{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d829e16",
   "metadata": {},
   "source": [
    "# Tree-Specific Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa7c930",
   "metadata": {},
   "source": [
    "Let $BT$ be a Boosted Tree composed of {$T_1,\\ldots, T_n$} regression trees and $x$ be an instance.\n",
    "* A **worst instance** extending $t$ given $BT$ is an instance $x'$ such that $t \\subseteq t_{x'}$ and: $$x' = \\mathit{argmin}_{x'': t \\subseteq t_{x''}}(\\{w(BT, x'')\\})$$\n",
    "* A **best instance** extending $t$ given $BT$ is an instance $x'$ such that $t \\subseteq t_{x'}$ and: $$x' = \\mathit{argmax}_{x'': t \\subseteq t_{x''}}(\\{w(BT, x'')\\})$$\n",
    "\n",
    "$W(t, BT)$ (resp. $B(t, BT)$) denotes the set of worst (resp. best) instances covered by $t$ given $BT$, and $w_\\downarrow(t, BT)$ (resp. $w_\\uparrow(t, BT)$) denotes the weight of the worst (resp. best) instance covered by $t$ given $F$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f48c34f8",
   "metadata": {},
   "source": [
    "In the multi-class setting, TS-explanations are defined on page dedicated to the [Tree-Specific Explanations for Classification](/documentation/classification/BTexplanations/treespecific/).\n",
    "\n",
    "For the regression case, TS-explanations can be defined as follows:\n",
    "\n",
    "Let $BT$ be a Boosted Tree composed of {$T_1,\\ldots, T_n$} regression trees and $x$ be an instance such that $BT(x) = r$. Let $I=[a,b]$ be an interval containing $r$. \n",
    "\n",
    "{: .note}\n",
    "> In order to get explanations that best respond to real problems, we do not explain why $BT(x) = r$ but why $BT(x) \\in [a,b]$. Note that explaining why $BT(x) = r$ corresponds to computing the [direct reason](/documentation/regression/BTregression/direct/) for $x$. \n",
    "\n",
    "\n",
    "Conceptually, $t$ is a **tree-specific** explanation for $x$ given $BT$ and be an interval $I=[a,b]$ if and only if $t$ is a subset of $t_{x}$ such that:\n",
    "- $w_\\downarrow(t, BT) \\geq a$ \n",
    "- $w_\\uparrow(t, BT) \\leq b$ \n",
    "- no proper subset of $t$ satisfies the latter condition.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424b7b69",
   "metadata": {},
   "source": [
    "In general, the notions of tree-specific explanation and of sufficient reason do not coincide. Indeed, a sufficient reason is a prime implicant (covering $x$) of the Boosted Tree $BT$, while a tree-specific explanation is an implicant $t$ (covering $x$). Since there is a simple, linear-time algorithm for computing $w_\\downarrow(t, F)$ and $w_\\uparrow(t, F)$ and for deriving a worst-case and a best-case instance, the **tree-specific** explanations for $x$ are much easier to compute than the sufficient reasons and they remain abductive.\n",
    "\n",
    "More information about tree-specific explanations for the classification task can be found in the paper [Computing Abductive Explanations for Boosted Trees](https://arxiv.org/abs/2209.07740). For the regression task, more information can be found in the [Computing Abductive Explanations for Boosted Regression Trees](https://www.ijcai.org/proceedings/2023/382) paper.\n",
    "\n",
    "The basic methods ([``initialize``](http://localhost:4000/pyxai/documentation/api/modules/explaining/), ```set_instance```, ```to_features```, ```is_reason```, …) of the Explainer module used in the next examples are described in the [Explainer Principles](/documentation/explainer/)   page.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf73429",
   "metadata": {},
   "source": [
    "The method ```ExplainerRegressionBT.tree_specific_reason``` allows to compute such explanations. To use it method, you must first call the method ```set_interval``` to define an interval.\n",
    "\n",
    "PyXAI also provides ```is_tree_specific_reason``` to check a reason."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc483010",
   "metadata": {},
   "source": [
    "## Example from Hand-Crafted Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f86e05b",
   "metadata": {},
   "source": [
    "To illustrate the generation of a tree-specific explanation, we take an example from the [Computing Abductive Explanations for Boosted Regression Trees](https://www.ijcai.org/proceedings/2023/382) paper.\n",
    "\n",
    "Let us consider a loan application scenario. The goal is to predict\n",
    "the amount of money that can be granted to an applicant described using three attributes ($A = \\{A_1, A_2, A_3\\}$). \n",
    "- $A_1$ is a numerical attribute giving the income per month of the applicant\n",
    "- $A_2$ is a categorical feature giving its employment status as ”employed”, ”unemployed” or ”self-employed”\n",
    "- $A_3$ is a Boolean feature set to true if the customer is married, false otherwise. \n",
    "\n",
    "<img src=\"attachment:BTbaseRegression.png\" alt=\"BTbase\" width=\"700\" />\n",
    "\n",
    "In this example:\n",
    "\n",
    "- $A_1$ is represented by the feature identifier $F_1$\n",
    "- $A_2$ has been one-hot encoded and is represented by feature identifiers $F_2$, $F_3$ and $F_4$, each of these features represents respectively the conditions $A_2 = employed$, $A_2 = unemployed$ and $A_2 = self-employed$\n",
    "- $A_3$ is represented by the feature identifier $F_5$ and the condition $(A_3 = 1)$ (”the applicant is married”)\n",
    "\n",
    "We consider the instance $x=(2200, 0, 0, 1, 1)$, corresponding to a person with a salary equal to 2200 per month, self-employed (one-hot encoded) and married. Then, $BT(x) = 1500 + 250 + 250 = 2000\\$.\n",
    "\n",
    "A tree-specific explanation for the instance $x = (2200, 0, 0, 1, 1)$ and $I=[1500, 2500]$ can be represented by $t = \\{A_1{>}2000\\}$. Indeed, we have $w_\\downarrow(t, BT) \\geq 1500$ and $w_\\uparrow(t, BT) \\leq 2500$. \n",
