anomaly_detection_challenge-1-1.ipynb

{
 "cells": [
  {
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   "metadata": {},
   "source": [
    "<b>AML2019</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Challenge 3</h3>\n",
    "<h1>Anomaly Detection (AD)</h1>\n",
    "<hr style=\"height:2px;border:none;color:#333;background-color:#333;\"/>\n",
    "<i>3<sup>th</sup> May 2019</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anomaly detection (AD) refers to the process of detecting data points that do not conform with the rest of observations. Applications of anomaly detection include fraud and fault detection, surveillance, diagnosis, data cleanup, predictive maintenance.\n",
    "\n",
    "When we talk about AD, we usually look at it as an unsupervised (or semi-supervised) task, where the concept of anomaly is often not well defined or, in the best case, just few samples are labeled as anomalous. In this challenge, you will look at AD from a different perspective!\n",
    "\n",
    "The dataset you are going to work on consists of monitoring data generated by IT systems; such data is then processed by a monitoring system that executes some checks and detects a series of anomalies. This is a multi-label classification problem, where each check is a binary label corresponding to a specific type of anomaly. Your goal is to develop a machine learning model (or multiple ones) to accurately detect such anomalies.\n",
    "\n",
    "This will also involve a mixture of data exploration, pre-processing, model selection, and performance evaluation. You will also be asked to try one or more rule learning models, and compare them with other ML models both in terms of predictive performances and interpretability. Interpreatibility is indeed a strong requirement especially in applications like AD where understanding the output of a model is as important as the output itself.\n",
    "\n",
    "Please, bear in mind that the purpose of this challenge is not simply to find the best-performing model. You should rather make sure to understand the difficulties that come with this AD task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "<hr style=\"height:1px;border:none;color:#333;background-color:#333;\" />    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beyond simply producing a well-performing model for making predictions, in this challenge we would like you to start developing your skills as a machine learning scientist.\n",
    "In this regard, your notebook should be structured in such a way as to explore the five following tasks that are expected to be carried out whenever undertaking such a project.\n",
    "The description below each aspect should serve as a guide for your work, but you are strongly encouraged to also explore alternative options and directions. \n",
    "Thinking outside the box will always be rewarded in these challenges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h3>1. Data Exploration</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first broad component of your notebook should enable you to familiarise yourselves with the given data, an outline of which is given at the end of this challenge specification.\n",
    "Among others, this section should investigate:\n",
    "\n",
    "- Data cleaning\n",
    "- Data visualisation;\n",
    "- Computing descriptive statistics, e.g. correlation.\n",
    "- etc.\n",
    "\n",
    "Data exploration is also useful to identify eventual errors in the dataset: for example, some features may have values that are outside the allowed range of values. Ranges are specified in the dataset description."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h3>2. Data Pre-processing</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous step should give you a better understanding of which pre-processing is required for the data.\n",
    "This may include:\n",
    "\n",
    "- Normalising and standardising the given data;\n",
    "- Removing outliers;\n",
    "- Carrying out feature selection;\n",
    "- Handling missing information in the dataset;\n",
    "- Handling errors in the dataset;\n",
    "- Combining existing features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h3>3. Model Selection</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, you should have a good understanding of the dataset, and have an idea about the possible candidate models. For example, you may try a multi-label classification model to predict all classes at ones, or train different models, one for each label. In any case, it is important to justify your choices and make a comparison among the candidate models.\n",
    "\n",
    "You are free to choose any model you want, but you should be aware about some factors which may influence your decision:\n",
    "\n",
    "- What is the model's complexity?\n",
    "- Is the model interpretable?\n",
    "- Is the model able to handle imbalanced datasets?\n",
    "- Is the model capable of handling both numerical and categorical data?\n",
    "- Is the model able to handle missing values?\n",
    "- Does the model return uncertainty estimates along with predictions?\n",
    "\n",
    "An in-depth evaluation of competing models in view of this and other criteria will elevate the quality of your submission and earn you a higher grade. You may also try to build new labels by combining one or more labels (for example by doing an OR) and check if this impacts the performance of the model(s)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h4>3.1 Interpretable Models</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Being able to understand the output of a model is important in many field, especially in anomaly detection. In linear regression, for example, the weights of the model can provide some hints on the importance of features, and this is a form of interpretability. Here, we focus on Rule learning, a specific field of interpretable machine learning that provides interpretability through the use of rules. Examples of rule-based models are: \n",
    "\n",
    "- RIPPER\n",
    "    - [Main Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.2612&rep=rep1&type=pdf)\n",
    "    - A fast and reliable implementation is JRIP by [WEKA](https://www.cs.waikato.ac.nz/~ml/weka/). You can also find unofficial python implementations on GitHub.\n",
    "- Bayesian Rule Sets (BRS)\n",
    "    - [Main Paper](http://jmlr.org/papers/volume18/16-003/16-003.pdf)\n",
    "    - You can find a good implementation [here](https://pypi.org/project/ruleset/). You will probably need to install \"fim\" (pip install fim) before installing BRS.\n",
    "- Scalable Bayesian Rule Lists (SBRL)\n",
    "    - [Main Paper](https://arxiv.org/pdf/1602.08610.pdf)\n",
    "    - You can find a good implementation [here](https://github.com/myaooo/pysbrl). You will probably need to install \"fim\" (pip install fim) before installing SBRL.\n",
    "- and so on... \n",
    "\n",
