project_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb7a1618-8a41-4137-828c-095f85e7e10d",
   "metadata": {},
   "source": [
    "# Reddit Analysis - ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47ca9a1-3136-4792-9235-8803bf1ae43f",
   "metadata": {},
   "source": [
    "The goal of this ML assignment is to do topic modeling to check which topic do the comemnts belong to and predict socre and awards associated with them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73100a28-fa5b-445e-90c2-244dc2b0d73d",
   "metadata": {},
   "source": [
    "## Spark Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0da93c0d-f07c-42cc-9381-df46efe24fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6012fb0-3890-437e-8442-dc219766ea30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark.sql.functions as F\n",
    "from pyspark.sql.functions import col, lit\n",
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20e34874-f235-467a-b207-b76a98371bf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ivy Default Cache set to: /home/hadoop/.ivy2/cache\n",
      "The jars for the packages stored in: /home/hadoop/.ivy2/jars\n",
      ":: loading settings :: url = jar:file:/usr/lib/spark/jars/ivy-2.4.0.jar!/org/apache/ivy/core/settings/ivysettings.xml\n",
      "com.johnsnowlabs.nlp#spark-nlp_2.12 added as a dependency\n",
      ":: resolving dependencies :: org.apache.spark#spark-submit-parent-0104c778-5a9b-49e4-974d-20e54712b3ae;1.0\n",
      "\tconfs: [default]\n",
      "\tfound com.johnsnowlabs.nlp#spark-nlp_2.12;3.4.2 in central\n",
      "\tfound com.typesafe#config;1.4.1 in central\n",
      "\tfound org.rocksdb#rocksdbjni;6.5.3 in central\n",
      "\tfound com.amazonaws#aws-java-sdk-bundle;1.11.603 in central\n",
      "\tfound com.github.universal-automata#liblevenshtein;3.0.0 in central\n",
      "\tfound com.google.code.findbugs#annotations;3.0.1 in central\n",
      "\tfound net.jcip#jcip-annotations;1.0 in central\n",
      "\tfound com.google.code.findbugs#jsr305;3.0.1 in central\n",
      "\tfound com.google.protobuf#protobuf-java-util;3.0.0-beta-3 in central\n",
      "\tfound com.google.protobuf#protobuf-java;3.0.0-beta-3 in central\n",
      "\tfound com.google.code.gson#gson;2.3 in central\n",
      "\tfound it.unimi.dsi#fastutil;7.0.12 in central\n",
      "\tfound org.projectlombok#lombok;1.16.8 in central\n",
      "\tfound org.slf4j#slf4j-api;1.7.21 in central\n",
      "\tfound com.navigamez#greex;1.0 in central\n",
      "\tfound dk.brics.automaton#automaton;1.11-8 in central\n",
      "\tfound org.json4s#json4s-ext_2.12;3.5.3 in central\n",
      "\tfound joda-time#joda-time;2.9.5 in central\n",
      "\tfound org.joda#joda-convert;1.8.1 in central\n",
      "\tfound com.johnsnowlabs.nlp#tensorflow-cpu_2.12;0.3.3 in central\n",
      "\tfound net.sf.trove4j#trove4j;3.0.3 in central\n",
      ":: resolution report :: resolve 507ms :: artifacts dl 14ms\n",
      "\t:: modules in use:\n",
      "\tcom.amazonaws#aws-java-sdk-bundle;1.11.603 from central in [default]\n",
      "\tcom.github.universal-automata#liblevenshtein;3.0.0 from central in [default]\n",
      "\tcom.google.code.findbugs#annotations;3.0.1 from central in [default]\n",
      "\tcom.google.code.findbugs#jsr305;3.0.1 from central in [default]\n",
      "\tcom.google.code.gson#gson;2.3 from central in [default]\n",
      "\tcom.google.protobuf#protobuf-java;3.0.0-beta-3 from central in [default]\n",
      "\tcom.google.protobuf#protobuf-java-util;3.0.0-beta-3 from central in [default]\n",
      "\tcom.johnsnowlabs.nlp#spark-nlp_2.12;3.4.2 from central in [default]\n",
      "\tcom.johnsnowlabs.nlp#tensorflow-cpu_2.12;0.3.3 from central in [default]\n",
      "\tcom.navigamez#greex;1.0 from central in [default]\n",
      "\tcom.typesafe#config;1.4.1 from central in [default]\n",
      "\tdk.brics.automaton#automaton;1.11-8 from central in [default]\n",
      "\tit.unimi.dsi#fastutil;7.0.12 from central in [default]\n",
      "\tjoda-time#joda-time;2.9.5 from central in [default]\n",
      "\tnet.jcip#jcip-annotations;1.0 from central in [default]\n",
      "\tnet.sf.trove4j#trove4j;3.0.3 from central in [default]\n",
      "\torg.joda#joda-convert;1.8.1 from central in [default]\n",
      "\torg.json4s#json4s-ext_2.12;3.5.3 from central in [default]\n",
      "\torg.projectlombok#lombok;1.16.8 from central in [default]\n",
      "\torg.rocksdb#rocksdbjni;6.5.3 from central in [default]\n",
      "\torg.slf4j#slf4j-api;1.7.21 from central in [default]\n",
      "\t---------------------------------------------------------------------\n",
      "\t|                  |            modules            ||   artifacts   |\n",
      "\t|       conf       | number| search|dwnlded|evicted|| number|dwnlded|\n",
      "\t---------------------------------------------------------------------\n",
      "\t|      default     |   21  |   0   |   0   |   0   ||   21  |   0   |\n",
      "\t---------------------------------------------------------------------\n",
      ":: retrieving :: org.apache.spark#spark-submit-parent-0104c778-5a9b-49e4-974d-20e54712b3ae\n",
      "\tconfs: [default]\n",
      "\t0 artifacts copied, 21 already retrieved (0kB/15ms)\n",
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "22/05/04 00:16:28 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-3.4.2.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.typesafe_config-1.4.1.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/org.rocksdb_rocksdbjni-6.5.3.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.603.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.navigamez_greex-1.0.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/org.json4s_json4s-ext_2.12-3.5.3.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.3.3.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/net.sf.trove4j_trove4j-3.0.3.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.google.code.findbugs_annotations-3.0.1.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.0.0-beta-3.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.google.protobuf_protobuf-java-3.0.0-beta-3.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/org.slf4j_slf4j-api-1.7.21.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/net.jcip_jcip-annotations-1.0.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.1.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/com.google.code.gson_gson-2.3.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/joda-time_joda-time-2.9.5.jar added multiple times to distributed cache.\n",
      "22/05/04 00:16:32 WARN Client: Same path resource file:///home/hadoop/.ivy2/jars/org.joda_joda-convert-1.8.1.jar added multiple times to distributed cache.\n"
     ]
    }
   ],
   "source": [
    "# Starting spark session\n",
    "spark = SparkSession.builder \\\n",
    "        .appName(\"RedditML\") \\\n",
    "        .config(\"spark.serializer\", \"org.apache.spark.serializer.KryoSerializer\") \\\n",
    "        .config(\"spark.jars.packages\", \"com.johnsnowlabs.nlp:spark-nlp_2.12:3.4.2\") \\\n",
    "    .master('yarn') \\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e297b70-6e33-4a0b-bc43-939c070f2554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - in-memory</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://ip-172-31-33-188.ec2.internal:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.0.0-amzn-0</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>yarn</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>RedditML</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7fd274152690>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0ddbc33-be1d-4f29-bd34-f35820baccf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sparknlp in /mnt/miniconda/lib/python3.7/site-packages (1.0.0)\n",
      "Requirement already satisfied: spark-nlp in /mnt/miniconda/lib/python3.7/site-packages (from sparknlp) (3.4.3)\n",
      "Requirement already satisfied: numpy in /mnt/miniconda/lib/python3.7/site-packages (from sparknlp) (1.21.2)\n",
      "Requirement already satisfied: altair in /mnt/miniconda/lib/python3.7/site-packages (4.2.0)\n",
      "Requirement already satisfied: jinja2 in /mnt/miniconda/lib/python3.7/site-packages (from altair) (3.0.3)\n",
      "Requirement already satisfied: pandas>=0.18 in /mnt/miniconda/lib/python3.7/site-packages (from altair) (1.3.5)\n",
      "Requirement already satisfied: jsonschema>=3.0 in /mnt/miniconda/lib/python3.7/site-packages (from altair) (4.4.0)\n",
      "Requirement already satisfied: entrypoints in /mnt/miniconda/lib/python3.7/site-packages (from altair) (0.4)\n",
      "Requirement already satisfied: numpy in /mnt/miniconda/lib/python3.7/site-packages (from altair) (1.21.2)\n",
      "Requirement already satisfied: toolz in /mnt/miniconda/lib/python3.7/site-packages (from altair) (0.11.2)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /mnt/miniconda/lib/python3.7/site-packages (from jsonschema>=3.0->altair) (0.18.0)\n",
      "Requirement already satisfied: importlib-metadata in /mnt/miniconda/lib/python3.7/site-packages (from jsonschema>=3.0->altair) (4.8.2)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /mnt/miniconda/lib/python3.7/site-packages (from jsonschema>=3.0->altair) (21.4.0)\n",
      "Requirement already satisfied: importlib-resources>=1.4.0 in /mnt/miniconda/lib/python3.7/site-packages (from jsonschema>=3.0->altair) (5.2.0)\n",
      "Requirement already satisfied: typing-extensions in /mnt/miniconda/lib/python3.7/site-packages (from jsonschema>=3.0->altair) (3.10.0.2)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /mnt/miniconda/lib/python3.7/site-packages (from importlib-resources>=1.4.0->jsonschema>=3.0->altair) (3.7.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /mnt/miniconda/lib/python3.7/site-packages (from pandas>=0.18->altair) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /mnt/miniconda/lib/python3.7/site-packages (from pandas>=0.18->altair) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in /mnt/miniconda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=0.18->altair) (1.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /mnt/miniconda/lib/python3.7/site-packages (from jinja2->altair) (2.0.1)\n",
      "Requirement already satisfied: imageio in /mnt/miniconda/lib/python3.7/site-packages (2.19.0)\n",
      "Requirement already satisfied: numpy in /mnt/miniconda/lib/python3.7/site-packages (from imageio) (1.21.2)\n",
      "Requirement already satisfied: pillow>=8.3.2 in /mnt/miniconda/lib/python3.7/site-packages (from imageio) (9.0.1)\n",
      "\u001b[31mERROR: Could not find a version that satisfies the requirement collections (from versions: none)\u001b[0m\n",
      "\u001b[31mERROR: No matching distribution found for collections\u001b[0m\n",
      "Requirement already satisfied: wordcloud in /mnt/miniconda/lib/python3.7/site-packages (1.8.1)\n",
      "Requirement already satisfied: numpy>=1.6.1 in /mnt/miniconda/lib/python3.7/site-packages (from wordcloud) (1.21.2)\n",
      "Requirement already satisfied: pillow in /mnt/miniconda/lib/python3.7/site-packages (from wordcloud) (9.0.1)\n",
      "Requirement already satisfied: matplotlib in /mnt/miniconda/lib/python3.7/site-packages (from wordcloud) (3.5.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /mnt/miniconda/lib/python3.7/site-packages (from matplotlib->wordcloud) (0.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /mnt/miniconda/lib/python3.7/site-packages (from matplotlib->wordcloud) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /mnt/miniconda/lib/python3.7/site-packages (from matplotlib->wordcloud) (4.25.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /mnt/miniconda/lib/python3.7/site-packages (from matplotlib->wordcloud) (3.0.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /mnt/miniconda/lib/python3.7/site-packages (from matplotlib->wordcloud) (21.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /mnt/miniconda/lib/python3.7/site-packages (from matplotlib->wordcloud) (1.3.2)\n",
