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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "# Tutorial: EqualizedOddsImprovement Metric\n",
+    "\n",
+    "This notebook demonstrates how to use the `EqualizedOddsImprovement` metric to evaluate fairness in synthetic data generation. We'll use the Adult dataset to show how synthetic data can potentially improve fairness in machine learning models.\n",
+    "\n",
+    "## What is Equalized Odds?\n",
+    "\n",
+    "Equalized odds is a fairness criterion that requires the True Positive Rate (TPR) and False Positive Rate (FPR) to be equal across different groups defined by a sensitive attribute (like gender, race, etc.). \n",
+    "\n",
+    "The `EqualizedOddsImprovement` metric compares how well a model trained on synthetic data maintains fairness compared to a model trained on real data, both evaluated on the same validation set.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Setup and Imports\n",
+    "\n",
+    "First, let's install and import all the necessary libraries:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install sdv\n",
+    "!pip install xgboost\n",
+    "!pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All libraries imported successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import json\n",
+    "\n",
+    "from sdv.single_table import TVAESynthesizer\n",
+    "from sdv.datasets.demo import download_demo\n",
+    "from sdv.sampling import Condition\n",
+    "\n",
+    "from sdmetrics.single_table.equalized_odds import EqualizedOddsImprovement\n",
+    "\n",
+    "print(\"All libraries imported successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Step 1: Load the Adult Dataset\n",
+    "\n",
+    "We'll use the Adult dataset from the SDV demo datasets. This dataset contains information about individuals and whether they earn more than $50K per year.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset shape: (32561, 15)\n",
+      "\n",
+      "First few rows:\n"
+     ]
+    },
+    {
+     "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>fnlwgt</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>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>native-country</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>177119</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>44</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>216481</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>256263</td>\n",
+       "      <td>Assoc-acdm</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>46</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>147640</td>\n",
+       "      <td>5th-6th</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Transport-moving</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>Amer-Indian-Eskimo</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1902</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>172822</td>\n",
+       "      <td>11th</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Transport-moving</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>76</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age workclass  fnlwgt     education  education-num      marital-status  \\\n",