    "Try to run at least one of the suggested models (you are free to try others as well) and comment:\n",
    "\n",
    "- Are rule-learning models able to provide the same predictive performances as previously tested models?\n",
    "- Are they faster or slower to train?\n",
    "- Do learned rules look meaningful to you?\n",
    "- How many rules do these models learn?\n",
    "- How many conditions/atoms have on average?\n",
    "\n",
    "<b>N.B.</b> Since most of the rule-learning implementations deal with binary labels, you can train the model to predict one label of your choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h3>4. Parameter Optimisation</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Irrespective of your choice, it is highly likely that your model will have one or more parameters that require tuning.\n",
    "There are several techniques for carrying out such a procedure, including cross-validation, Bayesian optimisation, and several others.\n",
    "As before, an analysis into which parameter tuning technique best suits your model is expected before proceeding with the optimisation of your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h3>5. Model Evaluation</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some form of pre-evaluation will inevitably be required in the preceding sections in order to both select an appropriate model and configure its parameters appropriately.\n",
    "In this final section, you may evaluate other aspects of the model such as:\n",
    "\n",
    "- Assessing the running time of your model;\n",
    "- Determining whether some aspects can be parallelised;\n",
    "- Training the model with smaller subsets of the data.\n",
    "- etc.\n",
    "\n",
    "For the evaluation of the classification results, you should use F1-score for each class and do the average.\n",
    "\n",
    "<b>N.B.</b> Please note that you are responsible for creating a sensible train/validation/test split. There is no predefined held-out test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h3>*. Optional</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you will see in the dataset description, the labels you are going to predict have no meaningful names. Try to understand which kind of anomalies these labels refer to and give sensible names. To do it, you could exploit the output of the interpretable models and/or use a statistical approach with the data you have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "    <b>N.B.</b> Please note that the items listed under each heading are neither exhaustive, nor are you expected to explore every given suggestion.\n",
    "    Nonetheless, these should serve as a guideline for your work in both this and upcoming challenges.\n",
    "    As always, you should use your intuition and understanding in order to decide which analysis best suits the assigned task.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h2>Submission Instructions</h2>\n",
    "    <hr style=\"height:1px;border:none;color:#333;background-color:#333;\" />    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The goal of this challenge is to construct one or more models to detect anomalies.\n",
    "- Your submission will be the <b>HTML version of your notebook</b> exploring the various modelling aspects described above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"\">\n",
    "    <h2>Dataset Description</h2>\n",
    "    <hr style=\"height:1px;border:none;color:#333;background-color:#333;\"/>    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * Location of the Dataset on zoe\n",
    "The data for this challenge is located at: `/mnt/datasets/anomaly`\n",
    "\n",
    "#### * Files\n",
    "\n",
    "You have a unique csv file with 36 features and 8 labels.\n",
    "Each record contains aggregate features computed over a given amount of time.\n",
    "\n",
    "#### * Attributes\n",
    "\n",
    "A brief outline of the available attributes is given below.\n",
    "\n",
    "1. <i>SessionNumber</i> (INTEGER): it identifies the session on which data is collected;\n",
    "* <i>SystemID</i> (INTEGER): it identifies the system generating the data;\n",
    "* <i>Date</i> (DATE): collection date;\n",
    "* <i>HighPriorityAlerts</i> (INTEGER [0, N]): number of high priority alerts in the session;\n",
    "* <i>Dumps</i> (INTEGER [0, N]): number of memory dumps;\n",
    "* <i>CleanupOOMDumps</i> (INTEGER) [0, N]): number of cleanup OOM dumps;\n",
    "* <i>CompositeOOMDums</i> (INTEGER [0, N]): number of composite OOM dumps;\n",
    "* <i>IndexServerRestarts</i> (INTEGER [0, N]): number of restarts of the index server;\n",
    "* <i>NameServerRestarts</i> (INTEGER [0, N]): number of restarts of the name server;\n",
    "* <i>XSEngineRestarts</i> (INTEGER [0, N]): number of restarts of the XSEngine;\n",
    "* <i>PreprocessorRestarts</i> (INTEGER [0, N]): number of restarts of the preprocessor;\n",
    "* <i>DaemonRestarts</i> (INTEGER [0, N]): number of restarts of the daemon process;\n",
    "* <i>StatisticsServerRestarts</i> (INTEGER [0, N]): number of restarts of the statistics server;\n",
    "* <i>CPU</i> (FLOAT [0, 100]): cpu usage;\n",
    "* <i>PhysMEM</i> (FLOAT [0, 100]): physical memory;\n",
    "* <i>InstanceMEM</i> (FLOAT [0, 100]): memory usage of one instance of the system;\n",
    "* <i>TablesAllocation</i> (FLOAT [0, 100]): memory allocated for tables;\n",
    "* <i>IndexServerAllocationLimit</i> (FLOAT [0, 100]): level of memory used by index server;\n",
    "* <i>ColumnUnloads</i> (INTEGER [0, N]): number of columns unloaded from the tables;\n",
    "* <i>DeltaSize</i> (INTEGER [0, N]): size of the delta store;\n",
    "* <i>MergeErrors</i> BOOLEAN [0, 1]: 1 if there are merge errors;\n",
    "* <i>BlockingPhaseSec</i> (INTEGER [0, N]): blocking phase duration in seconds;\n",
    "* <i>Disk</i> (FLOAT [0, 100]): disk usage;\n",
    "* <i>LargestTableSize</i> (INTEGER [0, N]): size of the largest table;\n",
    "* <i>LargestPartitionSize</i> (INTEGER [0, N]): size of the largest partition of a table;\n",
    "* <i>DiagnosisFiles</i> (INTEGER [0, N]): number of diagnosis files;\n",
    "* <i>DiagnosisFilesSize</i> (INTEGER [0, N]): size of diagnosis files;\n",
    "* <i>DaysWithSuccessfulDataBackups</i> (INTEGER [0, N]): number of days with successful data backups;\n",
    "* <i>DaysWithSuccessfulLogBackups</i> (INTEGER [0, N]): number of days with successful log backups;\n",
    "* <i>DaysWithFailedDataBackups</i> (INTEGER [0, N]): number of days with failed data backups;\n",
    "* <i>DaysWithFailedfulLogBackups</i> (INTEGER [0, N]): number of days with failed log backups;\n",
    "* <i>MinDailyNumberOfSuccessfulDataBackups</i> (INTEGER [0, N]): minimum number of successful data backups per day;\n",
    "* <i>MinDailyNumberOfSuccessfulLogBackups</i> (INTEGER [0, N]): minimum number of successful log backups per day;\n",
    "* <i>MaxDailyNumberOfFailedDataBackups</i> (INTEGER [0, N]): maximum number of failed data backups per day;\n",