      "Requirement already satisfied: six>=1.5 in /mnt/miniconda/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!/mnt/miniconda/bin/pip install sparknlp\n",
    "!/mnt/miniconda/bin/pip install altair\n",
    "!/mnt/miniconda/bin/pip install imageio\n",
    "!/mnt/miniconda/bin/pip install collections\n",
    "!/mnt/miniconda/bin/pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d27fdd4-6586-4dad-a2be-2d074acdbbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.sql import SparkSession\n",
    "import pyspark.sql.functions as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7baf00d2-0fb0-40fb-970f-0de920bc2a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70375c74-9556-4b7c-b674-49072e816269",
   "metadata": {},
   "source": [
    "## Basic Data Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e2068d6-dfff-44c7-bfaa-e5c1d1abf7be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "reddit_eda = spark.read.parquet('s3://aa2464-labdata2/reddit-eda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfc0b90f-485a-4930-9e88-a55e8e79686a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "reddit_sentiment = spark.read.parquet('s3://aa2464-labdata2/reddit-sentiment2/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78e835b4-8406-4493-be63-fee898fce7a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- document: string (nullable = true)\n",
      " |-- sarcasm_class: string (nullable = true)\n",
      " |-- sarcasm_value: double (nullable = true)\n",
      " |-- sentiment_class: string (nullable = true)\n",
      " |-- sentiment_value: double (nullable = true)\n",
      " |-- emotion_class: string (nullable = true)\n",
      " |-- score: long (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- Awards: string (nullable = true)\n",
      " |-- Jokes: string (nullable = true)\n",
      " |-- Covid: string (nullable = true)\n",
      " |-- created_date: string (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- reddit_age_post: integer (nullable = true)\n",
      " |-- is_award: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reddit_sentiment.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "926b019e-80db-4ea7-b147-e842c3a803d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- all_awardings: string (nullable = true)\n",
      " |-- associated_award: string (nullable = true)\n",
      " |-- author: string (nullable = true)\n",
      " |-- author_created_utc: double (nullable = true)\n",
      " |-- author_flair_richtext: string (nullable = true)\n",
      " |-- author_flair_text: string (nullable = true)\n",
      " |-- author_flair_text_color: string (nullable = true)\n",
      " |-- author_flair_type: string (nullable = true)\n",
      " |-- author_fullname: string (nullable = true)\n",
      " |-- author_patreon_flair: boolean (nullable = true)\n",
      " |-- author_premium: boolean (nullable = true)\n",
      " |-- awarders: string (nullable = true)\n",
      " |-- body: string (nullable = true)\n",
      " |-- can_gild: boolean (nullable = true)\n",
      " |-- can_mod_post: boolean (nullable = true)\n",
      " |-- collapsed: boolean (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- created_utc: long (nullable = true)\n",
      " |-- distinguished: string (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- gildings: string (nullable = true)\n",
      " |-- id: string (nullable = true)\n",
      " |-- is_submitter: boolean (nullable = true)\n",
      " |-- link_id: string (nullable = true)\n",
      " |-- locked: boolean (nullable = true)\n",
      " |-- no_follow: boolean (nullable = true)\n",
      " |-- parent_id: string (nullable = true)\n",
      " |-- permalink: string (nullable = true)\n",
      " |-- quarantined: boolean (nullable = true)\n",
      " |-- retrieved_on: long (nullable = true)\n",
      " |-- score: long (nullable = true)\n",
      " |-- send_replies: boolean (nullable = true)\n",
      " |-- stickied: boolean (nullable = true)\n",
      " |-- subreddit_id: string (nullable = true)\n",
      " |-- subreddit_name_prefixed: string (nullable = true)\n",
      " |-- subreddit_type: string (nullable = true)\n",
      " |-- total_awards_received: long (nullable = true)\n",
      " |-- treatment_tags: string (nullable = true)\n",
      " |-- editable: boolean (nullable = true)\n",
      " |-- ym_partition: integer (nullable = true)\n",
      " |-- filename: string (nullable = true)\n",
      " |-- length_body: integer (nullable = true)\n",
      " |-- author_created_date: string (nullable = true)\n",
      " |-- created_date: string (nullable = true)\n",
      " |-- reddit_age_post: integer (nullable = true)\n",
      " |-- Awards: string (nullable = true)\n",
      " |-- Partition: string (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- hour_post: integer (nullable = true)\n",
      " |-- month_post: integer (nullable = true)\n",
      " |-- year_post: integer (nullable = true)\n",
      " |-- body_count: integer (nullable = true)\n",
      " |-- Jokes: string (nullable = true)\n",
      " |-- Finance: string (nullable = true)\n",
      " |-- Covid: string (nullable = true)\n",
      " |-- Sports: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reddit_eda.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5c0a4e0-0ca3-46e8-81a5-994f88052e67",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7373839"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_sentiment.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf53521-a88f-499c-b599-691e9e97acb5",
   "metadata": {},
   "source": [
    "### Business Goal 1 : Which topic do the comments belong to?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee236e7-0979-4c0a-ad61-a73352baff43",
   "metadata": {},
   "source": [
    "Technical : We will run 2 models to achieve this. First is K means clustering which will be used to determine the optimal number of clusters using the Elbow method. USing this number w e welill cluster our comemnts using LDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad343d34-b154-4fdb-8db1-808074f2455c",
   "metadata": {},
   "source": [
    "#### K Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b93752-972b-4e1b-8733-c2312daab966",
   "metadata": {},
   "source": [
    "##### Building Pipeline for prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5fc1a8d4-4cb9-4592-8726-63a63f8f9d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sparknlp.base import DocumentAssembler,Finisher\n",
    "from sparknlp.annotator import Tokenizer,StopWordsCleaner\n",
    "from pyspark.ml.feature import StopWordsRemover\n",
    "from pyspark.ml.feature import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ddc9ed4-44ee-49b9-80cb-287bb3101ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "documentAssembler = DocumentAssembler()\\\n",
    "    .setInputCol(\"document\")\\\n",
    "    .setOutputCol(\"document2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92093fec-1ad2-456e-b71f-df09e709027f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenizer to break words\n",
    "tokenizer = Tokenizer() \\\n",
    "     .setInputCols(['document2']) \\\n",
    "     .setOutputCol('token')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d7e83dc7-cd58-43cb-b68f-ae8ae55d09a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stopwords_list(stop_file_path):\n",
    "    \"\"\"load stop words \"\"\"\n",
    "    \n",
    "    with open(stop_file_path, 'r', encoding=\"utf-8\") as f:\n",
    "        stopwords = f.readlines()\n",
    "        stop_set = set(m.strip() for m in stopwords)\n",
    "        return list(frozenset(stop_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "166c831f-746e-46ea-b606-0e126c7c8053",
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords_path = \"data_extra/english_stopwords.txt\"\n",
    "stopwords = get_stopwords_list(stopwords_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "66c67a20-96d6-483a-b81e-0888ce66452b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords_custom1 = ['abc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f12b7508-169a-4351-9652-7b6655d410a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Stopwords\n",
    "stopwords_cleaner = StopWordsCleaner() \\\n",
    "     .setInputCols(['token']) \\\n",
    "     .setOutputCol('clean_token') \\\n",
    "     .setStopWords(stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8e1de61b-be46-4840-883c-0bf3cbf9cb62",
   "metadata": {},
   "outputs": [],
   "source": [
    "finisher = Finisher() \\\n",
    "    .setInputCols([\"clean_token\"]) \\\n",
    "    .setOutputCols([\"final\"]) \\\n",
    "    .setOutputAsArray(True) \\\n",
    "    .setCleanAnnotations(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206b94e2-edde-48c0-8bdc-ca7c0cd4cbaa",
   "metadata": {},
   "source": [
    "Fitting the pipeline on empty data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8227863-390a-49ec-b2d1-6855f07da765",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline() \\\n",
    "     .setStages([\n",
    "           documentAssembler,\n",
    "           tokenizer,\n",
    "           stopwords_cleaner,\n",
    "           finisher\n",
    "     ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49776c12-c2ae-4422-a8c8-5a0f8ba4a5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "empty_df = spark.createDataFrame([['']]).toDF(\"document\")\n",
    "pipelineModel = pipeline.fit(empty_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c747380-1ab5-4ace-b93a-5940a79f01fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorised_reddit_comments = pipelineModel.transform(reddit_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0cab11c-a673-4c52-85ac-2162302c2024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- document: string (nullable = true)\n",
      " |-- sarcasm_class: string (nullable = true)\n",
      " |-- sarcasm_value: double (nullable = true)\n",
      " |-- sentiment_class: string (nullable = true)\n",
      " |-- sentiment_value: double (nullable = true)\n",
      " |-- emotion_class: string (nullable = true)\n",
      " |-- score: long (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- Awards: string (nullable = true)\n",
      " |-- Jokes: string (nullable = true)\n",
      " |-- Covid: string (nullable = true)\n",
      " |-- created_date: string (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- reddit_age_post: integer (nullable = true)\n",
      " |-- is_award: integer (nullable = true)\n",
      " |-- document2: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- clean_token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- final: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vectorised_reddit_comments.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f89c9e2a-cbb0-4dfb-9da3-dcc3d9a46fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_reddit_comments = vectorised_reddit_comments.withColumn(\"text\", F.concat_ws(\" \", \"final\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1683c658-83b2-44e4-8096-542ea22be005",
   "metadata": {},
   "outputs": [],
   "source": [
    "lem_comments = cleaned_reddit_comments.select(cleaned_reddit_comments.final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e071cf79-4802-48c1-82c3-8c0a019ff35b",
   "metadata": {},
   "outputs": [],
   "source": [
    "important_words = lem_comments.withColumn('final', F.explode(col('final')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "12b78251-591a-4642-bbc6-25a8ae5f3220",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_words = important_words.groupby('final').count().sort('count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd376172-7ea6-4886-9b7e-05380b6b3ec4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 3:>                                                        (0 + 24) / 26]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-------+\n",
      "|   final|  count|\n",
      "+--------+-------+\n",
      "|  people|1352441|\n",
      "|question| 798711|\n",
      "|    time| 698895|\n",
      "|    feel| 452123|\n",
      "|     lot| 415667|\n",
      "|     day| 314317|\n",
      "|    life| 291745|\n",
      "|  person| 276022|\n",
      "|  reason| 274265|\n",
      "|     bad| 270173|\n",
