371\n",
      "\u001b[1;;4mBest alpha's accuracy gap:\u001b[1;;2m 0.02772579533142916\n",
      "\u001b[1;;4mBest alpha's accuracy ratio:\u001b[1;;2m 1.0295879756148834\n"
     ]
    }
   ],
   "source": [
    "best_alpha = df2['ccp_alphas'][best_alpha_index]\n",
    "best_alpha_test_score = df2['test_scores'][best_alpha_index]\n",
    "best_alpha_accuracy_gap = df2['accuracy_gaps'][best_alpha_index]\n",
    "best_alpha_accuracy_ratio = df2['accuracy_ratios'][best_alpha_index]\n",
    "\n",
    "best_x, best_y, best_g, best_r = str(best_alpha), str(best_alpha_test_score), str(best_alpha_accuracy_gap), str(best_alpha_accuracy_ratio)\n",
    "\n",
    "underline_key(\"Best alpha:\", best_x)\n",
    "underline_key(\"Best alpha's test score\", best_y)\n",
    "underline_key(\"Best alpha's accuracy gap:\", best_g)\n",
    "underline_key(\"Best alpha's accuracy ratio:\", best_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A training accuracy that is subjectively far higher than test accuracy indicates over-fitting. - Andrew Ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;;4mSelected Decision Tree Parameters\u001b[1;;2m DecisionTreeClassifier(ccp_alpha=0.011443661971830986, class_weight=None,\n",
      "                       criterion='gini', max_depth=None, max_features=None,\n",
      "                       max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
      "                       min_impurity_split=None, min_samples_leaf=1,\n",
      "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "                       presort='deprecated', random_state=0, splitter='best')\n"
     ]
    }
   ],
   "source": [
    "underline_key(\"Selected Decision Tree Parameters\", str(clfs[best_alpha_index]))       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;;4mSelected Decision Tree Structure\u001b[1;;2m \n",
      "|--- concave_points_mean <= 0.05\n",
      "|   |--- area_worst <= 952.90\n",
      "|   |   |--- weights: [7.00, 245.00] class: 1\n",
      "|   |--- area_worst >  952.90\n",
      "|   |   |--- weights: [6.00, 2.00] class: 0\n",
      "|--- concave_points_mean >  0.05\n",
      "|   |--- area_worst <= 785.80\n",
      "|   |   |--- texture_worst <= 23.74\n",
      "|   |   |   |--- weights: [0.00, 14.00] class: 1\n",
      "|   |   |--- texture_worst >  23.74\n",
      "|   |   |   |--- weights: [13.00, 3.00] class: 0\n",
      "|   |--- area_worst >  785.80\n",
      "|   |   |--- weights: [133.00, 3.00] class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import export_text\n",
    "\n",
    "#SET PARAMETERS!!!!! FEATURE NAMES, CLASS NAMES\n",
    "r = export_text(clfs[best_alpha_index], feature_names=feature_names, max_depth=100, spacing=3, show_weights='True')\n",
    "underline_key(\"Selected Decision Tree Structure\",\"\")\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting\n",
    "### plot tree method - straight to inline vizualization\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html\n",
    "### .dot method - saved file that is called upon later to create vizualization\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html\n",
    "### export text method - inline text\n",
    "### export to png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#version 1 from https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure(figsize=(9,7))\n",
    "clf_selected = DecisionTreeClassifier(random_state=0, ccp_alpha=ccp_alphas[best_alpha_index])\n",
    "clf_selected.fit(X_train, y_train)\n",
    "\n",
    "plot_tree(clf_selected, feature_names=feature_names, class_names=class_names, filled=True,\n",
    "          node_ids=False, rotate=False, rounded=False, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for help https://bobswift.atlassian.net/wiki/spaces/GVIZ/pages/131924165/Graphviz+installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.5, 1.4],\n",
       "       [3. , 1.4],\n",
       "       [3.2, 1.3],\n",
       "       [3.1, 1.5],\n",
       "       [3.6, 1.4],\n",
       "       [3.9, 1.7],\n",
       "       [3.4, 1.4],\n",
       "       [3.4, 1.5],\n",
       "       [2.9, 1.4],\n",
       "       [3.1, 1.5],\n",
       "       [3.7, 1.5],\n",
       "       [3.4, 1.6],\n",
       "       [3. , 1.4],\n",
       "       [3. , 1.1],\n",
       "       [4. , 1.2],\n",
       "       [4.4, 1.5],\n",
       "       [3.9, 1.3],\n",
       "       [3.5, 1.4],\n",
       "       [3.8, 1.7],\n",
       "       [3.8, 1.5],\n",
       "       [3.4, 1.7],\n",
       "       [3.7, 1.5],\n",
       "       [3.6, 1. ],\n",
       "       [3.3, 1.7],\n",
       "       [3.4, 1.9],\n",
       "       [3. , 1.6],\n",
       "       [3.4, 1.6],\n",
       "       [3.5, 1.5],\n",
       "       [3.4, 1.4],\n",
       "       [3.2, 1.6],\n",
       "       [3.1, 1.6],\n",
       "       [3.4, 1.5],\n",
       "       [4.1, 1.5],\n",
       "       [4.2, 1.4],\n",
       "       [3.1, 1.5],\n",
       "       [3.2, 1.2],\n",
       "       [3.5, 1.3],\n",
       "       [3.6, 1.4],\n",
       "       [3. , 1.3],\n",
       "       [3.4, 1.5],\n",
       "       [3.5, 1.3],\n",
