tutorials/Anomaly Detection Tutorial Level Beginner - ANO101.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  <span style=\"color:orange\">Anomaly Detection Tutorial (ANO101) - Level Beginner</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Date Updated: May 02, 2020**\n",
    "\n",
    "# 1.0 Objective of Tutorial\n",
    "Welcome to Anomaly Detection Tutorial (#ANO101). This tutorial assumes that you are new to PyCaret and looking to get started with Anomaly Detection using `pycaret.anomaly` Module.\n",
    "\n",
    "In this tutorial we will learn:\n",
    "\n",
    "\n",
    "* **Getting Data:** How to import data from PyCaret repository?\n",
    "* **Setting up Environment:** How to setup experiment in PyCaret to get started with building anomaly models?\n",
    "* **Create Model:** How to create a model and assign anomaly labels to original dataset for analysis?\n",
    "* **Plot Model:** How to analyze model performance using various plots?\n",
    "* **Predict Model:** How to assign anomaly labels to new and unseen dataset based on trained model?\n",
    "* **Save / Load Model:**  How to save / load model for future use?\n",
    "\n",
    "Read Time : Approx. 25 Minutes\n",
    "\n",
    "\n",
    "## 1.1 Installing PyCaret\n",
    "First step to get started with PyCaret is to install PyCaret. Installing PyCaret is easy and takes few minutes only. Follow the instructions below:\n",
    "\n",
    "#### Installing PyCaret in Local Jupyter Notebook\n",
    "`pip install pycaret`  <br />\n",
    "\n",
    "#### Installing PyCaret on Google Colab or Azure Notebooks\n",
    "`!pip install pycaret`\n",
    "\n",
    "\n",
    "## 1.2 Pre-Requisites\n",
    "- Python 3.x\n",
    "- Latest version of PyCaret\n",
    "- Internet connection to load data from PyCaret's repository\n",
    "- Basic Knowledge of Anomaly Detection\n",
    "\n",
    "## 1.3 For Google colab users:\n",
    "If you are running this notebook on Google Colab, run the following code at top of your notebook to display interactive visuals.<br/>\n",
    "<br/>\n",
    "`from pycaret.utils import enable_colab` <br/>\n",
    "`enable_colab()`\n",
    "\n",
    "## 1.4 See also:\n",
    "- __[Anomaly Detectiom Tutorial (ANO102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20Level%20Intermediate%20-%20ANO102.ipynb)__\n",
    "- __[Anomaly Detection Tutorial (ANO103) - Level Expert](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20Level%20Expert%20-%20ANO103.ipynb)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.0 What is Anomaly Detection?\n",
    "\n",
    "Anomaly Detection is the task of identifying the rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. There are three broad categories of anomaly detection techniques exist:\n",
    "\n",
    "- **Unsupervised anomaly detection:** Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the dataset are normal by looking for instances that seem to fit least to the remainder of the data set. <br/>\n",
    "<br/>\n",
    "- **Supervised anomaly detection:** This technique requires a dataset that has been labeled as \"normal\" and \"abnormal\" and involves training a classifier. <br/>\n",
    "<br/>\n",
    "- **Semi-supervised anomaly detection:** This technique constructs a model representing normal behavior from a given normal training dataset, and then tests the likelihood of a test instance to be generated by the learnt model. <br/>\n",
    "\n",
    "`pycaret.anomaly` module supports the unsupervised and supervised anomaly detection technique. In this tutorial we will only cover unsupervised anomaly detection technique.\n",
    "\n",
    "__[Learn More about Anomaly Detection](https://en.wikipedia.org/wiki/Anomaly_detection)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.0 Overview of Anomaly Detection Module in PyCaret\n",
    "PyCaret's anomaly detection module (`pycaret.anomaly`) is a an unsupervised machine learning module which performs the task of identifying rare items, events or observations which raise suspicions by differing significantly from the majority of the data.\n",
    "\n",
    "PyCaret anomaly detection module provides several pre-processing features that can be configured when initializing the setup through `setup()` function. It has over 12 algorithms and few plots to analyze the results of anomaly detection. PyCaret's anomaly detection module also implements a unique function `tune_model()` that allows you to tune the hyperparameters of anomaly detection model to optimize the supervised learning objective such as `AUC` for classification or `R2` for regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.0 Dataset for the Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial we will use a dataset from UCI called **Mice Protein Expression**. The dataset consists of the expression levels of 77 proteins/protein modifications that produced detectable signals in the nuclear fraction of cortex. The dataset contains a total of 1080 measurements per protein. Each measurement can be considered as an independent sample/mouse.  __[Click Here](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression)__ to read more about the dataset.\n",
    "\n",
    "\n",
    "#### Dataset Acknowledgement:\n",
    "Clara Higuera Department of Software Engineering and Artificial Intelligence, Faculty of Informatics and the Department of Biochemistry and Molecular Biology, Faculty of Chemistry, University Complutense, Madrid, Spain.\n",
    "Email: clarahiguera@ucm.es\n",
    "\n",
    "Katheleen J. Gardiner, creator and owner of the protein expression data, is currently with the Linda Crnic Institute for Down Syndrome, Department of Pediatrics, Department of Biochemistry and Molecular Genetics, Human Medical Genetics and Genomics, and Neuroscience Programs, University of Colorado, School of Medicine, Aurora, Colorado, USA.\n",
    "Email: katheleen.gardiner@ucdenver.edu\n",
    "\n",
    "Krzysztof J. Cios is currently with the Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, USA, and IITiS Polish Academy of Sciences, Poland.\n",