      "+--------+-------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "count_words.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "73125277-f260-4a49-a7bb-a92deb61e09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_words2 = count_words.limit(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d889fc5a-91a6-4cf1-9130-0e26af30f027",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "count_words2_df = count_words2.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3438f6d5-4e87-4aef-8ce8-f631f266a13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>final</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>people</td>\n",
       "      <td>1352441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>question</td>\n",
       "      <td>798711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>time</td>\n",
       "      <td>698895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>feel</td>\n",
       "      <td>452123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lot</td>\n",
       "      <td>415667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>politician</td>\n",
       "      <td>16961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>imply</td>\n",
       "      <td>16949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>minor</td>\n",
       "      <td>16940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>meme</td>\n",
       "      <td>16890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>relative</td>\n",
       "      <td>16874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          final    count\n",
       "0        people  1352441\n",
       "1      question   798711\n",
       "2          time   698895\n",
       "3          feel   452123\n",
       "4           lot   415667\n",
       "..          ...      ...\n",
       "995  politician    16961\n",
       "996       imply    16949\n",
       "997       minor    16940\n",
       "998        meme    16890\n",
       "999    relative    16874\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_words2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fddbcf0e-593e-43c6-95d4-52ed9e76aba7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32a47d43-0c8d-441f-b1aa-88d57ee32ca3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed0b2e6a-9da9-4b25-82ce-510f4299288d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = imread('plots/images/reddit_logo.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ec88b7f-508a-4f84-bc30-f4eab982747a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'WordCloud' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_15801/1765012510.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcount_words2_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m wc = WordCloud(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mmax_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     background_color='#E7E6E1').generate(text)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'WordCloud' is not defined"
     ]
    }
   ],
   "source": [
    "text = \" \".join(i for i in count_words2_df.final)\n",
    "wc = WordCloud(\n",
    "    max_words=1000,\n",
    "    background_color='#E7E6E1').generate(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a642120e-6d20-44ef-8c8a-f4cbea315bc9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_15801/1226932301.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_figwidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_figheight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bilinear'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(16)\n",
    "\n",
    "plt.imshow(wc, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.savefig('plots/eda/most-words-wordcloud.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "738f4212-5d4f-46a7-b933-2b7fbd0dae1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.feature import CountVectorizer\n",
    "cv = CountVectorizer(inputCol=\"final\", outputCol=\"features\", vocabSize=500, minDF=3.0)\n",
    "# train the model\n",
    "cv_model = cv.fit(vectorised_reddit_comments)\n",
    "# transform the data. Output column name will be features.\n",
    "vectorized_tokens = cv_model.transform(vectorised_reddit_comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab48db9d-7983-4564-a814-d1fa5599aa5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- document: string (nullable = true)\n",
      " |-- sarcasm_class: string (nullable = true)\n",
      " |-- sarcasm_value: double (nullable = true)\n",
      " |-- sentiment_class: string (nullable = true)\n",
      " |-- sentiment_value: double (nullable = true)\n",
      " |-- emotion_class: string (nullable = true)\n",
      " |-- score: long (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- Awards: string (nullable = true)\n",
      " |-- Jokes: string (nullable = true)\n",
      " |-- Covid: string (nullable = true)\n",
      " |-- created_date: string (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- reddit_age_post: integer (nullable = true)\n",
      " |-- is_award: integer (nullable = true)\n",
      " |-- document2: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- clean_token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- final: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- features: vector (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vectorized_tokens.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9f0f06f6-54ec-4351-b5e7-6cb1448046db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.evaluation import ClusteringEvaluator\n",
    "from pyspark.ml.clustering import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caf2c751-f732-4046-9e5b-b315e0af86e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22/05/04 00:01:47 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS\n",
      "22/05/04 00:01:47 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS\n",
      "[Stage 89:====>                                                   (4 + 24) / 49]\r"
     ]
    }
   ],
   "source": [
    "# Running a loop to find optimal number of clusters\n",
    "cost = np.zeros(10)\n",
    "for k in range(2,10):\n",
    "    kmeans = KMeans().setK(k).setSeed(100).setFeaturesCol('features')\n",
    "    model = kmeans.fit(vectorized_tokens)\n",
    "    cost[k] = model.summary.trainingCost\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c142a4f0-be44-475f-a0b3-cbcdea5c0737",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the cost\n",
    "df_cost = pd.DataFrame(cost[2:])\n",
    "df_cost.columns = [\"cost\"]\n",
    "new_col = [2,3,4,5,6,7,8, 9]\n",
    "df_cost.insert(0, 'cluster', new_col)\n",
    "df_cost.to_csv('data_extra/clustering_cost.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ba67e993-ab2c-4ffb-93e5-013aaaa346c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cost = pd.read_csv('data_extra/clustering_cost.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cf01ea65-dee4-4a27-b34b-b52608f37c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1.097887e+08</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1.053069e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1.038906e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1.030784e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1.024634e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>1.015963e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>1.012754e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1.009122e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster          cost\n",
       "0        2  1.097887e+08\n",
       "1        3  1.053069e+08\n",
       "2        4  1.038906e+08\n",
       "3        5  1.030784e+08\n",
       "4        6  1.024634e+08\n",
       "5        7  1.015963e+08\n",
       "6        8  1.012754e+08\n",
       "7        9  1.009122e+08"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "430f2cc3-a5d6-4279-84f0-aaa40ee432c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.097887\n",
       "1    1.053069\n",
       "2    1.038906\n",
       "3    1.030784\n",
       "4    1.024634\n",
       "5    1.015963\n",
       "6    1.012754\n",
       "7    1.009122\n",
       "Name: cost_normal, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cost['cost_normal'] = df_cost['cost']/100000000\n",
    "df_cost['cost_normal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a96d3660-b7e7-406a-b7bd-c46db8b5683d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    if (outputDiv.id !== \"altair-viz-021484f3d3ec47be863f118aaa0bc50a\") {\n",
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       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
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       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cost['text_x'] = 4\n",
    "df_cost['text_y'] = 1.07\n",
    "\n",
    "elbow_plot = (\n",
    "        alt.Chart(df_cost)\n",
    "        .mark_line()\n",
    "        .encode(\n",
    "            x = alt.X(\"cluster\", title=\"Number of Clusters\"),\n",
    "            y=alt.Y(\"cost_normal\", title=\"Score\",scale = alt.Scale(domain=[1,1.15])\n",
    "                   ),\n",
    "            color=alt.value('#fc9272'),\n",
    "            tooltip=[alt.Tooltip('cost_normal')]\n",
    "        )\n",
    "    ).properties(width=600,title={\n",
    "      \"text\": [\"What is the optimal number of clusters?\"], \n",
    "      \"subtitle\": [\"Elbow Method - Topic Modelling\"]\n",
    "    },).interactive()\n",
    "\n",
    "elbow_plot_point = (\n",
    "        alt.Chart(df_cost)\n",
    "        .mark_point()\n",
    "        .encode(\n",
    "            x = alt.X(\"cluster\", title=\"Number of Clusters\",scale = alt.Scale(domain=[1.9,9.5])),\n",
    "            y=alt.Y(\"cost_normal\", title=\"Score\",scale = alt.Scale(domain=[1,1.15])\n",
    "                   ),\n",
    "            color=alt.value('#fc9272'),\n",
    "            tooltip=[alt.Tooltip('cost_normal')]\n",
    "        )\n",
    "    ).properties(width=600,title={\n",
    "      \"text\": [\"What is the optimal number of clusters?\"], \n",
    "      \"subtitle\": [\"Elbow Method - Topic Modelling\"]\n",
    "    },).interactive()\n",
    "\n",
    "\n",
    "text = alt.Chart(df_cost).mark_text(opacity = 0.1,size =20,\n",
    "    text='Elbow at k = 3', angle=0\n",
    ").encode(\n",
    "    x='text_x', y='text_y'\n",
    ")\n",
    "\n",
    "elbow_plot = alt.layer(\n",
    "    elbow_plot,elbow_plot_point,text#,rect,rect2,text2,text\n",
    ").configure_axis(\n",
    "    grid=False\n",
    ").configure_view(\n",
    "    strokeWidth=0\n",
    ")\n",
    "\n",
    "elbow_plot.save(\"plots/elbow_plot.html\")\n",
    "elbow_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9630b8-c2f9-42d3-981a-e19ee8d5dc43",
   "metadata": {},
   "source": [
    "Making the model with k = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3665b26-97d2-4696-97e5-fcc5164e3073",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22/04/29 20:28:07 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS\n",
      "22/04/29 20:28:07 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS\n",
      "[Stage 59:======================================================> (48 + 1) / 49]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette with squared euclidean distance = 0.5102599222082395\n",
      "Within Set Sum of Squared Errors = 55916169.73063542\n",
      "Cluster Centers: \n",
      "[1.67530499e-01 4.71509038e-02 1.21871499e+00 1.44044209e-01\n",
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      " 7.64462570e-03 9.78450064e-03 9.00724534e-03 7.35000523e-03\n",
      " 1.02555057e-02 1.04861625e-02 9.18169166e-03 3.53156897e-03\n",
      " 1.38413469e-02 7.47793254e-03 4.41155377e-03 7.93730787e-03]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Trains a 4 k-means model.\n",
    "kmeans = KMeans().setK(4).setSeed(100).setFeaturesCol('features')\n",
    "model = kmeans.fit(vectorized_tokens)\n",
    "# Make predictions\n",
    "predictions = model.transform(vectorized_tokens)\n",
    "# Evaluate clustering by computing Silhouette score\n",
