full train results

retroc.ipynb

@@ -0,0 +1,136 @@
+{
+ "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
+}