AmesHousingTests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for testing and demonstrating CIU on Ames Housing data set\n",
    "\n",
    "First the necessary imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import ciu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data. A lot of preprocessing required here! At least compared to R, where there's a \"AmesHousing\" package with everything prepared (maybe there's one for Python too?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/KaryFramling/py-ciu/master/ciu_tests/data/AmesHousing.csv')\n",
    "\n",
    "#Checking for missing data\n",
    "missing_data_count = df.isnull().sum()\n",
    "missing_data_percent = df.isnull().sum() / len(df) * 100\n",
    "\n",
    "missing_data = pd.DataFrame({\n",
    "    'Count': missing_data_count,\n",
    "    'Percent': missing_data_percent\n",
    "})\n",
    "\n",
    "missing_data = missing_data[missing_data.Count > 0]\n",
    "missing_data.sort_values(by='Count', ascending=False, inplace=True)\n",
    "\n",
    "#This one has spaces for some reason\n",
    "df.columns = df.columns.str.replace(' ', '')\n",
    "\n",
    "\n",
    "#Taking care of missing values\n",
    "from sklearn.impute import SimpleImputer\n",
    "# Group 1:\n",
    "group_1 = [\n",
    "    'PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'GarageType',\n",
    "    'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtQual', 'BsmtCond',\n",
    "    'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'MasVnrType'\n",
    "]\n",
    "df[group_1] = df[group_1].fillna(\"None\")\n",
    "\n",
    "# Group 2:\n",
    "group_2 = [\n",
    "    'GarageArea', 'GarageCars', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n",
    "    'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'MasVnrArea'\n",
    "]\n",
    "\n",
    "df[group_2] = df[group_2].fillna(0)\n",
    "\n",
    "# Group 3:\n",
    "group_3a = [\n",
    "    'Functional', 'MSZoning', 'Electrical', 'KitchenQual', 'Exterior1st',\n",
    "    'Exterior2nd', 'SaleType', 'Utilities'\n",
    "]\n",
    "\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "df[group_3a] = pd.DataFrame(imputer.fit_transform(df[group_3a]), index=df.index)\n",
    "\n",
    "df.LotFrontage = df.LotFrontage.fillna(df.LotFrontage.mean())\n",
    "df.GarageYrBlt = df.GarageYrBlt.fillna(df.YearBuilt)\n",
    "\n",
    "#Label encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "data = df.drop(columns=['SalePrice'])\n",
    "data = data.astype(float) # This is a \"quick fix\" to make everything into float. Some of these would rather need a category mapping. \n",
    "target = df.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Order', 'PID', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea',\n",
       "       'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt',\n",
       "       'YearRemod/Add', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd',\n",
       "       'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split into training and test set and train XGB model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</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\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=123)\n",
    "np.random.seed(26) # We want to always get the same Random Forest model here.\n",
    "model = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.5, learning_rate = 0.1, max_depth = 15, alpha = 10)\n",
    "model.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic CIU and explanation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimal price in the training set is $0 and the maximal price is $1031\n"
     ]
    }
   ],
   "source": [
    "out_minmaxs = pd.DataFrame({'mins': [float(min(y_train))], 'maxs': [float(max(y_train))]})\n",
    "out_minmaxs.index = ['SalePrice'] # Each row has to have the output name(s) as index! \n",
    "print(f\"The minimal price in the training set is ${min(y_train):.0f} and the maximal price is ${max(y_train):.0f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CI</th>\n",
       "      <th>CU</th>\n",
       "      <th>Cinfl</th>\n",
       "      <th>outname</th>\n",
       "      <th>outval</th>\n",
       "      <th>feature</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>inputs</th>\n",
       "      <th>invals</th>\n",
       "      <th>neutralCU</th>\n",
       "      <th>target_concept</th>\n",
       "      <th>target_inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order</th>\n",
       "      <td>0.031089</td>\n",
       "      <td>0.975402</td>\n",
       "      <td>0.014780</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>Order</td>\n",
       "      <td>708.957336</td>\n",
