Detect data bias with Amazon SageMaker Clarify.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detect data bias with Amazon SageMaker Clarify\n",
    "\n",
    "### Introduction\n",
    "\n",
    "\n",
    "Bias can be present in data before any model training occurs. Inspecting the dataset for bias can help detect collection gaps, inform your feature engineering, and understand societal biases the dataset may reflect. In this notebook we will analyze bias on the dataset, generate and analyze bias report, and prepare the dataset for the model training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's install and import required modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# please ignore warning messages during the installation\n",
    "!pip install --disable-pip-version-check -q sagemaker==2.35.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sess   = sagemaker.Session()\n",
    "bucket = sess.default_bucket()\n",
    "role = sagemaker.get_execution_role()\n",
    "region = boto3.Session().region_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-1.'></a>\n",
    "# 1. Analyze the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-1.1.'></a>\n",
    "### 1.1. Create a pandas data frame from the CSV file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a pandas dataframe from each of the product categories and concatenate them into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://dlai-practical-data-science/data/transformed/womens_clothing_ecommerce_reviews_transformed.csv to ./womens_clothing_ecommerce_reviews_transformed.csv\n"
     ]
    }
   ],
   "source": [
    "!aws s3 cp 's3://dlai-practical-data-science/data/transformed/womens_clothing_ecommerce_reviews_transformed.csv' ./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>sentiment</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment                                        review_body  \\\n",
       "0          1  If this product was in petite  i would get the...   \n",
       "1          1  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2          0  I had such high hopes for this dress and reall...   \n",
       "3          1  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4          1  This shirt is very flattering to all due to th...   \n",
       "\n",
       "  product_category  \n",
       "0          Blouses  \n",
       "1          Dresses  \n",
       "2          Dresses  \n",
       "3            Pants  \n",
       "4          Blouses  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = './womens_clothing_ecommerce_reviews_transformed.csv'\n",
    "\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you saw in the previous notebook, there are way more positive reviews than negative or neutral. Such a dataset is called unbalanced. \n",
    "\n",
    "In this case, using a relatively small data subset you could visualize the occurring unbalances. At scale, you would need to perform bias analysis. Let's use this dataset as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f88025dd9d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 278,
       "width": 490
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(data=df, x='sentiment', hue='product_category')\n",
    "\n",
    "plt.legend(loc='upper right',bbox_to_anchor=(1.3, 1.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-1.2.'></a>\n",
    "### 1.2. Upload the dataset to S3 bucket\n",
    "\n",
    "Upload the dataset to a private S3 bucket in a folder called `bias/unbalanced`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-east-1-186735670237/bias/unbalanced/womens_clothing_ecommerce_reviews_transformed.csv'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_s3_uri_unbalanced = sess.upload_data(bucket=bucket, \n",
    "                               key_prefix='bias/unbalanced', \n",
    "                               path='./womens_clothing_ecommerce_reviews_transformed.csv')\n",
    "data_s3_uri_unbalanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-2.'></a>\n",
    "# 2. Analyze class imbalance on the dataset with Amazon SageMaker Clarify\n",
    "Let's analyze bias in `sentiment` with respect to the `product_category` facet on the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-2.1.'></a>\n",
    "### 2.1. Configure a `DataConfig`\n",
    "\n",
    "Information about the input data needs to be provided to the processor. This can be done with the `DataConfig` of the Clarify container. It stores information about the dataset to be analyzed, for example the dataset file, its format, headers and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import clarify\n",
    "\n",
    "bias_report_unbalanced_output_path = 's3://{}/bias/generated_bias_report/unbalanced'.format(bucket)\n",
    "\n",
    "data_config_unbalanced = clarify.DataConfig(\n",
    "    ### BEGIN SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    s3_data_input_path=data_s3_uri_unbalanced, # Replace None\n",
    "    s3_output_path=bias_report_unbalanced_output_path, # Replace None\n",
    "    label='sentiment', # Replace None\n",
    "    ### END SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    headers=df.columns.to_list(),\n",
    "    dataset_type='text/csv'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-2.2.'></a>\n",
    "### 2.2. Configure `BiasConfig`\n",
    "Bias is measured by calculating a metric and comparing it across groups. To compute it, you will specify the required information in the `BiasConfig` API. SageMaker Clarify needs the sensitive columns (`facet_name`) and the desirable outcomes (`label_values_or_threshold`). Here `product_category` is the sensitive facet and the desired outcome is with the `sentiment==1`.\n",
    "\n",
    "\n",
    "SageMaker Clarify can handle both categorical and continuous data for `label_values_or_threshold`. In this case you are using categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bias_config_unbalanced = clarify.BiasConfig(\n",
    "    label_values_or_threshold=[1], # desired sentiment\n",
