25-fairness-indicators/C3_W4_Lab_3_Fairness_Indicators.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YwpE8n4v_JeT"
   },
   "source": [
    "# Ungraded Lab: Fairness Indicators\n",
    "\n",
    "In this lab, you will use [Fairness Indicators](https://www.tensorflow.org/tfx/guide/fairness_indicators) to explore the `Smiling` attribute in a large-scale face image dataset. Fairness Indicators is a suite of tools built on top of [TensorFlow Model Analysis](https://www.tensorflow.org/tfx/model_analysis/get_started) that enable regular evaluation of fairness metrics in product pipelines. This [Introductory Video](https://www.youtube.com/watch?v=pHT-ImFXPQo) provides more details and context on the real-world scenario presented here, one of primary motivations for creating Fairness Indicators. This notebook will teach you to:\n",
    "\n",
    "\n",
    "*   Train a simple neural network model to detect a person's smile in images using [TF Keras](https://www.tensorflow.org/guide/keras) and the [CelebFaces Attributes (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset.\n",
    "*   Evaluate model performance against a commonly used fairness metric across age groups using Fairness Indicators.\n",
    "\n",
    "*Credits: Some of the code and discussions are taken from this [Tensorflow tutorial](https://colab.research.google.com/github/tensorflow/fairness-indicators/blob/master/g3doc/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study.ipynb).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fiqohiU73cRf"
   },
   "source": [
    "## Imports\n",
    "\n",
    "You will import the main packages and some utilities you will need in this notebook. Notice that you are not importing `fairness-indicators` directly. As mentioned in the intro, this suite of tools is built on top of TFMA so you can just import TFMA to access it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "A66hFOyMorfQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow 2.11.0\n",
      "TFMA 0.43.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_model_analysis as tfma\n",
    "import tensorflow_datasets as tfds\n",
    "from tensorflow import keras\n",
    "from absl import logging as absl_logging\n",
    "from apache_beam import logging as beam_logging\n",
    "\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "absl_logging.set_verbosity('ERROR')\n",
    "beam_logging.getLogger().setLevel('ERROR')\n",
    "\n",
    "print(\"TensorFlow \" + tf.__version__)\n",
    "print(\"TFMA \" + tfma.VERSION_STRING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aLzC_ZvSEM_C"
   },
   "source": [
    "## Download and prepare the dataset\n",
    "\n",
    "[CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) is a large-scale face attributes dataset with more than 200,000 celebrity images, each with 40 attribute annotations (such as hair type, fashion accessories, facial features, etc.) and 5 landmark locations (eyes, mouth and nose positions). For more details, you can read more in [this paper](https://liuziwei7.github.io/projects/FaceAttributes.html).\n",
    "\n",
    "With the permission of the owners, this dataset is stored on Google Cloud Storage (GCS) and mostly accessed via [TensorFlow Datasets(`tfds`)](https://www.tensorflow.org/datasets). To save on download time and disk space, you will use the GCS bucket specified below as your data directory. This already contains the TFRecords. If you want to download it to your workspace, you can pass a local directory to the `data_dir` argument. Just take note that it will take time to complete the download.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "_tRPCjI9YxKN"
   },
   "outputs": [],
   "source": [
    "# URI of the dataset in Goocle Cloud Storage\n",
    "GCS_BASE_DIR = \"gs://celeb_a_dataset/\"\n",
    "\n",
    "# Load the data using TFDS\n",
    "data, data_info = tfds.load(\"celeb_a\", data_dir=GCS_BASE_DIR, with_info=True, builder_kwargs={'version':'2.0.0'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5c8Rsf-WamtK"
   },
   "source": [
    "You can preview some of the images in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "8r0ZMW3_ZnZU"
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 648x648 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take 6 examples and preview images\n",
    "fig = tfds.show_examples(data['train'].take(6), data_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nt8Ahn9fbBTh"
   },
   "source": [
    "You can also view the dataset as a dataframe to preview the other attributes in tabular format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "S7Ndy-sKbpbk"
   },
   "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>attributes/5_o_Clock_Shadow</th>\n",
       "      <th>attributes/Arched_Eyebrows</th>\n",
       "      <th>attributes/Attractive</th>\n",
       "      <th>attributes/Bags_Under_Eyes</th>\n",
       "      <th>attributes/Bald</th>\n",
       "      <th>attributes/Bangs</th>\n",
       "      <th>attributes/Big_Lips</th>\n",
       "      <th>attributes/Big_Nose</th>\n",
       "      <th>attributes/Black_Hair</th>\n",
       "      <th>attributes/Blond_Hair</th>\n",
       "      <th>...</th>\n",
       "      <th>landmarks/lefteye_x</th>\n",
       "      <th>landmarks/lefteye_y</th>\n",