    "* <i>MaxDailyNumberOfFailedLogBackups</i> (INTEGER [0, N]): maximum number of failed log backups per day;\n",
    "* <i>LogSegmentChange</i> (INTEGER [0, N]): changes in the number of log segments.\n",
    "\n",
    "#### * Labels\n",
    "\n",
    "Labels are binary. Each label refers to a different anomaly.\n",
    "\n",
    "* <i>Check1</i>;\n",
    "* <i>Check2</i>;\n",
    "* <i>Check3</i>;\n",
    "* <i>Check4</i>;\n",
    "* <i>Check5</i>;\n",
    "* <i>Check6</i>;\n",
    "* <i>Check7</i>;\n",
    "* <i>Check8</i>;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Data Exploration + Data Pre-processing </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "from ruleset import *\n",
    "from time import time\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start first by  loading our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "287030  228195  130  11/01/2018 04:58   0  0.0  0.0  0.0   0   0   0 ...    0   \n",
       "\n",
       "        35   36   37   38  39   40   41  42   43  \n",
       "287026 NaN  NaN  NaN  NaN NaN  NaN  1.0 NaN  0.0  \n",
       "287027 NaN  NaN  NaN  NaN NaN  NaN  1.0 NaN  0.0  \n",
       "287028 NaN  NaN  NaN  NaN NaN  NaN  0.0 NaN  0.0  \n",
       "287029 NaN  0.0  0.0  0.0 NaN  0.0  0.0 NaN  0.0  \n",
       "287030 NaN  NaN  NaN  NaN NaN  NaN  NaN NaN  0.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load data\n",
    "data=pd.read_csv('/mnt/datasets/anomaly/data.csv',sep=';',header=None)\n",
    "#Show samples of data\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We replace then columns name bythe actual column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['SessionNumber', 'SystemID' , 'Date', 'HighPriorityAlerts', 'Dumps','CleanupOOMDumps','CompositeOOMDums','IndexServerRestarts',\n",
    "                'NameServerRestarts','XSEngineRestarts','PreprocessorRestarts','DaemonRestarts','StatisticsServerRestarts','CPU','PhysMEM','InstanceMEM',\n",
    "                'TablesAllocation','IndexServerAllocationLimit','ColumnUnloads','DeltaSize','MergeErrors','BlockingPhaseSec','Disk','LargestTableSize',\n",
    "                'LargestPartitionSize','DiagnosisFiles','DiagnosisFilesSize','DaysWithSuccessfulDataBackups','DaysWithSuccessfulLogBackups','DaysWithFailedDataBackups',\n",
    "                'DaysWithFailedfulLogBackups','MinDailyNumberOfSuccessfulDataBackups','MinDailyNumberOfSuccessfulLogBackups','MaxDailyNumberOfFailedDataBackups',\n",
    "                'MaxDailyNumberOfFailedLogBackups','LogSegmentChange',\n",
    "                'Check1','Check2','Check3','Check4','Check5','Check6','Check7','Check8']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 287031 entries, 0 to 287030\n",
      "Data columns (total 44 columns):\n",
      "SessionNumber                            287031 non-null int64\n",
      "SystemID                                 287031 non-null int64\n",
      "Date                                     287031 non-null object\n",
      "HighPriorityAlerts                       287031 non-null int64\n",
      "Dumps                                    287028 non-null float64\n",
      "CleanupOOMDumps                          287028 non-null float64\n",
      "CompositeOOMDums                         287028 non-null float64\n",
      "IndexServerRestarts                      287031 non-null int64\n",
      "NameServerRestarts                       287031 non-null int64\n",
      "XSEngineRestarts                         287031 non-null int64\n",
      "PreprocessorRestarts                     287031 non-null int64\n",
      "DaemonRestarts                           287031 non-null int64\n",
      "StatisticsServerRestarts                 287031 non-null int64\n",
      "CPU                                      261822 non-null float64\n",
      "PhysMEM                                  266464 non-null float64\n",
      "InstanceMEM                              264914 non-null float64\n",
      "TablesAllocation                         284741 non-null float64\n",
      "IndexServerAllocationLimit               260587 non-null float64\n",
      "ColumnUnloads                            287031 non-null int64\n",
      "DeltaSize                                286825 non-null float64\n",
      "MergeErrors                              279298 non-null float64\n",
      "BlockingPhaseSec                         211177 non-null float64\n",
      "Disk                                     275652 non-null float64\n",
      "LargestTableSize                         270781 non-null float64\n",
      "LargestPartitionSize                     286881 non-null float64\n",
      "DiagnosisFiles                           265108 non-null float64\n",
      "DiagnosisFilesSize                       265108 non-null float64\n",
      "DaysWithSuccessfulDataBackups            287031 non-null int64\n",
      "DaysWithSuccessfulLogBackups             287031 non-null int64\n",
      "DaysWithFailedDataBackups                287031 non-null int64\n",
      "DaysWithFailedfulLogBackups              287031 non-null int64\n",
      "MinDailyNumberOfSuccessfulDataBackups    287031 non-null int64\n",
      "MinDailyNumberOfSuccessfulLogBackups     287031 non-null int64\n",
      "MaxDailyNumberOfFailedDataBackups        287031 non-null int64\n",
      "MaxDailyNumberOfFailedLogBackups         287031 non-null int64\n",
      "LogSegmentChange                         251482 non-null float64\n",
      "Check1                                   262520 non-null float64\n",
      "Check2                                   262545 non-null float64\n",
      "Check3                                   264463 non-null float64\n",
      "Check4                                   250384 non-null float64\n",
      "Check5                                   251997 non-null float64\n",
      "Check6                                   279647 non-null float64\n",
      "Check7                                   251309 non-null float64\n",
      "Check8                                   286979 non-null float64\n",
      "dtypes: float64(25), int64(18), object(1)\n",
      "memory usage: 96.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Except for date, all the reamining features are numerical (float and int)<br>\n",
    "We can see that some of the columns contains missing data<br>\n",
    "NaN in these columns: Check1-8, LogSegmentChange, CPU, PhysMEM, InstanceMEM, TablesAllocation, IndexServerAllocationLimit, DeltaSize, MergeErrors, BlockingPhaseSec, Disk, LargestTableSize, LargestPartitionSize, DiagnosisFiles, DiagnosisFilesSize<br>\n",
    "While we can fix that for features, we assessed that it will wrong final results if we filled empty lines for outputs.<br>\n",
    "We start first by fixing data integrity (In columns that are supposed to contain values between 0 and 100, we change values grater than 100 to 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We only keep data that have all checks filled\n",
