{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# MLflow Logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Setup\n",
    "\n",
    "For this tutorial we will simplify the approach by using MLflow's local client. One of MLflow's advantages is that it uses the exact same API to work both locally and in the cloud. So with a minor setup, the code shown here can be easily extended if you're working with MLflow in Kubernetes or in Databricks, for example. In order to get started, make sure you have both `mlflow` and `whylogs` installed in your environment by uncommenting the following cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Note: you may need to restart the kernel to use updated packages.\n",
    "!pip3 install --proxy http://hpeproxy.its.hpecorp.net:443/ 'whylogs[viz]'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We are also installing `pandas`, `scikit-learn` and `matplotlib` in order to have a very simple training example and show you how you can start profiling your training data with `whylogs`. So, if you still haven't, also run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!pip3 install --proxy http://hpeproxy.its.hpecorp.net:443/ graphviz scikit-learn matplotlib pandas mlflow-skinny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!pip3 install --proxy http://hpeproxy.its.hpecorp.net:443/ pydotplus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Get the data\n",
    "\n",
    "Now let us get an example dataset from the `scikit-learn` library and create a function that returns an aggregated dataframe with it. We will use this same function later on!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "def get_data() -> pd.DataFrame:\n",
    "    iris_data = load_iris()\n",
    "    dataframe = pd.DataFrame(iris_data.data, columns=iris_data.feature_names)\n",
    "    dataframe[\"target\"] = pd.DataFrame(iris_data.target)\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Train a model\n",
    "\n",
    "Let's define the simplest model to be trained with `scikit-learn`. We aren't interested in model performance nor deep ML concepts, but only in having some baseline model being trained and having the overall idea of how to use `whylogs` with your existing training pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "def train(dataframe: pd.DataFrame) -> None:\n",
    "    model = DecisionTreeClassifier(max_depth=2)\n",
    "    model.fit(dataframe.drop(\"target\", axis=1), y=dataframe[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from urllib.parse import urlparse\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json, datetime\n",
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "from mlflow import log_metric, log_param, log_artifact\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.inspection import permutation_importance\n",
    "from mlflow.models.signature import infer_signature\n",
    "from sklearn import tree\n",
    "\n",
    "from pydotplus import graph_from_dot_data\n",
    "import graphviz\n",
    "from IPython.display import Image\n",
    "\n",
    "import itertools, os\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "if os.path.exists(\"model_artifacts\"):\n",
    "    os.system(\"rm -rf model_artifacts\")\n",
    "os.mkdir(\"model_artifacts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could serialize a model, but we will take a shortcut here taking advantage of `mlflow`'s awesome `autolog` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/09/27 11:35:22 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmps6kk8phx/model/model.pkl, flavor: sklearn), fall back to return ['scikit-learn==1.0.2', 'cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback.\n"
     ]
    }
   ],
   "source": [
    "experiment_name = 'whylogs-mlflow01'\n",
    "mlflow.set_experiment(experiment_name)\n",
    "\n",
    "with mlflow.start_run() as run:\n",
    "    mlflow.sklearn.autolog()\n",
    "\n",
    "    df = get_data()\n",
    "    train(dataframe=df)\n",
    "\n",
    "    run_id = run.info.run_id\n",
    "\n",
    "    mlflow.end_run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Profile the training data with `whylogs`\n",
    "\n",
    "Now in order to profile your training data with `whylogs`, you'll basically need to use our `logger` API, which is as simple as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ No session found. Call whylogs.init() to initialize a session and authenticate. See https://docs.whylabs.ai/docs/whylabs-whylogs-init for more information.\n"
     ]
    }
   ],
   "source": [
    "import whylogs as why\n",
    "\n",
    "profile_result = why.log(df)\n",
    "profile_view = profile_result.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardinality/est</th>\n",
       "      <th>cardinality/lower_1</th>\n",
       "      <th>cardinality/upper_1</th>\n",
       "      <th>counts/inf</th>\n",
       "      <th>counts/n</th>\n",
       "      <th>counts/nan</th>\n",
       "      <th>counts/null</th>\n",
       "      <th>distribution/max</th>\n",
       "      <th>distribution/mean</th>\n",
       "      <th>distribution/median</th>\n",
       "      <th>...</th>\n",
       "      <th>type</th>\n",
       "      <th>types/boolean</th>\n",
       "      <th>types/fractional</th>\n",
       "      <th>types/integral</th>\n",
       "      <th>types/object</th>\n",
       "      <th>types/string</th>\n",
       "      <th>types/tensor</th>\n",
       "      <th>frequent_items/frequent_strings</th>\n",
       "      <th>ints/max</th>\n",
       "      <th>ints/min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>43.000004</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.002151</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>22.000001</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.001100</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>35.000003</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.001750</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>5.8</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>23.000001</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.001150</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000150</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[FrequentItem(value='1', est=50, upper=50, low...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   cardinality/est  cardinality/lower_1  cardinality/upper_1  \\\n",
       "column                                                                         \n",
