examples/credit_card_default/credit_card_default.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Arthur Sandbox Example: Credit Card Default \n",
    "\n",
    "In this guide, we'll use a tabular dataset and pre-computed predictions to onboard a new streaming model to the Arthur platform. Then we will use Arthur to analyze our model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Outline\n",
    "\n",
    "Read on for an overview of everything this notebook will cover. **[Click here to dive straight into the code.](#Imports)**\n",
    "\n",
    "## Onboarding\n",
    "\n",
    "Onboarding is the process of setting up your model to be monitored by Arthur. You specify the type of data your model ingests, send a reference dataset to provide a baseline of the distribution of your data, and you configure additional settings among the services Arthur offers.\n",
    "\n",
    "**Arthur does not need your model object itself to monitor performance - only predictions are required**\n",
    "\n",
    "All you need to monitor your model with Arthur is to upload the predictions your model makes: Arthur computes analytics about your model based on that prediction data. This data can be computed directly by your model in a script or notebook like this one to be uploaded to the platform, or can be fetched from an external database to be sent to Arthur.\n",
    "\n",
    "You may optionally provide your model itself to enable Explainability, which shows the features driving your model's decision-making, and What-If, which lets you see how your model would respond to feature adjustments through the Arthur Dashboard or API.\n",
    "\n",
    "### Getting Model Predictions\n",
    "We'll prepare a reference dataset of a sample of training data and predictions from a pre-trained model.\n",
    "\n",
    "### Registering Model with Arthur\n",
    "We'll build our model on the reference dataset, configure our model attributes, save the model to the Arthur platform, and activate explainability.\n",
    "\n",
    "### Sending Inferences\n",
    "We'll send model inferences (inputs and predictions) to the Arthur platform, and then send the corresponding ground truth values (which are matched to the predictions by inference IDs).\n",
    "\n",
    "### (Optional) Activating Enrichments\n",
    "We'll activate model enrichments, which are additional services Arthur provides with automated backend computation to make model monitoring and analysis easier for Arthur users:\n",
    "\n",
    "|  |  |\n",
    "| ---------- | ----------- |\n",
    "| **Anomaly Detection** | Anomaly scores for individual inferences help identify outliers in a model's data pipeline. |\n",
    "| **Explainability** | Model explanations estimate the relative impact of different input features on inferences.|\n",
    "| **Hotspots** | Hotspots are regions of data where Arthur automatically identifies low performance metrics.|\n",
    "| **Bias Mitigation** | Bias mitigation post-processes model outputs to balance performance with groupwise fairness. |\n",
    "\n",
    "## Model Monitoring and Analysis\n",
    "\n",
    "Once onboarding is complete and you have inferences uploaded to the platform, you can use Arthur to get model monitoring insights.\n",
    "\n",
    "### Performance\n",
    "We'll get accuracy, F1, and AUC performance metrics for our model.\n",
    "\n",
    "### Drift\n",
    "We'll get KL divergence metrics for our model, which measures how far our model's attributes have strayed from their baseline distribution.\n",
    "\n",
    "### Bias Monitoring\n",
    "We'll compare model metrics split by sensitive attributes in order to proactively understand how a model is underperforming on different groups in its dataset.\n",
    "\n",
    "### Alerts\n",
    "We'll configure model alerts, which send messages notifying of recent changes in model performance.\n",
    "\n",
    "### Enrichments\n",
    "We'll get extra insights on model performance with anomaly detection, model explanations, performance hotspots, and bias mitigation post-processing analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Setup & Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# ensure required packages are installed\n",
    "#  don't worry, our requirements are flexible!\n",
    "\n",
    "! pip install -r requirements.txt > /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T19:08:14.390559Z",
     "start_time": "2021-09-02T19:08:12.824796Z"
    },
    "scrolled": true,
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "from IPython.display import display, HTML\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import arthurai\n",
    "from arthurai import ArthurAI\n",
    "from arthurai.common.constants import InputType, OutputType, Stage, Enrichment\n",
    "from arthurai.util import generate_timestamps\n",
    "\n",
    "from utils.arthur_example import get_arthur_example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Onboarding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Loading the Data\n",
    "\n",
    "We'll load data split into \"train\" and \"inference\" sets. We'll use the training data as our baseline reference dataset, and later on we'll use the inference set to mimic production inferences our model would be making.\n",
    "\n",
    "Note that we load predictions from a pre-trained model as well. You might imagine fetching these predictions from a historical database if you have them saved, or running your model locally to re-compute them on the training and inference data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### The Dataset\n",
    "\n",
    "We're using the Credit Default dataset, which examines the likelihood of individuals defaulting on a credit card. This dataset is stored on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients), and was compiled by researchers from Chung Hua University and Tamkang University in Taiwan. \n",
    "\n",
    "- Each row in the dataset represents a bank customer. \n",
    "- The first feature is `LIMIT_BAL`, which shows the amount of credit available to the customer for a given month.\n",
    "- The next seven features, `PAY_0`...`PAY_6` indicate the status of the customer's history of past payment, with negative values indicating complete payments and positive values indicating the delay in months of payment.\n",
    "- The next six features, `BILL_AMT1`...`BILL_AMT6` represent the amount the customer currently owes each month to the bank from the previous 6 months.\n",
    "- The final six features, `PAY_AMT1`...`PAY_AMT6` represent the amount the customer paid to the bank each month from the previous 6 months.\n",
    "\n",
    "We split out sensitive features for the data that the model does not process. We will still track this data as sensitive attributes in our ArthurModel so that we can proactively monitor our model for bias against these attributes, even though the model does not directly use them as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>LIMIT_BAL</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>40000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36159</td>\n",
       "      <td>38241</td>\n",
       "      <td>39121</td>\n",
       "      <td>40400</td>\n",
       "      <td>40000</td>\n",
       "      <td>40000</td>\n",
       "      <td>3000</td>\n",
       "      <td>1800</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23582</th>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>90000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>3000</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25642</th>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>45675</td>\n",
       "      <td>41712</td>\n",
       "      <td>29756</td>\n",
       "      <td>32418</td>\n",
       "      <td>34983</td>\n",
       "      <td>18175</td>\n",
       "      <td>3630</td>\n",
       "      <td>2000</td>\n",
       "      <td>3500</td>\n",
       "      <td>2713</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29466</th>\n",
       "      <td>180000</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6  BILL_AMT1  \\\n",
       "14855      40000      0      0      0      0      0      0      36159   \n",
       "23582      80000      1     -2     -2     -2     -2     -2          0   \n",
       "331        90000     -1     -1     -1     -1     -1     -1       3000   \n",
       "25642      50000      0      0      0      0      0      2      45675   \n",
       "29466     180000      1     -2     -2     -2     -2     -2          0   \n",
       "\n",
       "       BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
       "14855      38241      39121      40400      40000      40000      3000   \n",
       "23582          0          0          0          0          0         0   \n",
       "331         6000          0       3000       3000       3000      6000   \n",
       "25642      41712      29756      32418      34983      18175      3630   \n",
       "29466          0          0          0          0          0         0   \n",
       "\n",
       "       PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \n",
       "14855      1800      2000         0         0         0  \n",
       "23582         0         0         0         0         0  \n",
       "331           0      3000      3000      3000      3000  \n",
       "25642      2000      3500      2713         0       193  \n",
       "29466         0         0         0         0       800  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_default_example = get_arthur_example('credit_card_default_drifty')\n",
    "X_train, X_inf = credit_card_default_example.get_inputs(split=True)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T19:08:17.299377Z",