       "      <th>landmarks/leftmouth_x</th>\n",
       "      <th>landmarks/leftmouth_y</th>\n",
       "      <th>landmarks/nose_x</th>\n",
       "      <th>landmarks/nose_y</th>\n",
       "      <th>landmarks/righteye_x</th>\n",
       "      <th>landmarks/righteye_y</th>\n",
       "      <th>landmarks/rightmouth_x</th>\n",
       "      <th>landmarks/rightmouth_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>111</td>\n",
       "      <td>64</td>\n",
       "      <td>149</td>\n",
       "      <td>75</td>\n",
       "      <td>138</td>\n",
       "      <td>109</td>\n",
       "      <td>113</td>\n",
       "      <td>114</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>111</td>\n",
       "      <td>68</td>\n",
       "      <td>151</td>\n",
       "      <td>90</td>\n",
       "      <td>133</td>\n",
       "      <td>108</td>\n",
       "      <td>112</td>\n",
       "      <td>109</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>112</td>\n",
       "      <td>67</td>\n",
       "      <td>151</td>\n",
       "      <td>84</td>\n",
       "      <td>132</td>\n",
       "      <td>109</td>\n",
       "      <td>111</td>\n",
       "      <td>110</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>111</td>\n",
       "      <td>69</td>\n",
       "      <td>153</td>\n",
       "      <td>85</td>\n",
       "      <td>136</td>\n",
       "      <td>107</td>\n",
       "      <td>111</td>\n",
       "      <td>108</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   attributes/5_o_Clock_Shadow  attributes/Arched_Eyebrows  \\\n",
       "0                        False                       False   \n",
       "1                         True                        True   \n",
       "2                        False                       False   \n",
       "3                        False                       False   \n",
       "\n",
       "   attributes/Attractive  attributes/Bags_Under_Eyes  attributes/Bald  \\\n",
       "0                   True                       False            False   \n",
       "1                  False                       False             True   \n",
       "2                   True                       False            False   \n",
       "3                   True                       False            False   \n",
       "\n",
       "   attributes/Bangs  attributes/Big_Lips  attributes/Big_Nose  \\\n",
       "0             False                 True                False   \n",
       "1             False                 True                 True   \n",
       "2              True                False                False   \n",
       "3             False                False                False   \n",
       "\n",
       "   attributes/Black_Hair  attributes/Blond_Hair  ...  landmarks/lefteye_x  \\\n",
       "0                  False                  False  ...                   68   \n",
       "1                  False                  False  ...                   69   \n",
       "2                  False                   True  ...                   68   \n",
       "3                  False                  False  ...                   69   \n",
       "\n",
       "   landmarks/lefteye_y  landmarks/leftmouth_x  landmarks/leftmouth_y  \\\n",
       "0                  111                     64                    149   \n",
       "1                  111                     68                    151   \n",
       "2                  112                     67                    151   \n",
       "3                  111                     69                    153   \n",
       "\n",
       "   landmarks/nose_x  landmarks/nose_y  landmarks/righteye_x  \\\n",
       "0                75               138                   109   \n",
       "1                90               133                   108   \n",
       "2                84               132                   109   \n",
       "3                85               136                   107   \n",
       "\n",
       "   landmarks/righteye_y  landmarks/rightmouth_x  landmarks/rightmouth_y  \n",
       "0                   113                     114                     153  \n",
       "1                   112                     109                     152  \n",
       "2                   111                     110                     152  \n",
       "3                   111                     108                     152  \n",
       "\n",
       "[4 rows x 51 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take 4 examples as a dataframe\n",
    "df = tfds.as_dataframe(data['train'].take(4), data_info)\n",
    "\n",
    "# View the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NqoM3nazby8C"
   },
   "source": [
    "Let's list the column header so you can see the attribute names in the dataset. For this notebook, you will just examine the `attributes/Young` and `attributes/Smiling` features but feel free to pick other features once you've gone over the whole exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "icDbLpkaGN0S"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['attributes/5_o_Clock_Shadow', 'attributes/Arched_Eyebrows',\n",
       "       'attributes/Attractive', 'attributes/Bags_Under_Eyes',\n",
       "       'attributes/Bald', 'attributes/Bangs', 'attributes/Big_Lips',\n",
