IrisTests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "/var/folders/65/868tj1315js3sr7vp6rnvvs00000gp/T/ipykernel_51766/4242516144.py:16: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
      "  iris_df = df.apply(pd.to_numeric, errors='ignore')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "import ciu\n",
    "\n",
    "iris=datasets.load_iris()\n",
    "\n",
    "df = pd.DataFrame(data = np.c_[iris['data'], iris['target']],\n",
    "              columns = iris['feature_names'] + ['target'])\n",
    "df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
    "df.columns = ['s_length', 's_width', 'p_length', 'p_width', 'target', 'species']\n",
    "iris_outnames = df['species'].cat.categories.tolist()\n",
    "iris_df = df.apply(pd.to_numeric, errors='ignore')\n",
    "iris_input_names = df.columns[range(4)]\n",
    "X = df[iris_input_names]\n",
    "y = df['species']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print (iris['feature_names'])\n",
    "df = pd.DataFrame({'mins': iris_df[iris_input_names].min(), 'maxs': iris_df[iris_input_names].max()})\n",
    "print(df)\n",
    "print(df.index)\n",
    "print(df.columns)\n",
    "print(df.loc['s_width'][['mins']])\n",
    "print(df[['mins']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_model = LinearDiscriminantAnalysis()\n",
    "iris_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance 42 in the test set is a virginica and it is predicted to be a virginica\n",
      "The instance values are:\n",
      "     s_length  s_width  p_length  p_width\n",
      "143       6.8      3.2       5.9      2.3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst_ind = 42\n",
    "instance = X_test.iloc[[inst_ind]]\n",
    "inst_class = y_test.iloc[inst_ind]\n",
    "pred = iris_model.predict(instance)[0]\n",
    "print(f\"Instance {inst_ind} in the test set is a {inst_class} and it is predicted to be a {pred}\")\n",
    "print(f\"The instance values are:\\n{instance}\")\n",
    "iris_model.predict_proba(instance).shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ciu_obj = ciu.CIU(iris_model.predict_proba, iris_outnames, data=X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "          mins  maxs\n",
      "s_length   4.3   7.9\n",
      "s_width    2.0   4.4\n",
      "p_length   1.0   6.9\n",
      "p_width    0.1   2.5\n",
      "Row by Position:\n",
      "2.0\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print(type(ciu_obj.in_minmaxs))\n",
    "print(ciu_obj.in_minmaxs)\n",
    "row_by_position = ciu_obj.in_minmaxs.loc['s_width']  # Gets the second row (index 1)\n",
    "\n",
    "print(\"Row by Position:\")\n",
    "print(row_by_position.iloc[0])\n",
    "print(ciu_obj.in_minmaxs.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          mins  maxs\n",
      "s_length   4.3   7.9\n",
      "s_width    2.0   4.4\n",
      "p_length   1.0   6.9\n",
      "p_width    0.1   2.5\n",
      "[4.3 2.  1.  0.1]\n",
      "[7.9 4.4 6.9 2.5]\n"
     ]
    }
   ],
   "source": [
    "print(ciu_obj.in_minmaxs)\n",
    "mins = np.array(ciu_obj.in_minmaxs.iloc[0:4,0])\n",
    "maxs = np.array(ciu_obj.in_minmaxs.iloc[0:4,1])\n",
    "print(mins)\n",
    "print(maxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0    1\n",
      "0  4.3  1.0\n",
      "1  4.3  6.9\n",
      "2  7.9  1.0\n",
      "3  7.9  6.9\n"
     ]
    }
   ],
   "source": [
    "from itertools import product\n",
    "minmaxgrid = pd.DataFrame(list(product(*ciu_obj.in_minmaxs.values[[0,2],:])))\n",
    "#minmaxgrid.values.reshape(8,4)\n",
    "print(minmaxgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.75974558e-52 4.97705992e-08 9.99762639e-01]\n",
      "[4.11531032e-33 2.37361325e-04 9.99999950e-01]\n"
     ]
    }
   ],
   "source": [
    "estimator = ciu.PerturbationMinMaxEstimator.PerturbationMinMaxEstimator(iris_model.predict_proba, ciu_obj.in_minmaxs, 150)\n",
    "mins, maxs = estimator.get_minmax_outvals(instance, [1])\n",
    "print(mins)\n",
    "print(maxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['virginica']\n"
     ]
    },
    {
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       "  <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>Coalition of 2 inputs</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>Coalition of 2 inputs</td>\n",
       "      <td>3.632469e-58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[2, 3]</td>\n",
       "      <td>[5.9, 2.3]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                        CI            CU  Cinfl outname        outval  \\\n",
       "Coalition of 2 inputs  1.0  8.510916e-43   -0.5  setosa  8.510916e-43   \n",
       "\n",
       "                                     feature          ymin  ymax  inputs  \\\n",
       "Coalition of 2 inputs  Coalition of 2 inputs  3.632469e-58   1.0  [2, 3]   \n",