    "evaluator = ClusteringEvaluator()\n",
    "silhouette = evaluator.evaluate(predictions)\n",
    "print(\"Silhouette with squared euclidean distance = \" + str(silhouette))\n",
    "# Evaluate clustering.\n",
    "cost = model.summary.trainingCost\n",
    "#cost = model.computeCost(dataset)\n",
    "print(\"Within Set Sum of Squared Errors = \" + str(cost))\n",
    "# Shows the result.\n",
    "print(\"Cluster Centers: \")\n",
    "ctr=[]\n",
    "centers = model.clusterCenters()\n",
    "for center in centers:\n",
    "    ctr.append(center)\n",
    "print(center)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "706bc4a8-b615-413b-8af7-64c3ed2cc370",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 115:=====================================================> (48 + 1) / 49]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette with squared euclidean distance = 0.7362034977520407\n",
      "Within Set Sum of Squared Errors = 56660952.02521935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Trains a 3 k-means model.\n",
    "kmeans = KMeans().setK(3).setSeed(100).setFeaturesCol('features')\n",
    "model = kmeans.fit(vectorized_tokens)\n",
    "# Make predictions\n",
    "predictions = model.transform(vectorized_tokens)\n",
    "# Evaluate clustering by computing Silhouette score\n",
    "evaluator = ClusteringEvaluator()\n",
    "silhouette = evaluator.evaluate(predictions)\n",
    "print(\"Silhouette with squared euclidean distance = \" + str(silhouette))\n",
    "# Evaluate clustering.\n",
    "cost = model.summary.trainingCost\n",
    "#cost = model.computeCost(dataset)\n",
    "print(\"Within Set Sum of Squared Errors = \" + str(cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d9469d2-ea6a-4265-bf32-b35b13860143",
   "metadata": {},
   "source": [
    "Silhoutte Score comes out to be 73.6% for K =3, which is not that bad. 0 here represents that the clusters could not be seprataed at all and 100% means that maximum separation. Given the type and amount of data, 74% seems like a great result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b2cfa772-ad3e-401b-ae00-47e2235d851a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- document: string (nullable = true)\n",
      " |-- sarcasm_class: string (nullable = true)\n",
      " |-- sarcasm_value: double (nullable = true)\n",
      " |-- sentiment_class: string (nullable = true)\n",
      " |-- sentiment_value: double (nullable = true)\n",
      " |-- emotion_class: string (nullable = true)\n",
      " |-- score: long (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- Awards: string (nullable = true)\n",
      " |-- Jokes: string (nullable = true)\n",
      " |-- Covid: string (nullable = true)\n",
      " |-- created_date: string (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- reddit_age_post: integer (nullable = true)\n",
      " |-- is_award: integer (nullable = true)\n",
      " |-- document2: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- clean_token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = false)\n",
      " |    |    |-- end: integer (nullable = false)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = false)\n",
      " |-- final: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- features: vector (nullable = true)\n",
      " |-- prediction: integer (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b91fdb69-5072-42fd-bab7-502262eed968",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_summary = predictions.groupBy(\"prediction\",\"is_award\").agg(\\\n",
    "                                                              F.count(\"prediction\").alias('Count'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a88b96e3-3746-4b4c-abf6-5506740ce126",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "prediction_summary_df = prediction_summary.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "91e10d13-f29a-4e15-b74b-a840f5e0152b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6927551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>306951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prediction  is_award    Count\n",
       "0           1         1       23\n",
       "1           0         0  6927551\n",
       "2           0         1    12540\n",
       "3           1         0   125109\n",
       "4           2         0   306951\n",
       "5           2         1     1665"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a68a7c-a5b8-435d-97c4-342dc7a1de4f",
   "metadata": {},
   "source": [
    "Saving Predictions of S3 bucket "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cb08bcf3-03f4-4c66-94d4-6043db1c99dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "predictions.write.parquet(\"s3://aa2464-labdata2/predictions_clustering/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d10580b0-20fe-465d-b4e2-76f68e26fa00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "model.write().save('s3://aa2464-labdata2/clustering_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186fee07-6f45-4229-b6a1-bc905ea71a0b",
   "metadata": {},
   "source": [
    "#### LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "20f16375-9c06-4ff6-961d-51f73941237e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 160:=====================================================> (48 + 1) / 49]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lower bound on the log likelihood of the entire corpus: -234656330.5974096\n",
      "The upper bound on perplexity: 5.909958750971146\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.clustering import LDA\n",
    "lda = LDA(k=3, maxIter=10)\n",
    "model = lda.fit(vectorized_tokens)\n",
    "ll = model.logLikelihood(vectorized_tokens)\n",
    "lp = model.logPerplexity(vectorized_tokens)\n",
    "print(\"The lower bound on the log likelihood of the entire corpus: \" + str(ll))\n",
    "print(\"The upper bound on perplexity: \" + str(lp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a489dc3e-a38a-4374-956f-297b00c8436c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract vocabulary from CountVectorizer\n",
    "vocab = cv_model.vocabulary\n",
    "topics = model.describeTopics()   \n",
    "topics_rdd = topics.rdd\n",
    "topics_words = topics_rdd\\\n",
    "       .map(lambda row: row['termIndices'])\\\n",
    "       .map(lambda idx_list: [vocab[idx] for idx in idx_list])\\\n",
    "       .collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b10dc4ba-eba1-4d9f-839b-5c1ece106309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic1</th>\n",
       "      <th>topic2</th>\n",
       "      <th>topic3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>question</td>\n",
       "      <td>time</td>\n",
       "      <td>people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>answer</td>\n",
       "      <td>people</td>\n",
       "      <td>day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>action</td>\n",
       "      <td>lot</td>\n",
       "      <td>person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>feel</td>\n",
       "      <td>woman</td>\n",
       "      <td>life</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>people</td>\n",
       "      <td>feel</td>\n",
       "      <td>time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>reason</td>\n",
       "      <td>pretty</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>remove</td>\n",
       "      <td>yeah</td>\n",
       "      <td>talk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>perform</td>\n",
       "      <td>happen</td>\n",
       "      <td>money</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pay</td>\n",
       "      <td>word</td>\n",
       "      <td>job</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bot</td>\n",
       "      <td>bad</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     topic1  topic2  topic3\n",
       "0  question    time  people\n",
       "1    answer  people     day\n",
       "2    action     lot  person\n",
       "3      feel   woman    life\n",
       "4    people    feel    time\n",
       "5    reason  pretty    live\n",
       "6    remove    yeah    talk\n",
       "7   perform  happen   money\n",
       "8       pay    word     job\n",
       "9       bot     bad    food"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_topic =  {'topic1': topics_words[0], 'topic2': topics_words[1], 'topic3': topics_words[2]}\n",
    "words_topic_df = pd.DataFrame(data = words_topic)\n",
    "words_topic_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a10dea4a-a93d-4469-852c-eb5edac41d20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a0862d5e-6759-4f67-9240-732c2b59b30e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_dict = {'vocab': vocab}\n",
    "vocab_df = pd.DataFrame(vocab_dict) \n",
    "vocab_df.to_csv('data/topics_vocab.csv',index = False)\n",
    "words_topic_df.to_csv('data/words_topics.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "32642c23-b499-4c74-908b-e62a760b4887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+--------------------+--------------------+\n",
      "|topic|         termIndices|         termWeights|\n",
      "+-----+--------------------+--------------------+\n",
      "|    0|[1, 10, 17, 3, 0,...|[0.07827456922192...|\n",
      "|    1|[2, 0, 4, 23, 3, ...|[0.02832096689602...|\n",
      "|    2|[0, 5, 7, 6, 2, 1...|[0.05522007183642...|\n",
      "+-----+--------------------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "topics.show(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2242d303-98bc-4b80-b7f5-3290760915b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imageio import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6a99a208-fc57-4bba-86cf-892ddc35627b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = imread('plots/reddit_logo.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5a416a69-873a-49d0-a2ca-196e81e02d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "70713cd3-55d4-4ba3-af2a-137de027ad1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = Counter(vocab)\n",
    "wc = WordCloud(\n",
    "    max_words=1000,\n",
    "    mask=mask,\n",
    "    background_color='#E7E6E1').fit_words(freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a27e31c8-24f8-404e-9223-8a05026f482c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "annotation = '''The top 500 words that are important for topic modelling\n",
    "until April 2021. Adverbs, interjections, nouns and verbs were lemmatized.\n",
    "Proper names and \"I\" were upper-cased all other words lower-cased.\n",
    "Author: Avi Arora\n",
    "Data: reddit.com'''\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(16)\n",
    "\n",
    "plt.imshow(wc, interpolation='bilinear')\n",
    "plt.annotate(annotation, xy=(0, 60), fontsize=12)\n",
    "plt.axis('off')\n",
    "plt.savefig('plots/reddit-topic-wordcloud.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5231962d-213a-49e4-b490-61f64f70c2a8",
   "metadata": {},
   "source": [
    "## Business Question - 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f6e04d-1eee-44d3-af69-58eeed2cf26d",
   "metadata": {},
   "source": [
    "Predicting popularity of a comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96128b6e-4440-4f89-a2b6-91ee416e5e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "reddit_modeled = spark.read.parquet('s3://aa2464-labdata2/predictions_clustering/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a10e3c0-6423-4720-8491-b3a70585f2b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- document: string (nullable = true)\n",
      " |-- sarcasm_class: string (nullable = true)\n",
      " |-- sarcasm_value: double (nullable = true)\n",
      " |-- sentiment_class: string (nullable = true)\n",
      " |-- sentiment_value: double (nullable = true)\n",
      " |-- emotion_class: string (nullable = true)\n",
      " |-- score: long (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- Awards: string (nullable = true)\n",
      " |-- Jokes: string (nullable = true)\n",
      " |-- Covid: string (nullable = true)\n",