     "start_time": "2021-09-02T19:08:17.293749Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>ground_truth_credit_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23582</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25642</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29466</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ground_truth_credit_default\n",
       "14855                            0\n",
       "23582                            1\n",
       "331                              0\n",
       "25642                            0\n",
       "29466                            1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, y_inf = credit_card_default_example.get_labels(split=True)\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>SEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23582</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25642</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29466</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SEX  AGE  EDUCATION  MARRIAGE\n",
       "14855    2   49          2         1\n",
       "23582    1   26          1         2\n",
       "331      2   33          1         2\n",
       "25642    2   22          2         2\n",
       "29466    2   32          2         1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sens_data_train, sens_data_inf = credit_card_default_example.get_sensitive_data(split=True)\n",
    "sens_data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>pred_proba_credit_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23582</th>\n",
       "      <td>0.24958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>0.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25642</th>\n",
       "      <td>0.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29466</th>\n",
       "      <td>0.90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pred_proba_credit_default\n",
       "14855                    0.00000\n",
       "23582                    0.24958\n",
       "331                      0.10000\n",
       "25642                    0.10000\n",
       "29466                    0.90000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_train, preds_inf = credit_card_default_example.get_predictions(split=True)\n",
    "preds_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Preparing `reference_data`\n",
    "\n",
    "A reference dataset is an example of what you expect data to look like during deployment. When your model ingests data during deployment, drift metrics and anomaly scores are computed by comparing new inferences against the reference set.\n",
    "\n",
    "We include the model's training data features in our reference dataset, as well as predicted model probabilities, sensitive attributes, and ground truth labels for these inferences.\n",
    "\n",
    "**Only model input attributes are required during onboarding, but others are recommended**: \n",
    "\n",
    "Including predicted model output values, sensitive attributes, and ground truth labels when onboarding to Arthur is **optional**. Including these attributes during onboarding, if they are available, is recommended because it can be used to track drift and evaluate future model performance against baseline model performance.\n",
    "\n",
    "Read more about [reference datasets in our documentation](https://docs.arthur.ai/user-guide/basic_concepts.html#reference-dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T19:08:20.451589Z",
     "start_time": "2021-09-02T19:08:19.113582Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>LIMIT_BAL</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>pred_proba_credit_default</th>\n",
       "      <th>ground_truth_credit_default</th>\n",
       "      <th>SEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>40000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36159</td>\n",
       "      <td>38241</td>\n",
       "      <td>39121</td>\n",
       "      <td>...</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23582</th>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24958</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>90000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>3000</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25642</th>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>45675</td>\n",
       "      <td>41712</td>\n",
       "      <td>29756</td>\n",
       "      <td>...</td>\n",
       "      <td>3500</td>\n",
       "      <td>2713</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29466</th>\n",
       "      <td>180000</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>800</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6  BILL_AMT1  \\\n",
       "14855      40000      0      0      0      0      0      0      36159   \n",
       "23582      80000      1     -2     -2     -2     -2     -2          0   \n",
       "331        90000     -1     -1     -1     -1     -1     -1       3000   \n",
       "25642      50000      0      0      0      0      0      2      45675   \n",
       "29466     180000      1     -2     -2     -2     -2     -2          0   \n",
       "\n",
       "       BILL_AMT2  BILL_AMT3  ...  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "14855      38241      39121  ...      2000         0         0         0   \n",
       "23582          0          0  ...         0         0         0         0   \n",
       "331         6000          0  ...      3000      3000      3000      3000   \n",
       "25642      41712      29756  ...      3500      2713         0       193   \n",
       "29466          0          0  ...         0         0         0       800   \n",
       "\n",
       "       pred_proba_credit_default  ground_truth_credit_default  SEX  AGE  \\\n",
       "14855                    0.00000                            0    2   49   \n",
       "23582                    0.24958                            1    1   26   \n",
       "331                      0.10000                            0    2   33   \n",
       "25642                    0.10000                            0    2   22   \n",
       "29466                    0.90000                            1    2   32   \n",
       "\n",
       "       EDUCATION  MARRIAGE  \n",
       "14855          2         1  \n",
       "23582          1         2  \n",
       "331            1         2  \n",
       "25642          2         2  \n",
       "29466          2         1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reference_data will contain the model's input features, \n",
    "# predicted probabilities, ground truth labels, and sensitive attributes,\n",
    "reference_data = pd.concat([\n",
    "    X_train,\n",
    "    preds_train,\n",
    "    y_train,\n",
    "    sens_data_train\n",
    "], axis=1)\n",
    "\n",
    "                           \n",
    "reference_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Registering Model With Arthur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Setting Up Connection\n",
    "Supply your login to authenticate with the platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# connect to Arthur\n",
    "# UNCOMMENT the two lines below and enter your details\n",
    "arthur = ArthurAI(\n",
    "    # url=\"https://beta.app.arthur.ai\",  # you can also pass this through the ARTHUR_ENDPOINT_URL environment variable\n",
    "    # login=\"<YOUR_USERNAME_OR_EMAIL>\",  # you can also pass this through the ARTHUR_LOGIN environment variable\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Registering Model Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We'll instantiate an [`ArthurModel`](https://docs.arthur.ai/sdk/sdk_v3/apiref/arthurai.core.models.ArthurModel.html) with the `ArthurAI.model()` method, which constructs a new local `ArthurModel` object. Later we'll use `ArthurModel.save()` to register this model with the Arthur platform.\n",
    "\n",
    "We give the model a user-friendly `display_name` and allow the unique `partner_model_id` field to be automatically generated, but you can supply a unique identifier if it helps you map your models in Arthur to your other MLOps systems.\n",
    "\n",
    "The `InputType` of a model specifies the general type of data your model ingests. The `OutputType` of a model specifies the modeling task at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T19:08:20.533507Z",
     "start_time": "2021-09-02T19:08:20.531272Z"
    },
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# instantiate an ArthurModel object to be registered \n",
    "# with the Arthur platform with a name and a type\n",
    "arthur_model = arthur.model(\n",
    "    display_name=\"Example: Credit Default\",\n",
    "    # partner_model_id=\"optional-unique-identifier\",\n",
    "    input_type=InputType.Tabular,\n",
    "    output_type=OutputType.Multiclass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We have just registered a streaming model, which is the default for an ArthurModel. A streaming model receives instances of data as they come into the deployed model. A batch model, in contrast, receives data in groups, and is often preferred if your model runs as a job rather than operating in realtime or over a data stream. To indicated a batch model, simply add an `is_batch=True` parameter to the call above.\n",
    "\n",
    "You can read more about [streaming and batch models in our documentation](https://docs.arthur.ai/user-guide/basic_concepts.html#basic-concepts-streaming-vs-batch)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Building the Model Schema on the Reference Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We'll use the `reference_data` prepared above to register the model schema for this tabular classifier model. The model schema is a record of the names, shapes, datatypes, and other properties for the attributes data your model uses (inputs, outputs, ground truth values, and other non-input attributes). \n",
    "\n",
