notebooks/figs_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c09ed35-55e2-44e0-a551-98e486e8954a",
   "metadata": {
    "incorrectly_encoded_metadata": "tags=[] jp-MarkdownHeadingCollapsed=true",
    "tags": []
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dfbf0aaa-a6db-4ac9-84e8-ded7015de160",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import plot_tree, DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "# TODo remove when package is updated\n",
    "import sys,os\n",
    "sys.path.append(os.path.expanduser('~/imodels'))\n",
    "\n",
    "# installable with: `pip install imodels`\n",
    "from imodels import FIGSClassifier\n",
    "import demo_helper\n",
    "np.random.seed(13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08c18d5-6f3e-4061-8858-63ab9e204c67",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's start by loading some data in...  \n",
    "Note, we need to still load the reg dataset first to get the same splits as in `imodels_demo.ipynb` due to the call to random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b207abea-6fd7-45e6-bf8e-7c1e0f615bcf",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# ames housing dataset: https://www.openml.org/search?type=data&status=active&id=43926\n",
    "X_train_reg, X_test_reg, y_train_reg, y_test_reg, feat_names_reg = demo_helper.get_ames_data()\n",
    "\n",
    "# diabetes dataset: https://www.openml.org/search?type=data&sort=runs&id=37&status=active\n",
    "X_train, X_test, y_train, y_test, feat_names = demo_helper.get_diabetes_data()\n",
    "    # feat_names meanings:\n",
    "    # [\"#Pregnant\", \"Glucose concentration test\", \"Blood pressure(mmHg)\",\n",
    "    # \"Triceps skin fold thickness(mm)\",\n",
    "    # \"2-Hour serum insulin (mu U/ml)\", \"Body mass index\", \"Diabetes pedigree function\", \"Age (years)\"]\n",
    "\n",
    "# load some data\n",
    "# print('Regression data training', X_train_reg.shape, 'Classification data training', X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee73b4c5-6531-40a8-ae7a-55415e6cc733",
   "metadata": {
    "tags": []
   },
   "source": [
    "***\n",
    "# FIGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdae480c-7ca2-41be-bc5b-cf7330c0ab28",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_figs = FIGSClassifier(max_rules=7, max_trees=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60e886a6-d541-4116-86c3-c3d07ebf9c8d",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model_figs.fit(X_train, y_train, feature_names=feat_names);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66f5e3ac-cf4a-4be5-8873-bf60c7c154df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> ------------------------------\n",
      "> FIGS-Fast Interpretable Greedy-Tree Sums:\n",
      "> \tPredictions are made by summing the \"Val\" reached by traversing each tree.\n",
      "> \tFor classifiers, a sigmoid function is then applied to the sum.\n",
      "> ------------------------------\n",
      "Glucose concentration test <= 99.500 (Tree #0 root)\n",
      "\tVal: 0.068 (leaf)\n",
      "\tGlucose concentration test <= 168.500 (split)\n",
      "\t\t#Pregnant <= 6.500 (split)\n",
      "\t\t\tBody mass index <= 30.850 (split)\n",
      "\t\t\t\tVal: 0.065 (leaf)\n",
      "\t\t\t\tBlood pressure(mmHg) <= 67.000 (split)\n",
      "\t\t\t\t\tVal: 0.705 (leaf)\n",
      "\t\t\t\t\tVal: 0.303 (leaf)\n",
      "\t\t\tVal: 0.639 (leaf)\n",
      "\t\tBlood pressure(mmHg) <= 93.000 (split)\n",
      "\t\t\tVal: 0.860 (leaf)\n",
      "\t\t\tVal: -0.009 (leaf)\n",
      "\n",
      "\t+\n",
      "Diabetes pedigree function <= 0.404 (Tree #1 root)\n",
      "\tVal: -0.088 (leaf)\n",
      "\tVal: 0.106 (leaf)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model_figs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc21536b-c9d5-4722-a4d7-6e6ced6ab226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------\n",
      "Glucose concentration test <= 99.500 65/192 (33.85%)\n",
      "\tΔRisk = 0.07 4/59 (6.78%)\n",
      "\tGlucose concentration test <= 168.500 61/133 (45.86%)\n",
      "\t\t#Pregnant <= 6.500 44/112 (39.29%)\n",
      "\t\t\tBody mass index <= 30.850 21/76 (27.63%)\n",
      "\t\t\t\tΔRisk = 0.06 2/31 (6.45%)\n",
      "\t\t\t\tBlood pressure(mmHg) <= 67.000 19/45 (42.22%)\n",
      "\t\t\t\t\tΔRisk = 0.71 10/14 (71.43%)\n",
      "\t\t\t\t\tΔRisk = 0.30 9/31 (29.03%)\n",
      "\t\t\tΔRisk = 0.64 23/36 (63.89%)\n",
      "\t\tBlood pressure(mmHg) <= 93.000 17/21 (80.95%)\n",
      "\t\t\tΔRisk = 0.86 17/19 (89.47%)\n",
      "\t\t\tΔRisk = -0.01 0/2 (0.0%)\n",
      "\n",
      "\t+\n",
      "Diabetes pedigree function <= 0.404 65/192 (33.85%)\n",
      "\tΔRisk = -0.09 25/105 (23.81%)\n",
      "\tΔRisk = 0.11 40/87 (45.98%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model_figs.print_tree(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4207ec0a-9930-4dfb-b5fe-ad24eaa932e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1050x1050 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_figs.plot(fig_size=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589feb6b-9dfc-4b1d-8157-27e3d0c36acb",