+       "0   27   Private  177119  Some-college             10            Divorced   \n",
+       "1   27   Private  216481     Bachelors             13       Never-married   \n",
+       "2   25   Private  256263    Assoc-acdm             12  Married-civ-spouse   \n",
+       "3   46   Private  147640       5th-6th              3  Married-civ-spouse   \n",
+       "4   45   Private  172822          11th              7            Divorced   \n",
+       "\n",
+       "         occupation   relationship                race     sex  capital-gain  \\\n",
+       "0      Adm-clerical      Unmarried               White  Female             0   \n",
+       "1    Prof-specialty  Not-in-family               White  Female             0   \n",
+       "2             Sales        Husband               White    Male             0   \n",
+       "3  Transport-moving        Husband  Amer-Indian-Eskimo    Male             0   \n",
+       "4  Transport-moving  Not-in-family               White    Male             0   \n",
+       "\n",
+       "   capital-loss  hours-per-week native-country  label  \n",
+       "0             0              44  United-States  <=50K  \n",
+       "1             0              40  United-States  <=50K  \n",
+       "2             0              40  United-States  <=50K  \n",
+       "3          1902              40  United-States  <=50K  \n",
+       "4          2824              76  United-States   >50K  "
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the adult dataset\n",
+    "real_data, metadata = download_demo('single_table', 'adult')\n",
+    "\n",
+    "print(f\"Dataset shape: {real_data.shape}\")\n",
+    "print(f\"\\nFirst few rows:\")\n",
+    "real_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Introduce Data Bias\n",
+    "\n",
+    "We'll introduce bias to the data by setting the label of 95% of female rows as '<=50K', while the other 5% are '>50K'. The labels are randomly chosen, ie they have no correlation with the data besides the gender column.\n",
+    "\n",
+    "For male rows the opposite will be done, ie 5% of the data labels will be '<=50K' and 95% will be '>50K'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "female_mask = real_data['sex'] == 'Female'\n",
+    "num_females = female_mask.sum()\n",
+    "\n",
+    "female_labels = np.random.choice(['<=50K', '>50K'], size=num_females, p=[0.95, 0.05])\n",
+    "real_data.loc[female_mask, 'label'] = female_labels\n",
+    "\n",
+    "male_mask = real_data['sex'] == 'Male'\n",
+    "num_males = male_mask.sum()\n",
+    "\n",
+    "male_labels = np.random.choice(['<=50K', '>50K'], size=num_males, p=[0.05, 0.95])\n",
+    "real_data.loc[male_mask, 'label'] = male_labels\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the distributions\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# income distribution by sex\n",
+    "crosstab_pct = pd.crosstab(real_data['sex'], real_data['label'], normalize='index') * 100\n",
+    "crosstab_pct.plot(kind='bar', ax=axes[0], rot=0)\n",
+    "axes[0].set_title('Income Distribution by Sex (%)')\n",
+    "axes[0].set_xlabel('Sex')\n",
+    "axes[0].set_ylabel('Percentage')\n",
+    "axes[0].legend(title='Income')\n",