    "\n",
    "The next figure represents the tree-specific reason $t = \\{A_1{>}2000\\}$ in red and the dark red leaves give the weights of $w_\\downarrow(t, BT)$ for each regression tree of $BT$. We can see that $w_\\downarrow(t, BT) = 1500 - 100 + 250 = 1650 \\geq 1500$. \n",
    "\n",
    "<img src=\"attachment:BTTS1Regression.png\" alt=\"BTTS1\" width=\"700\" />\n",
    "\n",
    "The next figure represents the tree-specific explanation $t = \\{A_1{>}2000\\}$ in blue and the dark blue leaves give the weights of $w_\\uparrow(t, BT)$ for each regression tree of $BT$. We can observe that $w_\\uparrow(t, BT) = 1750 + 250 + 100 = 2100 \\leq 2500$. \n",
    "\n",
    "<img src=\"attachment:BTTS2Regression.png\" alt=\"BTTS2\" width=\"700\" />\n",
    "\n",
    "\n",
    "We now show how to get those tree-specific explanations using PyXAI: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3667447b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyxai import Builder, Explaining, Learning\n",
    "\n",
    "node1_1 = Builder.DecisionNode(1, operator=Builder.GT, threshold=3000, left=1500, right=1750)\n",
    "node1_2 = Builder.DecisionNode(1, operator=Builder.GT, threshold=2000, left=1000, right=node1_1)\n",
    "node1_3 = Builder.DecisionNode(1, operator=Builder.GT, threshold=1000, left=0, right=node1_2)\n",
    "tree1 = Builder.DecisionTree(5, node1_3)\n",
    "\n",
    "\n",
    "node2_1 = Builder.DecisionNode(5, operator=Builder.EQ, threshold=1, left=100, right=250)\n",
    "node2_2 = Builder.DecisionNode(4, operator=Builder.EQ, threshold=1, left=-100, right=node2_1)\n",
    "node2_3 = Builder.DecisionNode(2, operator=Builder.EQ, threshold=1, left=node2_2, right=250)\n",
    "tree2 = Builder.DecisionTree(5, node2_3)\n",
    "\n",
    "node3_1 = Builder.DecisionNode(3, operator=Builder.EQ, threshold=1, left=500, right=250)\n",
    "node3_2 = Builder.DecisionNode(3, operator=Builder.EQ, threshold=1, left=250, right=100)\n",
    "node3_3 = Builder.DecisionNode(1, operator=Builder.GT, threshold=2000, left=0, right=node3_1)\n",
    "node3_4 = Builder.DecisionNode(4, operator=Builder.EQ, threshold=1, left=node3_3, right=node3_2)\n",
    "tree3 = Builder.DecisionTree(5, node3_4)\n",
    "\n",
    "\n",
    "BTs = Builder.BoostedTreesRegression([tree1, tree2, tree3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e07d5a6",
   "metadata": {},
   "source": [
    "This example can be found in the second part of the [Building Boosted Trees](/documentation/learning/builder/BTbuilder/) page. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84b411c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instance: (2200, 0, 0, 1, 1)\n",
      "---------   Theory Feature Types   -----------\n",
      "Before the encoding (without one hot encoded features), we have:\n",
      "Numerical features: 1\n",
      "Categorical features: 1\n",
      "Binary features: 1\n",
      "Number of features: 3\n",
      "Values of categorical features: {'f2': ['f{2,3,4}', 1, (1, 2, 3)], 'f3': ['f{2,3,4}', 2, (1, 2, 3)], 'f4': ['f{2,3,4}', 3, (1, 2, 3)]}\n",
      "\n",
      "Number of used features in the model (before the encoding): 3\n",
      "Number of used features in the model (after the encoding): 5\n",
      "----------------------------------------------\n",
      "prediction: 2000\n",
      "tree specific: ('f1 > 2000',)\n",
      "is tree :  True\n"
     ]
    }
   ],
   "source": [
    "instance = (2200, 0, 0, 1, 1) # 2200$, self employed (one hot encoded), married\n",
    "print(\"instance:\", instance)\n",
    "\n",
    "loan_types = {\n",
    "    \"numerical\": Learning.DEFAULT,\n",
    "    \"categorical\": {\"f{2,3,4}\": (1, 2, 3)},\n",
    "    \"binary\": [\"f5\"],\n",
    "}\n",
    "\n",
    "explainer = Explainer.initialize(BTs, instance, features_type=loan_types)\n",
    "\n",
    "print(\"prediction:\", explainer.predict(instance))\n",
    "explainer.set_interval(1500, 2500)\n",
    "\n",
    "tree_specific = explainer.tree_specific_reason()\n",
    "print(\"tree specific:\", explainer.to_features(tree_specific))\n",
    "print(\"is tree : \", explainer.is_tree_specific_reason(tree_specific))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac340495",
   "metadata": {},
   "source": [
    "## Example from a Real Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73754a55",
   "metadata": {},
   "source": [
    "For this example, we take the [Houses-prices](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques) dataset (this one [here](/assets/notebooks/dataset/houses-prices.csv)). We create a model using the hold-out approach (by default, the test size is set to 30%) and select a well-classified instance. As this dataset contains strings, we encode the data using PyXAI's [Preprocessor](/documentation/preprocessor/): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0cf26af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------   Information   ---------------\n",
      "Problem type: regression\n",
      "Instances type: tabular\n",
      "Labels type: None\n",
      "\n",
      "Dataset path: None\n",
      "---------------    Converter    ---------------\n",
      "Feature deleted:  Id\n",
      "One hot encoding new features for MSSubClass: 16\n",
      "-> The feature Street is boolean! No One Hot Encoding for this features.\n",
      "-> However, the boolean feature Street contains strings. A ordinal encoding must be performed.\n",
      "One hot encoding new features for LotShape: 4\n",
      "One hot encoding new features for LandContour: 4\n",
      "One hot encoding new features for LotConfig: 5\n",
