retroc2/run.ipynb

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   "execution_count": 7,
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   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import lzma\n",
    "import numpy as np\n",
    "\n",
    "X_train_raw = lzma.open(\"train/train.tsv.xz\", mode='rt').readlines()\n",
    "X_dev0 = open(\"dev-0/in.tsv\", \"r\").readlines()\n",
    "y_expected_dev0 = open(\"dev-0/expected.tsv\", \"r\").readlines()\n",
    "X_dev1 = open(\"dev-1/in.tsv\", \"r\").readlines()\n",
    "y_expected_dev1 = open(\"dev-1/expected.tsv\", \"r\").readlines()\n",
    "X_test = open(\"test-A/in.tsv\", \"r\").readlines()\n",
    "X = [i.split('\\t') for i in X_train_raw]\n",
    "X_train = [x[4] for x in X]\n",
    "y_expected_train = [x[0] for x in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=10000)\n",
    "X_train_tfidf = vectorizer.fit_transform(X_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "X_dev0_tfidf = vectorizer.transform(X_dev0)\n",
    "X_dev1_tfidf = vectorizer.transform(X_dev1)\n",
    "X_test_tfidf = vectorizer.transform(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train_tfidf, y_expected_train)\n",
    "y_predicted_dev0 = model.predict(X_dev0_tfidf)\n",
    "y_predicted_dev1 = model.predict(X_dev1_tfidf)\n",
    "y_predicted_test = model.predict(X_test_tfidf)"
   ],
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  },
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   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Dominik/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:63: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  return f(*args, **kwargs)\n",
      "/Users/Dominik/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:63: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "rmse_dev0 = mean_squared_error(y_expected_dev0, y_predicted_dev0, squared=False)\n",
    "rmse_dev1 = mean_squared_error(y_expected_dev1, y_predicted_dev1, squared=False)\n"
   ],
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     "name": "#%%\n"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.077488520623103 22.447122551358966\n"
     ]
    }
   ],
   "source": [
    "print(rmse_dev0, rmse_dev1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "open(\"dev-0/out.tsv\", mode='w').writelines([str(i)+'\\n' for i in y_predicted_dev0])\n",
    "open(\"dev-1/out.tsv\", mode='w').writelines([str(i)+'\\n' for i in y_predicted_dev1])\n",
    "open(\"test-A/out.tsv\", mode='w').writelines([str(i)+'\\n' for i in y_predicted_test])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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