+    "\n",
+    "# Overall income distribution\n",
+    "real_data['label'].value_counts().plot(kind='bar', ax=axes[1], rot=0)\n",
+    "axes[1].set_title('Overall Income Distribution')\n",
+    "axes[1].set_xlabel('Income')\n",
+    "axes[1].set_ylabel('Count')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Step 3: Split Data into Training and Validation Sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Training set shape: (22792, 15)\n",
+      "Validation set shape: (9769, 15)\n",
+      "\n",
+      "Training set combinations:\n",
+      "sex    Female   Male\n",
+      "label               \n",
+      "<=50K    7164    738\n",
+      ">50K      354  14536\n",
+      "\n",
+      "Validation set combinations:\n",
+      "sex    Female  Male\n",
+      "label              \n",
+      "<=50K    3089   336\n",
+      ">50K      164  6180\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_data, validation_data = train_test_split(\n",
+    "    real_data,\n",
+    "    test_size=0.3,\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "print(f\"\\nTraining set shape: {training_data.shape}\")\n",
+    "print(f\"Validation set shape: {validation_data.shape}\")\n",
+    "\n",
+    "# Verify all combinations exist in both sets\n",
+    "print(\"\\nTraining set combinations:\")\n",
+    "print(pd.crosstab(training_data['label'], training_data['sex']))\n",
+    "print(\"\\nValidation set combinations:\")\n",
+    "print(pd.crosstab(validation_data['label'], validation_data['sex']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Step 4: Generate Synthetic Data\n",
+    "\n",
+    "We'll use the TVAE (Tabular Variational AutoEncoder) synthesizer to generate synthetic data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training TVAE synthesizer...\n",
+      "Synthesizer training completed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training TVAE synthesizer...\")\n",
+    "\n",
+    "synthesizer = TVAESynthesizer(metadata=metadata)\n",
+    "synthesizer.fit(training_data)\n",
+    "\n",
+    "print(\"Synthesizer training completed!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Synthetic data shape: (22792, 15)\n",
+      "\n",
+      "Target and sensitive attribute distribution:\n",
+      "sex    Female   Male\n",
+      "label               \n",
+      "<=50K    6868    936\n",
+      ">50K       83  14905\n"
+     ]
+    }
+   ],
+   "source": [
+    "synthetic_data = synthesizer.sample(len(training_data))\n",
+    "\n",
+    "print(f\"Synthetic data shape: {synthetic_data.shape}\")\n",
+    "print(\"\\nTarget and sensitive attribute distribution:\")\n",
+    "print(pd.crosstab(synthetic_data['label'], synthetic_data['sex']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Step 5: Evaluate Synthetic Data\n",
+    "\n",
+    "Let's evaluate the synthetic data generated with the EqualizedOddsImprovement metric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score: 0.5337\n",
+      "\n",
+      "Score Interpretation:\n",
+      "- Score > 0.5 means synthetic data improves fairness\n",
+      "- Score < 0.5 means synthetic data worsens fairness\n",