       "petal length (cm)        43.000004                 43.0            43.002151   \n",
       "petal width (cm)         22.000001                 22.0            22.001100   \n",
       "sepal length (cm)        35.000003                 35.0            35.001750   \n",
       "sepal width (cm)         23.000001                 23.0            23.001150   \n",
       "target                    3.000000                  3.0             3.000150   \n",
       "\n",
       "                   counts/inf  counts/n  counts/nan  counts/null  \\\n",
       "column                                                             \n",
       "petal length (cm)           0       150           0            0   \n",
       "petal width (cm)            0       150           0            0   \n",
       "sepal length (cm)           0       150           0            0   \n",
       "sepal width (cm)            0       150           0            0   \n",
       "target                      0       150           0            0   \n",
       "\n",
       "                   distribution/max  distribution/mean  distribution/median  \\\n",
       "column                                                                        \n",
       "petal length (cm)               6.9           3.758000                  4.4   \n",
       "petal width (cm)                2.5           1.199333                  1.3   \n",
       "sepal length (cm)               7.9           5.843333                  5.8   \n",
       "sepal width (cm)                4.4           3.057333                  3.0   \n",
       "target                          2.0           1.000000                  1.0   \n",
       "\n",
       "                   ...                type  types/boolean  types/fractional  \\\n",
       "column             ...                                                        \n",
       "petal length (cm)  ...  SummaryType.COLUMN              0               150   \n",
       "petal width (cm)   ...  SummaryType.COLUMN              0               150   \n",
       "sepal length (cm)  ...  SummaryType.COLUMN              0               150   \n",
       "sepal width (cm)   ...  SummaryType.COLUMN              0               150   \n",
       "target             ...  SummaryType.COLUMN              0                 0   \n",
       "\n",
       "                   types/integral  types/object  types/string  types/tensor  \\\n",
       "column                                                                        \n",
       "petal length (cm)               0             0             0             0   \n",
       "petal width (cm)                0             0             0             0   \n",
       "sepal length (cm)               0             0             0             0   \n",
       "sepal width (cm)                0             0             0             0   \n",
       "target                        150             0             0             0   \n",
       "\n",
       "                                     frequent_items/frequent_strings  \\\n",
       "column                                                                 \n",
       "petal length (cm)                                                NaN   \n",
       "petal width (cm)                                                 NaN   \n",
       "sepal length (cm)                                                NaN   \n",
       "sepal width (cm)                                                 NaN   \n",
       "target             [FrequentItem(value='1', est=50, upper=50, low...   \n",
       "\n",
       "                   ints/max  ints/min  \n",
       "column                                 \n",
       "petal length (cm)       NaN       NaN  \n",
       "petal width (cm)        NaN       NaN  \n",
       "sepal length (cm)       NaN       NaN  \n",
       "sepal width (cm)        NaN       NaN  \n",
       "target                  2.0       0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_view.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing your profile to `mlflow`\n",
    "\n",
    "Now even more interesting than writing this profile locally is the ability to use `mlflow`'s API **together** with `whylogs`', in order to store the training data profile and analyze the results of your experiments over time. For that, we basically need to define a function that will\n",
    "\n",
    "1. Profile our training data\n",
    "2. Log the profile as an `mlflow` artifact\n",
    "\n",
    "Let's see how this function can be written:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_profile(dataframe: pd.DataFrame) -> None:\n",
    "    profile_result = why.log(dataframe)\n",
    "    profile_result.writer(\"mlflow\").write()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can call that function we defined in our `mlflow` run experiment, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/09/27 11:37:28 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp2uqvht97/model/model.pkl, flavor: sklearn), fall back to return ['scikit-learn==1.0.2', 'cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback.\n"
     ]
    }
   ],
   "source": [
    "with mlflow.start_run() as run:\n",
    "    mlflow.sklearn.autolog()\n",
    "\n",
    "    df = get_data()\n",
    "    train(dataframe=df)\n",
    "\n",
    "    log_profile(dataframe=df)\n",
    "\n",
    "    run_id = run.info.run_id\n",
    "\n",
    "    mlflow.end_run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can even use `mlflow`'s API to fetch and read back our profile, like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac12fcb794694d5b949b4909f41718fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlflow.tracking import MlflowClient\n",
    "\n",
    "client = MlflowClient()\n",
    "\n",
    "local_dir = \"/mnt/shared/artifact_downloads\"\n",
    "if not os.path.exists(local_dir):\n",
    "    os.mkdir(local_dir)\n",
    "local_path = client.download_artifacts(run_id, \"whylogs\", local_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['profile_2023-09-27 11:10:59.954347+00:00.bin',\n",
       " 'profile_2023-09-27 11:37:29.415083+00:00.bin',\n",
       " 'profile_2023-09-25 07:11:31.666995+00:00.bin']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(local_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_name = os.listdir(local_path)[0]\n",
