ArabicNewsCategoryPrediction_starting_kit.ipynb

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    "\n",
    "<img border=\"0\" src=\"https://www.ip-paris.fr/sites/default/files/logo.png\" width=\"20%\"> \n",
    "\n",
    "<center><h1>Arabic News Category Prediction Challenge (ANCP)</h1></center>\n",
    "<br/>\n",
    "<center>Ahmad CHAMMA, Hadi ABDINE, Youssef FARHAT</center>\n",
    "<br/>\n",
    "<center>Prof: Alexandre Gramfort, Thomas Moreau</center>"
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    "## <b><font size=\"5\" color=\"DodgerBlue\" face=\"Courier New\">Table of Contents:</font></b>\n",
    "1. [Introduction](#1.-Introduction)\n",
    "2. [Motivation](#2.-Motivation)\n",
    "3. [Business Model](#3.-Business-Model)\n",
    "4. [KPI](#4.-KPI)\n",
    "5. [Data](#5.-Data)\n",
    "6. [Metric](#6.-Metric)\n",
    "7. [Data exploaration](#7.-Data-exploration)\n",
    "8. [Predictions](#8.-Predictions)"
   ]
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  {
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   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">1. Introduction</font></b>"
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    "<table style=\"width:100%; background-color:transparent;\">\n",
    "      <tr style=\"background-color:transparent;\">\n",
    "            <td style=\"background-color:transparent;\">\n",
    "                <img src=\"files/images/NLP.png\" width=\"400\">\n",
    "            </td>\n",
    "            <td style=\"background-color:transparent;\">\n",
    "                <img src=\"files/images/arabic.png\" width=\"300\">\n",
    "            </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<br><br>"
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  {
   "cell_type": "markdown",
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    "<table style=\"width:100%; background-color:transparent;\">\n",
    "      <tr style=\"background-color:transparent;\">\n",
    "            <td style=\"background-color:transparent;\" style=\"vertical-align:middle\">\n",
    "                <div align='center'>\n",
    "                <img src=\"files/images/thinking.png\" width=\"50\" align='left'>\n",
    "                    <b><font size=\"5\" color=\"SapphireBlue\" face=\"Courier New\">Why is <I>Arabic</I> Natural Language Processing important?</font></b>\n",
    "                </div>\n",
    "          </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
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    "<table style=\"width:100%; background-color:transparent;\">\n",
    "    <tr style=\"background-color:transparent;\">\n",
    "        <td>\n",
    "            <img src='images/turing.jpg' width='50' align='left'>\n",
    "            <div align='center'>\n",
    "            <font size='4' face=\"Courier New\">A computer could be considered intelligent if it could carry on a conversation with a human being without the human realizing they were talking to a machine.<br><b>Alan Turing</b> </font><br> </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<br>"
   ]
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   "source": [
    "<font size='4' face=\"Courier New\"> By \"Natural Language\" we mean a language that is used for communication by humans. By combining the power of artificial intelligence, computational linguistics and computer science, __Natural Language Processing (NLP)__ helps machines to understand the natural language and communicate with humans in their own language. </font><br>\n",
    "\n",
    "<font size='4' face=\"Courier New\">With the ongoing growth of the World Wide Web and social media, there is a drastic increase in online data. As the amount of data increases, the mechanisms to process it with its unstructured format and to extract meaningful information from it becomes more challenging. However, the natural language is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context. And that is why the development of advanced NLP techniques has been a major topic of research in the last decade. </font><br>\n",
    "<img src='images/data.png'>"
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    "<font size='4' face=\"Courier New\"> With NLP, ambiguity in language can be resolved and it adds useful numeric structure to the previously unstructed data. From then, sky is the limit on what can be done and the applications that can be developed. For example, NLP makes it possible for computers to read text, translate it, hear speech, interpret it, measure sentiment and determine which parts are important, and that is just top of the iceberg.</font>"
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   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">2. Motivation</font></b>"
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    "<table style=\"width:100%; background-color:transparent;\">\n",
    "    <tr style=\"background-color:transparent;\">\n",
    "        <td>\n",
    "            <img src='images/alfonso.jpg' width='50' align='left'>\n",
    "            <div align='center'>\n",
    "            <font size='4' face=\"Courier New\">The Arabic language has surpassed all other languages in beauty and elegance. I am at a loss for words to describe its good qualitites.<br><b>Carlo Alfonso Nallino</b> </font><br> </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<br>"
   ]
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    "<table style=\"width:100%; background-color:transparent;\">\n",
    "    <tr style=\"background-color:transparent;\">\n",
    "        <td>\n",
    "            <div align='left'>\n",
    "            <font size='4' face=\"Courier New\"> In this project, we are going to be looking into Arabic Natural Language Processing (ANLP). Arabic is the fifth most spoken language in the world <b>(according to https://www.fluentin3months.com/most-spoken-languages/)</b> and is considered as the official language in 26 countries which qualifies it to be an important langauge. Because of that, a lot of textual data written in arabic language is generated whether it's from social media, a news website or google searches etc... Also, Arabic in its standard form, known as Modern Standard Arabic (MSA), is one of the 6 official languages of the United Nations. </font><br> </div>\n",
    "        </td>\n",
    "        <td>\n",
    "            <img src='images/world.svg' width='500' align='center'>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<br>"
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    "<table style=\"width:100%; background-color:transparent;\">\n",
    "    <tr style=\"background-color:transparent;\">\n",
    "        <div align='left'><font size='4' face=\"Courier New\"> However, according to <b>https://en.wikipedia.org/wiki/Languages_used_on_the_Internet</b>, the <b>Arabic</b> content on the Internet doesn't represent more than 0.7%. <U>As Arab people, we are responsable of spreading this beautiful language and introduce it with all the challenges within.</U> </font></div>\n",
    "    </tr>\n",
    "</table>"
   ]
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   "source": [
    "<table style=\"width:100%; background-color:transparent;\">\n",
    "    <tr style=\"background-color:transparent;\">\n",
    "        <td>\n",
    "            <div align='left'>\n",
    "            <font size='4' face=\"Courier New\"> Despite its importance and significance in the world, it has yet to have a major contribution in the NLP applications. Arabic has received comparatively little attention in modern computational linguistics. It is indeed a challenging topic due to the complexity of the language, its rich morphology, as well as the presence many different dialects. But with the constant advancement in the NLP field, as well as deep learning and big data, and with proper research and dedication, the creation of a great ANLP model will be considered a breakthrough in the computational liguistics. </font></div>\n",
    "        </td>\n",
    "        <td>\n",
    "            <img src='images/words.jpg' width='250' align='center'>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<br>"
   ]
  },
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    "id": "3LTh3Rvx-XUd"