      "One hot encoding new features for LandSlope: 3\n",
      "One hot encoding new features for Neighborhood: 25\n",
      "One hot encoding new features for Condition1: 9\n",
      "One hot encoding new features for Condition2: 8\n",
      "One hot encoding new features for BldgType: 5\n",
      "One hot encoding new features for HouseStyle: 8\n",
      "One hot encoding new features for OverallQual: 10\n",
      "One hot encoding new features for OverallCond: 9\n",
      "One hot encoding new features for RoofStyle: 6\n",
      "One hot encoding new features for RoofMatl: 8\n",
      "One hot encoding new features for ExterQual: 4\n",
      "One hot encoding new features for ExterCond: 5\n",
      "One hot encoding new features for Foundation: 6\n",
      "One hot encoding new features for Heating: 6\n",
      "One hot encoding new features for HeatingQC: 5\n",
      "-> The feature CentralAir is boolean! No One Hot Encoding for this features.\n",
      "-> However, the boolean feature CentralAir contains strings. A ordinal encoding must be performed.\n",
      "One hot encoding new features for PavedDrive: 3\n",
      "One hot encoding new features for SaleCondition: 6\n",
      "Dataset saved: ../../dataset/houses-prices-converted.csv\n",
      "Types saved: ../../dataset/houses-prices-converted.types\n",
      "-----------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n",
      "/home/audemard/expectation/softwares/pyxai-mlp/pyxai/sources/learners/preprocessor/tabular_preprocessor.py:500: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  self.data.insert(index, name, transformed_df[name], True)\n"
     ]
    }
   ],
   "source": [
    "from pyxai import Learning\n",
    "\n",
    "preprocessor = Learning.TabularPreprocessor(\"../../dataset/houses-prices.csv\", target_feature=\"SalePrice\", problem_type=\"regression\")\n",
    "\n",
    "preprocessor.unset_features([\"Id\"])\n",
    "\n",
    "preprocessor.set_categorical_features(features=[\n",
    "    \"MSSubClass\",\n",
    "    \"Street\",\n",
    "    \"LotShape\", \n",
    "    \"LandContour\", \n",
    "    \"LotConfig\", \n",
    "    \"LandSlope\", \n",
    "    \"Neighborhood\", \n",
    "    \"Condition1\", \n",
    "    \"Condition2\", \n",
    "    \"BldgType\", \n",
    "    \"HouseStyle\", \n",
    "    \"OverallQual\", \n",
    "    \"OverallCond\", \n",
    "    \"RoofStyle\", \n",
    "    \"RoofMatl\", \n",
    "    \"ExterQual\", \n",
    "    \"ExterCond\", \n",
    "    \"Foundation\", \n",
    "    \"Heating\", \n",
    "    \"HeatingQC\", \n",
    "    \"CentralAir\", \n",
    "    \"PavedDrive\", \n",
    "    \"SaleCondition\"])\n",
    "\n",
    "preprocessor.set_numerical_features({\n",
    "        \"LotArea\": None,\n",
    "        \"YearBuilt\": None, \n",
    "        \"YearRemodAdd\": None, \n",
    "        \"1stFlrSF\": None,\n",
    "        \"2ndFlrSF\": None,\n",
    "        \"LowQualFinSF\": None,\n",
    "        \"GrLivArea\": None,\n",
    "        \"FullBath\": None,\n",
    "        \"HalfBath\": None,\n",
    "        \"BedroomAbvGr\": None,\n",
    "        \"KitchenAbvGr\": None,\n",
    "        \"TotRmsAbvGrd\": None,\n",
    "        \"Fireplaces\": None,\n",
    "        \"WoodDeckSF\": None,\n",
    "        \"OpenPorchSF\": None,\n",
    "        \"EnclosedPorch\": None,\n",
    "        \"3SsnPorch\": None,\n",
    "        \"ScreenPorch\": None,\n",
    "        \"PoolArea\": None,\n",
    "        \"MiscVal\": None,\n",
    "        \"MoSold\": None,\n",
    "        \"YrSold\": None\n",
    "  })\n",
    "\n",
    "\n",
    "preprocessor.process()\n",
    "dataset_name = \"../../dataset/houses-prices.csv\".split(\"/\")[-1].split(\".\")[0]+\"-converted\" \n",
    "preprocessor.export(dataset_name, output_directory=\"../../dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3355045d",
   "metadata": {},
   "source": [
    "```console\n",
    "Index(['Id', 'MSSubClass', 'LotArea', 'Street', 'LotShape', 'LandContour',\n",
    "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
    "       'BldgType', 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt',\n",
    "       'YearRemodAdd', 'RoofStyle', 'RoofMatl', 'ExterQual', 'ExterCond',\n",
    "       'Foundation', 'Heating', 'HeatingQC', 'CentralAir', '1stFlrSF',\n",
    "       '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'FullBath', 'HalfBath',\n",
    "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
    "       'PavedDrive', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\n",
    "       'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold',\n",
    "       'SaleCondition', 'SalePrice'],\n",
    "      dtype='object')\n",
    "---------------    Converter    ---------------\n",
    "Feature deleted:  Id\n",
    "One hot encoding new features for MSSubClass: 16\n",
    "-> The feature Street is boolean! No One Hot Encoding for this features.\n",
    "-> However, the boolean feature Street contains strings. A ordinal encoding must be performed.\n",
    "One hot encoding new features for LotShape: 4\n",
    "One hot encoding new features for LandContour: 4\n",
    "One hot encoding new features for LotConfig: 5\n",
    "One hot encoding new features for LandSlope: 3\n",
    "One hot encoding new features for Neighborhood: 25\n",
    "One hot encoding new features for Condition1: 9\n",
    "One hot encoding new features for Condition2: 8\n",
    "One hot encoding new features for BldgType: 5\n",
    "One hot encoding new features for HouseStyle: 8\n",
    "One hot encoding new features for OverallQual: 10\n",
    "One hot encoding new features for OverallCond: 9\n",
    "One hot encoding new features for RoofStyle: 6\n",