    "Email: kcios@vcu.edu\n",
    "\n",
    "The original dataset and data dictionary can be __[found here.](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression)__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.0 Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the data from the original source __[found here](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression)__ and load it using pandas __[(Learn How)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)__ or you can use PyCaret's data respository to load the data using `get_data()` function (This will require internet connection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:28.667428Z",
     "start_time": "2020-05-02T09:14:27.913034Z"
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   "outputs": [
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       "      <th>H3MeK4_N</th>\n",
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       "  MouseID  DYRK1A_N   ITSN1_N    BDNF_N     NR1_N    NR2A_N    pAKT_N  \\\n",
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       "\n",
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       "4  0.173627   2.134014  0.192158  ...  0.110694  0.434154   0.118481   \n",
       "\n",
       "     EGR1_N  H3MeK4_N    CaNA_N  Genotype  Treatment  Behavior   class  \n",
       "0  0.131790  0.128186  1.675652   Control  Memantine       C/S  c-CS-m  \n",
       "1  0.135103  0.131119  1.743610   Control  Memantine       C/S  c-CS-m  \n",
       "2  0.133362  0.127431  1.926427   Control  Memantine       C/S  c-CS-m  \n",
       "3  0.147444  0.146901  1.700563   Control  Memantine       C/S  c-CS-m  \n",
       "4  0.140314  0.148380  1.839730   Control  Memantine       C/S  c-CS-m  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "dataset = get_data('mice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:28.670969Z",
     "start_time": "2020-05-02T09:14:28.668377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1080, 82)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the shape of data\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to demonstrate the `predict_model()` function on unseen data, a sample of 5% (54 samples) are taken out from original dataset to be used for predictions at the end of experiment. This should not be confused with train/test split. This particular split is performed to simulate real life scenario. Another way to think about this is that these 54 samples are not available at the time when this experiment was performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:28.679165Z",
     "start_time": "2020-05-02T09:14:28.672497Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (1026, 82)\n",
      "Unseen Data For Predictions: (54, 82)\n"
     ]
    }
   ],
   "source": [
    "data = dataset.sample(frac=0.95, random_state=786)\n",
    "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(data.shape))\n",
    "print('Unseen Data For Predictions: ' + str(data_unseen.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.0 Setting up Environment in PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`setup()` function initializes the environment in PyCaret and creates the transformation pipeline to prepare the data for modeling and deployment. `setup()` must be called before executing any other function in PyCaret. It takes only one mandatory parameter: pandas dataframe. All other parameters are optional and are used to customize pre-processing pipeline (we will see them in later tutorials).\n",
    "\n",
    "When `setup()` is executed, PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. Although, most of the times the data type is inferred correctly but it's not always the case. Therefore, after `setup()` is executed, PyCaret displays a table containing features and their inferred data types. At which stage, you can inspect and press `enter` to continue if all data types are correctly inferred or type `quit` to end the experiment. Identifying data types correctly is of fundamental importance in PyCaret as it automatically performs few pre-processing tasks which are imperative to perform any machine learning experiment. These pre-processing tasks are performed differently for each data type. As such, it is very important that data types are correctly configured.\n",
    "\n",
    "In later tutorials we will learn how to overwrite PyCaret's inferred data types using `numeric_features` and `categorical_features` parameter in `setup()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:28.683840Z",
     "start_time": "2020-05-02T09:14:28.680733Z"
    }
   },
   "outputs": [],
   "source": [
    "from pycaret.anomaly import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:41.178246Z",
     "start_time": "2020-05-02T09:14:28.685389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
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       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row3_col0\" class=\"data row3 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row3_col1\" class=\"data row3 col1\" >77</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row4_col0\" class=\"data row4 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row5_col0\" class=\"data row5 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row6_col0\" class=\"data row6 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row7_col0\" class=\"data row7 col0\" >Transformed Data </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row7_col1\" class=\"data row7 col1\" >(1026, 91)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row8_col0\" class=\"data row8 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row8_col1\" class=\"data row8 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row9_col0\" class=\"data row9 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row9_col1\" class=\"data row9 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row10_col0\" class=\"data