       "      <td>741.010498</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[1561.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PID</th>\n",
       "      <td>0.030742</td>\n",
       "      <td>0.807310</td>\n",
       "      <td>0.009447</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>PID</td>\n",
       "      <td>714.634338</td>\n",
       "      <td>746.329346</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[2700.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>MSSubClass</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[11.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>0.003970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001985</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>MSZoning</td>\n",
       "      <td>736.128540</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[6.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>0.035820</td>\n",
       "      <td>0.737216</td>\n",
       "      <td>0.008497</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>712.996338</td>\n",
       "      <td>749.926758</td>\n",
       "      <td>[4]</td>\n",
       "      <td>[20.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>0.000339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>MiscVal</td>\n",
       "      <td>739.872620</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[76]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>0.022351</td>\n",
       "      <td>0.537845</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>MoSold</td>\n",
       "      <td>727.828247</td>\n",
       "      <td>750.871704</td>\n",
       "      <td>[77]</td>\n",
       "      <td>[6.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>0.010858</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005429</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>YrSold</td>\n",
       "      <td>729.027527</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[78]</td>\n",
       "      <td>[2.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>0.042302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021151</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>SaleType</td>\n",
       "      <td>696.609192</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[79]</td>\n",
       "      <td>[9.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>0.006309</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>SaleCondition</td>\n",
       "      <td>733.717407</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[80]</td>\n",
       "      <td>[4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CI        CU     Cinfl    outname      outval  \\\n",
       "Order          0.031089  0.975402  0.014780  SalePrice  740.222046   \n",
       "PID            0.030742  0.807310  0.009447  SalePrice  740.222046   \n",
       "MSSubClass     0.000000  0.000000 -0.000000  SalePrice  740.222046   \n",
       "MSZoning       0.003970  1.000000  0.001985  SalePrice  740.222046   \n",
       "LotFrontage    0.035820  0.737216  0.008497  SalePrice  740.222046   \n",
       "...                 ...       ...       ...        ...         ...   \n",
       "MiscVal        0.000339  1.000000  0.000169  SalePrice  740.222046   \n",
       "MoSold         0.022351  0.537845  0.000846  SalePrice  740.222046   \n",
       "YrSold         0.010858  1.000000  0.005429  SalePrice  740.222046   \n",
       "SaleType       0.042302  1.000000  0.021151  SalePrice  740.222046   \n",
       "SaleCondition  0.006309  1.000000  0.003155  SalePrice  740.222046   \n",
       "\n",
       "                     feature        ymin        ymax inputs    invals  \\\n",
       "Order                  Order  708.957336  741.010498    [0]  [1561.0]   \n",
       "PID                      PID  714.634338  746.329346    [1]  [2700.0]   \n",
       "MSSubClass        MSSubClass  740.222046  740.222046    [2]    [11.0]   \n",
       "MSZoning            MSZoning  736.128540  740.222046    [3]     [6.0]   \n",
       "LotFrontage      LotFrontage  712.996338  749.926758    [4]    [20.0]   \n",
       "...                      ...         ...         ...    ...       ...   \n",
       "MiscVal              MiscVal  739.872620  740.222046   [76]     [0.0]   \n",
       "MoSold                MoSold  727.828247  750.871704   [77]     [6.0]   \n",
       "YrSold                YrSold  729.027527  740.222046   [78]     [2.0]   \n",
       "SaleType            SaleType  696.609192  740.222046   [79]     [9.0]   \n",
       "SaleCondition  SaleCondition  733.717407  740.222046   [80]     [4.0]   \n",
       "\n",
       "               neutralCU target_concept target_inputs  \n",
       "Order                0.5           None          None  \n",
       "PID                  0.5           None          None  \n",