    "train = data[data.Check1.notna() & data.Check2.notna() & data.Check3.notna() & data.Check4.notna() & data.Check5.notna()\n",
    "        & data.Check6.notna() & data.Check7.notna() & data.Check8.notna()]\n",
    "columns = train.columns[-8:]\n",
    "plt.figure(figsize=(20,8))\n",
    "for i in range(len(columns)):\n",
    "    plt.subplot(2,4,i+1)\n",
    "    plt.title(columns[i])\n",
    "    plt.hist(train[columns[i]])\n",
    "    plt.xticks([0,1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that we have more zeros than ones in our outpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Day\"] = train.Date.apply(lambda x:x.partition(\"/\")[0]).astype(int)\n",
    "train[\"Month\"] = train.Date.apply(lambda x:x.partition(\"/\")[2].partition(\"/\")[0]).astype(int)\n",
    "train[\"Year\"] = train.Date.apply(lambda x:x.partition(\"/\")[2].partition(\"/\")[2].split()[0]).astype(int)\n",
    "train[\"Hour\"] = train.Date.apply(lambda x:x.partition(\"/\")[2].partition(\"/\")[2].split()[1].partition(\":\")[0]).astype(int)\n",
    "train[\"Minute\"] = train.Date.apply(lambda x:x.partition(\"/\")[2].partition(\"/\")[2].split()[1].partition(\":\")[2]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Replace incorrect values\n",
    "#Columns must be less than 100. Any value grater than 100 will be reduced to 100\n",
    "train.CPU = train.CPU.apply(lambda x:100 if x>100 else x)\n",
    "train.PhysMEM = train.PhysMEM.apply(lambda x:100 if x>100 else x)\n",
    "train.InstanceMEM = train.InstanceMEM.apply(lambda x:100 if x>100 else x)\n",
    "train.TablesAllocation = train.TablesAllocation.apply(lambda x:100 if x>100 else x)\n",
    "train.IndexServerAllocationLimit = train.IndexServerAllocationLimit.apply(lambda x:100 if x>100 else x)\n",
    "train.Disk = train.Disk.apply(lambda x:100 if x>100 else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we visulaize the distribution of features that contain missing data.<br>\n",
    "These columns are: <br>\n",
    "Dumps, CleanupOOMDumps, CompositeOOMDums, CPU, PhysMEM, InstanceMEM, IndexServerAllocationLimit, DeltaSize, BlockingPhaseSec, Disk, LargestTableSize, LargestPartitionSize, DiagnosisFiles, DiagnosisFilesSize, LogSegmentChange   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'LogSegmentChange')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,12))\n",
    "plt.subplot(3,5,1);\n",
    "plt.hist(train.Dumps.dropna(),range=(0,100),bins=10);\n",
    "plt.title('Dumps')\n",
    "plt.subplot(3,5,2);\n",
    "plt.hist(train.CleanupOOMDumps.dropna(),bins=10);\n",
    "plt.title('CleanupOOMDumps')\n",
    "plt.subplot(3,5,3);\n",
    "plt.hist(train.CompositeOOMDums.dropna(),bins=10);\n",
    "plt.title('CompositeOOMDums')\n",
    "plt.subplot(3,5,4);\n",
    "plt.hist(train[train.CPU<100].CPU.dropna(),bins=10);\n",
    "plt.title('CPU')\n",
    "plt.subplot(3,5,5);\n",
    "plt.hist(train.PhysMEM.dropna(),bins=10);\n",
    "plt.title('PhysMEM')\n",
    "plt.subplot(3,5,6);\n",
    "plt.hist(train.InstanceMEM.dropna(),bins=10);\n",
    "plt.title('InstanceMEM')\n",
    "plt.subplot(3,5,7);\n",
    "plt.hist(train.IndexServerAllocationLimit.dropna(),bins=10);\n",
    "plt.title('IndexServerAllocationLimit')\n",
    "plt.subplot(3,5,8);\n",
    "plt.hist(train.DeltaSize.dropna(),bins=20);\n",
    "plt.title('DeltaSize')\n",
    "plt.subplot(3,5,9);\n",
    "plt.hist(train.BlockingPhaseSec.dropna(),bins=10);\n",
    "plt.title('BlockingPhaseSec')\n",
    "plt.subplot(3,5,10);\n",
    "plt.hist(train.Disk.dropna(),bins=10);\n",
    "plt.title('Disk')\n",
    "plt.subplot(3,5,11);\n",
    "plt.hist(train.LargestTableSize.dropna(),bins=10);\n",
    "plt.title('LargestTableSize')\n",
    "plt.subplot(3,5,12);\n",
    "plt.hist(train.LargestPartitionSize.dropna(),bins=10);\n",
    "plt.title('LargestPartitionSize')\n",
    "plt.subplot(3,5,13);\n",
    "plt.hist(train.DiagnosisFiles.dropna(),bins=10);\n",
    "plt.title('DiagnosisFiles')\n",
    "plt.subplot(3,5,14);\n",
    "plt.hist(train.DiagnosisFilesSize.dropna(),bins=10);\n",
    "plt.title('DiagnosisFilesSize')\n",
    "plt.subplot(3,5,15);\n",
    "plt.hist(train.LogSegmentChange.dropna(),bins=10);\n",
    "plt.title('LogSegmentChange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can identify two trends:<br>\n",
    "<ul>\n",
    "    <li> Some features seems to contains a dominating range. For this type of features we just fill with the mode of that feature.</li>\n",
    "    <li> The other features have a more spread distribution. To fill missing data for these features we will generate a distribution using multiple Gaussians (GMM) </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill features that have a dominating range\n",
    "train.Dumps = train.Dumps.fillna(train.Dumps.mode()[0])\n",
    "train.CompositeOOMDums = train.CompositeOOMDums.fillna(train.CompositeOOMDums.mode()[0])\n",
    "train.DeltaSize = train.CompositeOOMDums.fillna(train.DeltaSize.mode()[0])\n",
    "train.BlockingPhaseSec = train.BlockingPhaseSec.fillna(train.BlockingPhaseSec.mode()[0])\n",
    "train.DiagnosisFiles = train.DiagnosisFiles.fillna(train.DiagnosisFiles.mode()[0])\n",
    "train.DiagnosisFilesSize = train.DiagnosisFilesSize.fillna(train.DiagnosisFilesSize.mode()[0])\n",
    "train.LogSegmentChange = train.LogSegmentChange.fillna(train.LogSegmentChange.mode()[0])\n",
    "#Columns that have no impact inunderstanding output: They have a unique \n",
    "train = train.drop(columns=['CleanupOOMDumps','DaemonRestarts','PreprocessorRestarts']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use GMM, we first see how many components is best suited for each distribution. Therefore we check Akaike information criterion (AIC) which is an estimator of the best number of components to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operating now:  CPU\n",
      "Operating now:  PhysMEM\n",
      "Operating now:  InstanceMEM\n",
      "Operating now:  IndexServerAllocationLimit\n",
      "Operating now:  Disk\n",
      "Operating now:  LargestTableSize\n",
      "Operating now:  LargestPartitionSize\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fill features with GMM\n",
    "n_components = [10,20,30,40,50]\n",
    "columns = ['CPU','PhysMEM','InstanceMEM','IndexServerAllocationLimit','Disk','LargestTableSize','LargestPartitionSize']\n",
    "i = 1\n",
    "plt.figure(figsize=(25,12))\n",
    "for col in columns:\n",
    "    aics = []\n",
    "    print(\"Operating now: \",col)\n",
    "    #For eachcomponents number we calculate AIC\n",
    "    for n in n_components:\n",
    "        model = GaussianMixture(n, covariance_type='full', random_state=0)\n",
    "        tmp = train[train[col].notnull()]\n",
    "        aics.append(model.fit(tmp[col].to_frame()).aic(tmp[col].to_frame()))\n",
    "    plt.subplot(2,4,i)\n",
    "    plt.plot(n_components, aics);\n",
    "    plt.xlabel(\"Number of GMM components\")\n",
    "    plt.ylabel(\"AICS\")\n",
    "    plt.title(col)\n",
