{ "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 }