    "We'll do this with the [ArthurModel.build()](https://docs.arthur.ai/sdk/sdk_v3/apiref/arthurai.core.models.ArthurModel.html#arthurai.core.models.ArthurModel.build) method, which configures our model using the provided reference dataframe and a map that defines how predicted probabilities correspond to ground truth values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-02T19:08:22.654494Z",
     "start_time": "2021-09-02T19:08:22.503579Z"
    },
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-28 16:10:55,643 - arthurai.core.models - WARNING - Found one or more categorical attributes. It is suggested to use model.review() to verify all possible categories were inferred correctly for each categorical attribute. To update with new categories, use model.get_attribute(attr_name).set(categories=[cat_1, cat_2, cat_3])\n",
      "2023-01-28 16:10:55,648 - arthurai.core.models - WARNING - Found one or more categorical attributes. It is suggested to use model.review() to verify all possible categories were inferred correctly for each categorical attribute. To update with new categories, use model.get_attribute(attr_name).set(categories=[cat_1, cat_2, cat_3])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16:10:55 - arthurai - Please review the inferred schema. If everything looks correct, lock in your model by calling arthur_model.save()\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>name</th>\n",
       "      <th>stage</th>\n",
       "      <th>value_type</th>\n",
       "      <th>categorical</th>\n",
       "      <th>is_unique</th>\n",
       "      <th>categories</th>\n",
       "      <th>bins</th>\n",
       "      <th>range</th>\n",
       "      <th>monitor_for_bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[10000, 1000000]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAY_2</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAY_3</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAY_4</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 2}, {value: 3}, {value: 4...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAY_5</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 2}, {value: 3}, {value: 4...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PAY_6</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 2}, {value: 3}, {value: 4...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BILL_AMT1</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-14386, 964511]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BILL_AMT2</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-69777, 983931]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BILL_AMT3</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-61506, 565550]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BILL_AMT4</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-65167, 891586]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BILL_AMT5</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-53007, 927171]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BILL_AMT6</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-339603, 961664]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PAY_AMT1</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 873552]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PAY_AMT2</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 1227082]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PAY_AMT3</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 896040]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PAY_AMT4</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 621000]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PAY_AMT5</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 426529]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PAY_AMT6</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 403500]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SEX</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 1}, {value: 2}]</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AGE</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[21, 75]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MARRIAGE</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3}]</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ground_truth_credit_default</td>\n",
       "      <td>GROUND_TRUTH_CLASS</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}]</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pred_proba_credit_default</td>\n",
       "      <td>PREDICTED_VALUE</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name               stage value_type categorical  \\\n",
       "0                     LIMIT_BAL      PIPELINE_INPUT    INTEGER       False   \n",
       "1                         PAY_0      PIPELINE_INPUT    INTEGER        True   \n",
       "2                         PAY_2      PIPELINE_INPUT    INTEGER        True   \n",
       "3                         PAY_3      PIPELINE_INPUT    INTEGER        True   \n",
       "4                         PAY_4      PIPELINE_INPUT    INTEGER        True   \n",
       "5                         PAY_5      PIPELINE_INPUT    INTEGER        True   \n",
       "6                         PAY_6      PIPELINE_INPUT    INTEGER        True   \n",
       "7                     BILL_AMT1      PIPELINE_INPUT    INTEGER       False   \n",
       "8                     BILL_AMT2      PIPELINE_INPUT    INTEGER       False   \n",
       "9                     BILL_AMT3      PIPELINE_INPUT    INTEGER       False   \n",
       "10                    BILL_AMT4      PIPELINE_INPUT    INTEGER       False   \n",
       "11                    BILL_AMT5      PIPELINE_INPUT    INTEGER       False   \n",
       "12                    BILL_AMT6      PIPELINE_INPUT    INTEGER       False   \n",
       "13                     PAY_AMT1      PIPELINE_INPUT    INTEGER       False   \n",
       "14                     PAY_AMT2      PIPELINE_INPUT    INTEGER       False   \n",
       "15                     PAY_AMT3      PIPELINE_INPUT    INTEGER       False   \n",
       "16                     PAY_AMT4      PIPELINE_INPUT    INTEGER       False   \n",
       "17                     PAY_AMT5      PIPELINE_INPUT    INTEGER       False   \n",
       "18                     PAY_AMT6      PIPELINE_INPUT    INTEGER       False   \n",
       "19                          SEX      NON_INPUT_DATA    INTEGER        True   \n",
       "20                          AGE      NON_INPUT_DATA    INTEGER       False   \n",
       "21                    EDUCATION      NON_INPUT_DATA    INTEGER        True   \n",
       "22                     MARRIAGE      NON_INPUT_DATA    INTEGER        True   \n",
       "23  ground_truth_credit_default  GROUND_TRUTH_CLASS    INTEGER        True   \n",
       "24    pred_proba_credit_default     PREDICTED_VALUE      FLOAT       False   \n",
       "\n",
       "   is_unique                                         categories  bins  \\\n",
       "0      False                                                 []  None   \n",
       "1      False  [{value: 0}, {value: 1}, {value: 2}, {value: 3...  None   \n",
       "2      False  [{value: 0}, {value: 1}, {value: 2}, {value: 3...  None   \n",
       "3      False  [{value: 0}, {value: 1}, {value: 2}, {value: 3...  None   \n",
       "4      False  [{value: 0}, {value: 2}, {value: 3}, {value: 4...  None   \n",
       "5      False  [{value: 0}, {value: 2}, {value: 3}, {value: 4...  None   \n",
       "6      False  [{value: 0}, {value: 2}, {value: 3}, {value: 4...  None   \n",
       "7      False                                                 []  None   \n",
       "8      False                                                 []  None   \n",
       "9      False                                                 []  None   \n",
       "10     False                                                 []  None   \n",
       "11     False                                                 []  None   \n",
       "12     False                                                 []  None   \n",
       "13     False                                                 []  None   \n",
       "14     False                                                 []  None   \n",
       "15     False                                                 []  None   \n",
       "16     False                                                 []  None   \n",
       "17     False                                                 []  None   \n",
       "18     False                                                 []  None   \n",
       "19     False                           [{value: 1}, {value: 2}]  None   \n",
       "20     False                                                 []  None   \n",
       "21     False  [{value: 0}, {value: 1}, {value: 2}, {value: 3...  None   \n",
       "22     False   [{value: 0}, {value: 1}, {value: 2}, {value: 3}]  None   \n",
       "23     False                           [{value: 0}, {value: 1}]  None   \n",
       "24     False                                                 []  None   \n",
       "\n",
       "                range monitor_for_bias  \n",
       "0    [10000, 1000000]            False  \n",
       "1        [None, None]            False  \n",
       "2        [None, None]            False  \n",
       "3        [None, None]            False  \n",
       "4        [None, None]            False  \n",
       "5        [None, None]            False  \n",
       "6        [None, None]            False  \n",
       "7    [-14386, 964511]            False  \n",
       "8    [-69777, 983931]            False  \n",
       "9    [-61506, 565550]            False  \n",
       "10   [-65167, 891586]            False  \n",
       "11   [-53007, 927171]            False  \n",
       "12  [-339603, 961664]            False  \n",
       "13        [0, 873552]            False  \n",
       "14       [0, 1227082]            False  \n",
       "15        [0, 896040]            False  \n",
       "16        [0, 621000]            False  \n",