       "MSSubClass           0.5           None          None  \n",
       "MSZoning             0.5           None          None  \n",
       "LotFrontage          0.5           None          None  \n",
       "...                  ...            ...           ...  \n",
       "MiscVal              0.5           None          None  \n",
       "MoSold               0.5           None          None  \n",
       "YrSold               0.5           None          None  \n",
       "SaleType             0.5           None          None  \n",
       "SaleCondition        0.5           None          None  \n",
       "\n",
       "[81 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ames_instance = X_test.iloc[[345]]\n",
    "CIU_ames = ciu.CIU(model.predict, ['SalePrice'], data=X_train, out_minmaxs=out_minmaxs) #, category_mapping=category_mapping)\n",
    "CIUres = CIU_ames.explain(ames_instance)\n",
    "display(CIUres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic CIU plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = CIU_ames.plot_ciu(CIUres, figsize=(6,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ames_voc = {\n",
    "    \"Garage\":[c for c in df.columns if 'Garage' in c],\n",
    "    \"Basement\":[c for c in df.columns if 'Bsmt' in c],\n",
    "    \"Lot\":list(df.columns[[3,4,7,8,9,10,11]]),\n",
    "    \"Access\":list(df.columns[[13,14]]),\n",
    "    \"House_type\":list(df.columns[[1,15,16,21]]),\n",
    "    \"House_aesthetics\":list(df.columns[[22,23,24,25,26]]),\n",
    "    \"House_condition\":list(df.columns[[20,18,21,28,19,29]]),\n",
    "    \"First_floor_surface\":list(df.columns[[43]]),\n",
    "    \"Above_ground_living area\":[c for c in df.columns if 'GrLivArea' in c]\n",
    "}\n",
    "CIU_ames.vocabulary = ames_voc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CI</th>\n",
       "      <th>CU</th>\n",
       "      <th>Cinfl</th>\n",
       "      <th>outname</th>\n",
       "      <th>outval</th>\n",
       "      <th>feature</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>inputs</th>\n",
       "      <th>invals</th>\n",
       "      <th>neutralCU</th>\n",
       "      <th>target_concept</th>\n",
       "      <th>target_inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Garage</th>\n",
       "      <td>0.066393</td>\n",
       "      <td>0.805509</td>\n",
       "      <td>0.020284</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>Garage</td>\n",
       "      <td>685.084045</td>\n",
       "      <td>753.535156</td>\n",
       "      <td>[59, 60, 61, 62, 63, 64, 65]</td>\n",
       "      <td>[1.0, 103.0, 0.0, 2.0, 224.0, 5.0, 5.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basement</th>\n",
       "      <td>0.245923</td>\n",
       "      <td>0.905069</td>\n",
       "      <td>0.099616</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>Basement</td>\n",
       "      <td>510.745026</td>\n",
       "      <td>764.291504</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "      <td>[0.0, 5.0, 1.0, 2.0, 941.0, 6.0, 0.0, 81.0, 84...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot</th>\n",
       "      <td>0.062898</td>\n",
       "      <td>0.798055</td>\n",
       "      <td>0.018747</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>Lot</td>\n",
       "      <td>688.470215</td>\n",
       "      <td>753.317688</td>\n",
       "      <td>[3, 4, 7, 8, 9, 10, 11]</td>\n",
       "      <td>[6.0, 20.0, 1.0, 0.0, 1.0, 0.0, 4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Access</th>\n",
       "      <td>0.066235</td>\n",
       "      <td>0.998431</td>\n",
       "      <td>0.033013</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>Access</td>\n",
       "      <td>672.041138</td>\n",
       "      <td>740.329163</td>\n",
       "      <td>[13, 14]</td>\n",
       "      <td>[6.0, 2.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House_type</th>\n",
       "      <td>0.073230</td>\n",
       "      <td>0.686148</td>\n",
       "      <td>0.013632</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>House_type</td>\n",
       "      <td>688.417786</td>\n",
       "      <td>763.917908</td>\n",
       "      <td>[1, 15, 16, 21]</td>\n",
       "      <td>[2700.0, 2.0, 4.0, 55.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House_aesthetics</th>\n",
       "      <td>0.056205</td>\n",
       "      <td>0.938650</td>\n",
       "      <td>0.024654</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>House_aesthetics</td>\n",
       "      <td>685.829773</td>\n",
       "      <td>743.777100</td>\n",
       "      <td>[22, 23, 24, 25, 26]</td>\n",
       "      <td>[3.0, 1.0, 5.0, 5.0, 4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House_condition</th>\n",
