add 05 06
This commit is contained in:
Jakub Pokrywka
parent
ccf8b8dfa7
commit
81d0d0928a
|
|
@ -0,0 +1,256 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"<div class=\"alert alert-block alert-info\">\n",
|
||||
"<h1> Ekstrakcja informacji </h1>\n",
|
||||
"<h2> 5. <i>Ekstrakcja informacji z dokumentów</i> [ćwiczenia]</h2> \n",
|
||||
"<h3> Jakub Pokrywka (2021)</h3>\n",
|
||||
"</div>\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Zajecia: ekstrakcja informacji z dokumentów "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Przedstawianie zadania z wyszukiwarek\n",
|
||||
"\n",
|
||||
"Osoby, które zrobiły zadanie- proszę o krótką prezentację na początku zajęć oraz wklikanie hand-in w zadaniu na MS TEAMS"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Wyzwanie Kleister NDA\n",
|
||||
"Proszę pobrać z gonito https://gonito.net/challenge/kleister-nda\n",
|
||||
"\n",
|
||||
"### Metryki w klasyfikacji"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$TP$ = TRUE POSITIVE\n",
|
||||
"\n",
|
||||
"$TN$ = TRUE NEGATIVE\n",
|
||||
"\n",
|
||||
"$FP$ = FALSE POSITIVE\n",
|
||||
"\n",
|
||||
"$FN$ = FALSE POSITIVE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"ACCURACY = $\\frac{TP + TN}{TP + TN + FP + FN}$\n",
|
||||
"\n",
|
||||
"PRECISION = $\\frac{TP}{TP + FP}$\n",
|
||||
"\n",
|
||||
"RECALL = $\\frac{TP}{TP + FN}$\n",
|
||||
"\n",
|
||||
"F1 = $2 \\times \\frac{PRECISION \\times RECALL}{PRECISION + RECALL}$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"true = [0] * 30 + [1] * 70"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predicted = [0] * 30 + [1] * 70"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"accuracy_score(true, predicted)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"precision_score(true, predicted)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"recall_score(true, predicted)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"f1_score(true, predicted)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Automatyczna ocena kleister NDA\n",
|
||||
"\n",
|
||||
"W katalogu głównym projektu: \n",
|
||||
"\n",
|
||||
"`wget https://gonito.net/get/bin/geval`\n",
|
||||
"\n",
|
||||
"`chmod u+x geval`\n",
|
||||
"\n",
|
||||
"`./geval -t dev-0` (dla zbioru deweloperskiego)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### ZADANIE DOMOWE KLEISTER NDA\n",
|
||||
"- stworzyć regułowy ekstraktor informacji (np. na podstawie wyrażeń regularnych)\n",
|
||||
"- wygenerować pliki `train/out.tsv`, `dev-0/out.tsv`, `test-A/out.tsv`\n",
|
||||
"- dodać do commita w swoim forku powyższe pliki wraz z ze skryptem (skryptami), który pomógł je wygenerować. Skrypty powinny byc w formie tekstowej (jeżeli to jupyter to proszę przekonwertować do zwykłego pliku .py, np jupyter nbconvert --to script a.ipynb)\n",
|
||||
"- wynik zaliczający zadanie to więcej niż 0.1 wg metryki F1 dla zbioru test-A\n",
|
||||
"- punkty za zadanie: 40, a dla 5 osób które osiągną najwyższy wyniki dostaną 70 punktów zamiast 40\n",
|
||||
"- zadanie oddajemy do 03.05 w gonito "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### ZADANIE DOMOWE Przygotuj wyzwanie dotyczące tekstu zgodne ze standardami Gonito\n",
|
||||
"\n",
|
||||
"Warunki konieczne:\n",
|
||||
"- wyzwanie musi spełniać standardy Gonito\n",
|
||||
"- w szczególności musi przechodzić walidację (`geval --validate --expected-directory .`)\n",
|
||||
"- wyzwanie musi być dostarczone jako repozytorium git\n",
|
||||
"- leży również dostarczyć osobne repozytorium ze skryptami tworzącymi wyzwanie\n",
|
||||
"- leży także dostarczyć jedno przykładowe rozwiązanie - baseline (może być prosty/\"głupi\")\n",
|
||||
"- cały zbiór danych musi mieć co najmniej 5 tys. elementów \n",
|
||||
"Punktacja:\n",
|
||||
"- bazowa liczba punktów za spełnienie - 80 punktów\n",
|
||||
"- +30 - interesujący zbiór danych\n",
|
||||
"- +30 - rozmiar zbioru\n",
|
||||