       "       [2.3, 1.3],\n",
       "       [3.2, 1.3],\n",
       "       [3.5, 1.6],\n",
       "       [3.8, 1.9],\n",
       "       [3. , 1.4],\n",
       "       [3.8, 1.6],\n",
       "       [3.2, 1.4],\n",
       "       [3.7, 1.5],\n",
       "       [3.3, 1.4],\n",
       "       [3.2, 4.7],\n",
       "       [3.2, 4.5],\n",
       "       [3.1, 4.9],\n",
       "       [2.3, 4. ],\n",
       "       [2.8, 4.6],\n",
       "       [2.8, 4.5],\n",
       "       [3.3, 4.7],\n",
       "       [2.4, 3.3],\n",
       "       [2.9, 4.6],\n",
       "       [2.7, 3.9],\n",
       "       [2. , 3.5],\n",
       "       [3. , 4.2],\n",
       "       [2.2, 4. ],\n",
       "       [2.9, 4.7],\n",
       "       [2.9, 3.6],\n",
       "       [3.1, 4.4],\n",
       "       [3. , 4.5],\n",
       "       [2.7, 4.1],\n",
       "       [2.2, 4.5],\n",
       "       [2.5, 3.9],\n",
       "       [3.2, 4.8],\n",
       "       [2.8, 4. ],\n",
       "       [2.5, 4.9],\n",
       "       [2.8, 4.7],\n",
       "       [2.9, 4.3],\n",
       "       [3. , 4.4],\n",
       "       [2.8, 4.8],\n",
       "       [3. , 5. ],\n",
       "       [2.9, 4.5],\n",
       "       [2.6, 3.5],\n",
       "       [2.4, 3.8],\n",
       "       [2.4, 3.7],\n",
       "       [2.7, 3.9],\n",
       "       [2.7, 5.1],\n",
       "       [3. , 4.5],\n",
       "       [3.4, 4.5],\n",
       "       [3.1, 4.7],\n",
       "       [2.3, 4.4],\n",
       "       [3. , 4.1],\n",
       "       [2.5, 4. ],\n",
       "       [2.6, 4.4],\n",
       "       [3. , 4.6],\n",
       "       [2.6, 4. ],\n",
       "       [2.3, 3.3],\n",
       "       [2.7, 4.2],\n",
       "       [3. , 4.2],\n",
       "       [2.9, 4.2],\n",
       "       [2.9, 4.3],\n",
       "       [2.5, 3. ],\n",
       "       [2.8, 4.1],\n",
       "       [3.3, 6. ],\n",
       "       [2.7, 5.1],\n",
       "       [3. , 5.9],\n",
       "       [2.9, 5.6],\n",
       "       [3. , 5.8],\n",
       "       [3. , 6.6],\n",
       "       [2.5, 4.5],\n",
       "       [2.9, 6.3],\n",
       "       [2.5, 5.8],\n",
       "       [3.6, 6.1],\n",
       "       [3.2, 5.1],\n",
       "       [2.7, 5.3],\n",
       "       [3. , 5.5],\n",
       "       [2.5, 5. ],\n",
       "       [2.8, 5.1],\n",
       "       [3.2, 5.3],\n",
       "       [3. , 5.5],\n",
       "       [3.8, 6.7],\n",
       "       [2.6, 6.9],\n",
       "       [2.2, 5. ],\n",
       "       [3.2, 5.7],\n",
       "       [2.8, 4.9],\n",
       "       [2.8, 6.7],\n",
       "       [2.7, 4.9],\n",
       "       [3.3, 5.7],\n",
       "       [3.2, 6. ],\n",
       "       [2.8, 4.8],\n",
       "       [3. , 4.9],\n",
       "       [2.8, 5.6],\n",
       "       [3. , 5.8],\n",
       "       [2.8, 6.1],\n",
       "       [3.8, 6.4],\n",
       "       [2.8, 5.6],\n",
       "       [2.8, 5.1],\n",
       "       [2.6, 5.6],\n",
       "       [3. , 6.1],\n",
       "       [3.4, 5.6],\n",
       "       [3.1, 5.5],\n",
       "       [3. , 4.8],\n",
       "       [3.1, 5.4],\n",
       "       [3.1, 5.6],\n",
       "       [3.1, 5.1],\n",
       "       [2.7, 5.1],\n",
       "       [3.2, 5.9],\n",
       "       [3.3, 5.7],\n",
       "       [3. , 5.2],\n",
       "       [2.5, 5. ],\n",
       "       [3. , 5.2],\n",
       "       [3.4, 5.4],\n",
       "       [3. , 5.1]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "iris.data[:, [1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-104-de2e3208195a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdf_X_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_X\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\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      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_X_list\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----> 3\u001b[1;33m \u001b[0mdf_X_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "df_X_list = df_X.values.tolist()\n",
    "print(type(df_X_list))\n",
    "df_X_list[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-7e242e2cf770>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_X\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\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      2\u001b[0m \u001b[0mds_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_X\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\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      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#used in later part of loop\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mds_x\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df_X' is not defined"
     ]
    }
   ],
   "source": [
    "print(df_X.values)\n",
    "ds_x = df_X.values[:,[1,2]]\n",
    "\n",
    "#used in later part of loop\n",
    "ds_x[:,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area_mean\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(feature_names[[2,3][1]])\n",
    "\n",