    "    facet_name='product_category' # sensitive column (facet)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-2.3.'></a>\n",
    "### 2.3. Configure Amazon SageMaker Clarify as a processing job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you need to construct an object called `SageMakerClarifyProcessor`. This allows you to scale the process of data bias detection using two parameters, `instance_count` and `instance_type`. `Instance_count` represents how many nodes you want in the distributor cluster during the data detection. `Instance_type` specifies the processing capability (compute capacity, memory capacity) available for each one of those nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "clarify_processor_unbalanced = clarify.SageMakerClarifyProcessor(role=role,\n",
    "                                                      instance_count=1,\n",
    "                                                      instance_type='ml.m5.large',\n",
    "                                                      sagemaker_session=sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-2.4.'></a>\n",
    "### 2.4. Run the Amazon SageMaker Clarify processing job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Job Name:  Clarify-Pretraining-Bias-2021-06-19-04-52-05-771\n",
      "Inputs:  [{'InputName': 'dataset', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-186735670237/bias/unbalanced/womens_clothing_ecommerce_reviews_transformed.csv', 'LocalPath': '/opt/ml/processing/input/data', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'analysis_config', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/unbalanced/analysis_config.json', 'LocalPath': '/opt/ml/processing/input/config', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
      "Outputs:  [{'OutputName': 'analysis_result', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/unbalanced', 'LocalPath': '/opt/ml/processing/output', 'S3UploadMode': 'EndOfJob'}}]\n"
     ]
    }
   ],
   "source": [
    "clarify_processor_unbalanced.run_pre_training_bias(\n",
    "    ### BEGIN SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    data_config=data_config_unbalanced, # Replace None\n",
    "    data_bias_config=bias_config_unbalanced, # Replace None\n",
    "    ### END SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    methods=[\"CI\", \"DPL\", \"KL\", \"JS\", \"LP\", \"TVD\", \"KS\"],\n",
    "    wait=False,\n",
    "    logs=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clarify-Pretraining-Bias-2021-06-19-04-52-05-771\n"
     ]
    }
   ],
   "source": [
    "run_unbalanced_bias_processing_job_name = clarify_processor_unbalanced.latest_job.job_name\n",
    "print(run_unbalanced_bias_processing_job_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "running_processor = sagemaker.processing.ProcessingJob.from_processing_name(processing_job_name=run_unbalanced_bias_processing_job_name,\n",
    "                                                                            sagemaker_session=sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _This cell will take approximately 5-10 minutes to run._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!CPU times: user 4.2 ms, sys: 0 ns, total: 4.2 ms\n",
      "Wall time: 18.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "running_processor.wait(logs=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-2.6.'></a>\n",
    "### 2.6. Analyze unbalanced bias report\n",
    "In this run, you analyzed bias for `sentiment` relative to the `product_category` for the unbalanced data. Let's have a look at the bias report."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List the files in the output path `bias_report_unbalanced_output_path`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-19 04:57:36      31732 analysis.json\n",
      "2021-06-19 04:52:06        346 analysis_config.json\n",
      "2021-06-19 04:57:36     387134 report.html\n",
      "2021-06-19 04:57:36     121999 report.ipynb\n",
      "2021-06-19 04:57:36     139371 report.pdf\n"
     ]
    }
   ],
   "source": [
    "!aws s3 ls $bias_report_unbalanced_output_path/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download generated bias report from S3 bucket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/unbalanced/analysis_config.json to generated_bias_report/unbalanced/analysis_config.json\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/unbalanced/analysis.json to generated_bias_report/unbalanced/analysis.json\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/unbalanced/report.ipynb to generated_bias_report/unbalanced/report.ipynb\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/unbalanced/report.pdf to generated_bias_report/unbalanced/report.pdf\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/unbalanced/report.html to generated_bias_report/unbalanced/report.html\n"
     ]
    }
   ],
   "source": [
    "!aws s3 cp --recursive $bias_report_unbalanced_output_path ./generated_bias_report/unbalanced/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can Review the downloaded bias report (in pdf format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bias report shows a number of metrics, but here you can focus on just two of them: \n",
    "- Class Imbalance (CI). Measures the imbalance in the number of members between different facet values. Answers the question, does a `product_category` have disproportionately more reviews than others? Values of CI will become equal for even distribution between facets. Here, different CI values show the existence of imbalance.\n",
    "- Difference in Positive Proportions in Labels (DPL). Measures the imbalance of positive outcomes between different facet values. Answers the question, does a `product_category` have disproportionately higher ratings than others? With the range over the interval from -1 to 1, if there is no bias, you want to see this value as close as possible to zero. Here, non-zero values indicate the imbalances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-3.'></a>\n",
