1036 lines
24 KiB
Plaintext
1036 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Ekstrakcja informacji </h1>\n",
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"<h2> 3. <i>tfidf (1)</i> [ćwiczenia]</h2> \n",
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"<h3> Jakub Pokrywka (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Zajęcia 3\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import re"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## zbiór dokumentów"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"documents = ['Ala lubi zwierzęta i ma kota oraz psa!',\n",
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" 'Ola lubi zwierzęta oraz ma kota a także chomika!',\n",
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" 'I Jan jeździ na rowerze.',\n",
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" '2 wojna światowa była wielkim konfliktem zbrojnym',\n",
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" 'Tomek lubi psy, ma psa i jeździ na motorze i rowerze.',\n",
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" ]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### CZEGO CHCEMY?\n",
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"- chcemy zamienić teksty na zbiór słów\n",
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"\n",
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"\n",
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"### PYTANIE\n",
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"- czy możemy ztokenizować tekst np. documents.split(' ') jakie wystąpią wtedy problemy?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ODPOWIEDŹ\n",
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"- lepiej użyć preprocessingu i dopiero później tokenizacji"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## preprocessing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_str_cleaned(str_dirty):\n",
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" punctuation = '!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'\n",
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" new_str = str_dirty.lower()\n",
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" new_str = re.sub(' +', ' ', new_str)\n",
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" for char in punctuation:\n",
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" new_str = new_str.replace(char,'')\n",
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" return new_str\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_document = get_str_cleaned(documents[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'ala lubi zwierzęta i ma kota oraz psa'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sample_document"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## tokenizacja"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def tokenize_str(document):\n",
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" return document.split(' ')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['ala', 'lubi', 'zwierzęta', 'i', 'ma', 'kota', 'oraz', 'psa']"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenize_str(sample_document)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"documents_cleaned = [get_str_cleaned(d) for d in documents]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['ala lubi zwierzęta i ma kota oraz psa',\n",
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" 'ola lubi zwierzęta oraz ma kota a także chomika',\n",
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" 'i jan jeździ na rowerze',\n",
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" '2 wojna światowa była wielkim konfliktem zbrojnym',\n",
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" 'tomek lubi psy ma psa i jeździ na motorze i rowerze']"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"documents_cleaned"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"documents_tokenized = [tokenize_str(d) for d in documents_cleaned]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[['ala', 'lubi', 'zwierzęta', 'i', 'ma', 'kota', 'oraz', 'psa'],\n",
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" ['ola', 'lubi', 'zwierzęta', 'oraz', 'ma', 'kota', 'a', 'także', 'chomika'],\n",
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" ['i', 'jan', 'jeździ', 'na', 'rowerze'],\n",
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" ['2', 'wojna', 'światowa', 'była', 'wielkim', 'konfliktem', 'zbrojnym'],\n",
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" ['tomek',\n",
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" 'lubi',\n",
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" 'psy',\n",
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" 'ma',\n",
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" 'psa',\n",
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" 'i',\n",
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" 'jeździ',\n",
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" 'na',\n",
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" 'motorze',\n",
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" 'i',\n",
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" 'rowerze']]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"documents_tokenized"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PYTANIA\n",
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"- jaki jest następny krok w celu stworzenia wektórów TF lub TF-IDF\n",
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"- jakie wielkości będzie wektor TF lub TF-IDF?\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"vocabulary = []\n",
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"for document in documents_tokenized:\n",
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" for word in document:\n",
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" vocabulary.append(word)\n",
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"vocabulary = sorted(set(vocabulary))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['2',\n",
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" 'a',\n",
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" 'ala',\n",
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" 'była',\n",
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" 'chomika',\n",
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" 'i',\n",
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" 'jan',\n",
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" 'jeździ',\n",
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" 'konfliktem',\n",
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" 'kota',\n",
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" 'lubi',\n",
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" 'ma',\n",
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" 'motorze',\n",
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" 'na',\n",
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" 'ola',\n",
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" 'oraz',\n",
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" 'psa',\n",
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" 'psy',\n",
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" 'rowerze',\n",
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" 'także',\n",
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" 'tomek',\n",
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" 'wielkim',\n",
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" 'wojna',\n",
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" 'zbrojnym',\n",
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" 'zwierzęta',\n",
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" 'światowa']"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vocabulary"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ZADANIE 1 stworzyć funkcję word_to_index(word:str), funkcja ma zwarać one-hot vector w postaciu numpy array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"def word_to_index(word):\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"word_to_index('psa')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ZADANIE 2 NAPISAC FUNKCJĘ, która bierze listę słów i zamienia na wetktor TF\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"def tf(document):\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"array([0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 1., 0.])"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tf(documents_tokenized[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"documents_vectorized = list()\n",
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"for document in documents_tokenized:\n",
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" document_vector = tf(document)\n",
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" documents_vectorized.append(document_vector)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"[array([0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 1., 0.]),\n",
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" array([0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0.,\n",
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" 0., 0., 1., 0., 0., 0., 0., 1., 0.]),\n",
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" array([0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
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" 0., 1., 0., 0., 0., 0., 0., 0., 0.]),\n",
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" array([1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 1., 1., 1., 0., 1.]),\n",
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" array([0., 0., 0., 0., 0., 2., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 1.,\n",
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" 1., 1., 0., 1., 0., 0., 0., 0., 0.])]"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"documents_vectorized"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### IDF"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wersja bez żadnej normalizacji\n",
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"\n",
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"\n",
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"$idf_i = \\Large\\frac{|D|}{|\\{d : t_i \\in d \\}|}$\n",
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"\n",
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"\n",
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"$|D|$ - ilość dokumentów w korpusie\n",
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"$|\\{d : t_i \\in d \\}|$ - ilość dokumentów w korpusie, gdzie dany term występuje chociaż jeden raz"
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
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{
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|
"data": {
|
|
"text/plain": [
|
|
"array([5. , 5. , 5. , 5. , 5. ,\n",
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" 1.66666667, 5. , 2.5 , 5. , 2.5 ,\n",
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" 1.66666667, 1.66666667, 5. , 2.5 , 5. ,\n",
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" 2.5 , 2.5 , 5. , 2.5 , 5. ,\n",
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" 5. , 5. , 5. , 5. , 2.5 ,\n",
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" 5. ])"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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|
}
|
|
],
|
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"source": [
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"idf = np.zeros(len(vocabulary))\n",
|
|
"idf = len(documents_vectorized) / np.sum(np.array(documents_vectorized) != 0,axis=0)\n",
|
|
"display(idf)"
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
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|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for i in range(len(documents_vectorized)):\n",
|
|
" documents_vectorized[i] = documents_vectorized[i] * idf"
|
|
]
|
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### ZADANIE 3 Napisać funkcję similarity, która zwraca podobieństwo kosinusowe między dwoma dokumentami w postaci zwektoryzowanej"
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]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def similarity(query, document):\n",
|
|
" pass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ala lubi zwierzęta i ma kota oraz psa!'"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"documents[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 1.,\n",
|
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" 0., 0., 0., 0., 0., 0., 0., 1., 0.])"
