run.ipynb

@@ -1,116 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "835530c2-0129-4b5f-a41c-f1870ca1307f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import sklearn\n",
-    "import pandas as pd\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from gzip import open as open_gz\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": 21,
-   "id": "5db73671-80b9-4099-85a0-08ecf77250d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluation(x, path_out, model):\n",
-    "    results = model.predict(x)\n",
-    "\n",
-    "    with open(path_out, 'wt') as file:\n",
-    "        for r in results:\n",
-    "            file.write(str(r) + '\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2120f43e-d587-4481-a04c-dea9520cecec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train = pd.read_csv('train/train.tsv', header = None, sep = '\\t', error_bad_lines = False)\n",
-    "\n",
-    "x_train = train[1]\n",
-    "y_train = train[0]\n",
-    "x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')\n",
-    "x_dev = x_dev[0]\n",
-    "x_test = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')\n",
-    "x_test = x_test[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "cdfefa84-e535-48e8-844a-10e24b2a3555",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
-       "                ('multinomialnb', MultinomialNB())])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
-    "model.fit(x_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "e1b7fe0c-a21a-42cf-8cd4-46ee932d5282",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "evaluation(x_dev,'dev-0/out.tsv', model)\n",
-    "evaluation(x_test,'test-A/out.tsv', model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7a269ea0-eefd-4907-bf65-9bb53ad296b7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

run.py

@@ -1,31 +0,0 @@
-import os
-import sklearn
-import pandas as pd
-from sklearn.metrics import accuracy_score
-from gzip import open as open_gz
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.pipeline import make_pipeline
-
-def evaluation(x, path_out, model):
-    results = model.predict(x)
-
-    with open(path_out, 'wt') as file:
-        for r in results:
-            file.write(str(r) + '\n')
-            
-
-train = pd.read_csv('train/train.tsv', header = None, sep = '\t', error_bad_lines = False)
-
-x_train = train[1]
-y_train = train[0]
-x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')
-x_dev = x_dev[0]
-x_test = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')
-x_test = x_test[0]
-
-model = make_pipeline(TfidfVectorizer(), MultinomialNB())
-model.fit(x_train, y_train)
-
-evaluation(x_dev,'dev-0/out.tsv', model)
-evaluation(x_test,'test-A/out.tsv', model)