+      "- Score = 0.5 means no change in fairness\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_standard = EqualizedOddsImprovement.compute_breakdown(\n",
+    "    real_training_data=training_data,\n",
+    "    synthetic_data=synthetic_data,\n",
+    "    real_validation_data=validation_data,\n",
+    "    metadata=metadata.to_dict()['tables']['adult'],\n",
+    "    prediction_column_name='label',\n",
+    "    positive_class_label='>50K',\n",
+    "    sensitive_column_name='sex',\n",
+    "    sensitive_column_value='Female'\n",
+    ")\n",
+    "\n",
+    "print(f\"Score: {result_standard['score']:.4f}\")\n",
+    "print(f\"\\nScore Interpretation:\")\n",
+    "print(f\"- Score > 0.5 means synthetic data improves fairness\")\n",
+    "print(f\"- Score < 0.5 means synthetic data worsens fairness\")\n",
+    "print(f\"- Score = 0.5 means no change in fairness\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Full breakdown of the Equalized Odds Improvement metric:\n",
+      "{\n",
+      "  \"score\": 0.5337489169733715,\n",
+      "  \"real_training_data\": {\n",
+      "    \"equalized_odds\": 0.0008090614886731018,\n",
+      "    \"prediction_counts_validation\": {\n",
+      "      \"Female=True\": {\n",
+      "        \"true_positive\": 0,\n",
+      "        \"false_positive\": 15,\n",
+      "        \"true_negative\": 3074,\n",
+      "        \"false_negative\": 164\n",
+      "      },\n",
+      "      \"Female=False\": {\n",
+      "        \"true_positive\": 6175,\n",
+      "        \"false_positive\": 336,\n",
+      "        \"true_negative\": 0,\n",
+      "        \"false_negative\": 5\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"synthetic_data\": {\n",
+      "    \"equalized_odds\": 0.06830689543541602,\n",
+      "    \"prediction_counts_validation\": {\n",
+      "      \"Female=True\": {\n",
+      "        \"true_positive\": 14,\n",
+      "        \"false_positive\": 211,\n",
+      "        \"true_negative\": 2878,\n",
+      "        \"false_negative\": 150\n",
+      "      },\n",
+      "      \"Female=False\": {\n",
+      "        \"true_positive\": 6172,\n",
+      "        \"false_positive\": 336,\n",
+      "        \"true_negative\": 0,\n",
+      "        \"false_negative\": 8\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Full breakdown of the Equalized Odds Improvement metric:')\n",
+    "print(json.dumps(result_standard, indent=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Step 6: Generate Conditionally Sampled Synthetic Data\n",
+    "\n",
+    "Now let's try to improve fairness by using conditional sampling to create a more balanced dataset where each combination of target and sensitive attributes has equal representation (25% each)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating conditionally sampled synthetic data...\n",
+      "Each condition will have 25% of the data (equal representation)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling conditions: 100%|██████████| 22792/22792 [00:14<00:00, 1609.35it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated 22792 samples\n",
+      "\n",
+      "Target and sensitive attribute distribution:\n",
+      "sex    Female  Male\n",
+      "label              \n",