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"documents_vectorized[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ola lubi zwierzęta oraz ma kota a także chomika!'"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"documents[1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0.,\n",
|
|
" 0., 0., 1., 0., 0., 0., 0., 1., 0.])"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"documents_vectorized[1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.5892556509887895"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"similarity(documents_vectorized[0],documents_vectorized[1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def transform_query(query):\n",
|
|
" query_vector = tf(tokenize_str(get_str_cleaned(query)))\n",
|
|
" return query_vector"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
|
|
" 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"transform_query('psa')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.4999999999999999"
|
|
]
|
|
},
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"similarity(transform_query('psa kota'), documents_vectorized[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ala lubi zwierzęta i ma kota oraz psa!'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.4999999999999999"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ola lubi zwierzęta oraz ma kota a także chomika!'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.2357022603955158"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'I Jan jeździ na rowerze.'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'2 wojna światowa była wielkim konfliktem zbrojnym'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tomek lubi psy, ma psa i jeździ na motorze i rowerze.'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.19611613513818402"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# tak są obsługiwane 2 słowa\n",
|
|
"query = 'psa kota'\n",
|
|
"for i in range(len(documents)):\n",
|
|
" display(documents[i])\n",
|
|
" display(similarity(transform_query(query), documents_vectorized[i]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ala lubi zwierzęta i ma kota oraz psa!'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ola lubi zwierzęta oraz ma kota a także chomika!'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'I Jan jeździ na rowerze.'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.4472135954999579"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'2 wojna światowa była wielkim konfliktem zbrojnym'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tomek lubi psy, ma psa i jeździ na motorze i rowerze.'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.2773500981126146"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# dlatego potrzebujemy mianownik w cosine similarity\n",
|
|
"# dłuższe dokumenty, w który raz wystąpie słowo rower są gorzej punktowane od\n",
|
|
"# krótszych. Jeżeli słowo rower wystąpiło w bardzo krótki dokumencie, to znaczy\n",
|
|
"# że jest większe prawdopodobieństwo że dokument jest o rowerze\n",
|
|
"query = 'rowerze'\n",
|
|
"for i in range(len(documents)):\n",
|
|
" display(documents[i])\n",
|
|
" display(similarity(transform_query(query), documents_vectorized[i]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ala lubi zwierzęta i ma kota oraz psa!'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.35355339059327373"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Ola lubi zwierzęta oraz ma kota a także chomika!'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'I Jan jeździ na rowerze.'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.4472135954999579"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'2 wojna światowa była wielkim konfliktem zbrojnym'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tomek lubi psy, ma psa i jeździ na motorze i rowerze.'"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0.5547001962252291"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# dlatego potrzebujemy term frequency → wiecej wystąpień słowa w dokumencie\n",
|
|
"# znaczy bardziej dopasowany dokument\n",
|
|
"query = 'i'\n",
|
|
"for i in range(len(documents)):\n",
|
|
" display(documents[i])\n",
|
|
" display(similarity(transform_query(query), documents_vectorized[i]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'documents' 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<ipython-input-3-ca637083c8f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# słowo chomik ma większą wagę od i, ponieważ występuje w mniejszej ilości dokumentów\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[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'i chomika'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m)\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 5\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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[1;32m 6\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimilarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransform_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocuments_vectorized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\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[0;31mNameError\u001b[0m: name 'documents' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# dlatego IDF - żeby ważniejsze słowa miał większą wagę\n",
|
|
"# słowo chomik ma większą wagę od i, ponieważ występuje w mniejszej ilości dokumentów\n",
|
|
"query = 'i chomika'\n",
|
|
"for i in range(len(documents)):\n",
|
|
" display(documents[i])\n",
|
|
" display(similarity(transform_query(query), documents_vectorized[i]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Uwaga\n",
|
|
"Powyższe przykłady pokazują score dokuemntu. Aby zrobić wyszukiwarkę, powinniśmy posortować te dokumenty po score (od największego) i zaprezentwoać w tej kolejności."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"author": "Jakub Pokrywka",
|
|
"email": "kubapok@wmi.amu.edu.pl",
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"lang": "pl",
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.3"
|
|
},
|
|
"subtitle": "3.tfidf (1)[ćwiczenia]",
|
|
"title": "Ekstrakcja informacji",
|
|
"year": "2021"
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|