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   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">3. Business Model</font></b>"
   ]
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   "cell_type": "markdown",
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    "id": "DLQATtNTXWvl"
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   "source": [
    "<font size='4' face=\"Courier New\"> For a local news agency, it will receive hundreds of news per day of multiple categories. So, workers are needed to discover the main subject of each news in order to perform the best classification. </font>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "bkrJgRrGXWvs"
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   "source": [
    "<table style=\"width:100%; background-color:transparent;\">\n",
    "      <tr style=\"background-color:transparent;\">\n",
    "            <td style=\"background-color:transparent;\" style=\"vertical-align:middle\">\n",
    "                <div align='center'>\n",
    "                <img src=\"files/images/thinking.png\" width=\"50\" align='left'>\n",
    "                    <b><font size=\"5\" color=\"SapphireBlue\" face=\"Courier New\"> But why not let the machine do this task? In other words, why not automate it?</font></b>\n",
    "                </div>\n",
    "          </td>\n",
    "    </tr>\n",
    "</table>"
   ]
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   "cell_type": "markdown",
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   "source": [
    "<font size='4' face=\"Courier New\">One the most challenges faced when working with Arabic language is the limited ressources we could use. In order to perform this NLP work, we need a structured arabic dataset of news unfortunately not present at all over the internet (there is some new works on __Arabic__ with MIT).  So, the first step was to perform some __Crawling__.</font><br>"
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    "<table style=\"width:100%; background-color:transparent;\">\n",
    "    <tr style=\"background-color:transparent;\">\n",
    "        <td>\n",
    "            <div align='left'>\n",
    "                <font size='4' face=\"Courier New\">The <b>Crawling</b> was chosen for a website with no legal issues of Lebanese __Arabic__ News. With some basic python script, we were intergrating the html format of each page and taking the most important parts such as title, main text, image link ... <br>\n",
    "The task to challenge here is to construct a good and robust nlp model in order to give the highest metric possible with the given dataset (a combination of data cleaning, tuning ...)</font></div>\n",
    "        </td>\n",
    "        <td>\n",
    "            <img src='images/scrapping.png' width='400' align='center'>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<br>"
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   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">4. KPI</font></b>"
   ]
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   "cell_type": "markdown",
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    "id": "5X8X03CI-XU6"
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   "source": [
    "<font size='4' face=\"Courier New\">This predictive model can be integrated in any news providing medium. Some of the benefits and KPIs to help us track the impact of this project: </font><br>\n",
    "<font size='3' face=\"Courier New\">\n",
    "<p><b>&#x21AA; Trending category shown at first</b></p><br>\n",
    "<p><b>&#x21AA; Number of users or visits to the medium</b></p><br>\n",
    "<p><b>&#x21AA; User Satisfaction with the medium integrated</b></p>"
   ]
  },
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   "cell_type": "markdown",
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    "id": "k-I0LIIiXWv_"
   },
   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">5. Data</font></b>"
   ]
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   "cell_type": "markdown",
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   "source": [
    "<table style=\"width:100%; background-color:transparent;\">\n",
    "    <tr style=\"background-color:transparent;\">\n",
    "        <td>\n",
    "            <div align='left'>\n",
    "                <font size='4' face=\"Courier New\">As mentioned before, <b>Arabic</b> text data is hard to be found, and rare to find one already categorized so, the data that we will be using throughout the project is collected from a Lebanese news website <b>www.lebanonfiles.com</b> . This site contains all sorts of news such as economical, political news ... and what made this part challenging is that we had to extract the data ourselves in order to use it.The data can be downloaded using the provided <b>data_download.py</b? file</font></div>\n",
    "        </td>\n",
    "        <td>\n",
    "            <img src='images/files.jpg' width='400' align='center'>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<br>\n",
    "\n",
    "<font size='4' face=\"Courier New\"> The data collected:\n",
    "* `Id`: integer, identifier for the news\n",
    "* `Title`: string, title of the news which can tell a lot about the category\n",
    "* `Date`: date type, but written in arabic\n",
    "* `Desc`: string, description of the news\n",
    "* `Image`: string, some news may contain an image\n"
   ]
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   "source": [
    "## <b><font size=\"5\" color=\"SapphireBlue\" face=\"Courier New\"><p> &nbsp; Training and test</p></font></b>\n",
    "<font size='4' face=\"Courier New\"><p> &nbsp; The dataset has been split into 'training' and 'test' subsets. The shapes are:<br><br>\n",
    "\n",
    "* train: &nbsp; (116 409, 5)\n",
    "* <p> test: &nbsp; &nbsp; (38 804, 5) </p>\n"
   ]
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   "cell_type": "markdown",
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    "id": "nWRpkA04XWwP"
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   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">6. Metric</font></b>"
   ]
  },
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   "cell_type": "markdown",
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    "id": "rre7kAeV-XVw"
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   "source": [
    "<font size='4' face=\"Courier New\"> One of the main challenges added in this work is the imbalanced classification case. For this, we are willing to use as metric the __f1 score__ of sklearn with the parameter __average__ set to <U>weighted</U>. It's a combination of the __f1 score macro__ used for multi-class classification but respecting the imbalanced case.\n",
    "\n",
    "<img src='images/f1.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hqeR-6wVXWwX"
   },
   "source": [
    "## <b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\">Importing the necessary librairies</font></b>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 0,
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   "source": [
    "from bidi.algorithm import get_display\n",
    "import arabic_reshaper\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import datetime\n",
    "import dateutil.parser as dparser\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import unicodedata as ud\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HmERhxKEXWwl"
   },
   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">7. Data exploration</font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the data\n",
    "!python data_download.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tE6BZnwK-XXH"
   },
   "outputs": [],
   "source": [
    "from problem import get_train_data\n",
    "X_df, y_array = get_train_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 544,
     "status": "ok",
     "timestamp": 1580585124246,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "UgqUSZc3-XXe",
    "outputId": "12b9ac30-65ad-4eb0-f09f-a93c9c18918f"
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      "text/plain": [
       "        Id                                              Title  \\\n",
       "0  1313318  تكريم الشاعر موسى زغي ورئيس الجمهورية قلده وسا...   \n",
       "1  1413607  نائب معارض لمادورو: حزب الله يستثمر منجمين للذ...   \n",