row10 col0\" >Normalize </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row10_col1\" class=\"data row10 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row11_col0\" class=\"data row11 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row11_col1\" class=\"data row11 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row12_col0\" class=\"data row12 col0\" >Transformation </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row13_col0\" class=\"data row13 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row13_col1\" class=\"data row13 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row14_col0\" class=\"data row14 col0\" >PCA </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row15_col0\" class=\"data row15 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row15_col1\" class=\"data row15 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row16_col0\" class=\"data row16 col0\" >PCA components </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row17_col0\" class=\"data row17 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row18_col0\" class=\"data row18 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row19_col0\" class=\"data row19 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row19_col1\" class=\"data row19 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row20_col0\" class=\"data row20 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row20_col1\" class=\"data row20 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row21_col0\" class=\"data row21 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row21_col1\" class=\"data row21 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row22_col0\" class=\"data row22 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row22_col1\" class=\"data row22 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row23_col0\" class=\"data row23 col0\" >Group Features </td>\n",
       "                        <td id=\"T_5accb0fa_8c55_11ea_a164_a5e690e9dd48row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcc56632c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_ano101 = setup(data, normalize = True, \n",
    "                   ignore_features = ['MouseID'],\n",
    "                   session_id = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the setup is succesfully executed it prints the information grid that contains few important information. Much of the information is related to pre-processing pipeline which is constructed when `setup()` is executed. Much of these features are out of scope for the purpose of this tutorial. However, few important things to note at this stage are:\n",
    "\n",
    "- **session_id :**  A pseudo-random number distributed as a seed in all functions for later reproducibility. If no `session_id` is passed, a random number is automatically generated that is distributed to all functions. In this experiment session_id is set as `123` for later reproducibility.<br/>\n",
    "<br/>\n",
    "- **Missing Values :**  When there are missing values in original data it will show as `True`. Notice that `Missing Values` in the information grid above is `True` as the data contains missing values which are automatically imputed using `mean` for numeric features and `constant` for categorical features. The method of imputation can be changed using `numeric_imputation` and `categorical_imputation` parameter in `setup()`. <br/>\n",
    "<br/>\n",
    "- **Original Data :**  Displays the original shape of dataset. In this experiment (1026, 82) means 1026 samples and 82 features. <br/>\n",
    "<br/>\n",
    "- **Transformed Data :** Displays the shape of transformed dataset. Notice that the shape of original dataset (1026, 82) is transformed into (1026, 91). The number of features has increased due to encoding of categorical features in the dataset. <br/>\n",
    "<br/>\n",
    "- **Numeric Features :**  Number of features inferred as numeric. In this dataset, 77 out of 82 features are inferred as numeric. <br/>\n",
    "<br/>\n",
    "- **Categorical Features :**  Number of features inferred as categorical. In this dataset, 5 out of 82 features are inferred as categorical. Also notice, we have ignored one categorical feature i.e. `MouseID` using `ignore_feature` parameter. <br/>\n",
    "\n",
    "Notice that how few tasks such as missing value imputation and categorical encoding that are imperative to perform modeling are automatically handled. Most of the other parameters in `setup()` are optional and used for customizing pre-processing pipeline. These parameters are out of scope for this tutorial but as you progress to intermediate and expert level, we will cover them in much detail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.0 Create a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an anomaly detection model in PyCaret is simple and similar to how you would have created a model in supervised modules of PyCaret. The anomaly detection model is created using `create_model()` function which takes one mandatory parameter i.e. name of the model as a string. This function returns a trained model object. See the example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:41.413128Z",
     "start_time": "2020-05-02T09:14:41.180023Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "iforest = create_model('iforest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:41.416300Z",
     "start_time": "2020-05-02T09:14:41.414049Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
      "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\n",
      "    random_state=123, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "print(iforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have created Isolation Forest model using `create_model()`. Notice the `contamination` parameter is set `0.05` which is the default value when you do not pass `fraction` parameter in `create_model()`. `fraction` parameter determines the proportion of outliers in the dataset. In below example, we will create `One Class Support Vector Machine` model with `0.025` fraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:41.555029Z",