"- +10 - zbiór z ekstrakcji informacji (podobny do Kleister Charity/NDA)\n",
|
||||
"- maksimum: 150 punktów\n",
|
||||
"Uwaga: nie jest to wymaganie, ale zachęcamy do opracowywanie zbiorów danych potrzebnych do pracy magisterskiej.\n",
|
||||
"\n",
|
||||
"Termin 10 maj 2022 na zajęciach (proszę sie zgłosić do prowadzącego)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "5.ekEtrakcja informacji z dokumentCCow[ćwiczenia]",
|
||||
"title": "Ekstrakcja informacji",
|
||||
"year": "2021"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
|
@ -0,0 +1,935 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"<div class=\"alert alert-block alert-info\">\n",
|
||||
"<h1> Ekstrakcja informacji </h1>\n",
|
||||
"<h2> 6. <i>Klasyfikacja</i> [ćwiczenia]</h2> \n",
|
||||
"<h3> Jakub Pokrywka (2021)</h3>\n",
|
||||
"</div>\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Zajęcia klasyfikacja"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Zbiór kleister"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pathlib\n",
|
||||
"from collections import Counter\n",
|
||||
"from sklearn.metrics import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"KLEISTER_PATH = pathlib.Path('/home/kuba/kleister-nda')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Pytanie\n",
|
||||
"\n",
|
||||
"Czy jurysdykcja musi być zapisana explicite w umowie?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_expected_jurisdiction(filepath):\n",
|
||||
" dataset_expected_jurisdiction = []\n",
|
||||
" with open(filepath,'r') as train_expected_file:\n",
|
||||
" for line in train_expected_file:\n",
|
||||
" key_values = line.rstrip('\\n').split(' ')\n",
|
||||
" jurisdiction = None\n",
|
||||
" for key_value in key_values:\n",
|
||||
" key, value = key_value.split('=')\n",
|
||||
" if key == 'jurisdiction':\n",
|
||||
" jurisdiction = value\n",
|
||||
" if jurisdiction is None:\n",
|
||||
" jurisdiction = 'NONE'\n",
|
||||
" dataset_expected_jurisdiction.append(jurisdiction)\n",
|
||||
" return dataset_expected_jurisdiction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_expected_jurisdiction = get_expected_jurisdiction(KLEISTER_PATH/'train'/'expected.tsv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dev_expected_jurisdiction = get_expected_jurisdiction(KLEISTER_PATH/'dev-0'/'expected.tsv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"254"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(train_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"False"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"'NONE' in train_expected_jurisdiction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"31"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(set(train_expected_jurisdiction))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Czy wszystkie stany muszą występować w zbiorze trenującym w zbiorze kleister?\n",
|
||||
"\n",
|
||||
"https://en.wikipedia.org/wiki/U.S._state\n",
|
||||
"\n",
|
||||
"### Jaki jest baseline?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_counter = Counter(train_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[('New_York', 43),\n",
|
||||
" ('Delaware', 39),\n",
|
||||
" ('California', 32),\n",
|
||||
" ('Massachusetts', 15),\n",
|
||||
" ('Texas', 13),\n",
|
||||
" ('Illinois', 10),\n",
|
||||
" ('Oregon', 9),\n",
|
||||
" ('Florida', 9),\n",
|
||||
" ('Pennsylvania', 9),\n",
|
||||
" ('Missouri', 9),\n",
|
||||
" ('Ohio', 8),\n",
|
||||
" ('New_Jersey', 7),\n",
|
||||
" ('Georgia', 6),\n",
|
||||
" ('Indiana', 5),\n",
|
||||
" ('Nevada', 5),\n",
|
||||
" ('Colorado', 4),\n",
|
||||
" ('Virginia', 4),\n",
|
||||
" ('Washington', 4),\n",
|
||||
" ('Michigan', 3),\n",
|
||||
" ('Minnesota', 3),\n",
|
||||
" ('Connecticut', 2),\n",
|
||||
" ('Wisconsin', 2),\n",
|
||||
" ('Maine', 2),\n",
|
||||
" ('North_Carolina', 2),\n",
|
||||
" ('Kansas', 2),\n",
|
||||
" ('Utah', 2),\n",
|
||||
" ('Iowa', 1),\n",
|
