Naive bayes first try
This commit is contained in:
Th3NiKo
parent
fa78136b5f
commit
35d0bbd849
385
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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385
.ipynb_checkpoints/Untitled-checkpoint.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": 13,
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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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"\n",
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"train = pd.read_csv(\"train/in.tsv.xz\",header=None, compression='xz',sep=\"\\t\", names=[\"text\",\"time\"])\n",
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"expected = pd.read_csv(\"train/expected.tsv\", header=None)"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"train[\"expected\"] = expected"
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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": 34,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"count 185478.000000\n",
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"mean 303.405056\n",
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"std 494.328936\n",
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"min 3.000000\n",
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"25% 68.000000\n",
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"50% 151.000000\n",
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"75% 341.000000\n",
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"max 10251.000000\n",
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"Name: text, dtype: float64"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train[train[\"expected\"]==' S'][\"text\"].str.len().describe()"
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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": 35,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"count 104063.000000\n",
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"mean 298.150995\n",
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"std 504.984133\n",
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"min 3.000000\n",
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"25% 65.000000\n",
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"50% 146.000000\n",
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"75% 330.000000\n",
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"max 10161.000000\n",
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"Name: text, dtype: float64"
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]
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},
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"execution_count": 35,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train[train[\"expected\"]==' P'][\"text\"].str.len().describe()"
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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": 39,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package punkt to /home/th3niko/nltk_data...\n",
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"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
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]
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}
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],
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"source": [
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"import string\n",
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"from nltk import word_tokenize\n",
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"import nltk\n",
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"from nltk.corpus import stopwords\n",
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"stopwords = set(stopwords.words('english'))\n",
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"nltk.download(\"punkt\")\n",
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"\n",
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"def clean_text(text):\n",
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" text = word_tokenize(text)\n",
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" text = [word.lower() for word in text if word.isalpha()]\n",
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" punct = str.maketrans('','',string.punctuation)\n",
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" text = [word.translate(punct) for word in text]\n",
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" text = [word for word in text if not word in stopwords]\n",
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" return text\n",
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"\n",
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"train['text'] = train['text'].apply(clean_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": 40,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0 [medical, issues, recently]\n",
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"1 [supposedly, aluminum, barium, strontium, used...\n",
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"2 [nobel, prizes, make, rich]\n",
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"3 [came, article, stayed, doctor]\n",
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"4 [resorted, insults, got, owned, directly, afte...\n",
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" ... \n",
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"289536 [really, baby, shampoo, actually, highly, alka...\n",
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"289537 [gives, example, brendan, reilly, doctor, came...\n",
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"289538 [ca, fix, stupidity]\n",
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"289539 [excellent, points, also, looking, bit, progra...\n",
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"289540 [earlier, year, may, couple, days, ago, nov]\n",
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"Name: text, Length: 289541, dtype: object"
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]
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},
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"execution_count": 40,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train['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": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import Counter\n",
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"def counter(text):\n",
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" cnt = Counter()\n",
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" for msgs in text:\n",
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" for msg in msgs:\n",
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" cnt[msg] += 1\n",
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" return cnt\n",
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"\n",
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"text_cnt_s = counter(train[train['expected']==' S']['text'])\n",
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"text_cnt_p = counter(train[train['expected']==' P']['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": 58,
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"metadata": {},
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"outputs": [],
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"source": [
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"text_s = text_cnt_s.most_common(100)\n",
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"text_p = text_cnt_p.most_common(100)\n",
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"text_s = pd.DataFrame(text_s,columns = ['words','counts'])\n",
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"text_p = pd.DataFrame(text_p,columns = ['words','counts'])"
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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": 53,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"import seaborn as sns"
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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": 57,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/th3niko/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