    "One hot encoding new features for RoofMatl: 8\n",
    "One hot encoding new features for ExterQual: 4\n",
    "One hot encoding new features for ExterCond: 5\n",
    "One hot encoding new features for Foundation: 6\n",
    "One hot encoding new features for Heating: 6\n",
    "One hot encoding new features for HeatingQC: 5\n",
    "-> The feature CentralAir is boolean! No One Hot Encoding for this features.\n",
    "-> However, the boolean feature CentralAir contains strings. A ordinal encoding must be performed.\n",
    "One hot encoding new features for PavedDrive: 3\n",
    "One hot encoding new features for SaleCondition: 6\n",
    "Dataset saved: ../../dataset/houses-prices-converted_0.csv\n",
    "Types saved: ../../dataset/houses-prices-converted_0.types\n",
    "-----------------------------------------------\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d7b88c",
   "metadata": {},
   "source": [
    "Now we produce a model and pick up an instance: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1479f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------   Information   ---------------\n",
      "Problem type: regression\n",
      "Instances type: tabular\n",
      "Labels type: continuous-values\n",
      "\n",
      "Dataset path: ../../dataset/houses-prices-converted_0.csv\n",
      "nFeatures (nAttributes, with the labels): 179\n",
      "nInstances (nObservations): 2919\n",
      "---------------   Model creation, fitting and evaluation  ---------------\n",
      "Splitting method: hold-out\n",
      "Problem type: regression\n",
      "Models type: boosted-tree\n",
      "model_parameters: {}\n",
      "---------   Evaluation Information   ---------\n",
      "For the evaluation number 0:\n",
      "Metrics:\n",
      "   mean_squared_error: 2027744102.0068274\n",
      "   root_mean_squared_error: 45030.47969994132\n",
      "   mean_absolute_error: 31183.444598122052\n",
      "Number of Training instances: 2189\n",
      "Number of Testing instances: 730\n",
      "\n",
      "---------------   Explainer   ----------------\n",
      "For the split number 0:\n",
      "**Boosted Tree Regression model**\n",
      "nTrees: 100\n",
      "nVariables: 1077\n",
      "\n",
      "---------------   Instances   ----------------\n",
      "Number of instances selected: 1\n",
      "----------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from pyxai import Learning, Explaining\n",
    "\n",
    "learner = Learning.Xgboost(\"../../dataset/houses-prices-converted_0.csv\", problem_type=Learning.REGRESSION)\n",
    "model = learner.evaluate(splitting_method=Learning.HOLD_OUT, model_type=Learning.BT)\n",
    "instance, prediction = learner.get_instances(model, n=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0e2ec8",
   "metadata": {},
   "source": [
    "Finally, we display a tree-specific explanation for this instance. Note that we activate the theory created by the PyXAI's Preprocessor by adding the parameter ```features_type=\"../../dataset/houses-prices-converted_0.types\"``` to the ```initialize``` method. More information about theories are available on this [page](/documentation/explainer/theories/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29a4effc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature_names: ['MSSubClass_20', 'MSSubClass_30', 'MSSubClass_40', 'MSSubClass_45', 'MSSubClass_50', 'MSSubClass_60', 'MSSubClass_70', 'MSSubClass_75', 'MSSubClass_80', 'MSSubClass_85', 'MSSubClass_90', 'MSSubClass_120', 'MSSubClass_150', 'MSSubClass_160', 'MSSubClass_180', 'MSSubClass_190', 'LotArea', 'Street', 'LotShape_IR1', 'LotShape_IR2', 'LotShape_IR3', 'LotShape_Reg', 'LandContour_Bnk', 'LandContour_HLS', 'LandContour_Low', 'LandContour_Lvl', 'LotConfig_Corner', 'LotConfig_CulDSac', 'LotConfig_FR2', 'LotConfig_FR3', 'LotConfig_Inside', 'LandSlope_Gtl', 'LandSlope_Mod', 'LandSlope_Sev', 'Neighborhood_Blmngtn', 'Neighborhood_Blueste', 'Neighborhood_BrDale', 'Neighborhood_BrkSide', 'Neighborhood_ClearCr', 'Neighborhood_CollgCr', 'Neighborhood_Crawfor', 'Neighborhood_Edwards', 'Neighborhood_Gilbert', 'Neighborhood_IDOTRR', 'Neighborhood_MeadowV', 'Neighborhood_Mitchel', 'Neighborhood_NAmes', 'Neighborhood_NPkVill', 'Neighborhood_NWAmes', 'Neighborhood_NoRidge', 'Neighborhood_NridgHt', 'Neighborhood_OldTown', 'Neighborhood_SWISU', 'Neighborhood_Sawyer', 'Neighborhood_SawyerW', 'Neighborhood_Somerst', 'Neighborhood_StoneBr', 'Neighborhood_Timber', 'Neighborhood_Veenker', 'Condition1_Artery', 'Condition1_Feedr', 'Condition1_Norm', 'Condition1_PosA', 'Condition1_PosN', 'Condition1_RRAe', 'Condition1_RRAn', 'Condition1_RRNe', 'Condition1_RRNn', 'Condition2_Artery', 'Condition2_Feedr', 'Condition2_Norm', 'Condition2_PosA', 'Condition2_PosN', 'Condition2_RRAe', 'Condition2_RRAn', 'Condition2_RRNn', 'BldgType_1Fam', 'BldgType_2fmCon', 'BldgType_Duplex', 'BldgType_Twnhs', 'BldgType_TwnhsE', 'HouseStyle_1.5Fin', 'HouseStyle_1.5Unf', 'HouseStyle_1Story', 'HouseStyle_2.5Fin', 'HouseStyle_2.5Unf', 'HouseStyle_2Story', 'HouseStyle_SFoyer', 'HouseStyle_SLvl', 'OverallQual_1', 'OverallQual_2', 'OverallQual_3', 'OverallQual_4', 'OverallQual_5', 'OverallQual_6', 'OverallQual_7', 'OverallQual_8', 'OverallQual_9', 'OverallQual_10', 'OverallCond_1', 'OverallCond_2', 'OverallCond_3', 'OverallCond_4', 'OverallCond_5', 'OverallCond_6', 'OverallCond_7', 'OverallCond_8', 'OverallCond_9', 'YearBuilt', 'YearRemodAdd', 'RoofStyle_Flat', 'RoofStyle_Gable', 'RoofStyle_Gambrel', 'RoofStyle_Hip', 'RoofStyle_Mansard', 'RoofStyle_Shed', 'RoofMatl_ClyTile', 'RoofMatl_CompShg', 'RoofMatl_Membran', 'RoofMatl_Metal', 'RoofMatl_Roll', 'RoofMatl_Tar&Grv', 'RoofMatl_WdShake', 'RoofMatl_WdShngl', 'ExterQual_Ex', 'ExterQual_Fa', 'ExterQual_Gd', 'ExterQual_TA', 'ExterCond_Ex', 'ExterCond_Fa', 'ExterCond_Gd', 'ExterCond_Po', 'ExterCond_TA', 'Foundation_BrkTil', 'Foundation_CBlock', 'Foundation_PConc', 'Foundation_Slab', 'Foundation_Stone', 'Foundation_Wood', 'Heating_Floor', 'Heating_GasA', 'Heating_GasW', 'Heating_Grav', 'Heating_OthW', 'Heating_Wall', 'HeatingQC_Ex', 'HeatingQC_Fa', 'HeatingQC_Gd', 'HeatingQC_Po', 'HeatingQC_TA', 'CentralAir', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'PavedDrive_N', 'PavedDrive_P', 'PavedDrive_Y', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SaleCondition_Abnorml', 'SaleCondition_AdjLand', 'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal', 'SaleCondition_Partial']\n",