       "      <td>0.450672</td>\n",
       "      <td>0.775577</td>\n",
       "      <td>0.124195</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>379.855774</td>\n",
       "      <td>844.498474</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "      <td>[111.0, 6.0, 55.0, 2.0, 4.0, 4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>0.000295</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>Electrical</td>\n",
       "      <td>739.918396</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[43]</td>\n",
       "      <td>[4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.149149</td>\n",
       "      <td>0.894286</td>\n",
       "      <td>0.058807</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>602.705200</td>\n",
       "      <td>756.477905</td>\n",
       "      <td>[47]</td>\n",
       "      <td>[711.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CI        CU     Cinfl    outname      outval  \\\n",
       "Garage            0.066393  0.805509  0.020284  SalePrice  740.222046   \n",
       "Basement          0.245923  0.905069  0.099616  SalePrice  740.222046   \n",
       "Lot               0.062898  0.798055  0.018747  SalePrice  740.222046   \n",
       "Access            0.066235  0.998431  0.033013  SalePrice  740.222046   \n",
       "House_type        0.073230  0.686148  0.013632  SalePrice  740.222046   \n",
       "House_aesthetics  0.056205  0.938650  0.024654  SalePrice  740.222046   \n",
       "House_condition   0.450672  0.775577  0.124195  SalePrice  740.222046   \n",
       "Electrical        0.000295  1.000000  0.000147  SalePrice  740.222046   \n",
       "GrLivArea         0.149149  0.894286  0.058807  SalePrice  740.222046   \n",
       "\n",
       "                           feature        ymin        ymax  \\\n",
       "Garage                      Garage  685.084045  753.535156   \n",
       "Basement                  Basement  510.745026  764.291504   \n",
       "Lot                            Lot  688.470215  753.317688   \n",
       "Access                      Access  672.041138  740.329163   \n",
       "House_type              House_type  688.417786  763.917908   \n",
       "House_aesthetics  House_aesthetics  685.829773  743.777100   \n",
       "House_condition    House_condition  379.855774  844.498474   \n",
       "Electrical              Electrical  739.918396  740.222046   \n",
       "GrLivArea                GrLivArea  602.705200  756.477905   \n",
       "\n",
       "                                                        inputs  \\\n",
       "Garage                            [59, 60, 61, 62, 63, 64, 65]   \n",
       "Basement          [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]   \n",
       "Lot                                    [3, 4, 7, 8, 9, 10, 11]   \n",
       "Access                                                [13, 14]   \n",
       "House_type                                     [1, 15, 16, 21]   \n",
       "House_aesthetics                          [22, 23, 24, 25, 26]   \n",
       "House_condition                       [20, 18, 21, 28, 19, 29]   \n",
       "Electrical                                                [43]   \n",
       "GrLivArea                                                 [47]   \n",
       "\n",
       "                                                             invals  \\\n",
       "Garage                      [1.0, 103.0, 0.0, 2.0, 224.0, 5.0, 5.0]   \n",
       "Basement          [0.0, 5.0, 1.0, 2.0, 941.0, 6.0, 0.0, 81.0, 84...   \n",
       "Lot                            [6.0, 20.0, 1.0, 0.0, 1.0, 0.0, 4.0]   \n",
       "Access                                                   [6.0, 2.0]   \n",
       "House_type                                 [2700.0, 2.0, 4.0, 55.0]   \n",
       "House_aesthetics                          [3.0, 1.0, 5.0, 5.0, 4.0]   \n",
       "House_condition                   [111.0, 6.0, 55.0, 2.0, 4.0, 4.0]   \n",
       "Electrical                                                    [4.0]   \n",
       "GrLivArea                                                   [711.0]   \n",
       "\n",
       "                  neutralCU target_concept target_inputs  \n",
       "Garage                  0.5           None          None  \n",
       "Basement                0.5           None          None  \n",
       "Lot                     0.5           None          None  \n",
       "Access                  0.5           None          None  \n",
       "House_type              0.5           None          None  \n",
       "House_aesthetics        0.5           None          None  \n",
       "House_condition         0.5           None          None  \n",
