model_analysis/visualize_decision_trees_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of CatBoost decision trees tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/model_analysis/visualize_decision_trees_tutorial.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import catboost\n",
    "from catboost import CatBoostRegressor, CatBoostClassifier, Pool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load boston dataset from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()\n",
    "y = boston['target']\n",
    "X = boston['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = catboost.Pool(\n",
    "    data=X, \n",
    "    label=y\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create and fit CatBoost model with trees of depth 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CatBoostRegressor(depth=2, verbose=False, iterations=1).fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently only symmetric trees can be visualized.\n",
    "\n",
    "Let's consider symmetric decision tree. In such tree only one feature is used to build all splits at each tree level. There are three types of splits: \"FloatFeature\", \"OneHotFeature\" and \"OnlineCtr\". Model without categorical features contains only \"FloatFeature\" splits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In visualised tree each node represents one split. Since there are three types of splits there are three types of tree nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FloatFeature\n",
    "Let's look at the first tree of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "model.plot_tree(\n",
    "    tree_idx=0,\n",
    "    # pool=pool, \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model doesn't have categorical features, so there are only \"FloatFeature\" nodes in visualised tree.\n",
    "Node corresponding to \"FloatFeature\" split contains feature index and border value, which are used to split objects.\n",
    "\n",
    "In this example, the node of depth 0 shows that objects are splitted by their 0th feature with border value $393.795$. Analogously, nodes of depth 1 split objects by their 2nd feature with border value $279.5$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"pool\" parameter is optional for models without one hot features. Features are labeled with their external indexes from pool or features names if pool is provided, otherwise internal indexes are used. For semicolon-separated pool with 2 features  \"f1;label;f2\" external feature indexes are 0 and 2, internal indexes are 0 and 1 respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OneHotFeature\n",
    "\n",
    "We will use `catboost.datasets.titanic` dataset, which contains categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost.datasets import titanic\n",
    "titanic_df = titanic()\n",
    "\n",
    "X = titanic_df[0].drop('Survived',axis=1)\n",
    "y = titanic_df[0].Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "2            3       3                             Heikkinen, Miss. Laina   \n",
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   "source": [
    "X.head()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing NaN values in categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_cat = (X.dtypes != float)\n",
    "for feature, feat_is_cat in is_cat.to_dict().items():\n",
    "    if feat_is_cat:\n",
    "        X[feature].fillna(\"NAN\", inplace=True)\n",
    "\n",
    "cat_features_index = np.where(is_cat)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = Pool(X, y, cat_features=cat_features_index, feature_names=list(X.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define and fit CatBoost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CatBoostClassifier(\n",
    "    max_depth=2, verbose=False, max_ctr_complexity=1, random_seed=42, iterations=2).fit(pool)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
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     "execution_count": 40,
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   ],
   "source": [
    "model.plot_tree(\n",
    "    tree_idx=0,\n",
    "    pool=pool # \"pool\" is required parameter for trees with one hot features\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first tree contains only one split made by \"OneHotFeature\" `Sex`. This split puts objects with `Sex=female` to the left and other objects to the right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OnlineCtr features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at other trees, which contain \"OnlineCtr\" splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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    "model.plot_tree(tree_idx=1, pool=pool)"
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   "metadata": {},
   "source": [
    "The node of depth 0 corresponds to \"OnlineCtr\" split. This split is made by one feature `Pclass`."
   ]
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