forked from kubapok/retroc2
195 lines
4.7 KiB
Plaintext
195 lines
4.7 KiB
Plaintext
{
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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": "d103a6c5-a9b4-4547-9e10-f384d716972d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import sklearn\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.metrics import mean_squared_error\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": 2,
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"id": "6a2785e6-36b0-4649-91d1-aea8fd3599c1",
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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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]
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},
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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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"107463\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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"print(len(train))\n",
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"train = train.head(10000)"
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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": "8cc00b89-1007-4c4a-8ba7-c62b57459b79",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = train[4]\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": "dd454ce5-a06e-4fbd-a546-83fb94ad0390",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n",
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"x_dev = x_dev_data[0]\n",
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"x_dev[19999] = \"to jest tekst testowy\"\n",
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"x_dev[20000] = \"a ten tekst jest najbardziej testowy\"\n",
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"y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')"
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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": "0a1cce75-86a1-4f76-9416-e876e01699e3",
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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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" ('linearregression', LinearRegression())])"
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]
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},
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"execution_count": 5,
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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 = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
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"model.fit(x_train, 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": 6,
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"id": "cc1270d5-29dc-4f03-82c1-dc03f3e4fa00",
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"metadata": {},
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"outputs": [],
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"source": [
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"dev_predicted = model.predict(x_dev)\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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]
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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": "223de995-5e91-4254-9214-4fc871c985e9",
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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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"4086.3369441409172\n"
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]
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}
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],
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"source": [
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"print(mean_squared_error(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": 8,
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"id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
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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 = model.predict(x_test)\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": 9,
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"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
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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 1607 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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