       "2  1363260           هكذا استغل داعش الأطفال في هجمات الشيشان   \n",
       "3   971779            سلام ترأس اجتماعا لخلية الازمة الوزارية   \n",
       "4   960499  كنعان: وصلنا الى اتفاق حول اقتراح قانون استعاد...   \n",
       "\n",
       "                                    Date  \\\n",
       "0           الخميس 12 نيسان 2018 - 15:37   \n",
       "1   الاثنين 14 كانون الثاني 2019 - 15:58   \n",
       "2              الخميس 23 آب 2018 - 07:29   \n",
       "3    الاثنين 07 كانون الأول 2015 - 18:48   \n",
       "4  الأربعاء 11 تشرين الثاني 2015 - 15:01   \n",
       "\n",
       "                                                Desc  \\\n",
       "0  \\n\\r\\n\\tكرمت جامعة الروح القدس - الكسليك وجامع...   \n",
       "1  \\n\\n\\tكشف نائب معارض للرئيس الفنزويلي نيكولاس ...   \n",
       "2  \\n\\n\\tنشر تنظيم داعش مقطع فيديو، الأربعاء، يظه...   \n",
       "3  \\n\\r\\n\\tترأس رئيس مجلس الوزراء تمام سلام اجتما...   \n",
       "4  \\n\\r\\n\\tعقد نواب من \"التيار الوطني الحر\" والمس...   \n",
       "\n",
       "                                               Image  \n",
       "0  http://www.lebanonfiles.com/files/images/12-04...  \n",
       "1  http://www.lebanonfiles.com/files/images/14-01...  \n",
       "2  http://www.lebanonfiles.com/files/images/23-08...  \n",
       "3  http://www.lebanonfiles.com/files/images/07-12...  \n",
       "4  http://www.lebanonfiles.com/files/images/11-11...  "
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 539,
     "status": "ok",
     "timestamp": 1580585127821,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "VMUR0iNI-XXz",
    "outputId": "fe6094bb-741b-4b80-8004-76d688257b27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116409, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 127
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 585,
     "status": "ok",
     "timestamp": 1580585130185,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "I96ID2dr-XYK",
    "outputId": "306305bc-df5d-4d12-8f12-c3332db79d1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id       0.000000\n",
       "Title    0.000000\n",
       "Date     0.000000\n",
       "Desc     0.000807\n",
       "Image    0.036776\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# proportion of NaN values\n",
    "X_df.isna().sum() / X_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 127
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1139,
     "status": "ok",
     "timestamp": 1580585132473,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "bAY355iV-XYh",
    "outputId": "2d8caccc-df72-4272-fa9b-4a39f4426c7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id       116402\n",
       "Title    115474\n",
       "Date     111403\n",
       "Desc     116212\n",
       "Image    109996\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique values\n",
    "X_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 127
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 564,
     "status": "ok",
     "timestamp": 1580585134011,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "mB2WgbWU-XZB",
    "outputId": "9d8e5641-96fd-4a98-dacf-220c1a9e9916"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id        int64\n",
       "Title    object\n",
       "Date     object\n",
       "Desc     object\n",
       "Image    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1233,
     "status": "ok",
     "timestamp": 1580585136040,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "_LX78jZU-XZW",
    "outputId": "6004b281-5149-4f22-e2b7-dd75a35ba3a3"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Date</th>\n",
       "      <th>Desc</th>\n",
       "      <th>Image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.164090e+05</td>\n",
       "      <td>116409</td>\n",
       "      <td>116409</td>\n",
       "      <td>116315</td>\n",
       "      <td>112128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>115474</td>\n",
       "      <td>111403</td>\n",
       "      <td>116212</td>\n",
       "      <td>109996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>الدفاع المدني: مهمات إنقاذ وإسعاف وإخماد حرائق...</td>\n",
       "      <td>الثلاثاء 09 تشرين الأول 2018 - 11:58</td>\n",
       "      <td>\\n\\r\\n\\tحافظ الدولار الاميركي في سوق بيروت الم...</td>\n",
       "      <td>http://www.lebanonfiles.com/files/images/19-09...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>141</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.285414e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.239324e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000010e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.993410e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.372597e+06</td>\n",
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       "    <tr>\n",
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       "      <td>1.461524e+06</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.560310e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Id                                              Title  \\\n",
       "count   1.164090e+05                                             116409   \n",
       "unique           NaN                                             115474   \n",
       "top              NaN  الدفاع المدني: مهمات إنقاذ وإسعاف وإخماد حرائق...   \n",
       "freq             NaN                                                141   \n",
       "mean    1.285414e+06                                                NaN   \n",
       "std     2.239324e+05                                                NaN   \n",
       "min     9.000010e+05                                                NaN   \n",
       "25%     9.993410e+05                                                NaN   \n",
       "50%     1.372597e+06                                                NaN   \n",
       "75%     1.461524e+06                                                NaN   \n",
       "max     1.560310e+06                                                NaN   \n",
       "\n",
       "                                        Date  \\\n",
       "count                                 116409   \n",
       "unique                                111403   \n",
       "top     الثلاثاء 09 تشرين الأول 2018 - 11:58   \n",
       "freq                                      10   \n",
       "mean                                     NaN   \n",
       "std                                      NaN   \n",
       "min                                      NaN   \n",
       "25%                                      NaN   \n",
       "50%                                      NaN   \n",
       "75%                                      NaN   \n",
       "max                                      NaN   \n",
       "\n",
       "                                                     Desc  \\\n",
       "count                                              116315   \n",
       "unique                                             116212   \n",
       "top     \\n\\r\\n\\tحافظ الدولار الاميركي في سوق بيروت الم...   \n",
       "freq                                                    4   \n",
       "mean                                                  NaN   \n",
       "std                                                   NaN   \n",
       "min                                                   NaN   \n",
       "25%                                                   NaN   \n",
       "50%                                                   NaN   \n",
       "75%                                                   NaN   \n",
       "max                                                   NaN   \n",
       "\n",
       "                                                    Image  \n",
       "count                                              112128  \n",
       "unique                                             109996  \n",
       "top     http://www.lebanonfiles.com/files/images/19-09...  \n",
       "freq                                                    8  \n",
       "mean                                                  NaN  \n",
       "std                                                   NaN  \n",
       "min                                                   NaN  \n",
       "25%                                                   NaN  \n",