    "    i=i+1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We end up with these following values\n",
    "<table>\n",
    "    <tr>\n",
    "        <td> Columns\n",
    "        <td> CPU</td>\n",
    "        <td> PhysMEM</td>\n",
    "        <td> InstanceMEM</td>\n",
    "        <td> IndexServerAllocationLimit</td>\n",
    "        <td> Disk</td>\n",
    "        <td> LargestTableSize</td>\n",
    "        <td> LargestPartitionSize</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> Number of components\n",
    "        <td> 40</td>\n",
    "        <td> 30</td>\n",
    "        <td> 40</td>\n",
    "        <td> 40</td>\n",
    "        <td> 20</td>\n",
    "        <td> 40</td>\n",
    "        <td> 30</td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SessionNumber', 'SystemID', 'Date', 'HighPriorityAlerts', 'Dumps',\n",
       "       'CompositeOOMDums', 'IndexServerRestarts', 'NameServerRestarts',\n",
       "       'XSEngineRestarts', 'StatisticsServerRestarts', 'CPU', 'PhysMEM',\n",
       "       'InstanceMEM', 'TablesAllocation', 'IndexServerAllocationLimit',\n",
       "       'ColumnUnloads', 'DeltaSize', 'MergeErrors', 'BlockingPhaseSec', 'Disk',\n",
       "       'LargestTableSize', 'LargestPartitionSize', 'DiagnosisFiles',\n",
       "       'DiagnosisFilesSize', 'DaysWithSuccessfulDataBackups',\n",
       "       'DaysWithSuccessfulLogBackups', 'DaysWithFailedDataBackups',\n",
       "       'DaysWithFailedfulLogBackups', 'MinDailyNumberOfSuccessfulDataBackups',\n",
       "       'MinDailyNumberOfSuccessfulLogBackups',\n",
       "       'MaxDailyNumberOfFailedDataBackups', 'MaxDailyNumberOfFailedLogBackups',\n",
       "       'LogSegmentChange', 'Check1', 'Check2', 'Check3', 'Check4', 'Check5',\n",
       "       'Check6', 'Check7', 'Check8', 'Day', 'Month', 'Year', 'Hour', 'Minute'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_size = [40,30,40,40,20,40,30]\n",
    "columns = ['CPU','PhysMEM','InstanceMEM','IndexServerAllocationLimit','Disk','LargestTableSize','LargestPartitionSize']\n",
    "#For each column we fill missing data with samples generated with the pre-computed distributions\n",
    "for i in range(len(columns)):\n",
    "    col = columns[i]\n",
    "    tmp = train[train[col].notnull()]\n",
    "    #Number of missing data in column\n",
    "    num = len(train) - len(train[col].dropna())\n",
    "    #Calculate distribution with GMM using the best number of components\n",
    "    model = GaussianMixture(comp_size[i], covariance_type='full', random_state=0)\n",
    "    model.fit(tmp[col].to_frame())\n",
    "    #Sample and fill columns\n",
    "    train.loc[train[col].isnull(), col] =model.sample(n_samples=num)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(columns=\"Date\")\n",
    "to_normalize = ['HighPriorityAlerts', 'Dumps', 'CompositeOOMDums','IndexServerRestarts','NameServerRestarts',\n",
    "                'XSEngineRestarts', 'StatisticsServerRestarts', 'CPU', 'PhysMEM','InstanceMEM', 'TablesAllocation', \n",
    "                'IndexServerAllocationLimit','ColumnUnloads', 'DeltaSize', 'BlockingPhaseSec', 'Disk',\n",
    "                'LargestTableSize', 'LargestPartitionSize', 'DiagnosisFiles','DiagnosisFilesSize', \n",
    "                'DaysWithSuccessfulDataBackups','DaysWithSuccessfulLogBackups', 'DaysWithFailedDataBackups',\n",
    "                'DaysWithFailedfulLogBackups', 'MinDailyNumberOfSuccessfulDataBackups',\n",
    "                'MinDailyNumberOfSuccessfulLogBackups','MaxDailyNumberOfFailedDataBackups', \n",
    "                'MaxDailyNumberOfFailedLogBackups','LogSegmentChange']\n",
    "for col in to_normalize:\n",
    "    train[col] = normalize(train[col].to_frame(),axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Model Selection</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first split our dataset into train and test. We use one third of the dataset for test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop(columns=['Check1','Check2','Check3','Check4','Check5','Check6','Check7','Check8'])\n",
    "y = train[['Check1','Check2', 'Check3', 'Check4', 'Check5','Check6', 'Check7', 'Check8']]\n",
    "#Split dataset into trainand test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first use the Bayesian Rule Set(RBS). Therefore we create two dataframes one containing all the features and the other containing all the outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________Calculation for  Check1  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.9937, TP = 0,FP = 0, TN = 9937, FN = 63\n",
      " old is -999999999.9, pt_new is -402.18910208681586, prior_ChsRules=-17.905007283232408, likelihood_1 = 0.0, likelihood_2 = -384.28409480358346\n",
      " \n",
      "[]\n",
      "\n",
      "** chain = 1, max at iter = 1 ** \n",
      " accuracy = 0.9937, TP = 0,FP = 0, TN = 9937, FN = 63\n",
      " old is -402.08910208681584, pt_new is -384.3139454458094, prior_ChsRules=-0.029850642225937918, likelihood_1 = 0.0, likelihood_2 = -384.28409480358346\n",
      " \n",
      "[]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      1.00      1.00      9928\n",
      "          1       0.00      0.00      0.00        72\n",
      "\n",
      "avg / total       0.99      0.99      0.99     10000\n",
      "\n",
      "Time of execution:  13.24011516571045\n",
      "________Calculation for  Check2  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.965, TP = 50,FP = 73, TN = 9600, FN = 277\n",
      " old is -999999999.9, pt_new is -1423.1934783028353, prior_ChsRules=-17.905007283232408, likelihood_1 = -138.53871340207024, likelihood_2 = -1266.7497576175326\n",
      " \n",
      "['InstanceMEM<0.003698669365591986', '0.003972671388166479<=TablesAllocation', 'TablesAllocation<0.005924597054940531', '0.0034085486347295293<=LargestTableSize', 'LargestTableSize<0.006961312808225556']\n",
      "[3]\n",
      "\n",
      "** chain = 1, max at iter = 2 ** \n",
      " accuracy = 0.9683, TP = 93,FP = 83, TN = 9590, FN = 234\n",
      " old is -1423.0934783028354, pt_new is -1310.6088209244056, prior_ChsRules=-35.08701674177428, likelihood_1 = -166.47454467887883, likelihood_2 = -1109.0472595037525\n",
      " \n",
      "['InstanceMEM<0.003698669365591986', '0.003972671388166479<=TablesAllocation', 'TablesAllocation<0.005924597054940531', '0.0034085486347295293<=LargestTableSize', 'LargestTableSize<0.006961312808225556']\n",
      "['0.0034050780280517868<=IndexServerAllocationLimit', 'IndexServerAllocationLimit<0.0034405061676365703', '0.0008543528827167665<=LargestTableSize', 'MergeErrors<1.0', 'LargestTableSize<0.009083860443508989', '0.0<=MergeErrors']\n",
      "[3, 7]\n",
      "\n",
      "** chain = 1, max at iter = 9 ** \n",
      " accuracy = 0.9728, TP = 75,FP = 20, TN = 9653, FN = 252\n",
      " old is -1310.5088209244057, pt_new is -1275.1863868233638, prior_ChsRules=-35.08701674177428, likelihood_1 = -62.67542576291271, likelihood_2 = -1177.4239443186768\n",
