diff --git a/ROADMAP.md b/ROADMAP.md
index 0dfb06b..5135831 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -5,5 +5,6 @@ This page contains the roadmap for the XAI library.
 
 * Add AIC and BIC model evaluation metrics
 * Add numbers on the charts for accuracy metrics
+* Update XAI so it works with the latest version of pandas
 
 
diff --git a/env.yml b/env.yml
index 0e0b041..aef1d2c 100644
--- a/env.yml
+++ b/env.yml
@@ -5,10 +5,10 @@ channels:
 dependencies:
   - python==3.7.3
   - jupyter
-  - numpy
-  - pandas
-  - matplotlib
-  - scikit-learn
+  - numpy==1.15.4
+  - pandas==0.23.4
+  - matplotlib==3.0.2
+  - scikit-learn==0.20.1
   - seaborn
   - spacy
   - nb_conda
diff --git a/examples/XAI Example Usage.ipynb b/examples/XAI Example Usage.ipynb
deleted file mode 100644
index 91c724b..0000000
--- a/examples/XAI Example Usage.ipynb	
+++ /dev/null
@@ -1,1149 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys, os\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Use below for charts in dark jupyter theme\n",
-    "params = {\"ytick.color\" : \"w\",\n",
-    "          \"xtick.color\" : \"w\",\n",
-    "          \"axes.labelcolor\" : \"w\",\n",
-    "          \"axes.edgecolor\" : \"w\"} \n",
-    "plt.rcParams.update(params)\n",
-    "\n",
-    "import xai\n",
-    "import xai.data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "csv_path = 'data/adult.data'\n",
-    "categorical_cols = [\"gender\", \"workclass\", \"education\", \"education-num\", \"marital-status\",\n",
-    "                   \"occupation\", \"relationship\", \"ethnicity\", \"native-country\", \"loan\"]\n",
-    "csv_columns = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\",\n",
-    "                   \"occupation\", \"relationship\", \"ethnicity\", \"gender\", \"capital-gain\", \"capital-loss\",\n",
-    "                   \"hours-per-week\", \"native-country\", \"loan\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "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>age</th>\n",
-       "      <th>workclass</th>\n",
-       "      <th>education</th>\n",
-       "      <th>education-num</th>\n",
-       "      <th>marital-status</th>\n",
-       "      <th>occupation</th>\n",
-       "      <th>relationship</th>\n",
-       "      <th>ethnicity</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>capital-gain</th>\n",
-       "      <th>capital-loss</th>\n",
-       "      <th>hours-per-week</th>\n",
-       "      <th>loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>39</td>\n",
-       "      <td>State-gov</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Adm-clerical</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>2174</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Self-emp-not-inc</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Exec-managerial</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>38</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Divorced</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>53</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>11th</td>\n",
-       "      <td>7</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Prof-specialty</td>\n",
-       "      <td>Wife</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age          workclass   education  education-num       marital-status  \\\n",
-       "0   39          State-gov   Bachelors             13        Never-married   \n",
-       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
-       "2   38            Private     HS-grad              9             Divorced   \n",
-       "3   53            Private        11th              7   Married-civ-spouse   \n",
-       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
-       "\n",
-       "           occupation    relationship ethnicity   gender  capital-gain  \\\n",
-       "0        Adm-clerical   Not-in-family     White     Male          2174   \n",
-       "1     Exec-managerial         Husband     White     Male             0   \n",
-       "2   Handlers-cleaners   Not-in-family     White     Male             0   \n",
-       "3   Handlers-cleaners         Husband     Black     Male             0   \n",
-       "4      Prof-specialty            Wife     Black   Female             0   \n",
-       "\n",
-       "   capital-loss  hours-per-week    loan  \n",
-       "0             0              40   <=50K  \n",
-       "1             0              13   <=50K  \n",
-       "2             0              40   <=50K  \n",
-       "3             0              40   <=50K  \n",
-       "4             0              40   <=50K  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = xai.data.load_census()\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(csv_path, names=csv_columns)\n",
-    "del df[\"fnlwgt\"]\n",
-    "del df[\"native-country\"]\n",
-    "df.head()\n",
-    "df.to_csv(\"../xai/data/census.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target = \"loan\"\n",
-    "protected = [\"ethnicity\", \"gender\", \"age\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ims = xai.show_imbalances(df, protected)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "im = xai.show_imbalance(df, \"gender\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bal_df = xai.balance(df, \"gender\", upsample=0.8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "im = xai.show_imbalance(bal_df, \"gender\", cross=[\"loan\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bal_df = xai.balance(df, \"gender\", cross=[\"loan\"], upsample=1)\n",
-    "im = xai.show_imbalance(bal_df, \"gender\", cross=[\"loan\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1200, 12)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "proc_df = xai.normalize_numeric(bal_df)\n",
-    "proc_df = xai.convert_categories(proc_df)\n",
-    "x = proc_df.drop(\"loan\", axis=1)\n",
-    "y = proc_df[\"loan\"]\n",
-    "\n",
-    "x_train, y_train, x_test, y_test = xai.balanced_train_test_split(\n",
-    "            x, y, cross=[\"gender\"], \n",
-    "            categorical_cols=categorical_cols, min_per_class=300,\n",
-    "            fallback_type=\"half\")\n",
-    "\n",
-    "print(x_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_test = x_test.copy()\n",
-    "df_test[\"loan\"] = y_test\n",
-    "\n",
-    "_= xai.show_imbalance(df_test, \"gender\", cross=[\"loan\"], categorical_cols=categorical_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sklearn\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import classification_report, mean_squared_error, roc_curve, auc\n",
-    "\n",
-    "from keras.layers import Input, Dense, Flatten, \\\n",
-    "    Concatenate, concatenate, Dropout, Lambda\n",
-    "from keras.models import Model, Sequential\n",
-    "from keras.layers.embeddings import Embedding\n",
-    "\n",
-    "def build_model(X):\n",
-    "    input_els = []\n",
-    "    encoded_els = []\n",
-    "    dtypes = list(zip(X.dtypes.index, map(str, X.dtypes)))\n",
-    "    for k,dtype in dtypes:\n",
-    "        input_els.append(Input(shape=(1,)))\n",
-    "        if dtype == \"int8\":\n",
-    "            e = Flatten()(Embedding(X[k].max()+1, 1)(input_els[-1]))\n",
-    "        else:\n",
-    "            e = input_els[-1]\n",
-    "        encoded_els.append(e)\n",
-    "    encoded_els = concatenate(encoded_els)\n",
-    "\n",
-    "    layer1 = Dropout(0.5)(Dense(100, activation=\"relu\")(encoded_els))\n",
-    "    out = Dense(1, activation='sigmoid')(layer1)\n",
-    "\n",
-    "    # train model\n",
-    "    model = Model(inputs=input_els, outputs=[out])\n",
-    "    model.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=['accuracy'])\n",
-    "    return model\n",
-    "\n",
-    "\n",
-    "def f_in(X, m=None):\n",
-    "    \"\"\"Preprocess input so it can be provided to a function\"\"\"\n",
-    "    if m:\n",
-    "        return [X.iloc[:m,i] for i in range(X.shape[1])]\n",
-    "    else:\n",
-    "        return [X.iloc[:,i] for i in range(X.shape[1])]\n",
-    "\n",
-    "def f_out(probs, threshold=0.5):\n",
-    "    \"\"\"Convert probabilities into classes\"\"\"\n",
-    "    return list((probs >= threshold).astype(int).T[0])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50\n",
-      "59312/59312 [==============================] - 1s 10us/step - loss: 0.5508 - acc: 0.7170\n",
-      "Epoch 2/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.4199 - acc: 0.8065\n",
-      "Epoch 3/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3813 - acc: 0.8251\n",
-      "Epoch 4/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3714 - acc: 0.8280\n",
-      "Epoch 5/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3673 - acc: 0.8311\n",
-      "Epoch 6/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3662 - acc: 0.8318\n",
-      "Epoch 7/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3640 - acc: 0.8338\n",
-      "Epoch 8/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3626 - acc: 0.8343\n",
-      "Epoch 9/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3630 - acc: 0.8335\n",
-      "Epoch 10/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3619 - acc: 0.8356\n",
-      "Epoch 11/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3605 - acc: 0.8360\n",
-      "Epoch 12/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3605 - acc: 0.8359\n",
-      "Epoch 13/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3601 - acc: 0.8377\n",
-      "Epoch 14/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3590 - acc: 0.8360\n",
-      "Epoch 15/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3593 - acc: 0.8364\n",
-      "Epoch 16/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3589 - acc: 0.8363\n",
-      "Epoch 17/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3589 - acc: 0.8373\n",
-      "Epoch 18/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3586 - acc: 0.8365\n",
-      "Epoch 19/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3590 - acc: 0.8364\n",
-      "Epoch 20/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3586 - acc: 0.8364\n",
-      "Epoch 21/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3582 - acc: 0.8367\n",
-      "Epoch 22/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3581 - acc: 0.8370\n",
-      "Epoch 23/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3573 - acc: 0.8378\n",
-      "Epoch 24/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3575 - acc: 0.8372\n",
-      "Epoch 25/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3576 - acc: 0.8366\n",
-      "Epoch 26/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3574 - acc: 0.8370\n",
-      "Epoch 27/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3574 - acc: 0.8376\n",
-      "Epoch 28/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3570 - acc: 0.8375\n",
-      "Epoch 29/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3570 - acc: 0.8368\n",
-      "Epoch 30/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3573 - acc: 0.8377\n",
-      "Epoch 31/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3567 - acc: 0.8371\n",
