paranormal-or-skeptic-ISI-p.../Naiwny_bayes.ipynb
2021-05-09 18:26:42 +02:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"import sklearn\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import numpy as np\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.preprocessing import LabelEncoder "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def getInput(path):\n",
" with open(path,encoding='utf-8') as f:\n",
" return f.readlines()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/c/Users/mkoci/Desktop/naiwny_bayes\n"
]
}
],
"source": [
"!pwd"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"train_in=getInput('./train/in.tsv')\n",
"train_expected=getInput('./train/expected.tsv')\n",
"test_in=getInput('./test-A/in.tsv')\n",
"dev_in=getInput('./dev-0/in.tsv')\n",
"dev_expected=getInput('./dev-0/expected.tsv')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())\n",
"encTransform = LabelEncoder().fit_transform(train_expected)\n",
"model = pipeline.fit(train_in, encTransform)\n",
"dev_predicted = model.predict(dev_in)\n",
"test_predicted = model.predict(test_in)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"with open('./dev-0/out.tsv', \"w\") as result:\n",
" for out in dev_predicted:\n",
" result.write(str(out) + '\\n')\n",
"with open('./test-A/out.tsv', \"w\") as result:\n",
" for out in test_predicted:\n",
" result.write(str(out) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook Naiwny_bayes.ipynb to script\n",
"[NbConvertApp] Writing 1337 bytes to Naiwny_bayes.py\n"
]
}
],
"source": [
"!jupyter nbconvert --to script Naiwny_bayes.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}