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Author SHA1 Message Date
996589b699 full train results 2021-05-17 10:39:55 +02:00
393630083f solution 2021-05-17 00:18:26 +02:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import csv\n",
"from sklearn.linear_model import LinearRegression\n",
"from stop_words import get_stop_words\n",
"from sklearn.feature_extraction.text import TfidfVectorizer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#trening\n",
"\n",
"#dane treningowe\n",
"train_data = pd.read_csv('train/train.tsv.xz', compression='xz', header=None, sep='\\t')\n",
"\n",
"#regresja liniowa\n",
"LR = LinearRegression()\n",
"#vectorizer\n",
"VEC = TfidfVectorizer(stop_words=get_stop_words('polish'))\n",
"#wektoryzacja danych treningowych\n",
"train_x = VEC.fit_transform(train_data[4])\n",
"#średnia dat\n",
"dm = (train_data[0] + train_data[1])/2\n",
"#trening\n",
"LR.fit(train_x, dm)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#dev-0 predict\n",
"\n",
"#dane treningowe\n",
"dev0_data = pd.read_csv('dev-0/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
"\n",
"#wektoryzacja danych treningowych\n",
"dev0_x = VEC.transform(dev0_data[0])\n",
"#predykcja\n",
"dev0_y = LR.predict(dev0_x)\n",
"#zapis wyników\n",
"dev0_y.tofile('dev-0/out.tsv', sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#dev-1 predict\n",
"\n",
"#dane treningowe\n",
"dev1_data = pd.read_csv('dev-1/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
"\n",
"#wektoryzacja danych treningowych\n",
"dev1_x = VEC.transform(dev1_data[0])\n",
"#predykcja\n",
"dev1_y = LR.predict(dev1_x)\n",
"#zapis wyników\n",
"dev1_y.tofile('dev-1/out.tsv', sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#test-A predict\n",
"\n",
"#dane treningowe\n",
"testA_data = pd.read_csv('test-A/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
"\n",
"#wektoryzacja danych treningowych\n",
"testA_x = VEC.transform(testA_data[0])\n",
"#predykcja\n",
"testA_y = LR.predict(testA_x)\n",
"#zapis wyników\n",
"testA_y.tofile('test-A/out.tsv', sep='\\n')"
]
}
],
"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": "3.8.0"
},
"metadata": {
"interpreter": {
"hash": "d4bdc0d8028da516e3b937f3ab23da3f18f7264589053952c883afefa2219368"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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