       "       'attributes/Big_Nose', 'attributes/Black_Hair', 'attributes/Blond_Hair',\n",
       "       'attributes/Blurry', 'attributes/Brown_Hair',\n",
       "       'attributes/Bushy_Eyebrows', 'attributes/Chubby',\n",
       "       'attributes/Double_Chin', 'attributes/Eyeglasses', 'attributes/Goatee',\n",
       "       'attributes/Gray_Hair', 'attributes/Heavy_Makeup',\n",
       "       'attributes/High_Cheekbones', 'attributes/Male',\n",
       "       'attributes/Mouth_Slightly_Open', 'attributes/Mustache',\n",
       "       'attributes/Narrow_Eyes', 'attributes/No_Beard', 'attributes/Oval_Face',\n",
       "       'attributes/Pale_Skin', 'attributes/Pointy_Nose',\n",
       "       'attributes/Receding_Hairline', 'attributes/Rosy_Cheeks',\n",
       "       'attributes/Sideburns', 'attributes/Smiling',\n",
       "       'attributes/Straight_Hair', 'attributes/Wavy_Hair',\n",
       "       'attributes/Wearing_Earrings', 'attributes/Wearing_Hat',\n",
       "       'attributes/Wearing_Lipstick', 'attributes/Wearing_Necklace',\n",
       "       'attributes/Wearing_Necktie', 'attributes/Young', 'image',\n",
       "       'landmarks/lefteye_x', 'landmarks/lefteye_y', 'landmarks/leftmouth_x',\n",
       "       'landmarks/leftmouth_y', 'landmarks/nose_x', 'landmarks/nose_y',\n",
       "       'landmarks/righteye_x', 'landmarks/righteye_y',\n",
       "       'landmarks/rightmouth_x', 'landmarks/rightmouth_y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List dataframe header\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BO5Ld9oOYAvZ"
   },
   "source": [
    "In this notebook:\n",
    "* Your model will attempt to classify whether the subject of the image is smiling, as represented by the `Smiling` attribute<sup>*</sup>.\n",
    "*   Images will be resized from 218x178 to 28x28 to reduce the execution time and memory when training.\n",
    "*   Your model's performance will be evaluated across age groups, using the binary `Young` attribute. You will call this \"age group\" in this notebook.\n",
    "\n",
    "___\n",
    "\n",
    "<sup>*</sup> *While there is little information available about the labeling methodology for this dataset, you will assume that the \"Smiling\" attribute was determined by a pleased, kind, or amused expression on the subject's face. For the purpose of this example, you will take these labels as ground truth.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1uzyvjt8EyLT"
   },
   "source": [
    "### Caveats\n",
    "Before moving forward, there are several considerations to keep in mind when using CelebA:\n",
    "*   Although, in principle, this notebook could use any dataset of face images, CelebA was chosen because it contains public domain images of public figures.\n",
    "*   All of the attribute annotations in CelebA are operationalized as binary categories. For example, the `Young` attribute (as determined by the dataset labelers) is denoted as either present or absent in the image.\n",
    "*   CelebA's categorizations do not reflect real human diversity of attributes.\n",
    "*   For the purposes of this notebook, the feature containing the `Young` attribute is referred to as \"age group\". A `True` will put the image as a member of the `Young` age group and a `False` will put the image as a member of the `Not Young` age group. These are assumptions made as this information is not mentioned in the [original paper](http://openaccess.thecvf.com/content_iccv_2015/html/Liu_Deep_Learning_Face_ICCV_2015_paper.html).\n",
    "*   As such, performance in the models trained in this notebook is tied to the ways the attributes have been operationalized and annotated by the authors of CelebA.\n",
    "*   This model should not be used for commercial purposes as that would violate [CelebA's non-commercial research agreement](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6yDEIn3cE6Uo"
   },
   "source": [
    "### Setting Up Input Functions\n",
    "\n",
    "Now, you will define the preprocessing functions to prepare your data as model inputs. These include resizing images, normalizing pixels, casting to the right data type, and grouping the features and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "3PSk2eUUx9L8"
   },
   "outputs": [],
   "source": [
    "# Define Constants\n",
    "ATTR_KEY = \"attributes\"\n",
    "IMAGE_KEY = \"image\"\n",
    "LABEL_KEY = \"Smiling\"\n",
    "GROUP_KEY = \"Young\"\n",
    "IMAGE_SIZE = 28\n",
    "\n",
    "# Define Preprocessing Function\n",
    "def preprocess_input_dict(feat_dict):\n",
    "  ''' Picks the attributes to study and resizes the images\n",
    "  Args:\n",
    "    feat_dict (dictionary): features from the dataset\n",
    "\n",
    "  Returns:\n",
    "    dictionary containing the resized image, label, and age group\n",
    "  '''\n",
    "  # Separate out the image and target variable from the feature dictionary.\n",
    "  image = feat_dict[IMAGE_KEY]\n",
    "  label = feat_dict[ATTR_KEY][LABEL_KEY]\n",
    "  group = feat_dict[ATTR_KEY][GROUP_KEY]\n",
    "\n",
    "  # Resize and normalize image.\n",
    "  image = tf.cast(image, tf.float32)\n",
    "  image = tf.image.resize(image, [IMAGE_SIZE, IMAGE_SIZE])\n",
    "  image /= 255.0\n",
    "\n",