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"of pandas will change to not sort by default.\n",
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"\n",
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"To accept the future behavior, pass 'sort=False'.\n",
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"\n",
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"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
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"\n",
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" \"\"\"Entry point for launching an IPython kernel.\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>counts1</th>\n",
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" <th>counts2</th>\n",
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" <th>dataset</th>\n",
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" <th>words1</th>\n",
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" <th>words2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <td>39094.0</td>\n",
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" <td>NaN</td>\n",
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" <td>s</td>\n",
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" <td>would</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <td>36978.0</td>\n",
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" <td>NaN</td>\n",
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" <td>s</td>\n",
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" <td>like</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2</td>\n",
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" <td>36461.0</td>\n",
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" <td>NaN</td>\n",
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" <td>s</td>\n",
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" <td>people</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>3</td>\n",
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" <td>29143.0</td>\n",
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" <td>NaN</td>\n",
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" <td>s</td>\n",
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" <td>one</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>4</td>\n",
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" <td>26827.0</td>\n",
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" <td>NaN</td>\n",
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" <td>s</td>\n",
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" <td>think</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>95</td>\n",
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" <td>NaN</td>\n",
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" <td>3007.0</td>\n",
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" <td>p</td>\n",
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" <td>NaN</td>\n",
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" <td>kind</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>96</td>\n",
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" <td>NaN</td>\n",
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" <td>2990.0</td>\n",
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" <td>p</td>\n",
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" <td>NaN</td>\n",
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" <td>show</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>97</td>\n",
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" <td>NaN</td>\n",
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" <td>2970.0</td>\n",
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" <td>p</td>\n",
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" <td>NaN</td>\n",
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" <td>far</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>98</td>\n",
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" <td>NaN</td>\n",
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" <td>2964.0</td>\n",
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" <td>p</td>\n",
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" <td>NaN</td>\n",
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" <td>feel</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>99</td>\n",
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" <td>NaN</td>\n",
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" <td>2915.0</td>\n",
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" <td>p</td>\n",
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" <td>NaN</td>\n",
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" <td>try</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>200 rows × 5 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" counts1 counts2 dataset words1 words2\n",
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"0 39094.0 NaN s would NaN\n",
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"1 36978.0 NaN s like NaN\n",
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"2 36461.0 NaN s people NaN\n",
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"3 29143.0 NaN s one NaN\n",
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"4 26827.0 NaN s think NaN\n",
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".. ... ... ... ... ...\n",
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"95 NaN 3007.0 p NaN kind\n",
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"96 NaN 2990.0 p NaN show\n",
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"97 NaN 2970.0 p NaN far\n",
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"98 NaN 2964.0 p NaN feel\n",
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"99 NaN 2915.0 p NaN try\n",
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"\n",
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"[200 rows x 5 columns]"
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]
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},
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"execution_count": 57,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"concatenated = pd.concat([text_s.assign(dataset='s'), text_p.assign(dataset='p')])\n",
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"concatenated\n",
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"sns.set(style=\"whitegrid\")\n",
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"g = sns.catplot(x=\"words\", y=\"counts\", data=concatenated,\n",
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" height=6, kind=\"bar\", palette=\"muted\",style=\"dataset\")"
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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": 4
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}
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BIN
.ipynb_checkpoints/plot-checkpoint.png
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.ipynb_checkpoints/plot-checkpoint.png
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After Width: | Height: | Size: 109 KiB |
2656
dev-0/out.tsv
2656
dev-0/out.tsv
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mostUsed.txt
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mostUsed.txt
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Load Diff
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mostUsedP.txt
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mostUsedP.txt
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Load Diff
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mostUsedS.txt
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mostUsedS.txt
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predict.py
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predict.py
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#!/usr/bin/python3
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import sys
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import pickle
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from math import log
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from tokenizer import tokenize