     "start_time": "2020-05-02T09:14:41.417939Z"
    }
   },
   "outputs": [],
   "source": [
    "svm = create_model('svm', fraction = 0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:41.558863Z",
     "start_time": "2020-05-02T09:14:41.556379Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OCSVM(cache_size=200, coef0=0.0, contamination=0.025, degree=3, gamma='auto',\n",
      "   kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001,\n",
      "   verbose=False)\n"
     ]
    }
   ],
   "source": [
    "print(svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just by replacing `iforest` with `svm` inside `create_model()` we have now created `OCSVM` anomaly detection model. There are 12 models available ready-to-use in `pycaret.anomaly` module. To see the complete list, please see docstring. If you would like to read more about different models, their limitations and use cases, you may __[click here](https://pyod.readthedocs.io/en/latest/pyod.html)__ to read more about it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.0 Assign a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have created a model, we would like to assign the anomaly labels to our dataset (1080 samples) to analyze the results. We will achieve this by using `assign_model()` function. See an example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:41.591146Z",
     "start_time": "2020-05-02T09:14:41.559884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MouseID</th>\n",
       "      <th>DYRK1A_N</th>\n",
       "      <th>ITSN1_N</th>\n",
       "      <th>BDNF_N</th>\n",
       "      <th>NR1_N</th>\n",
       "      <th>NR2A_N</th>\n",
       "      <th>pAKT_N</th>\n",
       "      <th>pBRAF_N</th>\n",
       "      <th>pCAMKII_N</th>\n",
       "      <th>pCREB_N</th>\n",
       "      <th>...</th>\n",
       "      <th>H3AcK18_N</th>\n",
       "      <th>EGR1_N</th>\n",
       "      <th>H3MeK4_N</th>\n",
       "      <th>CaNA_N</th>\n",
       "      <th>Genotype</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Behavior</th>\n",
       "      <th>class</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3501_12</td>\n",
       "      <td>0.344930</td>\n",
       "      <td>0.626194</td>\n",
       "      <td>0.383583</td>\n",
       "      <td>2.534561</td>\n",
       "      <td>4.097317</td>\n",
       "      <td>0.303547</td>\n",
       "      <td>0.222829</td>\n",
       "      <td>4.592769</td>\n",
       "      <td>0.239427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252700</td>\n",
       "      <td>0.218868</td>\n",
       "      <td>0.249187</td>\n",
       "      <td>1.139493</td>\n",
       "      <td>Ts65Dn</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>S/C</td>\n",
       "      <td>t-SC-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3520_5</td>\n",
       "      <td>0.630001</td>\n",
       "      <td>0.839187</td>\n",
       "      <td>0.357777</td>\n",
       "      <td>2.651229</td>\n",
       "      <td>4.261675</td>\n",
       "      <td>0.253184</td>\n",
       "      <td>0.185257</td>\n",
       "      <td>3.816673</td>\n",
       "      <td>0.204940</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155008</td>\n",
       "      <td>0.153219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.642886</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073378</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3414_13</td>\n",
       "      <td>0.555122</td>\n",
       "      <td>0.726229</td>\n",
       "      <td>0.278319</td>\n",
       "      <td>2.097249</td>\n",
       "      <td>2.897553</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.174356</td>\n",
       "      <td>1.867880</td>\n",
       "      <td>0.203379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136109</td>\n",
       "      <td>0.155530</td>\n",
       "      <td>0.185484</td>\n",
       "      <td>1.657670</td>\n",
       "      <td>Ts65Dn</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>t-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.062198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3488_8</td>\n",
       "      <td>0.275849</td>\n",
       "      <td>0.430764</td>\n",
       "      <td>0.285166</td>\n",
       "      <td>2.265254</td>\n",
       "      <td>3.250091</td>\n",
       "      <td>0.189258</td>\n",
       "      <td>0.157837</td>\n",
       "      <td>2.917611</td>\n",
       "      <td>0.202594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127944</td>\n",
       "      <td>0.207671</td>\n",
       "      <td>0.175357</td>\n",
       "      <td>0.893598</td>\n",
       "      <td>Control</td>\n",
       "      <td>Saline</td>\n",
       "      <td>S/C</td>\n",
       "      <td>c-SC-s</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.078707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3501_7</td>\n",
       "      <td>0.304788</td>\n",
       "      <td>0.617299</td>\n",
       "      <td>0.335164</td>\n",
       "      <td>2.638236</td>\n",
       "      <td>4.876609</td>\n",
       "      <td>0.280590</td>\n",
       "      <td>0.199417</td>\n",
       "      <td>4.835421</td>\n",
       "      <td>0.236314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245277</td>\n",
       "      <td>0.202171</td>\n",
       "      <td>0.240372</td>\n",
       "      <td>0.795637</td>\n",
       "      <td>Ts65Dn</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>S/C</td>\n",
       "      <td>t-SC-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.070426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MouseID  DYRK1A_N   ITSN1_N    BDNF_N     NR1_N    NR2A_N    pAKT_N  \\\n",
       "0  3501_12  0.344930  0.626194  0.383583  2.534561  4.097317  0.303547   \n",
       "1   3520_5  0.630001  0.839187  0.357777  2.651229  4.261675  0.253184   \n",
