retroc.ipynb

@@ -1,136 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import csv\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from stop_words import get_stop_words\n",
-    "from sklearn.feature_extraction.text import TfidfVectorizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#trening\n",
-    "\n",
-    "#dane treningowe\n",
-    "train_data = pd.read_csv('train/train.tsv.xz', compression='xz', header=None, sep='\\t')\n",
-    "\n",
-    "#regresja liniowa\n",
-    "LR = LinearRegression()\n",
-    "#vectorizer\n",
-    "VEC = TfidfVectorizer(stop_words=get_stop_words('polish'))\n",
-    "#wektoryzacja danych treningowych\n",
-    "train_x = VEC.fit_transform(train_data[4])\n",
-    "#średnia dat\n",
-    "dm = (train_data[0] + train_data[1])/2\n",
-    "#trening\n",
-    "LR.fit(train_x, dm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#dev-0 predict\n",
-    "\n",
-    "#dane treningowe\n",
-    "dev0_data = pd.read_csv('dev-0/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
-    "\n",
-    "#wektoryzacja danych treningowych\n",
-    "dev0_x = VEC.transform(dev0_data[0])\n",
-    "#predykcja\n",
-    "dev0_y = LR.predict(dev0_x)\n",
-    "#zapis wyników\n",
-    "dev0_y.tofile('dev-0/out.tsv', sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#dev-1 predict\n",
-    "\n",
-    "#dane treningowe\n",
-    "dev1_data = pd.read_csv('dev-1/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
-    "\n",
-    "#wektoryzacja danych treningowych\n",
-    "dev1_x = VEC.transform(dev1_data[0])\n",
-    "#predykcja\n",
-    "dev1_y = LR.predict(dev1_x)\n",
-    "#zapis wyników\n",
-    "dev1_y.tofile('dev-1/out.tsv', sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#test-A predict\n",
-    "\n",
-    "#dane treningowe\n",
-    "testA_data = pd.read_csv('test-A/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
-    "\n",
-    "#wektoryzacja danych treningowych\n",
-    "testA_x = VEC.transform(testA_data[0])\n",
-    "#predykcja\n",
-    "testA_y = LR.predict(testA_x)\n",
-    "#zapis wyników\n",
-    "testA_y.tofile('test-A/out.tsv', sep='\\n')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.0"
-  },
-  "metadata": {
-   "interpreter": {
-    "hash": "d4bdc0d8028da516e3b937f3ab23da3f18f7264589053952c883afefa2219368"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}