      "---------   Theory Feature Types   -----------\n",
      "Before the one-hot encoding of categorical features:\n",
      "Numerical features: 22\n",
      "Categorical features: 21\n",
      "Binary features: 2\n",
      "Number of features: 45\n",
      "Characteristics of categorical features: {'MSSubClass_20': ['MSSubClass', 20, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_30': ['MSSubClass', 30, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_40': ['MSSubClass', 40, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_45': ['MSSubClass', 45, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_50': ['MSSubClass', 50, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_60': ['MSSubClass', 60, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_70': ['MSSubClass', 70, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_75': ['MSSubClass', 75, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_80': ['MSSubClass', 80, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_85': ['MSSubClass', 85, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_90': ['MSSubClass', 90, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_120': ['MSSubClass', 120, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_150': ['MSSubClass', 150, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_160': ['MSSubClass', 160, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_180': ['MSSubClass', 180, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'MSSubClass_190': ['MSSubClass', 190, [20, 30, 40, 45, 50, 60, 70, 75, 80, 85, 90, 120, 150, 160, 180, 190]], 'LotShape_IR1': ['LotShape', 'IR1', ['IR1', 'IR2', 'IR3', 'Reg']], 'LotShape_IR2': ['LotShape', 'IR2', ['IR1', 'IR2', 'IR3', 'Reg']], 'LotShape_IR3': ['LotShape', 'IR3', ['IR1', 'IR2', 'IR3', 'Reg']], 'LotShape_Reg': ['LotShape', 'Reg', ['IR1', 'IR2', 'IR3', 'Reg']], 'LandContour_Bnk': ['LandContour', 'Bnk', ['Bnk', 'HLS', 'Low', 'Lvl']], 'LandContour_HLS': ['LandContour', 'HLS', ['Bnk', 'HLS', 'Low', 'Lvl']], 'LandContour_Low': ['LandContour', 'Low', ['Bnk', 'HLS', 'Low', 'Lvl']], 'LandContour_Lvl': ['LandContour', 'Lvl', ['Bnk', 'HLS', 'Low', 'Lvl']], 'LotConfig_Corner': ['LotConfig', 'Corner', ['Corner', 'CulDSac', 'FR2', 'FR3', 'Inside']], 'LotConfig_CulDSac': ['LotConfig', 'CulDSac', ['Corner', 'CulDSac', 'FR2', 'FR3', 'Inside']], 'LotConfig_FR2': ['LotConfig', 'FR2', ['Corner', 'CulDSac', 'FR2', 'FR3', 'Inside']], 'LotConfig_FR3': ['LotConfig', 'FR3', ['Corner', 'CulDSac', 'FR2', 'FR3', 'Inside']], 'LotConfig_Inside': ['LotConfig', 'Inside', ['Corner', 'CulDSac', 'FR2', 'FR3', 'Inside']], 'LandSlope_Gtl': ['LandSlope', 'Gtl', ['Gtl', 'Mod', 'Sev']], 'LandSlope_Mod': ['LandSlope', 'Mod', ['Gtl', 'Mod', 'Sev']], 'LandSlope_Sev': ['LandSlope', 'Sev', ['Gtl', 'Mod', 'Sev']], 'Neighborhood_Blmngtn': ['Neighborhood', 'Blmngtn', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Blueste': ['Neighborhood', 'Blueste', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_BrDale': ['Neighborhood', 'BrDale', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_BrkSide': ['Neighborhood', 'BrkSide', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_ClearCr': ['Neighborhood', 'ClearCr', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_CollgCr': ['Neighborhood', 'CollgCr', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Crawfor': ['Neighborhood', 'Crawfor', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Edwards': ['Neighborhood', 'Edwards', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Gilbert': ['Neighborhood', 'Gilbert', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_IDOTRR': ['Neighborhood', 'IDOTRR', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_MeadowV': ['Neighborhood', 'MeadowV', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Mitchel': ['Neighborhood', 'Mitchel', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_NAmes': ['Neighborhood', 'NAmes', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_NPkVill': ['Neighborhood', 'NPkVill', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_NWAmes': ['Neighborhood', 'NWAmes', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_NoRidge': ['Neighborhood', 'NoRidge', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_NridgHt': ['Neighborhood', 'NridgHt', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_OldTown': ['Neighborhood', 'OldTown', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_SWISU': ['Neighborhood', 'SWISU', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Sawyer': ['Neighborhood', 'Sawyer', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_SawyerW': ['Neighborhood', 'SawyerW', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Somerst': ['Neighborhood', 'Somerst', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_StoneBr': ['Neighborhood', 'StoneBr', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Timber': ['Neighborhood', 'Timber', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Neighborhood_Veenker': ['Neighborhood', 'Veenker', ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NPkVill', 