       "2  3414_13  0.555122  0.726229  0.278319  2.097249  2.897553  0.222222   \n",
       "3   3488_8  0.275849  0.430764  0.285166  2.265254  3.250091  0.189258   \n",
       "4   3501_7  0.304788  0.617299  0.335164  2.638236  4.876609  0.280590   \n",
       "\n",
       "    pBRAF_N  pCAMKII_N   pCREB_N  ...  H3AcK18_N    EGR1_N  H3MeK4_N  \\\n",
       "0  0.222829   4.592769  0.239427  ...   0.252700  0.218868  0.249187   \n",
       "1  0.185257   3.816673  0.204940  ...   0.155008  0.153219       NaN   \n",
       "2  0.174356   1.867880  0.203379  ...   0.136109  0.155530  0.185484   \n",
       "3  0.157837   2.917611  0.202594  ...   0.127944  0.207671  0.175357   \n",
       "4  0.199417   4.835421  0.236314  ...   0.245277  0.202171  0.240372   \n",
       "\n",
       "     CaNA_N  Genotype  Treatment  Behavior   class  Label     Score  \n",
       "0  1.139493    Ts65Dn  Memantine       S/C  t-SC-m      0 -0.003593  \n",
       "1  1.642886   Control  Memantine       C/S  c-CS-m      0 -0.073378  \n",
       "2  1.657670    Ts65Dn  Memantine       C/S  t-CS-m      0 -0.062198  \n",
       "3  0.893598   Control     Saline       S/C  c-SC-s      0 -0.078707  \n",
       "4  0.795637    Ts65Dn  Memantine       S/C  t-SC-m      0 -0.070426  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iforest_results = assign_model(iforest)\n",
    "iforest_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that two columns `Label` and `Score` are added towards the end. 0 stands for inliers and 1 for outliers/anomalies. Score is the values computed by the algorithm. Outliers are assigned with larger anomaly scores. Notice that `iforest_results` also includes `MouseID` feature that we have dropped during `setup()`. It wasn't used for the model and is only appended to the dataset when you use `assign_model()`. In the next section we will see how to analyze the results of anomaly detection using `plot_model()`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.0 Plot a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot_model()` function can be used to analyze the anomaly detection model over different aspects. This function takes a trained model object and returns a plot. See the examples below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1 T-distributed Stochastic Neighbor Embedding (t-SNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:46.806093Z",
     "start_time": "2020-05-02T09:14:41.592212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
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     "output_type": "display_data"
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    "plot_model(iforest)"
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   "source": [
    "### 9.2 Uniform Manifold Approximation and Projection"
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    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:54.524117Z",
     "start_time": "2020-05-02T09:14:46.807099Z"
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{\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"uMAP Plot for Outliers\"}, \"width\": 900, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"0\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"1\"}}},\n",
       "                        {\"responsive\": true}\n",
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       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(iforest, plot = 'umap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10.0 Predict on Unseen Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`predict_model()` function is used to assign anomaly labels on the new unseen dataset. We will now use our `iforest` model to predict the data stored in `data_unseen`. This was created in the beginning of the experiment and it contains 54 new samples that were not exposed to PyCaret before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:54.691804Z",
     "start_time": "2020-05-02T09:14:54.525398Z"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MouseID</th>\n",
       "      <th>DYRK1A_N</th>\n",
       "      <th>ITSN1_N</th>\n",
       "      <th>BDNF_N</th>\n",
       "      <th>NR1_N</th>\n",
       "      <th>NR2A_N</th>\n",
       "      <th>pAKT_N</th>\n",
       "      <th>pBRAF_N</th>\n",
       "      <th>pCAMKII_N</th>\n",
       "      <th>pCREB_N</th>\n",
       "      <th>...</th>\n",
       "      <th>H3AcK18_N</th>\n",
       "      <th>EGR1_N</th>\n",
       "      <th>H3MeK4_N</th>\n",
       "      <th>CaNA_N</th>\n",
       "      <th>Genotype</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Behavior</th>\n",
       "      <th>class</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>309_6</td>\n",
       "      <td>0.447506</td>\n",
       "      <td>0.628176</td>\n",
       "      <td>0.367388</td>\n",
       "      <td>2.385939</td>\n",
       "      <td>4.807635</td>\n",
       "      <td>0.218578</td>\n",
       "      <td>0.176233</td>\n",
       "      <td>2.141282</td>\n",
       "      <td>0.195188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116657</td>\n",
       "      <td>0.140766</td>\n",
       "      <td>0.142180</td>\n",
       "      <td>1.816389</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.079135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>311_3</td>\n",
       "      <td>0.704633</td>\n",
       "      <td>0.802537</td>\n",
       "      <td>0.350110</td>\n",
       "      <td>2.467733</td>\n",
       "      <td>5.548400</td>\n",
       "      <td>0.205323</td>\n",
       "      <td>0.165058</td>\n",
       "      <td>2.107281</td>\n",
       "      <td>0.171401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111089</td>\n",
       "      <td>0.157731</td>\n",
       "      <td>0.158543</td>\n",
       "      <td>1.404481</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.063899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321_4</td>\n",
       "      <td>0.505093</td>\n",
       "      <td>0.695549</td>\n",