+      "<=50K    5698  5698\n",
+      ">50K     5698  5698\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Generating conditionally sampled synthetic data...\")\n",
+    "print(\"Each condition will have 25% of the data (equal representation)\")\n",
+    "\n",
+    "total_samples = len(training_data)\n",
+    "samples_per_condition = total_samples // 4\n",
+    "conditions = [\n",
+    "    Condition({'label': '>50K', 'sex': 'Female'}, num_rows=samples_per_condition),\n",
+    "    Condition({'label': '<=50K', 'sex': 'Female'}, num_rows=samples_per_condition),\n",
+    "    Condition({'label': '>50K', 'sex': 'Male'}, num_rows=samples_per_condition),\n",
+    "    Condition({'label': '<=50K', 'sex': 'Male'}, num_rows=samples_per_condition)\n",
+    "]\n",
+    "balanced_synthetic_data = synthesizer.sample_from_conditions(conditions=conditions)\n",
+    "print(f\"Generated {len(balanced_synthetic_data)} samples\")\n",
+    "\n",
+    "print(\"\\nTarget and sensitive attribute distribution:\")\n",
+    "balanced_crosstab = pd.crosstab(balanced_synthetic_data['label'], balanced_synthetic_data['sex'])\n",
+    "print(balanced_crosstab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Step 7: Evaluate Balanced Synthetic Data\n",
+    "\n",
+    "Now let's evaluate the balanced synthetic data to compare it with the standard synthetic data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score: 0.7693\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_balanced = EqualizedOddsImprovement.compute_breakdown(\n",
+    "    real_training_data=training_data,\n",
+    "    synthetic_data=balanced_synthetic_data,\n",
+    "    real_validation_data=validation_data,\n",
+    "    metadata=metadata.to_dict()['tables']['adult'],\n",
+    "    prediction_column_name='label',\n",
+    "    positive_class_label='>50K',\n",
+    "    sensitive_column_name='sex',\n",
+    "    sensitive_column_value='Female'\n",
+    ")\n",
+    "\n",
+    "print(f\"Score: {result_balanced['score']:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The full breakdown of the Equalized Odds Improvement metric is:\n",
+      "{\n",
+      "  \"score\": 0.7692813165995738,\n",
+      "  \"real_training_data\": {\n",
+      "    \"equalized_odds\": 0.0008090614886731018,\n",
+      "    \"prediction_counts_validation\": {\n",
+      "      \"Female=True\": {\n",
+      "        \"true_positive\": 0,\n",
+      "        \"false_positive\": 15,\n",
+      "        \"true_negative\": 3074,\n",
+      "        \"false_negative\": 164\n",
+      "      },\n",
+      "      \"Female=False\": {\n",
+      "        \"true_positive\": 6175,\n",
+      "        \"false_positive\": 336,\n",
+      "        \"true_negative\": 0,\n",
+      "        \"false_negative\": 5\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"synthetic_data\": {\n",
+      "    \"equalized_odds\": 0.5393716946878206,\n",
+      "    \"prediction_counts_validation\": {\n",
+      "      \"Female=True\": {\n",
+      "        \"true_positive\": 81,\n",
+      "        \"false_positive\": 1708,\n",
+      "        \"true_negative\": 1381,\n",
+      "        \"false_negative\": 83\n",
+      "      },\n",
+      "      \"Female=False\": {\n",