       "17        [0, 426529]            False  \n",
       "18        [0, 403500]            False  \n",
       "19       [None, None]            False  \n",
       "20           [21, 75]            False  \n",
       "21       [None, None]            False  \n",
       "22       [None, None]            False  \n",
       "23       [None, None]            False  \n",
       "24             [0, 1]            False  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map PredictedValue attribute to its corresponding GroundTruth attribute value.\n",
    "# This tells Arthur that the `pred_proba_credit_default` column represents \n",
    "# the probability that the ground truth column has the value 1\n",
    "pred_to_ground_truth_map = {'pred_proba_credit_default' : 1}\n",
    "\n",
    "# Build arthur_model schema on the reference dataset,\n",
    "# specifying which attribute represents ground truth\n",
    "# and which attributes are NonInputData.\n",
    "# Arthur will monitor NonInputData attributes \n",
    "# even though they are not model inputs.\n",
    "arthur_model.build(\n",
    "    reference_data, \n",
    "    ground_truth_column='ground_truth_credit_default',\n",
    "    pred_to_ground_truth_map=pred_to_ground_truth_map,\n",
    "    non_input_columns=sens_data_train.columns\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Configuring Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Monitoring Sensitive Attributes for Bias\n",
    "\n",
    "We will specify that a few attributes (`SEX`, `AGE`, and `EDUCATION`) should be [monitored for bias](https://docs.arthur.ai/user-guide/walkthroughs/bias_monitoring.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# monitoring attributes for bias\n",
    "for attr in sens_data_train.columns:\n",
    "    arthur_model.set_attribute_as_sensitive(attr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Binning Continuous Attributes\n",
    "\n",
    "Binning an attribute means placing a continuous value into one of a set of buckets. For example, one might bin an age attribute into an `old` bucket and a `young` bucket, using the bins `[None, 65, None]` to set the binned age value to be `young` when `age <= 65`, and the binned age value to be `old` when `age > 65`.\n",
    "\n",
    "The `bins` property of an attribute are used for two tasks in the Arthur system:\n",
    "\n",
    "1: Determining default settings for filters and attribute groupings in the online dashboard\n",
    "\n",
    "2: Determining the groups with which to measure bias. For example, monitoring the `age` attribute with the bins set above would monitor whether a model was biased towards people over/under age 65.\n",
    "\n",
    "Note that this property does not impact other metrics - for example, different bins are chosen when calculating drift, so the `bins` property of an attribute won't impact drift.\n",
    "\n",
    "Here we will breakup the continuous `AGE` attribute into discrete bins with the cutoff values 35 and 55. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ArthurAttribute(name='AGE', value_type='INTEGER', stage='NON_INPUT_DATA', id=None, label=None, position=1, categorical=False, min_range=21, max_range=75, monitor_for_bias=True, categories=[], bins=[AttributeBin(continuous_start=None, continuous_end=35), AttributeBin(continuous_start=35, continuous_end=55), AttributeBin(continuous_start=55, continuous_end=None)], is_unique=False, is_positive_predicted_attribute=False, attribute_link=None, gt_class_link=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# binning a continuous attribute\n",
    "# this will create 3 bins for the AGE attribute, \n",
    "# corresponding to the cutoffs [AGE < 35, 35 < AGE < 55, 55 < AGE]\n",
    "arthur_model.get_attribute(\"AGE\").set(\n",
    "    bins = [None, 35, 55, None], \n",
    "    categorical=False,\n",
    "    categories=[], \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Labelling Attributes\n",
    "\n",
    "We will configure labels for attributes and their values so that the information is more easily readable and understandable in the Arthur dashboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# configure attribute labels and attribute value labels\n",
    "# the labels parameter maps raw attribute values to \n",
    "# the labels you would like them to have in the dashboard\n",
    "\n",
    "arthur_model.set_attribute_labels(\n",
    "    attribute_name=\"SEX\",\n",
    "    labels={1: \"Male\", 2: \"Female\"})\n",
    "\n",
    "arthur_model.set_attribute_labels(\n",
    "    attribute_name=\"EDUCATION\",\n",
    "    labels={1: \"Graduate School\", 2: \"University\",\n",
    "            3: \"High School\", 4: \"Less Than High School\",\n",
    "            5: \"Unknown\", 6: \"Unreported\", 0: \"Other\"})\n",
    "\n",
    "arthur_model.set_attribute_labels(\n",
    "    attribute_name=\"MARRIAGE\",\n",
    "    labels={1: \"Married\", 2: \"Single\",\n",
    "            3: \"Other\", 0: \"Unknown\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Saving the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Before saving, be sure to review your model to make sure everything is correct. We already saw the model schema returned by `ArthurModel.build()`, but we have since changed our attribute congiruations. Therefore we call `ArthurModel.review()` to see that our changed attributes look correct before saving to the platform. See the [onboarding walkthrough on the Arthur docs](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/index.html#review-model) for tips on reviewing your model.\n",
    "\n",
    "Note that while we capture the ranges of the attributes in this schema, they don’t need to be exact and won’t affect any performance calculations. They’re used as metadata to configure plots in the online Arthur dashboard, but never affect data drift or any other computations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>name</th>\n",
       "      <th>stage</th>\n",
       "      <th>value_type</th>\n",
       "      <th>categorical</th>\n",
       "      <th>is_unique</th>\n",
       "      <th>categories</th>\n",
       "      <th>bins</th>\n",
       "      <th>range</th>\n",
       "      <th>monitor_for_bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[10000, 1000000]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAY_2</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAY_3</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}, {value: 2}, {value: 3...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAY_4</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 2}, {value: 3}, {value: 4...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAY_5</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 2}, {value: 3}, {value: 4...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PAY_6</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 2}, {value: 3}, {value: 4...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BILL_AMT1</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-14386, 964511]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BILL_AMT2</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-69777, 983931]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BILL_AMT3</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-61506, 565550]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BILL_AMT4</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-65167, 891586]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BILL_AMT5</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-53007, 927171]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BILL_AMT6</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[-339603, 961664]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PAY_AMT1</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 873552]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PAY_AMT2</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 1227082]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PAY_AMT3</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 896040]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PAY_AMT4</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 621000]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PAY_AMT5</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 426529]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PAY_AMT6</td>\n",
       "      <td>PIPELINE_INPUT</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 403500]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SEX</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{label: Male, value: 1}, {label: Female, valu...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AGE</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[AttributeBin(continuous_start=None, continuou...</td>\n",
       "      <td>[21, 75]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{label: Graduate School, value: 1}, {label: U...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MARRIAGE</td>\n",
       "      <td>NON_INPUT_DATA</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{label: Married, value: 1}, {label: Single, v...</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ground_truth_credit_default</td>\n",
       "      <td>GROUND_TRUTH_CLASS</td>\n",
       "      <td>INTEGER</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[{value: 0}, {value: 1}]</td>\n",
       "      <td>None</td>\n",
       "      <td>[None, None]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pred_proba_credit_default</td>\n",
       "      <td>PREDICTED_VALUE</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name               stage value_type categorical  \\\n",
       "0                     LIMIT_BAL      PIPELINE_INPUT    INTEGER       False   \n",
       "1                         PAY_0      PIPELINE_INPUT    INTEGER        True   \n",
       "2                         PAY_2      PIPELINE_INPUT    INTEGER        True   \n",
