183 lines
4.7 KiB
Plaintext
183 lines
4.7 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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"stworzyć klasyfikator bazujący na naiwnym bayessie (może być gotowa biblioteka), może też korzystać z gotowych implementacji tfidf\n",
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"\n",
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"stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
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"\n",
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"wynik accuracy sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.67\n",
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"\n",
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"proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, zadanie oddajemy w gonito, termin 10.05, 40 punktów\n",
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"\n",
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"Output label is the probability of a paranormal subreddit."
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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": 135,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.naive_bayes import MultinomialNB\n",
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"from sklearn.pipeline import make_pipeline"
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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": 136,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_1208\\74507919.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
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"\n",
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"\n",
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" data = pd.read_csv('train/train.tsv', sep='\\t', header=None, error_bad_lines=False)\n",
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"b'Skipping line 25706: expected 2 fields, saw 3\\nSkipping line 58881: expected 2 fields, saw 3\\nSkipping line 73761: expected 2 fields, saw 3\\n'\n"
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]
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}
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],
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"source": [
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"data = pd.read_csv('train/train.tsv', sep='\\t', header=None, error_bad_lines=False)"
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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": 137,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = data[1]\n",
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"\n",
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"with open('dev-0/in.tsv', 'r', encoding='utf8') as f:\n",
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" Xdev = f.readlines()\n",
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"Xdev = pd.Series(Xdev)\n",
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"\n",
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"with open('test-A/in.tsv', 'r', encoding='utf8') as f:\n",
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" Xtest = f.readlines()\n",
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"Xtest = pd.Series(Xtest)"
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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": 138,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = data[0].astype('string')\n",
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"\n",
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"ydev = pd.read_csv('dev-0/expected.tsv', sep='\\t', header=None)\n",
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"ydev = ydev.squeeze()"
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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": 139,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
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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": 140,
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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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"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
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" ('multinomialnb', MultinomialNB())])"
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]
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},
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"execution_count": 140,
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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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"model.fit(X, y)"
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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": 141,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictions_dev0 = model.predict(Xdev)\n",
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"predictions_dev0 = pd.Series(predictions_dev0)\n",
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"predictions_dev0 = predictions_dev0.astype('int')"
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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": 142,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('dev-0/out.tsv', 'wt') as f:\n",
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" for pred in predictions_dev0:\n",
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" f.write(str(pred)+'\\n')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 143,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictions_testA = model.predict(Xtest)\n",
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"predictions_testA = pd.Series(predictions_testA)\n",
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"predictions_testA = predictions_testA.astype('int')"
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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": 144,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('test-A/out.tsv', 'wt') as f:\n",
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" for pred in predictions_testA:\n",
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" f.write(str(pred)+'\\n')"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "df93b008b708122b991044997d8941ca5d5845b048d54848454a010a3b0bd41a"
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},
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"kernelspec": {
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"display_name": "Python 3.8.13 ('eks')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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