From 4797f14e208c10fe1efb5b29a884fe2863f6723f Mon Sep 17 00:00:00 2001
From: s443930 <iwochr2@st.amu.edu.pl>
Date: Sun, 8 May 2022 23:30:43 +0200
Subject: [PATCH] Add retro.ipynb

---
 retro.ipynb | 161 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 161 insertions(+)
 create mode 100644 retro.ipynb

diff --git a/retro.ipynb b/retro.ipynb
new file mode 100644
index 0000000..d948448
--- /dev/null
+++ b/retro.ipynb
@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lzma\n",
+    "import csv\n",
+    "import re\n",
+    "\n",
+    "def readInput(dir):\n",
+    "    X = []\n",
+    "    if 'xz' in dir:\n",
+    "        with lzma.open(dir) as f:\n",
+    "            for line in f:\n",
+    "                text = line.decode('utf-8')\n",
+    "                text = text.split('\\t')\n",
+    "                X.append(text)\n",
+    "    else:\n",
+    "        with open(dir, encoding='utf8', errors='ignore') as f:\n",
+    "            for line in f:\n",
+    "                X. append(line.replace('\\n',''))\n",
+    "    return X\n",
+    "\n",
+    "def writeOutput(output, dir):\n",
+    "    with open(dir, 'w', newline='') as f:\n",
+    "        writer = csv.writer(f)\n",
+    "        writer.writerows(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "train = pd.DataFrame(readInput('train/train.tsv.xz'), columns=['Beginning', 'End', 'Title', 'Source', 'X'])\n",
+    "train['Y'] = train.apply(lambda x: (float(x.Beginning) + float(x.End))/2, axis=1)\n",
+    "train = train.drop(columns=['Beginning', 'End', 'Title', 'Source'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import linear_model\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "estimators = [('tfidf', TfidfVectorizer()), ('linearRegression', linear_model.LinearRegression())]\n",
+    "model = Pipeline(estimators)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(train.X, train.Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev0X = readInput('dev-0/in.tsv')\n",
+    "dev0Expected = readInput('dev-0/expected.tsv')\n",
+    "dev0Predicted = model.predict(dev0X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('RMSE = ', np.sqrt(sklearn.metrics.mean_squared_error(dev0Expected, dev0Predicted)))\n",
+    "print('Model score = ', model.score(dev0X, dev0Expected))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\software\\python3\\lib\\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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE =  21.716380888138996\n",
+      "Model score =  0.8585103501633741\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\software\\python3\\lib\\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": [
+    "import sklearn.metrics\n",
+    "import numpy as np\n",
+    "\n",
+    "print('RMSE = ', np.sqrt(sklearn.metrics.mean_squared_error(dev0Expected, dev0Predicted)))\n",
+    "print('Model score = ', model.score(dev0X, dev0Expected))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "1b132c2ed43285dcf39f6d01712959169a14a721cf314fe69015adab49bb1fd1"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit",
+   "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": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}