    "print([1,2][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-124-a432785e3420>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh_pad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw_pad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;31m# ravel flattens array aka turn a list of lists into one continous list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m     \u001b[0mZ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m     \u001b[0mZ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m     \u001b[1;31m#generate background rectangles\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\\tree\\_classes.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    418\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 420\u001b[1;33m         \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\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    421\u001b[0m         \u001b[0mn_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.Tree.predict\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.Tree.predict\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this can take from permutation feature selcetion notebook\n",
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "\n",
    "# Parameters\n",
    "n_classes = 3\n",
    "plot_colors = \"ryb\"\n",
    "plot_step = 0.02\n",
    "\n",
    "# Load data\n",
    "iris = load_iris()\n",
    "\n",
    "#this iterates pariidx as index? and pair as small list in the large list in reference to columns in dataset\n",
    "#for future insert combonation of [x_column, y_column]\n",
    "for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n",
    "                                [1, 2], [1, 3], [2, 3]]):\n",
    "    # like said above, pair is used to select appropriate columns in dataset where : is selecting all rows\n",
    "    X = df_X.values[:, pair]\n",
    "    y = df_y\n",
    "\n",
    "    # Train\n",
    "    clf = DecisionTreeClassifier().fit(X, y)\n",
    "    \n",
    "    # Plot the decision boundary\n",
    "    #plt.subplot(nrows, ncols, index)\n",
    "    #pairidx is a index number from above to index small list out of large list\n",
    "    plt.subplot(2, 3, pairidx + 1)\n",
    "\n",
    "    # [:,] refers to the first out of two columns with all rows\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    #creates the granualarity of visual plot\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                         np.arange(y_min, y_max, plot_step))\n",
    "    # makes layout of visualization to have form factor\n",
    "    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
    "    # ravel flattens array aka turn a list of lists into one continous list\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    #generate background rectangles\n",
    "        #X and Y must both be 2-D with the same shape as Z (e.g. created via numpy.meshgrid),\n",
    "            #or they must both be 1-D such that len(X) == M \n",
    "            #is the number of columns in Z and len(Y) == N is the number of rows in Z.\n",
    "            # Z is the height values over which the contour is drawn.\n",
    "    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
    "\n",
    "    plt.xlabel(feature_names[pair[0]])\n",
    "    plt.ylabel(feature_names[pair[1]])\n",
    "\n",
    "    # Plot the training points\n",
    "    for i, color in zip(range(n_classes), plot_colors):\n",
    "        idx = np.where(y == i)\n",
    "        plt.scatter(X[idx, 0], X[idx, 1], c=color, label=feature_names[i],\n",
    "                    cmap=plt.cm.RdYlBu, edgecolor='black', s=15)\n",
    "\n",
    "plt.suptitle(\"Decision surface of a decision tree using paired features\")\n",
    "plt.legend(loc='lower right', borderpad=0, handletextpad=0)\n",
    "plt.axis(\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial dependence plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "plot_partial_dependence() got multiple values for argument 'X'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-e2e18adc9d9c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclfs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbest_alpha_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\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----> 8\u001b[1;33m \u001b[0mplot_partial_dependence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX_train\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      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m#partial_dependence(gb, features=[0], X=X, percentiles=(0, 1),\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: plot_partial_dependence() got multiple values for argument 'X'"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_hastie_10_2\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.inspection import plot_partial_dependence\n",
    "\n",
    "X, y = make_hastie_10_2(random_state=0)\n",
    "clf = clfs[best_alpha_index]\n",
    "features = [0, 1, (0, 1)]\n",
    "plot_partial_dependence(clf, features, X=X_train) \n",
    "\n",
    "#partial_dependence(gb, features=[0], X=X, percentiles=(0, 1), \n",
    "#grid_resolution=2) # doctest: +SKIP\n"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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