       "50%                                                   NaN  \n",
       "75%                                                   NaN  \n",
       "max                                                   NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VlwwSUyHXWyC"
   },
   "source": [
    "<b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\">Histogram of the classes</font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UqH9Ly3AXWyE",
    "outputId": "346436c6-abc6-42a4-8d9d-2d6976b89265"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "unique, counts = np.unique(y_array, return_counts=True)\n",
    "categories = ['أخبار محليّة', 'أخبار فنية', 'أخبار اقتصادية ومالية', 'أخبار رياضية', 'أخبار إقليمية ودولية']\n",
    "labels_arab = []\n",
    "for i in categories:\n",
    "    labels_arab.append(get_display(arabic_reshaper.reshape(i)))\n",
    "plt.pie(counts, labels=labels_arab, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZjE6N33PXWyQ"
   },
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> أخبار محليّة </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; </p></div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> Local News </div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> أخبار إقليمية ودولية </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; </p></div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> Regional and International News </div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> أخبار رياضية </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; </p></div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> Sports News </div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> أخبار اقتصادية ومالية </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; </p></div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> Economic and Financial News </div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> أخبار فنية </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; </p></div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> Art News </div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7vPW6xPaXWyT"
   },
   "source": [
    "## <b><font size=\"6\" color=\"DodgerBlue\" face=\"Courier New\">8. Predictions</font></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KnwDw0fj-XbB"
   },
   "source": [
    "<font size='3' face=\"Courier New\">1. Create a transformer that deals with missing values:</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zN9h5hoD-XbF"
   },
   "outputs": [],
   "source": [
    "# list of numerical columns\n",
    "num_cols = ['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hwpvyJzt-XbW"
   },
   "outputs": [],
   "source": [
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('impute', SimpleImputer(strategy='median'))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tJAyY8hHXWyu"
   },
   "source": [
    "<font size='3' face=\"Courier New\">2. Create a transformer to split `Date` into separate year, month, day columns:</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ky7jyaFp-Xbs"
   },
   "outputs": [],
   "source": [
    "def process_date(X):\n",
    "    date = X.Date.apply(convert_to_date)\n",
    "    return np.c_[date.dt.year, date.dt.month, date.dt.day]\n",
    "date_transformer = FunctionTransformer(process_date, validate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kozYOX3zXWy5"
   },
   "source": [
    "<font size='3' face=\"Courier New\">3. Process the text in `Title` and `Desc`. We must pay attention that these two columns aren't specified as type string. Therefore, the need to change the type.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H6BHTXglXWy_"
   },
   "source": [
    "<font size='3' face=\"Courier New\"><p>&#x21AC; <b>Convert the arabic date to datetime format</b></p></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WiTa8Y8PXWzC"
   },
   "source": [
    "<table style=\"width:40%; float:left\">\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> كانون الثاني </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; January</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> شباط </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; February</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> آذار </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; March</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> نيسان </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; April</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> أيار </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; May</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> حزيران </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; June</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> تموز </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; July</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> آب </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; August</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> أيلول </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; September</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> تشرين الأول </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; October</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> تشرين الثاني </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; November</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> كانون الأول </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; December</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "<table style=\"width:40%; float:right\">\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> الأحد </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; Sunday</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> الاثنين </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; Monday</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> الثلثاء </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; Tuesday</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> الأربعاء </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; Wednesday</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> الخميس </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; Thursday</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> الجمعة </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; Friday</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'> السبت </div></font>\n",
    "        </td>\n",
    "        <td>\n",
    "            <font size='4' face=\"Courier New\"><div align='center'><p> &#x27F6; Saturday</p></div></font>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XoJMhUhDXWzF"
   },
   "source": [
    "<font size='3' face=\"Courier New\"><p>&#x21AC; <b>Clean the \"Desc\" and \"Title\" column (remove \\t, weird symbols and stop words)</b></p></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QZa1gaBC-XcK"
   },
   "outputs": [],
   "source": [
    "stps_arabic = set(stopwords.words('arabic'))\n",
    "arabic_diacritics = re.compile(\"\"\"\n",
    "                             ّ    | # Tashdid\n",
    "                             َ    | # Fatha\n",
    "                             ً    | # Tanwin Fath\n",
    "                             ُ    | # Damma\n",
    "                             ٌ    | # Tanwin Damm\n",
    "                             ِ    | # Kasra\n",
    "                             ٍ    | # Tanwin Kasr\n",
    "                             ْ    | # Sukun\n",
    "                             ـ     # Tatwil/Kashida\n",
    "                         \"\"\", re.VERBOSE)\n",
    "\n",
    "def convert_to_date(x):\n",
    "    dicta =  {\"كانون الثاني\": \"january\" ,\"شباط\": \"february\", \"أيار\": \"may\",  \"نيسان\": \"April\",\n",
    "         \"آذار\": \"march\", \"حزيران\": \"june\", \"تموز\": \"july\", \"آب\": \"august\", \"أيلول\": \"september\",\n",
    "         \"تشرين الأول\": \"october\", \"تشرين الثاني\": \"november\", \"كانون الأول\": \"december\",\n",
    "         \"الاثنين\": \"monday\", \"الثلاثاء\": \"tuesday\", \"الأربعاء\": \"wednesday\", \"الخميس\": \"thursday\", \"الجمعة\": \"friday\",\n",
    "         \"السبت\": \"saturday\", \"الأحد\": \"sunday\", \"السبت\": \"saturday\"}\n",
    "    x_new = x\n",
    "    for arabic, english in dicta.items():\n",
    "        x_new = x_new.replace(arabic, english)\n",
