Prior_Probabilities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import s3fs\n",
    "from smart_open import open\n",
    "import boto3\n",
    "from io import StringIO # python3; python2: BytesIO \n",
    "from boto3.s3.transfer import TransferConfig\n",
    "import metrics\n",
    "import torch\n",
    "from transformers import *\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = [\"reply_timestamp\", \"retweet_timestamp\", \"retweet_with_comment_timestamp\", \"like_timestamp\"]\n",
    "train = pd.read_csv(open(\"s3://recsys-challenge-2020/training.tsv\"), encoding=\"utf-8\",\n",
    "                    names = all_features, usecols= [20, 21, 22, 23], sep=\"\\x01\"\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_not_reply = train.reply_timestamp.isnull().sum()/len(train)\n",
    "prior_reply = 1.0  - prior_not_reply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02744620947359211\n"
     ]
    }
   ],
   "source": [
    "print(prior_reply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_not_retweet = train.retweet_timestamp.isnull().sum()/len(train)\n",
    "prior_retweet = 1.0  - prior_not_retweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11308992797296735\n"
     ]
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   ],
   "source": [
    "print(prior_retweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_not_retweet_with_comment = train.retweet_with_comment_timestamp.isnull().sum()/len(train)\n",
    "prior_retweet_with_comment = 1.0  - prior_not_retweet_with_comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007789621111397471\n"
     ]
    }
   ],
   "source": [
    "print(prior_retweet_with_comment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_not_like = train.like_timestamp.isnull().sum()/len(train)\n",
    "prior_like = 1.0  - prior_not_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4391793109932398\n"
     ]
    }
   ],
   "source": [
    "print(prior_like)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "reply_probabilities = [0.028]*15127684"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "retweet_probabilities = [0.113]*15127684"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "retweet_with_comment_probabilities = [0.008]*15127684"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "like_probabilities = [0.439]*15127684"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(reply_probabilities).to_csv('s3://recsys-challenge-2020/submissions/priors/reply.csv', index = False, header = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(retweet_probabilities).to_csv('s3://recsys-challenge-2020/submissions/priors/retweet.csv', index = False, header = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(retweet_with_comment_probabilities).to_csv('s3://recsys-challenge-2020/submissions/priors/retweet_with_comment.csv', index = False, header = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(like_probabilities).to_csv('s3://recsys-challenge-2020/submissions/priors/like.csv', index = False, header = False)"
   ]
  }
 ],
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