-      "Epoch 32/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3567 - acc: 0.8368\n",
-      "Epoch 33/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3571 - acc: 0.8377\n",
-      "Epoch 34/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3567 - acc: 0.8386\n",
-      "Epoch 35/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3566 - acc: 0.8371\n",
-      "Epoch 36/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3558 - acc: 0.8375\n",
-      "Epoch 37/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3561 - acc: 0.8376\n",
-      "Epoch 38/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3565 - acc: 0.8372\n",
-      "Epoch 39/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3558 - acc: 0.8376\n",
-      "Epoch 40/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3558 - acc: 0.8374\n",
-      "Epoch 41/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3555 - acc: 0.8378\n",
-      "Epoch 42/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3555 - acc: 0.8389\n",
-      "Epoch 43/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3558 - acc: 0.8378\n",
-      "Epoch 44/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3562 - acc: 0.8371\n",
-      "Epoch 45/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3547 - acc: 0.8378\n",
-      "Epoch 46/50\n",
-      "59312/59312 [==============================] - 0s 3us/step - loss: 0.3548 - acc: 0.8381\n",
-      "Epoch 47/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3553 - acc: 0.8366\n",
-      "Epoch 48/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3550 - acc: 0.8383\n",
-      "Epoch 49/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3542 - acc: 0.8383\n",
-      "Epoch 50/50\n",
-      "59312/59312 [==============================] - 0s 4us/step - loss: 0.3545 - acc: 0.8384\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.callbacks.History at 0x7fcb03c264e0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = build_model(proc_df.drop(\"loan\", axis=1))\n",
-    "\n",
-    "model.fit(f_in(x_train), y_train, epochs=50, batch_size=512)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1200/1200 [==============================] - 0s 53us/step\n",
-      "Error 0.3332: \n",
-      "Accuracy 83.6667: \n"
-     ]
-    }
-   ],
-   "source": [
-    "score = model.evaluate(f_in(x_test), y_test, verbose=1)\n",
-    "print(\"Error %.4f: \" % score[0])\n",
-    "print(\"Accuracy %.4f: \" % (score[1]*100))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "probabilities = model.predict(f_in(x_test))\n",
-    "pred = f_out(probabilities)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_= xai.metrics_imbalance(\n",
-    "        x_test, \n",
-    "        y_test, \n",
-    "        probabilities)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/alejandro/anaconda3/lib/python3.6/site-packages/xai-0.0.3-py3.6.egg/xai/__init__.py:520: RuntimeWarning: invalid value encountered in long_scalars\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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nP/3pzs+1tbVZs2ZNSfcEAAAA6Eog1IOXXnopc+fOTZLcdtttOeqoo9Y7369fv9TX1+euu+5K0h7iLFiwIEly/PHH5/rrr0/S/vDpt956a71r//SnP2XPPffMhAkTcv7552f+/PnrnT/mmGPy61//Ou+++27eeeed3H333Tn66KM3y/cEAAAAikkg1IOGhobcfPPNaWxszOuvv55vfvObG7T513/919x4441pamrKIYccknvuuSdJcs0112T27NkZNGhQhg0blkWLFq133Zw5czJ48OAMGTIkM2bMyLe//e31zg8dOjTjxo3LiBEjcthhh+WCCy7IkCFDNt+XBQAAAAqn5qOWKG1mG9y4paUlDQ0NlailU2tra0455ZQ8++yzFa2j3Krht/0k6ibfV+kSqk5r37MrXUJVGlS/f6VLqEp3XmGJaU8aFpe23BdgUxi3bMi4pWfGLT0zbumZcQubYF6SQz+uUakzhE5KsiTJ80km93D+u0n+mGRhkt8l+UyJ/QIAAACwhZUSCNUm+UWSLyQ5OMlZHX+7eirt6VNjkl8l+ccy1rhF1dXV9brZQQAAAABdlRIIjUj7zKAXknyQ5PYkp3ZrMzvJux2fH0syoFwFAgAAAFBepQRC+yZ5ucv+so5jG3N+kt98kqIAAAAA2Hz6lNCmpodjG3sS9TlpXzp27EbOT+zYAAAAAKiQUgKhZUn267I/IMnyHtp9Psn/THsYtGojfU3t2JKNh0oAAAAAbEalLBl7IskBSeqTfCrJmUnu7dZmSJL/k2RMkv8oZ4EAAAAAlFcpM4TWJJmU5Ldpf+PYTUkWJbksyZNpD4f+d5Idk9zVcc1LaQ+HPpkf9f/EXazf34ry9lei1tbWnHLKKXn22WczZ86cXHXVVZk5c2ZFagEAAAAoJRBKklkdW1c/6PL58+Upp7q0tbWlra0t22xTykQqAAAAgK2DpKOb1tbWNDQ05MILL8zQoUNzyy235IgjjsjQoUMzduzYrFy5MknyxBNPZOTIkWlqasqIESPy9ttvp7W1NUcffXSGDh2aoUOH5tFHH63wtwEAAADYkECoB0uWLMm5556bBx54IDfeeGOam5szf/78HHroobn66qvzwQcf5Iwzzsg111yTBQsWpLm5Odttt1323HPPPPDAA5k/f37uuOOOXHzxxZX+KgAAAAAbKHXJWKF85jOfyeGHH56ZM2fmj3/8Y4488sgkyQcffJAjjjgiS5Ysyd57753hw4cnSfr165ckeeeddzJp0qQ8/fTTqa2tzdKlSyv2HQAAAAA2RiDUgx122CFJ+zOETjjhhNx2223rnV+4cGFqamo2uG7KlCnZa6+9smDBgqxbty59+/bdIvUCAAAAbApLxj7C4YcfnkceeSTPP/98kuTdd9/N0qVLc9BBB2X58uV54oknkiRvv/121qxZkxUrVmTvvffONttsk1tuuSVr166tZPkAAAAAParuGUIVek38h/bYY49MmzYtZ511VlatWpUk+fGPf5wDDzwwd9xxR771rW/lvffey3bbbZfm5uZceOGF+fKXv5y77rorxx13XOdMIwAAAIBqUt2BUAXU1dXl2Wef7dz/3Oc+1zkTqKvhw4fnscceW+/YAQcckIULF3buX3HFFRv0OWrUqIwaNWozVA4AAABQGkvGAAAAAApGIAQAAABQMAIhAAAAgIIRCAEAAAAUjEAIAAAAoGAEQlvQySefnDfffDNJcu2116ahoSFf/epXc++99+bKK6/8yGtHjhyZJGltbc2tt9662WsFAAAAeq+qfu38oJsHlbW/Z857pqz9bapZs2Z1fr7uuuvym9/8JvX19UmSMWPGfOS1jz76aJL/DITOPvvszVcoAAAA0KuZIdTNO++8ky9+8YtpamrKwIEDc8cdd6Suri6XXHJJRowYkREjRuT5559Pkrzyyiv58pe/nOHDh2f48OF55JFHkiQrV67M+PHjM2jQoDQ2NmbGjBlJkrq6urz66qv5xje+kRdeeCFjxozJlClTMm3atEyaNClJ8pe//CWnnXZampqa0tTU1BkE7bjjjkmSyZMn5+GHH87gwYMzZcqUHH300Xn66ac76z/yyCOzcOHCLfZ7AQAAAFufqp4hVAn3339/9tlnn9x3331JkhUrVuSSSy5Jv3798vjjj2f69On5zne+k5kzZ+bb3/52/vZv/zZHHXVUXnrppZx44olpaWnJ5Zdfnv79++eZZ9pnJL3xxhvr3eOGG27I/fffn9mzZ2f33XfPtGnTOs9dfPHFOfbYY3P33Xdn7dq1Wbly5XrXXnnllbnqqqsyc+bMJMmuu+6aadOm5ac//WmWLl2aVatWpbGxcTP+QgAAAMDWzgyhbgYNGpTm5uZccsklefjhh9O/f/8kyVlnndX5d+7cuUmS5ubmTJo0KYMHD86YMWPy1ltv5e23305zc3Muuuiizj532WWXku//4IMP5pvf/GaSpLa2tvP+GzN27NjMnDkzq1evzk033ZRx48ZtytcFAAAACsgMoW4OPPDAzJs3L7Nmzcr3v//9jB49OklSU1PT2ebDz+vWrcvcuXOz3XbbrddHW1vbeu03p+233z4nnHBC7rnnntx555158sknt8h9AQAAgK2XGULdLF++PNtvv33OOeecfO9738v8+fOTJHfccUfn3yOOOCJJMnr06Pz85z/vvPbDZ/l0P959ydhHOf7443P99dcnSdauXZu33nprvfM77bRT3n777fWOXXDBBbn44oszfPjw7LrrriXfCwAAACgmgVA3zzzzTEaMGJHBgwfnJz/5SS699NIkyapVq3LYYYflmmuuyZQpU5K0vzr+ySefTGNjYw4++ODccMMNSZJLL700b7zxRgYOHJimpqbMnj275Ptfc801mT17dgYNGpRhw4Zl0aJF651vbGxMnz590tTU1FnHsGHD0q9fv4wfP74cPwEAAADQy9W0tbVV6t4b3LilpSUNDQ2VqOUj1dXV5cknn8zuu+9e6VJ6tHz58owaNSqLFy/ONtv0nPFV629bqrrJ91W6hKrT2vfsSpdQlQbV71/pEqrSnVesqXQJValhcUulSwB6IeOWDRm39My4pWfGLT0zbmETzEty6Mc1MkNoKzd9+vQcdthh+clPfrLRMAgAAACgKw+VLkFra2ulS9ioc889N+eee26lywAAAAC2IqaUAAAAABSMQAgAAACgYARCAAAAAAUjEAIAAAAoGIEQAAAAQMFU9VvGWg5qKGt/DYtbytofAAAAwNbIDKEKWbNmTaVLAAAAAApKINSDL33pSxk2bFgOOeSQTJ06NUly//33Z+jQoWlqasrxxx+fJFm5cmXGjx+fQYMGpbGxMTNmzEiS7Ljjjp19/epXv8q4ceOSJOPGjct3v/vdHHfccbnkkkvy+OOPZ+TIkRkyZEhGjhyZJUuWJEnWrl2b733ve539/uxnP8vvfve7nHbaaZ39PvDAAzn99NO3xM8BAAAA9DJVvWSsUm666absuuuuee+99zJ8+PCceuqpmTBhQh566KHU19fn9ddfT5Jcfvnl6d+/f5555pkkyRtvvPGxfS9dujTNzc2pra3NW2+9lYceeih9+vRJc3Nz/v7v/z4zZszI1KlT8+KLL+app55Knz598vrrr2eXXXbJRRddlFdeeSV77LFHfvnLX2b8+PGb9XcAAAAAeieBUA+uvfba3H333UmSl19+OVOnTs0xxxyT+vr6JMmuu+6aJGlubs7tt9/eed0uu+zysX2PHTs2tbW1SZIVK1bkvPPOy3PPPZeampqsXr26s99vfOMb6dOnz3r3+9rXvpZ/+Zd/yfjx4zN37txMnz69TN8YAAAAKBKBUDdz5sxJc3Nz5s6dm+233z6jRo1KU1NT53Kurtra2lJTU7PB8a7H3n///fXO7bDDDp2f/+Ef/iHHHXdc7r777rS2tmbUqFEf2e/48ePz13/91+nbt2/Gjh3bGRgBAAAAbArPEOpmxYoV2WWXXbL99ttn8eLFeeyxx7Jq1ar827/9W1588cUk6VwyNnr06Pz85z/vvPbDJWN77bVXWlpasm7dus6ZRhu717777pskmTZtWufx0aNH54Ybbuh88PSH99tnn32yzz775Mc//nHnc4kAAAAANlVVTzGpxGviTzrppNxwww1pbGzMZz/72Rx++OHZY489MnXq1Jx++ulZt25d9txzzzzwwAO59NJLc9FFF2XgwIGpra3ND3/4w5x++um58sorc8opp2S//fbLwIEDs3Llyh7v9Xd/93c577zzcvXVV+dzn/tc5/ELLrggS5cuTWNjY7bddttMmDAhkyZNSpJ89atfzSuvvJKDDz54i/weAAAAQO9T09bWVql7b3DjlpaWNDQ0VKKWrcakSZMyZMiQnH/++Zt03db+29ZNvq/SJVSd1r5nV7qEqjSofv9Kl1CV7rxiTaVLqEqV+McD0PsZt2zIuKVnxi09M27pmXELm2BekkM/rlFVzxBifcOGDcsOO+yQf/qnf6p0KQAAAMBWTCC0FZk3b16lSwAAAAB6gap7qHQFl7D1Wn5TAAAAoKuqCoT69u2b1157TYBRRm1tbXnttdfSt2/fSpcCAAAAVImqWjI2YMCALFu2LK+88kqlS+lV+vbtmwEDBlS6DAAAAKBKlBoInZTkmiS1Sf5vkiu7nf90kulJhiV5LckZSVo3tZhtt9029fX1m3oZAAAAAJuglCVjtUl+keQLSQ5OclbH367OT/JGkr9KMiXJ/ypjjQAAAACUUSmB0Igkzyd5IckHSW5Pcmq3Nqcmubnj86+SHJ+kpkw1AgAAAFBGpQRC+yZ5ucv+so5jG2uzJsmKJLt94uoAAAAAKLtSniHU00yf7q8BK6VNkkzs2JJkZZIlJdwf0nrlFytdQhVaUekCqtIzye5JXq10HVXnvEoXAFAcxi09MW7piXHLRhi3wCf1mVIalRIILUuyX5f9AUmWb6TNso4++yd5vYe+pnZsAJvLk0kOrXQRAAAlMG4BKqaUJWNPJDkgSX2STyU5M8m93drcm//Mcb+S5MH0PEMIAAAAgAorZYbQmiSTkvw27W8cuynJoiSXpT3RvjfJjUluSfvDp19Pe2gEAAAAQBWqaWszkQfoVSbG0lQAYOtg3AJUjEAIAAAAoGBKeYYQAAAAAL2IQAgAAACgYARCAAAAAAVTylvGAKrZQUlOTbJvkrYky9P+9sOWShYFAABQzcwQArZmlyS5PUlNkseTPNHx+bYkkytYFwDAphhf6QKA4vGWMWBrtjTJIUlWdzv+qSSLkhywxSsCANh0LyXZv9JFAMViyRiwNVuXZJ8kf+p2fO+OcwAA1WLhRo7XJNlrSxYCkAiEgK3bd5L8LslzSV7uOLZ/kr9KMqlSRQEA9GCvJCcmeaPb8Zokj275coCiEwgBW7P7kxyYZETaHypdk2RZ2p8ltLaCdQEAdDczyY5Jnu7h3JwtWwqAZwgBAAAAFI63jAEAAAAUjEAIAAAAoGAEQgAAAAAFIxACAAAAKBiBEAAAAEDB/H+2cQ4zeDL0hAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_= xai.metrics_imbalances(\n",
-    "        x_test, \n",
-    "        y_test, \n",
-    "        probabilities,\n",
-    "        columns=protected,\n",
-    "        categorical_cols=categorical_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_= xai.roc_imbalance(\n",
-    "    x_test, \n",
-    "    y_test, \n",
-    "    probabilities)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_= xai.roc_imbalances(\n",
-    "    x_test, \n",
-    "    y_test, \n",
-    "    probabilities, \n",
-    "    columns=protected,\n",
-    "    categorical_cols=categorical_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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q70Nw9oERZ3X61KvumcE0X39FzCnfsLvLdwM9b3EjJQoRkZYMw0wUo+eCw/+jru1J9D0i+/rjOezPKf7y873le7Fh61GPxoIG3ImInKw8HyoPwTnf63IRk84bRs4Hh6irdrP86R3EOGzMvX48ezx7OK3vaSQ6E4MYsPVUoxARaSmvc4/FtiU2wcE3/+c8rr53BgBej8H2VUXsLt/NhP4TghFlSClRiIi0lPcBpI6G/mO6XdSwcSnc+Zd5JPSNJWVYHEU1RUxIVaIQEem56o9D/kqY+JWgFXmisoG6KheNA08AqEYhItKj7X0PGl2QeUXQiiw9bK6rXbCxCrvX0eOeeAIlChGRZrlvQvJQSD8jaEUOSE8CwFsSyzc2PkySJyVoZYeKEoWICEBDjflYbOZXWw+A6Ibk/vF87WezKOt/GIc3Foez533t9ryIRUSskPcBeOoh68qgF504NIZSuzlR4PZVhUEv32pKFCIiALlLoc8gGHl20IveVb6LXWnrANixuijo5VtNiUJExF1ndmRPvDygRYo6K7csl8qEEgBqyhv8TkMeiZQoRET2fwzuE5AVvKedWtpZupPUlGSu/tF0AMq/OGHJdayiRCEisvMNiE+BUedZUvy20m1MHjiZ2AQnAGkjky25jlWUKESkd2uogd1vw6SrwO4MevGldaUU1RQxddBUSgqqABikRCEi0oPsegvctTBlkSXFby3ZCmAmikPVOOPtpKRpUkARkZ5j2xJIOQ1Gdn7tiUBsLdmKI8ZB5oBMiguqcdc38tqjmyg/eoJGt9eSawabEoWI9F5VRyB/FUxdBBatYb21eCtZ/bOIs8cxZV46AMWHqln8y8/44G+5llwz2JQoRKT32vYqYMCU6y0p3t3oZmfZTqYMmgLA+NlD+M4fs7n09snEOGzEJgT/UVwraOEiEemdDAO2/QPSZ8OAsZZcYk/FHhoaGyiqKeLprU83X7regdczmj0xW9m3dbUl125pCGXdOr8ziWI4cNop51j/G4qIWOGL7VCcC1953LJLGIZBbEwsKw6vYMXhFV9+Pqp8MvO5ndcqXqZ4yyHLrt/kwRhPt84PNFE8ClwP5AKNvs8MOk4U84EnADvwV+CRNo5ZCDzkK28rcGOAMYmIdN3WJRDjhEnXWHaJ0wedzoabNmBgnPT5xrcPsnlPAY9m/JGpF6Zji7Gmf6TJmy/P6Nb5gSaKq4AJQEMnyrYDTwIXA4XARmApZrJpkgH8DDgXqADSOlG+iEjXNLph+z9h/KWQ2N/SS9nbmBIkOSUBgM9eP0BsrPPLTu5IFWhndj7Q2ZEos4E837kuYAlw6rSMt2Mmkwrf++JOXkNEpPN2vwMnimH6f4Tl8pPnDmf+tycDkJAU/EF+wRZojaIW2AJ8xMm1iu/7OWc4cLjF+0LgzFOOGe/7uQazBvIQsDzAmEREumbTs9BvJGRcHLYQXHVmv0FtlStsMQQq0ESx1Ld1RluNbsYp7x2YzU/ZQDrwCTAZqDzluDt8m4hI95TugwOr4YL7LZkpNlDOOPPr99N/7iMh2cmoKQOJjY/MB1EDbXp6AVgMfO7b/u77zJ9CYESL9+nAqXPrFgJvAm7gALAHM3Gc6hlglm8TEem6Tc+Zndgzvh7WMMbNTGP4hFQAPngul/wtJWGNx59AE0U2sA+zP+HPwF5gbgfnbMT80h8NxAKLaF0reQOY53s9ELMpKj/AmEREOsdVC1teMZc7TQr/szOX3z2F7JsmAJCUEhfmaNoXaKJ4HLgEOB8zQVwK/K6DczzA3cB7wC7