    "    x_new = dparser.parse(x_new, fuzzy=True)\n",
    "    return x_new\n",
    "\n",
    "def clean_txt(sent):\n",
    "    \"\"\"\n",
    "        text: a string\n",
    "        \n",
    "        return: modified initial string\n",
    "    \"\"\"    \n",
    "    text = sent.strip()\n",
    "    text = re.sub('[\\n\\r\\t\\xa0]', ' ', text)\n",
    "    text = re.sub(arabic_diacritics, '', text)\n",
    "    text = ''.join(c for c in text if not ud.category(c).startswith('P') and not c.isdigit())\n",
    "    res = re.sub(' +', ' ', text)\n",
    "    return [w for w in res.split() if w not in stps_arabic]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2oeUTfL5-Xca"
   },
   "source": [
    "<font size='3' face=\"Courier New\">Also, for some news without any description, we will fill naivly the `Title` into the `Desc`.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5sZYBWXO-Xce"
   },
   "outputs": [],
   "source": [
    "def process_text(X):\n",
    "    X['Desc'] = X['Desc'].astype('str')\n",
    "    X['Title'] = X['Title'].astype('str')\n",
    "    \n",
    "    X.loc[X['Desc']=='', 'Desc'] = X['Title']\n",
    "    X['Desc'] = X['Desc'].apply(clean_txt)\n",
    "    return np.c_[X['Desc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KzekvCYi-Xcq"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, LSTM, Dense, Dropout\n",
    "import keras.backend as K\n",
    "\n",
    "# The maximum number of words to be used. (Most frequent)\n",
    "vocab_size = 20000\n",
    "\n",
    "# The padding and truncating types used.\n",
    "trunc_type = 'post'\n",
    "padding_type = 'post'\n",
    "\n",
    "# The OOV token (Out Of Vocabulary) will be included within the dictionary\n",
    "oov_tok = '<OOV>'\n",
    "\n",
    "# Max number of words in each complaint.\n",
    "MAX_SEQUENCE_LENGTH = 200\n",
    "\n",
    "# This is fixed.\n",
    "EMBEDDING_DIM = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aUDHBOra-Xc2"
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class FeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    #Class Constructor \n",
    "    def __init__(self, feature_names, vocab_size, oov_tok, MAX_SEQUENCE_LENGTH, padding_type, trunc_type):\n",
    "        self.feature_names = feature_names\n",
    "        self.tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)\n",
    "        self.max_length = MAX_SEQUENCE_LENGTH\n",
    "        self.padding = padding_type\n",
    "        self.truncating = trunc_type\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        X_1 = process_text(X.loc[:, self.feature_names])\n",
    "        self.tokenizer.fit_on_texts(X_1[:, 0])\n",
    "        self.word_index = self.tokenizer.word_index\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        X_1 = process_text(X.loc[:, self.feature_names])\n",
    "        sequences = self.tokenizer.texts_to_sequences(X_1[:, 0])\n",
    "        X = pad_sequences(sequences, maxlen=self.max_length, padding=self.padding, truncating=self.truncating)\n",
    "        return X\n",
    "    \n",
    "    def fit_transform(self, X, y=None):\n",
    "        self.fit(X, y)\n",
    "        return self.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D2iWRfr3-XdC"
   },
   "source": [
    "<font size='3' face=\"Courier New\">Finally the pipelines for text and non-text components are assembled.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FrJnhUrQ-XdG"
   },
   "outputs": [],
   "source": [
    "num_col = ['Id']\n",
    "date_col = ['Date']\n",
    "text_cols = ['Desc', 'Title']\n",
    "drop_cols = ['Image']\n",
    "\n",
    "non_text_preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('date', make_pipeline(date_transformer), date_col),\n",
    "        ('num', numeric_transformer, num_col),\n",
    "        ('drop cols', 'drop', drop_cols),\n",
    "        ])\n",
    "\n",
    "text_preprocessor = Pipeline(steps=[\n",
    "    ('text', FeatureSelector(text_cols, vocab_size, oov_tok, MAX_SEQUENCE_LENGTH, padding_type, trunc_type)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IgOaEjXH-XdT"
   },
   "outputs": [],
   "source": [
    "# make sure it works\n",
    "X_train = text_preprocessor.fit_transform(X_df)\n",
    "X_extra = non_text_preprocessor.fit_transform(X_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 467,
     "status": "ok",
     "timestamp": 1580585402451,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "D8KCJpxm-Xdu",
    "outputId": "14dd4784-646e-4cba-fb2e-4c0f218bf030"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   35,     7,   173,   590,   139,     6,     3, 10920,  2147,\n",
       "        2238,  1552,   798,  3759, 16918,    64,   156,     1,  1552,\n",
       "        1073,  5638,  4688,  1673,  3819,  4387,  2859,   139,    24,\n",
       "         175,   566,  2668, 11612,   402,  4851,  1552,   798, 10822,\n",
       "         183,   308,     9,    56,     1,     7,   139,  4851,  1552,\n",
       "         798,   103,    58,    85,   615,  3042,  4572,     1,  4736,\n",
       "          27,  1552,   798,  1096,     1,   183,     1,   166, 19210,\n",
       "        3576,   141,   628,     1,    98,    27,   712,   646,  1623,\n",
       "         183,     1,     1,     1,  3350,   275,  4387,  2425,     1,\n",
       "         164,     1,   313,     1,   363,  6535,  1969,  1552,   798,\n",
       "           1,  5927,  8035,  6730,   139,     1,  3574,  8243,  5404,\n",
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       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0], dtype=int32)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BGvO9qhUXW0M"
   },
   "source": [
    "## <b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\"> Visualizing the format of the text used</font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 512,
     "status": "ok",
     "timestamp": 1580585406137,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "bdT3jblY-XeB",
    "outputId": "dafe4326-2a59-40fa-b289-d184991c27f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "قال الرئيس الأميركي دونالد ترامب اليوم ان إدارته تجري محادثات كوريا الجنوبية تزيد تمويلها بشكل كبير <OOV> كوريا الشمالية تصاعد التوترات شبه الجزيرة الكورية وكتب ترامب عبر تويتر مدى العقود العديدة الماضية دفعت كوريا الجنوبية القليل جدا المال العام الماضي <OOV> الرئيس ترامب دفعت كوريا الجنوبية مليون دولار وأضاف بدأت المحادثات لزيادة <OOV> للولايات المتحدة كوريا الجنوبية بلد <OOV> جدا <OOV> الآن بالتزام المساهمة الدفاع العسكري <OOV> الولايات المتحدة العلاقة البلدين جيدة جدا <OOV> <OOV> <OOV> أمني الحرب الكورية انتهت <OOV> وليس <OOV> سلام <OOV> ألف جندي أميركي كوريا الجنوبية <OOV> تهديدات بيونغ يانغ ترامب <OOV> مرارا تكاليف إبقاء القوات الأميركية البلد <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD>\n",
      "---\n",
      "\n",
      "\n",
      "\t قال الرئيس الأميركي دونالد ترامب، اليوم، ان إدارته تجري محادثات مع كوريا الجنوبية لكي تزيد تمويلها \"بشكل كبير\" لحمايتها من كوريا الشمالية، مع تصاعد التوترات في شبه الجزيرة الكورية.\n",
      "\n",
      "\tوكتب ترامب عبر \"تويتر\": \"مدى العقود العديدة الماضية دفعت كوريا الجنوبية القليل جدا من المال، ولكن العام الماضي، وبطلب من الرئيس ترامب، دفعت كوريا الجنوبية 990 مليون دولار\".\n",
      "\n",
      "\tوأضاف \"بدأت المحادثات لزيادة الدفعات للولايات المتحدة. كوريا الجنوبية بلد ثري جدا ويشعر الآن بالتزام المساهمة في الدفاع العسكري الذي توفره الولايات المتحدة. العلاقة بين البلدين جيدة جدا\".\n",
      "\n",
      "\tوالبلدان يرتبطان بتحالف أمني منذ الحرب الكورية (1950-1953) التي انتهت بهدنة وليس بمعاهدة سلام. وينتشر أكثر من 28 ألف جندي أميركي في كوريا الجنوبية لحمايتها من تهديدات بيونغ يانغ. \n",
      "\n",
      "\tإلا أن ترامب اشتكى مرارا من تكاليف إبقاء القوات الأميركية في هذا البلد.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "id_to_word = {value:key for key,value in text_preprocessor['text'].word_index.items()}\n",
    "id_to_word[0] = '<PAD>'\n",
    "print(' '.join(id_to_word[id] for id in X_train[10]))\n",
    "print('---')\n",
    "print(X_df.loc[10]['Desc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HkKZ2OnCXW0W"
   },
   "source": [
    "## <b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\"> Transform y_array to one hot encoding</font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kOHJmlNf-XeT"
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "enc = preprocessing.OneHotEncoder()\n",
