dtreeviz code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import dtreeviz\n",
    "from sklearn.datasets import *\n",
    "from dtreeviz.trees import *\n",
    "import graphviz.backend as be\n",
    "from IPython.display import Image,display_svg, SVG"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-26T00:58:34.903698Z",
     "end_time": "2023-04-26T00:58:36.446881Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(max_depth=2)",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2)</pre></div></div></div></div></div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clas=tree.DecisionTreeClassifier(max_depth=2)\n",
    "iris=load_iris()\n",
    "\n",
    "X_train=iris.data\n",
    "y_train= iris.target\n",
    "clas.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-26T00:58:36.451762Z",
     "end_time": "2023-04-26T00:58:36.475075Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-26T00:58:36.468773Z",
     "end_time": "2023-04-26T00:58:36.492626Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "[Text(0.4, 0.8333333333333334, 'x[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n Text(0.2, 0.5, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n Text(0.6, 0.5, 'x[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n Text(0.4, 0.16666666666666666, 'gini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n Text(0.8, 0.16666666666666666, 'gini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]')]"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree(clas)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-26T00:58:36.476178Z",
     "end_time": "2023-04-26T00:58:36.837656Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# using detreeviz"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Arial' not found.\n",
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      "findfont: Font family 'Arial' not found.\n"
     ]
    }
   ],
   "source": [
    "import dtreeviz\n",
    "viz = dtreeviz.model(clas,\n",
    "                     X_train,\n",
    "                     y_train,\n",
    "                     feature_names=iris.feature_names,\n",
    "                     class_names=[\"setosa\",\"versicolor\",\"virginica\"]\n",
    "\n",
    "                     )\n",
    "v= viz.view()\n",
    "v.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-26T01:03:47.506253Z",
     "end_time": "2023-04-26T01:03:48.416826Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Regressin"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeRegressor()",
      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr=tree.DecisionTreeRegressor()\n",
    "diabetes=load_diabetes()\n",
    "\n",
    "X_train = diabetes.data\n",
    "y_train = diabetes.target\n",
    "\n",
    "regr.fit(X_train,y_train)\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-26T01:06:31.273193Z",
     "end_time": "2023-04-26T01:06:31.316162Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n        87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n       259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n       128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n       150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n       200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n       173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n        60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n       197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n        59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n       237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n       143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n       142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n        77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n       154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n       150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n        60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n       220.,  57.])"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-26T01:06:38.253359Z",
     "end_time": "2023-04-26T01:06:38.259692Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
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      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "X has 10 features, but DecisionTreeClassifier is expecting 4 features as input.",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[10], line 9\u001B[0m\n\u001B[1;32m      1\u001B[0m viz \u001B[38;5;241m=\u001B[39m dtreeviz\u001B[38;5;241m.\u001B[39mmodel(clas,\n\u001B[1;32m      2\u001B[0m                      X_train,\n\u001B[1;32m      3\u001B[0m                      y_train,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m      7\u001B[0m \n\u001B[1;32m      8\u001B[0m                      )\n\u001B[0;32m----> 9\u001B[0m v\u001B[38;5;241m=\u001B[39m \u001B[43mviz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mview\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     10\u001B[0m v\u001B[38;5;241m.\u001B[39mshow()\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/dtreeviz/trees.py:553\u001B[0m, in \u001B[0;36mDTreeVizAPI.view\u001B[0;34m(self, precision, orientation, instance_orientation, show_root_edge_labels, show_node_labels, show_just_path, fancy, histtype, leaftype, highlight_path, x, max_X_features_LR, max_X_features_TD, depth_range_to_display, label_fontsize, ticks_fontsize, fontname, title, title_fontsize, colors, scale)\u001B[0m\n\u001B[1;32m    551\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mshadow_tree\u001B[38;5;241m.\u001B[39mis_classifier():\n\u001B[1;32m    552\u001B[0m     nbins \u001B[38;5;241m=\u001B[39m _get_num_bins(histtype, n_classes)\n\u001B[0;32m--> 553\u001B[0m     node_heights \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mshadow_tree\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_split_node_heights\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnbins\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnbins\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    555\u001B[0m internal \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m    556\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m node \u001B[38;5;129;01min\u001B[39;00m get_internal_nodes():\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/dtreeviz/models/shadow_decision_tree.py:281\u001B[0m, in \u001B[0;36mShadowDecTree.get_split_node_heights\u001B[0;34m(self, X_train, y_train, nbins)\u001B[0m\n\u001B[1;32m    277\u001B[0m     overall_feature_range \u001B[38;5;241m=\u001B[39m (np\u001B[38;5;241m.\u001B[39mmin(X_feature), np\u001B[38;5;241m.\u001B[39mmax(X_feature))\n\u001B[1;32m    279\u001B[0m bins \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mlinspace(overall_feature_range[\u001B[38;5;241m0\u001B[39m],\n\u001B[1;32m    280\u001B[0m                    overall_feature_range[\u001B[38;5;241m1\u001B[39m], nbins \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m--> 281\u001B[0m X, y \u001B[38;5;241m=\u001B[39m X_feature[\u001B[43mnode\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msamples\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m], y_train[node\u001B[38;5;241m.