936 lines
22 KiB
Plaintext
936 lines
22 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> 6. <i>Klasyfikacja</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 klasyfikacja"
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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 kleister"
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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 pathlib\n",
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"from collections import Counter\n",
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"from sklearn.metrics import *"
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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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"KLEISTER_PATH = pathlib.Path('/home/kuba/kleister-nda')"
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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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"### Pytanie\n",
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"\n",
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"Czy jurysdykcja musi być zapisana explicite w umowie?"
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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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"source": [
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"def get_expected_jurisdiction(filepath):\n",
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" dataset_expected_jurisdiction = []\n",
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" with open(filepath,'r') as train_expected_file:\n",
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" for line in train_expected_file:\n",
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" key_values = line.rstrip('\\n').split(' ')\n",
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" jurisdiction = None\n",
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" for key_value in key_values:\n",
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" key, value = key_value.split('=')\n",
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" if key == 'jurisdiction':\n",
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" jurisdiction = value\n",
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" if jurisdiction is None:\n",
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" jurisdiction = 'NONE'\n",
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" dataset_expected_jurisdiction.append(jurisdiction)\n",
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" return dataset_expected_jurisdiction"
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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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"train_expected_jurisdiction = get_expected_jurisdiction(KLEISTER_PATH/'train'/'expected.tsv')"
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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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"source": [
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"dev_expected_jurisdiction = get_expected_jurisdiction(KLEISTER_PATH/'dev-0'/'expected.tsv')"
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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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{
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"data": {
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"text/plain": [
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"254"
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]
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},
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"execution_count": 10,
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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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"len(train_expected_jurisdiction)"
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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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"False"
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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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"'NONE' in train_expected_jurisdiction"
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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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{
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"data": {
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"text/plain": [
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"31"
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]
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},
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"execution_count": 12,
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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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"len(set(train_expected_jurisdiction))"
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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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"### Czy wszystkie stany muszą występować w zbiorze trenującym w zbiorze kleister?\n",
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"\n",
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"https://en.wikipedia.org/wiki/U.S._state\n",
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"\n",
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"### Jaki jest baseline?"
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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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"source": [
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"train_counter = Counter(train_expected_jurisdiction)"
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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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{
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"data": {
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"text/plain": [
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"[('New_York', 43),\n",
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" ('Delaware', 39),\n",
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" ('California', 32),\n",
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" ('Massachusetts', 15),\n",
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" ('Texas', 13),\n",
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" ('Illinois', 10),\n",
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" ('Oregon', 9),\n",
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" ('Florida', 9),\n",
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" ('Pennsylvania', 9),\n",
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" ('Missouri', 9),\n",
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" ('Ohio', 8),\n",
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" ('New_Jersey', 7),\n",
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" ('Georgia', 6),\n",
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" ('Indiana', 5),\n",
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" ('Nevada', 5),\n",
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" ('Colorado', 4),\n",
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" ('Virginia', 4),\n",
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" ('Washington', 4),\n",
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" ('Michigan', 3),\n",
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" ('Minnesota', 3),\n",
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" ('Connecticut', 2),\n",
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" ('Wisconsin', 2),\n",