       "      <td>0.376029</td>\n",
       "      <td>2.915585</td>\n",
       "      <td>5.917957</td>\n",
       "      <td>0.226734</td>\n",
       "      <td>0.174271</td>\n",
       "      <td>2.663039</td>\n",
       "      <td>0.190038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131515</td>\n",
       "      <td>0.188391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.699260</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.042825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3415_12</td>\n",
       "      <td>0.429133</td>\n",
       "      <td>0.563175</td>\n",
       "      <td>0.258429</td>\n",
       "      <td>2.028151</td>\n",
       "      <td>3.542553</td>\n",
       "      <td>0.214075</td>\n",
       "      <td>0.176759</td>\n",
       "      <td>3.165139</td>\n",
       "      <td>0.167430</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118223</td>\n",
       "      <td>0.171071</td>\n",
       "      <td>0.173702</td>\n",
       "      <td>1.405727</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.090049</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3415_15</td>\n",
       "      <td>0.373648</td>\n",
       "      <td>0.471165</td>\n",
       "      <td>0.257909</td>\n",
       "      <td>1.860032</td>\n",
       "      <td>2.938526</td>\n",
       "      <td>0.218262</td>\n",
       "      <td>0.150380</td>\n",
       "      <td>2.610132</td>\n",
       "      <td>0.142571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086785</td>\n",
       "      <td>0.126537</td>\n",
       "      <td>0.112690</td>\n",
       "      <td>0.790975</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>1</td>\n",
       "      <td>0.038967</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
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      "text/plain": [
       "   MouseID  DYRK1A_N   ITSN1_N    BDNF_N     NR1_N    NR2A_N    pAKT_N  \\\n",
       "0    309_6  0.447506  0.628176  0.367388  2.385939  4.807635  0.218578   \n",
       "1    311_3  0.704633  0.802537  0.350110  2.467733  5.548400  0.205323   \n",
       "2    321_4  0.505093  0.695549  0.376029  2.915585  5.917957  0.226734   \n",
       "3  3415_12  0.429133  0.563175  0.258429  2.028151  3.542553  0.214075   \n",
       "4  3415_15  0.373648  0.471165  0.257909  1.860032  2.938526  0.218262   \n",
       "\n",
       "    pBRAF_N  pCAMKII_N   pCREB_N  ...  H3AcK18_N    EGR1_N  H3MeK4_N  \\\n",
       "0  0.176233   2.141282  0.195188  ...   0.116657  0.140766  0.142180   \n",
       "1  0.165058   2.107281  0.171401  ...   0.111089  0.157731  0.158543   \n",
       "2  0.174271   2.663039  0.190038  ...   0.131515  0.188391       NaN   \n",
       "3  0.176759   3.165139  0.167430  ...   0.118223  0.171071  0.173702   \n",
       "4  0.150380   2.610132  0.142571  ...   0.086785  0.126537  0.112690   \n",
       "\n",
       "     CaNA_N  Genotype  Treatment  Behavior   class  Label     Score  \n",
       "0  1.816389   Control  Memantine       C/S  c-CS-m      0 -0.079135  \n",
       "1  1.404481   Control  Memantine       C/S  c-CS-m      0 -0.063899  \n",
       "2  1.699260   Control  Memantine       C/S  c-CS-m      0 -0.042825  \n",
       "3  1.405727   Control  Memantine       C/S  c-CS-m      0 -0.090049  \n",
       "4  0.790975   Control  Memantine       C/S  c-CS-m      1  0.038967  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(iforest, data=data_unseen)\n",
    "unseen_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Label` column indicates the outlier (1 = outlier, 0 = inlier). `Score` is the values computed by the algorithm. Outliers are assigned with larger anomaly scores. You can also use `predict_model()` function to label the training data. See example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:54.909604Z",
     "start_time": "2020-05-02T09:14:54.692808Z"
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   },
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>3501_12</td>\n",
       "      <td>0.344930</td>\n",
       "      <td>0.626194</td>\n",
       "      <td>0.383583</td>\n",
       "      <td>2.534561</td>\n",
       "      <td>4.097317</td>\n",
       "      <td>0.303547</td>\n",
       "      <td>0.222829</td>\n",
       "      <td>4.592769</td>\n",
       "      <td>0.239427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252700</td>\n",
       "      <td>0.218868</td>\n",
       "      <td>0.249187</td>\n",
       "      <td>1.139493</td>\n",
       "      <td>Ts65Dn</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>S/C</td>\n",
       "      <td>t-SC-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003593</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3520_5</td>\n",
       "      <td>0.630001</td>\n",
       "      <td>0.839187</td>\n",
       "      <td>0.357777</td>\n",
       "      <td>2.651229</td>\n",
       "      <td>4.261675</td>\n",
       "      <td>0.253184</td>\n",
       "      <td>0.185257</td>\n",
       "      <td>3.816673</td>\n",
       "      <td>0.204940</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155008</td>\n",
       "      <td>0.153219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.642886</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073378</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3414_13</td>\n",
       "      <td>0.555122</td>\n",
       "      <td>0.726229</td>\n",
       "      <td>0.278319</td>\n",
       "      <td>2.097249</td>\n",
       "      <td>2.897553</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.174356</td>\n",
       "      <td>1.867880</td>\n",
       "      <td>0.203379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136109</td>\n",
       "      <td>0.155530</td>\n",
       "      <td>0.185484</td>\n",
       "      <td>1.657670</td>\n",
       "      <td>Ts65Dn</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>t-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.062198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3488_8</td>\n",