    "  # Cast label and group to float32.\n",
    "  label = tf.cast(label, tf.float32)\n",
    "  group = tf.cast(group, tf.float32)\n",
    "\n",
    "  # Put the computed values in a dictionary\n",
    "  feat_dict[IMAGE_KEY] = image\n",
    "  feat_dict[ATTR_KEY][LABEL_KEY] = label\n",
    "  feat_dict[ATTR_KEY][GROUP_KEY] = group\n",
    "\n",
    "  return feat_dict\n",
    "\n",
    "# Define lambda functions to group features and labels for training and evaluation\n",
    "get_image_and_label = lambda feat_dict: (feat_dict[IMAGE_KEY], feat_dict[ATTR_KEY][LABEL_KEY])\n",
    "get_image_label_and_group = lambda feat_dict: (feat_dict[IMAGE_KEY], feat_dict[ATTR_KEY][LABEL_KEY], feat_dict[ATTR_KEY][GROUP_KEY])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IQOsJKPshhmJ"
   },
   "source": [
    "## Prepare train and test splits\n",
    "\n",
    "This next helper function will help split, shuffle, batch and preprocess your training data. For this notebook, you will just develop a model that accepts the image as input and outputs the `Smiling` attribute (i.e. label)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "-CfC6ZF4pc7I"
   },
   "outputs": [],
   "source": [
    "def celeb_a_train_data_wo_group(data, batch_size):\n",
    "  '''\n",
    "  Args:\n",
    "    data (TF dataset) - dataset to preprocess\n",
    "    batch_size (int) - batch size\n",
    "  \n",
    "  Returns:\n",
    "    Batches of preprocessed datasets containing tuples with (image, label)\n",
    "  '''\n",
    "  celeb_a_train_data = data.shuffle(1024).repeat().batch(batch_size).map(preprocess_input_dict)\n",
    "  return celeb_a_train_data.map(get_image_and_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GZYWhFB-lal-"
   },
   "source": [
    "The `test` split does not need to be shuffled so you can just preprocess it like below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "hLkOPoVwdhKh"
   },
   "outputs": [],
   "source": [
    "# Prepare test data\n",
    "celeb_a_test_data = data['test'].batch(1).map(preprocess_input_dict).map(get_image_label_and_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xcQwGY4JnsT6"
   },
   "source": [
    "As a sanity check, you can examine the contents of a one example in the test data. You should see that it is successfully reshaped and the pixels should be normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "svHIiXWPmfy4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type: <class 'tuple'>\n",
      "Number of elements: 3\n",
      "Shape of 1st element: (1, 28, 28, 3)\n",
      "Shape of 2nd element: (1,)\n",
      "Shape of 3rd element: (1,)\n",
      "Contents: \n",
      "(<tf.Tensor: shape=(1, 28, 28, 3), dtype=float32, numpy=\n",
      "array([[[[0.81317526, 0.7912615 , 0.72465986],\n",
      "         [0.85708785, 0.83748   , 0.7542867 ],\n",
      "         [0.8946379 , 0.86615646, 0.77189875],\n",
      "         ...,\n",
      "         [0.527636  , 0.3903811 , 0.16293013],\n",
      "         [0.5096639 , 0.3841737 , 0.15280116],\n",
      "         [0.5019608 , 0.3882353 , 0.15294118]],\n",
      "\n",
      "        [[0.7951531 , 0.75985897, 0.69963485],\n",
      "         [0.7954932 , 0.7562775 , 0.7156613 ],\n",
      "         [0.80069023, 0.76064926, 0.7249299 ],\n",
      "         ...,\n",
      "         [0.5149909 , 0.3777361 , 0.1502851 ],\n",
      "         [0.50980395, 0.38431373, 0.15294118],\n",
      "         [0.5019608 , 0.3882353 , 0.15294118]],\n",
      "\n",
      "        [[0.74019605, 0.6931372 , 0.6382353 ],\n",
      "         [0.757418  , 0.71035916, 0.6554572 ],\n",
      "         [0.7790216 , 0.7319077 , 0.6804222 ],\n",
      "         ...,\n",
      "         [0.49884957, 0.3802521 , 0.15848838],\n",
      "         [0.5060224 , 0.3883753 , 0.13767505],\n",
      "         [0.5058824 , 0.38431373, 0.16078432]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[0.4265211 , 0.3478997 , 0.25378206],\n",
      "         [0.37941152, 0.28501377, 0.18291293],\n",
      "         [0.5415068 , 0.4387057 , 0.31951806],\n",
      "         ...,\n",
      "         [0.4872593 , 0.35395598, 0.10908321],\n",
      "         [0.5692585 , 0.4866355 , 0.23984672],\n",
      "         [0.1599792 , 0.1325282 , 0.06978311]],\n",
      "\n",
      "        [[0.322579  , 0.21025406, 0.09330729],\n",
      "         [0.2881203 , 0.189941  , 0.07271411],\n",
      "         [0.29329234, 0.1928722 , 0.08236799],\n",
      "         ...,\n",
      "         [0.5442577 , 0.44201183, 0.17365956],\n",
      "         [0.152111  , 0.13096255, 0.01717705],\n",
      "         [0.3653809 , 0.2843435 , 0.1575379 ]],\n",
      "\n",
      "        [[0.26529136, 0.16094968, 0.07551543],\n",
      "         [0.27388975, 0.18761528, 0.09587853],\n",
      "         [0.22688586, 0.1515357 , 0.0604993 ],\n",
      "         ...,\n",
      "         [0.2677126 , 0.18372396, 0.03662009],\n",
      "         [0.18214768, 0.14041106, 0.0327629 ],\n",
      "         [0.5616248 , 0.45547238, 0.18067256]]]], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([1.], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([1.], dtype=float32)>)\n"
     ]
    }
   ],
   "source": [
    "# Print info about the test data records\n",
    "for sample in celeb_a_test_data.take(1):\n",
