GettingTheSimilarFilmsUsingSynopses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import gensim\n",
    "import operator\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv(r'test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "synopses=list(data[\"ملخص\"])\n",
    "names=list(data[\"اسم الفيلم\"])\n",
    "s=[]\n",
    "n=[]\n",
    "for i in range(len(synopses)):\n",
    "    if synopses[i]!=\"ملخص\":\n",
    "        s.append(synopses[i])\n",
    "        n.append(names[i])\n",
    "synopses=s\n",
    "names=n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.index(\"حب وجنون\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 23.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def tagged_document(synopses):\n",
    "    for i in range(len(synopses)):\n",
    "        yield gensim.models.doc2vec.TaggedDocument(synopses[i].split(\" \"),[i])\n",
    "\n",
    "data_for_training = list(tagged_document(synopses))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gensim.models.doc2vec.Doc2Vec(vector_size=40, min_count=2, epochs=30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.build_vocab(data_for_training)\n",
    "model.train(data_for_training, total_examples=model.corpus_count, epochs=model.epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarFilms(movieName):\n",
    "    movieIndex=names.index(movieName)\n",
    "    dic={}\n",
    "    vec1=model.dv[movieIndex]\n",
    "    for i in range(len(names)):\n",
    "        if i!=movieIndex:\n",
    "            vec2=model.dv[i]\n",
    "            dic[i]=TS_SS(vec1,vec2)\n",
    "    dic=dict(sorted(dic.items(), key=operator.itemgetter(1),reverse=False))\n",
    "    dic=list(dic)\n",
    "\n",
    "    answer=[]\n",
    "    answer.append(names[dic[0]])\n",
    "    answer.append(names[dic[1]])\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def Cosine(vec1, vec2) :\n",
    "    result = InnerProduct(vec1,vec2) / (VectorSize(vec1) * VectorSize(vec2))\n",
    "    return result\n",
    "\n",
    "def VectorSize(vec) :\n",
    "    return math.sqrt(sum(math.pow(v,2) for v in vec))\n",
    "\n",
    "def InnerProduct(vec1, vec2) :\n",
    "    return sum(v1*v2 for v1,v2 in zip(vec1,vec2))\n",
    "\n",
    "def Euclidean(vec1, vec2) :\n",
    "    return math.sqrt(sum(math.pow((v1-v2),2) for v1,v2 in zip(vec1, vec2)))\n",
    "\n",
    "def Theta(vec1, vec2) :\n",
    "    return math.acos(Cosine(vec1,vec2)) + math.radians(10)\n",
    "\n",
    "def Triangle(vec1, vec2) :\n",
    "    theta = math.radians(Theta(vec1,vec2))\n",
    "    return (VectorSize(vec1) * VectorSize(vec2) * math.sin(theta)) / 2\n",
    "\n",
    "def Magnitude_Difference(vec1, vec2) :\n",
    "    return abs(VectorSize(vec1) - VectorSize(vec2))\n",
    "\n",
    "def Sector(vec1, vec2) :\n",
    "    ED = Euclidean(vec1, vec2)\n",
    "    MD = Magnitude_Difference(vec1, vec2)\n",
    "    theta = Theta(vec1, vec2)\n",
    "    return math.pi * math.pow((ED+MD),2) * theta/360\n",
    "\n",
    "def TS_SS(vec1, vec2) :\n",
    "    return Triangle(vec1, vec2) * Sector(vec1, vec2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['خد الجميل', 'معبودة الجماهير']"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarFilms(\"حب وجنون\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['المتمرد', 'صقر العرب']"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarFilms(\"ابن الشيطان\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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