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" ('Maine', 2),\n",
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" ('North_Carolina', 2),\n",
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" ('Kansas', 2),\n",
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" ('Utah', 2),\n",
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" ('Iowa', 1),\n",
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" ('Idaho', 1),\n",
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" ('South_Dakota', 1),\n",
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" ('South_Carolina', 1),\n",
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" ('Rhode_Island', 1)]"
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]
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},
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"execution_count": 14,
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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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"train_counter.most_common(100)"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"most_common_answer = train_counter.most_common(100)[0][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": 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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"'New_York'"
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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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"most_common_answer"
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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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"dev_predictions_jurisdiction = [most_common_answer] * len(dev_expected_jurisdiction)"
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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": 18,
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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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"['New_York',\n",
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" 'New_York',\n",
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" 'Delaware',\n",
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" 'Massachusetts',\n",
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" 'Delaware',\n",
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" 'Washington',\n",
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" 'Delaware',\n",
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" 'New_Jersey',\n",
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" 'New_York',\n",
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" 'NONE',\n",
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" 'NONE',\n",
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" 'Delaware',\n",
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" 'Delaware',\n",
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" 'Delaware',\n",
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" 'New_York',\n",
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" 'Massachusetts',\n",
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" 'Minnesota',\n",
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" 'California',\n",
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" 'New_York',\n",
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" 'California',\n",
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" 'Iowa',\n",
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" 'California',\n",
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" 'Virginia',\n",
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" 'North_Carolina',\n",
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" 'Arizona',\n",
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" 'Indiana',\n",
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" 'New_Jersey',\n",
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" 'California',\n",
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" 'Delaware',\n",
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" 'Georgia',\n",
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" 'New_York',\n",
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" 'New_York',\n",
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" 'California',\n",
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" 'Minnesota',\n",
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" 'California',\n",
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" 'Kentucky',\n",
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" 'Minnesota',\n",
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" 'Ohio',\n",
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" 'Michigan',\n",
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" 'California',\n",
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" 'Minnesota',\n",
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" 'California',\n",
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" 'Delaware',\n",
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" 'Illinois',\n",
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" 'Minnesota',\n",
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" 'Texas',\n",
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" 'New_Jersey',\n",
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" 'Delaware',\n",
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" 'Washington',\n",
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" 'NONE',\n",
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" 'Delaware',\n",
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" 'Oregon',\n",
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" 'Delaware',\n",
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" 'Delaware',\n",
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" 'Delaware',\n",
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" 'Massachusetts',\n",
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" 'California',\n",
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" 'NONE',\n",
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" 'Delaware',\n",
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" 'Illinois',\n",
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" 'Idaho',\n",
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" 'Washington',\n",
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" 'New_York',\n",
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" 'New_York',\n",
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" 'California',\n",
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" 'Utah',\n",
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" 'Delaware',\n",
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" 'Washington',\n",
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" 'Virginia',\n",
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" 'New_York',\n",
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" 'New_York',\n",
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" 'Illinois',\n",
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" 'California',\n",
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" 'Delaware',\n",
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" 'NONE',\n",
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" 'Texas',\n",
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" 'California',\n",
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" 'Washington',\n",
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" 'Delaware',\n",
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" 'Washington',\n",
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" 'New_York',\n",