    "# 3. Balance the dataset by `product_category` and `sentiment`\n",
    "Let's balance the dataset by `product_category` and `sentiment`. Then you can configure and run SageMaker Clarify processing job to analyze the bias of it. Which metrics values do you expect to see in the bias report?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped_by = df.groupby(['product_category', 'sentiment'])\n",
    "df_balanced = df_grouped_by.apply(lambda x: x.sample(df_grouped_by.size().min()).reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_category</th>\n",
       "      <th>sentiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Blouses</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">-1</th>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>I was struggling with the options above-- does...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>Beautiful in pictures and i loved it before i ...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1</td>\n",
       "      <td>This blouse is stunning on the website ;howeve...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>I saw this top online and ordered immediately!...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>Very cute top. i went down a size because for ...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Trend</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>This sweater tee is made from a fine but subst...</td>\n",
       "      <td>Trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Never spent this much on a dress so it needs t...</td>\n",
       "      <td>Trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>So different than anything in my closet. it's ...</td>\n",
       "      <td>Trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>I love this skirt. i was happy with the qualit...</td>\n",
       "      <td>Trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>I tried on a size 8 in the store just for fun....</td>\n",
       "      <td>Trend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>486 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              sentiment  \\\n",
       "product_category sentiment                \n",
       "Blouses          -1        0         -1   \n",
       "                           1         -1   \n",
       "                           2         -1   \n",
       "                           3         -1   \n",
       "                           4         -1   \n",
       "...                                 ...   \n",
       "Trend             1        4          1   \n",
       "                           5          1   \n",
       "                           6          1   \n",
       "                           7          1   \n",
       "                           8          1   \n",
       "\n",
       "                                                                    review_body  \\\n",
       "product_category sentiment                                                        \n",
       "Blouses          -1        0  I was struggling with the options above-- does...   \n",
       "                           1  Beautiful in pictures and i loved it before i ...   \n",
       "                           2  This blouse is stunning on the website ;howeve...   \n",
       "                           3  I saw this top online and ordered immediately!...   \n",
       "                           4  Very cute top. i went down a size because for ...   \n",
       "...                                                                         ...   \n",
       "Trend             1        4  This sweater tee is made from a fine but subst...   \n",
       "                           5  Never spent this much on a dress so it needs t...   \n",
       "                           6  So different than anything in my closet. it's ...   \n",
       "                           7  I love this skirt. i was happy with the qualit...   \n",
       "                           8  I tried on a size 8 in the store just for fun....   \n",
       "\n",
       "                             product_category  \n",
       "product_category sentiment                     \n",
       "Blouses          -1        0          Blouses  \n",
       "                           1          Blouses  \n",
       "                           2          Blouses  \n",
       "                           3          Blouses  \n",
       "                           4          Blouses  \n",
       "...                                       ...  \n",
       "Trend             1        4            Trend  \n",
       "                           5            Trend  \n",
       "                           6            Trend  \n",
       "                           7            Trend  \n",
       "                           8            Trend  \n",
       "\n",
       "[486 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_balanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the distribution of review sentiment in the balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f87f92926d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 278,
       "width": 471
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(data=df_balanced, x='sentiment', hue='product_category')\n",
    "\n",
    "plt.legend(loc='upper right',bbox_to_anchor=(1.3, 1.1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-4.'></a>\n",
    "# 4. Analyze bias on balanced dataset with Amazon SageMaker Clarify\n",
    "Let's analyze bias in `sentiment` with respect to the `product_category` facet on your balanced dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save and upload balanced data to S3 bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-east-1-186735670237/bias/balanced/womens_clothing_ecommerce_reviews_balanced.csv'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_balanced = './womens_clothing_ecommerce_reviews_balanced.csv'\n",
    "df_balanced.to_csv(path_balanced, index=False, header=True)\n",
    "\n",