gVWAn8CugadL394AyzCehVgA/9r0XEQm+nf+G+uNwxm3hjgQAh9OOxzff06rFe8McTfsCbRBzYjYLNdlLYE9BLfNtLT3Q4rUB3OPbRESstfFZGDQRTjs33JF8acLsIXz66j5qKuoxDAObRXNOdUegNYpNwLOYTVDZwP9h9lWIiPQMR3LgyGaY9U3LJgDsivgkJ0mpcXhcXv712OcROaNsoIniu5jNRt8H/hOzqeg7VgUlIhJ0G/4KzkRzptgIc+nt5piKYweqOHawKszRtBZoomgA/h9wDXA1Zv9EZ0Zpi4iET9URcwLAaTdCfL9wR9PKkDH9OOuqMQD0HZgQ5mha66iP4lXMuZi203oMBMCUoEckIhJs6/8MhhfO+V64I2nX8ZI64vs46ZMSG+5QWukoUfyn7+flVgciImKJukrY9DxMuhpSR4U7mnYVH6yisdGL4TWw2SOnDwU6bno66vtZijkdxyEgDphK68FzIiKRZ9Oz4KqGc/+z42PDqLbKhbu+kZf+ax0njkdWy36gfRSrgXjM+Zs+Ar4BPG9RTCIiweGuh/VPwdgLYWhkt5R/9fvTAKipaOD5n65hya830FDXvXUkgiXQRGHDnBjwGuCPmB3aWVYFJSISFFv/bs4SO+cH4Y6kQ4NGJPOdP2Z/2aldVlRDdVl9mKMydSZRnA3cBLzj+ywyZ68SEQHwNsLaP8KwGTDqvHBHExC7M4aZ80dxxldGAfDRC7l8/OKu8AZF4F/2P8BcYOh1zPEUYzCn3BARiUy7lkJ5Pix8MaIG2AUifWIqxw5WU1pYza51R2n0mIPwUgYncsZXRoc8nkATxSrf1iQf/2tRiIiEV87L5s9XwztLbIcyvwrXv3zSR8MyUhmWkcqO1UXkfFDAFweqqKt24W00mLVgVMin+egoUfweszbxFm2Po7iijc9ERMLvnO/D8JnhjsK/NX8AT/sLF02eO5zJc4cD8O7T26k4eiIsc0F1lChe8v38X6sDEREJqjHnm1ukOl4Iqx4NOMaq0jr6DgrPqO2OEkXTxH+bgDqgabYqO+Z4ChER6YoDq82fozta2gcMw6Dii1oGDk+yOKi2BfrU00dAYov3CcCHwQ9HRKSXOLAaEgdA2qQOD/W4vDS6vexe/wU7PykKQXAnCzRRxAM1Ld7XcHLiEBGRQBkG5K8yaxMxHX8NO+PsnH+juRLemtfyQr5saqCJ4gQwo8X7mZhNUSIi0llleVB9JKBmpyaTzhvG9EtG4m5oDPlAvM6Mo/gnzfM7DQWutyQiEZFod8A32mB04J3tNpuNsTPSyHm/gOQB8RYF1rZAE8VGYCIwAXOU9m7AbVVQIiJRLX8V9E2H/mM6dVpVqdmQ0y/ETz8F2vSUCPwUc9rx7cAoNPW4iEjneb1w8BPzsdhOjoloShShrlEEmij+Brgw53sCKAR+Y0lEIiLR7Nh2qKvoVLNTk6qyeuKTnMTGh3aqvUATxVjgMZqbm+owm6BERKQz8pv6JwLvyG5SU1aPYRgU5JYFOSj/Ak0ULsyxE03TeIxFa2aLiHTegdUwcDz0HdrpU8fOTAMDPvnHPgsCa1+gieJBYDkwAngFcwDeT6wKSkQkKnlccGhtl2oTAFnnDqP/0D646j3szykOcnDtCyRRND3ldA1wK7AYmAWstCwqEZFodGQzuE90qX+iSVL/eGqrXKx7fX8QA/MvkB4RA3gDc5DdOx0cKyIi7clfBdhg1JwuF3HJbZOoKa8nxh66buJAm57WA2dYGYiISNQ7sNpcuzuxf7eKqSqrD+kjsoEminmYyWI/sA1zLMU2q4ISEYk6rloo3NCtZieARreXE8cbcNU1cqIyNM8UBfow7mWWRiEiEu0OrYVGV/cThcdLjM1G/pYSnHF2LvpGVpACbF9HNYp4zHmefgzMB4qAQy02EREJxK43ITapW/0TALEJDr7+8Dk4nDE44uwYRluLjwZXR4niBcwnnLZj1ioetzwiEZFo0+iGXW/BhAXg7H7fQlyCA4/by87VRXz0/K4gBOhfR4kiC7gZeBq4DjjP8ohERKJN/ipz2o5JVwelOEesnUtum0Ri31j2fPYFL/3XWqrLrZt6vKNE0XKGWE8Xyp8P7AHygPv8HHcd5mO4s7pwDRGRyLbz3xDXF8ZdGLQiM84YzJyvZQBQVVrPiz9fy6Ed1kzt0VFn9lSgyvfahjmNR5XvtQH09XOuHXgSuBhzEsGNwFIg95TjkoHvA591JnARkR7B44Jdb8PEr4AjLqhFZ5wxmBFZ/dm+spANbx3A67Wmv6KjGoUdMxn0xfxCd7R47S9JAMzGrEnkY84VtQS4so3jfo054WBol2wSEQmF/R9Dw3GYdI0lxcf3cTJoRDIAicmxllwj0HEUXTEcONzifaHvs5amY84f9baFcYiIhM/O1yE+BcZkW3aJ2moXAAl9nZaUb+Wk5m2NL29ZL4oBfoc5f1RH7vBtbXK73RQWFlJfH7mVkvj4eNLT03E6rfkPKSIRyF0Pu9+BSVeCw5q/9gFqq8xEYVWNwspEUYhZW2iSTvOa22A2X02meXLBIZh9GFcAm04p6xnfBicnG/NChYUkJyczatQobJ1cMSoUDMOgrKyMwsJCRo8eHe5wRCRUCjeAqxpyXoEtiy27TN3xW3HaLsDx8KA29zuNwd0q38pEsRHIAEZjDtRbBNzYYv9xYGCL9yuBe2mdJDpUX18fsUkCzEXRBwwYQElJSbhDEZFQGjoVLrgf3HWWXqZu/XgcxQ6OTHyAoQOqW6+w+unfu1W+lYnCA9wNvIfZKf4csBP4FWYyWBrMi0VqkmgS6fGJiAXi+8Hcey2/jHf/duoOl/D6iilcdc90ho9PPfmAR7qXKKzszAZYBozHXBHvt77PHqDtJJFNF2oTkWT58uVMmDCBcePG8cgjj4Q7HBHpJS74j0zOvmYsAHZH8L/WrU4UvUZjYyN33XUX7777Lrm5uSxevJjc3FOHjIiIBF9sgoOEJLMjO8GCDm0liiDZsGED48aNY8yYMcTGxrJo0SLefPPNcIclIr1EXdMjssnBf7LSyj6KsPjlWzvJPVLV8YGdkDWsLw9+dZLfY4qKihgxovkhr/T0dD77TIPNRSQ0aqtdOGJjiI0P/te6ahRB0tZUv+rAFpFQqat2WdLsBFFYo+joL3+rpKenc/hw80D0wsJChg0bFpZYRKT3qat2W5YoVKMIkjPOOIN9+/Zx4MABXC4XS5Ys4Yorrgh3WCLSS9RWuUjsq0QR0RwOB3/605+49NJLyczMZOHChUyaFJ7ajYj0PvU1bgp3lfPKg+upPFYb1LKjrukpnBYsWMCCBQvCHYaI9EKzFoziwJYSCnLLqa6oJ2VwYtDKVqIQEYkCk+cOJyHZSUFuOQlJwX1EVk1PIiJRor7GXJR036ZiCvdUBK1cJQoRkSgRn+QEG2xefoiVr+wOWrlKFCIiUWLs9DTueOJ8Tps8AIfTHrRylShERKKIM9aOu6GR2AQlChERaYer3oO30aBwdzmuek+3y1OiCJJvfvObpKWlMXny5HCHIiK9nM1m49iBKt78/RY+ezO/2+UpUQTJrbfeyvLly8MdhogIX7lrClf/aDrOeDteb+t56DpL4yiCZO7cuRw8eDDcYYiI0KdfHH36xWE0Gjhju99XEX2J4t374IvtwS1zyOlwmVasE5Gew/AaeNxeHLHdbzhS05OISBTyuL0AOFSjaIP+8hcRweNqBIKTKFSjEBGJQm5fonDGqekpYtxwww2cffbZ7Nmzh/T0dJ599tlwhyQivZjHpaaniLN48eJwhyAi8iU1PYmIiF/NiUJNTyIi0ga3r+kpGOMolChERKKQmp5ERMSv5s5sNT2JiEgb3A2+x2NVoxARkbY0TS/ujFeiiBiHDx9m3rx5ZGZmMmnSJJ544olwhyQivZir1oMtxoYzTuMoIobD4eDxxx9nxowZVFdXM3PmTC6++GKysrLCHZqI9EINdR7iEhzYbLZul6UaRZAMHTqUGTNmAJCcnExmZiZFRUVhjkpEequGWg+xicGpC0RdjeLRDY+yu3x3UMuc2H8iP53904CPP3jwIDk5OZx55plBjUNEJFAuX40iGKyuUcwH9gB5wH1t7L8HyAW2AR8Bp1kcj+Vqamq49tpr+f3vf0/fvn3DHY6I9FINtR5ig5QorKxR2IEngYuBQmAjsBQzMTTJAWYBtcB3gceA67tz0c785R9sbreba6+9lptuuolrrrkmbHGIiLjqPaT0SwxKWVbWKGZj1iTyARewBLjylGNWYCYJgPVAuoXxWMowDG677TYyMzO55557wh2OiPRyDbU9o+lpOHC4xftC32e+f1qcAAAJKElEQVTtuQ14t519dwCbfFtEWrNmDS+99BIff/wx06ZNY9q0aSxbtizcYYlIL9VQ1zM6s9t6Jsto59ibMZugzm9n/zO+zV8ZYTVnzhwMIyJDE5FeptHjxdPQGLQahZWJohAY0eJ9OnCkjeMuAn6BmSQaLIxHRKRXqD/hBmDbikL2fPYFJ0Y80K3yrEwUG4EMYDRQBCwCbjzlmOnA05hPRxVbGIuISK+RmBzL1AtGUFvtAiDvi5RulWdlovAAdwPvYT4B9RywE/gVZl/DUuB/gCTgn75zCoArLIxJRCTq2WJszFmY8eX7/LW5fo7umNUD7pb5tpZa1oEusvj6IiLSTZrCQ0RE/FKiEBERv5QogqS+vp7Zs2czdepUJk2axIMPPhjukEREgiLqJgUMl7i4OD7++GOSkpJwu93MmTOHyy67jLPOOivcoYmIdItqFEFis9lISkoCzDmf3G53UOaBFxEJt6irUXzx3/9Nw67gTjMelzmRIT//eYfHNTY2MnPmTPLy8rjrrrs0zbiIRAXVKILIbrezZcsWCgsL2bBhAzt27Ah3SCIi3RZ1NYpA/vK3WkpKCtnZ2SxfvpzJkyeHOxwRkW5RjSJISkpKqKysBKCuro4PP/yQiRMnhjkqEZHui7oaRbgcPXqUW265hcbGRrxeLwsXLuTyyy8Pd1giIt2mRBEkU6ZMIScnJ9xhiIgEnZqeRETELyUKERHxS4lCRET8UqIQERG/lChERMQvJQoREfFLiSLIGhsbmT59usZQiEjUUKIIsieeeILMzMxwhyEiEjRKFEFUWFjIO++8w7e+9a1whyIiEjRRNzL7k1f3Unq4JqhlDhyRxHkLx3d43A9+8AMee+wxqqurg3p9EZFwUo0iSN5++23S0tKYOXNmuEMREQmqqKtRBPKXvxXWrFnD0qVLWbZsGfX19VRVVXHzzTfz8ssvhyUeEZFgUY0iSB5++GEKCws5ePAgS5Ys4YILLlCSEJGooEQhIiJ+RV3TUyTIzs4mOzs73GGIiASFahQiIuKXEoWIiPilRCEiIn5FTaIwDCPcIfgV6fGJiLQnKhJFfHw8ZWVlEftlbBgGZWVlxMfHhzsUEZFOi4qnntLT0yksLKSkpCTcobQrPj6e9PT0cIchItJpVieK+cATgB34K/DIKfvjgBeBmUAZcD1wsLMXcTqdjB49uluBiohI26xserIDTwKXAVnADb6fLd0GVADjgN8Bj1oYj4iIdIGViWI2kAfkAy5gCXDlKcdcCbzge/0acCFgszAmERHpJCsTxXDgcIv3hb7P2jvGAxwHBlgYk4iIdJKVfRRt1QxOfSwpkGMA7vBtAA3Ajm7EFU0GAqXhDiJC6F40071opnsBfOfZrwFM6Or5ViaKQmBEi/fpwJF2jin0xdIPKG+jrGd8G8AmYFZQI+25dC+a6V40071opnvRbFNXT7Sy6WkjkAGMBmKBRcDSU45ZCtzie30d8DFt1yhERCRMrKxReIC7gfcwn4B6DtgJ/Aozsy0FngVewuz0LsdMJiIiEkGsHkexzLe19ECL1/XA1zpZ5jMdH9Jr6F40071opnvRTPeiWZfvhS1Sp70QEZHIEBVzPYmIiHUiOVHMB/Zg9l/c18b+OOAfvv2fAaNCFlnodXQv7gFygW3AR8BpoQst5Dq6F02uw3wwIpqfeAnkXizE/H9jJ/D3EMUVDh3di5HACiAH89/JgtCFFlLPAcW0P4TABvwB8z5tA2YEVKphGJG42Q3D2G8YxhjDMGINw9hqGEbWKcfcaRjGU77XiwzD+EcExB2uezHPMIxE3+vv9vJ7gWEYyYZhrDYMY71hGLMiIO5w3YsMwzByDMNI9b1Pi4C4w3UvnjHMfxv49h2MgLit2OYahjHDMIwd7exfYBjGu4Zh2AzDOMswjM8CKTdSaxSa/qNZIPdiBVDre70ec8xKNArkXgD8GngM82GJaBXIvbgdc761Ct/74pBFF1qB3AsD6Ot73Y/WY7qixWraHovW5ErMiVgNzO+KFGBoR4VGaqLQ9B/NArkXLd0GvGtpROETyL2YjjmI8+1QBRUmgdyL8b5tDeaXwvzQhBZygdyLh4CbffuWAd8LSWSRp7PfJ0DkrkcRzOk/errO/J43Y7bJn29dOGHV0b2IwZyF+NaQRBNegfx/4cAc9JqNWcv8BJgMVFoaWegFci9uAJ4HHgfOxhy/NRnwWhpZ5OnS92ak1ig6M/0H+J/+o6cL5F4AXAT8ArgCcz6saNTRvUjG/Me/EnNdk7MwB3ZGY4d2oP9G3gTcwAHMzt6MkEQXWoHci9uAV32v1wHxmPNA9TaBfp+cJFIThab/aBbIvZgOPI2ZJKK1HRo6vhfHMf/xj/Jt6zHvSZfnuIlggfx/8QYwz/d6IGYzVH6oAgyhQO5FAWY/JkAmZqKI3CUxrbMU+DpmzeIszH8zRzs6KVKbnjT9R7NA7sX/AEnAP33nFGB+QUabQO5FbxHIvXgPuATz8dhG4MeYK0lGm0DuxY+A/wN+iPkH5a1E5x+WizGbGgdi1h4eBJy+fU9h9s8swPzerAW+EUihGpktIiJ+RWrTk4iIRAglChER8UuJQkRE/FKiEBERv5QoRETELyUKkdYagS2YM3C+hTkfTjDdCvzJ9/oh4N4gly8SVEoUIq3VAdMwR3mXA3eFNxyR8FKiEPFvHSdPmvZjzJHA24Bftvj8677PtmIOBAX4KuZaKTnAh8Bgq4MVsUKkjswWiQR2zGkfnvW9vwRzqojZmFMgLAXmYo52/gVwLlAK9Pcd/ynmNAkG8C3gJ5gjhEV6FCUKkdYSMPsoRgGfAx/4Pr/Et+X43idhJo6pmGuilPo+b5qcMh1zFcahmHMQHbA4bhFLqOlJpLWmPorTML/gm/oobMDDvn3TgHGYtQ0bbc8b9EfMTuvTgW9jTkQn0uMoUYi07zjwfcynkpyYk859E7MmAWbfRRrmOuULaV44q6npqR9Q5HvdNNOxSI+jpicR/3IwO6gXYXZSZ2J2cAPUYC4WtRP4LbAK89HaHMxHYB/CnNG3CHPK89GhC1skeDR7rIiI+KWmJxER8UuJQkRE/FKiEBERv5QoRETELyUKERHxS4lCRET8UqIQERG/lChERMSv/w/omO+nYJS0KQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_= xai.pr_imbalances(\n",
-    "    x_test, \n",
-    "    y_test, \n",
-    "    probabilities, \n",
-    "    columns=protected,\n",
-    "    categorical_cols=categorical_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/alejandro/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  self._setitem_with_indexer(indexer, value)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d = xai.smile_imbalance(\n",
-    "    y_test, \n",
-    "    probabilities)\n",
-    "\n",
-    "# d.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/alejandro/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  self._setitem_with_indexer(indexer, value)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d = xai.smile_imbalance(\n",
-    "    y_test, \n",
-    "    probabilities,\n",
-    "    display_breakdown=True)\n",
-    "# d.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/alejandro/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  self._setitem_with_indexer(indexer, value)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d = xai.smile_imbalance(\n",
-    "    y_test, \n",
-    "    probabilities,\n",
-    "    bins=9,\n",
-    "    threshold=0.75,\n",
-    "    manual_review=0.375,\n",
-    "    display_breakdown=False)\n",
-    "# d.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "age\n",
-      "workclass\n",
-      "education\n",
-      "education-num\n",
-      "marital-status\n",
-      "occupation\n",
-      "relationship\n",
-      "ethnicity\n",
-      "gender\n",
-      "capital-gain\n",
-      "capital-loss\n",
-      "hours-per-week\n"
-     ]
-    }
-   ],
-   "source": [
-    "for c in x.columns: \n",
-    "    print(c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "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>age</th>\n",
-       "      <th>workclass</th>\n",
-       "      <th>education</th>\n",
-       "      <th>education-num</th>\n",
-       "      <th>marital-status</th>\n",
-       "      <th>occupation</th>\n",
-       "      <th>relationship</th>\n",
-       "      <th>ethnicity</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>capital-gain</th>\n",
-       "      <th>capital-loss</th>\n",
-       "      <th>hours-per-week</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.028167</td>\n",
-       "      <td>-0.0015</td>\n",
-       "      <td>-0.00125</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>0.092333</td>\n",
-       "      <td>0.0175</td>\n",
-       "      <td>0.01975</td>\n",
-       "      <td>0.002083</td>\n",
-       "      <td>0.005917</td>\n",
-       "      <td>0.044917</td>\n",
-       "      <td>0.006917</td>\n",
-       "      <td>0.01225</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        age  workclass  education  education-num  marital-status  occupation  \\\n",
-       "0  0.028167    -0.0015   -0.00125          0.037        0.092333      0.0175   \n",
-       "\n",
-       "   relationship  ethnicity    gender  capital-gain  capital-loss  \\\n",
-       "0       0.01975   0.002083  0.005917      0.044917      0.006917   \n",
-       "\n",
-       "   hours-per-week  \n",
-       "0         0.01225  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "dark"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def get_avg(x, y):\n",
-    "    return model.evaluate(f_in(x), y, verbose=0)[1]\n",
-    "\n",
-    "imp = xai.feature_importance(x_test, y_test, get_avg)\n",
-    "\n",
-    "imp.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/XAI Tabular Data Example Usage.ipynb b/examples/XAI Tabular Data Example Usage.ipynb
new file mode 100644
