retroc2/run.py

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "greenhouse-technician",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "from gzip import open as open_gz\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acoustic-dividend",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_year(x, path_out, model):\n",
    "    results = model.predict(x)\n",
    "    with open(path_out, 'wt') as file:\n",
    "        for r in results:\n",
    "            file.write(str(r) + '\\n')       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "senior-harassment",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
    "    train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
    "    \n",
    "#train = train[0:10000]\n",
    "train_x = train['Text']\n",
    "train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
    "train_y=train['Date1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "polyphonic-coach",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
       "                ('linearregression', LinearRegression())])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "varying-wright",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
    "    x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
    "x_dev0 = x_dev0[0]  \n",
    "x_dev0[19999] = 'nie jest'\n",
    "x_dev0[20000] = 'nie wiem'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "frozen-ticket",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dev-1/in.tsv', 'r', encoding='utf8') as file:\n",
    "    x_dev1 = pd.read_csv(file, header=None, sep='\\t')\n",
    "x_dev1 = x_dev1[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e3a18db-f966-45e4-b881-4b336f188055",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
    "    x_test = pd.read_csv(file, header=None, sep='\\t')\n",
    "x_test = x_test[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "traditional-amount",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
    "#y_dev = y_dev[0]\n",
    "#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
    "#y_dev_exp = y_dev_exp[0]\n",
    "#RMSE_dev = mean_squared_error(y_dev_exp, y_dev) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "close-clinton",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_year(x_dev0, 'dev-0/out.tsv', model)\n",
    "predict_year(x_dev1,'dev-1/out.tsv', model)\n",
    "predict_year(x_test,'test-A/out.tsv', model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "official-sweet",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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