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This commit is contained in:
parent
9cb2fb2612
commit
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.ipynb_checkpoints/run-checkpoint.ipynb
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203
.ipynb_checkpoints/run-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a8bcddf9-596c-4493-bf2a-8e32255115ce",
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import sklearn\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.metrics import accuracy_score"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "da067d47-0543-48b3-bdf4-844061f827c9",
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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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"D:\\Programy\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
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"\n",
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"\n",
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" exec(code_obj, self.user_global_ns, self.user_ns)\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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"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
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"train = train.head(2000)"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": 3,
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||||
"id": "94390d90-898c-42df-8482-0e1b8a3ea706",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = train[1]\n",
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"y_train = train[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": 4,
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"id": "df870ce3-c258-4de0-bbda-f5d71a53163c",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
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"x_dev = x_dev[0]\n",
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"y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t', 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": 5,
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"id": "ce5621d9-655a-46d7-b235-8638daac733e",
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"metadata": {},
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"outputs": [],
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"source": [
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"vectorizer = TfidfVectorizer()"
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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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"id": "bca4dc07-fdcd-4ae5-8f24-584a3cda3b79",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = vectorizer.fit_transform(x_train)\n",
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"x_dev = vectorizer.transform(x_dev)"
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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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"id": "96840a5e-bfb9-4fae-a5f9-7acc0d7e4c53",
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"metadata": {},
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"outputs": [],
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"source": [
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"gnb = GaussianNB()"
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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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"id": "aed08803-9aef-43e2-8ee2-1d79458b49ac",
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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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"GaussianNB()"
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]
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},
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"execution_count": 8,
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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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"gnb.fit(x_train.toarray(), y_train)"
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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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"id": "7461bb8d-3b3d-4164-9d47-a62b73dc0e36",
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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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"0.9418561995597946\n"
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]
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}
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],
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"source": [
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"dev_predicted = gnb.predict(x_dev.toarray())\n",
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"\n",
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"with open('dev-0/out.tsv', 'wt') as f:\n",
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" for i in dev_predicted:\n",
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" f.write(str(i)+'\\n')\n",
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"\n",
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"dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n",
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"dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n",
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"print(accuracy_score(dev_out, dev_expected))"
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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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"id": "2e18bdbe-6d06-42e3-b952-0d5e7bc60325",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:\n",
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" x_test = f.readlines()\n",
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" \n",
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"x_test = pd.Series(x_test)\n",
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"x_test = vectorizer.transform(x_test)\n",
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"\n",
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"test_predicted = gnb.predict(x_test.toarray())\n",
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"\n",
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"with open('test-A/out.tsv', 'wt') as f:\n",
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" for i in test_predicted:\n",
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" f.write(str(i)+'\\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": 11,
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"id": "9463e664-5f74-4a96-8959-03eb224715e7",
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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 1502 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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5452
dev-0/.ipynb_checkpoints/expected-checkpoint.tsv
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5452
dev-0/.ipynb_checkpoints/expected-checkpoint.tsv
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dev-0/.ipynb_checkpoints/in-checkpoint.tsv
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dev-0/.ipynb_checkpoints/in-checkpoint.tsv
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dev-0/.ipynb_checkpoints/out-checkpoint.tsv
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dev-0/.ipynb_checkpoints/out-checkpoint.tsv
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5452
dev-0/out.tsv
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5452
dev-0/out.tsv
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203
run.ipynb
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run.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a8bcddf9-596c-4493-bf2a-8e32255115ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import sklearn\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.metrics import accuracy_score"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "da067d47-0543-48b3-bdf4-844061f827c9",
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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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"D:\\Programy\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
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"\n",
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"\n",
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" exec(code_obj, self.user_global_ns, self.user_ns)\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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"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
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"train = train.head(2000)"
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]
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},
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||||
{
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||||
"cell_type": "code",
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"execution_count": 3,
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||||