       "3                         PAY_3      PIPELINE_INPUT    INTEGER        True   \n",
       "4                         PAY_4      PIPELINE_INPUT    INTEGER        True   \n",
       "5                         PAY_5      PIPELINE_INPUT    INTEGER        True   \n",
       "6                         PAY_6      PIPELINE_INPUT    INTEGER        True   \n",
       "7                     BILL_AMT1      PIPELINE_INPUT    INTEGER       False   \n",
       "8                     BILL_AMT2      PIPELINE_INPUT    INTEGER       False   \n",
       "9                     BILL_AMT3      PIPELINE_INPUT    INTEGER       False   \n",
       "10                    BILL_AMT4      PIPELINE_INPUT    INTEGER       False   \n",
       "11                    BILL_AMT5      PIPELINE_INPUT    INTEGER       False   \n",
       "12                    BILL_AMT6      PIPELINE_INPUT    INTEGER       False   \n",
       "13                     PAY_AMT1      PIPELINE_INPUT    INTEGER       False   \n",
       "14                     PAY_AMT2      PIPELINE_INPUT    INTEGER       False   \n",
       "15                     PAY_AMT3      PIPELINE_INPUT    INTEGER       False   \n",
       "16                     PAY_AMT4      PIPELINE_INPUT    INTEGER       False   \n",
       "17                     PAY_AMT5      PIPELINE_INPUT    INTEGER       False   \n",
       "18                     PAY_AMT6      PIPELINE_INPUT    INTEGER       False   \n",
       "19                          SEX      NON_INPUT_DATA    INTEGER        True   \n",
       "20                          AGE      NON_INPUT_DATA    INTEGER       False   \n",
       "21                    EDUCATION      NON_INPUT_DATA    INTEGER        True   \n",
       "22                     MARRIAGE      NON_INPUT_DATA    INTEGER        True   \n",
       "23  ground_truth_credit_default  GROUND_TRUTH_CLASS    INTEGER        True   \n",
       "24    pred_proba_credit_default     PREDICTED_VALUE      FLOAT       False   \n",
       "\n",
       "   is_unique                                         categories  \\\n",
       "0      False                                                 []   \n",
       "1      False  [{value: 0}, {value: 1}, {value: 2}, {value: 3...   \n",
       "2      False  [{value: 0}, {value: 1}, {value: 2}, {value: 3...   \n",
       "3      False  [{value: 0}, {value: 1}, {value: 2}, {value: 3...   \n",
       "4      False  [{value: 0}, {value: 2}, {value: 3}, {value: 4...   \n",
       "5      False  [{value: 0}, {value: 2}, {value: 3}, {value: 4...   \n",
       "6      False  [{value: 0}, {value: 2}, {value: 3}, {value: 4...   \n",
       "7      False                                                 []   \n",
       "8      False                                                 []   \n",
       "9      False                                                 []   \n",
       "10     False                                                 []   \n",
       "11     False                                                 []   \n",
       "12     False                                                 []   \n",
       "13     False                                                 []   \n",
       "14     False                                                 []   \n",
       "15     False                                                 []   \n",
       "16     False                                                 []   \n",
       "17     False                                                 []   \n",
       "18     False                                                 []   \n",
       "19     False  [{label: Male, value: 1}, {label: Female, valu...   \n",
       "20     False                                                 []   \n",
       "21     False  [{label: Graduate School, value: 1}, {label: U...   \n",
       "22     False  [{label: Married, value: 1}, {label: Single, v...   \n",
       "23     False                           [{value: 0}, {value: 1}]   \n",
       "24     False                                                 []   \n",
       "\n",
       "                                                 bins              range  \\\n",
       "0                                                None   [10000, 1000000]   \n",
       "1                                                None       [None, None]   \n",
       "2                                                None       [None, None]   \n",
       "3                                                None       [None, None]   \n",
       "4                                                None       [None, None]   \n",
       "5                                                None       [None, None]   \n",
       "6                                                None       [None, None]   \n",
       "7                                                None   [-14386, 964511]   \n",
       "8                                                None   [-69777, 983931]   \n",
       "9                                                None   [-61506, 565550]   \n",
       "10                                               None   [-65167, 891586]   \n",
       "11                                               None   [-53007, 927171]   \n",
       "12                                               None  [-339603, 961664]   \n",
       "13                                               None        [0, 873552]   \n",
       "14                                               None       [0, 1227082]   \n",
       "15                                               None        [0, 896040]   \n",
       "16                                               None        [0, 621000]   \n",
       "17                                               None        [0, 426529]   \n",
       "18                                               None        [0, 403500]   \n",
       "19                                               None       [None, None]   \n",
       "20  [AttributeBin(continuous_start=None, continuou...           [21, 75]   \n",
       "21                                               None       [None, None]   \n",
       "22                                               None       [None, None]   \n",
       "23                                               None       [None, None]   \n",
       "24                                               None             [0, 1]   \n",
       "\n",
       "   monitor_for_bias  \n",
       "0             False  \n",
       "1             False  \n",
       "2             False  \n",
       "3             False  \n",
       "4             False  \n",
       "5             False  \n",
       "6             False  \n",
       "7             False  \n",
       "8             False  \n",
       "9             False  \n",
       "10            False  \n",
       "11            False  \n",
       "12            False  \n",
       "13            False  \n",
       "14            False  \n",
       "15            False  \n",
       "16            False  \n",
       "17            False  \n",
       "18            False  \n",
       "19             True  \n",
       "20             True  \n",
       "21             True  \n",
       "22             True  \n",
       "23            False  \n",
       "24            False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the model attribute properties in the model schema\n",
    "arthur_model.review()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now, we save the model. \n",
    "\n",
    "Note that this will be the first call to send data to the Arthur platform so far in this example - no information has been sent yet to the platform.\n",
    "\n",
    "The method `ArthurModel.save()` sends an API request to Arthur to validate your model - if there are any problems with your model schema, this method will result in an error informing you how to correct your model's configuration. If no errors are found, the model will be saved to the platform, and the reference data we passed to `ArthurModel.build()` will be saved as the model's [reference set](https://docs.arthur.ai/user-guide/basic_concepts.html#reference-dataset), which serves as the default baseline for drift metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16:11:24 - arthurai - Starting upload (1.141 MB in 1 files), depending on data size this may take a few minutes\n",
      "16:11:25 - arthurai - Upload completed: /var/folders/fg/t2njc_1s2z3bsbzw5bhn_n0c0000gn/T/tmp8q4fqtct/35212780-2353-4c31-85a2-5fb1afe1a7bf-0.parquet\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'35212780-2353-4c31-85a2-5fb1afe1a7bf'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validate the model and save it onto the Arthur platform\n",
    "arthur_model.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### (Optional) Activating Enrichments\n",
    "\n",
    "**💡 Enrichments are an advanced feature only available to paid users. For first-time users we recommend [jumping ahead to Sending Inferences](#sending_inferences_content)**.\n",
    "\n",
    "Enrichments are additional services Arthur provides with automated backend computation to make model monitoring and analysis easier for Arthur users.\n",
    "\n",
    "Next we will enable the Hotspots & Bias Mitigation enrichments with a single call to `ArthurModel.update_enrichments()`. After that, we will enable the Explainability enrichment with a call to `ArthurModel.enable_explainability()`. The Anomaly Detection enrichment is configured by default, so we omit that here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Enabling Hotspots and Bias Mitigation\n",
    "We will activate two model enrichments, enabling us to access these services when monitoring our model after onboarding. This configuration code will use the default setting for each of these enrichments. Extra settings can be provided in the `config` parameters. See the [Arthur API Enrichments endpoint](https://docs.arthur.ai/api-documentation/v3-api-docs.html#tag/enrichments) to see the configurable parameters for each of these enrichments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# enable hotspots and bias mitigation\n",