'NWAmes', 'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker']], 'Condition1_Artery': ['Condition1', 'Artery', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_Feedr': ['Condition1', 'Feedr', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_Norm': ['Condition1', 'Norm', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_PosA': ['Condition1', 'PosA', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_PosN': ['Condition1', 'PosN', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_RRAe': ['Condition1', 'RRAe', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_RRAn': ['Condition1', 'RRAn', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_RRNe': ['Condition1', 'RRNe', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition1_RRNn': ['Condition1', 'RRNn', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNe', 'RRNn']], 'Condition2_Artery': ['Condition2', 'Artery', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'Condition2_Feedr': ['Condition2', 'Feedr', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'Condition2_Norm': ['Condition2', 'Norm', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'Condition2_PosA': ['Condition2', 'PosA', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'Condition2_PosN': ['Condition2', 'PosN', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'Condition2_RRAe': ['Condition2', 'RRAe', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'Condition2_RRAn': ['Condition2', 'RRAn', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'Condition2_RRNn': ['Condition2', 'RRNn', ['Artery', 'Feedr', 'Norm', 'PosA', 'PosN', 'RRAe', 'RRAn', 'RRNn']], 'BldgType_1Fam': ['BldgType', '1Fam', ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE']], 'BldgType_2fmCon': ['BldgType', '2fmCon', ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE']], 'BldgType_Duplex': ['BldgType', 'Duplex', ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE']], 'BldgType_Twnhs': ['BldgType', 'Twnhs', ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE']], 'BldgType_TwnhsE': ['BldgType', 'TwnhsE', ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE']], 'HouseStyle_1.5Fin': ['HouseStyle', '1.5Fin', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'HouseStyle_1.5Unf': ['HouseStyle', '1.5Unf', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'HouseStyle_1Story': ['HouseStyle', '1Story', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'HouseStyle_2.5Fin': ['HouseStyle', '2.5Fin', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'HouseStyle_2.5Unf': ['HouseStyle', '2.5Unf', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'HouseStyle_2Story': ['HouseStyle', '2Story', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'HouseStyle_SFoyer': ['HouseStyle', 'SFoyer', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'HouseStyle_SLvl': ['HouseStyle', 'SLvl', ['1.5Fin', '1.5Unf', '1Story', '2.5Fin', '2.5Unf', '2Story', 'SFoyer', 'SLvl']], 'OverallQual_1': ['OverallQual', 1, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_2': ['OverallQual', 2, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_3': ['OverallQual', 3, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_4': ['OverallQual', 4, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_5': ['OverallQual', 5, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_6': ['OverallQual', 6, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_7': ['OverallQual', 7, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_8': ['OverallQual', 8, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_9': ['OverallQual', 9, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallQual_10': ['OverallQual', 10, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], 'OverallCond_1': ['OverallCond', 1, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_2': ['OverallCond', 2, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_3': ['OverallCond', 3, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_4': ['OverallCond', 4, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_5': ['OverallCond', 5, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_6': ['OverallCond', 6, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_7': ['OverallCond', 7, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_8': ['OverallCond', 8, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'OverallCond_9': ['OverallCond', 9, [1, 2, 3, 4, 5, 6, 7, 8, 9]], 'RoofStyle_Flat': ['RoofStyle', 'Flat', ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed']], 'RoofStyle_Gable': ['RoofStyle', 'Gable', ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed']], 'RoofStyle_Gambrel': ['RoofStyle', 'Gambrel', ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed']], 'RoofStyle_Hip': ['RoofStyle', 'Hip', ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed']], 'RoofStyle_Mansard': ['RoofStyle', 'Mansard', ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed']], 'RoofStyle_Shed': ['RoofStyle', 'Shed', ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed']], 'RoofMatl_ClyTile': ['RoofMatl', 'ClyTile', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'RoofMatl_CompShg': ['RoofMatl', 'CompShg', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'RoofMatl_Membran': ['RoofMatl', 'Membran', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'RoofMatl_Metal': ['RoofMatl', 'Metal', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'RoofMatl_Roll': ['RoofMatl', 'Roll', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'RoofMatl_Tar&Grv': ['RoofMatl', 'Tar&Grv', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'RoofMatl_WdShake': ['RoofMatl', 'WdShake', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'RoofMatl_WdShngl': ['RoofMatl', 'WdShngl', ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl']], 'ExterQual_Ex': ['ExterQual', 'Ex', ['Ex', 'Fa', 'Gd', 'TA']], 'ExterQual_Fa': ['ExterQual', 'Fa', ['Ex', 'Fa', 'Gd', 'TA']], 'ExterQual_Gd': ['ExterQual', 'Gd', ['Ex', 'Fa', 'Gd', 'TA']], 'ExterQual_TA': ['ExterQual', 'TA', ['Ex', 'Fa', 'Gd', 'TA']], 'ExterCond_Ex': ['ExterCond', 'Ex', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'ExterCond_Fa': ['ExterCond', 'Fa', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'ExterCond_Gd': ['ExterCond', 'Gd', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'ExterCond_Po': ['ExterCond', 'Po', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'ExterCond_TA': ['ExterCond', 'TA', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'Foundation_BrkTil': ['Foundation', 'BrkTil', ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood']], 'Foundation_CBlock': ['Foundation', 'CBlock', ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood']], 'Foundation_PConc': ['Foundation', 'PConc', ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood']], 'Foundation_Slab': ['Foundation', 'Slab', ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood']], 'Foundation_Stone': ['Foundation', 'Stone', ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood']], 'Foundation_Wood': ['Foundation', 'Wood', ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood']], 'Heating_Floor': ['Heating', 'Floor', ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall']], 'Heating_GasA': ['Heating', 'GasA', ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall']], 'Heating_GasW': ['Heating', 'GasW', ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall']], 'Heating_Grav': ['Heating', 'Grav', ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall']], 'Heating_OthW': ['Heating', 'OthW', ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall']], 'Heating_Wall': ['Heating', 'Wall', ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall']], 'HeatingQC_Ex': ['HeatingQC', 'Ex', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'HeatingQC_Fa': ['HeatingQC', 'Fa', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'HeatingQC_Gd': ['HeatingQC', 'Gd', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'HeatingQC_Po': ['HeatingQC', 'Po', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'HeatingQC_TA': ['HeatingQC', 'TA', ['Ex', 'Fa', 'Gd', 'Po', 'TA']], 'PavedDrive_N': ['PavedDrive', 'N', ['N', 'P', 'Y']], 'PavedDrive_P': ['PavedDrive', 'P', ['N', 'P', 'Y']], 'PavedDrive_Y': ['PavedDrive', 'Y', ['N', 'P', 'Y']], 'SaleCondition_Abnorml': ['SaleCondition', 'Abnorml', ['Abnorml', 'AdjLand', 'Alloca', 'Family', 'Normal', 'Partial']], 'SaleCondition_AdjLand': ['SaleCondition', 'AdjLand', ['Abnorml', 'AdjLand', 'Alloca', 'Family', 'Normal', 'Partial']], 'SaleCondition_Alloca': ['SaleCondition', 'Alloca', ['Abnorml', 'AdjLand', 'Alloca', 'Family', 'Normal', 'Partial']], 'SaleCondition_Family': ['SaleCondition', 'Family', ['Abnorml', 'AdjLand', 'Alloca', 'Family', 'Normal', 'Partial']], 'SaleCondition_Normal': ['SaleCondition', 'Normal', ['Abnorml', 'AdjLand', 'Alloca', 'Family', 'Normal', 'Partial']], 'SaleCondition_Partial': ['SaleCondition', 'Partial', ['Abnorml', 'AdjLand', 'Alloca', 'Family', 'Normal', 'Partial']]}\n",
      "\n",
      "Number of used features in the model (before the encoding of categorical features): 45\n",
      "Number of used features in the model (after the encoding of categorical features): 151\n",
      "----------------------------------------------\n",
      "instance: MSSubClass_20            0\n",
      "MSSubClass_30            0\n",
      "MSSubClass_40            0\n",
      "MSSubClass_45            0\n",
      "MSSubClass_50            0\n",
      "                        ..\n",
      "SaleCondition_AdjLand    0\n",
      "SaleCondition_Alloca     0\n",
      "SaleCondition_Family     0\n",
      "SaleCondition_Normal     1\n",
      "SaleCondition_Partial    0\n",
      "Name: 0, Length: 179, dtype: int64\n",
      "prediction: 195191.86\n",
      "delta1: 43351.5540875\n",
      "delta2: 86703.108175\n",
      "delta3: 173406.21635\n",
      "delta4: 346812.4327\n",
      "\n",
      "The PyQt6 module is not installed or produces an import error.\n",
      "Please use 'python3 -m pip install pyxai[gui]' to install this extras dependency.\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'PyQt6'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 30\u001b[39m\n\u001b[32m     27\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mdelta4:\u001b[39m\u001b[33m\"\u001b[39m, delta4)\n\u001b[32m     28\u001b[39m tree_specific_reason = explainer.tree_specific_reason()\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m \u001b[43mexplainer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvisualisation\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgui\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/expectation/softwares/pyxai-mlp/pyxai/sources/explainers/Visualisation.py:89\u001b[39m, in \u001b[36mVisualisation.gui\u001b[39m\u001b[34m(self, image, time_series)\u001b[39m\n\u001b[32m     86\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m feature \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m feature_names:\n\u001b[32m     87\u001b[39m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mThe feature \u001b[39m\u001b[33m\"\u001b[39m + \u001b[38;5;28mstr\u001b[39m(\n\u001b[32m     88\u001b[39m                     feature) + \u001b[33m\"\u001b[39m\u001b[33m in the `time_series` parameter is not an available feature name.