    "  print(f'Data type: {type(sample)}')\n",
    "  print(f'Number of elements: {len(sample)}')\n",
    "  print(f'Shape of 1st element: {sample[0].shape}')\n",
    "  print(f'Shape of 2nd element: {sample[1].shape}')\n",
    "  print(f'Shape of 3rd element: {sample[2].shape}')\n",
    "  print(f'Contents: \\n{sample}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bZ7SCKIEF1IC"
   },
   "source": [
    "## Build a simple DNN Model\n",
    "\n",
    "With the dataset prepared, you will now assemble a simple `tf.keras.Sequential` model to classify your images. The model consists of:\n",
    "\n",
    "1. An input layer that represents the flattened 28x28x3 image.\n",
    "2. A fully connected layer with 64 units activated by a ReLU function.\n",
    "3. A single-unit readout layer to output real-scores instead of probabilities.\n",
    "\n",
    "You may be able to greatly improve model performance by adding some complexity (e.g., more densely-connected layers, exploring different activation functions, increasing image size), but that may distract from the goal of demonstrating how easy it is to apply the indicators when working with Keras. For that reason, you will first keep the model simple — but feel free to explore this space later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "tpNlbyyapfbL"
   },
   "outputs": [],
   "source": [
    "def create_model():\n",
    "  '''Builds the simple DNN binary classifier'''\n",
    "\n",
    "  # Build the model using the Sequential API\n",
    "  model = keras.Sequential([\n",
    "      keras.layers.Flatten(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), name='image'),\n",
    "      keras.layers.Dense(64, activation='relu'),\n",
    "      keras.layers.Dense(1, activation=None)\n",
    "  ])\n",
    "\n",
    "  # Compile the model with hinge loss and binary accuracy metric\n",
    "  model.compile(\n",
    "      optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "      loss='hinge',\n",
    "      metrics='binary_accuracy')\n",
    "  \n",
    "  return model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zLVPrZGYGwiI"
   },
   "source": [
    "## Train & Evaluate Model\n",
    "\n",
    "You’re now ready to train your model. To cut back on the amount of execution time and memory, you will train the model by slicing the data into small batches with only a few repeated iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "spMUm9wNp0nW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1000/1000 [==============================] - 33s 25ms/step - loss: 0.4863 - binary_accuracy: 0.7789\n",
      "Epoch 2/5\n",
      "1000/1000 [==============================] - 22s 22ms/step - loss: 0.3668 - binary_accuracy: 0.8366\n",
      "Epoch 3/5\n",
      "1000/1000 [==============================] - 22s 22ms/step - loss: 0.3656 - binary_accuracy: 0.8410\n",
      "Epoch 4/5\n",
      "1000/1000 [==============================] - 23s 23ms/step - loss: 0.3396 - binary_accuracy: 0.8484\n",
      "Epoch 5/5\n",
      "1000/1000 [==============================] - 22s 22ms/step - loss: 0.3346 - binary_accuracy: 0.8510\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f83fc77f1d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "# Build the model\n",
    "model = create_model()\n",
    "\n",
    "# Train the model\n",
    "model.fit(celeb_a_train_data_wo_group(data['train'], BATCH_SIZE), epochs=5, steps_per_epoch=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CgYTmRdcHYOK"
   },
   "source": [
    "Evaluating the model on the test data should result in a final accuracy score of just over 85%. Not bad for a simple model with no fine tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "tlPUI-ovqL-F"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19962/19962 [==============================] - 27s 1ms/step - loss: 0.2137 - binary_accuracy: 0.8602\n"
     ]
    }
   ],
   "source": [
    "# Evaluate trained model on the test data\n",
    "results = model.evaluate(celeb_a_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HKdvynqKrWfn"
   },
   "source": [
    "You will then save the model so you can analyze it in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Jk06C_O3SIkx"
   },
   "outputs": [],
   "source": [
    "# Define model directory\n",
    "MODEL_LOCATION = 'saved_model'\n",
    "\n",
    "# Save the model\n",
    "model.save(MODEL_LOCATION, save_format='tf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WGJl6pJeGYzD"
   },
   "source": [
    "## Model Analysis\n",
    "\n",
    "As you already know, it is usually not enough to just measure your model's performance on global metrics. For instance, performance evaluated across age groups may reveal some shortcomings.\n",
    "\n",
    "To explore this further, you will evaluate the model with Fairness Indicators via TFMA. In particular, you will see whether there is a significant gap in performance between \"Young\" and \"Not Young\" categories when evaluated on false positive rate (FPR).\n",
    "\n",
    "A false positive error occurs when the model incorrectly predicts the positive class. In this context, a false positive outcome occurs when the ground truth is an image of a celebrity 'Not Smiling' and the model predicts 'Smiling'. While this seems like a relatively mundane error, false positive errors can sometimes cause more problematic behaviors when deployed in a real world application. For instance, a false positive error in a spam classifier could cause a user to miss an important email.\n",
