first solution

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8Inspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+          <option value="W29" />
+          <option value="E501" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/retroc2.iml" filepath="$PROJECT_DIR$/.idea/retroc2.iml" />
+    </modules>
+  </component>
+</project>

.idea/other.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PySciProjectComponent">
+    <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
+  </component>
+</project>

.idea/retroc2.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

run.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "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)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "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"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "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"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

run.py

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+from sklearn.linear_model import LinearRegression
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics import mean_squared_error
+import lzma
+import numpy as np
+
+X_train_raw = lzma.open("train/train.tsv.xz", mode='rt').readlines()
+X_dev0 = open("dev-0/in.tsv", "r").readlines()
+y_expected_dev0 = open("dev-0/expected.tsv", "r").readlines()
+X_dev1 = open("dev-1/in.tsv", "r").readlines()
+y_expected_dev1 = open("dev-1/expected.tsv", "r").readlines()
+X_test = open("test-A/in.tsv", "r").readlines()
+X = [i.split('\t') for i in X_train_raw]
+X_train = [x[4] for x in X]
+y_expected_train = [x[0] for x in X]
+
+vectorizer = TfidfVectorizer(max_features=10000)
+X_train_tfidf = vectorizer.fit_transform(X_train)
+
+X_dev0_tfidf = vectorizer.transform(X_dev0)
+X_dev1_tfidf = vectorizer.transform(X_dev1)
+X_test_tfidf = vectorizer.transform(X_test)
+
+model = LinearRegression()
+model.fit(X_train_tfidf, y_expected_train)
+y_predicted_dev0 = model.predict(X_dev0_tfidf)
+y_predicted_dev1 = model.predict(X_dev1_tfidf)
+y_predicted_test = model.predict(X_test_tfidf)
+
+rmse_dev0 = mean_squared_error(y_expected_dev0, y_predicted_dev0, squared=False)
+rmse_dev1 = mean_squared_error(y_expected_dev1, y_predicted_dev1, squared=False)
+
+print(rmse_dev0, rmse_dev1)
+
+open("dev-0/out.tsv", mode='w').writelines([str(i)+'\n' for i in y_predicted_dev0])
+open("dev-1/out.tsv", mode='w').writelines([str(i)+'\n' for i in y_predicted_dev1])
+open("test-A/out.tsv", mode='w').writelines([str(i)+'\n' for i in y_predicted_test])