229 lines
4.5 KiB
Plaintext
229 lines
4.5 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.cluster import KMeans\n",
|
|
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
|
"import numpy as np\n",
|
|
"import pandas as pd"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## FUNKCJE"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def inertia_list(all_doc):\n",
|
|
" list_inter = []\n",
|
|
" K_max = int(len(all_doc)/2)\n",
|
|
" while K_max > 100:\n",
|
|
" K_max = int(K_max/2)\n",
|
|
" K = range(1,K_max)\n",
|
|
" for k in K:\n",
|
|
" FitMean = KMeans(n_clusters=k).fit(doc_vectors)\n",
|
|
" list_inter.append(FitMean.inertia_)\n",
|
|
" return list_inter"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def BestK(list_inter):\n",
|
|
" position = -10\n",
|
|
" for i in range(0, len(list_inter)-1):\n",
|
|
" if (int(list_inter[i]) == (int(list_inter[i+1]))):\n",
|
|
" position = i\n",
|
|
" if position == -10 :\n",
|
|
" position = len(list_inter)-1\n",
|
|
" return position"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## PLIK DEV-0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"infile = open('dev-0/in.tsv', 'r', encoding=\"utf-8\")\n",
|
|
"outfile = open(\"dev-0/out.tsv\", \"w\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"all_doc = infile.readlines()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vectorizer = TfidfVectorizer()\n",
|
|
"doc_vectors = vectorizer.fit_transform(all_doc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"list_inter = inertia_list(all_doc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"position = BestK(list_inter)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"FitMean = KMeans(n_clusters=position).fit_predict(doc_vectors)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for x in FitMean:\n",
|
|
" outfile.write(str(x) + '\\n')\n",
|
|
"infile.close()\n",
|
|
"outfile.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## PLIK TEST-A"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"infile = open('test-A/in.tsv', 'r', encoding=\"utf-8\")\n",
|
|
"outfile = open(\"test-A/out.tsv\", \"w\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"all_doc = infile.readlines()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vectorizer = TfidfVectorizer()\n",
|
|
"doc_vectors = vectorizer.fit_transform(all_doc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"list_inter = inertia_list(all_doc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"position = BestK(list_inter)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"FitMean = KMeans(n_clusters=position).fit_predict(doc_vectors)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for x in FitMean:\n",
|
|
" outfile.write(str(x) + '\\n')\n",
|
|
"infile.close()\n",
|
|
"outfile.close()"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|