index 0000000..777501d
--- /dev/null
+++ b/examples/XAI Tabular Data Example Usage.ipynb	
@@ -0,0 +1,14384 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys, os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Use below for charts in dark jupyter theme\n",
+    "params = {\"ytick.color\" : \"w\",\n",
+    "          \"xtick.color\" : \"w\",\n",
+    "          \"axes.labelcolor\" : \"w\",\n",
+    "          \"axes.edgecolor\" : \"w\"} \n",
+    "plt.rcParams.update(params)\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "import xai\n",
+    "import xai.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from importlib import reload\n",
+    "xai = reload(xai)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "csv_path = 'data/adult.data'\n",
+    "categorical_cols = [\"gender\", \"workclass\", \"education\", \"education-num\", \"marital-status\",\n",
+    "                   \"occupation\", \"relationship\", \"ethnicity\", \"native-country\", \"loan\"]\n",
+    "csv_columns = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\",\n",
+    "                   \"occupation\", \"relationship\", \"ethnicity\", \"gender\", \"capital-gain\", \"capital-loss\",\n",
+    "                   \"hours-per-week\", \"native-country\", \"loan\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>ethnicity</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>39</td>\n",
+       "      <td>State-gov</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>50</td>\n",
+       "      <td>Self-emp-not-inc</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>53</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>11th</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Wife</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          workclass   education  education-num       marital-status  \\\n",
+       "0   39          State-gov   Bachelors             13        Never-married   \n",
+       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
+       "2   38            Private     HS-grad              9             Divorced   \n",
+       "3   53            Private        11th              7   Married-civ-spouse   \n",
+       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
+       "\n",
+       "           occupation    relationship ethnicity   gender  capital-gain  \\\n",
+       "0        Adm-clerical   Not-in-family     White     Male          2174   \n",
+       "1     Exec-managerial         Husband     White     Male             0   \n",
+       "2   Handlers-cleaners   Not-in-family     White     Male             0   \n",
+       "3   Handlers-cleaners         Husband     Black     Male             0   \n",
+       "4      Prof-specialty            Wife     Black   Female             0   \n",
+       "\n",
+       "   capital-loss  hours-per-week  loan  \n",
+       "0             0              40  True  \n",
+       "1             0              13  True  \n",
+       "2             0              40  True  \n",
+       "3             0              40  True  \n",
+       "4             0              40  True  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = xai.data.load_census()\n",
+    "df[\"loan\"] = df[\"loan\"].apply(lambda x: x == \" <=50K\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = \"loan\"\n",
+    "protected = [\"ethnicity\", \"gender\", \"age\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ims = xai.show_imbalances(df, protected)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "im = xai.show_imbalance(df, \"gender\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bal_df = xai.balance(df, \"gender\", upsample=0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "im = xai.show_imbalance(bal_df, \"gender\", cross=[\"loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bal_df = xai.balance(df, \"gender\", cross=[\"loan\"], upsample=1)\n",
+    "im = xai.show_imbalance(bal_df, \"gender\", cross=[\"loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1200, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "proc_df = xai.normalize_numeric(bal_df)\n",
+    "proc_df = xai.convert_categories(proc_df)\n",
+    "x = proc_df.drop(\"loan\", axis=1)\n",
+    "y = proc_df[\"loan\"]\n",
+    "\n",
+    "x_train, y_train, x_test, y_test = xai.balanced_train_test_split(\n",
+    "            x, y, cross=[\"gender\"], \n",
+    "            categorical_cols=categorical_cols, min_per_class=300,\n",
+    "            fallback_type=\"half\")\n",
+    "\n",
+    "print(x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_test = x_test.copy()\n",
+    "df_test[\"loan\"] = y_test\n",
+    "\n",
+    "_= xai.show_imbalance(df_test, \"gender\", cross=[\"loan\"], categorical_cols=categorical_cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report, mean_squared_error, roc_curve, auc\n",
+    "\n",
+    "from keras.layers import Input, Dense, Flatten, \\\n",
+    "    Concatenate, concatenate, Dropout, Lambda\n",
+    "from keras.models import Model, Sequential\n",
+    "from keras.layers.embeddings import Embedding\n",
+    "\n",
+    "def build_model(X):\n",
+    "    input_els = []\n",
+    "    encoded_els = []\n",
+    "    dtypes = list(zip(X.dtypes.index, map(str, X.dtypes)))\n",
+    "    for k,dtype in dtypes:\n",
+    "        input_els.append(Input(shape=(1,)))\n",
+    "        if dtype == \"int8\":\n",
+    "            e = Flatten()(Embedding(X[k].max()+1, 1)(input_els[-1]))\n",
+    "        else:\n",
+    "            e = input_els[-1]\n",
+    "        encoded_els.append(e)\n",
+    "    encoded_els = concatenate(encoded_els)\n",
+    "\n",
+    "    layer1 = Dropout(0.5)(Dense(100, activation=\"relu\")(encoded_els))\n",
+    "    out = Dense(1, activation='sigmoid')(layer1)\n",
+    "\n",
+    "    # train model\n",
+    "    model = Model(inputs=input_els, outputs=[out])\n",
+    "    model.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "def f_in(X, m=None):\n",
+    "    \"\"\"Preprocess input so it can be provided to a function\"\"\"\n",
+    "    if m:\n",
+    "        return [X.iloc[:m,i] for i in range(X.shape[1])]\n",
+    "    else:\n",
+    "        return [X.iloc[:,i] for i in range(X.shape[1])]\n",
+    "\n",
+    "def f_out(probs, threshold=0.5):\n",
+    "    \"\"\"Convert probabilities into classes\"\"\"\n",
+    "    return list((probs >= threshold).astype(int).T[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "38022/38022 [==============================] - 1s 34us/step - loss: 0.5471 - acc: 0.7482\n",
+      "Epoch 2/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.3625 - acc: 0.8392\n",
+      "Epoch 3/50\n",
+      "38022/38022 [==============================] - 0s 7us/step - loss: 0.3088 - acc: 0.8608\n",
+      "Epoch 4/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2965 - acc: 0.8641\n",
+      "Epoch 5/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2894 - acc: 0.8661\n",
+      "Epoch 6/50\n",
+      "38022/38022 [==============================] - 0s 7us/step - loss: 0.2879 - acc: 0.8684\n",
+      "Epoch 7/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2863 - acc: 0.8700\n",
+      "Epoch 8/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2842 - acc: 0.8698\n",
+      "Epoch 9/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2829 - acc: 0.8707\n",
+      "Epoch 10/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2821 - acc: 0.8712\n",
+      "Epoch 11/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2827 - acc: 0.8700\n",
+      "Epoch 12/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2819 - acc: 0.8708\n",
+      "Epoch 13/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2810 - acc: 0.8725\n",
+      "Epoch 14/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2809 - acc: 0.8722\n",
+      "Epoch 15/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2807 - acc: 0.8718\n",
+      "Epoch 16/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2797 - acc: 0.8724\n",
+      "Epoch 17/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2804 - acc: 0.8729\n",
+      "Epoch 18/50\n",
+      "38022/38022 [==============================] - 0s 9us/step - loss: 0.2798 - acc: 0.8726\n",
+      "Epoch 19/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2788 - acc: 0.8724\n",
+      "Epoch 20/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2789 - acc: 0.8721\n",
+      "Epoch 21/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2786 - acc: 0.8723\n",
+      "Epoch 22/50\n",
+      "38022/38022 [==============================] - 0s 7us/step - loss: 0.2783 - acc: 0.8735\n",
+      "Epoch 23/50\n",
+      "38022/38022 [==============================] - 0s 7us/step - loss: 0.2783 - acc: 0.8733\n",
+      "Epoch 24/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2776 - acc: 0.8730\n",
+      "Epoch 25/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2781 - acc: 0.8727\n",
+      "Epoch 26/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2784 - acc: 0.8723\n",
+      "Epoch 27/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2782 - acc: 0.8738\n",
+      "Epoch 28/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2771 - acc: 0.8735\n",
+      "Epoch 29/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2769 - acc: 0.8738\n",
+      "Epoch 30/50\n",
+      "38022/38022 [==============================] - 0s 7us/step - loss: 0.2773 - acc: 0.8744\n",
+      "Epoch 31/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2775 - acc: 0.8740\n",
+      "Epoch 32/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2768 - acc: 0.8736\n",
+      "Epoch 33/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2771 - acc: 0.8749\n",
+      "Epoch 34/50\n",
+      "38022/38022 [==============================] - 0s 9us/step - loss: 0.2770 - acc: 0.8732\n",
+      "Epoch 35/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2768 - acc: 0.8735\n",
+      "Epoch 36/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2768 - acc: 0.8742\n",
+      "Epoch 37/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2772 - acc: 0.8734\n",
+      "Epoch 38/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2774 - acc: 0.8739\n",
+      "Epoch 39/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2759 - acc: 0.8747\n",
+      "Epoch 40/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2757 - acc: 0.8743\n",
+      "Epoch 41/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2766 - acc: 0.8744\n",
+      "Epoch 42/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2759 - acc: 0.8749\n",
+      "Epoch 43/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2764 - acc: 0.8738\n",
+      "Epoch 44/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2760 - acc: 0.8747\n",
+      "Epoch 45/50\n",
+      "38022/38022 [==============================] - 0s 9us/step - loss: 0.2754 - acc: 0.8739\n",
+      "Epoch 46/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2753 - acc: 0.8755\n",
+      "Epoch 47/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2751 - acc: 0.8756\n",
+      "Epoch 48/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2747 - acc: 0.8752\n",