    "data_s3_uri_balanced = sess.upload_data(bucket=bucket, key_prefix='bias/balanced', path=path_balanced)\n",
    "data_s3_uri_balanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-4.1.'></a>\n",
    "### 4.1. Configure a `DataConfig`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import clarify\n",
    "\n",
    "bias_report_balanced_output_path = 's3://{}/bias/generated_bias_report/balanced'.format(bucket)\n",
    "\n",
    "data_config_balanced = clarify.DataConfig(\n",
    "    ### BEGIN SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    s3_data_input_path=data_s3_uri_balanced, # Replace None\n",
    "    s3_output_path=bias_report_balanced_output_path, # Replace None\n",
    "    label='sentiment', # Replace None\n",
    "    ### END SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    headers=df_balanced.columns.to_list(),\n",
    "    dataset_type='text/csv'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-4.2.'></a>\n",
    "### 4.2. Configure `BiasConfig`\n",
    "\n",
    "`BiasConfig` for the balanced dataset will have the same settings as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "bias_config_balanced = clarify.BiasConfig(\n",
    "    label_values_or_threshold=[1], # desired sentiment\n",
    "    facet_name='product_category' # sensitive column (facet)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-4.3.'></a>\n",
    "### 4.3. Configure SageMaker Clarify as a processing job\n",
    "\n",
    "`SageMakerClarifyProcessor` object will also have the same parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "clarify_processor_balanced = clarify.SageMakerClarifyProcessor(role=role,\n",
    "                                                      instance_count=1,\n",
    "                                                      instance_type='ml.m5.large',\n",
    "                                                      sagemaker_session=sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-4.4.'></a>\n",
    "### 4.4. Run the Amazon SageMaker Clarify processing job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Job Name:  Clarify-Pretraining-Bias-2021-06-19-05-06-23-361\n",
      "Inputs:  [{'InputName': 'dataset', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-186735670237/bias/balanced/womens_clothing_ecommerce_reviews_balanced.csv', 'LocalPath': '/opt/ml/processing/input/data', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'analysis_config', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/balanced/analysis_config.json', 'LocalPath': '/opt/ml/processing/input/config', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
      "Outputs:  [{'OutputName': 'analysis_result', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/balanced', 'LocalPath': '/opt/ml/processing/output', 'S3UploadMode': 'EndOfJob'}}]\n"
     ]
    }
   ],
   "source": [
    "clarify_processor_balanced.run_pre_training_bias(\n",
    "    ### BEGIN SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    data_config=data_config_balanced, # Replace None\n",
    "    data_bias_config=bias_config_balanced, # Replace None\n",
    "    ### END SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    methods=[\"CI\", \"DPL\", \"KL\", \"JS\", \"LP\", \"TVD\", \"KS\"],\n",
    "    wait=False,\n",
    "    logs=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clarify-Pretraining-Bias-2021-06-19-05-06-23-361\n"
     ]
    }
   ],
   "source": [
    "run_balanced_bias_processing_job_name = clarify_processor_balanced.latest_job.job_name\n",
    "print(run_balanced_bias_processing_job_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "running_processor = sagemaker.processing.ProcessingJob.from_processing_name(processing_job_name=run_balanced_bias_processing_job_name,\n",
    "                                                                            sagemaker_session=sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _This cell will take approximately 5-10 minutes to run._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..........................................................!CPU times: user 235 ms, sys: 30.6 ms, total: 265 ms\n",
      "Wall time: 4min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "running_processor.wait(logs=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w2-4.6.'></a>\n",
    "### 4.6. Analyze balanced bias report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List the files in the output path `bias_report_balanced_output_path`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-19 05:11:51      29889 analysis.json\n",
      "2021-06-19 05:06:24        346 analysis_config.json\n",
      "2021-06-19 05:11:51     394888 report.html\n",
      "2021-06-19 05:11:51     129753 report.ipynb\n",
      "2021-06-19 05:11:51     141422 report.pdf\n"
     ]
    }
   ],
   "source": [
    "!aws s3 ls $bias_report_balanced_output_path/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download generated bias report from S3 bucket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/balanced/analysis.json to generated_bias_report/balanced/analysis.json\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/balanced/analysis_config.json to generated_bias_report/balanced/analysis_config.json\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/balanced/report.ipynb to generated_bias_report/balanced/report.ipynb\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/balanced/report.pdf to generated_bias_report/balanced/report.pdf\n",
      "download: s3://sagemaker-us-east-1-186735670237/bias/generated_bias_report/balanced/report.html to generated_bias_report/balanced/report.html\n"
     ]
    }
   ],
   "source": [
    "!aws s3 cp --recursive $bias_report_balanced_output_path ./generated_bias_report/balanced/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can Review the downloaded bias report (in pdf format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect Bias on Previous Notebook's transformed dataset\n",
    "## Make it balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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