       "Electrical              0.5           None          None  \n",
       "GrLivArea               0.5           None          None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_res = CIU_ames.explain_voc(ames_instance, nsamples=10000)\n",
    "display(top_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = CIU_ames.plot_ciu(top_res, figsize=(6,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also test textual explanation. Here using Markdown effects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "The explained value is ***SalePrice*** with the value 740.22 (CU=0.72), which is **higher than average utility**.<br>Feature *Garage* has **very low importance (CI=0.07)** and has value(s) [  1. 103.   0.   2. 224.   5.   5.], which is **high utility (CU=0.81)**<br>Feature *Basement* has **low importance (CI=0.25)** and has value(s) [  0.   5.   1.   2. 941.   6.   0.  81. 847.   1.   1.], which is **high utility (CU=0.91)**<br>Feature *Lot* has **very low importance (CI=0.06)** and has value(s) [ 6. 20.  1.  0.  1.  0.  4.], which is **high utility (CU=0.80)**<br>Feature *Access* has **very low importance (CI=0.07)** and has value(s) [6. 2.], which is **high utility (CU=1.00)**<br>Feature *House_type* has **very low importance (CI=0.07)** and has value(s) [2.7e+03 2.0e+00 4.0e+00 5.5e+01], which is **higher than average utility (CU=0.69)**<br>Feature *House_aesthetics* has **very low importance (CI=0.06)** and has value(s) [3. 1. 5. 5. 4.], which is **high utility (CU=0.94)**<br>Feature *House_condition* has **normal importance (CI=0.45)** and has value(s) [111.   6.  55.   2.   4.   4.], which is **high utility (CU=0.78)**<br>Feature *Electrical* has **very low importance (CI=0.00)** and has value(s) 4.0, which is **high utility (CU=1.00)**<br>Feature *GrLivArea* has **very low importance (CI=0.15)** and has value(s) 711.0, which is **high utility (CU=0.89)**<br>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown\n",
    "markdown_text = CIU_ames.textual_explanation(use_markdown_effects=True)\n",
    "Markdown(markdown_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way of explaining intermediate concepts is to just indicate the target concept (intermediate concept):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CI</th>\n",
       "      <th>CU</th>\n",
       "      <th>Cinfl</th>\n",
       "      <th>outname</th>\n",
       "      <th>outval</th>\n",
       "      <th>feature</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>inputs</th>\n",
       "      <th>invals</th>\n",
       "      <th>neutralCU</th>\n",
       "      <th>target_concept</th>\n",
       "      <th>target_inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.219308</td>\n",
       "      <td>0.889584</td>\n",
       "      <td>0.085439</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>650.217224</td>\n",
       "      <td>751.393555</td>\n",
       "      <td>[20]</td>\n",
       "      <td>[111.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.635583</td>\n",
       "      <td>0.647663</td>\n",
       "      <td>0.093852</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>OverallQual</td>\n",
       "      <td>553.617737</td>\n",
       "      <td>841.737061</td>\n",
       "      <td>[18]</td>\n",
       "      <td>[6.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemod/Add</th>\n",
       "      <td>0.111436</td>\n",
       "      <td>0.969996</td>\n",
       "      <td>0.052375</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>YearRemod/Add</td>\n",
       "      <td>690.913513</td>\n",
       "      <td>741.747253</td>\n",
       "      <td>[21]</td>\n",
       "      <td>[55.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>0.126410</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063205</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>ExterQual</td>\n",
       "      <td>682.560303</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[28]</td>\n",
       "      <td>[2.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>0.004681</td>\n",
       "      <td>0.244806</td>\n",
       "      <td>-0.001195</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>OverallCond</td>\n",
       "      <td>739.692688</td>\n",
       "      <td>741.855042</td>\n",
       "      <td>[19]</td>\n",
       "      <td>[4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond</th>\n",
       "      <td>0.000083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>ExterCond</td>\n",
       "      <td>740.184387</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[29]</td>\n",
       "      <td>[4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CI        CU     Cinfl    outname      outval  \\\n",
       "YearBuilt      0.219308  0.889584  0.085439  SalePrice  740.222046   \n",