       "\n",
       "                           invals  neutralCU target_concept target_inputs  \n",
       "Coalition of 2 inputs  [5.9, 2.3]        0.5           None          None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(iris_model.predict(instance))\n",
    "res = ciu_obj.explain_core([2,3], instance, nsamples=1000)\n",
    "petal_ciu=pd.concat(res)\n",
    "display(petal_ciu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"target coalition\" can be indicated with the `target_inputs` parameter. Here `[2,3]` signifies the two petal features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "  <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>p_length</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.510917e-43</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>p_length</td>\n",
       "      <td>4.286756e-55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>[2, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "           CI            CU  Cinfl outname        outval   feature  \\\n",
       "p_length  1.0  8.510917e-43   -0.5  setosa  8.510916e-43  p_length   \n",
       "\n",
       "                  ymin  ymax inputs invals  neutralCU target_concept  \\\n",
       "p_length  4.286756e-55   1.0    [2]  [5.9]        0.5           None   \n",
       "\n",
       "         target_inputs  \n",
       "p_length        [2, 3]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_ic = ciu_obj.explain_core([2], instance, nsamples=1000, target_inputs=[2,3])\n",
    "display(pd.concat(res_ic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if CIU has already been calculated for the target coalition (i.e., intermediate concept), then it is better to use that result directly and pass the `ymin` and `ymax` values dorectly to the parameter `out_minmaxs`. That guarantees consistent CIU values between calculations and also avoid re-doing the CIU calculation for the target intermediate concept. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <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>p_length</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.510917e-43</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>p_length</td>\n",
       "      <td>4.286756e-55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           CI            CU  Cinfl outname        outval   feature  \\\n",
       "p_length  1.0  8.510917e-43   -0.5  setosa  8.510916e-43  p_length   \n",
       "\n",
       "                  ymin  ymax inputs invals  neutralCU target_concept  \\\n",
       "p_length  4.286756e-55   1.0    [2]  [5.9]        0.5           None   \n",
       "\n",
       "         target_inputs  \n",
       "p_length          None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_ic = ciu_obj.explain_core([2], instance, nsamples=1000, out_minmaxs=petal_ciu.loc[:,['ymin','ymax']])\n",
    "display(pd.concat(res_ic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          mins  maxs\n",
      "s_length   4.3   7.9\n",
      "s_width    2.0   4.4\n",
      "p_length   1.0   6.9\n",
      "p_width    0.1   2.5\n",
      "4.3\n"
     ]
    }
   ],
   "source": [
    "print(ciu_obj.in_minmaxs)\n",
    "print(ciu_obj.in_minmaxs.loc['s_length'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to get CIU results for all outputs in one go into the same DataFrame (even though that's not usually what you want but can be more efficient and stable):"
   ]
  },
  {
   "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>s_length</th>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.023350</td>\n",
       "      <td>-0.000070</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>s_length</td>\n",
       "      <td>6.727409e-10</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[6.8]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_length</th>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.976650</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>s_length</td>\n",
       "      <td>9.998528e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[6.8]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_width</th>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.014275</td>\n",
       "      <td>-0.000115</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>s_width</td>\n",
       "      <td>4.977060e-08</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3.2]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_width</th>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.985725</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>s_width</td>\n",
       "      <td>9.997626e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3.2]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_length</th>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>-0.499996</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>p_length</td>\n",