    "# configuring each enrichment in an enrichment_configs dictionary\n",
    "# and providing enrichment_configs to ArthurModel.update_enrichments()\n",
    "# we leave the `config` parameters empty to use \n",
    "# the default setting for each enrichment\n",
    "enrichment_configs = {\n",
    "    Enrichment.Hotspots: {'enabled': True, 'config': {}},\n",
    "    Enrichment.BiasMitigation: {'enabled': True, 'config': {}},   \n",
    "}\n",
    "arthur_model.update_enrichments(enrichment_configs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Enabling Explainability\n",
    "Next, we will activate the [Explainability enrichment](https://docs.arthur.ai/user-guide/walkthroughs/explainability.html), which requires:\n",
    "\n",
    "1) Configuring our model's [project directory in a specific way](https://docs.arthur.ai/user-guide/walkthroughs/explainability.html#setting-up-project-directory)\n",
    "\n",
    "2) Providing access to an entrypoint file with the [model's `predict` function](https://docs.arthur.ai/user-guide/walkthroughs/explainability.html#prediction-function), as well as its [environment requirements file](https://docs.arthur.ai/user-guide/walkthroughs/explainability.html#requirements-file)\n",
    "\n",
    "For most Arthur services, the model itself is not needed - only its input and output data is required to get performance metrics, drift, other enrichments, and the rest of the model monitoring tools Arthur provides. But to enable explainability and generate model explanations, Arthur needs access to the model artifact itself to set up a model server that will generate new model inferences on sample data. If you are unable to or would prefer to not share your model with Arthur, skip this step.\n",
    "\n",
    "Note that the `streaming_explainability_enabled` parameter (set to `False` by default) can be set to `True` if you would like the Arthur platform to automatically generate explanations for every inference that comes through a streaming model (this can be used to compute a model's global feature importance scores)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Setting up directory\n",
    "\n",
    "Arthur creates an explainer by packaging a folder in your project directory. For this sandbox example, the folder we package for the explainer is `explainability`, and it contains three files:\n",
    "\n",
    "```\n",
    "project directory/\n",
    "    ...\n",
    "    other stuff/\n",
    "    ...\n",
    "    explainability/\n",
    "\n",
    "        model_entrypoint.py (a model prediction script)\n",
    "        credit_model.pkl (a model object that makes predictions in the prediction script)\n",
    "        requirements.txt (the requirements for the environment that runs the prediction script)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Creating model prediction script\n",
    "\n",
    "In the file `model_entrypoint.py`, we load a pre-trained model called `credit_model.pkl` and define a `predict` function which outputs the model's predicted probability:\n",
    "\n",
    "```\n",
    "# model_entrypoint.py\n",
    "\n",
    "import joblib\n",
    "model = joblib.load(\"credit_model.pkl\")\n",
    "def predict(x):\n",
    "    return model.predict_proba(x)[:,1]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Train and save a sample model\n",
    "\n",
    "For this sandbox example, we train and save `credit_model.pkl` here in the notebook so that you can run the demo.\n",
    "\n",
    "To adapt this sandbox example to set up explainability for your own model, replace the model loaded in `model_entrypoint.py` and make sure the `predict` function outputs your model's outputs on an input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators=10, random_state=278487)\n",
    "clf.fit(X_train, y_train)\n",
    "pickle.dump(clf, open('explainability/credit_model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Creating requirements file\n",
    "\n",
    "We create the `requirements.txt` file for the `model_entrypoint.py` script to be run in its own python environment automatically. The following shell command will save the versions of the packages needed to run the script into a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!pip freeze | grep \"^scikit-learn==\\|^joblib==\" > explainability/requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Enabling explainability\n",
    "\n",
    "With the above pieces set up in our `explainability` directory, we can now enable explainability for our Arthur model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "arthur_model.enable_explainability(\n",
    "    df=reference_data,\n",
    "    project_directory=Path(Path.cwd() / 'explainability'),\n",
    "    requirements_file=\"requirements.txt\",\n",
    "    user_predict_function_import_path=\"model_entrypoint\",\n",
    "    explanation_algo=\"lime\",\n",
    "    streaming_explainability_enabled=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<a id='sending_inferences_content'></a>\n",
    "\n",
    "## Sending Inferences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Sending Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-02T22:02:22.489559Z",
     "start_time": "2021-08-02T22:02:22.481988Z"
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We'll create some timestamps to mimic sending the data over a period of time. If these are left out the\n",
    "current time will be populated as the inference timestamp, but we'll include them so that our inferences will appear as having been made over time like a production model.\n",
    "\n",
    "The `arthur_model.send_inferences()` method has a `partner_inference_ids` parameter, which we populate with a custom ID + timestamp unique identifier for each inference. We will use these identifiers to upload ground truth values to Arthur separately from the inference predictions, since in a production setting ground truth information will only become available at a later date. See the [model onboarding guide](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/index.html#partner-inference-ids) for more information on matching predictions to ground truth values using `partner_inference_ids`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Preparing `inference_data`\n",
    "\n",
    "The inference data you send to Arthur should have the same `ModelPipelineInput` and `PredictedValue` attributes you registered in your model schema. \n",
    "\n",
    "It is optional to include sensitive attributes and ground truth labels in the inference data, but recommended if they are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>LIMIT_BAL</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>pred_proba_credit_default</th>\n",
       "      <th>SEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23652</th>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47067</td>\n",
       "      <td>48410</td>\n",
       "      <td>49218</td>\n",
       "      <td>...</td>\n",
       "      <td>2200</td>\n",
       "      <td>824</td>\n",
       "      <td>700</td>\n",
       "      <td>900</td>\n",
       "      <td>852</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16721</th>\n",
       "      <td>80000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1894</td>\n",
       "      <td>316</td>\n",
       "      <td>316</td>\n",
       "      <td>...</td>\n",
       "      <td>316</td>\n",
       "      <td>514</td>\n",
       "      <td>316</td>\n",
       "      <td>1605</td>\n",
       "      <td>316</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>230000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23396</td>\n",
       "      <td>25723</td>\n",
       "      <td>49223</td>\n",
       "      <td>...</td>\n",
       "      <td>30029</td>\n",
       "      <td>27417</td>\n",
       "      <td>1016</td>\n",
       "      <td>640</td>\n",
       "      <td>260</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48017</td>\n",
       "      <td>47913</td>\n",
       "      <td>49225</td>\n",
       "      <td>...</td>\n",
       "      <td>2800</td>\n",
       "      <td>1610</td>\n",
       "      <td>918</td>\n",
       "      <td>1214</td>\n",
       "      <td>468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47569</td>\n",
       "      <td>48768</td>\n",
       "      <td>49234</td>\n",
       "      <td>...</td>\n",
       "      <td>1452</td>\n",
       "      <td>1200</td>\n",
       "      <td>300</td>\n",
       "      <td>27000</td>\n",
       "      <td>1412</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6  BILL_AMT1  \\\n",
       "23652      50000      2      0      0      0      0      0      47067   \n",
       "16721      80000     -1     -1     -1     -1     -1     -1       1894   \n",
       "4534      230000     -1     -1      0      0      0      0      23396   \n",
       "8752       50000      0      0      0      0      0      0      48017   \n",
       "776        50000      0      0      0      0      0      0      47569   \n",
       "\n",
       "       BILL_AMT2  BILL_AMT3  ...  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  \\\n",
       "23652      48410      49218  ...      2200       824       700       900   \n",
       "16721        316        316  ...       316       514       316      1605   \n",
       "4534       25723      49223  ...     30029     27417      1016       640   \n",
       "8752       47913      49225  ...      2800      1610       918      1214   \n",
       "776        48768      49234  ...      1452      1200       300     27000   \n",
       "\n",
       "       PAY_AMT6  pred_proba_credit_default  SEX  AGE  EDUCATION  MARRIAGE  \n",
       "23652       852                        0.5    1   55          3         3  \n",