       "OverallQual    0.635583  0.647663  0.093852  SalePrice  740.222046   \n",
       "YearRemod/Add  0.111436  0.969996  0.052375  SalePrice  740.222046   \n",
       "ExterQual      0.126410  1.000000  0.063205  SalePrice  740.222046   \n",
       "OverallCond    0.004681  0.244806 -0.001195  SalePrice  740.222046   \n",
       "ExterCond      0.000083  1.000000  0.000041  SalePrice  740.222046   \n",
       "\n",
       "                     feature        ymin        ymax inputs   invals  \\\n",
       "YearBuilt          YearBuilt  650.217224  751.393555   [20]  [111.0]   \n",
       "OverallQual      OverallQual  553.617737  841.737061   [18]    [6.0]   \n",
       "YearRemod/Add  YearRemod/Add  690.913513  741.747253   [21]   [55.0]   \n",
       "ExterQual          ExterQual  682.560303  740.222046   [28]    [2.0]   \n",
       "OverallCond      OverallCond  739.692688  741.855042   [19]    [4.0]   \n",
       "ExterCond          ExterCond  740.184387  740.222046   [29]    [4.0]   \n",
       "\n",
       "               neutralCU   target_concept             target_inputs  \n",
       "YearBuilt            0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "OverallQual          0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "YearRemod/Add        0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "ExterQual            0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "OverallCond          0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "ExterCond            0.5  House_condition  [20, 18, 21, 28, 19, 29]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x150 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tconcept = \"House_condition\"\n",
    "res = CIU_ames.explain(ames_instance, target_concept=tconcept, nsamples=1000)\n",
    "display(res)\n",
    "p = CIU_ames.plot_ciu(res, figsize=(6,1.5), main=f\"Explained concept is {tconcept}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, for concistency of CIU values and for avoiding to re-calculate CIU for the target intermediate concept, it is better to pass the existing CIU result for the target concept (that may include rows also for other features or intermediate concepts)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CI</th>\n",
       "      <th>CU</th>\n",
       "      <th>Cinfl</th>\n",
       "      <th>outname</th>\n",
       "      <th>outval</th>\n",
       "      <th>feature</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>inputs</th>\n",
       "      <th>invals</th>\n",
       "      <th>neutralCU</th>\n",
       "      <th>target_concept</th>\n",
       "      <th>target_inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.217751</td>\n",
       "      <td>0.889584</td>\n",
       "      <td>0.084832</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>650.217224</td>\n",
       "      <td>751.393555</td>\n",
       "      <td>[20]</td>\n",
       "      <td>[111.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.620088</td>\n",
       "      <td>0.647663</td>\n",
       "      <td>0.091564</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>OverallQual</td>\n",
       "      <td>553.617737</td>\n",
       "      <td>841.737061</td>\n",
       "      <td>[18]</td>\n",
       "      <td>[6.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemod/Add</th>\n",
       "      <td>0.109404</td>\n",
       "      <td>0.969996</td>\n",
       "      <td>0.051419</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>YearRemod/Add</td>\n",
       "      <td>690.913513</td>\n",
       "      <td>741.747253</td>\n",
       "      <td>[21]</td>\n",
       "      <td>[55.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>0.124099</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062050</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>ExterQual</td>\n",
       "      <td>682.560303</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[28]</td>\n",
       "      <td>[2.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>0.004654</td>\n",
       "      <td>0.244806</td>\n",
       "      <td>-0.001188</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>OverallCond</td>\n",
       "      <td>739.692688</td>\n",
       "      <td>741.855042</td>\n",
       "      <td>[19]</td>\n",
       "      <td>[4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>ExterCond</td>\n",
       "      <td>740.184387</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[29]</td>\n",
       "      <td>[4.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>House_condition</td>\n",
       "      <td>[20, 18, 21, 28, 19, 29]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CI        CU     Cinfl    outname      outval  \\\n",