       "      <td>0.275849</td>\n",
       "      <td>0.430764</td>\n",
       "      <td>0.285166</td>\n",
       "      <td>2.265254</td>\n",
       "      <td>3.250091</td>\n",
       "      <td>0.189258</td>\n",
       "      <td>0.157837</td>\n",
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       "      <td>0.202594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127944</td>\n",
       "      <td>0.207671</td>\n",
       "      <td>0.175357</td>\n",
       "      <td>0.893598</td>\n",
       "      <td>Control</td>\n",
       "      <td>Saline</td>\n",
       "      <td>S/C</td>\n",
       "      <td>c-SC-s</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.078707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3501_7</td>\n",
       "      <td>0.304788</td>\n",
       "      <td>0.617299</td>\n",
       "      <td>0.335164</td>\n",
       "      <td>2.638236</td>\n",
       "      <td>4.876609</td>\n",
       "      <td>0.280590</td>\n",
       "      <td>0.199417</td>\n",
       "      <td>4.835421</td>\n",
       "      <td>0.236314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245277</td>\n",
       "      <td>0.202171</td>\n",
       "      <td>0.240372</td>\n",
       "      <td>0.795637</td>\n",
       "      <td>Ts65Dn</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>S/C</td>\n",
       "      <td>t-SC-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.070426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MouseID  DYRK1A_N   ITSN1_N    BDNF_N     NR1_N    NR2A_N    pAKT_N  \\\n",
       "0  3501_12  0.344930  0.626194  0.383583  2.534561  4.097317  0.303547   \n",
       "1   3520_5  0.630001  0.839187  0.357777  2.651229  4.261675  0.253184   \n",
       "2  3414_13  0.555122  0.726229  0.278319  2.097249  2.897553  0.222222   \n",
       "3   3488_8  0.275849  0.430764  0.285166  2.265254  3.250091  0.189258   \n",
       "4   3501_7  0.304788  0.617299  0.335164  2.638236  4.876609  0.280590   \n",
       "\n",
       "    pBRAF_N  pCAMKII_N   pCREB_N  ...  H3AcK18_N    EGR1_N  H3MeK4_N  \\\n",
       "0  0.222829   4.592769  0.239427  ...   0.252700  0.218868  0.249187   \n",
       "1  0.185257   3.816673  0.204940  ...   0.155008  0.153219       NaN   \n",
       "2  0.174356   1.867880  0.203379  ...   0.136109  0.155530  0.185484   \n",
       "3  0.157837   2.917611  0.202594  ...   0.127944  0.207671  0.175357   \n",
       "4  0.199417   4.835421  0.236314  ...   0.245277  0.202171  0.240372   \n",
       "\n",
       "     CaNA_N  Genotype  Treatment  Behavior   class  Label     Score  \n",
       "0  1.139493    Ts65Dn  Memantine       S/C  t-SC-m      0 -0.003593  \n",
       "1  1.642886   Control  Memantine       C/S  c-CS-m      0 -0.073378  \n",
       "2  1.657670    Ts65Dn  Memantine       C/S  t-CS-m      0 -0.062198  \n",
       "3  0.893598   Control     Saline       S/C  c-SC-s      0 -0.078707  \n",
       "4  0.795637    Ts65Dn  Memantine       S/C  t-SC-m      0 -0.070426  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_predictions = predict_model(iforest, data = data)\n",
    "data_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11.0 Saving the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now finished the experiment by using our `iforest` model to predict outlier labels on unseen data. This brings us to the end of our experiment but one question is still to be asked. What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is No, you don't need to rerun the entire experiment and reconstruct the pipeline to generate predictions on new data. PyCaret's inbuilt function `save_model()` allows you to save the model along with entire transformation pipeline for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:54.956974Z",
     "start_time": "2020-05-02T09:14:54.910555Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(iforest,'Final IForest Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12.0 Loading the Saved Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load a saved model on a future date in the same or different environment, we would use the PyCaret's `load_model()` function and then easily apply the saved model on new unseen data for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:54.995091Z",
     "start_time": "2020-05-02T09:14:54.958303Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "saved_iforest = load_model('Final IForest Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the model is loaded in the environment, you can simply use it to predict on any new data using the same `predict_model()` function . Below we have applied the loaded model to predict the same `data_unseen` that we have used in section 10 above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:55.150854Z",
     "start_time": "2020-05-02T09:14:54.996173Z"
    }
   },
   "outputs": [],
   "source": [
    "new_prediction = predict_model(saved_iforest, data=data_unseen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-02T09:14:55.170517Z",
     "start_time": "2020-05-02T09:14:55.151946Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MouseID</th>\n",
       "      <th>DYRK1A_N</th>\n",
       "      <th>ITSN1_N</th>\n",
       "      <th>BDNF_N</th>\n",
       "      <th>NR1_N</th>\n",
       "      <th>NR2A_N</th>\n",
       "      <th>pAKT_N</th>\n",
       "      <th>pBRAF_N</th>\n",
       "      <th>pCAMKII_N</th>\n",
       "      <th>pCREB_N</th>\n",
       "      <th>...</th>\n",
       "      <th>H3AcK18_N</th>\n",
       "      <th>EGR1_N</th>\n",
       "      <th>H3MeK4_N</th>\n",
       "      <th>CaNA_N</th>\n",
       "      <th>Genotype</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Behavior</th>\n",
       "      <th>class</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>309_6</td>\n",
       "      <td>0.447506</td>\n",
       "      <td>0.628176</td>\n",
       "      <td>0.367388</td>\n",
       "      <td>2.385939</td>\n",
       "      <td>4.807635</td>\n",
