71 lines
1.6 KiB
Plaintext
71 lines
1.6 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f7e1ae0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import csv\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.cluster import KMeans"
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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": 2,
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"id": "7582a8dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"#dev0\n",
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"dev0_data = pd.read_csv('dev-0/in.tsv', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
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"\n",
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"dev0_y = KMeans(n_clusters=50).fit_predict(TfidfVectorizer().fit_transform(dev0_data[0].values))\n",
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"\n",
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"#zapis wyników\n",
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"dev0_y.tofile('dev-0/out.tsv', sep='\\n')"
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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": 3,
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"id": "d3c75abc",
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"metadata": {},
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"outputs": [],
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"source": [
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"#TestA\n",
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"testA_data = pd.read_csv('test-A/in.tsv', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
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"\n",
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"testA_y = KMeans(n_clusters=50).fit_predict(TfidfVectorizer().fit_transform(testA_data[0].values))\n",
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"\n",
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"#zapis wyników\n",
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"testA_y.tofile('test-A/out.tsv', sep='\\n')"
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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": 5
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}
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