    "result = why.read(path=f\"{local_path}/{profile_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardinality/est</th>\n",
       "      <th>cardinality/lower_1</th>\n",
       "      <th>cardinality/upper_1</th>\n",
       "      <th>counts/inf</th>\n",
       "      <th>counts/n</th>\n",
       "      <th>counts/nan</th>\n",
       "      <th>counts/null</th>\n",
       "      <th>distribution/max</th>\n",
       "      <th>distribution/mean</th>\n",
       "      <th>distribution/median</th>\n",
       "      <th>...</th>\n",
       "      <th>type</th>\n",
       "      <th>types/boolean</th>\n",
       "      <th>types/fractional</th>\n",
       "      <th>types/integral</th>\n",
       "      <th>types/object</th>\n",
       "      <th>types/string</th>\n",
       "      <th>types/tensor</th>\n",
       "      <th>frequent_items/frequent_strings</th>\n",
       "      <th>ints/max</th>\n",
       "      <th>ints/min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>43.000004</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.002151</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>22.000001</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.001100</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>35.000003</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.001750</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>5.8</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>23.000001</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.001150</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000150</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>SummaryType.COLUMN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[FrequentItem(value='1', est=50, upper=50, low...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   cardinality/est  cardinality/lower_1  cardinality/upper_1  \\\n",
       "column                                                                         \n",
       "petal length (cm)        43.000004                 43.0            43.002151   \n",
       "petal width (cm)         22.000001                 22.0            22.001100   \n",
       "sepal length (cm)        35.000003                 35.0            35.001750   \n",
       "sepal width (cm)         23.000001                 23.0            23.001150   \n",
       "target                    3.000000                  3.0             3.000150   \n",
       "\n",
       "                   counts/inf  counts/n  counts/nan  counts/null  \\\n",
       "column                                                             \n",
       "petal length (cm)           0       150           0            0   \n",
       "petal width (cm)            0       150           0            0   \n",
       "sepal length (cm)           0       150           0            0   \n",
       "sepal width (cm)            0       150           0            0   \n",
       "target                      0       150           0            0   \n",
       "\n",
       "                   distribution/max  distribution/mean  distribution/median  \\\n",
       "column                                                                        \n",
       "petal length (cm)               6.9           3.758000                  4.4   \n",
       "petal width (cm)                2.5           1.199333                  1.3   \n",
       "sepal length (cm)               7.9           5.843333                  5.8   \n",
       "sepal width (cm)                4.4           3.057333                  3.0   \n",
       "target                          2.0           1.000000                  1.0   \n",
       "\n",
       "                   ...                type  types/boolean  types/fractional  \\\n",
       "column             ...                                                        \n",
       "petal length (cm)  ...  SummaryType.COLUMN              0               150   \n",
       "petal width (cm)   ...  SummaryType.COLUMN              0               150   \n",
       "sepal length (cm)  ...  SummaryType.COLUMN              0               150   \n",
       "sepal width (cm)   ...  SummaryType.COLUMN              0               150   \n",
       "target             ...  SummaryType.COLUMN              0                 0   \n",
       "\n",
       "                   types/integral  types/object  types/string  types/tensor  \\\n",
       "column                                                                        \n",
       "petal length (cm)               0             0             0             0   \n",
       "petal width (cm)                0             0             0             0   \n",
       "sepal length (cm)               0             0             0             0   \n",
       "sepal width (cm)                0             0             0             0   \n",
       "target                        150             0             0             0   \n",
       "\n",
       "                                     frequent_items/frequent_strings  \\\n",
       "column                                                                 \n",
       "petal length (cm)                                                NaN   \n",
       "petal width (cm)                                                 NaN   \n",
       "sepal length (cm)                                                NaN   \n",
       "sepal width (cm)                                                 NaN   \n",
       "target             [FrequentItem(value='1', est=50, upper=50, low...   \n",
       "\n",
       "                   ints/max  ints/min  \n",
       "column                                 \n",
       "petal length (cm)       NaN       NaN  \n",
       "petal width (cm)        NaN       NaN  \n",
       "sepal length (cm)       NaN       NaN  \n",
       "sepal width (cm)        NaN       NaN  \n",
       "target                  2.0       0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.view().to_pandas()"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And with those few lines we have successfully fetched the profile artifact from our experiment. Over time, we will be able to track down some very relevant information on how our data behaves, **why** is our model generating the results and walk towards a more Robust and Responsible AI field.\n",
    "![image.png](attachment:92d758bf-afd6-4a4c-b61c-c11fff74d6b4.png)\n",
    "![image-1.png](attachment:a4c855d8-0176-4a71-8504-d6ef9b910b30.png)"
   ]
  }
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