    "\n",
    "You will mostly follow the same steps as you did in the first ungraded lab of this week. Namely, you will:\n",
    "\n",
    "* Create a TFRecord of the test dataset.\n",
    "* Write an `EvalConfig` file\n",
    "* Create an `EvalSharedModel`\n",
    "* Define a `Schema` message\n",
    "* Run model analysis with TFMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kuPDsIdmClFa"
   },
   "source": [
    "### Create a TFRecord for the Test Set\n",
    "\n",
    "You will need to serialize the preprocessed test dataset so it can be read by TFMA. We've provided a helper function to do just that. Notice that the age group feature is transformed into a string ('Young' or 'Not Young'). This will come in handy in the visualization so the tags are easier to interpret (compared to just 1 or 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "-zrBYHAFteT6"
   },
   "outputs": [],
   "source": [
    "# Define filename\n",
    "TFRECORD_FILE = 'celeb_a_test.tfrecord'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "LY1QtzKBtG7p"
   },
   "outputs": [],
   "source": [
    "def celeb_ds_to_tfrecord(dataset, tfrecord_file):\n",
    "  ''' Helper function to convert a TF Dataset to TFRecord\n",
    "\n",
    "  Args:\n",
    "    dataset (TF Dataset) - dataset to save as TFRecord\n",
    "    tfrecord_file (string) - filename to use when writing the TFRecord\n",
    "  '''\n",
    "\n",
    "  # Initialize examples list\n",
    "  examples = []\n",
    "\n",
    "  for row in dataset:\n",
    "    # Get image, label, and group tensors\n",
    "    image = row[0]\n",
    "    label = row[1]\n",
    "    group = row[2]\n",
    "\n",
    "    # Flatten image\n",
    "    image = tf.reshape(image, [-1])\n",
    "\n",
    "    # Instantiate Example\n",
    "    output = tf.train.Example()\n",
    "\n",
    "    # Assign features' numpy arrays to the Example feature values\n",
    "    output.features.feature[IMAGE_KEY].float_list.value.extend(image.numpy().tolist())\n",
    "    output.features.feature[LABEL_KEY].float_list.value.append(label.numpy()[0])\n",
    "    output.features.feature[GROUP_KEY].bytes_list.value.append(b\"Young\" if group.numpy()[0] else b'Not Young')\n",
    "    \n",
    "    # Append to examples list\n",
    "    examples.append(output)\n",
    "\n",
    "  # Serialize examples and save as tfrecord\n",
    "  with tf.io.TFRecordWriter(tfrecord_file) as writer:\n",
    "    for example in examples:\n",
    "      writer.write(example.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "hPsF2Uu3uh7U"
   },
   "outputs": [],
   "source": [
    "# Use the helper function to serialize the test dataset\n",
    "celeb_ds_to_tfrecord(celeb_a_test_data, TFRECORD_FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KYEFWyrZHqyV"
   },
   "source": [
    "### Write the EvalConfig file\n",
    "\n",
    "Next, you will define the model, metrics, and slicing specs in an eval config file. As mentioned, you will slice the data across age groups to see if there is an underlying problem. For metrics, you will include the `FairnessIndicators` class. These are commonly-identified fairness metrics for binary and multiclass classifiers. Moreover, you will configure a list of thresholds. These will allow you to observe if the model predicts better when the threshold to determine between the two classes is changed (e.g. will the FPR be lower if the model predicts \"Smiling\" for outputs greater than 0.22?). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "KNM8_wn1S5-P"
   },
   "outputs": [],
   "source": [
    "# Import helper module\n",
    "from google.protobuf import text_format\n",
    "\n",
    "# Write EvalConfig string\n",
    "eval_config_pbtxt = \"\"\"\n",
    "      model_specs {\n",
    "        label_key: \"%s\"\n",
    "      }\n",
    "      metrics_specs {\n",
    "        metrics {\n",
    "          class_name: \"FairnessIndicators\"\n",
    "          config: '{ \"thresholds\": [0.22, 0.5, 0.75] }'\n",
    "        }\n",
    "        metrics {\n",
    "          class_name: \"ExampleCount\"\n",
    "        }\n",
    "      }\n",
    "      slicing_specs {}\n",
    "      slicing_specs { feature_keys: \"%s\" }\n",
    "    \"\"\" % (LABEL_KEY, GROUP_KEY)\n",
    "\n",
    "# Parse as a Message\n",
    "eval_config = text_format.Parse(eval_config_pbtxt, tfma.EvalConfig())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iN-67hysIycZ"
   },
   "source": [
    "### Create the EvalSharedModel\n",
    "\n",
    "This will be identical to the command you ran in an earlier lab. This is needed so TFMA will know how to load and configure your model from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "_3cBZNlOvGC1"
   },
   "outputs": [],
   "source": [
    "# Create EvalSharedModel\n",
    "eval_shared_model = tfma.default_eval_shared_model(\n",
    "      eval_saved_model_path=MODEL_LOCATION, eval_config=eval_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5DOHO4VoJ99b"
   },
   "source": [
    "### Create a Schema\n",
