solution

retroc.ipynb

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+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python38332bit715560a51b8a44948ee59d26a58cf272",
+   "display_name": "Python 3.8.3 32-bit"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "d4bdc0d8028da516e3b937f3ab23da3f18f7264589053952c883afefa2219368"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#trening\n",
+    "\n",
+    "#dane treningowe\n",
+    "train_data = pd.read_csv('train/train.tsv.xz', compression='xz', 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[2])\n",
+    "#średnia dat\n",
+    "dm = mean([train_data[0],train_data[1]])\n",
+    "#trening\n",
+    "LR.fit(train_x, dm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dev-0 predict\n",
+    "\n",
+    "#dane treningowe\n",
+    "dev0_data = pd.read_csv('dev-0/in.tsv', 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": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#dev-1 predict\n",
+    "\n",
+    "#dane treningowe\n",
+    "dev1_data = pd.read_csv('dev-1/in.tsv', 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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#test-A predict\n",
+    "\n",
+    "#dane treningowe\n",
+    "testA_data = pd.read_csv('testA/in.tsv', 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('testA/out.tsv', sep='\\n')"
+   ]
+  }
+ ]
+}