       "      <td>1.468630e-09</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.499997</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>p_length</td>\n",
       "      <td>6.464905e-19</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_width</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>-0.499997</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>p_width</td>\n",
       "      <td>2.347277e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2.3]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_width</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.499997</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>p_width</td>\n",
       "      <td>4.378181e-08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2.3]</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   feature  \\\n",
       "s_length  0.000147  0.023350 -0.000070  versicolor  0.000003  s_length   \n",
       "s_length  0.000147  0.976650  0.000070   virginica  0.999997  s_length   \n",
       "s_width   0.000237  0.014275 -0.000115  versicolor  0.000003   s_width   \n",
       "s_width   0.000237  0.985725  0.000115   virginica  0.999997   s_width   \n",
       "p_length  0.999998  0.000003 -0.499996  versicolor  0.000003  p_length   \n",
       "p_length  1.000000  0.999997  0.499997   virginica  0.999997  p_length   \n",
       "p_width   1.000000  0.000003 -0.499997  versicolor  0.000003   p_width   \n",
       "p_width   1.000000  0.999997  0.499997   virginica  0.999997   p_width   \n",
       "\n",
       "                  ymin      ymax inputs invals  neutralCU target_concept  \\\n",
       "s_length  6.727409e-10  0.000147    [0]  [6.8]        0.5           None   \n",
       "s_length  9.998528e-01  1.000000    [0]  [6.8]        0.5           None   \n",
       "s_width   4.977060e-08  0.000237    [1]  [3.2]        0.5           None   \n",
       "s_width   9.997626e-01  1.000000    [1]  [3.2]        0.5           None   \n",
       "p_length  1.468630e-09  0.999998    [2]  [5.9]        0.5           None   \n",
       "p_length  6.464905e-19  1.000000    [2]  [5.9]        0.5           None   \n",
       "p_width   2.347277e-07  1.000000    [3]  [2.3]        0.5           None   \n",
       "p_width   4.378181e-08  1.000000    [3]  [2.3]        0.5           None   \n",
       "\n",
       "         target_inputs  \n",
       "s_length          None  \n",
       "s_length          None  \n",
       "s_width           None  \n",
       "s_width           None  \n",
       "p_length          None  \n",
       "p_length          None  \n",
       "p_width           None  \n",
       "p_width           None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIUres = ciu_obj.explain(instance, output_inds=[1,2])\n",
    "display(CIUres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_length\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(ciu_obj.input_names[2])\n",
    "ciu_obj.plot_input_output(ind_input=2, output_inds=[0,1,2], illustrate_CIU=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is usually smarter to call `explain()` with only one `output_inds` value and then pass the result directly to the plotting methods. However, it can be more efficient (and stable) to get CIU values for all outputs with one single call. In that case, one can extract the values for a specific output like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>s_length</th>\n",
       "      <td>1.263354e-37</td>\n",
       "      <td>6.736764e-06</td>\n",
       "      <td>-6.316684e-38</td>\n",
       "      <td>setosa</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>s_length</td>\n",
       "      <td>1.500777e-54</td>\n",
       "      <td>1.263354e-37</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[6.8]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_length</th>\n",
       "      <td>1.471875e-04</td>\n",
       "      <td>2.334993e-02</td>\n",
       "      <td>-7.015693e-05</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>3.437490e-06</td>\n",
       "      <td>s_length</td>\n",
       "      <td>6.727409e-10</td>\n",
       "      <td>1.471882e-04</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[6.8]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_length</th>\n",
       "      <td>1.471875e-04</td>\n",
       "      <td>9.766501e-01</td>\n",
       "      <td>7.015693e-05</td>\n",
       "      <td>virginica</td>\n",
       "      <td>9.999966e-01</td>\n",
       "      <td>s_length</td>\n",
       "      <td>9.998528e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[6.8]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_width</th>\n",
       "      <td>4.115310e-33</td>\n",
       "      <td>2.068110e-10</td>\n",