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m89\u001b[39m \u001b[43mcheck_PyQt6\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     91\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyxai\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msources\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtools\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mGUIQT\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m GraphicalInterface\n\u001b[32m     92\u001b[39m graphical_interface = GraphicalInterface(\u001b[38;5;28mself\u001b[39m, image=image, time_series=time_series)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/expectation/softwares/pyxai-mlp/pyxai/sources/tools/utils.py:33\u001b[39m, in \u001b[36mcheck_PyQt6\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m     31\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mThe PyQt6 module is not installed or produces an import error.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     32\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mPlease use \u001b[39m\u001b[33m'\u001b[39m\u001b[33mpython3 -m pip install pyxai[gui]\u001b[39m\u001b[33m'\u001b[39m\u001b[33m to install this extras dependency.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m error\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/expectation/softwares/pyxai-mlp/pyxai/sources/tools/utils.py:37\u001b[39m, in \u001b[36m_check_PyQt6\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m     35\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_check_PyQt6\u001b[39m():\n\u001b[32m     36\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m37\u001b[39m         \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mPyQt6\u001b[39;00m\n\u001b[32m     38\u001b[39m         \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mPyQt6\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mQtWidgets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m QSplitter, QApplication, QAbstractItemView, QMessageBox, QFileDialog, QLabel, QSizePolicy, QScrollArea,  QMainWindow, QTableWidgetItem, QMenu, QGroupBox, QListWidget, QWidget, QVBoxLayout, QGridLayout, QTableWidget\n\u001b[32m     39\u001b[39m         \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mPyQt6\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mQtGui\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m QAction, QPixmap, QColor\n",
      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'PyQt6'"
     ]
    }
   ],
   "source": [
    "explainer = Explaining.initialize(model, instance, features_type=\"../../dataset/houses-prices-converted_0.types\")\n",
    "print(\"instance:\", instance)\n",
    "print(\"prediction:\", prediction)\n",
    "\n",
    "def compute_delta(percent):\n",
    "    extremum_range = explainer.extremum_range()\n",
    "    difference = extremum_range[1] - extremum_range[0]\n",
    "    return (percent/100)*difference \n",
    "\n",
    "delta1 = compute_delta(2.5)\n",
    "explainer.set_interval(prediction - delta1, prediction + delta1)\n",
    "print(\"delta1:\", delta1)\n",
    "tree_specific_reason = explainer.tree_specific_reason()\n",
    "\n",
    "delta2 = compute_delta(5)\n",
    "explainer.set_interval(prediction - delta2, prediction + delta2)\n",
    "print(\"delta2:\", delta2)\n",
    "tree_specific_reason = explainer.tree_specific_reason()\n",
    "\n",
    "delta3 = compute_delta(10)\n",
    "explainer.set_interval(prediction - delta3, prediction + delta3)\n",
    "print(\"delta3:\", delta3)\n",
    "tree_specific_reason = explainer.tree_specific_reason()\n",
    "\n",
    "delta4 = compute_delta(20)\n",
    "explainer.set_interval(prediction - delta4, prediction + delta4)\n",
    "print(\"delta4:\", delta4)\n",
    "tree_specific_reason = explainer.tree_specific_reason()\n",
    "\n",
    "# explainer.visualisation.gui()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e77f26",
   "metadata": {},
   "source": [
    "In order to set correctly the intervals, we use the ```compute_delta``` function in order to compute the delta values. A delta value is the quantity to be removed or added to calculate the interval $[a,b]$ as a percentage of the model extreme regression values (minimal and maximal values of possible regression values). For example, the prediction for this instance is $199248.22$, delta1 $\\approx 46155$ and the interval for the first tree-specific explanation reported above is $[a,b] = [199248.22 - 46155, 199248.22 + 46155]$. This interval corresponds to $2.5\\%$ of values in relation to the model extreme regression minimal and maximal values.\n",
    "\n",
    "The results are presented in the PyXAI's GUI thanks to the last lines of ```explainer.visualisation.gui()```\n",
    "\n",
    "<img src=\"attachment:GUI-Regression-TS.png\" alt=\"BTTS2\" width=\"900\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb19745",
   "metadata": {},
   "source": [
    "We can observe that the larger the interval I (the percentage and the delta value), the smaller the reason, both in terms of the number of binary variables and the number of features: \n",
    "- For $2.5\\%$, the tree specific explanation has $45$ binary variables and $34$ features \n",
    "- For $5\\%$, the tree specific explanation has $44$ binary variables and $32$ features\n",
    "- For $10\\%$, the tree specific explanation has $35$ binary variables and $27$ features\n",
    "- For $20\\%$, the tree specific explanation has $20$ binary variables and $15$ features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c79111f",
   "metadata": {},
   "source": [
    "This observation applies to all datasets. The larger the interval, the smaller the explanation. \n",
    "We therefore recommend testing several possible intervals, depending on the problem and possible regression values. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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