143 lines
3.8 KiB
Plaintext
143 lines
3.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "3312dc2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import vowpalwabbit\n",
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"import pandas as pd\n",
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"import re"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "a5d2718d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def prediction(path_in, path_out, model, categories):\n",
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" data = pd.read_csv(path_in, header=None, sep='\\t')\n",
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" data = data.drop(1, axis=1)\n",
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" data.columns = ['year', 'text']\n",
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"\n",
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" data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
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"\n",
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" with open(path_out, 'w', encoding='utf-8') as file:\n",
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" for example in data['train_input']:\n",
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" predicted = model.predict(example)\n",
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" text_predicted = dict((value, key) for key, value in categories.items()).get(predicted)\n",
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" file.write(str(text_predicted) + '\\n')\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": 25,
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"id": "2273a549",
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"metadata": {},
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"outputs": [],
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"source": [
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"def to_vowpalwabbit(row, categories):\n",
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" text = row['text'].replace('\\n', ' ').lower().strip()\n",
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" text = re.sub(\"[^a-zA-Z -']\", '', text)\n",
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" text = re.sub(\" +\", ' ', text)\n",
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" year = row['year']\n",
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" try:\n",
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" category = categories[row['category']]\n",
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" except KeyError:\n",
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" category = ''\n",
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"\n",
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" vw = f\"{category} | year:{year} text:{text}\\n\"\n",
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"\n",
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" return vw"
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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": 37,
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"id": "83f7c5b5",
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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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"{'news': 1, 'sport': 2, 'opinion': 3, 'business': 4, 'culture': 5, 'lifestyle': 6, 'removed': 7}\n"
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]
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}
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],
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"source": [
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"x_train = pd.read_csv('train/in.tsv', header=None, sep='\\t')\n",
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"x_train = x_train.drop(1, axis=1)\n",
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"x_train.columns = ['year', 'text']\n",
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"\n",
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"y_train = pd.read_csv('train/expected.tsv', header=None, sep='\\t')\n",
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"y_train.columns = ['category']\n",
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"\n",
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"data = pd.concat([x_train, y_train], axis=1)\n",
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"\n",
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"categories = {}\n",
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"\n",
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"for i, x in enumerate(data['category'].unique()):\n",
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" categories[x] = i+1\n",
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"\n",
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"print(categories)\n",
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" \n",
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"data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
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"\n",
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"model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.99')\n",
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"\n",
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"for example in data['train_input']:\n",
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" model.learn(example)\n",
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"\n",
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"prediction('dev-0/in.tsv', 'dev-0/out.tsv', model, categories)\n",
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"prediction('test-A/in.tsv', 'test-A/out.tsv', model, categories)\n",
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"prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)"
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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": 38,
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"id": "caa9bb3b",
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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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"[NbConvertApp] Converting notebook run.ipynb to script\n",
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"[NbConvertApp] Writing 1952 bytes to run.py\n"
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]
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}
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],
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"source": [
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"!jupyter nbconvert --to script run.ipynb"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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