       "      <td>-2.057655e-33</td>\n",
       "      <td>setosa</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>s_width</td>\n",
       "      <td>1.759746e-52</td>\n",
       "      <td>4.115310e-33</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3.2]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_width</th>\n",
       "      <td>2.373116e-04</td>\n",
       "      <td>1.427541e-02</td>\n",
       "      <td>-1.152681e-04</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>3.437490e-06</td>\n",
       "      <td>s_width</td>\n",
       "      <td>4.977060e-08</td>\n",
       "      <td>2.373613e-04</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3.2]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_width</th>\n",
       "      <td>2.373116e-04</td>\n",
       "      <td>9.857246e-01</td>\n",
       "      <td>1.152681e-04</td>\n",
       "      <td>virginica</td>\n",
       "      <td>9.999966e-01</td>\n",
       "      <td>s_width</td>\n",
       "      <td>9.997626e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3.2]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_length</th>\n",
       "      <td>9.999999e-01</td>\n",
       "      <td>8.510917e-43</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>setosa</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>p_length</td>\n",
       "      <td>4.286756e-55</td>\n",
       "      <td>9.999999e-01</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_length</th>\n",
       "      <td>9.999984e-01</td>\n",
       "      <td>3.436027e-06</td>\n",
       "      <td>-4.999958e-01</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>3.437490e-06</td>\n",
       "      <td>p_length</td>\n",
       "      <td>1.468630e-09</td>\n",
       "      <td>9.999984e-01</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_length</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.999966e-01</td>\n",
       "      <td>4.999966e-01</td>\n",
       "      <td>virginica</td>\n",
       "      <td>9.999966e-01</td>\n",
       "      <td>p_length</td>\n",
       "      <td>6.464905e-19</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5.9]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_width</th>\n",
       "      <td>2.303921e-16</td>\n",
       "      <td>3.690970e-27</td>\n",
       "      <td>-1.151960e-16</td>\n",
       "      <td>setosa</td>\n",
       "      <td>8.510916e-43</td>\n",
       "      <td>p_width</td>\n",
       "      <td>7.211918e-46</td>\n",
       "      <td>2.303921e-16</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2.3]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_width</th>\n",
       "      <td>9.999997e-01</td>\n",
       "      <td>3.202763e-06</td>\n",
       "      <td>-4.999967e-01</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>3.437490e-06</td>\n",
       "      <td>p_width</td>\n",
       "      <td>2.347277e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2.3]</td>\n",
       "      <td>0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_width</th>\n",
       "      <td>9.999997e-01</td>\n",
       "      <td>9.999968e-01</td>\n",
       "      <td>4.999967e-01</td>\n",
       "      <td>virginica</td>\n",
       "      <td>9.999966e-01</td>\n",
       "      <td>p_width</td>\n",
       "      <td>4.378181e-08</td>\n",
       "      <td>9.999998e-01</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2.3]</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",
       "s_length  1.263354e-37  6.736764e-06 -6.316684e-38      setosa  8.510916e-43   \n",
       "s_length  1.471875e-04  2.334993e-02 -7.015693e-05  versicolor  3.437490e-06   \n",
       "s_length  1.471875e-04  9.766501e-01  7.015693e-05   virginica  9.999966e-01   \n",
       "s_width   4.115310e-33  2.068110e-10 -2.057655e-33      setosa  8.510916e-43   \n",
       "s_width   2.373116e-04  1.427541e-02 -1.152681e-04  versicolor  3.437490e-06   \n",
       "s_width   2.373116e-04  9.857246e-01  1.152681e-04   virginica  9.999966e-01   \n",
       "p_length  9.999999e-01  8.510917e-43 -5.000000e-01      setosa  8.510916e-43   \n",
       "p_length  9.999984e-01  3.436027e-06 -4.999958e-01  versicolor  3.437490e-06   \n",
       "p_length  1.000000e+00  9.999966e-01  4.999966e-01   virginica  9.999966e-01   \n",
       "p_width   2.303921e-16  3.690970e-27 -1.151960e-16      setosa  8.510916e-43   \n",
       "p_width   9.999997e-01  3.202763e-06 -4.999967e-01  versicolor  3.437490e-06   \n",
       "p_width   9.999997e-01  9.999968e-01  4.999967e-01   virginica  9.999966e-01   \n",
       "\n",
       "           feature          ymin          ymax inputs invals  neutralCU  \\\n",
       "s_length  s_length  1.500777e-54  1.263354e-37    [0]  [6.8]        0.5   \n",
       "s_length  s_length  6.727409e-10  1.471882e-04    [0]  [6.8]        0.5   \n",