+      "        \"true_positive\": 5899,\n",
+      "        \"false_positive\": 317,\n",
+      "        \"true_negative\": 19,\n",
+      "        \"false_negative\": 281\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('The full breakdown of the Equalized Odds Improvement metric is:')\n",
+    "print(json.dumps(result_balanced, indent=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "## Step 8: Compare Results and Analysis\n",
+    "\n",
+    "Let's compare the results from both approaches to analyze the impact of balanced sampling on fairness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5vkqlUmooZkRcHsTmwMxCbF6VuBxojHF5bWLzek2if/7552nixImpQ4cOVZbH5TfeeKPG23zyySc1rh/LazJo0KB0wgknTLa8U6dOqb4MHT683u4baPxWXHHF+h5Cg/P40MfrewhAI1Zfn6uxk9C+ffvUEMyIuDyIzYGZjdi8KnE50Fg/U38tNq/XJPqMENU0lStkJk2alL788ss0zzzzpCZNmtTr2KCmX79iJ/L9999P7dq1q+/hADRqPlNpqKLKJYL0jh07plmN2JzGwncIQN3yuUpjj83rNYk+77zzpmbNmqVPP/20yvK4vMACC9R4m1hem/VbtWqVT5XNOeecv3nsMD3FF4ovFYC64TOVhqihVKDPyLg8iM1pbHyHANQtn6s01ti8Xg8s2rJly7TyyiunYcOGValGics9e/as8TaxvPL6YejQoYXrAwAAUyYuBwCA1HDbucR0zl133TWtssoqabXVVkuDBw9O33//ferXr1++vm/fvmmhhRbK/RPDwQcfnNZbb710xhlnpE033TQNGTIkPfvss+niiy+u50cCAACNl7gcAAAaaBJ9++23T2PGjEnHHXdcPghRjx490n333VdxkKLRo0enpk1/KZhfc80103XXXZeOOeaYdNRRR6WuXbum22+/PS277LL1+CigbsT05oEDB042zRmA2vOZCrUjLodf+A4BqFs+V2nsmpSiezoAAAAAANCweqIDAAAAAEBDJokOAAAAAAAFJNEBAAAAAKCAJDo0Au+++25q0qRJeuGFFxrVtgHqyvHHH58PctjQ+UwFmLmJywHE5syaJNGhwJgxY9Kf/vSntMgii+SjRy+wwAKpd+/e6YknnsjXxwfx7bffXt/DBGjwdtttt/yZWT7NM888aaONNkovvfRSfQ8NgEZAXA5Qd8TmMG0k0aHANttsk0aMGJGuuuqq9NZbb6U777wzrb/++umLL75IjdH48ePrewjALCwC848//jifhg0blpo3b54222yz+h4WAI2AuBygbonNofYk0aEGX3/9dXrsscfSqaeemn73u9+lRRddNK222mppwIABaYsttkidO3fO62211Vb5l9vy5ZEjR6Ytt9wydejQIc0xxxxp1VVXTQ8++GCVbce6J598ctp9991T27Ztc0XNxRdfXGWdp59+Oq244oqpdevWaZVVVsk7DZVNnDgx7bHHHqlLly5pttlmS0sttVQ6++yzJ/t1uU+fPumkk05KHTt2zOtMzbYBpody5WCcYurnkUcemd5///1cXRiOOOKItOSSS6Y2bdqkxRZbLB177LHp559/LtzeM888kzbYYIM077zzpvbt26f11lsvPf/881XWic/nSy+9NH9Wx3a7du2aEy+Vvfrqq3mHoV27dvkzeZ111smf5WVx+27duuXPzKWXXjpdcMEFVW7vMxVg+hKXA9Q9sTnUniQ61CAC7TjFtNBx48bV+AURrrjiivzLbfnyd999lzbZZJP8S258WMevu5tvvnkaPXp0ldufccYZFR/o++23X56e+uabb1ZsI740unfvnp577rnca+ywww6rcvtJkyalhRdeON10003ptddeS8cdd1w66qij0o033lhlvRhHbHfo0KHp7rvvnqptA0xv8Vl07bXXpiWWWCJPHw0RJF955ZX5My2SD5dcckk666yzCrfx7bffpl133TU9/vjj6amnnspBeHz+xvLKTjjhhLTddtvl6alx/c4775y+/PLLfN2HH36Y1l133bwT8dBDD+XPxUikTJgwIV//z3/+M3++RtLj9ddfz4mW2IGISsjy4/CZCjB9icsBpi+xOUylElCjm2++uTTXXHOVWrduXVpzzTVLAwYMKL344osV18efz2233far21lmmWVK5557bsXlRRddtPTHP/6x4vKkSZNK888/f+nCCy/Ml//xj3+U5plnntKPP/5YsU5cF/c3YsSIwvvZf//9S9tss03F5V133bXUoUOH0rhx4yqWTeu2AX6L+Dxq1qxZafbZZ8+n+MxZcMEFS88991zhbf7+97+XVl555YrLAwcOLK2wwgqF60+cOLHUtm3b0l133VWxLO7nmGOOqbj83Xff5WX/+te/8uX4XO/SpUtp/PjxNW5z8cUXL1133XVVlp144omlnj175vM+UwFmDHE5QN0Rm8O0UYkOU+i9+NFHH+XpRVG58sgjj6SVVlop/xpbJH75jF86Y3rRnHPOmatm4hfS6hUvyy+/fJUpTTGF6rPPPsuXY/24PqYflfXs2XOy+zr//PPTyiuvnOabb758PzH1tPr9LLfccqlly5YVl6d22wB1Labgv/DCC/kU0yzjgHAbb7xxeu+99/L1N9xwQ1prrbXy52F8ph1zzDGTfaZV9umnn6a99torV7nElNGY8hmfwVP6vJ199tnzeuXP2xhLTBFt0aLFZNv//vvv89TRmKJfroKM09/+9reKKaU+UwFmDHE5QN0Sm0PtNZ+G28AsIz58o69XnGKa0J577pkGDhyY+xrWJAL1mKJ5+umn56lQ0Rdx2223nezgQdW/FCJgj6mgU2vIkCH5vmL6aXwpxFSrv//97+k///lPlfXiSwmgIYjPo/hcrNzPMALsmBq66aab5qmcMb0zAvhYHp9z8RlXJKaLxgHlYnpp9MeNaZ/xeVibz9v4jC4SQX+I8a2++upVrmvWrFktHz0Av5W4HKDuiM2h9iTRoRait1b0Yyx/+MeBhCp74oknciAfB8oof9C/++67tbqPqJa55ppr0k8//VTxC2r0FKt+P2uuuWbu21hW+WAbv2XbADNCBMxNmzZNP/74Yxo+fHgOto8++uiK68tVMEXiczAOJBS9FEMcCOnzzz+v1RiiUiV6KMZBkqoH9HEgujj42zvvvJN3ImriMxWg/ojLAeqO2Bx+nXYuUIP4BfX//u//8sE14oAXo0aNygcLOu2009KWW26Z1+ncuXM+QNAnn3ySvvrqq7wspi7deuuteRrSiy++mHbaaadaVbKEuE18gcVUqDiIx7333psraCqL+3n22WfT/fffn956661cjVM+iNJv3TbA9BAHg4vPyzjFVMsDDzwwJzTiIG/xmRZTPaPCJRIP55xzTrrtttumuL24TQTJsa2o9otgekrVKzU54IAD0tixY9MOO+yQP1PffvvtvM3yAeWi+mbQoEF5PPFZ+/LLL+cD15155pn5ep+pANOfuByg7onNofYk0aEG0VsrpgjF0afj6NDLLrtsDojjw/i8887L68RUppgi2qlTp7TiiivmZfHhPddcc+VqlPjyialP0a+xtvd911135S+E2G78+nvqqadWWWefffZJW2+9ddp+++3zOGPnonL1y2/ZNsD0cN9996UFF1wwn+JzKxIMkQRZf/310xZbbJEOPfTQHDj36NEjV7/EZ+6UXHbZZTlREp+xu+yySzrooIPS/PPPX6sxzTPPPOmhhx7KOwzrrbde7mcbU0TLlS/RKiCmtkZwHr1sY53ov9ulS5d8vc9UgOlPXA5Q98TmUHtN4uii03A7AAAAAACY6alEBwAAAACAApLoAAAAAABQQBIdAAAAAAAKSKIDAAAAAEABSXRoQI4//vh89OvpIY6yfcghh0yXbQM0RD5TAZhWvkMA6pbPVRo7SXSowZgxY9Kf/vSntMgii6RWrVqlBRZYIPXu3Ts98cQTdXYfTZo0Sbfffnuqa4888kje9tdff11l+a233ppOPPHEOr8/gF/jMxWAaeU7BKBu+VyFadN8Gm8HM7VtttkmjR8/Pl111VVpscUWS59++mkaNmxY+uKLL1JjNffcc9f3EIBZlM9UAKaV7xCAuuVzFaZRCajiq6++KsWfxiOPPFLj9f369SttuummVZaNHz++NN9885UuvfTSfHm99dYrHXjggaXDDz+8NNdcc5U6dOhQGjhwYMX6iy66aL6P8ikuh1hnhRVWKF199dV5Wbt27Urbb799aezYsRW3nThxYunkk08ude7cudS6devS8ssvX7rpppvydaNGjaqy3TjtuuuuFWM6+OCDK7bz008/lf7yl7+UFl544VLLli1Liy++eMX4AeqKz1QAppXvEIC65XMVpp0kOlTz888/l+aYY47SIYcckj94q3viiSdKzZo1K3300UcVy2699dbS7LPPXvr2228rPsDjC+H4448vvfXWW6Wrrrqq1KRJk9IDDzyQr//ss8/yB/4VV1xR+vjjj/Pl8pdK3PfWW29devnll0uPPvpoaYEFFigdddRRFff1t7/9rbT00kuX7rvvvtLIkSPzNlq1apW/BCdMmFC65ZZb8rbffPPNvO2vv/66xi+V7bbbrtSpU6c89tjOgw8+WBoyZMh0fGaBWZHPVACmle8QgLrlcxWmnSQ61ODmm2/Ov6jGL59rrrlmacCAAaUXX3yx4vru3buXTj311IrLm2++eWm33XaruBwf4GuvvXaVba666qqlI444ouJyfPDfdtttVdaJL5U2bdpU+SU2ft1dffXV8/n4kovrhw8fXuV2e+yxR2nHHXfM5x9++OG87fiFubLKXyrxhRPrDB06dBqfIYCp5zMVgGnlOwSgbvlchWnjwKJQ0CPso48+SnfeeWfaaKON8sErVlpppXTllVfm6/fcc890xRVX5PPRP+xf//pX2n333atsY/nll69yecEFF0yfffbZr953586dU9u2bWu83X//+9/0ww8/pA022CDNMcccFaerr746jRw5cqof3wsvvJCaNWuW1ltvvam+DcC08pkKwLTyHQJQt3yuwrRxYFEo0Lp16/zhHadjjz02f5EMHDgw7bbbbqlv377pyCOPTE8++WQaPnx46tKlS1pnnXWq3L5FixZVLscRpCdNmvSr9zul23333Xf5/3vuuScttNBCVdaLo2pPrdlmm22q1wWoCz5TAZhWvkMA6pbPVag9SXSYSt27d0+33357Pj/PPPOkPn365F9n44ulX79+td5efHlMnDix1mOIL4/Ro0cX/qrasmXL/P+Utr3ccsvlL6p///vfqVevXrUcOcBv5zMVgGnlOwSgbvlchV8niQ7VfPHFF+kPf/hDnq4UU5RiqtGzzz6bTjvttLTllltWrBe/1G622Wb5w3vXXXet9f3ENKZhw4altdZaK39RzDXXXL96mxjLYYcdlg499ND8pbD22munb775Jj3xxBOpXbt2eRyLLrpo/jX37rvvTptsskn+FTamQFW/71g3HuM555yTVlhhhfTee+/laVTbbbddrR8LQBGfqT5TAaaV7xDfIUDd8rnqc5Vppyc6VBMfwKuvvno666yz0rrrrpuWXXbZPL1pr732Suedd17FevGLZvTv6t27d+rYsWOt7+eMM85IQ4cOTZ06dUorrrjiVN/uxBNPzOMZNGhQ6tatW+5hFtOdYopViGlPJ5xwQp5+1aFDh3TAAQfUuJ0LL7wwbbvttmm//fZLSy+9dH5833//fa0fB8CU+EwFYFr5DgGoWz5XYdo1iaOL/obbwywr+nXFB3hMcdp6663rezgAjZrPVACmle8QgLrlcxUmp50L1FJMK/r888/zL6tzzjln2mKLLep7SACNls9UAKaV7xCAuuVzFYpJokMtxUEuYirRwgsvnK688srUvLk/I4Bp5TMVgGnlOwSgbvlchWLauQAAAAAAQAEHFgUAAAAAgAKS6AAAAAAAUEASHQAAAAAACkiiAwAAAABAAUl0AAAAAAAoIIkOAAAAAAAFJNEBAAAAAKCAJDoAAAAAABSQRAcAAAAAgFSz/wclB9sH0uhWpAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