       "YearBuilt      0.217751  0.889584  0.084832  SalePrice  740.222046   \n",
       "OverallQual    0.620088  0.647663  0.091564  SalePrice  740.222046   \n",
       "YearRemod/Add  0.109404  0.969996  0.051419  SalePrice  740.222046   \n",
       "ExterQual      0.124099  1.000000  0.062050  SalePrice  740.222046   \n",
       "OverallCond    0.004654  0.244806 -0.001188  SalePrice  740.222046   \n",
       "ExterCond      0.000081  1.000000  0.000041  SalePrice  740.222046   \n",
       "\n",
       "                     feature        ymin        ymax inputs   invals  \\\n",
       "YearBuilt          YearBuilt  650.217224  751.393555   [20]  [111.0]   \n",
       "OverallQual      OverallQual  553.617737  841.737061   [18]    [6.0]   \n",
       "YearRemod/Add  YearRemod/Add  690.913513  741.747253   [21]   [55.0]   \n",
       "ExterQual          ExterQual  682.560303  740.222046   [28]    [2.0]   \n",
       "OverallCond      OverallCond  739.692688  741.855042   [19]    [4.0]   \n",
       "ExterCond          ExterCond  740.184387  740.222046   [29]    [4.0]   \n",
       "\n",
       "               neutralCU   target_concept             target_inputs  \n",
       "YearBuilt            0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "OverallQual          0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "YearRemod/Add        0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "ExterQual            0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "OverallCond          0.5  House_condition  [20, 18, 21, 28, 19, 29]  \n",
       "ExterCond            0.5  House_condition  [20, 18, 21, 28, 19, 29]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x150 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tconcept = \"House_condition\"\n",
    "res = CIU_ames.explain(ames_instance, nsamples=1000, target_concept=tconcept, target_ciu=top_res)\n",
    "display(res)\n",
    "p = CIU_ames.plot_ciu(res, figsize=(6,1.5), main=f\"Explained concept is {tconcept}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CI</th>\n",
       "      <th>CU</th>\n",
       "      <th>Cinfl</th>\n",
       "      <th>outname</th>\n",
       "      <th>outval</th>\n",
       "      <th>feature</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>inputs</th>\n",
       "      <th>invals</th>\n",
       "      <th>neutralCU</th>\n",
       "      <th>target_concept</th>\n",
       "      <th>target_inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>0.062191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031096</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtQual</td>\n",
       "      <td>724.453613</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[31]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>0.022566</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011283</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtCond</td>\n",
       "      <td>734.500488</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[32]</td>\n",
       "      <td>[5.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>0.018410</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtExposure</td>\n",
       "      <td>735.554260</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[33]</td>\n",
       "      <td>[1.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>0.049979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024989</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtFinType1</td>\n",
       "      <td>727.550171</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[34]</td>\n",
       "      <td>[2.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>0.452218</td>\n",
       "      <td>0.942100</td>\n",
       "      <td>0.199926</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtFinSF1</td>\n",
       "      <td>632.202515</td>\n",
       "      <td>746.860718</td>\n",
       "      <td>[35]</td>\n",
       "      <td>[941.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>0.016954</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.008477</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtFinType2</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>744.520691</td>\n",
       "      <td>[36]</td>\n",
       "      <td>[6.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>0.005796</td>\n",
       "      <td>0.315628</td>\n",
       "      <td>-0.001069</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtFinSF2</td>\n",
       "      <td>739.758179</td>\n",
       "      <td>741.227844</td>\n",
       "      <td>[37]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>0.153336</td>\n",
       "      <td>0.684075</td>\n",
       "      <td>0.028225</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtUnfSF</td>\n",