       "16721       316                        0.3    2   38          2         1  \n",
       "4534        260                        0.1    1   34          1         2  \n",
       "8752        468                        0.0    1   47          2         2  \n",
       "776        1412                        0.4    2   44          3         1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine model inputs, predictions, and sensitive data attributes\n",
    "inference_data = pd.concat([\n",
    "    X_inf,\n",
    "    preds_inf,\n",
    "    sens_data_inf\n",
    "], axis=1)\n",
    "\n",
    "inference_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Preparing `inference_timestamps`\n",
    "\n",
    "When you send your inferences to Arthur, you also log when your model made its predictions through the inference_timestamp field. Since our inference data in this example does not come with actual timestamps, we generate timestamps spanning the last 30 days to be used in place of real ones. If you're fetching your historical inferences from a data store, you're encouraged to supply or estimate the actual time of model inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First and last timestamps: 2022-12-29 to 2023-01-28\n"
     ]
    }
   ],
   "source": [
    "# generate fake timestamps for each inference over the last month\n",
    "inference_timestamps = list(generate_timestamps(len(inference_data), '30d', 'now', 'D'))\n",
    "\n",
    "print(f\"First and last timestamps: {inference_timestamps[0].strftime('%Y-%m-%d')} to {inference_timestamps[-1].strftime('%Y-%m-%d')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Preparing `partner_inference_ids`\n",
    "\n",
    "These are unique identifiers that we send with our inferences so that when we are sending ground truth values to the platform, Arthur knows which predictions each ground truth corresponds to. We will format these as `index_in_dataset - year - month - day`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 3 partner inference IDs: ['23652-2022-12-29', '16721-2022-12-29', '4534-2022-12-29']\n"
     ]
    }
   ],
   "source": [
    "# use the timestamps and the dataframe's original unique ID to make a partner_inference_id for each inference\n",
    "# these IDs just represent the order in the original credit_default_dataset dataframe\n",
    "# we will use the unique ID for each inference to match with ground truth values later on\n",
    "partner_inference_ids = [f'{x}-{t.date()}' for x, t in zip(inference_data.index.values, inference_timestamps)]\n",
    "\n",
    "print(f\"First 3 partner inference IDs: {partner_inference_ids[:3]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Using `ArthurModel.send_inferences()`\n",
    "\n",
    "We send our inference data to the platform along with their unique IDs and timestamps.\n",
    "\n",
    "Note that we added our predictions to the DataFrame and passed the timestamps and inference IDs as parameters to the `send_inferences()` method. However, you can also send the predictions through a parameter or include the IDs and timestamps in your DataFrame. See [our API docs for sending inferences](https://docs.arthur.ai/api-documentation/v3-api-docs.html#tag/inferences/paths/~1models~1%7Bmodel_id%7D~1inferences/post) for the full specification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of successful and # of failing inference uploads\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'failure': 0, 'success': 9000, 'total': 9000}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Send the inferences to Arthur with their unique IDs and their timestamps\n",
    "inference_result = arthur_model.send_inferences(\n",
    "    inference_data, \n",
    "    partner_inference_ids=partner_inference_ids,\n",
    "    inference_timestamps=inference_timestamps)\n",
    "\n",
    "print('# of successful and # of failing inference uploads')\n",
    "inference_result['counts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Updating with Ground Truth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "When your ground truth values are available for your model's inferences, you can [update each inference](https://docs.arthur.ai/sdk/sdk_v3/apiref/arthurai.core.models.ArthurModel.html#arthurai.core.models.ArthurModel.update_inference_ground_truths) by Partner Inference ID with its corresponding label. \n",
    "\n",
    "You will need the `partner_inference_ids` for your data so that we can include them with our upload of ground truth values and match each prediction with its corresponding ground truth. We chose the IDs above because they're easy to reproduce in the future: we can use the index to match the labels back to the right predictions. Check out our documentation for [more info on Partner Inference IDs](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/index.html#partner-inference-ids)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-02T21:54:20.699496Z",
     "start_time": "2021-08-02T21:54:20.689864Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16:22:14 - arthurai - 9000 rows were missing ground_truth_timestamp fields, so the current time was populated\n",
      "# of successful and # of failing ground truth uploads\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'failure': 0, 'success': 9000, 'total': 9000}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the partner inference ID is currently of the form\n",
    "# index_in_dataset-year-month-day\n",
    "# so we get the corresponding ground truth values from Y_inf\n",
    "# by using the index_in_dataset as the index of each inference in Y_inf\n",
    "\n",
    "# split out the index_in_dataset from each partner inference ID \n",
    "# (formatted as index_in_dataset-year-month-day)\n",
    "idx_in_dataset = [int(x.split('-')[0]) for x in partner_inference_ids]\n",
    "\n",
    "ground_truth_df = pd.DataFrame({\n",
    "    'partner_inference_id': partner_inference_ids,\n",
    "    'ground_truth_credit_default': y_inf.loc[idx_in_dataset].values.flatten()\n",
    "})\n",
    "\n",
    "ground_truth_result = arthur_model.update_inference_ground_truths(ground_truth_df)\n",
    "\n",
    "print('# of successful and # of failing ground truth uploads')\n",
    "ground_truth_result['counts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## See Model in Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br> <a style=\"font-size:200%\" href=https://beta.app.arthur.ai/model/35212780-2353-4c31-85a2-5fb1afe1a7bf/overview>See your model (Example: Credit Default) in the Arthur Dashboard</a> <br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the code below will render a link for you to view your model in the Arthur Dashboard\n",
    "\n",
    "def render_arthur_model_dashboard_link(arthur, arthur_model):\n",
    "    url = 'https://' + ''.join(arthur.client.api_base_url.split('/')[1:-2])\n",
    "    link_text = f\"See your model ({arthur_model.display_name}) in the Arthur Dashboard\"\n",
    "    href_string = f\"{url}/model/{arthur_model.id}/overview\"\n",
    "    html_string = f'<br> <a style=\"font-size:200%\" href={href_string}>{link_text}</a> <br>'\n",
    "    display(HTML(html_string))\n",
    "\n",
    "render_arthur_model_dashboard_link(arthur, arthur_model) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Once your inference and ground truth data have been uploaded to the platform, you can see your model by following the above link to the model dashboard page to see an overview of the model and browse its inference data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Model Monitoring and Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Await Inferences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Inferences usually become available for analysis in seconds, but sometimes it can take up to a few minutes for inferences to finish being saved in the Arthur system. This helper function, `arthur_model.await_inferences()`, will report a green-light message once the inferences are ready:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# Wait until some inferences land in Arthur\n",
    "arthur_model.await_inferences()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "If this function has returned the green-light message, you are ready to submit queries to get performance results for your model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Getting performance results for your model involves sending a query to the Arthur platform for data. \n",
    "\n",
    "One way to get performance results is to write out a query object and call `ArthurModel.query()`. You can see [examples of quick queries](https://docs.arthur.ai/user-guide/api-query-guide/query_quickstart.html), an [overview of the structure of a query](https://docs.arthur.ai/user-guide/api-query-guide/endpoint_overview.html), and more helpful information on querying in the [Arthur API Query Guide](https://docs.arthur.ai/user-guide/api-query-guide/index.html).\n",
    "\n",
    "Another way to get performance results is to use functions from the Arthur Python SDK that send these queries for you automatically. Below, we demonstrate some of the functions from the `viz` module of an `ArthurModel` to visualize performance results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Get Overall Accuracy\n",
    "\n",
    "Here we use the `ArthurModel.query()` method, which sends a query object to the Arthur platform to receive performance results for our model. In this query, we select the accuracy and provide no other parameters to the query object, which means we will receive the overall accuracy across all the model's inferences so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>accuracyRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.816778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accuracyRate\n",
       "0      0.816778"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query model accuracy across the batches\n",
