final commit
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Piotr Biskup
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bayes_1 .html
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bayes_1 .html
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bayes_1.ipynb
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bayes_1.ipynb
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import fetch_20newsgroups\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"import numpy as np\n",
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"import sklearn.metrics\n",
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"import gensim"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"newsgroups = fetch_20newsgroups()\n",
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"newsgroups_text = newsgroups['data']\n",
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"newsgroups_text_tokenized = [list(set(gensim.utils.tokenize(x, lowercase = True))) for x in newsgroups_text]"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"Y = newsgroups['target']\n",
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"Y_names = newsgroups['target_names']"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_prob3(index, document_tokenized):\n",
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" talks_topic = [x for x,y in zip(newsgroups_text_tokenized,Y) if y == index]\n",
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"\n",
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" if len(talks_topic) == 0:\n",
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" return 0.0\n",
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" \n",
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" p1_list = []\n",
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" for word in document_tokenized:\n",
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" to_p1 = len([x for x in talks_topic if word in x]) / len(talks_topic)\n",
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" p1_list.append(to_p1)\n",
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" \n",
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" p1 = np.prod(p1_list)\n",
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" \n",
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" p2 = len(talks_topic) / len(Y)\n",
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" \n",
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" return (p1 * p2) # / p3 --- mianownik dla wszystkich klas będzie taki sam dlatego można go pominąć, bez \n",
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" # bez wpływu na działanie klasyfikatora"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"def print_results(list_of_words):\n",
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" probs = []\n",
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" for i in range(len(Y_names)):\n",
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" p = get_prob3(i, list_of_words)\n",
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" probs.append(p)\n",
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" print(\"%.5f\" % p,'\\t\\t', Y_names[i])"
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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": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.00001 \t\t alt.atheism\n",
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"0.00000 \t\t comp.graphics\n",
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"0.00000 \t\t comp.os.ms-windows.misc\n",
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"0.00000 \t\t comp.sys.ibm.pc.hardware\n",
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"0.00000 \t\t comp.sys.mac.hardware\n",
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"0.00000 \t\t comp.windows.x\n",
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"0.00000 \t\t misc.forsale\n",
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"0.00000 \t\t rec.autos\n",
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"0.00002 \t\t rec.motorcycles\n",
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"0.00000 \t\t rec.sport.baseball\n",
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"0.00001 \t\t rec.sport.hockey\n",
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"0.00001 \t\t sci.crypt\n",
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"0.00000 \t\t sci.electronics\n",
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"0.00000 \t\t sci.med\n",
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"0.00000 \t\t sci.space\n",
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"0.00000 \t\t soc.religion.christian\n",
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"0.00087 \t\t talk.politics.guns\n",
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"0.00003 \t\t talk.politics.mideast\n",
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"0.00005 \t\t talk.politics.misc\n",
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"0.00006 \t\t talk.religion.misc\n"
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]
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}
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],
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"source": [
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"print_results(['i','love','guns'])"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.00004 \t\t alt.atheism\n",
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"0.00000 \t\t comp.graphics\n",
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"0.00000 \t\t comp.os.ms-windows.misc\n",
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"0.00000 \t\t comp.sys.ibm.pc.hardware\n",
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"0.00000 \t\t comp.sys.mac.hardware\n",
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"0.00000 \t\t comp.windows.x\n",
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"0.00000 \t\t misc.forsale\n",
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"0.00000 \t\t rec.autos\n",
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"0.00000 \t\t rec.motorcycles\n",
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"0.00000 \t\t rec.sport.baseball\n",
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"0.00000 \t\t rec.sport.hockey\n",
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"0.00000 \t\t sci.crypt\n",
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"0.00000 \t\t sci.electronics\n",
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"0.00000 \t\t sci.med\n",
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"0.00000 \t\t sci.space\n",
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"0.00012 \t\t soc.religion.christian\n",
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"0.00004 \t\t talk.politics.guns\n",
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"0.00007 \t\t talk.politics.mideast\n",
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"0.00003 \t\t talk.politics.misc\n",
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"0.00008 \t\t talk.religion.misc\n"
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]
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}
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],
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"source": [
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"print_results(['is','there','life','after','death'])"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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