    "enc.fit(y_array.reshape(-1, 1))\n",
    "Y_train = enc.transform(y_array.reshape(-1, 1)).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 541,
     "status": "ok",
     "timestamp": 1580585409810,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "8weSCicG9l2B",
    "outputId": "abaa9510-aceb-4d87-ccc0-9adb477f5842"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 4, ..., 4, 1, 3])"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 146
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 477,
     "status": "ok",
     "timestamp": 1580585412129,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "Z2NpMvd5-Xef",
    "outputId": "f5db48e3-68c8-416c-fbd6-6c7bc0183146"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yRkuEaVUXW0m"
   },
   "source": [
    "<b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\"> Implementing the f1_score function (not integrated in Keras)</font></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bzfDhRxmXW0m"
   },
   "source": [
    "<font size=\"3\" face=\"Courier New\"> We will use a Keras model to test with `Desc`:</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gfpgueMc-Xe4"
   },
   "outputs": [],
   "source": [
    "def get_f1(y_true, y_pred):\n",
    "    \n",
    "    y_true = K.argmax(y_true, -1)\n",
    "    y_pred = K.argmax(y_pred, -1)\n",
    "    \n",
    "    prec_num = 0\n",
    "    prec_den = 0\n",
    "    recall_num = 0\n",
    "    recall_den = 0\n",
    "    \n",
    "    equal = K.equal(y_true, y_pred)\n",
    "    not_equal = K.not_equal(y_true, y_pred)\n",
    "    \n",
    "    for i in range(5):\n",
    "        aux = K.sum(K.cast(K.all(K.stack([equal, K.equal(y_true, i)], axis=0), axis=0), 'int32'))\n",
    "        prec_num += aux\n",
    "        prec_den += aux + K.sum(K.cast(K.all(K.stack([not_equal, K.equal(y_pred, i)], axis=0), axis=0), 'int32'))\n",
    "        recall_num += aux\n",
    "        recall_den += aux + K.sum(K.cast(K.all(K.stack([not_equal, K.equal(y_true, i)], axis=0), axis=0), 'int32'))\n",
    "        \n",
    "    prec_num = K.cast(prec_num, 'float')\n",
    "    prec_den = K.cast(prec_den, 'float')\n",
    "    recall_num = K.cast(recall_num, 'float')\n",
    "    recall_den = K.cast(recall_den, 'float')\n",
    "    \n",
    "    precision = prec_num / prec_den\n",
    "    recall = recall_num / recall_den\n",
    "    f1_val = 2*(precision * recall)/(precision + recall)\n",
    "    return f1_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AUjdY-F4XW0r"
   },
   "source": [
    "<b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\">  Implementing the keras model to use</font></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ehJAKefDXW0r"
   },
   "source": [
    "<img src='images/model.png' width='600'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K2NG1E91-XfJ"
   },
   "outputs": [],
   "source": [
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wxWaOp5c-XfV"
   },
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(vocab_size, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(LSTM(32))\n",
    "    model.add(Dense(5, activation='softmax'))\n",
    "    # Compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "epochs = 5\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_brvaNHW-Xfg"
   },
   "outputs": [],
   "source": [
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "model = KerasClassifier(build_fn=create_model, epochs=epochs, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 866,
     "status": "ok",
     "timestamp": 1580585439550,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "z7feMvVO-Xft",
    "outputId": "8eba7675-d2fd-46eb-8485-93a0b6d040c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 200, 100)          2000000   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 200, 100)          0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 32)                17024     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 5)                 165       \n",
      "=================================================================\n",
      "Total params: 2,017,189\n",
      "Trainable params: 2,017,189\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "create_model().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2C7wt8-EXW05"
   },
   "source": [
    "<font size=\"3\" face=\"Courier New\"><b>The summary of the used model. As we can notice, this model has about 2 millions paramter to train. Thus, it needs a lot of time.</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6XPyZkX7XW05"
   },
   "source": [
    "<b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\"> Cross validation and training the model</font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HD8vgIWh-XgW"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "clf = Pipeline(steps=[\n",
    "    ('preprocessing', text_preprocessor),\n",
    "    ('classifier', model),\n",
    "    ])\n",
    "\n",
    "cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=1996)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4380000,
     "status": "ok",
     "timestamp": 1580592164365,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "W0lx0iFX-Xgr",
    "outputId": "13c11073-3edd-4f27-a3c1-6c222b2cbcef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "58204/58204 [==============================] - 259s 4ms/step - loss: 1.0710 - accuracy: 0.6219\n",
      "Epoch 2/5\n",
      "58204/58204 [==============================] - 257s 4ms/step - loss: 0.8920 - accuracy: 0.6790\n",
      "Epoch 3/5\n",
      "58204/58204 [==============================] - 258s 4ms/step - loss: 0.8976 - accuracy: 0.6816\n",
      "Epoch 4/5\n",
      "58204/58204 [==============================] - 258s 4ms/step - loss: 0.6884 - accuracy: 0.7640\n",
      "Epoch 5/5\n",
      "58204/58204 [==============================] - 258s 4ms/step - loss: 0.5097 - accuracy: 0.8450\n",
      "Epoch 1/5\n",
      "58205/58205 [==============================] - 252s 4ms/step - loss: 0.9307 - accuracy: 0.6752\n",
      "Epoch 2/5\n",
      "58205/58205 [==============================] - 251s 4ms/step - loss: 0.7288 - accuracy: 0.7663\n",
      "Epoch 3/5\n",
      "58205/58205 [==============================] - 252s 4ms/step - loss: 0.8585 - accuracy: 0.7235\n",
      "Epoch 4/5\n",
      "58205/58205 [==============================] - 252s 4ms/step - loss: 0.7704 - accuracy: 0.7388\n",
      "Epoch 5/5\n",
      "58205/58205 [==============================] - 252s 4ms/step - loss: 0.6591 - accuracy: 0.7885\n",
      "mean: 8.482821e-01 (+/- 6.029652e-03)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "cross_scores = []\n",
    "for train, test in cv.split(X_df, np.argmax(Y_train, -1)):\n",
    "    clf.fit(X_df.loc[train], Y_train[train])\n",
    "    y_pred = clf.predict(X_df.loc[test])\n",
    "    cross_scores.append(f1_score(np.argmax(Y_train[test], -1), y_pred, average='weighted'))\n",
    "\n",
    "cross_scores = np.array(cross_scores)\n",
    "print(\"mean: %e (+/- %e)\" % (cross_scores.mean(), cross_scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CQu3-_ipXW1E"
   },
   "source": [
    "<b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\"> Finding the F1-Score of the test data-set</font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8tlqFFRk-Xg8"
   },
   "outputs": [],
   "source": [
    "from problem import get_test_data\n",
    "X_test, y_test = get_test_data()\n",
    "\n",
    "X_test = text_preprocessor.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 50738,
     "status": "ok",
     "timestamp": 1580592679203,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "OeJmGsOK-XiI",
    "outputId": "72fb3c92-c165-488b-b294-7b3c4024eba6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5264578339954473"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, model.predict(X_test), average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0YRUlr5EXW1X"
   },
   "source": [
    "<b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\"> Confusion Matrix Visualizaton</font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cgB6b6uDXW1Y",
    "outputId": "5ee2303d-ef36-4848-aa8f-a53a7f5da829"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21255,    88,   312,   855,  1151],\n",