    "\n",
    "This is an additional step from your previous TFMA workflow. It is needed particularly because, unlike the TFMA ungraded lab, you didn't include a serving signature with the model. If you remember, the function called by that signature took care of parsing the tfrecords, converting them to the correct data type, and preprocessing. Since that part is not included in this lab, you will need to provide a schema so TFMA will know what data types are in the serialized examples when it parses the tfrecord into a dictionary of features. You will also need to define the dimensions of the image since that is expected by your model input. That is handled by the `tensor_representation_group` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "BtiJuufKvDV8"
   },
   "outputs": [],
   "source": [
    "from tensorflow_metadata.proto.v0 import schema_pb2\n",
    "from google.protobuf import text_format\n",
    "\n",
    "# Define Schema message as string\n",
    "schema_pbtxt = \"\"\"\n",
    "      tensor_representation_group {\n",
    "        key: \"\"\n",
    "        value {\n",
    "          tensor_representation {\n",
    "            key: \"%s\"\n",
    "            value {\n",
    "              dense_tensor {\n",
    "                column_name: \"%s\"\n",
    "                shape {\n",
    "                  dim { size: 28 }\n",
    "                  dim { size: 28 }\n",
    "                  dim { size: 3 }\n",
    "                }\n",
    "              }\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "      feature {\n",
    "        name: \"%s\"\n",
    "        type: FLOAT\n",
    "      }\n",
    "      feature {\n",
    "        name: \"%s\"\n",
    "        type: FLOAT\n",
    "      }\n",
    "      feature {\n",
    "        name: \"%s\"\n",
    "        type: BYTES\n",
    "      }\n",
    "      \"\"\" % (IMAGE_KEY, IMAGE_KEY, IMAGE_KEY, LABEL_KEY, GROUP_KEY)\n",
    "\n",
    "# Parse the schema string to a message\n",
    "schema = text_format.Parse(schema_pbtxt, schema_pb2.Schema())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N8GUBgybKq39"
   },
   "source": [
    "### Run TFMA\n",
    "\n",
    "You will pass the objects you created in the previous sections to `tfma.run_model_analysis()`. As you've done previously, this will take care of loading the model and data, and computing the metrics on the data slices you specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "dbpqTuHbTKFH"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "\n        if (typeof window.interactive_beam_jquery == 'undefined') {\n          var jqueryScript = document.createElement('script');\n          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n          jqueryScript.type = 'text/javascript';\n          jqueryScript.onload = function() {\n            var datatableScript = document.createElement('script');\n            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n            datatableScript.type = 'text/javascript';\n            datatableScript.onload = function() {\n              window.interactive_beam_jquery = jQuery.noConflict(true);\n              window.interactive_beam_jquery(document).ready(function($){\n                \n              });\n            }\n            document.head.appendChild(datatableScript);\n          };\n          document.head.appendChild(jqueryScript);\n        } else {\n          window.interactive_beam_jquery(document).ready(function($){\n            \n          });\n        }"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define output directory\n",
    "OUTPUT_PATH = 'tfma_output'\n",
    "\n",
    "# Run model analysis\n",
    "eval_results = tfma.run_model_analysis(\n",
    "    eval_shared_model=eval_shared_model,\n",
    "    eval_config=eval_config,\n",
    "    data_location=TFRECORD_FILE,\n",
    "    schema=schema,\n",
    "    output_path=OUTPUT_PATH\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i5BvsF8OH3CE"
   },
   "source": [
    "Now you can view the fairness metrics you specified. The FPR should already be selected (if not, please tick the `False Positive Rate` checkbox on the left) and you can see that it is considerably higher for the `Not Young` age group. Try to explore the widget and see if you can make other findings. Here are some suggestions:\n",
    "\n",
    "* Toggle the threshold drop down and see how the FPR changes with different thresholds\n",
    "\n",
    "* Change the baseline to `Young: Young` so the percentage difference (in the table below the chart) will be measured against it.\n",
    "\n",
    "* Deselect the `Overall` slice so you can compare the two age groups side by side.\n",
    "\n",
    "* Select other metrics to display and observe their charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Q0R5OlNDqWwS"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c0e02e2f9994e738b636a5048e7c4e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Young', 'slice': 'Young:Young', 'metrics': {'binary_ac…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the fairness metrics\n",
    "# NOTE: If you don't see a visualization on the first try, please re-run this cell to generate it again.\n",
    "tfma.addons.fairness.view.widget_view.render_fairness_indicator(eval_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pr5DrvGZO2B6"