+    "\n",
+    "# Improvement scores comparison\n",
+    "scores = [result_standard['score'], result_balanced['score']]\n",
+    "labels = ['Standard\\nSynthetic', 'Balanced\\nSynthetic']\n",
+    "colors = ['lightcoral', 'lightgreen']\n",
+    "\n",
+    "bars1 = axes[0].bar(labels, scores, color=colors, alpha=0.7, edgecolor='black')\n",
+    "axes[0].axhline(y=0.5, color='red', linestyle='--', alpha=0.7, label='No Improvement Baseline')\n",
+    "axes[0].set_ylim(0, 1)\n",
+    "axes[0].set_ylabel('Improvement Score')\n",
+    "axes[0].set_title('Overall Score Comparison')\n",
+    "axes[0].legend()\n",
+    "\n",
+    "# Add score labels on bars\n",
+    "for bar, score in zip(bars1, scores):\n",
+    "    axes[0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
+    "                f'{score:.4f}', ha='center', va='bottom', fontweight='bold')\n",
+    "\n",
+    "# Equalized odds scores comparison\n",
+    "eq_scores = [result_standard['synthetic_data']['equalized_odds'], result_balanced['synthetic_data']['equalized_odds']]\n",
+    "bars2 = axes[1].bar(labels, eq_scores, color=colors, alpha=0.7, edgecolor='black')\n",
+    "axes[1].set_ylim(0, 1)\n",
+    "axes[1].set_ylabel('Equalized Odds Score')\n",
+    "axes[1].set_title('Equalized Odds Score Comparison')\n",
+    "\n",
+    "# Add score labels on bars\n",
+    "for bar, score in zip(bars2, eq_scores):\n",
+    "    axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
+    "                f'{score:.4f}', ha='center', va='bottom', fontweight='bold')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "a",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/resources/visualize.png b/resources/visualize.png
deleted file mode 100644
index 1d9924cb..00000000
Binary files a/resources/visualize.png and /dev/null differ
diff --git a/sdmetrics/single_table/equalized_odds.py b/sdmetrics/single_table/equalized_odds.py
index d6b543d3..d343797f 100644
--- a/sdmetrics/single_table/equalized_odds.py
+++ b/sdmetrics/single_table/equalized_odds.py
@@ -272,6 +272,11 @@ def _validate_parameters(
         required_columns = [prediction_column_name, sensitive_column_name]
         _validate_required_columns(dataframes_dict, required_columns)
 
+        # Use base class validation for real_training_data and synthetic_data
+        real_training_data, synthetic_data, metadata = cls._validate_inputs(
+            real_training_data, synthetic_data, metadata
+        )
+
         # Validate data and metadata consistency for prediction column
         _validate_data_and_metadata(
             real_training_data,
@@ -286,11 +291,6 @@ def _validate_parameters(
         column_value_pairs = [(sensitive_column_name, sensitive_column_value)]
         _validate_column_values_exist(dataframes_dict, column_value_pairs)
 
-        # Use base class validation for real_training_data and synthetic_data
-        real_training_data, synthetic_data, metadata = cls._validate_inputs(
-            real_training_data, synthetic_data, metadata
-        )
-
         # Validate the validation data separately (not part of standard _validate_inputs)
         real_validation_data = real_validation_data.copy()