    "query = {\n",
    "    \"select\": [\n",
    "        {\n",
    "            \"function\": \"accuracyRate\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "query_result = arthur_model.query(query)\n",
    "pd.DataFrame(query_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Visualize Metrics Over Time\n",
    "\n",
    "We use `ArthurModel.viz.metric_series` to view the drift in our model's accuracy and its true positive rate at a hour-level time resolution.\n",
    "\n",
    "Based on this comparison, we can determine to what extent our model is struggling with *class imbalance*. A model can have high accuracy but a low true positive rate - meaning it struggles to identify instances of the positive class correctly, but its accuracy numbers are still high due to the higher prevalence of one class over another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize model performance metrics over time\n",
    "arthur_model.viz.metric_series([\"accuracyRate\", \"truePositiveRate\"], time_resolution=\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Drift\n",
    "\n",
    "### Get Drift Metrics\n",
    "\n",
    "We get the amount of drift of recent model inferences from the baseline distribution of data in the reference set.\n",
    "\n",
    "Note that drift queries communicate with a different API endpoint than other queries, so we indicate `query_type='drift'` as a parameter to the `ArthurModel.query()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <td>0.771567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <td>0.868068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <td>1.101928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <td>0.807456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <td>0.669267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <td>0.609053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <td>0.151564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_0</th>\n",
       "      <td>0.218127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_2</th>\n",
       "      <td>0.249253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_3</th>\n",
       "      <td>0.291525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_4</th>\n",
       "      <td>0.311003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_5</th>\n",
       "      <td>0.266545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_6</th>\n",
       "      <td>0.256499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <td>0.358568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <td>0.344368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <td>0.275515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <td>0.265094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <td>0.254058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <td>0.239919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "BILL_AMT1  0.771567\n",
       "BILL_AMT2  0.868068\n",
       "BILL_AMT3  1.101928\n",
       "BILL_AMT4  0.807456\n",
       "BILL_AMT5  0.669267\n",
       "BILL_AMT6  0.609053\n",
       "LIMIT_BAL  0.151564\n",
       "PAY_0      0.218127\n",
       "PAY_2      0.249253\n",
       "PAY_3      0.291525\n",
       "PAY_4      0.311003\n",
       "PAY_5      0.266545\n",
       "PAY_6      0.256499\n",
       "PAY_AMT1   0.358568\n",
       "PAY_AMT2   0.344368\n",
       "PAY_AMT3   0.275515\n",
       "PAY_AMT4   0.265094\n",
       "PAY_AMT5   0.254058\n",
       "PAY_AMT6   0.239919"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query drift of recent inferences from reference set\n",
    "# we track drift in the 10-binned representation of the inputs\n",
    "# to compare which feature's overall distribution is most drifted from the reference data\n",
    "data_drift_query = {\n",
    "    \"properties\": arthur_model.get_attribute_names(Stage.ModelPipelineInput),\n",
    "    \"num_bins\": 10,\n",
    "    \"base\": {\n",
    "      \"source\": \"reference\",\n",
    "    },\n",
    "    \"target\": {\n",
    "      \"source\": \"inference\",\n",
    "    },\n",
    "  \"metric\": \"KLDivergence\"\n",
    "}\n",
    "data_drift_response = arthur_model.query(data_drift_query, query_type='drift')\n",
    "\n",
    "pd.DataFrame(data_drift_response).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Visualize Drift Over Time\n",
    "The second drift method we use is `drift_series`, which visualizes the drift in our model's attributes at a given time resolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize drift of recent inferences from reference set\n",
    "arthur_model.viz.drift_series(\n",
    "    ['LIMIT_BAL',\n",
    "     'PAY_0',\n",
    "     'PAY_2',\n",
    "     'PAY_3',\n",
    "     'PAY_4',\n",
    "     'PAY_5',\n",
    "     'PAY_6',\n",
    "     'BILL_AMT1',\n",
    "     'BILL_AMT2',\n",
    "     'BILL_AMT3'], \n",
    "    drift_metric=\"KLDivergence\", \n",
    "    time_resolution=\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Bias Monitoring\n",
    "\n",
    "### Get Overall Bias Metrics\n",
    "\n",
    "We get bias metrics for the model's sensitive attributes, which during onboarding were configured with the parameter `monitor_for_bias=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# query bias metrics for sensitive attributes\n",
    "sensitive_attr_names = [attr.name for attr in arthur_model.get_attributes(stage=Stage.NonInputData)]\n",
    "\n",
    "dem_parity = {}\n",
    "confusion_matrices = {}\n",
    "for sens_attr_name in sensitive_attr_names:\n",
    "    dem_parity[sens_attr_name] = arthur_model.bias.metrics.demographic_parity(sens_attr_name)\n",
    "    confusion_matrices[sens_attr_name] = arthur_model.bias.metrics.group_confusion_matrices(sens_attr_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.07692307692307693,\n",
       " 1: 0.17302615790736742,\n",
       " 2: 0.1528864453372806,\n",
       " 3: 0.18681318681318682}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem_parity['MARRIAGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>accuracy_rate</th>\n",
       "      <th>balanced_accuracy_rate</th>\n",
       "      <th>f1</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>false_positive_rate</th>\n",
       "      <th>precision</th>\n",
       "      <th>true_negative_rate</th>\n",
       "      <th>true_positive_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.798168</td>\n",
       "      <td>0.637149</td>\n",
       "      <td>0.436871</td>\n",
       "      <td>0.665877</td>\n",
       "      <td>0.059826</td>\n",
       "      <td>0.630872</td>\n",
       "      <td>0.940174</td>\n",
       "      <td>0.334123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.829196</td>\n",
       "      <td>0.651177</td>\n",
       "      <td>0.451190</td>\n",
       "      <td>0.641438</td>\n",
       "      <td>0.056208</td>\n",
       "      <td>0.608347</td>\n",
       "      <td>0.943792</td>\n",
       "      <td>0.358562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accuracy_rate  balanced_accuracy_rate        f1  false_negative_rate  \\\n",
       "1       0.798168                0.637149  0.436871             0.665877   \n",
       "2       0.829196                0.651177  0.451190             0.641438   \n",
       "\n",
       "   false_positive_rate  precision  true_negative_rate  true_positive_rate  \n",
       "1             0.059826   0.630872            0.940174            0.334123  \n",
       "2             0.056208   0.608347            0.943792            0.358562  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrices['SEX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Alerts\n",
    "\n",
    "We configure an alert rule based on a metric and a threshold - when the metric crosses the threshold, the alert will be triggered and send a notification we can configure.\n",
    "\n",
    "The rule below will notify us if overall accuracy rate drops below 60% in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AlertRule(bound='lower', threshold=0.6, metric_id='8c6f26ba-9fbc-402a-a0a0-4ddb9b9e266f', severity='critical', name='Accuracy Rate lower bound', lookback_period=5, subsequent_alert_wait_time=0, enabled=True, id='0194bbbe-9caf-49e1-95d2-e5ce3ced15f4', metric_name='Accuracy Rate')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup and alert rule to notify when the accuracy dips below 60%\n",
    "arthur_model.create_alert_rule(\n",
    "    metric_id=[x for x in arthur_model.get_metrics() if x.name=='Accuracy Rate'][0].id,\n",
    "    bound=\"lower\",\n",
    "    threshold=0.6,\n",
    "    severity=\"critical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Querying Enrichments\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Anomaly Scores\n",
    "\n",
    "#### Get Anomaly Scores\n",
    "\n",
    "We get the anomaly scores for a group of inferences, which measures how far these inferences stray from the baseline distribution of the model's reference set.\n",
    "\n",
    "Note that anomaly scores, like all Enrichments, are a paid feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# query anomaly scores for inferences (as well as their their timestamp)\n",
    "def anomaly_scores_for_inferences(model):\n",
    "    query = {\n",
    "      \"select\": [\n",
    "          {\n",
    "              \"property\": \"anomaly_score\",\n",
    "          },\n",
    "          {\n",
    "              \"property\": \"inference_timestamp\"\n",
    "          }\n",
    "      ],\n",
    "      \"from\": \"enriched\",\n",
    "    }\n",
    "    return pd.DataFrame(model.query(query))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "anomaly_score_result = anomaly_scores_for_inferences(arthur_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "anomaly_score_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d485cb42aaa05640787d51ff6b98576e6591c8fe1b0e087ca1599e137e7b48f2"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}