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" 'Washington',\n",
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" 'Illinois']"
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]
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},
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"execution_count": 18,
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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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"dev_expected_jurisdiction"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"accuracy: 0.14457831325301204\n"
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]
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}
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],
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"source": [
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"counter = 0 \n",
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"for pred, exp in zip(dev_predictions_jurisdiction, dev_expected_jurisdiction):\n",
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" if pred == exp:\n",
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" counter +=1\n",
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"print('accuracy: ', counter/len(dev_predictions_jurisdiction))"
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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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{
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"data": {
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"text/plain": [
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"0.14457831325301204"
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]
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},
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"execution_count": 20,
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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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"accuracy_score(dev_predictions_jurisdiction, dev_expected_jurisdiction)"
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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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"### Co jeżeli nazwy klas nie występują explicite w zbiorach?"
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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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"https://git.wmi.amu.edu.pl/kubapok/paranormal-or-skeptic-ISI-public\n",
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" \n",
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"https://git.wmi.amu.edu.pl/kubapok/sport-text-classification-ball-ISI-public"
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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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"SPORT_PATH='/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia6_klasyfikacja/repos/sport-text-classification-ball'\n",
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"\n",
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"SPORT_TRAIN=$SPORT_PATH/train/train.tsv.gz\n",
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" \n",
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"SPORT_DEV_EXP=$SPORT_PATH/dev-0/expected.tsv"
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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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"### jaki jest baseline dla sport classification ball?\n"
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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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"zcat $SPORT_TRAIN | awk '{print $1}' | wc -l"
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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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"zcat $SPORT_TRAIN | awk '{print $1}' | grep 1 | wc -l"
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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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"cat $SPORT_DEV_EXP | wc -l\n",
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"\n",
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"grep 1 $SPORT_DEV_EXP | wc -l"
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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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"### Sprytne podejście do klasyfikacji tekstu? Naiwny bayess"
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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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|
"outputs": [],
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"source": [
|
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"from sklearn.datasets import fetch_20newsgroups\n",
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"# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
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"\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"import numpy as np\n",
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"import sklearn.metrics\n",
|
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"import gensim"
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]
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},
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{
|
|
"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"newsgroups = fetch_20newsgroups()"
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]
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},
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{
|
|
"cell_type": "code",
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|
"execution_count": 23,
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"metadata": {},
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|
"outputs": [],
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"source": [
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"newsgroups_text = newsgroups['data']"
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
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|
"metadata": {},
|
|
"outputs": [],
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|
"source": [
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"newsgroups_text_tokenized = [list(set(gensim.utils.tokenize(x, lowercase = True))) for x in newsgroups_text]"
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|
]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
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|
"metadata": {},
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|
"outputs": [
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{
|
|
"name": "stdout",
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|
"output_type": "stream",
|
|
"text": [
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"From: lerxst@wam.umd.edu (where's my thing)\n",
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"Subject: WHAT car is this!?\n",
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"Nntp-Posting-Host: rac3.wam.umd.edu\n",
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"Organization: University of Maryland, College Park\n",
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"Lines: 15\n",
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"\n",
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" I was wondering if anyone out there could enlighten me on this car I saw\n",
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"the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
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"early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