       "      <td>713.626709</td>\n",
       "      <td>752.504517</td>\n",
       "      <td>[38]</td>\n",
       "      <td>[81.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>0.391867</td>\n",
       "      <td>0.932756</td>\n",
       "      <td>0.169583</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>647.546753</td>\n",
       "      <td>746.903137</td>\n",
       "      <td>[39]</td>\n",
       "      <td>[847.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>0.189185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.094592</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtFullBath</td>\n",
       "      <td>692.254944</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[48]</td>\n",
       "      <td>[1.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>SalePrice</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>BsmtHalfBath</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>740.222046</td>\n",
       "      <td>[49]</td>\n",
       "      <td>[1.0]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Basement</td>\n",
       "      <td>[31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    CI        CU     Cinfl    outname      outval  \\\n",
       "BsmtQual      0.062191  1.000000  0.031096  SalePrice  740.222046   \n",
       "BsmtCond      0.022566  1.000000  0.011283  SalePrice  740.222046   \n",
       "BsmtExposure  0.018410  1.000000  0.009205  SalePrice  740.222046   \n",
       "BsmtFinType1  0.049979  1.000000  0.024989  SalePrice  740.222046   \n",
       "BsmtFinSF1    0.452218  0.942100  0.199926  SalePrice  740.222046   \n",
       "BsmtFinType2  0.016954  0.000000 -0.008477  SalePrice  740.222046   \n",
       "BsmtFinSF2    0.005796  0.315628 -0.001069  SalePrice  740.222046   \n",
       "BsmtUnfSF     0.153336  0.684075  0.028225  SalePrice  740.222046   \n",
       "TotalBsmtSF   0.391867  0.932756  0.169583  SalePrice  740.222046   \n",
       "BsmtFullBath  0.189185  1.000000  0.094592  SalePrice  740.222046   \n",
       "BsmtHalfBath  0.000000  0.000000 -0.000000  SalePrice  740.222046   \n",
       "\n",
       "                   feature        ymin        ymax inputs   invals  neutralCU  \\\n",
       "BsmtQual          BsmtQual  724.453613  740.222046   [31]    [0.0]        0.5   \n",
       "BsmtCond          BsmtCond  734.500488  740.222046   [32]    [5.0]        0.5   \n",
       "BsmtExposure  BsmtExposure  735.554260  740.222046   [33]    [1.0]        0.5   \n",
       "BsmtFinType1  BsmtFinType1  727.550171  740.222046   [34]    [2.0]        0.5   \n",
       "BsmtFinSF1      BsmtFinSF1  632.202515  746.860718   [35]  [941.0]        0.5   \n",
       "BsmtFinType2  BsmtFinType2  740.222046  744.520691   [36]    [6.0]        0.5   \n",
       "BsmtFinSF2      BsmtFinSF2  739.758179  741.227844   [37]    [0.0]        0.5   \n",
       "BsmtUnfSF        BsmtUnfSF  713.626709  752.504517   [38]   [81.0]        0.5   \n",
       "TotalBsmtSF    TotalBsmtSF  647.546753  746.903137   [39]  [847.0]        0.5   \n",
       "BsmtFullBath  BsmtFullBath  692.254944  740.222046   [48]    [1.0]        0.5   \n",
       "BsmtHalfBath  BsmtHalfBath  740.222046  740.222046   [49]    [1.0]        0.5   \n",
       "\n",
       "             target_concept                                 target_inputs  \n",
       "BsmtQual           Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtCond           Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtExposure       Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtFinType1       Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtFinSF1         Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtFinType2       Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtFinSF2         Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtUnfSF          Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "TotalBsmtSF        Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtFullBath       Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  \n",
       "BsmtHalfBath       Basement  [31, 32, 33, 34, 35, 36, 37, 38, 39, 48, 49]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tconcept = \"Basement\"\n",
    "res = CIU_ames.explain(ames_instance, target_concept=tconcept, target_ciu=top_res, nsamples=1000)\n",
    "display(res)\n",
    "p = CIU_ames.plot_ciu(res, figsize=(6,2.5), main=f\"Explained concept is {tconcept}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THERE SHOULD STILL BE A TEST FOR USING INPUT CONCEPTS TOGETHER WITH A TARGET CONCEPT! I.E. calling ``explain_voc``. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py-ciu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
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