131 lines
2.6 KiB
Plaintext
131 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.naive_bayes import MultinomialNB\n",
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"from sklearn.pipeline import make_pipeline\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"train/in.tsv\") as f:\n",
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" x_train = f.readlines()\n",
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"\n",
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"with open(\"train/expected.tsv\") as f:\n",
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" y_train = f.readlines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 0, 0, ..., 0, 0, 1])"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"y_train = LabelEncoder().fit_transform(y_train)\n",
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"y_train"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = pipeline.fit(x_train, y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"dev-0/in.tsv\") as f:\n",
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" x_dev = f.readlines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction = model.predict(x_dev)\n",
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"np.savetxt(\"dev-0/out.tsv\", prediction, fmt='%d')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"test-A/in.tsv\") as f:\n",
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" x_test = f.readlines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction = model.predict(x_test)\n",
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"np.savetxt(\"test-A/out.tsv\", prediction, fmt='%d')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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