task

README.md

@@ -1,10 +1,14 @@
 
-Sport Texts Classification Challenge
+Sport Texts Classification Challenge - Ball
-====================================
+======================
 
-Guess the sport discipline for a Polish article.
+Guess whether the sport is connected to the ball for a Polish article. Evaluation metrics: Accuracy, Likelihood.
 
-Possible disciplines: pilka-nozna, siatkowka, sporty-walki, pilka-reczna, koszykowka, tenis, moto, zimowe. Evaluation metric is Accuracy.
+Classes
+-------
+
+* `1` — ball
+* `0` — no-ball
 
 Directory structure
 -------------------

config.txt

@@ -1 +1 @@
---metric LikelihoodHashed --metric Accuracy --precision 5
+--metric Likelihood --metric Accuracy --precision 5

run.ipynb

@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
-    "from sklearn.naive_bayes import MultinomialNB\n",
-    "from sklearn.pipeline import make_pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\User\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
-      "\n",
-      "\n",
-      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "b'Skipping line 3249: expected 2 fields, saw 3\\nSkipping line 66393: expected 2 fields, saw 3\\nSkipping line 76415: expected 2 fields, saw 3\\n'\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = pd.read_csv('train/train.tsv', sep='\\t', header=None, error_bad_lines=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = data[1]\n",
-    "\n",
-    "with open('dev-0/in.tsv', 'r', encoding='utf8') as f:\n",
-    "    Xdev = f.readlines()\n",
-    "Xdev = pd.Series(Xdev)\n",
-    "\n",
-    "with open('test-A/in.tsv', 'r', encoding='utf8') as f:\n",
-    "    Xtest = f.readlines()\n",
-    "Xtest = pd.Series(Xtest)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = data[0].astype('string')\n",
-    "\n",
-    "ydev = pd.read_csv('dev-0/expected.tsv', sep='\\t', header=None)\n",
-    "ydev = ydev.squeeze()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
-       "                ('multinomialnb', MultinomialNB())])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions_dev0 = model.predict(Xdev)\n",
-    "predictions_dev0 = pd.Series(predictions_dev0)\n",
-    "predictions_dev0 = predictions_dev0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('dev-0/out.tsv', 'wt') as f:\n",
-    "    for pred in predictions_dev0:\n",
-    "        f.write(str(pred)+'\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions_testA = model.predict(Xtest)\n",
-    "predictions_testA = pd.Series(predictions_testA)\n",
-    "predictions_testA = predictions_testA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('test-A/out.tsv', 'wt') as f:\n",
-    "    for pred in predictions_testA:\n",
-    "        f.write(str(pred)+'\\n')"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "f08154012ddadd8e950e6e9e035c7a7b32c136e7647e9b7c77e02eb723a8bedb"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.9.7 ('base')",
-   "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": "3.9.7"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

run.py

@@ -21,7 +21,7 @@ Xtest = pd.Series(Xtest)
 
 
 
-y = data[0].astype('string')
+y = data[0].astype('str')
 
 ydev = pd.read_csv('dev-0/expected.tsv', sep='\t', header=None)
 ydev = ydev.squeeze()
@@ -34,7 +34,7 @@ model.fit(X, y)
 
 predictions_dev0 = model.predict(Xdev)
 predictions_dev0 = pd.Series(predictions_dev0)
-predictions_dev0 = predictions_dev0
+predictions_dev0 = predictions_dev0.astype('int')
 
 
 with open('dev-0/out.tsv', 'wt') as f:
@@ -44,7 +44,7 @@ with open('dev-0/out.tsv', 'wt') as f:
 
 predictions_testA = model.predict(Xtest)
 predictions_testA = pd.Series(predictions_testA)
-predictions_testA = predictions_testA
+predictions_testA = predictions_testA.astype('int')