      " |-- created_date: string (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- reddit_age_post: integer (nullable = true)\n",
      " |-- is_award: integer (nullable = true)\n",
      " |-- document2: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = true)\n",
      " |    |    |-- end: integer (nullable = true)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = true)\n",
      " |-- token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = true)\n",
      " |    |    |-- end: integer (nullable = true)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = true)\n",
      " |-- clean_token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = true)\n",
      " |    |    |-- end: integer (nullable = true)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = true)\n",
      " |-- final: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- features: vector (nullable = true)\n",
      " |-- prediction: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reddit_modeled.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dd41ff81-deb2-4312-a667-8fc9d8f9293e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 39:================================================>       (43 + 4) / 50]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+\n",
      "|summary|             score|\n",
      "+-------+------------------+\n",
      "|  count|           7373839|\n",
      "|   mean| 6.438911806997685|\n",
      "| stddev|119.37104052051218|\n",
      "|    min|              -629|\n",
      "|    max|             26483|\n",
      "+-------+------------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "## summary statistics of a column (numeric column)\n",
    "reddit_modeled.select('score').describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d4d159c-bc03-4a43-85e7-05aa2fe30da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-------+\n",
      "|sarcasm_class|  Count|\n",
      "+-------------+-------+\n",
      "|         null|      0|\n",
      "|      sarcasm| 715520|\n",
      "|       normal|6584329|\n",
      "+-------------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reddit_modeled.groupBy(\"sarcasm_class\").agg(F.count(\"sarcasm_class\").alias('Count')).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "52c17fee-8482-4823-9227-ab270e578523",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keeping the rows with only Sarcasm and Normal as sentiment\n",
    "reddit_modeled = reddit_modeled.filter((reddit_modeled.sarcasm_class == 'sarcasm') | (reddit_modeled.sarcasm_class == 'normal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ed847132-158b-448d-964f-47771b8000bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 68:===========================================>              (3 + 1) / 4]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-------+\n",
      "|emotion_class|  Count|\n",
      "+-------------+-------+\n",
      "|          joy|1748782|\n",
      "|         fear|3087079|\n",
      "|      sadness|1148227|\n",
      "|     surprise|1315761|\n",
      "+-------------+-------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "reddit_modeled.groupBy(\"emotion_class\").agg(F.count(\"sarcasm_class\").alias('Count')).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6343761a-1388-4f49-91f8-8772934487ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keeping the rows with only Sarcasm and Normal as sentiment\n",
    "reddit_modeled = reddit_modeled.filter((reddit_modeled.emotion_class == 'joy') | (reddit_modeled.emotion_class == 'fear') | (reddit_modeled.emotion_class == 'sadness') | (reddit_modeled.emotion_class == 'surprise'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ce8f3a60-76fc-4027-8556-fd1aca22b65c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 81:======================================================> (49 + 1) / 50]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+-------+\n",
      "|sentiment_class|  Count|\n",
      "+---------------+-------+\n",
      "|       positive|4737765|\n",
      "|        neutral| 329456|\n",
      "|       negative|2232628|\n",
      "+---------------+-------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "reddit_modeled.groupBy(\"sentiment_class\").agg(F.count(\"sentiment_class\").alias('Count')).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "602b476f-6629-4709-b921-a90050035b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keeping the rows with only Sarcasm and Normal as sentiment\n",
    "reddit_modeled = reddit_modeled.filter((reddit_modeled.sentiment_class == 'negative') | (reddit_modeled.sentiment_class == 'neutral') | (reddit_modeled.sentiment_class == 'positive'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafbd440-e3e1-4529-b259-1803ec28fde0",
   "metadata": {},
   "source": [
    "#### Making target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b6ab032-3bf4-4976-a215-dd0668a88318",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 9:=======================================================> (49 + 1) / 50]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+\n",
      "|75th|\n",
      "+----+\n",
      "| 3.0|\n",
      "+----+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Checking 75th percentile\n",
    "reddit_modeled.agg(F.expr('percentile(score, array(0.75))')[0].alias(\"75th\")).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc6d5794-eaa0-4818-accb-64f0ba915019",
   "metadata": {},
   "source": [
    "We define that every comment with a score of 3+ is a popular comment. This can also be seen as a classificaiton of comments that are liked more and the ones that are disliked more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "238f2ca4-0a4e-4c92-8606-ada9bbe1fac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import when\n",
    "reddit_modeled = reddit_modeled.withColumn(\"is_popular\", when(reddit_modeled.score >= 3,\"Yes\")\\\n",
    "                                 .when(reddit_modeled.score < 3,\"No\")\\\n",
    "                                 .otherwise(\"No\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f06b428f-873c-40f2-9e25-d2eeff7768db",
   "metadata": {},
   "outputs": [],
   "source": [
    "balance_target = reddit_modeled.groupBy(\"is_popular\").agg(F.count(\"is_popular\").alias('Count'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2f5057fb-205d-4634-a7d4-2a27c4baa100",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "balance_target_df = balance_target.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6abe8f82-a0b9-4c11-a014-42e961bd9c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_popular</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1833549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>5466300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  is_popular    Count\n",
       "0        Yes  1833549\n",
       "1         No  5466300"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance_target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd80c31f-4a04-4a4a-ac6c-22785a42838f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-56914059f78e4a3394747bc9b1d6fcaa\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-56914059f78e4a3394747bc9b1d6fcaa\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-56914059f78e4a3394747bc9b1d6fcaa\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}, \"axis\": {\"grid\": false}}, \"layer\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"#fc9272\"}, \"tooltip\": [{\"field\": \"Count\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"is_popular\", \"title\": \"Is a Popular comment?\", \"type\": \"nominal\"}, \"y\": {\"field\": \"Count\", \"title\": \"Count of comments\", \"type\": \"quantitative\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"title\": {\"text\": [\"What is the balance of target variable?\"], \"subtitle\": [\"Count of comments for popular and not popular comments\"]}, \"width\": 600}, {\"mark\": {\"type\": \"text\", \"angle\": 0, \"opacity\": 1, \"size\": 13, \"text\": \"Target Variable is imbalanced towards No\"}, \"encoding\": {\"x\": {\"field\": \"text_x\", \"type\": \"nominal\"}, \"y\": {\"field\": \"text_y\", \"type\": \"quantitative\"}}}], \"data\": {\"name\": \"data-528e341487d64528161b4c08c319cdca\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-528e341487d64528161b4c08c319cdca\": [{\"is_popular\": \"Yes\", \"Count\": 1833549, \"text_x\": \"Yes\", \"text_y\": 4000000}, {\"is_popular\": \"No\", \"Count\": 5466300, \"text_x\": \"Yes\", \"text_y\": 4000000}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance_target_df['text_x'] = \"Yes\"\n",
    "balance_target_df['text_y'] = 4000000\n",
    "\n",
    "balance_plot = (\n",
    "        alt.Chart(balance_target_df)\n",
    "        .mark_bar()\n",
    "        .encode(\n",
    "            y = alt.X(\"Count\", title=\"Count of comments\"),\n",
    "            x=alt.Y(\"is_popular\", title=\"Is a Popular comment?\"#,scale = alt.Scale(domain=[1,1.15])\n",
    "                   ),\n",
    "            color=alt.value('#fc9272'),\n",
    "            tooltip=[alt.Tooltip('Count')]\n",
    "        )\n",
    "    ).properties(width=600,title={\n",
    "      \"text\": [\"What is the balance of target variable?\"], \n",
    "      \"subtitle\": [\"Count of comments for popular and not popular comments\"]\n",
    "    },).interactive()\n",
    "\n",
    "\n",
    "\n",
    "text = alt.Chart(balance_target_df).mark_text(opacity = 1,size =13,\n",
    "    text='Target Variable is imbalanced towards No', angle=0\n",
    ").encode(\n",
    "    x='text_x', y='text_y'\n",
    ")\n",
    "\n",
    "balance_plot = alt.layer(\n",
    "    balance_plot,text#,elbow_plot_point,#,rect,rect2,text2,text\n",
    ").configure_axis(\n",
    "    grid=False\n",
    ").configure_view(\n",
    "    strokeWidth=0\n",
    ")\n",
    "\n",
    "balance_plot.save(\"plots/ml/balance_plot.html\")\n",
    "balance_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41608526-df58-457b-9a6e-cd50efb6ed75",
   "metadata": {},
   "source": [
    "We can see that our data is not balanced, so we will have to do downsampling to balance our dataset before moving on to our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49285ca2-6fec-46df-ad10-42c4c9244ea4",
   "metadata": {},
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d426be8-c054-4bb8-b9e8-fc77d087bd35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1db897c-3aa0-4bdc-9359-ea6ecb0c5dee",
   "metadata": {},
   "source": [
    "Cos and Sin have been taken of time variables to retain their cyclic nature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e911d13-4c32-44ab-aec5-78775b358b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"hour_post\", F.hour(\"created_date\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "849c9e99-3f15-4e34-a7b8-53c33b9a01f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"hour_cos\",F.cos((2 * math.pi * reddit_modeled[\"hour_post\"]) / 24))\n",
    "reddit_modeled = reddit_modeled.withColumn(\"hour_sin\",F.sin((2 * math.pi * reddit_modeled[\"hour_post\"]) / 24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d40d043f-cfe3-4bb8-90d0-6b77c46ff5d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"month_post\", F.month(\"created_date\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5fc5a19e-c384-4a5b-ab58-7dbfad9cc14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"month_cos\",F.cos((2 * math.pi * reddit_modeled[\"month_post\"]) / 12))\n",
    "reddit_modeled = reddit_modeled.withColumn(\"month_sin\",F.sin((2 * math.pi * reddit_modeled[\"month_post\"]) / 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "74f00ab1-bb55-49b9-bfc7-5504ea3c4a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"year_post\", F.year(\"created_date\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d27621e3-fcaf-4294-bc07-a8326243d377",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"day_week\", F.dayofweek(\"created_date\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "66aa67b9-5e7d-454e-a85e-cf892894cd5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"day_week_cos\",F.cos((2 * math.pi * reddit_modeled[\"day_week\"]) / 7))\n",