+      "Epoch 49/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2759 - acc: 0.8746\n",
+      "Epoch 50/50\n",
+      "38022/38022 [==============================] - 0s 8us/step - loss: 0.2754 - acc: 0.8744\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f24b81a2828>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = build_model(proc_df.drop(\"loan\", axis=1))\n",
+    "\n",
+    "model.fit(f_in(x_train), y_train, epochs=50, batch_size=512)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1200/1200 [==============================] - 0s 241us/step\n",
+      "Error 0.4962: \n",
+      "Accuracy 76.7500: \n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(f_in(x_test), y_test, verbose=1)\n",
+    "print(\"Error %.4f: \" % score[0])\n",
+    "print(\"Accuracy %.4f: \" % (score[1]*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "probabilities = model.predict(f_in(x_test))\n",
+    "pred = f_out(probabilities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_= xai.metrics_imbalance(\n",
+    "        x_test, \n",
+    "        y_test, \n",
+    "        probabilities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_= xai.metrics_imbalances(\n",
+    "        x_test, \n",
+    "        y_test, \n",
+    "        probabilities,\n",
+    "        columns=protected,\n",
+    "        categorical_cols=categorical_cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  max_open_warning, RuntimeWarning)\n"
+     ]
+    },
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded while calling a Python object",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-188-b04ecd14ca1f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     probabilities)\n\u001b[0m",
+      "\u001b[0;32m~/Programming/ethical/xai/xai/__init__.py\u001b[0m in \u001b[0;36mroc_imbalance\u001b[0;34m(x_df, y_valid, y_pred, col_name, cross, categorical_cols, bins, plot)\u001b[0m\n\u001b[1;32m    647\u001b[0m         \u001b[0mcross_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcross_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m         \u001b[0mcategorical_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m         bins=bins)\n\u001b[0m\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    651\u001b[0m def _curve(\n",
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+      "\u001b[0;32m~/Programming/ethical/xai/xai/__init__.py\u001b[0m in \u001b[0;36m_curve\u001b[0;34m(actual, predicted, curve_type, df, cross_cols, categorical_cols, bins, plot)\u001b[0m\n\u001b[1;32m    704\u001b[0m         \u001b[0mgroup_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"predicted\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 706\u001b[0;31m         \u001b[0mr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurve_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    707\u001b[0m         \u001b[0mr1s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m         \u001b[0mr2s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "... last 2 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m~/Programming/ethical/xai/xai/__init__.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(actual, predicted, df, cross_cols, categorical_cols, bins, plot)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \u001b[0mcross_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcross_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m         \u001b[0mcategorical_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m         bins=bins)\n\u001b[0m\u001b[1;32m    632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m def pr_curve(\n",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
+     ]
+    },
+    {
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+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "_= xai.roc_imbalance(\n",
+    "    x_test, \n",
+    "    y_test, \n",
+    "    probabilities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_tmp = x_test.copy()\n",
+    "df_tmp[\"target\"] = y_test\n",
+    "df_tmp[\"predicted\"] = probabilities\n",
+    "from xai import group_by_columns\n",
+    "p = group_by_columns(df_tmp, [\"target\", \"gender\"], bins=6,\n",
+    "                    categorical_cols=categorical_cols+[\"target\"])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = xai.roc_plot(y_test, probabilities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = [xai.roc(\n",
+    "    y_test, \n",
+    "    probabilities, \n",
+    "    df=x_test, \n",
+    "    cross_cols=[p],\n",
+    "    categorical_cols=categorical_cols) for p in protected]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_= xai.pr_plot(y_test, probabilities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KwvABSRRCiF5p+lXDSEyNYu+6AspO1FKcV03FqTpJFB2ot9dTWl9K/+D+GPQGj5xT2qEJIXqtQaNimP/TMcxaNByA9cuzfBxR7/VR9kec//b5TH5zMpe+fynP7HjGY+eWRCGE6PViEkMBKDpWxWv/853M3d3KdcOvY3L8ZC4bfBn3jL+HYGMwFQ1tDZvXNfLoSQjR6wWHm1nw83FkfpLD8ewKNn98hLEXDyRhaISvQ+sV7p105hDnb+x/w6PnlxKFEMIvDEiLZu6tGeh0cHh7Eds/O+rrkPoMSRRCCL8RFm3ltr/PJDoxhFO5lXz4xDaqyxp8HVavtLNoJ7/d8Fvu/OxOrvzwym6dSxKFEMKvWIJN9BsYRk1FIwWHynn1wQ3sXVeAUjJVTZOEkAQOlR3iu4LvqLHVMDRyaLfOJ3UUQgi/c9GSdGbfOJJX//c76iob+ebNgxzPrmDQ6BhCIy19fqyoN+a/gUJh0ps6PrgTJFEIIfySwaTnx49Op7K4jo//tYODm05wcNMJAC6+JZ3+KeF9duhyo96zt3ZJFEIIv2WyGIhJCuWa+ydx6mglR3YUcWjTSb54eR8ANz4yjch+fTNZeJIkCiGE3wuLthIWbWXo+H5kzEhiw/vZnMqt5M2HNpI8Mooh4+IYPTvZ12H6LanMFkIElMTUSK759UT6pYSjN+rIP1DG2mWHyNpy0teh+S0pUQghAo5er+O6ByahlOLgxhN8+ep+PnthLxFxQfQbFO7r8PyOt0sU84CDQDbwQBv7BwJfA9uBXcB8L8cjhOhDdDodI6cnNE+UtPzPW8jKlJLFufJmicIAPAPMBfKBTGAFsK/FMb8B3gX+DaQDq4AUL8YkhOiDLl6SRtzAML57P5u1yw7xxcv7GJgRzaDRsQxMjyY8NsjXIfZq3ixRTEErSRwBGoFlQOvugQpoKgdGAIVejEcI0UfpDXrGXTyAoRP6UV9jw+lU5O4u4du3DpK5KtfX4fV63ixRJAF5Lbbzgamtjvkd8BnwMyAEuLidc93pWoQQokt0Oh3z7hwFgMPupOhYFWue34PT7vRxZL2fN0sUujZea93HfhHwCpCMVj/xejsxPQ9Mci1CCNEtBqOe+CERGEzS8LMzvHmV8oEBLbaTOfvR0m1odRQA3wNWINaLMQkhRDOn3UlJQTXKKeNEuePNRJEJpAKDATOwEK0yu6VjwEWu9TS0RFHkxZiEEKKZ06EoKagh/0CZr0Pp1byZKOzA3cCnwH60ksNe4BFggeuYXwF3ADuBt4ElnP14SgghvOL864YB0FBn93EkvZu3O9ytci0tPdRifR9wvpdjEEKINjVNsSpDlLsnPbOFEH2Xq8nNZy/s5fsPDjP2wgEYTHpKj9cQmxxK+vmJvo2vl5BEIYTos6LiQxiQHs3x7HKqSupZvzyreV9EvyBJFC6SKIQQfZZer2PBPeMAOLKjCL1BR3RCCN9/eJjsLafY+WUeQ8bHERZt9XGkviWNiIUQAhgyLo6U0bGExwaRMlprpb9+eRYfPrHNx5H5npQohBCilRFT47EEG9n+2TFKC2t8HY7PSYlCCCHakDI6lpjEEABqKxvJP1BKTUWDj6PyDSlRCCGEG/U1Nl7+9XoA9EYdEy4ZhN3mJG16AsERZoxmPUaTwcdRepckCiGEaMfwqfE4nYqo+BDWL8/CaVdscY02u+PzY83HDR4bi8GoZ9ai4QSFmn0UrfdIohBCiHbED4kgfkgEAMMm9qO2shFrqImdX+ZRXdZARVEtxXnV5OwsBiDtvAQGZsT4MmSvkEQhhBCdEBJpISTSAsCM61KbX2+otVFaWMN//ha4raOkMlsIIbrBEmxCp9e6eBflVfk4Gu+QRCGEEN1ksmiV2Rs/PMKhzBM+jsbzJFEIIUQ3RSeGMHXBYAA+f3EfJQXVPo7IsyRRCCFEN+l0OibNH0zGTG1sqP0bjvs4Is+SRCGEEB4yaX4KADu/ymPnl3m+DcaDJFEIIYSHhEZZmbVwOAA5uwJnsk5JFEIIr2u0O6lrdPg6jB4xenYy/VLCKThYHjCtoKQfhRDC45RSZJ2qZl1WMeuzith4pJToEDMbHrjQ16H1iKj4YE7lVvLuHzOZs3gk6TP8e14LbyeKecCTgAF4AXis1f5/AHNc68FAPyDSyzEJIbygqKqBDdnFWnLILuJkpTaA3pDYEJKigjhWWuvjCHvOxUvSMZj07FtXSOYnOUTGB5M4zH9vbd5MFAbgGWAukA9kAivQ5slu8ssW6z8DxnsxHiGEB9XbHGzOKWV9djFrDxVx4IT2mCUq2MR5w2KZlRrLjNQ4kiKDeGz1AV7akOPjiHvWnBtHUl9l48iOIj742zZufGQakf2CfR1Wl3gzUUwBsoEjru1lwJWcmShaWgQ87MV4hBDd4HQq9h2vZH12MeuyisjMLaPR7sRs0DNxUBT3XTqCWalxZCSGo3f1VO7rpv1wCMERZvZ8W8DHT+3kgkXDOZVbhd3mQKfXkTI6FqNJT3RCSHPvbk+rtzk4dLKKMcldL9F4M1EkAS3bh+UDU9s5dhAwGPiqnf13uhYhRA86XlHnqmcoZkN2MSU1jQCM6B/Gj6cNYmZqLFMGRxNslurOtkTFhzBpfgp7vi2gsqiOj/+184z9Wz7JbV6ffMVgYhJDGNqNzyuvbWRfYSV7CyvZd7ySvYUVHC6qweFU5D52eZfP683/3bbSo2rn2IXAe0B7zSKedy3uziGE6KbqBjubjpSwLksrNRwu0mZ3iw21MGt4HDNTY5kxLJZ+4X17DulzERJhYdHDU8nbX0pMUijWECO2Bid5+0pApyNzpfZILnNlDpH9g4mLicRgCSLEzTmVUhSU12kJwZUY9h+vpKC8rvmY+HAr6YnhXJoRT3pCeLf+DR0lins72P+Em335wIAW28lAYTvHLgSWdvBZQggPczgVu/LLWZ+lVUJvO1aG3amwmvRMGRzDwskDmTk8lhH9w9Dp5HFSV0UnhBCdcOatP2GoNnz5qFlJVJbUsW3NUXJ2FvP6yV8AEJmzkcv/awwhsVYOF1WfLim4SgsVdTYAdDqtwcDEQVH8ePogMhLDSUsIJzbU4rH4O0oUYd04dyaQivZIqQAtGdzQxnEjgCjg+258lhCik46V1LIuu6j5cVJlvR2AUUnh3D5zCDNTY5k4KAprgM/a1lsEh5sJDjczZk4ylnAzldvWUtbYj/KTcO+LW1hfW0Oj3QmAxahnZEI480cnkJEYTnpiOCPjw7z+6K+js/++G+e2A3cDn6K1gHoJ2As8AmxBawEFWiX2MuSRkhBeUVFn4/vDxa7HScXNzVQTI6zMGxXPjNQ4zh8aQ4wHv4GKjhVVNbC3sMJVl1DJ/sJKckpqWBHyDEdN6Rwtv4Ngs4El41JITwgnIzGcwbEhGA0930+6o0Txrw7239PB/lWupaWHWm3/roNzCCHOgc3hZPuxctZnFbEuu5ideeU4FYSYDUwfGsOt56cwIzWOoXEh8jipBzidiqOlta5HR6cTQ1FVQ/MxyVFBZCSGc+W4JAbtDSHSGMnRcvjvS0aQMibWh9FrOkoUW3skCiFElymlOFxUoyWGrGI2HimhptGBXgdjB0Ry95xhzEiNY/zASEw++DbalzTYHWSdrNYSQotK5hrX8CVGvY5h/UKZmRpLRmIE6Qna46OIINPpkxw2Uu8aXcnp7B0PWjpKFK/2SBRCiHNSUt3AhsMlrDtUxPrsYo5X1AMwKCaYH45PYmZqLNOHxBIRbOrgTKKrKupszRXLTYkh+1Q1dtfNPcRsIC0hnGsnJpOeGE5GYgTD+oWeU93P6md3s/RZ3w970tkakDjgfiAdaNkuzvf/AtFjiqoaiA4xY5DOVD3O7nCyOaeUta5mq3sLKwEItxo5f1gsd18Yy8xhcQyM8c+ev73dqap6duVVuPonaD/zy043RY0Ls5CRGM6FI/tpJYXEcAZFB3e542FsaAk6vQ7lVOxdV0Dq5P6Yrb7rq9LZT34TeAe4HLgLuBkInDF0RYdW7irkF8t28Nsr0rn5vBRfh9NnZJ+qYvmWfP6zvYCiqgaMeh0TBkbxq7nDmZEay5jkSEncHqaUIqe4hi25ZWzOLSUzt5SjJVoDAJ0OBseEMHZAJDdMHdj86KhfmGf7leh1iqkLBrPxwyN88+ZBvnnzILNvHMHIaQkYTL2vMrtJDPAi8HPg2xaL6APezczjgf/swqmgqt7m63ACXkWdjY93FrJ8az4788ox6nXMGdmPayYkMSM1jlCL9IL2JLvDyf7jVVpSyClly9FSiqu1HuhRwSYmpUSzeOogxg+MZGRCeI9d/zFzBtA/JZyP/rkDgG/ePIhOp/PJSLSd/Rc33R2Oo5UqCtE60IkA9/KGHH7/8T7OGxrDd4dLfB1OwHI6Fd8dLuHdLXl8uvcEDXYnI/qH8ZvL07hyXBJxYdJ01VPqGh1szysjM6eMLUdL2Xa0rLmyOTkqiFmpcUxKiWbK4CiGxIb6bNwqk8VA8shofvp/c8jZUcSa5/dga/DNnB6dTRR/ACKAXwFPAeGcOfKrCDBKKZ75Opu/fXaISzP688T148h4+FNfhxVwjpbU8P7WfN7fVkBBeR3hViPXTxrAdZOSGZ0UIc1XPaCsppEtR8vIzC1lc04pewoqsDsVOp02ZtXVE5KZPDiaySlRJEQE+Trcs+j1OpJHRgGQt7+UsRcN6OAdntfZRLHS9bOC0/NHiACllOKxNQd47tsjXD0+icevHSO9IT2opsHOqt3HWb41n805peh0MDM1jgcuG8nc9P7SI7obmsZA0pJCGVtyS8k6VQ2A2aBnTHIEd8wawuSUKCYOjPabVmF6o1YvcXRPCSue3M6Cn/fsjAydTRSvotVPlLu2o4C/A7d6IyjhO06n4qEVe3hj4zFunDqQR68chV6vw+Zw+jo0v6aUYsvRMt7NzGPV7uPUNDpIiQnmvktHcPWEpF75TdYfOJ2KQ6eqyMwpJTNXKzU0NRUOsxiZmBLFD8cnMTklmjHJEX6bhE1mA1f9agIf/H0befvLOLKjiCHj4nrs8zubKMZwOkkAlCGTDAUcu8PJr9/bxX+2F/CTC4bwwLyR8uijm45X1PH+1nze25pPbkktIWYDl49J4LpJA5g0KEqu7zlqsDvYnV/RnBS25JY2j1XVL8zC5MHRTEmJZlJKFCPjwwOqRVhiaiSX/WQ0q5/bzepnd3PXM7Mx9FAHys4mCj1aKaLMtR19Du8VfqDB7uCet7fz6d6T/Pclw1k6Z5jcxLqo3ubgs30nWb4lj/XZxSgFUwdHc/eFqVw2Kp4QabXUaVX1Nra66hcyc8vYmVdOg2uAvCFxIcwfnaBVPKdEMyA6KOB/Z4eMP12KWPPsbi5fOrZHPrezv7F/B75DmzNCAdcDf/RWUKJn1TU6uPP1LazLKuahK9K5dcZgX4fkd5RS7MqvYPnWPFbsKKSy3k5SZBA/mzOMayYmMyjG3ewCosmpyno255ZqfRhySjlwohKnAoNex6jEcBZPG8TkFK3iua8OYrj40em88dvvyd1dQkVRLRFx3u9k2dlE8RraiK8Xok1IdDXtT2kq/EhlvY3bXslk69EyHr9mDNdP7vkWFf6sqKqBD7cXsHxrHodOVmMx6pk3Kp7rJg7gvKExMiWoG00d25orno+e7tgWZDIwfmAkP7swlckp0YwfGCklMZeIuCAmzBvEtjVH2fVVPjN/NNzrn3kuVz4aqAFeRhvSYzDQt2ZLDzClNY3c/NJm9h+v5F+LxnPFmJ7vyOOPbA4nXx04xfIt+Xxz8BR2p2LcgEj+eNUorhiTeOYAb+I0Bbvyy9mco5UYWnZsiw4xM2lQFIunDmLy4GgyEsNlAEM3pi0YwvZPj6J6aNDAziaKh4FJaJMMvQyYgDeA870Ul/CyU5X13PjCJo6W1vL8TRO5cGR/X4fU6x04UcnyLfl8uL2AkppG4sIs3DZjMNdOTCa1f3fm+OobGh1OFjy9AYAB0VrHNq3/QrQMeX6OdHodlmATe9cXcjK3kusenOzVz+tsorgKrZXTNtd2Id2b/U74UF5pLYtf3ERRVQOv3DKZ84b6frz73qq8tpEVOwtZviWf3QUVmAw6LhrZn+smJXPB8DifTCLjj3760LxsAAAYxUlEQVQ4PhGH08no5EimpEQTHyFzbndXxsxEsracpLSwxuuf1dlE0YhWid1UzpGaOT91uKiaxS9soqbBzhu3T2XCwChfh9TrOJyKdVlFLN+az+d7T9LocJKWEM5DV6Tzw/FJRIeYfR2i3xkZH87/Xp7u6zACyrQfDsXpUGz//Bilx2vOmpPbkzqbKN4FngMigTvQOtq94K2ghHfsK6zkxy9uQqeDd34ynbSEcF+H1KscKarmva35/GdbAScq64kMNnHD1IFcOzGZUUkRvg5PiLNYw7T6sK1rcpl7S4bXPqezieJvwFygEq2e4iHg8068bx7wJNqc2S8Aj7VxzPVo06EqYCdwQydjEudg27Eylry0mRCLkTdun8rQuFBfh9QrVDfY+WSX9mhpy9Ey9Dq4YHgcD/0gnYvS+mEx+mdPXtE3jL94INvWHMXp8G6l9rm0evqc08nBANyINk9FewzAM2gJJh/IBFZwZrPaVOBBtErxMqDfOcQjOum77GJuf20LcWEW3rx9KslRfXtyG6dTsSmnlOVb81i9+wR1NgdD4kK4f95Irp6QRP9weX4u/INOryMozIy3mwF0lCjCgaVAEtpN/nPX9n3ADtwniilANnDEtb0MuJIzE8UdaMmkqcf3qXOIXXTCl/tP8tM3t5ESE8wbt02lXx++CeaX1fL+1gLe25ZHXmkdoRYjPxyfyLUTBzBhYKS0uhGiHR0litfRbuLfA7ejJQgz2g1/RwfvTQLyWmznA1NbHdPUU2QDWgnkd8CajoIWnfPxzkJ++c4O0hPDefWWKUT10UrYkuoGnvj8EG9vPoZTwXlDY7h37nDmZSQQZJZHS0J0pKNEMQQY7Vp/ASgGBgJVnTh3W1/PWj9IM6I9fpqNNhHSOmAUZw5ACHCnaxGd9E7mMR74z24mD4rmxSWTCLP2vU5gDXYHr2zI5emvsqmzObhpegq3zRjMgOi+/ehNBBa7zUFhdgVOhxN9e821bfVg6vrThI4SRct5Lx1oPbE7kyRAK0G0HA8iGa3/RetjNro+Jwc4iJY4Mlsd97xrgbOTjWjlpfU5PLJyH7OGx/Hc4ol97luzUopP957kT6v2c6y0lotG9uN/Lk+TCnwRkJQTasob+PfSb1j86HQiogxwah8Ubj+9nNoHD3V9hsqOEsVYtJZOoJUQglzbOrQbtrv2lZloN/3BQAGwkLNbNH0ILAJeAWLRHkUdQXSJUoqnv8rm758fYl5GPE8uGtfnWu3sKajg0ZX72JRTyoj+Ybx+2xRmpvbcuP1C9CiHnR8sCmHZvxsAWPfX17gi7Dfg0IZGwRoJiePhvHu69TEdJYru3GXswN3Ap67zvATsBR5BG2BwhWvfJWgV3A60OhCZmLkLlFI8tvoAz609PStdX+o1fKqynr99dpDlW/OJCjbzhx+OYuHkAX3qGogA53RASfaZJYXju4ix13FXfyP/7+SbOHRmmHqXlhwSx0NUCnigkYa3h2Nc5VpaeqjFugLudS2ii5xOxW8/2sObm47x42mD+P2CjD4zamm9zcGL63N45utsbA4nd8wcwtI5w2RgPuHflIKSw62Swk5o1KZ1xRQCCWNh0q2QOB5D4nhiXyxHFxQPl9zi8XBk3F4/Z3c4ue+9XXywvYC7LhjK/fNG9IlmnkopVu46zmOrD1BQXselGf158LI0UmJldBkRALI+1RYAoxXiR8O4G06XFGKHg771A58t5O0r5Zm7vmL0nGRmeXD4cUkUfqzB7uBnb23ns30nue/SESydM8zXIfWIHXnlPLpyH1uPlpGeEM7frhvL9KExvg5LCM+Y8hPIz4SkCVpSiBsJho5LyEPGx1FX1UhlcT2FWa0bjnaPJAo/Vdto5yevb2VdVjEP/yCdW84P/FnpjlfU8fiag3ywvYDYUAt/uWY0104cEFDzIgvB+Bu15RxNuGQQEy4ZxMdP7eDY3lLqqhoJCvNM3ylJFH7ojFnprh3D9ZMCe1a62kY7z317hOfWHsapYOmcofx09jBCZcYzIc5iCdZKH/u/O86ESwd55Jzyl+ZnSmsauemlTRw4XsVTiyZw+ZgEX4fkNU6n4sMdBTy+5iAnKuu5YkwC988bKR3mhHBj9g0jyMo8yfcfHGbMnGSMHuhHJYnCj5ysrGfxC5s4VlrL/7tpEnNGBu4YiltyS3l05T525lcwNjmCp28Yz6SUaF+HJUSvZ7Ia0Om0hlMVRXXEJHW/o6k0MvcTeaW1XPfs9xSW1/HKLVMCNknklday9K1tXPvs95ysbOCJ68fywX+dL0lCiE7S6XRccvsoAL58db9HzhkQJQqbzUZ+fj719fW+DqVdVquVEFPXKl2zT2mz0tXZHLxx+1TGB+CsdNUNdv7v62xeWJ+DXgc/vyiVn1wwhGBzQPyKCtGjElMjASg6VsVnL+5l9o0jMFu7/rcUEH+F+fn5hIWFkZKS0iv7ECilKCkpYVFGCN8Xndt79xZWcNOLm9HpYNmd0wJuVjqHU/He1jz++ukhiqsbuGp8Er+eN4KEiCBfhyaE3woONzP/p6NZ9e/dZGWe5Mj2Iu56enaXzxcQj57q6+uJiYnplUkCtKJgTEwM8aHnVqm09WgZi57fiMWo590AnLr0+8Ml/OCp9dz//m4GRgfx4dLz+cePxkmSEMIDBo+NY9HD2swODoezW+cKiBIF0GuTRBOdTse5NPdvmpWuX5iFNwJsVrqjJTX8adV+Pt17kqTIIJ5aNJ4rxiT0+v9DIfxNdEIIY+Ykc3DTiW6dJyBKFL3FmjVrGDFiBMOGDeOxx9qaHrxzvth3kiWvZDIgKph3fzI9YJJEZb2NP63az8VPfMu6rGLuu3QEX/7qAn4wNlGShBC9WMCUKHzN4XCwdOlSPv/8c5KTk5k8eTILFiwgPT39nM6zYmch9wbYrHR2h5NlmXk88fkhymobuW5iMv99yYg+PS2rEP5EEoWHbN68mWHDhjFkyBAAFi5cyEcffXROiWLZ5mM8+MFuJqdE8+LNgTEr3dpDRfzhk30cOlnN1MHR/PaKdEYlRfg6LCH6lIZae7feH3CJ4vcf72VfYWXHB56D9MRwHv5BhttjCgoKGDDg9FAaycnJbNq0qdOf8eL6HB5duY8LhsfxbADMSpd9qpo/rdrPVwdOMTA6mGcXT+DSjHh5xCREDxsyLo7aqsZunSPgEoWvKHX2DK2duSkqpfjXl9n844tDXDYqnn8u9O9Z6cprG/nnF1m8sfEoQSYDD142kiXnp/j1v0kIf5Y0IoqkEd3rexVwiaKjb/7ekpycTF5eXvN2fn4+iYmJbt+jlOLPqw/w/NojXD0hicev8d9Z6WwOJ29sPMo/v8iiqt7GwikDuXfucGJDLb4OTQjRTQGXKHxl8uTJZGVlkZOTQ1JSEsuWLeOtt95q93inU/Gbj/bw1qZj3DR9EL/7gX/OSqeU4uuDp/jDJ/s5UlTDjGGx/OaKNEbGB1afDyH6Mm8ninnAk2hzZr8AtG4zugT4K1Dg2n7adZzfMRqNPP3001x66aU4HA5uvfVWMjLaLt3YHE7uW76TD3cU8tPZQ/n1pf45K93BE1X84ZN9rMsqZkhsCC/ePIkLR/bzy3+LEKJ93kwUBuAZYC6QD2QCK4B9rY57B7jbi3H0mPnz5zN//ny3xzTaHfzXm9v43I9npSupbuCJzw/x9uZjhFlNPHRFOounDcJs9M/HZkII97yZKKYA2cAR1/Yy4ErOThR9yqd7TwLw+wUZ3Hxeim+D6YLP95/iuW+PUGtzcNP0FH5+UWpA9PUQQrihlPLWcq1S6oUW2z9WSj3d6pglSqnjSqldSqn3lFIDOjpvZmamWrZsmVJKqf79+ytArV69WmVmZjYvNTU1Simltm3bdsbrTUuTrVu3ut2/ZcuWbu1v/frq1asV6NS7mceUUkrpdDoFnLEYDIbm97e132QyNe9vvQ9QVqvV7f6QkBC3+yMjI5VSSm3fvr3N/brgSLXkpU3qH8++3Ob+gQMHKqWUevzxx9vcn5aWppRS6p577mlz/+TJk5VSSi1atKjN/XPnzlVKKTV37tw29y9atEgppdTkyZPb3H/PPfcopZRKS0trc//jjz+ulFJq4MCBbe5v/bvXetm+fbtSSqnIyMg29zcJCQlxu99qtbrdbzKZzv6/0ema9xsMBrf7/fF3r3///koppZYtWya/e1373duiung/9+azgrYeVKtW2x8DKcAY4Avg1XbOdSewxbX4NZ0OrvPjqUsjg0y8fMsUEiJl4D4h+gqdaqP9v4dMB34HXOraftD188/tHG8ASoGOuu2eFfD+/ftJS0vrQog9y1/iFEIEpK3ApK680ZslikwgFRgMmIGFaJXZLbWc8HkB4JnpmIQQQniMNyuz7WitmT5FKy28BOwFHkF7hLQCuActQdjRShNLvBiPEEKILvB2P4pVrqWlh1qsP8jpR1J+7dZbb2XlypX069ePPXv2+DocIYTwGGn47iFLlixhzZo1vg5DCCE8ThKFh8yaNYvo6GhfhyGEEB4XeGM9rX4ATuz27DnjR8NlXZ+xTggh/JmUKIQQQrgVeCUK+eYvhBAeJSUKIYQQbkmi8JBFixYxffp0Dh48SHJyMi+++KKvQxJCCI8IvEdPPvL222/7OgQhhPAKKVEIIYRwSxKFEEIItyRRCCGEcEsShRBCCLckUQghhHBLEoUQQgi3JFF4SF5eHnPmzCEtLY2MjAyefPJJX4ckhBAeIf0oPMRoNPL3v/+dCRMmUFVVxcSJE5k7dy7p6em+Dk0IIbpFShQekpCQwIQJEwAICwsjLS2NgoICH0clhBDdF3Alir9s/gsHSg949Jwjo0dy/5T7O318bm4u27dvZ+rUqR6NQwghfMHbJYp5wEEgG3jAzXHXAgqY5OV4vK66upprrrmGf/7zn4SHh/s6HCGE6DZvligMwDPAXCAfyARWAPtaHRcG3ANs8sSHnss3f0+z2Wxcc8013HjjjVx99dU+i0MIITzJmyWKKWgliSNAI7AMuLKN4x4FHgfqvRiL1ymluO2220hLS+Pee+/1dThCCOEx3kwUSUBei+1812stjQcGACu9GEeP2LBhA6+//jpfffUV48aNY9y4caxatcrXYQkhRLd589GTro3XVIt1PfAPYEknznWna+m1ZsyYgVKq4wOFEMLPeLNEkY9WWmiSDBS22A4DRgHfALnANLQ6jLYqtJ93ve73ld1CCOFvvJkoMoFUYDBgBhaiJYImFUAskOJaNgILgC1ejEkIIcQ58maisAN3A58C+4F3gb3AI2gJQQghhB/wdoe7Va6lpYfaOXa2d0MRQgjRFTKEhxBCCLckUQghhHBLEoWH1NfXM2XKFMaOHUtGRgYPP/ywr0MSQgiPCLhBAX3FYrHw1VdfERoais1mY8aMGVx22WVMmzbN16EJIUS3SInCQ3Q6HaGhoYA25pPNZkOna6vPoRBC+JeAK1Gc+NOfaNjv2WHGLWkjif+f/+nwOIfDwcSJE8nOzmbp0qUyzLgQIiBIicKDDAYDO3bsID8/n82bN7Nnzx5fhySEEN0WcCWKznzz97bIyEhmz57NmjVrGDVqlK/DEUKIbpEShYcUFRVRXl4OQF1dHV988QUjR470cVRCCNF9AVei8JXjx49z880343A4cDqdXH/99VxxxRW+DksIIbpNEoWHjBkzhu3bt/s6DCGE8Dh59CSEEMItSRRCCCHckkQhhBDCLUkUQggh3JJEIYQQwi1JFEIIIdySROFhDoeD8ePHSx8KIUTAkEThYU8++SRpaWm+DkMIITzG24liHnAQyAYeaGP/XcBuYAewHkj3cjxelZ+fzyeffMLtt9/u61CEEMJjvNkz2wA8A8wF8oFMYAWwr8UxbwHPutYXAE+gJZcuW/fuIYrzqrtzirPEDghl5vXDOzzuF7/4BY8//jhVVVUe/XwhhPAlb5YopqCVJI4AjcAy4MpWx1S2WA8BlBfj8aqVK1fSr18/Jk6c6OtQhBDCo7xZokgC8lps5wNtzeSzFLgXMAMXtnOuO11Lhzrzzd8bNmzYwIoVK1i1ahX19fVUVlayePFi3njjDZ/EI4QQnuLNEkVb84C2VWJ4BhgK3A/8pp1zPQ9Mci290p///Gfy8/PJzc1l2bJlXHjhhZIkhBABwZuJIh8Y0GI7GSh0c/wy4IdejEcIIUQXeDNRZAKpwGC0x0oL0SqzW0ptsX45kOXFeHrM7NmzWblypa/DEEIIj/BmHYUduBv4FK0F1EvAXuARYAta0rgbuBiwAWXAzV6MRwghRBd4e+KiVa6lpYdarP/cy58vhBCim6RnthBCCLcCJlEo1bu7YPT2+IQQoj0BkSisVislJSW99maslKKkpASr1errUIQQ4px5u46iRyQnJ5Ofn09RUZGvQ2mX1WolOTnZ12EIIcQ50/XWb+Fu+F3AQgjRC2yli52WA+LRkxBCCO+RRCGEEMItSRRCCCHc8sfK7Gq0yZAExALFvg6il5BrcZpci9PkWpw2oqtv9MdEcZBePIpsD9uCXIsmci1Ok2txmlyL07Z09Y3y6EkIIYRbkiiEEEK45Y+J4nlfB9CLyLU4Ta7FaXItTpNrcVqXr4U/drgTQgjRg/yxRCGEEKIH9eZEMQ+thVM28EAb+y3AO679m4CUHous53V0Le4F9gG7gC+BQT0XWo/r6Fo0uRZtuJdAbvHSmWtxPdrvxl7grR6Kyxc6uhYDga+B7Wh/J/N7LrQe9RJwCtjTzn4d8C+067QLmNCpsyqleuNiUEodVkoNUUqZlVI7lVLprY75L6XUs671hUqpd3pB3L66FnOUUsGu9Z/28WuBUipMKbVWKbVRKTWpF8Ttq2uRqpTarpSKcm336wVx++paPK+0vw1c+3J7QdzeWGYppSYopfa0s3++Umq1UkqnlJqmlNrUmfP21hLFFLSMdwRoBJYBV7Y65krgVdf6e8BFaNky0HTmWnwN1LrWNwKBOkxtZ64FwKPA40B9z4XW4zpzLe4AnkGbZhi0b5qBqDPXQgHhrvUIoLDHoutZa4FSN/uvBF5Dux4bgUggoaOT9tZEkQTktdjOd73W3jF2oAKI8X5oPa4z16Kl24DVXo3IdzpzLcYDA4CVPRWUj3TmWgx3LRvQbgrzeia0HteZa/E7YLFr3yrgZz0SWe9zrvcToPf2zG6rZNC6eVZnjgkE5/LvXIz2TP4C74XjUx1dCz3wD2BJj0TjW535vTACqcBstFLmOmAUUO7VyHpeZ67FIuAV4O/AdOB1tGvh9GpkvU+X7pu9tUSRj/atsEkyZxcVWx5jRCtOuity+avOXAuAi4H/BRYADT0Qly90dC3C0P74vwFygWnACgKzQruzfyMfATYgB62yN7VHoutZnbkWtwHvuta/B6xo40D1NZ29n5yhtyaKTLRf6MGAGViI9gff0grgZtf6tcBXBGaJojPXYjzwHFqSCNTn0NDxtahA++NPcS0b0a5Jl8e46cU683vxITDHtR6L9hjqSE8F2IM6cy2OodVjAqShJYreOyWm96wAbkIrWUxD+5s53tGbeuujJztwN/ApYEBr8rUXeATtj34F8CJa8TEbrSSx0CeRel9nrsVfgVBgues9x9BukIGmM9eir+jMtfgUuASteawDuA8o8UWwXtaZa/Er4P8Bv0T7QrmEwPxi+Tbao8ZYtNLDw4DJte9ZtPqZ+Wj3zVrgls6cVHpmCyGEcKu3PnoSQgjRS0iiEEII4ZYkCiGEEG5JohBCCOGWJAohhBBuSaIQfZUD2IE2yuZyINgD55yENjJnexLRxiUTwq9I81jRV1Wj9T0BeBPYCjzRYr/OtfS1IR6EOIuUKITQxkAahtabez/wf8A2tKEOLkEb8mEbWsmjKblMBr4DdgKb0YYPmc3pwQgvQCux7ECbAyHMdf6meQKswMvAbtf+ph7US4D/AGuALLRRcIXwKUkUoq8zApeh3bABRqANwzweqAF+gzaO1gS0Xr73og0T8Q7wc2Csa39dq/P+N7AUGAfMbGP/UtfP0WgD1r2KljxwvedHrn0/4syxeYTocb11CA8hvC0I7ds+aCWKF9HqEI6ijREF2lg46WjDdIOWIL5HSybH0cYYAqhs4/wb0B5lvYlWQshvtX8G8JRr/YDrc4e7tr9EG4MHtOE3BnHm0NBC9ChJFKKvqkP75t5aTYt1HfA52jf+lsbQ8ThBjwGfoI2rsxGt1NFyIiV3k2y1HP3XgfydCh+TR09CtG8jcD5a/QVoLaOGo5UAEtHqKUCrf2h9Mx+K9jjrL2iPrEa22r8WuNG1PhxtTueDHoxdCI+RRCFE+4rQKpffRpuIfiPaDb8Rre7gKbTK7M85Xb/Q5BdoFdc70UovrWcd/D+0kU53o9V3LCFw5xERfk6axwohhHBLShRCCCHckkQhhBDCLUkUQggh3JJEIYQQwi1JFEIIIdySRCGEEMItSRRCCCHckkQhhBDCrf8PfLmvt16dfW8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xai = reload(xai)\n",
+    "_ = [xai.pr_plot(\n",
+    "    y_test, \n",
+    "    probabilities, \n",
+    "    df=x_test, \n",
+    "    cross_cols=[p],\n",
+    "    categorical_cols=categorical_cols) for p in protected]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d = xai.smile_imbalance(\n",
+    "    y_test, \n",
+    "    probabilities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LFi2iR48e3H777bRv355WrVrRvn17Pv744x32f+ONNygoKKCgoIBWrVqxZs0aAO68804KCwvJz8/nlltuqfC1hg0bxoUXXsiJJ55I06ZNy87xFovFuO6662jRogUtW7akqKgIgBUrVtCxY0cKCgpo0aIF06ZNA6BJkyZ89dVXDBkyhIULF1JQUMB1113H4sWLadGiBQDHHnssc+fOLXvtzp07M2vWLNatW8eAAQMoLCykVatWPPvsswDMnTuXtm3bUlBQQH5+PgsWLEhSD0uSJEnaXY7A7cTYsWP55z//yeuvv061atX47W9/S5UqVXjllVcYOnQoTz311Db7jxo1ijFjxtChQwfWrl1LjRo1mDx5MgsWLGDmzJnEYjF69OjB1KlT6dix4w6vN3v2bGbMmMG6deto1aoVp59+OtOnT6e4uJgPPviAr776isLCQjp27Mj48ePp2rUrN954I6WlpXz77bfbPNfIkSOZM2cOxcXFQBiR26Jv375MmDCB4cOHs2LFCpYvX06bNm0YOnQoJ554Io8++iirV6+mbdu2nHzyyYwdO5arr76afv368f3331NaWpr8zpYkSZKUEANcAkpKSujfvz8LFiwgJyeHjRs37rBPhw4d+M1vfkO/fv04++yzady4MZMnT2by5Mm0atUKgLVr17JgwYIKA1zPnj2pWbMmNWvWpEuXLsycOZM333yT8847j9zcXA4++GA6derEO++8Q2FhIQMGDGDjxo306tWLgoKChP8t5557LqeccgrDhw9nwoQJ9O7dG4DJkyczadIkRo0aBcD69etZsmQJxx13HH/4wx9YtmwZZ599Nk2bNt2TLpQkSZKUBE6hTMBNN91Ely5dmDNnDs899xzr16/fYZ8hQ4bwpz/9ie+++4527doxf/58YrEYN9xwA8XFxRQXF/Ppp58ycOBAxowZUzbdcvny5QDk5ORs83w5OTnEYrEK6+nYsSNTp06lUaNGXHjhhTz++OMJ/1saNWpE/fr1mT17NkVFRfTt2xcI0zWfeuqpslqXLFlCs2bNOP/885k0aRI1a9aka9euvPbaawm/liRJkqTkMsAloKSkhEaNGgHw2GOPVbjPwoULadmyJddffz3HHHMM8+fPp2vXrjz66KOsXbsWgP/85z+sXLmSwYMHlwWlhg0bAvDss8+yfv16Vq1axZQpU8qmSxYVFVFaWsqXX37J1KlTadu2LZ9//jkNGjTgkksuYeDAgbz33nvb1FKnTp2yY/Aq0rdvX+644w5KSkpo2TKs7tm1a1fuvffestD4/vvvA7Bo0SIOO+wwrrrqKnr06MHs2bP3vCMlSZIk7ZXITKFMZNn/VPn9739P//79ueuuuzjxxBMr3Oeee+7h9ddfJzc3l+bNm9O9e3eqV6/OvHnzOO644wCoXbs2TzzxBA0aNNjh8W3btuX0009nyZIl3HTTTTRs2JCzzjqL6dOnc/TRR5OTk8Mdd9zBT37yE8aNG8edd95J1apVqV279g4jcPXr16dDhw60aNGC7t27M3jw4G3uP+ecc7j66qu56aabytpuuukmrrnmGvLz84nFYjRp0oTnn3+eoqIinnjiCapWrcpPfvITbr755r3tTkmSJEl7KGdn0/TSbIci5s2bR7NmzTJRS9oNGzaM2rVr87vf/S7TpSRVZfoZSoq2Pg9OB6Do0uMyXIkkZb8mQ17IdAl7bPHI01P59LOAY3a1k1MoJUmSJCkiIjOFMpsNGzYs0yVIkiRJigBH4CRJkiQpIgxwkiRJkhQRBjhJkiRJiggDnCRJkiRFRGQWMZl3VHKXo282f94u9xk9ejQPPPAArVu35sknn9zh/ilTpjBq1Cief/75pNaWClOmTKFatWq0b98egLFjx7Lffvtx0UUXZbgySZIkSYmKTIDLhPvvv5+XXnqJvLy8TJey16ZMmULt2rXLAtxll12W4YokSZIk7S6nUO7EZZddxqJFi+jRowe333477du3p1WrVrRv356PP/54h/3feOMNCgoKKCgooFWrVqxZswaAO++8k8LCQvLz87nlllsqfK1hw4YxYMAAOnfuzGGHHcbo0aPL7nviiSdo27YtBQUFXHrppZSWlgLwyCOPcMQRR9C5c2cuueQSrrzySgCee+45jj32WFq1asXJJ5/MF198weLFixk7dix33303BQUFTJs2jWHDhjFq1CjmzZtH27Zty15v8eLF5OfnAzBr1iw6depEmzZt6Nq1KytWrADCyGTz5s3Jz8+nb9++SehtSZIkSYkwwO3E2LFjadiwIa+//jqXX345U6dO5f3332fEiBEMHTp0h/1HjRrFmDFjKC4uZtq0adSsWZPJkyezYMECZs6cSXFxMbNmzWLq1KkVvt78+fP517/+xcyZMxk+fDgbN25k3rx5FBUV8dZbb1FcXExubi5PPvkky5cv59Zbb2XGjBm8/PLLzJ8/v+x5jj/+eGbMmMH7779P3759ueOOO2jSpAmXXXYZ1157LcXFxZxwwgll+zdr1ozvv/+eRYsWAVBUVMS5