      " \n",
      "['0.0034050780280517868<=IndexServerAllocationLimit', 'IndexServerAllocationLimit<0.0034405061676365703', '0.0008543528827167665<=LargestTableSize', 'MergeErrors<1.0', 'LargestTableSize<0.009083860443508989', '0.0<=MergeErrors']\n",
      "['0.003972671388166479<=TablesAllocation', 'TablesAllocation<0.005924597054940531', 'SessionNumber<32924', '0.002095765554955084<=PhysMEM', 'PhysMEM<0.0028454001979803063', '2299<=SessionNumber']\n",
      "[7, 2]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      1.00      0.98      9647\n",
      "          1       0.56      0.17      0.26       353\n",
      "\n",
      "avg / total       0.96      0.97      0.96     10000\n",
      "\n",
      "Time of execution:  69.4027738571167\n",
      "________Calculation for  Check3  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.9983, TP = 276,FP = 14, TN = 9707, FN = 3\n",
      " old is -999999999.9, pt_new is -107.69527510488342, prior_ChsRules=-17.905007283232408, likelihood_1 = -59.42317896018335, likelihood_2 = -30.367088861467664\n",
      " \n",
      "['0.0028248826294088106<=PhysMEM', '0.0<=MinDailyNumberOfSuccessfulLogBackups', 'PhysMEM<0.002954827230361616', '4.811101596683855e-06<=LargestTableSize', 'LargestTableSize<0.00868993550421905', 'MinDailyNumberOfSuccessfulLogBackups<0.04425951568792845']\n",
      "[1]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00      9681\n",
      "          1       0.95      0.98      0.97       319\n",
      "\n",
      "avg / total       1.00      1.00      1.00     10000\n",
      "\n",
      "Time of execution:  28.09778642654419\n",
      "________Calculation for  Check4  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.9047, TP = 0,FP = 0, TN = 9047, FN = 953\n",
      " old is -999999999.9, pt_new is -3156.524914982884, prior_ChsRules=-0.029850642225937918, likelihood_1 = 0.0, likelihood_2 = -3156.4950643406582\n",
      " \n",
      "[]\n",
      "\n",
      "** chain = 1, max at iter = 1 ** \n",
      " accuracy = 0.9969, TP = 941,FP = 19, TN = 9028, FN = 12\n",
      " old is -3156.4249149828843, pt_new is -207.06456315362402, prior_ChsRules=-17.905007283232408, likelihood_1 = -95.19507472408873, likelihood_2 = -93.96448114630289\n",
      " \n",
      "['IndexServerAllocationLimit<0.0035807726794620394', '0.003253966167373833<=IndexServerAllocationLimit', 'LargestPartitionSize<0.008273594151636483', '0.0<=HighPriorityAlerts', '5.359596571625022e-06<=LargestPartitionSize', 'HighPriorityAlerts<0.008944356092570303']\n",
      "[2]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00      9025\n",
      "          1       0.98      0.99      0.99       975\n",
      "\n",
      "avg / total       1.00      1.00      1.00     10000\n",
      "\n",
      "Time of execution:  149.29844570159912\n",
      "________Calculation for  Check5  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.9896, TP = 0,FP = 0, TN = 9896, FN = 104\n",
      " old is -999999999.9, pt_new is -598.629303931574, prior_ChsRules=-17.905007283232408, likelihood_1 = 0.0, likelihood_2 = -580.7242966483416\n",
      " \n",
      "[]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      1.00      0.99      9888\n",
      "          1       0.00      0.00      0.00       112\n",
      "\n",
      "avg / total       0.98      0.99      0.98     10000\n",
      "\n",
      "Time of execution:  16.520994901657104\n",
      "________Calculation for  Check6  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.9906, TP = 2596,FP = 0, TN = 7310, FN = 94\n",
      " old is -999999999.9, pt_new is -523.5266484977487, prior_ChsRules=-13.992984334967332, likelihood_1 = -3.294354286043699, likelihood_2 = -506.2393098767377\n",
      " \n",
      "['0.0001269464014584703<=DiagnosisFiles', '5.578829836335855e-06<=DiagnosisFilesSize']\n",
      "[0]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      1.00      0.99      7230\n",
      "          1       1.00      0.97      0.98      2770\n",
      "\n",
      "avg / total       0.99      0.99      0.99     10000\n",
      "\n",
      "Time of execution:  70.02631735801697\n",
      "________Calculation for  Check7  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.972, TP = 0,FP = 0, TN = 9720, FN = 280\n",
      " old is -999999999.9, pt_new is -1298.7921879070443, prior_ChsRules=-17.905007283232408, likelihood_1 = 0.0, likelihood_2 = -1280.887180623812\n",
      " \n",
      "[]\n",
      "\n",
      "** chain = 1, max at iter = 1 ** \n",
      " accuracy = 0.972, TP = 0,FP = 0, TN = 9720, FN = 280\n",
      " old is -1298.6921879070444, pt_new is -1280.9170312660378, prior_ChsRules=-0.029850642225937918, likelihood_1 = 0.0, likelihood_2 = -1280.887180623812\n",
      " \n",
      "[]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      1.00      0.98      9689\n",
      "          1       0.00      0.00      0.00       311\n",
      "\n",
      "avg / total       0.94      0.97      0.95     10000\n",
      "\n",
      "Time of execution:  26.644855976104736\n",
      "________Calculation for  Check8  started________\n",
      "\n",
      "** chain = 1, max at iter = 0 ** \n",
      " accuracy = 0.9899, TP = 0,FP = 0, TN = 9899, FN = 101\n",
      " old is -999999999.9, pt_new is -584.9032756613112, prior_ChsRules=-17.905007283232408, likelihood_1 = 0.0, likelihood_2 = -566.9982683780788\n",
      " \n",
      "[]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      1.00      0.99      9899\n",
      "          1       0.00      0.00      0.00       101\n",
      "\n",
      "avg / total       0.98      0.99      0.98     10000\n",
      "\n",
      "Time of execution:  15.677063465118408\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "columns = ['Check1','Check2', 'Check3', 'Check4', 'Check5','Check6', 'Check7', 'Check8']\n",
    "for col in columns:\n",
    "    print(\"________Calculation for \", col, \" started________\")\n",
    "    t = time()\n",
    "    n = 10000\n",
    "    \n",
    "    y_solo=y_train[col][:n].as_matrix()\n",
    "    model = BayesianRuleSet(method='forest',max_rules=10,max_iter=10)\n",
    "    model.fit(X_train[:n], y_solo.astype(int))\n",
    "    yhat = model.predict(X_test[:n])\n",
    "    print(classification_report(y_test[col].astype(int)[:n],yhat))\n",
    "    print(\"Time of execution: \", str(time()-t))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In order to train the bayesian ruleset (RBS), we just use 10000 examples otherwise Zoe crashed. for each target (check_n with n in [1:8]), we apply the bayesian ruleset and to see the performance of the applied model we choose to display the classification report (precision, recall, f1-score and support; we might as well haveused ROC AUC as it gives basically the similar information although F1 score is more suited to our case since f1-score is more effective on an unbalanced target distribution) and the confusion matrix(Check following cell). <br>\n",
    "<ul>\n",