       "       [ 1731,   179,    35,   399,   555],\n",
       "       [ 2415,    18,    84,   133,   459],\n",
       "       [ 1705,    41,   101,   188,   478],\n",
       "       [ 4848,    68,   110,   258,  1338]])"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, model.predict(X_test))\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8pFBJkSDXW1b",
    "outputId": "a174179f-e0d3-4e6c-a571-41548622c926"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "#\n",
    "# Assuming you have \"2D\" dataset like the following that you need\n",
    "# to plot.\n",
    "#\n",
    "data_2d = cm\n",
    "#\n",
    "# Create a figure for plotting the data as a 3D histogram.\n",
    "#\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "#\n",
    "# Create an X-Y mesh of the same dimension as the 2D data. You can\n",
    "# think of this as the floor of the plot.\n",
    "#\n",
    "lx= len(data_2d[0])            # Work out matrix dimensions\n",
    "ly= len(data_2d[:,0])\n",
    "xpos = np.arange(0,lx,1)    # Set up a mesh of positions\n",
    "ypos = np.arange(0,ly,1)\n",
    "x_data, y_data = np.meshgrid(xpos+0.5, ypos+0.5)\n",
    "\n",
    "#\n",
    "# Flatten out the arrays so that they may be passed to \"ax.bar3d\".\n",
    "# Basically, ax.bar3d expects three one-dimensional arrays:\n",
    "# x_data, y_data, z_data. The following call boils down to picking\n",
    "# one entry from each array and plotting a bar to from\n",
    "# (x_data[i], y_data[i], 0) to (x_data[i], y_data[i], z_data[i]).\n",
    "#\n",
    "x_data = x_data.flatten()\n",
    "y_data = y_data.flatten()\n",
    "z_data = data_array.flatten()\n",
    "ax.bar3d( x_data,\n",
    "          y_data,\n",
    "          np.zeros(len(z_data)),\n",
    "          1, 1, z_data )\n",
    "#\n",
    "# Finally, display the plot.\n",
    "#\n",
    "ticksx = np.arange(1, 6, 1)\n",
    "plt.xticks(ticksx, labels_arab, rotation=90)\n",
    "\n",
    "ticksy = np.arange(1, 6, 1)\n",
    "plt.yticks(ticksy, labels_arab, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SZB1UKLgXW1e"
   },
   "source": [
    "As we can see from the confusion matrix there is lot of wrong prediction of classes 1 to 3, the most of them are predicted as class 0 which represents the highest number of samples so we have to solve this problem.<br>\n",
    "hint: one can use attention model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uCGyQgZvXW1f"
   },
   "source": [
    "<b><font size=\"5\" color=\"DarkBlue\" face=\"Courier New\"> Ramp test</font></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load features extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import re\n",
    "import datetime\n",
    "import dateutil.parser as dparser\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import unicodedata as ud\n",
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, SpatialDropout1D, LSTM, Dense\n",
    "import keras.backend as K\n",
    "import os\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "class FeatureExtractor(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def fit(self, X_df, y_array):\n",
    "        pass\n",
    "\n",
    "    def transform(self, X_df):\n",
    "        X_encoded = X_df\n",
    "\n",
    "        path = os.path.dirname(__file__)\n",
    "        \n",
    "        numeric_transformer = Pipeline(steps=[\n",
    "            ('impute', SimpleImputer(strategy='median'))])\n",
    "\n",
    "        def convert_to_date(x):\n",
    "            dicta =  {\"كانون الثاني\": \"january\" ,\"شباط\": \"february\", \"أيار\": \"may\",  \"نيسان\": \"April\",\n",
    "                \"آذار\": \"march\", \"حزيران\": \"june\", \"تموز\": \"july\", \"آب\": \"august\", \"أيلول\": \"september\",\n",
    "                \"تشرين الأول\": \"october\", \"تشرين الثاني\": \"november\", \"كانون الأول\": \"december\",\n",
    "                \"الاثنين\": \"monday\", \"الثلاثاء\": \"tuesday\", \"الأربعاء\": \"wednesday\", \"الخميس\": \"thursday\", \"الجمعة\": \"friday\",\n",
    "                \"السبت\": \"saturday\", \"الأحد\": \"sunday\", \"السبت\": \"saturday\"}\n",
    "            x_new = x\n",
    "            for arabic, english in dicta.items():\n",
    "                x_new = x_new.replace(arabic, english)\n",
    "            x_new = dparser.parse(x_new, fuzzy=True)\n",
    "            return x_new\n",
    "        def process_date(X):\n",
    "            date = X.Date.apply(convert_to_date)\n",
    "            return np.c_[date.dt.year, date.dt.month, date.dt.day]\n",
    "        date_transformer = FunctionTransformer(process_date, validate=False)\n",
    "\n",
    "\n",
    "        def clean_txt(sent):\n",
    "            \"\"\"\n",
    "                text: a string\n",
    "                return: modified initial string\n",
    "            \"\"\"    \n",
    "            stps_arabic = set(stopwords.words('arabic'))\n",
    "            arabic_diacritics = re.compile(\"\"\"\n",
    "                                        ّ    | # Tashdid\n",
    "                                        َ    | # Fatha\n",
    "                                        ً    | # Tanwin Fath\n",
    "                                        ُ    | # Damma\n",
    "                                        ٌ    | # Tanwin Damm\n",
    "                                        ِ    | # Kasra\n",
    "                                        ٍ    | # Tanwin Kasr\n",
    "                                        ْ    | # Sukun\n",
    "                                        ـ     # Tatwil/Kashida\n",
    "                                    \"\"\", re.VERBOSE)\n",
    "            sent = str(sent)\n",
    "            text = sent.strip()\n",
    "            text = re.sub('[\\n\\r\\t\\xa0]', ' ', text)\n",
    "            text = re.sub(arabic_diacritics, '', text)\n",
    "            text = ''.join(c for c in text if not ud.category(c).startswith('P') and not c.isdigit())\n",
    "            res = re.sub(' +', ' ', text)\n",
    "            return [w for w in text.split() if w not in stps_arabic]\n",
    "\n",
    "        def process_text(X):\n",
    "            X['Desc'] = X['Desc'].astype('str')\n",
    "            X['Title'] = X['Title'].astype('str')\n",
    "    \n",
    "            X.loc[X['Desc']=='', 'Desc'] = X['Title']\n",
    "            X['Desc'] = X['Desc'].apply(clean_txt)\n",
    "            return np.c_[X['Desc']]\n",
    "\n",
    "        # The maximum number of words to be used. (Most frequent)\n",
    "        vocab_size = 20000\n",
    "        # The padding and truncating types used.\n",
    "        trunc_type = 'post'\n",
    "        padding_type = 'post'\n",
    "        # The OOV token (Out Of Vocabulary) will be included within the dictionary\n",
    "        oov_tok = '<OOV>'\n",
    "        # Max number of words in each complaint.\n",
    "        MAX_SEQUENCE_LENGTH = 200\n",
    "        # This is fixed.\n",
    "        EMBEDDING_DIM = 100\n",
    "\n",
    "        class FeatureSelector(BaseEstimator, TransformerMixin):\n",
    "            #Class Constructor \n",
    "            def __init__(self, feature_names, vocab_size, oov_tok, MAX_SEQUENCE_LENGTH, padding_type, trunc_type):\n",
    "                self.feature_names = feature_names\n",
    "                self.tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)\n",
    "                self.max_length = MAX_SEQUENCE_LENGTH\n",
    "                self.padding = padding_type\n",
    "                self.truncating = trunc_type\n",
    "        \n",
    "            def fit(self, X, y=None):\n",
    "                X_1 = process_text(X.loc[:, self.feature_names])\n",
    "                self.tokenizer.fit_on_texts(X_1[:, 0])\n",
    "                self.word_index = self.tokenizer.word_index\n",
    "                return self\n",
    "    \n",
    "            def transform(self, X):\n",
    "                X_1 = process_text(X.loc[:, self.feature_names])\n",
    "                sequences = self.tokenizer.texts_to_sequences(X_1[:, 0])\n",
    "                X = pad_sequences(sequences, maxlen=self.max_length, padding=self.padding, truncating=self.truncating)\n",
    "                return X\n",
    "    \n",
    "            def fit_transform(self, X, y=None):\n",
    "                self.fit(X, y)\n",