       "s_length  s_length  9.998528e-01  1.000000e+00    [0]  [6.8]        0.5   \n",
       "s_width    s_width  1.759746e-52  4.115310e-33    [1]  [3.2]        0.5   \n",
       "s_width    s_width  4.977060e-08  2.373613e-04    [1]  [3.2]        0.5   \n",
       "s_width    s_width  9.997626e-01  1.000000e+00    [1]  [3.2]        0.5   \n",
       "p_length  p_length  4.286756e-55  9.999999e-01    [2]  [5.9]        0.5   \n",
       "p_length  p_length  1.468630e-09  9.999984e-01    [2]  [5.9]        0.5   \n",
       "p_length  p_length  6.464905e-19  1.000000e+00    [2]  [5.9]        0.5   \n",
       "p_width    p_width  7.211918e-46  2.303921e-16    [3]  [2.3]        0.5   \n",
       "p_width    p_width  2.347277e-07  1.000000e+00    [3]  [2.3]        0.5   \n",
       "p_width    p_width  4.378181e-08  9.999998e-01    [3]  [2.3]        0.5   \n",
       "\n",
       "         target_concept target_inputs  \n",
       "s_length           None          None  \n",
       "s_length           None          None  \n",
       "s_length           None          None  \n",
       "s_width            None          None  \n",
       "s_width            None          None  \n",
       "s_width            None          None  \n",
       "p_length           None          None  \n",
       "p_length           None          None  \n",
       "p_length           None          None  \n",
       "p_width            None          None  \n",
       "p_width            None          None  \n",
       "p_width            None          None  "
      ]
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       "  <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",
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       "      <td>0.499997</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>p_length</td>\n",
       "      <td>6.464905e-19</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>[5.9]</td>\n",
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      "text/plain": [
       "                CI        CU     Cinfl    outname    outval   feature  \\\n",
       "s_length  0.000147  0.976650  0.000070  virginica  0.999997  s_length   \n",
       "s_width   0.000237  0.985725  0.000115  virginica  0.999997   s_width   \n",
       "p_length  1.000000  0.999997  0.499997  virginica  0.999997  p_length   \n",
       "p_width   1.000000  0.999997  0.499997  virginica  0.999997   p_width   \n",
       "\n",
       "                  ymin  ymax inputs invals  neutralCU target_concept  \\\n",
       "s_length  9.998528e-01   1.0    [0]  [6.8]        0.5           None   \n",
       "s_width   9.997626e-01   1.0    [1]  [3.2]        0.5           None   \n",
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       "p_width   4.378181e-08   1.0    [3]  [2.3]        0.5           None   \n",
       "\n",
       "         target_inputs  \n",
       "s_length          None  \n",
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RaUhu7dq18Pb2xo0bN7B69Wo4ODgAAHJzczFz5ky9BmhIDg4OytiJiIgsCYfkjEunpm3SpAnmzJmDhIQE9O7dW1k+e/ZsTJ069bGDCg4ORmRkJCIjI+Hs7AxXV1csWrQIQoh6j/voo4/Qo0cP5ec9e/ZAkiQkJSUpy0JCQrBw4UIAtYfkFAoFoqKi0KxZM7Ro0QJvv/22yjUnTZqEI0eOICEhAZIkQZIkZGdnK/enp6fDz88P9vb26NevHzIzM+uMtaysDIWFhSobERGRpviUnHHp3LSff/45+vfvj1atWuG3334DAMTHx+Obb77RS2BbtmyBtbU10tLSkJCQgLi4OGzcuLHeYwYNGoSLFy/i9u3bAIAjR47A1dUVqampAICKigqcOnUKwcHBao+PjY3F5s2bsWnTJhw/fhx3797F7t27lfsTEhIQGBiIadOmITc3F7m5ucrJ7wCwYMECxMbG4qeffoK1tTUmT55cZ6wxMTFwdnZWbn89DxERUUP0+ZRcYmIivL29YWtri4CAAKSlpdVb//79+4iIiICnpyfkcjk6d+6M/fv363ZxC6FTwrR+/XpERUXh2Wefxf3795UTvZs1a4b4+Hi9BObl5YW1a9eiS5cuGD9+PF5//XWsXbu23mN69OiB5s2b48iRIwAeTcZ+6623lJ/T0tJQUVGBfv36qT0+Pj4e8+fPx0svvYRu3bohKSkJzs7Oyv3Ozs6wsbGBvb09PDw84OHhASsrK+X+FStWYNCgQejevTvmzZuHkydPorS0VO215s+fj4KCAuVWPYmeiIhIE/rqYdqxYweioqIQHR2Ns2fPwsfHB6Ghobh165ba+uXl5Rg6dCiys7Oxa9cuZGZmYsOGDWjduvVj3pF50ylhWrduHTZs2IAFCxaoJAx+fn64cOGCXgL729/+Bukvs9cCAwNx5cqVep/CkyQJAwcORGpqKu7fv4+LFy9i5syZKCsrw+XLl3HkyBH813/9F+zt7WsdW1BQgNzcXJVlEaytreHn56dxzL169VL+7OnpCQB1/sLJ5XI4OTmpbERERJqyUpMsWf3nr3rNKR9lZWV1nicuLg7Tpk1DeHg4unfvjqSkJNjb22PTpk1q62/atAl3797Fnj17EBQUBG9vbwwaNAg+Pj6GuE2zoVPCdP36dZW5S9XkcjmKi4sfO6jHERwcjNTUVBw7dgy9e/eGk5OTMok6cuQIBg0aZLBrN2nSRPlzdbJXVVVlsOsREVHjVd+QnJeXl8q0j5iYGLXnKC8vR3p6unJ5IACQyWQICQnBqVOn1B7z7bffIjAwEBEREXB3d0ePHj2wcuVKi1pWSBc6JUzt2rVDRkZGrfLk5GR069btcWMCAJw5c0bl8+nTp9GpUyeVHi11qucx7dy5UzlXKTg4GIcOHcKJEyfqnL/k7OwMT09PletWVlYiPT1dpZ6Njc0T/0tBRETmr74huRs3bqhM+5g/f77ac9y5cwcKhQLu7u4q5e7u7sjLy1N7TFZWFnbt2gWFQoH9+/dj0aJFiI2NxXvvvafX+zM3Oi0rEBUVhYiICJSWlkIIgbS0NGzbtg0xMTENTszWVE5ODqKiovCPf/wDZ8+exbp16xAbG9vgcb169YKLiwu2bt2KvXv3AniUMM2ZMweSJCEoKKjOY2fNmoVVq1ahU6dO6Nq1K+Li4motRunt7Y0zZ84gOzsbDg4OaN68+WPdJxERkS7qW1bAkFM9qqqq4Obmhk8++QRWVlbo27cvbt68iQ8++ADR0dEGuaY50Clhmjp1Kuzs7LBw4UKUlJRg3LhxaNWqFRISEvDKK6/oJbCJEyfi4cOH8Pf3h5WVFWbNmoXp06c3eJwkSRgwYAD27duH/v37A3iURDk5OaFLly5o2rRpnce+9dZbyM3NRVhYGGQyGSZPnowXX3wRBQUFyjpz5sxBWFgYunfvjocPH+L69euPf7NERERaUvdUnLZPybm6usLKygr5+fkq5fn5+fDw8FB7jKenJ5o0aaIy4tOtWzfk5eWhvLwcNjY22gVhISTR0OJGNVRWVmLr1q0IDQ2Fu7s7SkpKUFRUBDc3N70FFRwcDF9fX709cWcJCgsL4ezsDPd57si3zW/4ACIiMkvupe7IX5WPgoICg/TyVP+9OHsdcKhx+qJCoE87aHXtgIAA+Pv7Y926dQAe9SA99dRTiIyMxLx582rVf/fdd7F161ZkZWVBJnvUpZWQkID3338ff/zxx+PdnBnTeg6TtbU1ZsyYoXxc3t7eXq/JEhERETWsvqfktBEVFYUNGzZgy5YtuHTpEl577TUUFxcjPDwcwKMRn7/OgXrttddw9+5dzJo1C7/++iv27duHlStXIiIiQl+3ZpZ0GpLz9/fHuXPn0LZtW33HU69jx44p31unTlFRkRGjISIiMh19DMkBwJgxY3D79m0sXrwYeXl58PX1RXJysnIieE5OjrInCXj0BN6BAwcwe/Zs9OrVC61bt8asWbPwzjvvPM7tmD2th+QA4Msvv8T8+fMxe/Zs9O3bt9a8oL+uR6RPDx8+xM2bN+vcb0kv/q2JQ3JERE8GYw3JXfodcKxx+geFQLc22g3JkWZ06mGqntj9xhtvKMskSYIQApIkGeyxezs7O4tOioiIiPRFXz1MpBmdEiY+GUZERGRa9S0rQPqnU8Jk7LlLREREpMpKTcKky6Rv0oxOCdNnn31W7/6JEyfqFAwBLdACMt0WYCciIjPgAhfkw/BzUTkkZ1w6JUyzZs1S+VxRUYGSkhLY2NjA3t6eCdNjeAEvwBa2pg6DiIh0VIpSXMRFg1+HQ3LGpVPCdO/evVplV65cwWuvvYa5c+c+dlCN2dChQ2Fvb2/qMMyaTCaDl5cXbty4wZcb14PtpDm2lWbYTpopKSnBqlWrDH4dJkzGpVPCpE6nTp2watUqvPrqq7h8+bK+TtvouLi41Pv6Fnr0pe3p6Yni4mJ+adeD7aQ5tpVm2E6akcvlRrkOh+SMS28JE/BoFfAneVl0IiIic8EeJuPSKWH69ttvVT4LIZCbm4uPPvoIQUFBegmMiIiI6iYpHm01y8gwdEqYRo0apfJZkiS0bNkSzzzzDGJjY/URFxEREdWn6j9bzTIyCJ0676qqqlQ2hUKBvLw8bN26FZ6envqOkYiIiGqqrGNrZPLy8vD666+jffv2kMvl8PLywogRI5CSkgLgUafOnj17ah03adKkWh1A9dEpYVq2bBlKSkpqlT98+BDLli3T5ZT0H6WlpaYOgYiILAETJmRnZ6Nv37748ccf8cEHH+DChQtITk7G4MGDERERoddr6ZQwLV26FEVFRbXKS0pKsHTp0scOqjF7+PChqUMgIiJLUFXH1ojMnDkTkiQhLS0Nf//739G5c2c8/fTTiIqKwunTp/V6LZ0SpuqX7NZ0/vx5NG/e/LGDIiIiogYoULt3qRFN+r579y6Sk5MRERGhdjmeZs2a6fV6Wk36dnFxgSRJkCQJnTt3VkmaFAoFioqKMGPGDL0GSERERGqoG4JrRENyV69ehRACXbt2Ncr1tEqY4uPjIYTA5MmTsXTpUjg7Oyv32djYwNvbG4GBgXoPkoiIiGpo5E/JCSGMej2tEqawsDAAQLt27dCvXz80adLEIEERERFRAxp5D1OnTp0gSVKDbxdxdHREQUFBrfL79++rdPw0RKc5TIMGDVImS6WlpSgsLFTZiIiIyMAa+VNyzZs3R2hoKBITE1FcXFxr//379wEAXbp0QXp6uso+hUKB8+fPo3PnzhpfT6eEqaSkBJGRkXBzc0PTpk3h4uKishEREZGB8Sk5JCYmQqFQwN/fH1999RWuXLmCS5cu4cMPP1ROEYqKisLGjRvx8ccf48qVK8jIyMD06dNx7949TJ06VeNr6ZQwzZ07Fz/++CPWr18PuVyOjRs3YunSpWjVqhU+++wzXU5JRERE2tDjU3KJiYnw9vaGra0tAgICkJaWptFx27dvhyRJWi0AqU/t27fH2bNnMXjwYLz11lvo0aMHhg4dipSUFKxfvx4AMHbsWGzcuBGbNm1C3759MXz4cOTl5eHo0aNwd3fX+Fo6vRrlu+++w2effYbg4GCEh4djwIAB6NixI9q2bYsvvvgC48eP1+W0REREpCk9zWHasWMHoqKikJSUhICAAMTHxyM0NBSZmZlwc3Or87js7GzMmTMHAwYM0P6ieuTp6YmPPvoIH330UZ11xo0bh3Hjxj3WdXTqYbp79y7at28PAHBycsLdu3cBAP3798fRo0cfKyAiIiLSgJ6G5OLi4jBt2jSEh4eje/fuSEpKgr29PTZt2lTnMQqFAuPHj8fSpUuV+cCTTqeEqX379rh+/ToAoGvXrvjyyy8BPOp50vdCUYaUmpoKSZKUE8PU2bx5s0b3VNe7aoiIiAyinknfNR/GKisrU3uK8vJypKenIyQkRFkmk8kQEhKCU6dO1XnpZcuWwc3NDVOmTNHX3Zg9nRKm8PBwnD9/HgAwb948JCYmwtbWFrNnz8bcuXP1GqAh9evXD7m5uVo9VrhkyRL4+voaLigiIiJN1JMweXl5wdnZWbnFxMSoPcWdO3egUChqzeVxd3dHXl6e2mOOHz+Of/7zn9iwYYO+7sQi6DSHafbs2cqfQ0JCcPnyZaSnp6Njx47o1auX3oIzNBsbG3h4eJg6DCIiIu0J1B6C+89ajjdu3ICTk5OyWC6X6+WSDx48wIQJE7Bhwwa4urrq5ZyWQqeE6a9KS0vRtm1btG3bVh/xqNi1axeWLl2Kq1evwt7eHr1798Y333yj9p0xAPDzzz+jV69eyM/PR8uWLXH37l24urpi9OjR2L59OwDgvffeQ3JyMo4fP47U1FQMHjwY9+7dUw67bd68GYsXL8adO3cQGhqK/v37K8+/efNm5cuFq18L8+mnn2LSpEkAHmXqL774Ig4cOIDWrVsjNjYWL7zwgtpYy8rKVLpI/7p+lUymU8dfo1Hd9pIksa3qwXbSHNtKM2wnzah716pB1DPp28nJSSVhqourqyusrKyQn5+vUp6fn6+2Q+HatWvIzs7GiBEjlGVVVY+yNmtra2RmZqJDhw5a3YbFEDqorKwUy5YtE61atRJWVlbi2rVrQgghFi5cKDZu3KjLKWv5448/hLW1tYiLixPXr18X//73v0ViYqJ48OBBncdUVVUJV1dXsXPnTiGEEHv27BGurq7Cw8NDWSckJEQsWLBACCHE4cOHBQBx7949IYQQp0+fFjKZTLz//vsiMzNTJCQkiGbNmglnZ2chhBAlJSXirbfeEk8//bTIzc0Vubm5oqSkRAghBADRpk0bsXXrVnHlyhXxxhtvCAcHB/Hnn3+qjTU6Olrg0b8FVLbMzMzHbToiIjKhgoICAUAUFBQY9vz7IESq6lawD1pf29/fX0RGRio/KxQK0bp1axETE1Or7sOHD8WFCxdUtpEjR4pnnnlGXLhwQZSVlenlHs2RTj1MK1aswJYtW7B69WpMmzZNWd6jRw/Ex8frZRJYbm4uKisr8dJLLyl7r3r27FnvMZIkYeDAgUhNTcXLL7+M1NRUhIeHY+PGjbh8+TI6dOiAkydP4u2331Z7fEJCAoYPH67c37lzZ5w8eRLJyckAADs7Ozg4OMDa2lpt5j1p0iSMHTsWALBy5Up8+OGHSEtLw/Dhw2vVnT9/PqKiopSfCwsL4eXlhd9//53/cmuAJEno0KEDrl27ZvR3CVkStpPm2FaaYTtppqioyDgX0tO75KKiohAWFgY/Pz/4+/sjPj4excXFCA8PBwBMnDgRrVu3RkxMDGxtbdGjRw+V46tHaGqWP2l0Spg+++wzfPLJJxgyZAhmzJihLPfx8WnwnS6a8vHxwZAhQ9CzZ0+EhoZi2LBhePnllxtcSXzQoEH45JNPAABHjhzBypUr8euvvyI1NRV3795FRUUFgoKC1B576dIlvPjiiyplgYGByoSpIX+dv9W0aVM4OTnh1q1bauvK5fI6x5SruzdJveqEUgjBtqoH20lzbCvNsJ00Y7RkUk/rMI0ZMwa3b9/G4sWLkZeXB19fXyQnJysngufk5PAf8tAxYbp58yY6duxYq7yqqgoVFRWPHRQAWFlZ4eDBgzh58iR++OEHrFu3DgsWLMCZM2fQrl27Oo8LDg7Gm2++iStXruDixYvo378/Ll++jNTUVNy7dw9+fn6wt7fXS4w11XwZsSRJ/FIhIiLDqF7pu2aZDiIjIxEZGal2X2pqar3Hbt68WbeLWhidUsbu3bvj2LFjtcp37dqF3r17P3ZQ1SRJQlBQEJYuXYpz587BxsYGu3fvrveYnj17wsXFBe+99x58fX3h4OCA4OBgHDlyBKmpqQgODq7z2G7duuHMmTMqZadPn1b5bGNjA4VCx99IIiIifeG75IxKpx6mxYsXIywsDDdv3kRVVRW+/vprZGZm4rPPPsPevXv1EtiZM2eQkpKCYcOGwc3NDWfOnMHt27fRrVu3eo+rnsf0xRdfYM6cOQAeDZWVlZUhJSVFZd5QTW+88QaCgoKwZs0ajBw5EgcOHKg1HOft7Y3r168jIyMDbdq0gaOjo94e1yQiItKYnobkSDNa9TBlZWVBCIGRI0fiu+++w6FDh9C0aVMsXrwYly5dwnfffYehQ4fqJTAnJyccPXoUzz33HDp37oyFCxciNjYWzz77bIPHDho