    "reddit_modeled = reddit_modeled.withColumn(\"day_week_sin\",F.sin((2 * math.pi * reddit_modeled[\"day_week\"]) / 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "188dcfd4-df05-4d62-a40c-254f31aae7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"day_month\", F.dayofmonth(\"created_date\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c0c77e65-02ab-4f6b-89e7-8f8d0f0e1fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_modeled = reddit_modeled.withColumn(\"day_month_cos\",F.cos((2 * math.pi * reddit_modeled[\"day_month\"]) / 31))\n",
    "reddit_modeled = reddit_modeled.withColumn(\"day_month_sin\",F.sin((2 * math.pi * reddit_modeled[\"day_month\"]) / 31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "26c64ba9-4da6-44ac-aa26-b6fd521973c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- document: string (nullable = true)\n",
      " |-- sarcasm_class: string (nullable = true)\n",
      " |-- sarcasm_value: double (nullable = true)\n",
      " |-- sentiment_class: string (nullable = true)\n",
      " |-- sentiment_value: double (nullable = true)\n",
      " |-- emotion_class: string (nullable = true)\n",
      " |-- score: long (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- Awards: string (nullable = true)\n",
      " |-- Jokes: string (nullable = true)\n",
      " |-- Covid: string (nullable = true)\n",
      " |-- created_date: string (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- reddit_age_post: integer (nullable = true)\n",
      " |-- is_award: integer (nullable = true)\n",
      " |-- document2: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = true)\n",
      " |    |    |-- end: integer (nullable = true)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = true)\n",
      " |-- token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = true)\n",
      " |    |    |-- end: integer (nullable = true)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = true)\n",
      " |-- clean_token: array (nullable = true)\n",
      " |    |-- element: struct (containsNull = true)\n",
      " |    |    |-- annotatorType: string (nullable = true)\n",
      " |    |    |-- begin: integer (nullable = true)\n",
      " |    |    |-- end: integer (nullable = true)\n",
      " |    |    |-- result: string (nullable = true)\n",
      " |    |    |-- metadata: map (nullable = true)\n",
      " |    |    |    |-- key: string\n",
      " |    |    |    |-- value: string (valueContainsNull = true)\n",
      " |    |    |-- embeddings: array (nullable = true)\n",
      " |    |    |    |-- element: float (containsNull = true)\n",
      " |-- final: array (nullable = true)\n",
      " |    |-- element: string (containsNull = true)\n",
      " |-- features: vector (nullable = true)\n",
      " |-- prediction: integer (nullable = true)\n",
      " |-- is_popular: string (nullable = false)\n",
      " |-- hour_post: integer (nullable = true)\n",
      " |-- hour_cos: double (nullable = true)\n",
      " |-- hour_sin: double (nullable = true)\n",
      " |-- month_post: integer (nullable = true)\n",
      " |-- month_cos: double (nullable = true)\n",
      " |-- month_sin: double (nullable = true)\n",
      " |-- year_post: integer (nullable = true)\n",
      " |-- day_week: integer (nullable = true)\n",
      " |-- day_week_cos: double (nullable = true)\n",
      " |-- day_week_sin: double (nullable = true)\n",
      " |-- day_month: integer (nullable = true)\n",
      " |-- day_month_cos: double (nullable = true)\n",
      " |-- day_month_sin: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reddit_modeled.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d2f1f16-b78f-421a-b846-a45b5c50878f",
   "metadata": {},
   "source": [
    "#### Balancing target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "70a7be38-bd6d-417e-9ee7-ec99cc0cf849",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22/05/04 00:19:26 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#Downsamping as controversiality is imbalanced\n",
    "major_df = reddit_modeled.filter(F.col(\"is_popular\") == \"No\")\n",
    "minor_df = reddit_modeled.filter(F.col(\"is_popular\") == \"Yes\")\n",
    "ratio = int(major_df.count()/minor_df.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "380bf142-cac6-43a9-ac8b-07eda18cecb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_majority_df = major_df.sample(False, 1/3.2)\n",
    "downsampled_df = sampled_majority_df.unionAll(minor_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f1690409-6aac-4726-86bc-3746fa88c075",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------+\n",
      "|is_popular|  count|\n",
      "+----------+-------+\n",
      "|       Yes|1833549|\n",
      "|        No|1707139|\n",
      "+----------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "downsampled_df.groupBy('is_popular').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "30c71702-2aab-46fa-a15f-ecd51855f82c",
   "metadata": {},
   "outputs": [],
   "source": [
    "downsampled_df2 = downsampled_df.select(['sarcasm_class', \\\n",
    "                                        'sentiment_class', \\\n",
    "                                        'emotion_class', \\\n",
    "                                        'gilded', \\\n",
    "                                        'controversiality',\\\n",
    "                                        'is_award',\\\n",
    "                                        'Jokes',\\\n",
    "                                        'Covid', \\\n",
    "                                        'is_weekend',\\\n",
    "                                        'reddit_age_post',\\\n",
    "                                        'prediction',\\\n",
    "                                       'is_popular',\\\n",
    "                                       'hour_cos',\\\n",
    "                                        'hour_sin',\\\n",
    "                                        'month_cos',\\\n",
    "                                        'month_sin',\\\n",
    "                                        'day_week_cos',\\\n",
    "                                        'day_week_sin',\\\n",
    "                                        'day_month_cos',\\\n",
    "                                        'day_month_sin',\\\n",
    "                                        'year_post',\\\n",
    "                                        ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "86c25a6e-66e6-40b7-ad67-8bd4489494a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "null_values_s = downsampled_df2.select(*[\n",
    "    (\n",
    "        F.count(F.when((F.isnan(c) | F.col(c).isNull()), c)) if t not in (\"boolean\",\"timestamp\", \"date\")\n",
    "        else F.count(F.when(F.col(c).isNull(), c))\n",
    "    ).alias(c)\n",
    "    for c, t in downsampled_df2.dtypes if c in downsampled_df2.columns\n",
    "]).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e0e73f58-ab05-4ef6-94e3-cb13c97e6ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "null_values = pd.melt(null_values_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6d88eb63-c253-4002-8d37-2c8df6635a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sarcasm_class</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sentiment_class</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>emotion_class</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gilded</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>controversiality</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>is_award</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jokes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Covid</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>is_weekend</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>reddit_age_post</td>\n",
       "      <td>927493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prediction</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>is_popular</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hour_cos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hour_sin</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>month_cos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>month_sin</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>day_week_cos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>day_week_sin</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>day_month_cos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>day_month_sin</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>year_post</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            variable   value\n",
       "0      sarcasm_class       0\n",
       "1    sentiment_class       0\n",
       "2      emotion_class       0\n",
       "3             gilded       0\n",
       "4   controversiality       0\n",
       "5           is_award       0\n",
       "6              Jokes       0\n",
       "7              Covid       0\n",
       "8         is_weekend       0\n",
       "9    reddit_age_post  927493\n",
       "10        prediction       0\n",
       "11        is_popular       0\n",
       "12          hour_cos       0\n",
       "13          hour_sin       0\n",
       "14         month_cos       0\n",
       "15         month_sin       0\n",
       "16      day_week_cos       0\n",
       "17      day_week_sin       0\n",
       "18     day_month_cos       0\n",
       "19     day_month_sin       0\n",
       "20         year_post       0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c4ef28-b1ef-4148-ae91-e4d793907efa",
   "metadata": {},
   "source": [
    "We see that reddit age has a lot of null values, although it is very important variable, but so many nnull values will spoil our model. Thus we drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4c076a4b-f5f8-43d0-ad50-26b325284d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "downsampled_df3 = downsampled_df2.drop(\"reddit_age_post\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "103ec2c6-f13e-47d4-a420-c030a83fdb39",
   "metadata": {},
   "outputs": [],
   "source": [
    "downsampled_df3 = downsampled_df3.withColumn(\"is_joke\", when(reddit_modeled.Jokes == \"\",0)\\\n",
    "                                 .otherwise(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a0acc1d9-2fb1-4059-8920-14ceec3f0a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "downsampled_df3 = downsampled_df3.withColumn(\"is_covid\", when(reddit_modeled.Covid == \"\",0)\\\n",
    "                                 .otherwise(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bcefef-9ad3-4105-af5a-decaa648840a",
   "metadata": {},
   "source": [
    "#### Feature Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "85ed2245-e8d6-4ff3-8cd4-b038b9136343",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------+\n",
      "|is_popular|  Count|\n",
      "+----------+-------+\n",
      "|       Yes|1833549|\n",
      "|        No|1707139|\n",
      "+----------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "downsampled_df3.groupBy(\"is_popular\").agg(F.count(\"is_popular\").alias('Count')).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "35ebd131-c1af-4abd-abf9-06964462d741",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import OneHotEncoder, StringIndexer, IndexToString, VectorAssembler\n",
    "from pyspark.ml.classification import RandomForestClassifier, GBTClassifier, LogisticRegression, LinearSVC,DecisionTreeClassifier\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator, MulticlassClassificationEvaluator, RegressionEvaluator\n",
    "from pyspark.ml import Pipeline, Model\n",
    "from pyspark.ml.pipeline import PipelineModel\n",
    "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
    "from pyspark.mllib.evaluation import BinaryClassificationMetrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a75f7686-5b77-4e11-8dfd-5f047e970136",
   "metadata": {},
   "outputs": [],
   "source": [
    "downsampled_df3 = downsampled_df3.drop(\"Jokes\")\n",