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"the front bumper was separate from the rest of the body. This is \n",
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"all I know. If anyone can tellme a model name, engine specs, years\n",
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"of production, where this car is made, history, or whatever info you\n",
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"have on this funky looking car, please e-mail.\n",
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"\n",
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"Thanks,\n",
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"- IL\n",
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" ---- brought to you by your neighborhood Lerxst ----\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(newsgroups_text[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['s', 'day', 'was', 'it', 'know', 'is', 'where', 'nntp', 'on', 'body', 'i', 'my', 'il', 'wam', 'maryland', 'model', 'history', 'could', 'really', 'host', 'all', 'subject', 'wondering', 'brought', 'umd', 'edu', 'posting', 'funky', 'bumper', 'rac', 'saw', 'the', 'lines', 'what', 'doors', 'enlighten', 'early', 'out', 'thanks', 'bricklin', 'lerxst', 'front', 'were', 'production', 'other', 'neighborhood', 'late', 'please', 'to', 'rest', 'university', 'park', 'addition', 'can', 'by', 'car', 'whatever', 'tellme', 'anyone', 'sports', 'organization', 'me', 'mail', 'be', 'e', 'if', 'looking', 'years', 'door', 'in', 'separate', 'have', 'there', 'made', 'specs', 'thing', 'engine', 'info', 'you', 'of', 'college', 'small', 'or', 'your', 'called', 'name', 'from', 'a', 'this', 'looked']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(newsgroups_text_tokenized[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Y = newsgroups['target']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([7, 4, 4, ..., 3, 1, 8])"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Y_names = newsgroups['target_names']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['alt.atheism',\n",
|
|
" 'comp.graphics',\n",
|
|
" 'comp.os.ms-windows.misc',\n",
|
|
" 'comp.sys.ibm.pc.hardware',\n",
|
|
" 'comp.sys.mac.hardware',\n",
|
|
" 'comp.windows.x',\n",
|
|
" 'misc.forsale',\n",
|
|
" 'rec.autos',\n",
|
|
" 'rec.motorcycles',\n",
|
|
" 'rec.sport.baseball',\n",
|
|
" 'rec.sport.hockey',\n",
|
|
" 'sci.crypt',\n",
|
|
" 'sci.electronics',\n",
|
|
" 'sci.med',\n",
|
|
" 'sci.space',\n",
|
|
" 'soc.religion.christian',\n",
|
|
" 'talk.politics.guns',\n",
|
|
" 'talk.politics.mideast',\n",
|
|
" 'talk.politics.misc',\n",
|
|
" 'talk.religion.misc']"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Y_names"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'talk.politics.guns'"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Y_names[16]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"$P('talk.politics.guns' | 'gun')= ?$ \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n",
|
|
"$P(A|B) * P(A) = P(B) * P(B|A)$\n",
|
|
"\n",
|
|
"$P(A|B) = \\frac{P(B) * P(B|A)}{P(A)}$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"$P('talk.politics.guns' | 'gun') * P('gun') = P('gun'|'talk.politics.guns') * P('talk.politics.guns')$\n",
|
|
"\n",
|
|
"\n",
|
|
"$P('talk.politics.guns' | 'gun') = \\frac{P('gun'|'talk.politics.guns') * P('talk.politics.guns')}{P('gun')}$\n",
|
|
"\n",
|
|
"\n",
|
|
"$p1 = P('gun'|'talk.politics.guns')$\n",
|
|
"\n",
|
|
"\n",
|
|
"$p2 = P('talk.politics.guns')$\n",
|
|
"\n",
|
|
"\n",
|
|
"$p3 = P('gun')$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## obliczanie $p1 = P('gun'|'talk.politics.guns')$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# samodzielne wykonanie"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## obliczanie $p2 = P('talk.politics.guns')$\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# samodzielne wykonanie"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## obliczanie $p3 = P('gun')$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# samodzielne wykonanie"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## ostatecznie"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"(p1 * p2) / p3"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_prob(index ):\n",
|
|
" talks_topic = [x for x,y in zip(newsgroups_text_tokenized,Y) if y == index]\n",
|
|
"\n",
|
|
" len([x for x in talks_topic if 'gun' in x])\n",
|
|
"\n",
|
|
" if len(talks_topic) == 0:\n",
|
|
" return 0.0\n",
|
|
" p1 = len([x for x in talks_topic if 'gun' in x]) / len(talks_topic)\n",
|
|
" p2 = len(talks_topic) / len(Y)\n",
|
|
" p3 = len([x for x in newsgroups_text_tokenized if 'gun' in x]) / len(Y)\n",
|
|
"\n",
|
|
" if p3 == 0:\n",
|
|
" return 0.0\n",
|
|
" else: \n",
|
|
" return (p1 * p2)/ p3\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"probs = []\n",
|
|
"for i in range(len(Y_names)):\n",
|
|
" probs.append(get_prob(i))\n",
|
|
" print(\"%.5f\" % get_prob(i),'\\t\\t', Y_names[i])\n",
|
|
" \n",
|
|
"print(\"%.5f\" % sum(probs), '\\t\\tsuma',)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### zadanie samodzielne"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_prob2(index, word ):\n",
|
|
" pass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# listing dla get_prob2, słowo 'god'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## założenie naiwnego bayesa"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"$P(class | word1, word2, word3) = \\frac{P(word1, word2, word3|class) * P(class)}{P(word1, word2, word3)}$\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**przy założeniu o niezależności zmiennych losowych $word1$, $word2$, $word3$**:\n",
|
|
"\n",
|
|
"\n",
|
|
"$P(word1, word2, word3|class) = P(word1|class)* P(word2|class) * P(word3|class)$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**ostatecznie:**\n",
|
|
"\n",
|
|
"\n",
|
|
"$P(class | word1, word2, word3) = \\frac{P(word1|class)* P(word2|class) * P(word3|class) * P(class)}{\\sum_k{P(word1|class_k)* P(word2|class_k) * P(word3|class_k) * P(class_k)}}$\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## zadania domowe naiwny bayes1 ręcznie"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"- analogicznie zaimplementować funkcję get_prob3(index, document_tokenized), argument document_tokenized ma być zbiorem słów dokumentu. funkcja ma być naiwnym klasyfikatorem bayesowskim (w przypadku wielu słów)\n",
|
|
"- odpalić powyższy listing prawdopodobieństw z funkcją get_prob3 dla dokumentów: {'i','love','guns'} oraz {'is','there','life','after'\n",
|
|
",'death'}\n",
|
|
"- zadanie proszę zrobić w jupyterze, wygenerować pdf (kod + wyniki odpalenia) i umieścić go jako zadanie w teams\n",
|
|
"- termin 10.05, punktów: 40\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n",
|
|
"## zadania domowe naiwny bayes2 gotowa biblioteka"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"- wybrać jedno z poniższych repozytoriów i je sforkować:\n",
|
|
" - https://git.wmi.amu.edu.pl/kubapok/paranormal-or-skeptic-ISI-public\n",
|
|
" - https://git.wmi.amu.edu.pl/kubapok/sport-text-classification-ball-ISI-public\n",
|
|
"- stworzyć klasyfikator bazujący na naiwnym bayessie (może być gotowa biblioteka), może też korzystać z gotowych implementacji tfidf\n",
|
|
"- stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
|
|
"- wynik accuracy sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.67\n",
|
|
"- proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, zadanie oddajemy w gonito, termin 10.05, 40 punktów\n"
|
|
]
|
|
}
|
|
],
|
|
"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": "6.Klasyfikacja[ćwiczenia]",
|
|
"title": "Ekstrakcja informacji",
|
|
"year": "2021"
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|