||||
" ('Idaho', 1),\n",
|
||||
" ('South_Dakota', 1),\n",
|
||||
" ('South_Carolina', 1),\n",
|
||||
" ('Rhode_Island', 1)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"train_counter.most_common(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"most_common_answer = train_counter.most_common(100)[0][0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'New_York'"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"most_common_answer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dev_predictions_jurisdiction = [most_common_answer] * len(dev_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['New_York',\n",
|
||||
" 'New_York',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Massachusetts',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Washington',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'New_Jersey',\n",
|
||||
" 'New_York',\n",
|
||||
" 'NONE',\n",
|
||||
" 'NONE',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'New_York',\n",
|
||||
" 'Massachusetts',\n",
|
||||
" 'Minnesota',\n",
|
||||
" 'California',\n",
|
||||
" 'New_York',\n",
|
||||
" 'California',\n",
|
||||
" 'Iowa',\n",
|
||||
" 'California',\n",
|
||||
" 'Virginia',\n",
|
||||
" 'North_Carolina',\n",
|
||||
" 'Arizona',\n",
|
||||
" 'Indiana',\n",
|
||||
" 'New_Jersey',\n",
|
||||
" 'California',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Georgia',\n",
|
||||
" 'New_York',\n",
|
||||
" 'New_York',\n",
|
||||
" 'California',\n",
|
||||
" 'Minnesota',\n",
|
||||
" 'California',\n",
|
||||
" 'Kentucky',\n",
|
||||
" 'Minnesota',\n",
|
||||
" 'Ohio',\n",
|
||||
" 'Michigan',\n",
|
||||
" 'California',\n",
|
||||
" 'Minnesota',\n",
|
||||
" 'California',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Illinois',\n",
|
||||
" 'Minnesota',\n",
|
||||
" 'Texas',\n",
|
||||
" 'New_Jersey',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Washington',\n",
|
||||
" 'NONE',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Oregon',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Massachusetts',\n",
|
||||
" 'California',\n",
|
||||
" 'NONE',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Illinois',\n",
|
||||
" 'Idaho',\n",
|
||||
" 'Washington',\n",
|
||||
" 'New_York',\n",
|
||||
" 'New_York',\n",
|
||||
" 'California',\n",
|
||||
" 'Utah',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Washington',\n",
|
||||
" 'Virginia',\n",
|
||||
" 'New_York',\n",
|
||||
" 'New_York',\n",
|
||||
" 'Illinois',\n",
|
||||
" 'California',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'NONE',\n",
|
||||
" 'Texas',\n",
|
||||
" 'California',\n",
|
||||
" 'Washington',\n",
|
||||
" 'Delaware',\n",
|
||||
" 'Washington',\n",
|
||||
" 'New_York',\n",
|
||||
" 'Washington',\n",
|
||||
" 'Illinois']"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dev_expected_jurisdiction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"accuracy: 0.14457831325301204\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"counter = 0 \n",
|
||||
"for pred, exp in zip(dev_predictions_jurisdiction, dev_expected_jurisdiction):\n",
|
||||
" if pred == exp:\n",
|
||||
" counter +=1\n",
|
||||
"print('accuracy: ', counter/len(dev_predictions_jurisdiction))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.14457831325301204"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"accuracy_score(dev_predictions_jurisdiction, dev_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Co jeżeli nazwy klas nie występują explicite w zbiorach?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"https://git.wmi.amu.edu.pl/kubapok/paranormal-or-skeptic-ISI-public\n",
|
||||
" \n",
|
||||
"https://git.wmi.amu.edu.pl/kubapok/sport-text-classification-ball-ISI-public"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"SPORT_PATH='/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia6_klasyfikacja/repos/sport-text-classification-ball'\n",
|
||||
"\n",
|
||||