    "downsampled_df3 = downsampled_df3.drop(\"Covid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dfb274a0-e7d0-4da7-ad2f-2b11bb5e3d0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "downsampled_df3 = downsampled_df3.withColumnRenamed(\"prediction\",\"topic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dff0e67e-27d7-4beb-b85d-2d926924a2ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sarcasm_class: string (nullable = true)\n",
      " |-- sentiment_class: string (nullable = true)\n",
      " |-- emotion_class: string (nullable = true)\n",
      " |-- gilded: long (nullable = true)\n",
      " |-- controversiality: long (nullable = true)\n",
      " |-- is_award: integer (nullable = true)\n",
      " |-- is_weekend: integer (nullable = true)\n",
      " |-- topic: integer (nullable = true)\n",
      " |-- is_popular: string (nullable = false)\n",
      " |-- hour_cos: double (nullable = true)\n",
      " |-- hour_sin: double (nullable = true)\n",
      " |-- month_cos: double (nullable = true)\n",
      " |-- month_sin: double (nullable = true)\n",
      " |-- day_week_cos: double (nullable = true)\n",
      " |-- day_week_sin: double (nullable = true)\n",
      " |-- day_month_cos: double (nullable = true)\n",
      " |-- day_month_sin: double (nullable = true)\n",
      " |-- year_post: integer (nullable = true)\n",
      " |-- is_joke: integer (nullable = false)\n",
      " |-- is_covid: integer (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "downsampled_df3.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ba0531b9-6e4c-4919-8e63-a42f9c902d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data into train test and predict for our creating the model\n",
    "train_data, test_data = downsampled_df3.randomSplit([0.8, 0.2], 2464)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7a200b0b-ddc3-494e-aaa0-3612027d81a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 42:======================================================>(99 + 1) / 100]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------+\n",
      "|is_popular|  Count|\n",
      "+----------+-------+\n",
      "|       Yes|1467417|\n",
      "|        No|1365966|\n",
      "+----------+-------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "train_data.groupBy(\"is_popular\").agg(F.count(\"is_popular\").alias('Count')).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "15956dce-633d-4a81-904e-e34bc6573d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 5961:====================================================>(99 + 1) / 100]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+------+\n",
      "|is_popular| Count|\n",
      "+----------+------+\n",
      "|       Yes|366132|\n",
      "|        No|545326|\n",
      "+----------+------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "test_data.groupBy(\"is_popular\").agg(F.count(\"is_popular\").alias('Count')).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ea1d1178-fa45-46ee-8cb2-db56f7302ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "##StringIndexer for fitting in our pipeline for all of the string data into indices \n",
    "stringIndexer_sarcasm_class = StringIndexer(inputCol=\"sarcasm_class\", outputCol=\"sarcasm_class_idx\")\n",
    "stringIndexer_sentiment_class = StringIndexer(inputCol=\"sentiment_class\", outputCol=\"sentiment_class_idx\")\n",
    "stringIndexer_emotion_class = StringIndexer(inputCol=\"emotion_class\", outputCol=\"emotion_class_idx\")\n",
    "stringIndexer_popular = StringIndexer(inputCol=\"is_popular\", outputCol=\"is_popular_idx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ae59be18-0964-4ddc-bd43-c7a70da8aeea",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Hot encoding for categorical values having more tha 2 categories\n",
    "onehot_sentiment = OneHotEncoder(inputCol=\"sentiment_class_idx\", outputCol=\"sentiment_class_vec\")\n",
    "onehot_emotion = OneHotEncoder(inputCol=\"emotion_class_idx\", outputCol=\"emotion_class_vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ad3c8a6d-8c31-429a-815e-411b6a02a54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Picking features for models\n",
    "vectorAssembler_features = VectorAssembler(\n",
    "    inputCols=[\"sarcasm_class_idx\",\"sentiment_class_vec\",\"emotion_class_vec\",\\\n",
    "               \"gilded\",\"controversiality\",\"is_award\",\"is_weekend\",\"topic\",\\\n",
    "               \"hour_cos\",\"hour_sin\",\"month_cos\",\"month_sin\",\"day_week_cos\",\\\n",
    "               \"day_week_sin\",\"day_month_cos\",\"day_month_sin\",\"year_post\",\"is_joke\",\"is_covid\"], \n",
    "    outputCol= \"features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26e7835-e760-429c-8350-32f814cc5407",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b0b68f9b-4904-4996-af4b-9decfd228c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create initial LogisticRegression model\n",
    "lr = LogisticRegression(labelCol=\"is_popular_idx\", featuresCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d68d73f6-8cc0-4875-bcec-c9b6a16568f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Giving our predicted indices a label\n",
    "labelConverter = IndexToString(inputCol=\"prediction\", \n",
    "                               outputCol=\"predicted_popularity\", \n",
    "                               labels= [\"Popular\",\"Not Popular\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "63323333-c972-43c9-b214-7901ac4b93e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a pipeline\n",
    "pipeline_lr = Pipeline(stages=[stringIndexer_sarcasm_class, stringIndexer_sentiment_class,stringIndexer_emotion_class,stringIndexer_popular,\\\n",
    "                               onehot_sentiment,onehot_emotion,\\\n",
    "                               vectorAssembler_features, \\\n",
    "                               lr,labelConverter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3660f7f1-ff9b-460c-be34-a6899199d142",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create ParamGrid for Hyper parameter tuning\n",
    "lrparamGrid2 = ParamGridBuilder()\\\n",
    "             .addGrid(lr.regParam, [0.01, 0.1, 0.5])\\\n",
    "             .addGrid(lr.elasticNetParam, [0.25, 0.5, 0.75])\\\n",
    "             .addGrid(lr.maxIter, [5, 10, 20])\\\n",
    "             .build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "44ba6c9a-7825-4dde-9e03-aa3cd217c752",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate model\n",
    "lrevaluator2 = BinaryClassificationEvaluator().setLabelCol(\"is_popular_idx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c5e488d1-4e20-4516-9927-dafb866a50d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 5-fold CrossValidator\n",
    "lrcv2 = CrossValidator(estimator = pipeline_lr,\n",
    "                    estimatorParamMaps = lrparamGrid2,\n",
    "                    evaluator = lrevaluator2,\n",
    "                    numFolds = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "28ff3e37-95f6-482f-b679-451aa14c5d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22/05/04 00:24:14 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS\n",
      "22/05/04 00:24:14 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS\n",
      "22/05/04 00:24:16 WARN BlockManager: Asked to remove block broadcast_68, which does not exist\n",
      "22/05/04 00:36:19 WARN BlockManager: Asked to remove block broadcast_4124, which does not exist\n",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Run cross validations\n",
    "lrcvModel2 = lrcv2.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ba55958-e733-4f00-b09a-4fcd0c0120ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "72f54580-1fae-40ce-8319-5658132556d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model pipeline into s3\n",
    "lrcvModel2.write().save('s3://aa2464-labdata2/lrcvModel3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "51a6565d-85e1-4d6f-a9f9-ff16ca53d0ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use test set here so we can measure the accuracy of our model on new data\n",
    "lrprediction2 = lrcvModel2.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c7126f26-6589-4da8-85d0-3715e8802ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 6303:=================================================>    (44 + 4) / 48]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.523832\n",
      "Test Error = 0.476168\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# cvModel uses the best model found from the Cross Validation\n",
    "# Evaluate best model\n",
    "lr_acc2 = lrevaluator2.evaluate(lrprediction2)\n",
    "\n",
    "#Displaying the results\n",
    "print(\"Accuracy = %g\" % lr_acc2)\n",
    "print(\"Test Error = %g\" % (1.0 - lr_acc2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d3e1c2fc-c547-45e1-a5f8-0b93c6034080",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#Confusion Matrix \n",
    "#y_pred has the predicted labels\n",
    "y_pred_lr=lrprediction2.select(\"prediction\").collect()\n",
    "#y_orig are the actual labels \n",
    "y_orig_lr =lrprediction2.select(\"is_popular_idx\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f8537207-60fa-4aa7-b5b0-dbfb03d15d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "87537cf9-afd5-42a3-9ea4-3bb47cd0fa78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[360755   5377]\n",
      " [333401   7772]]\n"
     ]
    }
   ],
   "source": [
    "#Creating the confusion matrix \n",
    "cm = confusion_matrix(y_orig_lr, y_pred_lr)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2638d2b2-3490-4709-a0da-0133fc98f133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cm_lm = sns.heatmap(cm/np.sum(cm), annot=True,fmt='.2%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "89f80caf-dfab-462c-b4be-de47acc3268b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ExtractFeatureImp(featureImp, dataset, featuresCol):\n",
    "    list_extract = []\n",
    "    for i in dataset.schema['features'].metadata[\"ml_attr\"][\"attrs\"]:\n",
    "        list_extract = list_extract + dataset.schema['features'].metadata[\"ml_attr\"][\"attrs\"][i]\n",
    "    varlist = pd.DataFrame(list_extract)\n",
    "    varlist['score'] = varlist['idx'].apply(lambda x: featureImp[x])\n",
    "    return(varlist.sort_values('score', ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21fb140e-5edd-48ba-ad65-7a6bef69002e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "59c08118-ed50-46f5-a8e6-17baf46638fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance_gbt=ExtractFeatureImp(lrcvModel2.bestModel.stages[-2].coefficients, lrprediction2, \"features\").head(10)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "098cdacf-b4f1-4e63-8281-4c51af273277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idx</th>\n",
       "      <th>name</th>\n",
       "      <th>vals</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>controversiality</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.503633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21</td>\n",
       "      <td>is_covid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.368393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>hour_cos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>topic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>sentiment_class_vec_positive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>hour_sin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>year_post</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>day_month_cos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>emotion_class_vec_joy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>is_joke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    idx                          name vals     score\n",
       "1     7              controversiality  NaN  0.503633\n",
       "15   21                      is_covid  NaN  0.368393\n",
       "5    11                      hour_cos  NaN  0.029245\n",
       "4    10                         topic  NaN  0.011459\n",
       "17    1  sentiment_class_vec_positive  NaN  0.007478\n",