557Lxo0b+fWvf83EiROZNWsWAwYM4MYbbwRg5MiRvP/++8yePZuxY8cms9slSZIk/QCnUCagpKSE/v37s2DBAnJycti4ceMO+3To0IHf/OY39OvXj7PPPpvGjRszefJkJk+eTKtWrQBYu3YtCxYsoGPHjjs8/vTTT6d69epUr16dBg0a8MUXX/Dqq68ya9YsCgsLAfjuu+9o0KABM2fOpFOnThx44IEA9O7dm08++QSAZcuW0adPH1asWMH333+f0PTPc889lwkTJjBkyBCKioooKiri448/Zs6cOZxyyikAlJaWcsghhwCQn59Pv3796NWrF7169dqDHpUkSZK0JxyBS8BNN91Ely5dmDNnDs899xzr16/fYZ8hQ4bwpz/9ie+++4527doxf/58YrEYN9xwA8XFxRQXF/Ppp58ycOBAxowZUzbdcvny5QBUr1697Llyc3PZtGkTsViM/v37lz3+448/ZtiwYcRisZ3W+utf/5orr7ySDz/8kAcffLDCWrfXp08fJkyYwCeffEJOTg5NmzYlFovxi1/8ouy1P/zwQyZPngzACy+8wODBg5k1axZt2rRh06ZNu9ulkiRJkvaAAS4BJSUlNGrUCIDHHnuswn0WLlxIy5Ytuf766znmmGOYP38+Xbt25dFHH2Xt2rUA/Oc//2HlypUMHjy4LBg1bNhwp6970kknMXHiRFauXAnA119/zeeff07btm154403+Oabb9i0aRNPPfVUhbWOGzeurL1OnTplx+Vt7/DDDyc3N5dbb72VPn36AHDkkUfy5ZdfMn36dAA2btzI3Llz2bx5M0uXLqVLly7ccccdrF69uuzfJ0mSJCm1IjOFMpFl/1Pl97//Pf379+euu+7ixBNPrHCfe+65h9dff53c3FyaN29O9+7dqV69OvPmzeO4444DoHbt2jzxxBM0aNAgoddt3rw5t912G6eeeiqbN2+matWqjBkzhnbt2jF06FCOPfZYGjZsSPPmzalbty4QFkTp3bs3jRo1ol27dnz22WcAnHnmmZxzzjk8++yz3HvvvTu8Vp8+fbjuuuvK9q9WrRoTJ07kqquuoqSkhE2bNnHNNddwxBFHcMEFF1BSUkIsFuPaa6+lXr16u92nkiRJknZfzg9Nx9tOLvAu8B/gDCAP+BtwIPAecCHwPVAdeBxoA6wC+gCLd/HcOxQxb948mjVL7rnfssnatWupXbs2mzZt4qyzzmLAgAGcddZZmS5rG/4MJUVFnwfDbIOiS4/LcCWSlP2aDHkh0yXsscUjT0/l088CjtnVTrszhfJqoPww2O3A3UBT4BtgYLx9YPz2z+P3374br6EEDRs2jIKCAlq0aEFeXp6LiUiSJEmVQKJTKBsDpwN/AH4D5AAnAufH7x8HDAMeAHrGtwEmAvfF9094qE+7NmrUqEyXIEmSJCnNEh2Buwf4PbA5frs+sBrYsvzgMqBRfLsRsDS+vQkoie+/vUGEKZnv7uxFd2N6p/Yx/uwkSZKk5EskwJ0BrCTMydwip4L9YgncV95DhDmeFc7zrFGjBqtWrTIIRFAsFmPVqlXUqFEj06VIkiRJWSWRKZQdgB7AaUANYH/CiFy9+OM3EaZYLo/vvww4NH5dBagLfL27hTVu3Jhly5bx5Zdf7u5DtQ+oUaMGjRs3znQZkiRJUlZJJMDdEL8AdAZ+B/QD/g6cQ1iJsj/wbHyfSfHb0+P3v8YeHP9WtWpV8vLydvdhkiRJkpS19uZE3tcTFjT5lHCM2yPx9kfitz+N3z9kbwqUJEmSJAW7eyLvKfELwCKgbQX7rAd673lJkiRJkqSK7M0InCRJkiQpjQxwkiRJkhQRBjhJkiRJiggDnCRJkiRFhAFOkiRJkiLCACdJkiRJEWGAkyRJkqSIMMBJkiRJUkQY4CRJkiQpIgxwkiRJkhQRBjhJkiRJiggDnCRJkiRFhAFOkiRJkiLCACdJkiRJEWGAkyRJkqSIMMBJkiRJUkQY4CRJkiQpIgxwkiRJkhQRBjhJkiRJiggDnCRJkiRFhAFOkiRJkiLCACdJkiRJEWGAkyRJkqSIMMBJkiRJUkQY4CRJkiQpIgxwkiRJkhQRBjhJkiRJiggDnCRJkiRFhAFOkiRJkiLCACdJkiRJEWGAkyRJkqSIMMBJkiRJUkQY4CRJkiQpIgxwkiRJkhQRBjhJkiRJiggDnCRJkiRFhAFOkiRJkiLCACdJkiRJEWGAkyRJkqSIMMBJkiRJUkQY4CRJkiQpIgxwkiRJkhQRiQS4GsBM4ANgLjA83p4H/BtYABQB1eLt1eO3P43f3yR55UqSJElS5ZVIgNsAnAgcDRQA3YB2wO3A3UBT4BtgYHz/gfHbP4/ff3tyS5YkSZKkyimRABcD1sa3q8YvMUKomxhvHwf0im/3jN8mfv9JQE4yipUkSZKkyizRY+BygWJgJfAysBBYDWyK378MaBTfbgQsjW9vAkqA+skoVpIkSZIqs0QDXClh+mRjoC3QrIJ9YvHrikbbYhW0DQLejV8kSZIkSbuwu6tQrgamEI6BqwdUibc3BpbHt5cBh8a3qwB1ga8reK6HgGPiF0mSJEnSLiQS4A4ihDWAmsDJwDzgdeCceHt/4Nn49qT4beL3v0bFI3CSJEmSpN1QZde7cAhhUZJcQuCbADwPfAT8DbgNeB94JL7/I8BfCKcR+Brom9ySJUmSJKlySiTAzQZaVdC+iHA83PbWA733pihJkiRJ0o529xg4SZIkSVKGGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEYkEuEOB14F5wFzg6nj7gcDLwIL49QHx9hxgNPApMBtoncR6JUmSJKnSSiTAbQJ+CzQD2gGDgebAEOBVoGn8ekh8/+7xtqbAIOCB5JYsSZIkSZVTIgFuBfBefHsNYSSuEdATGBdvHwf0im/3BB4HYsAMoB5wSJLqlSRJkqRKa3ePgWsCtAL+DRxMCHfErxvEtxsBS8s9Zlm8bXuDgHfjF0mSJEnSLlTZjX1rA08B1wD//YH9cipoi1XQ9lD8srP7JUmSJEnlJDoCV5UQ3p4E/hFv+4KtUyMPAVbGt5cRFj7ZojGwfO/KlCRJkiQlEuBygEcIx77dVa59EtA/vt0feLZc+0Xxx7UDStg61VKSJEmStIcSmULZAbgQ+BAojrcNBUYCE4CBwBKgd/y+F4GluMeuAAAR/klEQVTTCKcR+Bb4VRLrlSRJkqRKK5EA9yYVH9cGcFIFbTHCqQYkSZIkSUm0u6tQSpIkSZIyxAAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRGRSIB7FFgJzCnXdiDwMrAgfn1AvD0HGA18CswGWietUkmSJEmq5BIJcI8B3bZrGwK8CjSNXw+Jt3ePtzUFBgEPJKVKSZIkSVJCAW4q8PV2bT2BcfHtcUCvcu2PAzFgBlAPOGTvy5QkSZIk7ekxcAcDK+LbK4AG8e1GwNJy+y2Lt0mSJEmS9lKVJD9fTgVtsZ3sOyh+kSRJkiQlYE8D3BeEqZEr4tcr4+3LgEPL7dcYWL6T53gofoGdhzxJkiQpZVqOa5npEvbYh/0/zHQJyoA9nUI5Cegf3+4PPFuu/SLCSFw7oIStUy0lSZIkSXshkRG4vwKdgR8TRthuAUYCE4CBwBKgd3zfF4HTCKcR+Bb4VXLLlSRJyl6OBknalUQC3Hk7aT+pgrYYMHjPy5EkSZIk7UyyFzGRlAHzjmqW6RL2WLP58zJdgiRJUmQY4CRJkZDKLyq+Pf7y8Bp3D0jJ8/tFhSQpWbI/wA2rm+kK9tywkkxXIEmSJGkfkv0BTpJSwGmrqgxcUEOS9j0GOCkLNOu7s9MtSpIkKZvs6XngJEmSJElp5gicJCkSzr0hdX+yvv08J6Wv4WQ+SVKyGOAkaQ84bVWSJGWCUyglSZIkKSIMcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiPA8cEq6eUc1y3QJe6zZ/HmZLkGSJEnaKQOcks4THEuSJEmp4RRKSZIkSYoIA5wkSZIkRYQBTpIkSZIiwmPgJEmSVGmtmTcy0yVIu8UROEmSJEmKCAOcJEmSJEWEUyglSZHw4WdLUvbcfTasB6Aoha8hSVIyOAInSZIkSRFhgJMkSZKkiHAKpSRJ0j7CFREl7YojcJIkSZIUEQY4SZIkSYoIA5wkSZIkRYQBTpIkSZIiwgAnSZIkSRFhgJMkSZKkiDDASZIkSVJEeB44SZJUIc9JJkn7HkfgJEmSJCkiDHCSJEmSFBEGOEmSJEmKCAOcJEmSJEWEAU6SJEmSIsIAJ0mSJEkRYYCTJEmSpIhI1XngugH/D8gF/gR4IhlJ0l5psn58ZF9jcUqeVZJUGaViBC4XGAN0B5oD58WvJUmSJEl7IRUBri3wKbAI+B74G9AzBa8jSZIkSZVKKgJcI2BpudvL4m2SJEmSpL2QE4vFkv2cvYGuwMXx2xcSRuV+vd1+g+IXgCOBj5NdSJr8GPgq00VUMvZ5+tnn6Wefp599nn72efrZ5+lnn6dfVPv8Z8BBu9opFYuYLAMOLXe7MbC8gv0eil+i7l3gmEwXUcnY5+lnn6effZ5+9nn62efpZ5+nn32eflnd56mYQvkO0BTIA6oBfYFJKXgdSZIkSapUUjECtwm4EvgXYUXKR4G5KXgdSZIkSapUUnUeuBfjl8ogG6aBRo19nn72efrZ5+lnn6effZ5+9nn62efpl9V9nopFTCRJkiRJKZCKY+AkSZIkSSlggJMkSZKkiDDASZIkSVJEGOAkSZIkKSIMcMmR1SvdZFAucClwK9Bhu/v+v/SXU2m0BQrj282B3wCnZa6cSunxTBeQ5a4Efhzf/jkwFVgN/Btomamistw/gAuA2pkupBI5jHAqp9sI/f4wMAf4O9Akc2VlvcOB3wH/D/hf4DKgbkYrqjwOBloDreLbWctVKBN34E7ac4APgMZprKWy+BOwHzATuBB4gxAmAN4j/CdVct0CdCecYuRl4FhgCnAy4dyOf8hYZdlr0na3c4AuwGvx2z3SW06lMBf4RXz7BcJ7zdNAZ8Lv+PZfGGnv/QeYDpwIvAL8ldD332eyqCw3ldDPdQnh+c/ABOBUoB/hZ6Hkugo4k/B55TSgGPgGOAu4gvD3VMlXAIwl/K7/J97WmPDF3BWEz4xZxQCXuFLgc8KHqy1i8duNgGqZKCrLzQby49tVgPsJ35qfB8wgfMOi5PqQ8EZYHfg/whvgf4GahNGJ/J0/VHvoPeAjQojY8p7yV6Bv/P43MlRXNvsYODK+/Q5bR5xh2/cdJc/7hPfsOkAvwvt4IfA84fd9cuZKy1pb+hxgCfDTndyn5NnyN7SU8AX0i4Qvhn4KPIt9nirFhBlb/96uvR3wIHB02itKMadQJm4R4T9hXrnLYfHrLzJXVlYrH4o3AYMI/0lfw2k4qbKJ8IfnW2AhIbwBfAdszlRRWe4YYBZwI1BC+Ib2O0JwM7ylxkTgMcJ7+NPANYQPWL8ifNBV8m35tngN8BfC6MSRhA9cQzJVVJbbDBxBCMr7Ed5rIEwbzs1UUZVAlfh1dcIXFhDeV6pmppxKoRY7hjcIX/bXSnMtaVFl17so7h7gACr+435HmmupLN4FugH/LNc2AlgOPJCRirLf94Q/9N8Cbcq118UAlyqbgbsJx6XcTfhCyPfm1LoR+CVh5OdwwgetQcAzhKllSr61FbR9TZj2NDbNtVQWvweeI7zH9AJuIIxE7A9cksG6stmfCKP6M4COwO3x9oMIv+9KjZcIU7IfB5bG2w4FLmLbz5BZwymUksqrDmyooP3HwCGE6SFKrdMJx2ANzXQhkrLOjwnHZJVmupAs9gugGWHBmPkZrqUy6Q70JBzWlAMsIxxj/mImi0oVA1xynEJY8EHJtz/hm6uF27XnE45VUfJtmVq9mTCNtQWwGL89TKcrCMd8Kj3yCMemfIQfuFLlp8BKYD3hw9UvCQtRfURYHXFTxirLXj0Ii09V9KWcUuenhMMPVhNW+zyG8L4yJ4M1Kct4DFxyPJLpArLUuYQ3vacIq8aVX2jgsUwUVAn0AlYQVnHqCUwDRhHC8pkZrCub/aaCy4hy20q+Z8pt9yQcV3sm4dvaX2aioErgRbZ+5hhJGGn+N+F93VPxpEYR4b18yzGHHveWekMIxy7PAC4mTN/rTvhZ+H6eGYMyXUAqeJxF4rZf6nuLHKB+OgupRIYSjsNaQTg32V/ibf9g29VAlTy3EI6RqEk4PUYhYcW+nxGC9HOZKy1rDSd8uJ3L1t/rXLYe/K7k+1m57esJy6l/Rphe9ip+QZQKPyIcWwvhtCSFhFH+JwjvNUq++YTf7XOA3xJOI/A04dhPF0hKjQsJ50/djzBz5TDgS7YusnFXxiqrvLLy86IBLnEnEM6jsv2B2DmEcKHkyyWENwjngutCWHK6MVtXNFPy/V/8egkhvEE4hYYj9qnxC8If9VqEMPct0D++rdQo//5RhRDeAL7CxXpSZSkhTLxG+GB7KOF9xS9AUydGON7t4fjlJ4SZLSMJf0cPzVxpWauUsIrw9/HrVfH2dRmrSFl5rkkDXOJmED5YVfSt1ccVtGnvrSGsELfl+LcVhFM5PMPWk/Aq+X5E+BA7oFxbLp7rMFWWEL4h70k4lvbuzJZTKRxNOEYlh7Bwz08IX1xUw2lmqXIxYYW4YYTTZRQTzkV2AE4tS5XtRx7+Dxgdv/xsx92VBO8B4wlfyL0KjCNMozyRcLyn0m84YfQ5q7iIifZlRxNC84Lt2qsSvkV8Mu0VZb9CwkqT67drbwIcT5jupNTZj/DH5ljCEtRKr3qE1eOmZ7qQLNaMcG6yKoRV4t7BUc9U6Uw4r6TSpwrQmzD6OZHwXn4e4Yu6MTgSlyo7W9Quh/B+Uz2NtaSFAU6S9j2tCd/kStlqf6ApsIgwzU/KVj8mTM9W6nwBdGXH95Ic4G2gYdorSjGPaUkOz42VGocCfyOshDiUMPK2xTMVPkJ76yi2nhDzcMJiDqsJxyA2y1xZWa11BZdJhGXtW2ewrmxWfnpwY8JUp9WEP/RHZKSi7PcE4YMshA9acwknOS4mjFgo+b4mnFj6JLJ0IYd9UHfCMbVvEt7D5xIOwVlG+DkoNZ4HahOOqy1/WUyWjkI7Ape4s3fSngOMJZyrTMn1MmHlwxnAQMKKlGcSDgp+n/DmqOSaCtxJeCMcSVihrwg4A7gG/wClwmbC73j5czW1i7fFCMdOKLneY2s4nkAIcA8TjkO8En/PU+FDoGV8+23gfMKHqy0rfx6dmbKy2sfAvYQpfE0IU/r+SnhvUWoUE/q7HiFUnE7o72aEwz78Uk5JYYBL3EbCf76KOuwcXPI7FYqBgnK3LwBuIJyc9O/4RpgK5YPxp8DPy91X/kOvkucc4NeE0YgX422fEU4urdQo/7u8/fuMXw6lxlzgOMLiMW8SjvHcXO4+F6ZKvvK/5z8F+sYv9QizW4ZmqK5sVr7Pl7LtSp/bv9dIe8xVKBM3m3BC4zkV3HdymmupLKoCNdi6oMYThFW0/kVY4UnJV34Fvu3PV+MqlKkxkbBK2a3Arwjna/KbtdRqTFiJL4cwe6Iq4Us62HaqtpJnOPA6YSGHtwhfwj1LGGH+Zwbrymblp00uAe6IX44kBDkl32rgUsIxnt8A1xJG+U9mx9NQSXssd9iwYZmuISrmASsJyx9vbxqwPL3lVArVCYHi83Jtiwj9fTThxN5Krs2EaTffE1aH2+LnhKlOftBKje8JX0wsIEzJbkyYyqrUWEk4LclywvS9zwhfFP2EMDrxauZKy1pzCeeAO5VwnGFVoC7hC4z7M1hXNjuS8L6yvVV4Iu9UeQ3oQ3gfuZgwbfguwnv6r3ExEyWJUyglad+SQ5iS/d9MFyJJkvY9rkKZHGdkuoBKyD5PP/s8PWJsDW/2efrZ5+lnn6effZ5+9rmSxgCXHIWZLqASss/Tzz5PP/s8/ezz9LPP088+Tz/7XEnjFMrdcxRhmelGhG/JlxPO1zQvk0VlOfs8/ezz9LPP088+Tz/7PP3s8/Szz5VyjsAl7nrCsrs5hJMavxPf/iswJIN1ZTP7PP3s8/Szz9PPPk8/+zz97PP0s8+VFo7AJe4TwnlqNm7XXo2wulbTtFeU/ezz9LPP088+Tz/7PP3s8/Szz9PPPldaOAKXuM1AwwraD2HryUiVXPZ5+tnn6Wefp599nn72efrZ5+lnnystPJF34q4hnBtoAbA03vZTwvmxrsxUUVnOPk8/+zz97PP0s8/Tzz5PP/s8/exzpYVTKHfPj4C2hANTc4BlhPnNpZksKsvZ5+lnn6effZ5+9nn62efpZ5+nn32ulDPASZIkSVJEeAycJEmSJEWEAU6SJEmSIsIAJ0nSjqYAx2S6CEmStmeAkyRVVrmZLkCSpN1lgJMkZaMmwHxgHDAbmAjsBywGbgbeBHoDBcCM+D5PAweUe44LgLeBOYRV5QA6AcXxy/tAnZT+KyRJ2o4BTpKUrY4EHgLygf8CV8Tb1wPHA38DHgeuj+/zIXBLucfXAtrHH/dovO13wGBC8DsB+C6l/wJJkrZjgJMkZaulwFvx7ScIoQ2gKH5dF6gHvBG/PQ7oWO7xf41fTwX2j+/7FnAXcFX89qZUFC5J0s4Y4CRJ2Wr7E51uub1uLx4/ErgYqEmYennUHlcnSdIeMMBJkrLVT4Hj4tvnEY57K68E+IYwFRLgQraOxgH0iV8fH9+3BDicMNXyduBdDHCSpDQzwEmSstU8oD9hgZIDgQcq2Kc/cGd8nwJgRLn7viEsYjIWGBhvu4awqMkHhOPfXkpF4ZIk7UxOLLb9DBFJkiKvCfA80CLDdUiSlFSOwEmSJElSRDgCJ0mSJEkR4QicJEmSJEWEAU6SJEmSIsIAJ0mSJEkRYYCTJEmSpIgwwEmSJElSRBjgJEmSJCki/n9Fn5w2UQCMaQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d = xai.smile_imbalance(\n",
+    "    y_test, \n",
+    "    probabilities,\n",
+    "    display_breakdown=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d = xai.smile_imbalance(\n",
+    "    y_test, \n",
+    "    probabilities,\n",
+    "    bins=9,\n",
+    "    threshold=0.75,\n",
+    "    manual_review=0.375,\n",
+    "    display_breakdown=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "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>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>ethnicity</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.03225</td>\n",
+       "      <td>-0.002917</td>\n",
+       "      <td>0.000417</td>\n",
+       "      <td>0.049333</td>\n",
+       "      <td>0.05475</td>\n",
+       "      <td>0.041417</td>\n",
+       "      <td>0.04225</td>\n",
+       "      <td>0.000917</td>\n",
+       "      <td>0.003833</td>\n",
+       "      <td>0.050083</td>\n",
+       "      <td>0.006333</td>\n",
+       "      <td>0.009917</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       age  workclass  education  education-num  marital-status  occupation  \\\n",
+       "0  0.03225  -0.002917   0.000417       0.049333         0.05475    0.041417   \n",
+       "\n",
+       "   relationship  ethnicity    gender  capital-gain  capital-loss  \\\n",
+       "0       0.04225   0.000917  0.003833      0.050083      0.006333   \n",
+       "\n",
+       "   hours-per-week  \n",
+       "0        0.009917  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "dark"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def get_avg(x, y):\n",
+    "    return model.evaluate(f_in(x), y, verbose=0)[1]\n",
+    "\n",
+    "imp = xai.feature_importance(x_test, y_test, get_avg)\n",
+    "\n",
+    "imp.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/xai/__init__.py b/xai/__init__.py
index e67b7ce..5a2219c 100644
--- a/xai/__init__.py
+++ b/xai/__init__.py
@@ -7,6 +7,7 @@
 import random, math
 # TODO: Remove Dependencies, starting with Sklearn
 from sklearn.metrics import roc_curve, precision_recall_curve
+import logging
 