"SPORT_TRAIN=$SPORT_PATH/train/train.tsv.gz\n",
|
||||
" \n",
|
||||
"SPORT_DEV_EXP=$SPORT_PATH/dev-0/expected.tsv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### jaki jest baseline dla sport classification ball?\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"zcat $SPORT_TRAIN | awk '{print $1}' | wc -l"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"zcat $SPORT_TRAIN | awk '{print $1}' | grep 1 | wc -l"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"cat $SPORT_DEV_EXP | wc -l\n",
|
||||
"\n",
|
||||
"grep 1 $SPORT_DEV_EXP | wc -l"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Sprytne podejście do klasyfikacji tekstu? Naiwny bayess"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import fetch_20newsgroups\n",
|
||||
"# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
|
||||
"\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"import numpy as np\n",
|
||||
"import sklearn.metrics\n",
|
||||
"import gensim"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"newsgroups = fetch_20newsgroups()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"newsgroups_text = newsgroups['data']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"newsgroups_text_tokenized = [list(set(gensim.utils.tokenize(x, lowercase = True))) for x in newsgroups_text]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"From: lerxst@wam.umd.edu (where's my thing)\n",
|
||||
"Subject: WHAT car is this!?\n",
|
||||
"Nntp-Posting-Host: rac3.wam.umd.edu\n",
|
||||
"Organization: University of Maryland, College Park\n",
|
||||
"Lines: 15\n",
|
||||
"\n",
|
||||
" I was wondering if anyone out there could enlighten me on this car I saw\n",
|
||||
"the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
|
||||
"early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
|
||||
"the front bumper was separate from the rest of the body. This is \n",
|
||||
"all I know. If anyone can tellme a model name, engine specs, years\n",
|
||||
"of production, where this car is made, history, or whatever info you\n",
|
||||
"have on this funky looking car, please e-mail.\n",
|
||||
"\n",
|
||||
"Thanks,\n",
|
||||
"- IL\n",
|
||||
" ---- brought to you by your neighborhood Lerxst ----\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
|
|
@ -0,0 +1,990 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"<div class=\"alert alert-block alert-info\">\n",
|
||||
"<h1> Ekstrakcja informacji </h1>\n",
|
||||
"<h2> 6. <i>Klasyfikacja</i> [ćwiczenia]</h2> \n",
|
||||
"<h3> Jakub Pokrywka (2021)</h3>\n",
|
||||
"</div>\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Zajęcia klasyfikacja"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Zbiór kleister"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pathlib\n",
|
||||
"from collections import Counter\n",
|
||||
"from sklearn.metrics import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"KLEISTER_PATH = pathlib.Path('/home/kuba/Syncthing/przedmioty/2020-02/IE/applica/kleister-nda')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Pytanie\n",
|
||||
"\n",
|
||||
"Czy jurysdykcja musi być zapisana explicite w umowie?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_expected_jurisdiction(filepath):\n",
|
||||
" dataset_expected_jurisdiction = []\n",
|
||||
" with open(filepath,'r') as train_expected_file:\n",
|
||||
" for line in train_expected_file:\n",
|
||||
" key_values = line.rstrip('\\n').split(' ')\n",
|
||||
" jurisdiction = None\n",
|
||||
" for key_value in key_values:\n",
|
||||
" key, value = key_value.split('=')\n",
|
||||
" if key == 'jurisdiction':\n",
|
||||
" jurisdiction = value\n",
|
||||
" if jurisdiction is None:\n",
|
||||
" jurisdiction = 'NONE'\n",
|
||||
" dataset_expected_jurisdiction.append(jurisdiction)\n",
|
||||