0CAqFQtmbJJPJMHDgQGWPVV3+9re/YcOGDUhISICPjw9++OEHLFy4UKXO3//+dwwfPhyDBw9Gy5YtsW3btse6TyIiIp3Us3Al6Z8ktJidZmVlhdzcXOXL+MaMGYMPP/xQq7f9knqFhYVwdnZGSkoK2rRpY+pwzJpMJkPHjh1x9epVzhGrB9tJc2wrzbCdNFNcXIw+ffqgoKBAo7WQtFX996JgM+BUY0puYQngPAkGu3ZjptWQXM3c6vvvv0dxcbFeAyIiIiINcEjOqB5rpW9TrMPh4OBQ577vv/8eAwYMMGI0REREJqLHp+SoYVolTJIk1Vry3WhLwP9HRkZGnftat25tvECIiIhMSU8LV5JmtB6SmzRpkvKpsNLSUsyYMaPWu92+/vpr/UVYg7r1n4iIiBodDskZlVYJU1hYmMrnV199Va/BEBERkYaYMBmVVgnTp59+aqg4iIiISBsckjOqx5r0TURERCbCHiajYsJkZuzs7EwdAhERWQI+JWdUTJjMjK2tralDICIiS8AhOaNiwkRERGSJOCRnVEyYiIiILBETJqNiwkRERGSJOCRnVEyYiIiILBEnfRsVEyYiIiJLVAnASk0ZGYTM1AGQqtLSUlOHQERElqCqjo0Mgj1MZubhw4emDoGIiCwBe5iMij1MRERElqiyjk0HiYmJ8Pb2hq2tLQICApCWllZn3Q0bNmDAgAFwcXGBi4sLQkJC6q3/pGDCREREZIn0NCS3Y8cOREVFITo6GmfPnoWPjw9CQ0Nx69YttfVTU1MxduxYHD58GKdOnYKXlxeGDRuGmzdv6n4vFoAJExERkSWqfkrur5sOT8nFxcVh2rRpCA8PR/fu3ZGUlAR7e3ts2rRJbf0vvvgCM2fOhK+vL7p27YqNGzeiqqoKKSkput+LBWDCREREZInqGZIrLCxU2crKytSeory8HOnp6QgJCVGWyWQyhISE4NSpUxqFUVJSgoqKCjRv3vxx7sbsMWEiIiKyRPUMyXl5ecHZ2Vm5xcTEqD3FnTt3oFAo4O7urlLu7u6OvLw8jcJ455130KpVK5Wk60nEp+SIiIgsUSVqd3v8p4fpxo0bcHJyUhbL5XKDhLBq1Sps374dqampT/zL45kwERERWaJ6EiYnJyeVhKkurq6usLKyQn5+vkp5fn4+PDw86j12zZo1WLVqFQ4dOoRevXppEbhl4pAcERGRJRKoPRwntDuFjY0N+vbtqzJhu3oCd2BgYJ3HrV69GsuXL0dycjL8/Px0CN7yWGzCNGnSJIwaNcrUYQAAsrOzIUkSMjIyTB0KERE1FnpahykqKgobNmzAli1bcOnSJbz22msoLi5GeHg4AGDixImYP3++sv7777+PRYsWYdOmTfD29kZeXh7y8vJQVFSkh5syXxyS09KkSZNw//597Nmzx9ShEBFRY1YJQFJTpqUxY8bg9u3bWLx4MfLy8uDr64vk5GTlRPCcnBzIZP/fv7J+/XqUl5fj5ZdfVjlPdHQ0lixZon0AFoIJExERkSVSt1Clju+Si4yMRGRkpNp9qampKp+zs7N1u4iFM4shuV27dqFnz56ws7NDixYtEBISguLiYq3OUVVVhZiYGLRr1w52dnbw8fHBrl27lPtTU1MhSRJSUlLg5+cHe3t79OvXD5mZmSrnee+99+Dm5gZHR0dMnToV8+bNg6+vLwBgyZIl2LJlC7755htIkgRJklR+kbKysjB48GDY29vDx8dH4zUsiIiItKbHV6NQw0zew5Sbm4uxY8di9erVePHFF/HgwQMcO3YMQmg3cy0mJgb/+7//i6SkJHTq1AlHjx7Fq6++ipYtW2LQoEHKegsWLEBsbCxatmyJGTNmYPLkyThx4gSAR6uXrlixAh9//DGCgoKwfft2xMbGol27dgCAOXPm4NKlSygsLMSnn34KAGjevDn++OMP5bnXrFmDTp06YcGCBRg7diyuXr0Ka+vazVxWVqaykFhhYaHy5792fVJtkiQp/8u2qhvbSXNsK82wnTRT3U4Gp0DtITkdVvomDQkTS09PFwBEdna2VseFhYWJkSNHCiGEKC0tFfb29uLkyZMqdaZMmSLGjh0rhBDi8OHDAoA4dOiQcv++ffsEAPHw4UMhhBABAQEiIiJC5RxBQUHCx8dH7XWrXb9+XQAQGzduVJb98ssvAoC4dOmS2vijo6MFHj3PoLJlZmZq1Q5ERGReCgoKBABRUFBg2PM/AyGGqW4Fz8Cg127MTN7D5OPjgyFDhqBnz54IDQ3FsGHD8PLLL8PFxUXjc1y9ehUlJSUYOnSoSnl5eTl69+6tUvbXtSI8PT0BALdu3cJTTz2FzMxMzJw5U6W+v78/fvzxR43iqOvcXbt2rVV3/vz5iIqKUn4uLCyEl5cXfv/9d/7LrQGSJKFDhw64du2a1j2RjQnbSXNsK82wnTRjtKfF1A2/cUjOYEyeMFlZWeHgwYM4efIkfvjhB6xbtw4LFizAmTNnlENhDan+5dy3bx9at26tsq/m6qZNmjRR/lzdbVpVpeMsuRq0ObdcLq9z5VV9xfOkqk4ohRBsq3qwnTTHttIM20kzRksmmTAZlVl0ZUiShKCgICxduhTnzp2DjY0Ndu/erfHx3bt3h1wuR05ODjp27KiyeXl5aXyeLl264F//+pdKWc3PNjY2UCg4SExERCZWz7vkSP9M3sN05swZpKSkYNiwYXBzc8OZM2dw+/ZtdOvWTeNzODo6Ys6cOZg9ezaqqqrQv39/FBQU4MSJE3ByckJYWJhG53n99dcxbdo0+Pn5oV+/ftixYwf+/e9/o3379so63t7eOHDgADIzM9GiRQs4Oztrfc9ERESPjT1MRmXyhMnJyQlHjx5FfHw8CgsL0bZtW8TGxuLZZ5/V6jzLly9Hy5YtERMTg6ysLDRr1gx9+vTBu+++q/E5xo8fj6ysLMyZMwelpaUYPXo0Jk2ahLS0NGWdadOmITU1FX5+figqKsLhw4fh7e2tVaxERESPTd1gBwdADEYSnLlXr6FDh8LDwwOff/65Qa9TWFgIZ2dnpKSkoE2bNga9lqWTyWTo2LEjrl69ynkU9WA7aY5tpRm2k2aKi4vRp08fFBQUaPQCXG1V/70o8AGcrGrsUwDO52GwazdmJu9hMiclJSVISkpCaGgorKyssG3bNhw6dAgHDx40dWhERESqKlH7ZbvsYTIYs02YHBwc6tz3/fffY8CAAXq/piRJ2L9/P1asWIHS0lJ06dIFX331FUJCQvR+LSIiosfChMmozDZhysjIqHNfzaUD9MXOzg6HDh0yyLmJiIj0qgq1V/rmSKnBmG3C1LFjR1OHQEREZL4UqN3DxITJYMw2YSIiIqJ6VKL2aopMmAyGCRMREZElUpccMWEyGCZMRERElog9TEbFhImIiMgSsYfJqJgwmRk7OztTh0BERJaAPUxGxYTJzNja2po6BCIisgQK1E6Q+O4Og2HCREREZInUrcPEhMlganbmERERkSWorGPTQWJiIry9vWFra4uAgACVl86rs3PnTnTt2hW2trbo2bMn9u/fr9uFLQgTJiIiIkukp4Rpx44diIqKQnR0NM6ePQsfHx+Ehobi1q1bauufPHkSY8eOxZQpU3Du3DmMGjUKo0aNws8//6z7vVgAJkxERESWqKqOTUtxcXGYNm0awsPD0b17dyQlJcHe3h6bNm1SWz8hIQHDhw/H3Llz0a1bNyxfvhx9+vTBRx99pPu9WADOYTITQjwaeC4qKlL+TOpJkoTCwkI8ePCAbVUPtpPm2FaaYTtppri4GAAM3kaFal60W1j938JClXK5XA65XF6rfnl5OdLT0zF//nxlmUwmQ0hICE6dOqX2uqdOnUJUVJRKWWhoKPbs2aNV/JaGCZOZ+PPPPwEAAwcONHEkRESkD3/++SecnZ31fl4bGxt4eHjAKy9P7X4HBwd4eXmplEVHR2PJkiW16t65cwcKhQLu7u4q5e7u7rh8+bLa8+fl5amtn1dHPE8KJkxmonnz5gCAnJwcg/wP9iQpLCyEl5cXbty4AScnJ1OHY7bYTppjW2mG7aSZgoICPPXUU8rvdX2ztbXF9evXUV5erna/EAKSpPr4nLreJdIOEyYzIZM9mk7m7OzMLyINOTk5sa00wHbSHNtKM2wnzVR/rxuCra2tXtbtc3V1hZWVFfLz81XK8/Pz4eHhofYYDw8Preo/KTjpm4iIqJGysbFB3759kZKSoiyrqqpCSkoKAgMD1R4TGBioUh8ADh48WGf9JwV7mIiIiBqxqKgohIWFwc/PD/7+/oiPj0dxcTHCw8MBABMnTkTr1q0RExMDAJg1axYGDRqE2NhYPP/889i+fTt++uknfPLJJ6a8DYNjwmQm5HI5oqOjOc6sAbaVZthOmmNbaYbtpBlLa6cxY8bg9u3bWLx4MfLy8uDr64vk5GTlxO6cnByV4cV+/fph69atWLhwId5991106tQJe/bsQY8ePUx1C0YhCT4bSkRERFQvzmEiIiIiagATJiIiIqIGMGEiIiIiagATJiIiIqIGMGEyosTERHh7e8PW1hYBAQFIS0urt/7OnTvRtWtX2NraomfPnti/f7+RIjU9bdpqw4YNGDBgAFxcXODi4oKQkJAG2/ZJoe3vVLXt27dDkiSMGjXKsAGaCW3b6f79+4iIiICnpyfkcjk6d+7caP7/07at4uPj0aVLF9jZ2cHLywuzZ89GaWmpkaI1jaNHj2LEiBFo1aoVJEnS6B1qqamp6NOnD+RyOTp27IjNmzcbPE7SM0FGsX37dmFjYyM2bdokfvnlFzFt2jTRrFkzkZ+fr7b+iRMnhJWVlVi9erW4ePGiWLhwoWjSpIm4cOGCkSM3Pm3baty4cSIxMVGcO3dOXLp0SUyaNEk4OzuL33//3ciRG5e27VTt+vXronXr1mLAgAFi5MiRxgnWhLRtp7KyMuHn5yeee+45cfz4cXH9+nWRmpoqMjIyjBy58WnbVl988YWQy+Xiiy++ENevXxcHDhwQnp6eYvbs2UaO3Lj2798vFixYIL7++msBQOzevbve+llZWcLe3l5ERUWJixcvinXr1gkrKyuRnJxsnIBJL5gwGYm/v7+IiIhQflYoFKJVq1YiJiZGbf3Ro0eL559/XqUsICBA/OMf/zBonOZA27aqqbKyUjg6OootW7YYKkSzoEs7VVZWin79+omNGzeKsLCwRpEwadtO69evF+3btxfl5eXGCtFsaNtWERER4plnnlEpi4qKEkFBQQaN05xokjC9/fbb4umnn1YpGzNmjAgNDTVgZKRvHJIzgvLycqSnpyMkJERZJpPJEBISglOnTqk95tSpUyr1ASA0NLTO+k8KXdqqppKSElRUVBjsxZfmQNd2WrZsGdzc3DBlyhRjhGlyurTTt99+i8DAQERERMDd3R09evTAypUroVAojBW2SejSVv369UN6erpy2C4rKwv79+/Hc889Z5SYLUVj/T5/0nClbyO4c+cOFAqFctXUau7u7rh8+bLaY/Ly8tTWz8vLM1ic5kCXtqrpnXfeQatWrWp9QT1JdGmn48eP45///CcyMjKMEKF50KWdsrKy8OOPP2L8+PHYv38/rl69ipkzZ6KiogLR0dHGCNskdGmrcePG4c6dO+jfvz+EEKisrMSMGTPw7rvvGiNki1HX93lhYSEePnwIOzs7E0VG2mAPEz1RVq1ahe3bt2P37t16eZP3k+LBgweYMGECNmzYAFdXV1OHY9aqqqrg5uaGTz75BH379sWYMWOwYMECJCUlmTo0s5OamoqVK1fi448/xtmzZ/H1119j3759WL58ualDI9I79jAZgaurK6ysrJCfn69Snp+fDw8PD7XHeHh4aFX/SaFLW1Vbs2YNVq1ahUOHDqFXr16GDNPktG2na9euITs7GyNGjFCWVVVVAQCsra2RmZmJDh06GDZoE9Dl98nT0xNNmjSBlZWVsqxbt27Iy8tDeXk5bGxsDBqzqejSVosWLcKECRMwdepUAEDPnj1RXFyM6dOnY8GCBSrvH2vM6vo+d3JyYu+SBeFvsxHY2Nigb9++SElJUZZVVVUhJSUFgYGBao8JDAxUqQ8ABw8erLP+k0KXtgKA1atXY/ny5UhOToafn58xQjUpbdupa9euuHDhAjIyMpTbCy+8gMGDByMjIwNeXl7GDN9odPl9CgoKwtWrV5UJJQD8+uuv8PT0fGKTJUC3tiopKamVFFUnmoKvKVVqrN/nTxxTzzpvLLZv3y7kcrnYvHmzuHjxopg+fbpo1qyZyMvLE0IIMWHCBDFv3jxl/RMnTghra2uxZs0acenSJREdHd2olhXQpq1WrVolbGxsxK5du0Rubq5ye/DggaluwSi0baeaGstTctq2U05OjnB0dBSRkZEiMzNT7N27V7i5uYn33nvPVLdgNNq2VXR0tHB0dBTbtm0TWVlZ4ocffhAdOnQQo0ePNtUtGMWDBw/EuXPnxLlz5wQAERcXJ86dOyd+++03IYQQ8+bNExMmTFDWr15WYO7cueLSpUsiMTGRywpYICZMRrRu3Trx1FNPCRsbG+Hv7y9Onz6t3Ddo0CARFhamUv/LL78UnTt3FjY2NuLpp58W+/btM3LEpqNNW7Vt21YAqLVFR0cbP3Aj0/Z36q8aS8IkhPbtdPLkSREQECDkcrlo3769WLFihaisrDRy1KahTVtVVFSIJUuWiA4dOghbW1vh5eUlZs6cKe7du2f8wI3o8OHDar9zqtsmLCxMDBo0qNYxvr6+wsbGRrRv3158+umnRo+bHo8kBPtNiYiIiOrDOUxEREREDWDCRERERNQAJkxEREREDWDCRERERNQAJkxEREREDWDCRERERNQAJkxEREREDWDCRERERNQAJkxEREREDWDCREQGlZeXh9dffx3t27eHXC6Hl5cXRowYoXwZqbe3N+Lj400bJBFRA6xNHQARPbmys7MRFBSEZs2a4YMPPkDPnj1RUVGBAwcOICIiApcvXzZ1iEREGmHCREQGM3PmTEiShLS0NDRt2lRZ/vTTT2Py5MkmjIyISDsckiMig7h79y6Sk5MRERGhkixVa9asmfGDIiLSERMmIjKIq1evQgiBrl27mjoUIqLHxoSJiAxCCGHqEIiI9IYJExEZRKdOnSBJEid2E9ETgQkTERlE8+bNERoaisTERBQXF9faf//+feMHRUSkIyZMRGQwiYmJUCgU8Pf3x1dffYUrV67g0qVL+PDDDxEYGGjq8IiINMZlBYjIYNq3b4+zZ89ixYoVeOutt5Cbm4uWLVuib9++WL9+vanDIyLSmCQ4M5OIiIioXhySIyIiImoAEyYiIiKiBjBhIiIiImoAEyYiIiKiBjBhIiIiImoAEyYiIiKiBjBhIiIiImoAEyYiIiKiBjBhIiIiImoAEyYiIiKiBjBhIiIiImrA/wGYhRcwFue7EwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIUres = ciu_obj.explain(instance, output_inds=[0,1,2])\n",
    "display(CIUres)\n",
    "CIUres = CIUres[CIUres['outname'] == 'virginica']\n",
    "display(CIUres)\n",
    "ciu_obj.plot_ciu(CIUres, figsize=(6,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ciu_obj.plot_ciu(CIUres, plot_mode='overlap', figsize=(6,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ciu_obj.plot_ciu(CIUres, color_blind='protanopia', figsize=(6,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ciu_obj.plot_influence(CIUres, figsize=(6,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The explained value is virginica with the value 1.00 (CU=1.00), which is high utility.\n",
      "Feature s_length has very low importance (CI=0.00) and has value(s) 6.8, which is high utility (CU=0.98)\n",
      "Feature s_width has very low importance (CI=0.00) and has value(s) 3.2, which is high utility (CU=0.99)\n",
      "Feature p_length has very high importance (CI=1.00) and has value(s) 5.9, which is high utility (CU=1.00)\n",
      "Feature p_width has very high importance (CI=1.00) and has value(s) 2.3, which is high utility (CU=1.00)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "text_ciu = ciu_obj.textual_explanation(CIUres)\n",
    "print(text_ciu)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "The explained value is ***virginica*** with the value 1.00 (CU=1.00), which is **high utility**.<br>Feature *s_length* has **very low importance (CI=0.00)** and has value(s) 6.8, which is **high utility (CU=0.98)**<br>Feature *s_width* has **very low importance (CI=0.00)** and has value(s) 3.2, which is **high utility (CU=0.99)**<br>Feature *p_length* has **very high importance (CI=1.00)** and has value(s) 5.9, which is **high utility (CU=1.00)**<br>Feature *p_width* has **very high importance (CI=1.00)** and has value(s) 2.3, which is **high utility (CU=1.00)**<br>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown\n",
    "markdown_text = ciu_obj.textual_explanation(CIUres, use_markdown_effects=True)\n",
    "Markdown(markdown_text)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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,
 "nbformat_minor": 4
}