    "    <li>Check1: we get 1 as a f1_score for the class 0 however we get 0 for the class 1 and this is a bad prediction. it's same for the Check8, Check7, Check5. </li>\n",
    "    <li>Check2: we get a good f1_score for the class 0 however we get 0.26 as f1_score for the class 1.</li>\n",
    "    <li>Check6, Check4, Check3: the bayesian ruleset did well, we get a good f1_score for both the class 1 and the class 0.</li>\n",
    "</ul>\n",
    "\n",
    "- As we can see for each target we display the rules (the questions) of how the model do the predicition. This makes this model more interpretable than other classification that we saw (NN, SVM). The fact that RBS relies on decision trees to make its decision makes it more humanly understandble: The same can be said on models that rely on decision trees (Random Forest, XGBoost...). <br> \n",
    "- However the algorithm seems to struggle with unbalanced data. For example, we have a good accuray for Check1, Check5 and Check8 for class 0 (since we have many training samples) but very poor one for class 1. <br>\n",
    "<table>\n",
    "    <tr>\n",
    "        <td> Label</td>\n",
    "        <td> F1-score for class 0</td>\n",
    "        <td> F1-score for class 1</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> Check1</td>\n",
    "        <td> 100%</td>\n",
    "        <td> 0%</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> Check5</td>\n",
    "        <td> 100%</td>\n",
    "        <td> 0%</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> Check8</td>\n",
    "        <td> 99%</td>\n",
    "        <td> 0%</td>\n",
    "    </tr>\n",
    " </table>\n",
    "-When we have somewhat balanced classes, the algorithm perform much better. We ca see it in the result of Check3, Check4 and Check6 \n",
    "<table>\n",
    "    <tr>\n",
    "        <td> Label</td>\n",
    "        <td> F1-score for class 0</td>\n",
    "        <td> F1-score for class 1</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> Check3</td>\n",
    "        <td> 100%</td>\n",
    "        <td> 97%</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> Check4</td>\n",
    "        <td> 99%</td>\n",
    "        <td> 0%</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> Check6</td>\n",
    "        <td> 99%</td>\n",
    "        <td> 98%</td>\n",
    "    </tr>\n",
    " </table>\n",
    "- As for Check2, we have a good accuracy for class 0 but not for class 1 (F1-score 26%). We might not have good seperation criteria: Among the 10 allowed rules the model used only 5 to distinguish between classes<br>\n",
    "- As for Check7 we have many samples for class 1 yet we do not manage to predict a single one meaning that the model is poor and that there might not be a good rule to seperate the classes.<br>\n",
    "\n",
    "The complexity and time of execution depends heavily on the training set: The time of training increases very much when we have more rules. RBS seem to be very slow compared to usual models especially if we have many rules (5 rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(l,title):\n",
    "    sns.heatmap(l,fmt='g',annot=l, cmap=\"Blues\", cbar=False)\n",
    "    plt.xlabel(\"Predicted labels\")\n",
    "    plt.ylabel(\"Actual labels\")\n",
    "    plt.title(title)\n",
    "    \n",
    "confusions = []\n",
    "title = ['Check1','Check2','Check3','Check4','Check5','Check6','Check7','Check8']\n",
    "#Check1\n",
    "TP = 0\n",
    "FP = 0\n",
    "TN = 9937\n",
    "FN = 63 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "#Check2\n",
    "TP = 75\n",
    "FP = 20\n",
    "TN = 9653\n",
    "FN = 252 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "#Check 3\n",
    "TP = 276\n",
    "FP = 14\n",
    "TN = 9707\n",
    "FN = 3 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "#Check4\n",
    "TP = 941\n",
    "FP = 19\n",
    "TN = 9028\n",
    "FN = 12 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "#Check 5\n",
    "TP = 0\n",
    "FP = 0\n",
    "TN = 9896\n",
    "FN = 104 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "#Check6\n",
    "TP = 2596\n",
    "FP = 0\n",
    "TN = 7310\n",
    "FN = 94 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "#Check7\n",
    "TP = 0\n",
    "FP = 0\n",
    "TN = 9720\n",
    "FN = 280 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "#Check8\n",
    "TP = 0\n",
    "FP = 0\n",
    "TN = 9899\n",
    "FN = 101 \n",
    "l = np.array([[TN,FP],[FN,TP]])\n",
    "confusions.append(l)\n",
    "plt.figure(figsize=(15,10))\n",
    "for i in range(len(confusions)):\n",
    "    plt.subplot(2,4,i+1)\n",
    "    plot(confusions[i],title[i])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Xgboost</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________Calculation for Check1 started________\n",
      "[0]\ttrain-logloss:0.600557\ttest-logloss:0.600455\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.004331\ttest-logloss:0.00389\n",
      "[99]\ttrain-logloss:0.000751\ttest-logloss:0.000407\n",
      "Confusion Matrix:\n",
      "\n",
      "[[31518     1]\n",
      " [    0   187]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     31518\n",
      "          1       1.00      0.99      1.00       188\n",
      "\n",
      "avg / total       1.00      1.00      1.00     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 7.631629705429077 \n",
      "\n",
      "________Calculation for Check2 started________\n",
      "[0]\ttrain-logloss:0.598538\ttest-logloss:0.598507\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.00791\ttest-logloss:0.007593\n",
      "[99]\ttrain-logloss:0.002871\ttest-logloss:0.0027\n",
      "Confusion Matrix:\n",
      "\n",
      "[[30634    28]\n",
      " [    1  1043]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     30635\n",
      "          1       1.00      0.97      0.99      1071\n",
      "\n",
      "avg / total       1.00      1.00      1.00     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 6.127545595169067 \n",
      "\n",
      "________Calculation for Check3 started________\n",
      "[0]\ttrain-logloss:0.60917\ttest-logloss:0.609467\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.010231\ttest-logloss:0.009993\n",
      "[99]\ttrain-logloss:0.00525\ttest-logloss:0.005011\n",
      "Confusion Matrix:\n",
      "\n",
      "[[30633    50]\n",
      " [    3  1020]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     30636\n",
      "          1       1.00      0.95      0.97      1070\n",
      "\n",
      "avg / total       1.00      1.00      1.00     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 8.052610874176025 \n",
      "\n",
      "________Calculation for Check4 started________\n",
      "[0]\ttrain-logloss:0.598975\ttest-logloss:0.598901\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.012733\ttest-logloss:0.011982\n",
      "[99]\ttrain-logloss:0.005445\ttest-logloss:0.00502\n",
      "Confusion Matrix:\n",
      "\n",
      "[[28503    58]\n",