" return dataset_expected_jurisdiction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_expected_jurisdiction = get_expected_jurisdiction(KLEISTER_PATH/'train'/'expected.tsv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dev_expected_jurisdiction = get_expected_jurisdiction(KLEISTER_PATH/'dev-0'/'expected.tsv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"254"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(train_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"False"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"'NONE' in train_expected_jurisdiction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"31"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(set(train_expected_jurisdiction))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Czy wszystkie stany muszą występować w zbiorze trenującym w zbiorze kleister?\n",
|
||||
"\n",
|
||||
"https://en.wikipedia.org/wiki/U.S._state\n",
|
||||
"\n",
|
||||
"### Jaki jest baseline?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_counter = Counter(train_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[('New_York', 43),\n",
|
||||
" ('Delaware', 39),\n",
|
||||
" ('California', 32),\n",
|
||||
" ('Massachusetts', 15),\n",
|
||||
" ('Texas', 13),\n",
|
||||
" ('Illinois', 10),\n",
|
||||
" ('Oregon', 9),\n",
|
||||
" ('Florida', 9),\n",
|
||||
" ('Pennsylvania', 9),\n",
|
||||
" ('Missouri', 9),\n",
|
||||
" ('Ohio', 8),\n",
|
||||
" ('New_Jersey', 7),\n",
|
||||
" ('Georgia', 6),\n",
|
||||
" ('Indiana', 5),\n",
|
||||
" ('Nevada', 5),\n",
|
||||
" ('Colorado', 4),\n",
|
||||
" ('Virginia', 4),\n",
|
||||
" ('Washington', 4),\n",
|
||||
" ('Michigan', 3),\n",
|
||||
" ('Minnesota', 3),\n",
|
||||
" ('Connecticut', 2),\n",
|
||||
" ('Wisconsin', 2),\n",
|
||||
" ('Maine', 2),\n",
|
||||
" ('North_Carolina', 2),\n",
|
||||
" ('Kansas', 2),\n",
|
||||
" ('Utah', 2),\n",
|
||||
" ('Iowa', 1),\n",
|
||||
" ('Idaho', 1),\n",
|
||||
" ('South_Dakota', 1),\n",
|
||||
" ('South_Carolina', 1),\n",
|
||||
" ('Rhode_Island', 1)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"train_counter.most_common(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"most_common_answer = train_counter.most_common(100)[0][0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'New_York'"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"most_common_answer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dev_predictions_jurisdiction = [most_common_answer] * len(dev_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dev_expected_jurisdiction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"accuracy: 0.14457831325301204\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"counter = 0 \n",
|
||||
"for pred, exp in zip(dev_predictions_jurisdiction, dev_expected_jurisdiction):\n",
|
||||
" if pred == exp:\n",
|
||||
" counter +=1\n",
|
||||
"print('accuracy: ', counter/len(dev_predictions_jurisdiction))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.14457831325301204"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"accuracy_score(dev_predictions_jurisdiction, dev_expected_jurisdiction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Co jeżeli nazwy klas nie występują explicite w zbiorach?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"https://git.wmi.amu.edu.pl/kubapok/paranormal-or-skeptic-ISI-public\n",
|
||||
" \n",
|
||||
"https://git.wmi.amu.edu.pl/kubapok/sport-text-classification-ball-ISI-public"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"SPORT_PATH='/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia6_klasyfikacja/repos/sport-text-classification-ball'\n",
|
||||
"\n",
|
||||
"SPORT_TRAIN=$SPORT_PATH/train/train.tsv.gz\n",
|
||||
" \n",
|
||||
"SPORT_DEV_EXP=$SPORT_PATH/dev-0/expected.tsv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### jaki jest baseline dla sport classification ball?\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"zcat $SPORT_TRAIN | awk '{print $1}' | wc -l"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"zcat $SPORT_TRAIN | awk '{print $1}' | grep 1 | wc -l"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"cat $SPORT_DEV_EXP | wc -l\n",