   "metadata": {},
   "source": [
    "## Gini Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5945bdd6-bbc1-43db-b9a2-4eb1e7e54318",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "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>feat_names</th>\n",
       "      <th>feature</th>\n",
       "      <th>importance_gini</th>\n",
       "      <th>importance_gini_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Diabetes pedigree function</td>\n",
       "      <td>6</td>\n",
       "      <td>0.421422</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Glucose concentration test</td>\n",
       "      <td>1</td>\n",
       "      <td>0.275414</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#Pregnant</td>\n",
       "      <td>0</td>\n",
       "      <td>0.123990</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Body mass index</td>\n",
       "      <td>5</td>\n",
       "      <td>0.092911</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blood pressure(mmHg)</td>\n",
       "      <td>2</td>\n",
       "      <td>0.086263</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Triceps skin fold thickness(mm)</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2-Hour serum insulin (mu U/ml)</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age (years)</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        feat_names  feature  importance_gini  \\\n",
       "0       Diabetes pedigree function        6         0.421422   \n",
       "1       Glucose concentration test        1         0.275414   \n",
       "2                        #Pregnant        0         0.123990   \n",
       "3                  Body mass index        5         0.092911   \n",
       "4             Blood pressure(mmHg)        2         0.086263   \n",
       "5  Triceps skin fold thickness(mm)        3         0.000000   \n",
       "6   2-Hour serum insulin (mu U/ml)        4         0.000000   \n",
       "7                      Age (years)        7         0.000000   \n",
       "\n",
       "   importance_gini_pct  \n",
       "0                1.000  \n",
       "1                0.875  \n",
       "2                0.750  \n",
       "3                0.625  \n",
       "4                0.500  \n",
       "5                0.250  \n",
       "6                0.250  \n",
       "7                0.250  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfp_importance = pd.DataFrame({'feat_names': feat_names})\n",
    "dfp_importance['feature'] = dfp_importance.index\n",
    "dfp_importance_gini = pd.DataFrame({'importance_gini': model_figs.feature_importances_})\n",
    "dfp_importance_gini['feature'] = dfp_importance_gini.index\n",
    "dfp_importance_gini['importance_gini_pct'] = dfp_importance_gini['importance_gini'].rank(pct=True)\n",
    "dfp_importance = pd.merge(dfp_importance, dfp_importance_gini, on='feature', how='left')\n",
    "dfp_importance = dfp_importance.sort_values(by=['importance_gini', 'feature'], ascending=[False, True]).reset_index(drop=True)\n",
    "display(dfp_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c269b216-f668-4911-8e28-d252de2df2f6",
   "metadata": {
    "tags": []
   },
   "source": [
    "***\n",
    "# `dtreeviz` Integration\n",
    "One tree at a time only, showing tree 0 here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2fdddb1-0837-4e6a-bf2f-0f5e4109c74d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dtreeviz\n",
    "from imodels.tree.viz_utils import extract_sklearn_tree_from_figs\n",
    "\n",
    "dt = extract_sklearn_tree_from_figs(model_figs, tree_num=0, n_classes=2)\n",
    "viz_model = dtreeviz.model(dt, X_train=X_train, y_train=y_train, feature_names=feat_names, target_name='y', class_names=[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3830851-c78a-4dfb-a386-440aa1a37136",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_params = {'classes': dtreeviz.colors.mpl_colors, 'hist_bar': 'C0', 'legend_edge': None}\n",
    "for _ in ['axis_label', 'title', 'legend_title', 'text', 'arrow', 'node_label', 'tick_label', 'leaf_label', 'wedge', 'text_wedge']:\n",
    "    color_params[_] = 'black'\n",
    "dtv_params_gen = {'colors': color_params, 'fontname': 'Arial', 'figsize': (4, 3)}\n",
    "dtv_params = {'leaftype': 'barh',\n",
    "              'label_fontsize': 10,\n",
    "              'colors': dtv_params_gen['colors'],\n",
    "              'fontname': dtv_params_gen['fontname']\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b93acd6-63a7-4c91-b41f-2fe9a3eec35e",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<dtreeviz.utils.DTreeVizRender at 0x7f16bde821d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz_model.view(**dtv_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7263d2c8-4dc0-4299-b1d7-ee4cefdbd770",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Pregnant</th>\n",