\u001B[39msamples()]\n\u001B[1;32m    283\u001B[0m \u001B[38;5;66;03m# in case there is a categorical split node, we can convert the values to numbers because we need them\u001B[39;00m\n\u001B[1;32m    284\u001B[0m \u001B[38;5;66;03m# only for getting the distribution values\u001B[39;00m\n\u001B[1;32m    285\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m node\u001B[38;5;241m.\u001B[39mis_categorical_split():\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/dtreeviz/models/shadow_decision_tree.py:502\u001B[0m, in \u001B[0;36mShadowDecTreeNode.samples\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    500\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msamples\u001B[39m(\u001B[38;5;28mself\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m List[\u001B[38;5;28mint\u001B[39m]:\n\u001B[1;32m    501\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Returns samples indexes from this node\"\"\"\u001B[39;00m\n\u001B[0;32m--> 502\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mshadow_tree\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_node_samples\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m[\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mid]\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/dtreeviz/models/sklearn_decision_trees.py:56\u001B[0m, in \u001B[0;36mShadowSKDTree.get_node_samples\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m     53\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnode_to_samples \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m     54\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnode_to_samples\n\u001B[0;32m---> 56\u001B[0m dec_paths \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtree_model\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdecision_path\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mX_train\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     58\u001B[0m \u001B[38;5;66;03m# each sample has path taken down tree\u001B[39;00m\n\u001B[1;32m     59\u001B[0m node_to_samples \u001B[38;5;241m=\u001B[39m defaultdict(\u001B[38;5;28mlist\u001B[39m)\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/sklearn/tree/_classes.py:503\u001B[0m, in \u001B[0;36mBaseDecisionTree.decision_path\u001B[0;34m(self, X, check_input)\u001B[0m\n\u001B[1;32m    481\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mdecision_path\u001B[39m(\u001B[38;5;28mself\u001B[39m, X, check_input\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m):\n\u001B[1;32m    482\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Return the decision path in the tree.\u001B[39;00m\n\u001B[1;32m    483\u001B[0m \n\u001B[1;32m    484\u001B[0m \u001B[38;5;124;03m    .. versionadded:: 0.18\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    501\u001B[0m \u001B[38;5;124;03m        indicates that the samples goes through the nodes.\u001B[39;00m\n\u001B[1;32m    502\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 503\u001B[0m     X \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_X_predict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcheck_input\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    504\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtree_\u001B[38;5;241m.\u001B[39mdecision_path(X)\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/sklearn/tree/_classes.py:392\u001B[0m, in \u001B[0;36mBaseDecisionTree._validate_X_predict\u001B[0;34m(self, X, check_input)\u001B[0m\n\u001B[1;32m    390\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Validate the training data on predict (probabilities).\"\"\"\u001B[39;00m\n\u001B[1;32m    391\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m check_input:\n\u001B[0;32m--> 392\u001B[0m     X \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mDTYPE\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maccept_sparse\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcsr\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreset\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m    393\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m issparse(X) \u001B[38;5;129;01mand\u001B[39;00m (\n\u001B[1;32m    394\u001B[0m         X\u001B[38;5;241m.\u001B[39mindices\u001B[38;5;241m.\u001B[39mdtype \u001B[38;5;241m!=\u001B[39m np\u001B[38;5;241m.\u001B[39mintc \u001B[38;5;129;01mor\u001B[39;00m X\u001B[38;5;241m.\u001B[39mindptr\u001B[38;5;241m.\u001B[39mdtype \u001B[38;5;241m!=\u001B[39m np\u001B[38;5;241m.\u001B[39mintc\n\u001B[1;32m    395\u001B[0m     ):\n\u001B[1;32m    396\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo support for np.int64 index based sparse matrices\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/sklearn/base.py:569\u001B[0m, in \u001B[0;36mBaseEstimator._validate_data\u001B[0;34m(self, X, y, reset, validate_separately, **check_params)\u001B[0m\n\u001B[1;32m    566\u001B[0m     out \u001B[38;5;241m=\u001B[39m X, y\n\u001B[1;32m    568\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m no_val_X \u001B[38;5;129;01mand\u001B[39;00m check_params\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mensure_2d\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mTrue\u001B[39;00m):\n\u001B[0;32m--> 569\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_n_features\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreset\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreset\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    571\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m out\n",
      "File \u001B[0;32m~/anaconda3/lib/python3.10/site-packages/sklearn/base.py:370\u001B[0m, in \u001B[0;36mBaseEstimator._check_n_features\u001B[0;34m(self, X, reset)\u001B[0m\n\u001B[1;32m    367\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[1;32m    369\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m n_features \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_features_in_:\n\u001B[0;32m--> 370\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m    371\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mX has \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mn_features\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m features, but \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    372\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mis expecting \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_features_in_\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m features as input.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    373\u001B[0m     )\n",
      "\u001B[0;31mValueError\u001B[0m: X has 10 features, but DecisionTreeClassifier is expecting 4 features as input."
     ]
    }
   ],
   "source": [
    "viz = dtreeviz.model(clas,\n",
    "                     X_train,\n",
    "                     y_train,\n",
    "                     target_name=0,\n",
    "                     feature_names=diabetes.feature_names\n",
    "\n",
    "\n",
    "                     )\n",
    "v= viz.view()\n",
    "v.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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