|
||||
"\n",
|
||||
"grep 1 $SPORT_DEV_EXP | wc -l"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Sprytne podejście do klasyfikacji tekstu? Naiwny bayess"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import fetch_20newsgroups\n",
|
||||
"# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
|
||||
"\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"import numpy as np\n",
|
||||
"import sklearn.metrics\n",
|
||||
"import gensim"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"newsgroups = fetch_20newsgroups()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"newsgroups_text = newsgroups['data']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"newsgroups_text_tokenized = [list(set(gensim.utils.tokenize(x, lowercase = True))) for x in newsgroups_text]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"From: lerxst@wam.umd.edu (where's my thing)\n",
|
||||
"Subject: WHAT car is this!?\n",
|
||||
"Nntp-Posting-Host: rac3.wam.umd.edu\n",
|
||||
"Organization: University of Maryland, College Park\n",
|
||||
"Lines: 15\n",
|
||||
"\n",
|
||||
" I was wondering if anyone out there could enlighten me on this car I saw\n",
|
||||
"the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
|
||||
"early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
|
||||
"the front bumper was separate from the rest of the body. This is \n",
|
||||
"all I know. If anyone can tellme a model name, engine specs, years\n",
|
||||
"of production, where this car is made, history, or whatever info you\n",
|
||||
"have on this funky looking car, please e-mail.\n",
|
||||
"\n",
|
||||
"Thanks,\n",
|
||||
"- IL\n",
|
||||
" ---- brought to you by your neighborhood Lerxst ----\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(newsgroups_text[0])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['lerxst', 'on', 'be', 'name', 'brought', 'late', 'front', 'umd', 'bumper', 'door', 'there', 'subject', 'day', 'early', 'history', 'me', 'neighborhood', 'university', 'mail', 'doors', 'by', 'funky', 'if', 'engine', 'know', 'years', 'maryland', 'your', 'rest', 'is', 'info', 'body', 'have', 'tellme', 'out', 'anyone', 'small', 'wam', 'il', 'organization', 'thanks', 'park', 'made', 'whatever', 'other', 'specs', 'wondering', 'lines', 'from', 'was', 'a', 'what', 'the', 's', 'or', 'please', 'all', 'rac', 'i', 'looked', 'really', 'edu', 'where', 'to', 'e', 'my', 'it', 'car', 'addition', 'can', 'of', 'production', 'in', 'saw', 'separate', 'you', 'thing', 'posting', 'bricklin', 'could', 'enlighten', 'nntp', 'model', 'were', 'host', 'looking', 'this', 'college', 'sports', 'called']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(newsgroups_text_tokenized[0])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Y = newsgroups['target']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([7, 4, 4, ..., 3, 1, 8])"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Y"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Y_names = newsgroups['target_names']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"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": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Y_names"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'talk.politics.guns'"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"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": 28,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"talk_politics_guns = [x for x,y in zip(newsgroups_text_tokenized,Y) if y == 16]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"546"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(talk_politics_guns)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"253"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len([x for x in talk_politics_guns if 'gun' in x])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"p1 = len([x for x in talk_politics_guns if 'gun' in x]) / len(talk_politics_guns)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.4633699633699634"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"p1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## obliczanie $p2 = P('talk.politics.guns')$\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"p2 = len(talk_politics_guns) / len(Y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.048258794414000356"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"p2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## obliczanie $p3 = P('gun')$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"p3 = len([x for x in newsgroups_text_tokenized if 'gun' in x]) / len(Y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.03270284603146544"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"p3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## ostatecznie"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.6837837837837839"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"(p1 * p2) / p3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"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": 39,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.01622 \t\t alt.atheism\n",
|
||||
"0.00000 \t\t comp.graphics\n",
|
||||
"0.00541 \t\t comp.os.ms-windows.misc\n",
|
||||
"0.01892 \t\t comp.sys.ibm.pc.hardware\n",
|
||||
"0.00270 \t\t comp.sys.mac.hardware\n",
|
||||
"0.00000 \t\t comp.windows.x\n",
|
||||
"0.01351 \t\t misc.forsale\n",
|
||||
"0.04054 \t\t rec.autos\n",
|
||||
"0.01892 \t\t rec.motorcycles\n",
|
||||
"0.00270 \t\t rec.sport.baseball\n",
|
||||
"0.00541 \t\t rec.sport.hockey\n",
|
||||
"0.03784 \t\t sci.crypt\n",
|
||||
"0.02973 \t\t sci.electronics\n",
|
||||
"0.00541 \t\t sci.med\n",
|
||||
"0.01622 \t\t sci.space\n",
|
||||
"0.00270 \t\t soc.religion.christian\n",
|
||||
"0.68378 \t\t talk.politics.guns\n",
|
||||
"0.04595 \t\t talk.politics.mideast\n",
|
||||
"0.03784 \t\t talk.politics.misc\n",
|
||||
"0.01622 \t\t talk.religion.misc\n",
|
||||
"1.00000 \t\tsuma\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "code",
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_prob2(index, word ):\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 word in x])\n",
|
||||
"\n",
|
||||
" if len(talks_topic) == 0:\n",
|
||||
" return 0.0\n",
|
||||
" p1 = len([x for x in talks_topic if word in x]) / len(talks_topic)\n",
|
||||
" p2 = len(talks_topic) / len(Y)\n",
|
||||
" p3 = len([x for x in newsgroups_text_tokenized if word 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": 44,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.20874 \t\t alt.atheism\n",
|
||||
"0.00850 \t\t comp.graphics\n",
|
||||
"0.00364 \t\t comp.os.ms-windows.misc\n",
|
||||
"0.00850 \t\t comp.sys.ibm.pc.hardware\n",
|
||||
"0.00243 \t\t comp.sys.mac.hardware\n",
|
||||
"0.00485 \t\t comp.windows.x\n",
|
||||
"0.00607 \t\t misc.forsale\n",
|
||||
"0.01092 \t\t rec.autos\n",
|
||||
"0.02063 \t\t rec.motorcycles\n",
|
||||
"0.01456 \t\t rec.sport.baseball\n",
|
||||
"0.01092 \t\t rec.sport.hockey\n",
|
||||
"0.00485 \t\t sci.crypt\n",
|
||||
"0.00364 \t\t sci.electronics\n",
|
||||
"0.00364 \t\t sci.med\n",
|
||||
"0.01092 \t\t sci.space\n",
|
||||
"0.41748 \t\t soc.religion.christian\n",
|
||||
"0.03398 \t\t talk.politics.guns\n",
|
||||
"0.02791 \t\t talk.politics.mideast\n",
|
||||
"0.02549 \t\t talk.politics.misc\n",
|
||||
"0.17233 \t\t talk.religion.misc\n",
|
||||
"1.00000 \t\tsuma\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"probs = []\n",
|
||||
"for i in range(len(Y_names)):\n",
|
||||
" probs.append(get_prob2(i,'god'))\n",
|
||||
" print(\"%.5f\" % get_prob2(i,'god'),'\\t\\t', Y_names[i])\n",
|
||||
" \n",
|
||||
"print(\"%.5f\" % sum(probs), '\\t\\tsuma',)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
Loading…
Reference in New Issue