       "      <td>0.218578</td>\n",
       "      <td>0.176233</td>\n",
       "      <td>2.141282</td>\n",
       "      <td>0.195188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116657</td>\n",
       "      <td>0.140766</td>\n",
       "      <td>0.142180</td>\n",
       "      <td>1.816389</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.079135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>311_3</td>\n",
       "      <td>0.704633</td>\n",
       "      <td>0.802537</td>\n",
       "      <td>0.350110</td>\n",
       "      <td>2.467733</td>\n",
       "      <td>5.548400</td>\n",
       "      <td>0.205323</td>\n",
       "      <td>0.165058</td>\n",
       "      <td>2.107281</td>\n",
       "      <td>0.171401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111089</td>\n",
       "      <td>0.157731</td>\n",
       "      <td>0.158543</td>\n",
       "      <td>1.404481</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.063899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321_4</td>\n",
       "      <td>0.505093</td>\n",
       "      <td>0.695549</td>\n",
       "      <td>0.376029</td>\n",
       "      <td>2.915585</td>\n",
       "      <td>5.917957</td>\n",
       "      <td>0.226734</td>\n",
       "      <td>0.174271</td>\n",
       "      <td>2.663039</td>\n",
       "      <td>0.190038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131515</td>\n",
       "      <td>0.188391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.699260</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.042825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3415_12</td>\n",
       "      <td>0.429133</td>\n",
       "      <td>0.563175</td>\n",
       "      <td>0.258429</td>\n",
       "      <td>2.028151</td>\n",
       "      <td>3.542553</td>\n",
       "      <td>0.214075</td>\n",
       "      <td>0.176759</td>\n",
       "      <td>3.165139</td>\n",
       "      <td>0.167430</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118223</td>\n",
       "      <td>0.171071</td>\n",
       "      <td>0.173702</td>\n",
       "      <td>1.405727</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.090049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3415_15</td>\n",
       "      <td>0.373648</td>\n",
       "      <td>0.471165</td>\n",
       "      <td>0.257909</td>\n",
       "      <td>1.860032</td>\n",
       "      <td>2.938526</td>\n",
       "      <td>0.218262</td>\n",
       "      <td>0.150380</td>\n",
       "      <td>2.610132</td>\n",
       "      <td>0.142571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086785</td>\n",
       "      <td>0.126537</td>\n",
       "      <td>0.112690</td>\n",
       "      <td>0.790975</td>\n",
       "      <td>Control</td>\n",
       "      <td>Memantine</td>\n",
       "      <td>C/S</td>\n",
       "      <td>c-CS-m</td>\n",
       "      <td>1</td>\n",
       "      <td>0.038967</td>\n",
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       "<p>5 rows × 84 columns</p>\n",
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       "   MouseID  DYRK1A_N   ITSN1_N    BDNF_N     NR1_N    NR2A_N    pAKT_N  \\\n",
       "0    309_6  0.447506  0.628176  0.367388  2.385939  4.807635  0.218578   \n",
       "1    311_3  0.704633  0.802537  0.350110  2.467733  5.548400  0.205323   \n",
       "2    321_4  0.505093  0.695549  0.376029  2.915585  5.917957  0.226734   \n",
       "3  3415_12  0.429133  0.563175  0.258429  2.028151  3.542553  0.214075   \n",
       "4  3415_15  0.373648  0.471165  0.257909  1.860032  2.938526  0.218262   \n",
       "\n",
       "    pBRAF_N  pCAMKII_N   pCREB_N  ...  H3AcK18_N    EGR1_N  H3MeK4_N  \\\n",
       "0  0.176233   2.141282  0.195188  ...   0.116657  0.140766  0.142180   \n",
       "1  0.165058   2.107281  0.171401  ...   0.111089  0.157731  0.158543   \n",
       "2  0.174271   2.663039  0.190038  ...   0.131515  0.188391       NaN   \n",
       "3  0.176759   3.165139  0.167430  ...   0.118223  0.171071  0.173702   \n",
       "4  0.150380   2.610132  0.142571  ...   0.086785  0.126537  0.112690   \n",
       "\n",
       "     CaNA_N  Genotype  Treatment  Behavior   class  Label     Score  \n",
       "0  1.816389   Control  Memantine       C/S  c-CS-m      0 -0.079135  \n",
       "1  1.404481   Control  Memantine       C/S  c-CS-m      0 -0.063899  \n",
       "2  1.699260   Control  Memantine       C/S  c-CS-m      0 -0.042825  \n",
       "3  1.405727   Control  Memantine       C/S  c-CS-m      0 -0.090049  \n",
       "4  0.790975   Control  Memantine       C/S  c-CS-m      1  0.038967  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_prediction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that results of `unseen_predictions` and `new_prediction` are identical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 16.0 Wrap-up / Next Steps?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we have covered in this tutorial is the entire machine learning pipeline from data ingestion, pre-processing, training the anomaly detector, predicting on unseen data and saving the model for later use. We have completed all this in less than 10 commands which are naturally constructed and very intuitive to remember such as `create_model()`, `assign_model()`, `plot_model()`. Re-creating the entire experiment without PyCaret would have taken well over 100 lines of code in most of the libraries.\n",
    "\n",
    "In this tutorial, we have only covered the basics of `pycaret.anomaly`. In the following tutorials, we will go deeper into advanced pre-processing techniques that allows you to fully customize your machine learning pipeline that are must to know for any data scientist. \n",
    "\n",
    "See you at the next tutorial. Follow the link to __[Anomaly Detection Tutorial (ANO102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Anomaly%20Detection%20Tutorial%20Level%20Intermediate%20-%20ANO102.ipynb)__"
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