       "6    12                      hour_sin  NaN  0.002945\n",
       "13   19                     year_post  NaN  0.000011\n",
       "11   17                 day_month_cos  NaN  0.000000\n",
       "20    4         emotion_class_vec_joy  NaN  0.000000\n",
       "14   20                       is_joke  NaN  0.000000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_gbt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "62ab6659-7dc5-4922-ac7e-00112b19aae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-b088342d713c4e24b10133a90976c4ee\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-b088342d713c4e24b10133a90976c4ee\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-b088342d713c4e24b10133a90976c4ee\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-e85ded97c02edd366d174dd9a06daf2f\"}, \"mark\": {\"type\": \"bar\", \"color\": \"#B589D3\"}, \"encoding\": {\"x\": {\"field\": \"name\", \"sort\": [\"controversiality\", \"is_covid\", \"hour_cos\", \"topic\", \"sentiment_class_vec_positive\", \"hour_sin\", \"year_post\", \"day_month_cos\", \"emotion_class_vec_joy\", \"is_joke\"], \"title\": \"Feature Name\", \"type\": \"nominal\"}, \"y\": {\"field\": \"score\", \"title\": \"Importance Score\", \"type\": \"quantitative\"}}, \"height\": 300, \"title\": \"Feature Importance in Logistic Regression\", \"width\": 500, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-e85ded97c02edd366d174dd9a06daf2f\": [{\"idx\": 7, \"name\": \"controversiality\", \"vals\": null, \"score\": 0.50363303828394}, {\"idx\": 21, \"name\": \"is_covid\", \"vals\": null, \"score\": 0.3683933266501019}, {\"idx\": 11, \"name\": \"hour_cos\", \"vals\": null, \"score\": 0.029244583436378122}, {\"idx\": 10, \"name\": \"topic\", \"vals\": null, \"score\": 0.011458840698813132}, {\"idx\": 1, \"name\": \"sentiment_class_vec_positive\", \"vals\": null, \"score\": 0.007477863497930833}, {\"idx\": 12, \"name\": \"hour_sin\", \"vals\": null, \"score\": 0.0029448513160813573}, {\"idx\": 19, \"name\": \"year_post\", \"vals\": null, \"score\": 1.061838510758141e-05}, {\"idx\": 17, \"name\": \"day_month_cos\", \"vals\": null, \"score\": 0.0}, {\"idx\": 4, \"name\": \"emotion_class_vec_joy\", \"vals\": null, \"score\": 0.0}, {\"idx\": 20, \"name\": \"is_joke\", \"vals\": null, \"score\": 0.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def chart_imp_features(imp, model):\n",
    "    categoryNames= list(imp.name)\n",
    "    chart= alt.Chart(imp).mark_bar().encode(\n",
    "       x=alt.X(\"name\", title='Feature Name',sort=categoryNames),\n",
    "       y=alt.Y(\"score\", title='Importance Score')\n",
    "       ).properties(\n",
    "           title=\"Feature Importance in \"+ model, \n",
    "           height=300, width=500\n",
    "       )\n",
    "    return chart\n",
    "\n",
    "chart2_1= chart_imp_features(feature_importance_gbt,'Logistic Regression')\n",
    "chart2_1=chart2_1.mark_bar(color='#B589D3')\n",
    "chart2_1.save(\"plots/ml/fetaure_imp.html\")\n",
    "chart2_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "432ab9fb-73ec-4e7d-9756-5c2944b342b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f85102a-cea3-47fe-bb06-7c7a565da210",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "674087c5-6fce-44f5-9d2a-93b41aee8ac6",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "318c6619-f65e-4625-8360-cf68c4a44dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(maxDepth=3, labelCol=\"is_popular_idx\", leafCol=\"leafId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "01a85598-d5f7-4df2-ad51-cd7b89f9e81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a pipeline\n",
    "pipeline_dt = Pipeline(stages=[stringIndexer_sarcasm_class, stringIndexer_sentiment_class,stringIndexer_emotion_class,stringIndexer_popular,\\\n",
    "                               onehot_sentiment,onehot_emotion,\\\n",
    "                               vectorAssembler_features, \\\n",
    "                               dt,labelConverter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1f2dbfd7-b48b-4d8d-b410-fa53c59fecce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Fitting the pipeline for train data\n",
    "model_dt = pipeline_dt.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "857daff8-61b7-4254-9915-c165e54728de",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Transforming data for prediction\n",
    "prediction_train_dt  = model_dt.transform(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "87669cb3-9a34-44f8-a5c6-485e1c6517d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Predicting labels for test data\n",
    "predictions = model_dt.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2dcf6c83-c25a-44b9-be3e-9615a7bbcd76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Evaluating accuracy and test error for test data\n",
    "evaluatorDT = MulticlassClassificationEvaluator(labelCol=\"is_popular_idx\", predictionCol=\"prediction\", metricName=\"accuracy\")\n",
    "accuracy = evaluatorDT.evaluate(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eb99409e-48ae-4814-88f3-7ecbf619ff70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.530172\n",
      "Test Error = 0.469828\n"
     ]
    }
   ],
   "source": [
    "##Accuracy and Test Error for test_data \n",
    "print(\"Accuracy = %g\" % accuracy)\n",
    "print(\"Test Error = %g\" % (1.0 - accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ec745b3f-ddf3-4f68-92e8-e444c135feaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Creating a confusion matrix for our predictions\n",
    "#y_pred has the predicted labels\n",
    "y_pred=predictions.select(\"prediction\").collect()\n",
    "#y_orig are the actual labels \n",
    "y_orig=predictions.select(\"is_popular_idx\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2a866e23-9e13-47a0-a673-244ec3dad228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[364078   2054]\n",
      " [330258  10915]]\n"
     ]
    }
   ],
   "source": [
    "##Creating the confusion matrix \n",
    "cm = confusion_matrix(y_orig, y_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e97b1786-37d5-4152-bb9b-e5d3c662b123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cm_lm = sns.heatmap(cm/np.sum(cm), annot=True,fmt='.2%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeb1c1ec-606a-4ccf-ac68-a6b20d797867",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daeb8e79-28ba-430a-8959-48543a08a801",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0674fb9-f013-4ec9-bb05-19dfbcc8dbc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fb965bc5-a4ff-49f1-be90-736a9705ee07",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "e2b5056d-b058-4f4f-aae8-46edd2b59e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##intialising random forest classifier\n",
    "rf = RandomForestClassifier(labelCol=\"is_popular_idx\", featuresCol=\"features\", numTrees=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "e69d6862-0121-47ae-8b89-234fe278b3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a pipeline\n",
    "pipeline_rf = Pipeline(stages=[stringIndexer_sarcasm_class, stringIndexer_sentiment_class,stringIndexer_emotion_class,stringIndexer_popular,\\\n",
    "                               onehot_sentiment,onehot_emotion,\\\n",
    "                               vectorAssembler_features, \\\n",
    "                               rf,labelConverter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "0e491660-62eb-42a2-be1b-c702bcceb00a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Fitting the pipeline for train data\n",
    "model_rf = pipeline_rf.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "223f5554-1b16-48f7-8811-f4ffa6c42f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Transforming data for prediction\n",
    "prediction_train  = model_rf.transform(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "394b5b91-4251-40ef-ae24-8522e74a4056",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Predicting labels for test data\n",
    "predictions = model_rf.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "75f6d3e3-c991-498a-8e03-3110a9fb0382",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Evaluating accuracy and test error for test data\n",
    "evaluatorRF = MulticlassClassificationEvaluator(labelCol=\"is_popular_idx\", predictionCol=\"prediction\", metricName=\"accuracy\")\n",
    "accuracy = evaluatorRF.evaluate(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "2b4fd4a3-b99c-4517-a2f0-759e7add3463",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.600448\n",
      "Test Error = 0.399552\n"
     ]
    }
   ],
   "source": [
    "##Accuracy and Test Error for test_data \n",
    "print(\"Accuracy = %g\" % accuracy)\n",
    "print(\"Test Error = %g\" % (1.0 - accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3b971f3-aee1-4ef8-9855-518b400c03f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Changing Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "600369d4-f67b-4586-b607-5733471d9fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "##intialising random forest classifier\n",
    "rf = RandomForestClassifier(labelCol=\"is_popular_idx\", featuresCol=\"features\", numTrees=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "c5740344-852a-4383-af45-9e807f2df1c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a pipeline\n",
    "pipeline_rf = Pipeline(stages=[stringIndexer_sarcasm_class, stringIndexer_sentiment_class,stringIndexer_emotion_class,stringIndexer_popular,\\\n",
    "                               onehot_sentiment,onehot_emotion,\\\n",
    "                               vectorAssembler_features, \\\n",
    "                               rf,labelConverter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "b7fe3854-e9d4-4027-b785-0c85d15bfa95",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Fitting the pipeline for train data\n",
    "model_rf = pipeline_rf.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "f2fa3d0f-f0d1-4964-878f-b2ceffc7a1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Transforming data for prediction\n",
    "prediction_train  = model_rf.transform(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "3dccb355-5431-4624-9fbd-81b70b5a5361",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Predicting labels for test data\n",
    "predictions = model_rf.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "c5754909-daca-47ce-8039-b1a172f30e39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Evaluating accuracy and test error for test data\n",
    "evaluatorRF = MulticlassClassificationEvaluator(labelCol=\"is_popular_idx\", predictionCol=\"prediction\", metricName=\"accuracy\")\n",
    "accuracy = evaluatorRF.evaluate(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "3211f411-1126-4239-b724-c2d2b5edcc43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.600451\n",
      "Test Error = 0.399549\n"
     ]
    }
   ],
   "source": [
    "##Accuracy and Test Error for test_data \n",
    "print(\"Accuracy = %g\" % accuracy)\n",
    "print(\"Test Error = %g\" % (1.0 - accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "9f60b6eb-13a2-49da-9309-5b87a948bf57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "##Creating a confusion matrix for our predictions\n",
    "#y_pred has the predicted labels\n",
    "y_pred=predictions.select(\"prediction\").collect()\n",
    "#y_orig are the actual labels \n",
    "y_orig=predictions.select(\"is_popular_idx\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "5f9f192b-d295-4889-9d94-61159f2e32c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[544973    353]\n",
      " [363819   2313]]\n"
     ]
    }
   ],
   "source": [
    "##Creating the confusion matrix \n",
    "cm = confusion_matrix(y_orig, y_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "790d6f2b-afca-47de-a8a2-1e419b212995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cm_lm = sns.heatmap(cm/np.sum(cm), annot=True,fmt='.2%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88405fc4-6ed9-4520-b74f-ff110c4d69d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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