    "                return self.transform(X)\n",
    "\n",
    "        num_col = ['Id']\n",
    "        date_col = ['Date']\n",
    "        text_cols = ['Desc', 'Title']\n",
    "        drop_cols = ['Image']\n",
    "\n",
    "        non_text_preprocessor = ColumnTransformer(\n",
    "                transformers=[\n",
    "                    ('date', make_pipeline(date_transformer), date_col),\n",
    "                    ('num', numeric_transformer, num_col),\n",
    "                    ('drop cols', 'drop', drop_cols),\n",
    "                    ])\n",
    "\n",
    "        text_preprocessor = Pipeline(steps=[\n",
    "            ('text', FeatureSelector(text_cols, vocab_size, oov_tok, MAX_SEQUENCE_LENGTH, padding_type, trunc_type)),\n",
    "        ])\n",
    "        X_train = text_preprocessor.fit_transform(X_df)\n",
    "        X_extra = non_text_preprocessor.fit_transform(X_df)\n",
    "        return X_extra, X_train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, SpatialDropout1D, LSTM, Dense\n",
    "import keras.backend as K\n",
    "from sklearn import preprocessing\n",
    "import os\n",
    "import tensorflow as tf\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "class Classifier(object):\n",
    "    def __init__(self):\n",
    "        self.vocab_size = 20000\n",
    "        self.max_length = 200\n",
    "        self.EMBEDDING_DIM = 100\n",
    "        self.model = Sequential()\n",
    "        self.enc = preprocessing.OneHotEncoder()\n",
    "        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "        K.clear_session()\n",
    "        self.model.add(Embedding(self.vocab_size, self.EMBEDDING_DIM, input_length=self.max_length))\n",
    "        self.model.add(SpatialDropout1D(0.25))\n",
    "        self.model.add(LSTM(32))\n",
    "        self.model.add(Dense(5, activation='softmax'))\n",
    "        self.model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[self.get_f1])\n",
    "        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "    def get_f1(self, y_true, y_pred):\n",
    "        \n",
    "        y_true = K.argmax(y_true, -1)\n",
    "        y_pred = K.argmax(y_pred, -1)\n",
    "        \n",
    "        prec_num = 0\n",
    "        prec_den = 0\n",
    "        recall_num = 0\n",
    "        recall_den = 0\n",
    "        \n",
    "        equal = K.equal(y_true, y_pred)\n",
    "        not_equal = K.not_equal(y_true, y_pred)\n",
    "        \n",
    "        for i in range(5):\n",
    "            tmp = K.sum(K.cast(K.all(K.stack([equal, K.equal(y_true, i)], axis=0), axis=0), 'int32'))\n",
    "            prec_num += tmp\n",
    "            prec_den += tmp + K.sum(K.cast(K.all(K.stack([not_equal, K.equal(y_pred, i)], axis=0), axis=0), 'int32'))\n",
    "            recall_num += tmp\n",
    "            recall_den += tmp + K.sum(K.cast(K.all(K.stack([not_equal, K.equal(y_true, i)], axis=0), axis=0), 'int32'))\n",
    "            \n",
    "        prec_num = K.cast(prec_num, 'float')\n",
    "        prec_den = K.cast(prec_den, 'float')\n",
    "        recall_num = K.cast(recall_num, 'float')\n",
    "        recall_den = K.cast(recall_den, 'float')\n",
    "        \n",
    "        precision = prec_num / prec_den\n",
    "        recall = recall_num / recall_den\n",
    "        f1_val = 2*(precision * recall)/(precision + recall)\n",
    "        return f1_val\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.enc.fit(y.reshape(-1, 1))\n",
    "        Y_train = self.enc.transform(y.reshape(-1, 1)).toarray()\n",
    "        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "        self.model.fit(X[1], Y_train, epochs=4, batch_size=32, verbose=0)\n",
    "    \n",
    "    def predict_proba(self, X):\n",
    "        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "        return self.model.predict_proba(X[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11367682,
     "status": "ok",
     "timestamp": 1580654643339,
     "user": {
      "displayName": "Hadi Abdine",
      "photoUrl": "",
      "userId": "09769333924622491433"
     },
     "user_tz": -60
    },
    "id": "B-7yusSg47wl",
    "outputId": "fac8a225-e493-4bd1-ced0-d67fd0afef2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;178m\u001b[1mTesting Arabic News Category prediction\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading train and test files from ./data ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading cv ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mTraining submissions/starting_kit ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 0\u001b[0m\n",
      "Using TensorFlow backend.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:107: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:111: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "\t\u001b[38;5;178m\u001b[1mscore  f1 score         time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m     \u001b[38;5;10m\u001b[1m0.507\u001b[0m  \u001b[38;5;150m2333.597978\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m     \u001b[38;5;12m\u001b[1m0.507\u001b[0m   \u001b[38;5;105m377.271598\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m      \u001b[38;5;1m\u001b[1m0.476\u001b[0m   \u001b[38;5;218m126.435108\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 1\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  f1 score         time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m     \u001b[38;5;10m\u001b[1m0.497\u001b[0m  \u001b[38;5;150m2347.471491\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m     \u001b[38;5;12m\u001b[1m0.497\u001b[0m   \u001b[38;5;105m367.431516\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m      \u001b[38;5;1m\u001b[1m0.471\u001b[0m   \u001b[38;5;218m125.013485\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 2\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  f1 score         time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m     \u001b[38;5;10m\u001b[1m0.511\u001b[0m  \u001b[38;5;150m2324.469800\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m     \u001b[38;5;12m\u001b[1m0.512\u001b[0m   \u001b[38;5;105m372.488616\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m      \u001b[38;5;1m\u001b[1m0.489\u001b[0m   \u001b[38;5;218m124.265731\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 3\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  f1 score         time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m     \u001b[38;5;10m\u001b[1m0.535\u001b[0m  \u001b[38;5;150m2339.621962\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m     \u001b[38;5;12m\u001b[1m0.532\u001b[0m   \u001b[38;5;105m378.818315\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m      \u001b[38;5;1m\u001b[1m0.528\u001b[0m   \u001b[38;5;218m124.835876\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mMean CV scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore        f1 score           time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m   \u001b[38;5;10m\u001b[1m0.512\u001b[0m \u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m \u001b[38;5;150m0.014\u001b[0m  \u001b[38;5;150m2336.3\u001b[0m \u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m \u001b[38;5;150m8.41\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.512\u001b[0m \u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m \u001b[38;5;105m0.0128\u001b[0m   \u001b[38;5;105m374.0\u001b[0m \u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m \u001b[38;5;105m4.45\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.491\u001b[0m \u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m \u001b[38;5;218m0.0224\u001b[0m    \u001b[38;5;218m125.1\u001b[0m \u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m \u001b[38;5;218m0.8\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mBagged scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  f1 score\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m     \u001b[38;5;12m\u001b[1m0.518\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m      \u001b[38;5;1m\u001b[1m0.478\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!ramp_test_submission "
   ]
  }
 ],
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   "name": "ArabicNewsCategoryPrediction_starting_kit.ipynb",
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