"id": "94390d90-898c-42df-8482-0e1b8a3ea706",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
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"x_train = train[1]\n",
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"y_train = train[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": 4,
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"id": "df870ce3-c258-4de0-bbda-f5d71a53163c",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
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"x_dev = x_dev[0]\n",
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"y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t', 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": 5,
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"id": "ce5621d9-655a-46d7-b235-8638daac733e",
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"metadata": {},
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"outputs": [],
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"source": [
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"vectorizer = TfidfVectorizer()"
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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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"id": "bca4dc07-fdcd-4ae5-8f24-584a3cda3b79",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = vectorizer.fit_transform(x_train)\n",
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"x_dev = vectorizer.transform(x_dev)"
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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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"id": "96840a5e-bfb9-4fae-a5f9-7acc0d7e4c53",
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"metadata": {},
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"outputs": [],
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"source": [
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"gnb = GaussianNB()"
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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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"id": "aed08803-9aef-43e2-8ee2-1d79458b49ac",
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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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"GaussianNB()"
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]
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},
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"execution_count": 8,
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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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"gnb.fit(x_train.toarray(), y_train)"
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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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"id": "7461bb8d-3b3d-4164-9d47-a62b73dc0e36",
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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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"0.9418561995597946\n"
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]
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}
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],
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"source": [
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"dev_predicted = gnb.predict(x_dev.toarray())\n",
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"\n",
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"with open('dev-0/out.tsv', 'wt') as f:\n",
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" for i in dev_predicted:\n",
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" f.write(str(i)+'\\n')\n",
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"\n",
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"dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n",
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"dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n",
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"print(accuracy_score(dev_out, dev_expected))"
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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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"id": "2e18bdbe-6d06-42e3-b952-0d5e7bc60325",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:\n",
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" x_test = f.readlines()\n",
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" \n",
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"x_test = pd.Series(x_test)\n",
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"x_test = vectorizer.transform(x_test)\n",
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"\n",
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"test_predicted = gnb.predict(x_test.toarray())\n",
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"\n",
|
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"with open('test-A/out.tsv', 'wt') as f:\n",
|
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" for i in test_predicted:\n",
|
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" f.write(str(i)+'\\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": 11,
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"id": "9463e664-5f74-4a96-8959-03eb224715e7",
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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 1502 bytes to run.py\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!jupyter nbconvert --to script run.ipynb"
|
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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",
|
||||
"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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90
run.py
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run.py
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#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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import numpy as np
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import pandas as pd
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import sklearn
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from sklearn.naive_bayes import GaussianNB
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics import accuracy_score
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# In[2]:
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train = pd.read_csv('train/train.tsv', header=None, sep='\t', error_bad_lines=False)
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train = train.head(2000)
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# In[3]:
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x_train = train[1]
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y_train = train[0]
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# In[4]:
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x_dev = pd.read_csv('dev-0/in.tsv', header=None, sep='\t', error_bad_lines=False)
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x_dev = x_dev[0]
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y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t', error_bad_lines=False)
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# In[5]:
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vectorizer = TfidfVectorizer()
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# In[6]:
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x_train = vectorizer.fit_transform(x_train)
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x_dev = vectorizer.transform(x_dev)
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# In[7]:
|
||||
|
||||
|
||||
gnb = GaussianNB()
|
||||
|
||||
|
||||
# In[8]:
|
||||
|
||||
|
||||
gnb.fit(x_train.toarray(), y_train)
|
||||
|
||||
|
||||
# In[9]:
|
||||
|
||||
|
||||
dev_predicted = gnb.predict(x_dev.toarray())
|
||||
|
||||
with open('dev-0/out.tsv', 'wt') as f:
|
||||
for i in dev_predicted:
|
||||
f.write(str(i)+'\n')
|
||||
|
||||
dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\t')
|
||||
dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
|
||||
print(accuracy_score(dev_out, dev_expected))
|
||||
|
||||
|
||||
# In[10]:
|
||||
|
||||
|
||||
with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:
|
||||
x_test = f.readlines()
|
||||
|
||||
x_test = pd.Series(x_test)
|
||||
x_test = vectorizer.transform(x_test)
|
||||
|
||||
test_predicted = gnb.predict(x_test.toarray())
|
||||
|
||||
with open('test-A/out.tsv', 'wt') as f:
|
||||
for i in test_predicted:
|
||||
f.write(str(i)+'\n')
|
||||
|
||||
5447
test-A/.ipynb_checkpoints/out-checkpoint.tsv
Normal file
5447
test-A/.ipynb_checkpoints/out-checkpoint.tsv
Normal file
File diff suppressed because it is too large
Load Diff
5447
test-A/out.tsv
Normal file
5447
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
98132
train/.ipynb_checkpoints/train-checkpoint.tsv
Normal file
98132
train/.ipynb_checkpoints/train-checkpoint.tsv
Normal file
File diff suppressed because it is too large
Load Diff
98132
train/train.tsv
Normal file
98132
train/train.tsv
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user