{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "import numpy as np\n",
    "import faiss\n",
    "from sklearn.metrics import ndcg_score, dcg_score, average_precision_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sprawy organizacyjne- zaliczenie\n",
    "\n",
    "dodatkowy mini projekt/zadanie domowe? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_relevance = np.asarray([[10, 2, 0, 1, 5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.asarray([[9, 5, 2, 1, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "idealny- 9,2,5\n",
    "\n",
    "\n",
    "nasz- 10,2,0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dla p = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "CG =  10 + 2 + 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log2(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "DCG = 10 / np.log2(2) + 2 / np.log2(3) +  0 / np.log2(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.261859507142916"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DCG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.261859507142916"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcg_score(true_relevance, scores, k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "IDCG = 10 / np.log2(2) + 5 / np.log2(3) +  2 / np.log2(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.154648767857287"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IDCG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7956297391650307"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DCG / IDCG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7956297391650307"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndcg_score(true_relevance, scores, k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pytanie:\n",
    "jak to się odnosi do praktyki?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ZADANIE\n",
    "\n",
    "policz ręcznie CG, DCG, nDCG i sprawdź czy zgadza się to z scikit-learn:\n",
    "dla k = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRUE =     np.asarray([[1,0,1,0,0,0,0,1,0,1,0,0,0,0,1]])\n",
    "PREDICTED = np.asarray([[15,14,13,12,11,10,9,8,7,6,5,4,3,2,1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wyszukiwarka TFIDF - jakie są plusy i minusy?\n",
    "\n",
    "W jaki sposób można zrobić lepszą wyszukiwarkę (wykorzystując transformery lub inne modele neuronowe)?\n",
    "Jakie są potencjalne zalety i wady takiego podejścia?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "sentences = [\"Hello World\", \"Hallo Welt\"]\n",
    "\n",
    "model = SentenceTransformer('LaBSE')\n",
    "embeddings = model.encode(sentences)\n",
    "print(embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie \n",
    "1. zainstaluj faiss i zrób tutorial: https://github.com/facebookresearch\n",
    "2. wczytaj treści artykułów z BBC News Train.csv\n",
    "3. Użyj któregoś z transformerów (możesz użyć biblioteki sentence-transformers) do stworzenia embeddingów dokumentów\n",
    "4. wczytaj embeddingi do bazy danych faiss\n",
    "5. wyszukaj query 'consumer electronics market'"
   ]
  }
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