   },
   "source": [
    "After studying the discrepancies in your predictions, you can then investigate why that happens and have a plan on remidiating it. Aside from changing your model architecture, you can also look first at your training data. `fairness-indicators` is also packaged with TFDV so you can use it to generate statistics from your data. Here is a short review on how to do that. \n",
    "\n",
    "First, you will load the dataset from the GCS bucket into your local workspace. You can use the [gsutil](https://cloud.google.com/storage/docs/gsutil) tool to help with that. (*Note: The files are already pre-downloaded into your workspace so some lines are commented out in the next two cells*) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Rd-oUa1z-yjQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A newer version of gsutil (5.26) is available than the version you are\r\n",
      "running (4.61). NOTE: This is a major new version, so it is strongly\r\n",
      "recommended that you review the release note details at\r\n",
      "https://pub.storage.googleapis.com/gsutil_ReleaseNotes.txt before\r\n",
      "updating to this version, especially if you use gsutil in scripts.\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00000-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00001-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00002-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00003-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00004-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00005-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00006-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00007-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00008-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00009-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00010-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00011-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00012-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00013-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00014-of-00016\r\n",
      "gs://celeb_a_dataset/celeb_a/2.0.0/celeb_a-train.tfrecord-00015-of-00016\r\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_data_validation as tfdv\n",
    "\n",
    "# Define training directory\n",
    "TRAIN_DIR = f'{GCS_BASE_DIR}celeb_a/2.0.0/celeb_a-train.tfrecord*'\n",
    "\n",
    "# View tfrecord filenames in GCS\n",
    "!gsutil ls {TRAIN_DIR}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "pbwyiEgZO2L0"
   },
   "outputs": [],
   "source": [
    "# Define the data directory\n",
    "DATA_DIR = './data/celeb_a-train-tfrecords'\n",
    "\n",
    "# Create the data directory\n",
    "# !mkdir {DATA_DIR}\n",
    "\n",
    "# Download the dataset into the local directory (this is already done for you in Coursera Labs)\n",
    "# !gsutil -m cp {TRAIN_DIR} {DATA_DIR}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8LdZy5ZyOyYh"
   },
   "source": [
    "Now you can generate the statistics for a specific feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "xGteF9i1FyhA"
   },
   "outputs": [
    {
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   ],
   "source": [
    "# Filter features to observe\n",
    "stats_options = tfdv.StatsOptions(feature_allowlist=['attributes/Young'])\n",
    "\n",
    "# Compute the statistics for all tf records under the data directory\n",
    "statistics = tfdv.generate_statistics_from_tfrecord(f'{DATA_DIR}/', stats_options=stats_options)\n",
    "\n",
    "# Visualize the statistics\n",
    "tfdv.visualize_statistics(statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q6huPG-SQNyV"
   },
   "source": [
    "The statistics show that the `Not Young` age group (i.e. `0` in the `attributes/Young` column) has very few images compared to the `Young` age group. Maybe that's why the model learns on the `Young` images better. You could try adding more `Not Young` images and see if your model performs better on this slice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6hhF68-0Q0nZ"
   },
   "source": [
    "## Wrap Up\n",
    "\n",
    "In this lab, you prepared an image dataset and trained a model to predict one of its attributes (i.e. `Smiling`). You then sliced the data based on age groups and computed fairness metrics from the `Fairness Indicators` package via TFMA. Though the outcome looks simple, it is an important step in production ML projects because not detecting these problems can greatly affect the experience of your users. Improving these metrics will help you commit to fairness in your applications. We encourage you to try exploring more slices of the dataset and see what findings you can come up with.\n",
    "\n",
    "For more practice, [here](https://colab.research.google.com/github/tensorflow/fairness-indicators/blob/master/g3doc/tutorials/Fairness_Indicators_Example_Colab.ipynb) is an official tutorial that uses fairness indicators on text data. It uses the [What-If-Tool](https://pair-code.github.io/what-if-tool/) which is another package that comes with `Fairness Indicators`. You will also get to explore that in this week's programming assignment."
   ]
  }
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