       "      <th>Glucose concentration test</th>\n",
       "      <th>Blood pressure(mmHg)</th>\n",
       "      <th>Triceps skin fold thickness(mm)</th>\n",
       "      <th>2-Hour serum insulin (mu U/ml)</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Diabetes pedigree function</th>\n",
       "      <th>Age (years)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.743</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Pregnant  Glucose concentration test  Blood pressure(mmHg)  \\\n",
       "0        0.0                       131.0                  88.0   \n",
       "\n",
       "   Triceps skin fold thickness(mm)  2-Hour serum insulin (mu U/ml)  \\\n",
       "0                              0.0                             0.0   \n",
       "\n",
       "   Body mass index  Diabetes pedigree function  Age (years)  \n",
       "0             31.6                       0.743         32.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_example = X_train[13]\n",
    "display(pd.DataFrame([{col: value for col,value in zip(feat_names, x_example)}]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "270ea631-4475-4c9f-b130-b6d7d037a07b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Pregnant < 6.5\n",
      "99.5 <= Glucose concentration test  < 168.5\n",
      "67.0 <= Blood pressure(mmHg) \n",
      "30.85 <= Body mass index \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(viz_model.explain_prediction_path(x=x_example))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c3e5055-bffd-4e63-aa08-4a24ccc28fb4",
   "metadata": {},
   "outputs": [
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       "      <use xlink:href=\"#ArialMT-31\" />\n",
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       "  </g>\n",
       " </g>\n",
       "</svg></g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<dtreeviz.utils.DTreeVizRender at 0x7f16bc165a50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz_model.view(**dtv_params, x=x_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bbc71fe6-5b2d-48b6-86d9-2d5ad0655835",
   "metadata": {},
   "outputs": [
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       "<title>node6</title>\n",
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       "<title>node6-&gt;leaf7</title>\n",
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       "<title>node6-&gt;leaf8</title>\n",
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       "</svg></g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<dtreeviz.utils.DTreeVizRender at 0x7f16c00f4950>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz_model.view(**dtv_params, show_node_labels=True, fancy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b24f39d4-5418-4a20-8eef-f00893989ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz_model.ctree_leaf_distributions(**dtv_params_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23c76035-61be-4831-95e5-0c675fb857d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz_model.leaf_purity(display_type='plot', **dtv_params_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c59a2544-a225-409a-8be1-f8335e5ddea8",
   "metadata": {},
   "source": [
    "***\n",
    "# `SKompiler` Integration\n",
    "One tree at a time only, showing tree 0 here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f09de956-054e-4bd1-a313-efd0689d16aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from skompiler import skompile\n",
    "from imodels.tree.viz_utils import extract_sklearn_tree_from_figs\n",
    "\n",
    "dt = extract_sklearn_tree_from_figs(model_figs, tree_num=0, n_classes=2)\n",
    "expr = skompile(dt.predict_proba, feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "735c90be-5191-4375-a746-8b03eff8f176",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Currently broken, see https://github.com/konstantint/SKompiler/issues/16\n",
    "# print(expr.to('sqlalchemy/sqlite', component=1, assign_to='tree_0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63705ea9-ab3a-4e71-b8bb-109663711e36",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(np.array([0.9322033898305084, 0.06779661016949153]) if \n",
      "    Glucose concentration test <= 99.5 else ((np.array([\n",
      "    0.9354838709677419, 0.06451612903225806]) if Body mass index <= \n",
      "    30.84999942779541 else np.array([0.2857142857142857, \n",
      "    0.7142857142857143]) if Blood pressure(mmHg) <= 67.0 else np.array(\n",
      "    [0.7096774193548387, 0.2903225806451613])) if #Pregnant <= 6.5 else\n",
      "    np.array([0.3611111111111111, 0.6388888888888888])) if \n",
      "    Glucose concentration test <= 168.5 else np.array([\n",
      "    0.10526315789473684, 0.8947368421052632]) if Blood pressure(mmHg) <= \n",
      "    93.0 else np.array([1.0, 0.0]))\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(expr.to('python/code'))"
   ]
  }
 ],
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