      " [    1  3144]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     28504\n",
      "          1       1.00      0.98      0.99      3202\n",
      "\n",
      "avg / total       1.00      1.00      1.00     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 7.304170370101929 \n",
      "\n",
      "________Calculation for Check5 started________\n",
      "[0]\ttrain-logloss:0.598519\ttest-logloss:0.598465\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.007653\ttest-logloss:0.007027\n",
      "[99]\ttrain-logloss:0.003472\ttest-logloss:0.003203\n",
      "Confusion Matrix:\n",
      "\n",
      "[[31283    12]\n",
      " [   11   400]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     31294\n",
      "          1       0.97      0.97      0.97       412\n",
      "\n",
      "avg / total       1.00      1.00      1.00     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 7.485370397567749 \n",
      "\n",
      "________Calculation for Check6 started________\n",
      "[0]\ttrain-logloss:0.66972\ttest-logloss:0.669574\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.033863\ttest-logloss:0.033846\n",
      "[99]\ttrain-logloss:0.016771\ttest-logloss:0.016992\n",
      "Confusion Matrix:\n",
      "\n",
      "[[22935    65]\n",
      " [  184  8522]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.99      0.99     23119\n",
      "          1       0.98      0.99      0.99      8587\n",
      "\n",
      "avg / total       0.99      0.99      0.99     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 6.680163145065308 \n",
      "\n",
      "________Calculation for Check7 started________\n",
      "[0]\ttrain-logloss:0.605807\ttest-logloss:0.605773\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.068731\ttest-logloss:0.067625\n",
      "[99]\ttrain-logloss:0.059575\ttest-logloss:0.058775\n",
      "Confusion Matrix:\n",
      "\n",
      "[[30633   142]\n",
      " [  596   335]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.98      0.99     31229\n",
      "          1       0.36      0.70      0.48       477\n",
      "\n",
      "avg / total       0.99      0.98      0.98     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 8.57551908493042 \n",
      "\n",
      "________Calculation for Check8 started________\n",
      "[0]\ttrain-logloss:0.599683\ttest-logloss:0.599609\n",
      "Multiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-logloss hasn't improved in 80 rounds.\n",
      "[50]\ttrain-logloss:0.008597\ttest-logloss:0.008399\n",
      "[99]\ttrain-logloss:0.002257\ttest-logloss:0.002217\n",
      "Confusion Matrix:\n",
      "\n",
      "[[31410     0]\n",
      " [   12   284]]\n",
      "\n",
      " classification Report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     31422\n",
      "          1       0.96      1.00      0.98       284\n",
      "\n",
      "avg / total       1.00      1.00      1.00     31706\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time execution : 8.098164319992065 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import xgboost\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import plot_importance\n",
    "ls_model=[]\n",
    "for i in range(8):\n",
    "    print(\"________Calculation for Check\" +str(i+1) +\" started________\")\n",
    "    start=time()\n",
    "    X_train_, X_val_, y_train_, y_val_ = train_test_split(X_train,y_train['Check'+str(i+1)], test_size=0.2, random_state=42)\n",
    "\n",
    "    xgtrain = xgboost.DMatrix(X_train_, y_train_)\n",
    "    xgtest = xgboost.DMatrix(X_val_, y_val_)\n",
    "    eta = 0.1\n",
    "    max_depth = 1\n",
    "    subsample = 0.8\n",
    "    colsample_bytree = 0.8\n",
    "    params = {\n",
    "        \"objective\": \"binary:logistic\",\n",
    "        \"booster\" : \"gbtree\",\n",
    "        \"eval_metric\": \"logloss\",\n",
    "        \"eta\": eta,\n",
    "        \"max_depth\": max_depth,\n",
    "        \"subsample\": subsample,\n",
    "        \"colsample_bytree\": colsample_bytree,\n",
    "        \"silent\": 1,\n",
    "        \"seed\": 42\n",
    "    }\n",
    "    model = xgboost.train(\n",
    "        params=list(params.items()),  \n",
    "        early_stopping_rounds=80, \n",
    "        verbose_eval=50, \n",
    "        dtrain=xgtrain,\n",
    "        evals=[(xgtrain, 'train'), (xgtest, 'test')],\n",
    "        num_boost_round=100,\n",
    "    )\n",
    "\n",
    "    X_test_=xgboost.DMatrix(X_val_)\n",
    "    p=model.predict(X_test_)\n",
    "    p=[1 if p[i]>0.5 else 0 for i in range(len(p))]\n",
    "\n",
    "\n",
    "\n",
    "    print('Confusion Matrix:\\n')\n",
    "    print(confusion_matrix(y_val_, p))\n",
    "    print('\\n classification Report:\\n')\n",
    "    report = classification_report(y_pred=y_val_, y_true=p)\n",
    "    print(report)\n",
    "    plot_importance(model)\n",
    "    plt.show()\n",
    "    print('\\n time execution : {} \\n'.format(time()-start))\n",
    "    ls_model.append(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Using Xgboost, we get a good result when predicting all the check except for the check7 seven where we get 0.99 as f1_score for class 0 however we get 0.48 as f1_score for class 1, but it still better than the bayesian ruleset.<br>\n",
    "- The xgboost is based on the decision tree model so we can interpret the result of xgboost and this by displaying the feature importance. as we can notice, for example to predict the check8, the two features \"IndexServerRestarts\" and \"NameServerRestarts\" are important to predict whether the check8 is 0 or 1 .<br>\n",
    "- The XGBoost is faster than the Bayesian Ruleset and has a better performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Summary</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problems we faced during this lab:<br>\n",
    "<ul>\n",
    "    <li> First, some data was not adequate with the spec of features. Sowe had to adjust the values </li>\n",
    "    <li> Second, we had missing data in some columns. To adjust that, depends on the feature we either fill it with mode or with sampling using the distribution generated with GMM.</li>\n",
    "    <li> Finally, we had to choose a model. We tried two models that are based on decision trees: RBS and XGBoost.\n",
    "    </li>\n",
    "<ul>\n",
    "The good thing with this typeof models is that we can interpret result: see how we managed to distinguish between classes; based on which criteria we were able to seperate the classes.<br>\n",
    "One problem with RBS is that depending on how many criteria we need to classify, we increase time of execution and it becomes very slow. RBS is also heavy in terms of computation. We couldn't apply it on our whole training set as it crashes after some time.<br>\n",
    "On the other hand XGBoost is less computationally heavy and time-consuming and allows s to know n which \n",
    "    "
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