 # TODO: Make categorical_cols optional argument (None) to 
 # avoid ambiguity when there are no categorical cols
@@ -611,173 +612,125 @@ def metrics_imbalances(
 
     return results
 
-
-
-def roc_imbalance(
-        x_df,
-        y_valid,
-        y_pred,
-        col_name=None,
-        cross=[],
-        categorical_cols=None,
+def roc_plot(
+        actual,
+        predicted,
+        df=pd.DataFrame(),
+        cross_cols=[],
+        categorical_cols=[],
         bins=6,
         plot=True):
 
-    x_tmp = x_df.copy()
-    x_tmp["target"] = y_valid
-    x_tmp["predicted"] = y_pred
-
-    if col_name is None:
-        grouped = [("target", x_tmp),]
-    else:
-        cols = cross + [col_name]
-        grouped = group_by_columns(
-            x_tmp,
-            cols,
-            bins=bins,
-            categorical_cols=categorical_cols)
-
-    if plot:
-        plt.figure()
-
-    fprs = tprs = []
-
-    for group, group_df in grouped:
-        group_valid = group_df["target"]
-        group_pred = group_df["predicted"]
-
-        fpr, tpr, _ = roc_curve(group_valid, group_pred)
-        fprs.append(fpr)
-        tprs.append(tpr)
-
-        if plot:
-            plt.plot(fpr, tpr, label=group)
-            plt.plot([0, 1], [0, 1], 'k--')
-
-    if plot:
-        plt.xlim([0.0, 1.0])
-        plt.ylim([0.0, 1.05])
-        plt.xlabel('False Positive Rate')
-        plt.ylabel('True Positive Rate')
-        plt.legend(loc="lower right")
-        plt.show()
-
-    return fprs, tprs
-
-def roc_imbalances(
-        x_test,
-        y_test,
-        predictions,
-        columns=[],
+    return _curve(
+        actual=actual,
+        predicted=predicted,
+        curve_type="roc",
+        df=df,
+        cross_cols=cross_cols,
+        categorical_cols=categorical_cols,
+        bins=bins,
+        plot=plot)
+
+def pr_plot(
+        actual,
+        predicted,
+        df=pd.DataFrame(),
+        cross_cols=[],
         categorical_cols=[],
-        cross=[],
         bins=6,
         plot=True):
 
-    if not len(columns):
-        columns = x_test.columns
-
-    if not len(categorical_cols):
-        categorical_cols = x_test.select_dtypes(include=[np.object, np.bool]).columns
-
-    results = []
-    for col in columns:
-        r = roc_imbalance(
-            x_test,
-            y_test,
-            predictions,
-            col,
-            cross=cross,
-            categorical_cols=categorical_cols,
-            bins=6,
-            plot=True)
-        results.append(r)
-
-    return results
-
-
-
-def pr_imbalance(
-        x_df,
-        y_valid,
-        y_pred,
-        col_name=None,
-        cross=[],
-        categorical_cols=None,
+    return _curve(
+        actual=actual,
+        predicted=predicted,
+        curve_type="pr",
+        df=df,
+        cross_cols=cross_cols,
+        categorical_cols=categorical_cols,
+        bins=bins,
+        plot=plot)
+
+def _curve(
+        actual,
+        predicted,
+        curve_type="roc",
+        df=pd.DataFrame(),
+        cross_cols=[],
+        categorical_cols=[],
         bins=6,
         plot=True):
 
-    x_tmp = x_df.copy()
-    x_tmp["target"] = y_valid
-    x_tmp["predicted"] = y_pred
-
-    if col_name is None:
-        grouped = [("target", x_tmp),]
+    if curve_type == "roc":
+        curve_func = roc_curve
+        y_label = 'False Positive Rate'
+        x_label = 'True Positive Rate'
+        p1 = [0,1]
+        p2 = [0,1]
+        y_lim = [0, 1.05]
+        legend_loc = "lower right"
+    elif curve_type == "pr":
+        curve_func = precision_recall_curve 
+        y_label = "Recall"
+        x_label = "Precision"
+        p1 = [1,0]
+        p2 = [0.5,0.5]
+        y_lim = [0.25, 1.05]
+        legend_loc = "lower left"
+    else:
+        raise ValueError("Curve function provided not valid. "
+                f" curve_func provided: {curve_func}")
+
+    if not all(c in df.columns for c in cross_cols):
+        raise KeyError("Cross columns don't match columns in dataframe provided.")
+
+    df_tmp = df.copy()
+    df_tmp["target"] = actual
+    df_tmp["predicted"] = predicted
+
+    if not categorical_cols and cross_cols:
+        categorical_cols = df_tmp.select_dtypes(
+                include=[np.object, np.bool, np.int8]).columns
+        logging.warn("No categorical_cols passed so inferred using np.object, "
+                f"np.int8 and np.bool: {categorical_cols}. If these are not "
+                "correct, please provide them as a string array as: "
+                "categorical_cols=['col1', 'col2', ...]")
+
+    if not cross_cols:
+        grouped = [("target", df_tmp),]
     else:
-        cols = cross + [col_name]
         grouped = group_by_columns(
-            x_tmp,
-            cols,
+            df_tmp,
+            cross_cols,
             bins=bins,
             categorical_cols=categorical_cols)
 
     if plot:
         plt.figure()
 
-    prs = rcs = []
+    r1s = r2s = []
 
     for group, group_df in grouped:
         group_valid = group_df["target"]
         group_pred = group_df["predicted"]
 
-        pr, rc, _ = precision_recall_curve(group_valid, group_pred)
-        prs.append(pr)
-        rcs.append(rc)
+        r1, r2, _ = curve_func(group_valid, group_pred)
+        r1s.append(r1)
+        r2s.append(r2)
 
         if plot:
-            plt.plot(pr,rc, label=group)
+            if curve_type == "pr": r1,r2 = r2,r1
+            plt.plot(r1, r2, label=group)
+            plt.plot(p1, p2, 'k--')
 
     if plot:
         plt.xlim([0.0, 1.0])
-        plt.ylim([0.0, 1.05])
-        plt.xlabel('Recall')
-        plt.ylabel('Precision')
-        plt.legend(loc="lower left")
+        plt.ylim(y_lim)
+        plt.xlabel(x_label)
+        plt.ylabel(y_label)
+        plt.legend(loc=legend_loc)
         plt.show()
 
-    return prs, rcs
-
-def pr_imbalances(
-        x_test,
-        y_test,
-        predictions,
-        columns=[],
-        categorical_cols=[],
-        cross=[],
-        bins=6,
-        plot=True):
-
-    if not len(columns):
-        columns = x_test.columns
-
-    if not len(categorical_cols):
-        categorical_cols = x_test.select_dtypes(include=[np.object, np.bool]).columns
-
-    results = []
-    for col in columns:
-        r = pr_imbalance(
-            x_test,
-            y_test,
-            predictions,
-            col,
-            cross=cross,
-            categorical_cols=categorical_cols,
-            bins=6,
-            plot=True)
-        results.append(r)
-
-    return results
-
+    return r1s, r2s
 
 def smile_imbalance(
         y_test,