s478855

README.md

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+
+Sport Texts Classification Challenge - Ball
+======================
+
+Guess whether the sport is connected to the ball for a Polish article. Evaluation metrics: Accuracy, Likelihood.
+
+Classes
+-------
+
+* `1` — ball
+* `0` — no-ball
+
+Directory structure
+-------------------
+
+* `README.md` — this file
+* `config.txt` — configuration file
+* `train/` — directory with training data
+* `train/train.tsv` — sample train set
+* `dev-0/` — directory with dev (test) data
+* `dev-0/in.tsv` — input data for the dev set
+* `dev-0/expected.tsv` — expected (reference) data for the dev set
+* `test-A` — directory with test data
+* `test-A/in.tsv` — input data for the test set
+* `test-A/expected.tsv` — expected (reference) data for the test set

config.txt

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+--metric Likelihood --metric Accuracy --precision 5

run.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "stworzyć klasyfikator bazujący na naiwnym bayessie (może być gotowa biblioteka), może też korzystać z gotowych implementacji tfidf\n",
+    "\n",
+    "stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
+    "\n",
+    "wynik accuracy sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.67\n",
+    "\n",
+    "proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, zadanie oddajemy w gonito, termin 10.05, 40 punktów\n",
+    "\n",
+    "Output label is the probability of a paranormal subreddit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "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": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_1208\\74507919.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
+      "\n",
+      "\n",
+      "  data = pd.read_csv('train/train.tsv', sep='\\t', header=None, error_bad_lines=False)\n",
+      "b'Skipping line 25706: expected 2 fields, saw 3\\nSkipping line 58881: expected 2 fields, saw 3\\nSkipping line 73761: 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": 137,
+   "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": 138,
+   "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": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
+       "                ('multinomialnb', MultinomialNB())])"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_dev0 = model.predict(Xdev)\n",
+    "predictions_dev0 = pd.Series(predictions_dev0)\n",
+    "predictions_dev0 = predictions_dev0.astype('int')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "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": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_testA = model.predict(Xtest)\n",
+    "predictions_testA = pd.Series(predictions_testA)\n",
+    "predictions_testA = predictions_testA.astype('int')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "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": "df93b008b708122b991044997d8941ca5d5845b048d54848454a010a3b0bd41a"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.13 ('eks')",
+   "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": "3.8.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

run.py

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+import pandas as pd
+import numpy as np
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+
+
+data = pd.read_csv('train/train.tsv', sep='\t', header=None, error_bad_lines=False)
+
+
+X = data[1]
+
+with open('dev-0/in.tsv', 'r', encoding='utf8') as f:
+    Xdev = f.readlines()
+Xdev = pd.Series(Xdev)
+
+with open('test-A/in.tsv', 'r', encoding='utf8') as f:
+    Xtest = f.readlines()
+Xtest = pd.Series(Xtest)
+
+
+
+y = data[0].astype('string')
+
+ydev = pd.read_csv('dev-0/expected.tsv', sep='\t', header=None)
+ydev = ydev.squeeze()
+
+
+model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+
+model.fit(X, y)
+
+
+predictions_dev0 = model.predict(Xdev)
+predictions_dev0 = pd.Series(predictions_dev0)
+predictions_dev0 = predictions_dev0.astype('int')
+
+
+with open('dev-0/out.tsv', 'wt') as f:
+    for pred in predictions_dev0:
+        f.write(str(pred)+'\n')
+
+
+predictions_testA = model.predict(Xtest)
+predictions_testA = pd.Series(predictions_testA)
+predictions_testA = predictions_testA.astype('int')
+
+
+
+with open('test-A/out.tsv', 'wt') as f:
+    for pred in predictions_testA:
+        f.write(str(pred)+'\n')
+
+
+
+
+
+