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model = pickle.load(open("model.pkl","rb"))
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pskeptic, vocabulary_size, skeptic_words_total, paranormal_words_total, skeptic_count, paranormal_count = model
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for line in sys.stdin:
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document = line.rstrip()
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fields = document.split('\t')
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document = fields[0]
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terms = tokenize(document)
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log_prob_sketpic = log(pskeptic)
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log_prob_paranormal = log(1 - pskeptic)
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for term in terms:
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if term not in skeptic_count:
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skeptic_count[term] = 0
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if term not in paranormal_count:
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paranormal_count[term] = 0
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log_prob_sketpic += log((skeptic_count[term] + 1) / (skeptic_words_total + vocabulary_size))
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log_prob_paranormal += log((paranormal_count[term] + 1) / (paranormal_words_total + vocabulary_size))
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if log_prob_sketpic > log_prob_paranormal:
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print('S')
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else:
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print('P')
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solve.py
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solve.py
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#!/usr/bin/env python3
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import pandas as pd
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import re
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import sys
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# sort | uniq -c
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#train = pd.read_csv("./train/in.tsv.xz", delimiter='\t')
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#import sys
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#for line in sys.stdin
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#if re.search(r'UFO', line) print("P")
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for line in sys.stdin:
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if re.search(r'(video|paranormal|happened|alien|camera|ghost|sleep|dream|moving|sky|contact|sightings|footage|photo|phenomena|phenomenon|spirit|shadow|board|window|creepy|wake|eye|film|circles|lol|extraterrestrial|floating|disclosure|civilization|record|glitch|driving|ufo|flash|sharing)', line.lower()):
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print("P")
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else:
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print("S")
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2566
test-A/out.tsv
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test-A/out.tsv
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tokenizer.py
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tokenizer.py
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#!/usr/bin/python3
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from nltk.tokenize import word_tokenize
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from nltk.corpus import stopwords
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from nltk.stem.porter import PorterStemmer
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import nltk
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import re
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import string
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wordlist = set(nltk.corpus.words.words())
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porter = PorterStemmer()
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stop_words = set(stopwords.words('english'))
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printable = set(string.printable)
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def tokenize(d):
|
||||
d = re.sub(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', 'thereisasimplelinkinside', d, flags=re.MULTILINE)
|
||||
d = re.sub(r'\\n',' ',d)
|
||||
d = re.sub(r'\*|\'|\"|\/|~|_|=|-',' ',d)
|
||||
d = ''.join(filter(lambda x: x in printable, d))
|
||||
tokenized = word_tokenize(d)
|
||||
#tokenized = re.split(r'\/|\\| ', d)
|
||||
lower = [w.lower() for w in tokenized]
|
||||
words = [w for w in lower if not w in stop_words]
|
||||
return words
|
||||
58
train.py
Normal file
58
train.py
Normal file
@ -0,0 +1,58 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import sys
|
||||
import pickle
|
||||
from tokenizer import tokenize
|
||||
|
||||
|
||||
def train():
|
||||
documents_total = 0
|
||||
skeptic_documents_total = 0
|
||||
|
||||
vocabulary = set()
|
||||
|
||||
skeptic_words_total = 0
|
||||
paranormal_words_total = 0
|
||||
|
||||
skeptic_count = {}
|
||||
paranormal_count = {}
|
||||
|
||||
for line in sys.stdin:
|
||||
line = line.rstrip()
|
||||
fields = line.split('\t')
|
||||
label = fields[0].strip()
|
||||
document = fields[1]
|
||||
terms = tokenize(document)
|
||||
|
||||
for t in terms:
|
||||
vocabulary.add(t)
|
||||
|
||||
documents_total += 1
|
||||
if label == 'S':
|
||||
skeptic_documents_total += 1
|
||||
skeptic_words_total += len(terms)
|
||||
for term in terms:
|
||||
if term in skeptic_count:
|
||||
skeptic_count[term] += 1
|
||||
else:
|
||||
skeptic_count[term] = 1
|
||||
else:
|
||||
paranormal_words_total += len(terms)
|
||||
for term in terms:
|
||||
if term in paranormal_count:
|
||||
paranormal_count[term] += 1
|
||||
else:
|
||||
paranormal_count[term] = 1
|
||||
|
||||
|
||||
|
||||
psketpic = skeptic_documents_total / documents_total
|
||||
vocabulary_size = len(vocabulary)
|
||||
|
||||
model = (psketpic, vocabulary_size, skeptic_words_total,paranormal_words_total, skeptic_count, paranormal_count)
|
||||
pickle.dump(model, open("model.pkl", "wb"))
|
||||
|
||||
print(paranormal_count)
|
||||
print(skeptic_words_total)
|
||||
|
||||
train()
|
||||
@ -1,12 +0,0 @@
|
||||
#!/bin/bash
|
||||
input="../mostUsedP.txt"
|
||||
while IFS= read -r line
|
||||
do
|
||||
p=`xzcat in.tsv.xz | paste expected.tsv - |grep "P.* $line" | wc -l`
|
||||
s=`xzcat in.tsv.xz | paste expected.tsv - |grep "S.* $line" | wc -l`
|
||||
diff=$((p-s))
|
||||
if [ $p -ge $s ]
|
||||
then
|
||||
echo "$line, $diff"
|
||||
fi
|
||||
done < "$input"
|
||||
@ -1,202 +0,0 @@
|
||||
video, 1790
|
||||
UFO, 3604
|
||||
saw, 958
|
||||
light, 1910
|
||||
paranormal, 1871
|
||||
looks, 459
|
||||
happened, 569
|
||||
story, 324
|
||||
night, 1327
|
||||
alien, 1511
|
||||
house, 1054
|
||||
camera, 1611
|
||||
aliens, 794
|
||||
experience, 342
|
||||
lights, 1214
|
||||
looked, 193
|
||||
object, 508
|
||||
came, 1026
|
||||
UFOs, 1097
|
||||
room, 273
|
||||
seeing, 99
|
||||
ghost, 1301
|
||||
videos, 645
|
||||
nI, 0
|
||||
sleep, 503
|
||||
weird, 608
|
||||
flying, 584
|
||||
picture, 718
|
||||
dream, 1191
|
||||
stories, 385
|
||||
moving, 494
|
||||
space, 268
|
||||
felt, 10
|
||||
strange, 436
|
||||
objects, 531
|
||||
experiences, 519
|
||||
technology, 189
|
||||
watching, 8
|
||||
sky, 769
|
||||
fake, 698
|
||||
military, 235
|
||||
dont, 223
|
||||
door, 401
|
||||
contact, 333
|
||||
planet, 45
|
||||
sightings, 620
|
||||
phone, 114
|
||||
craft, 681
|
||||
footage, 612
|
||||
advanced, 176
|
||||
cool, 83
|
||||
dreams, 532
|
||||
ghosts, 319
|
||||
pictures, 455
|
||||
experienced, 300
|
||||
eyes, 97
|
||||
photo, 1113
|
||||
moved, 254
|
||||
phenomena, 273
|
||||
phenomenon, 220
|
||||
air, 298
|
||||
image, 174
|
||||
happening, 116
|
||||
spirit, 470
|
||||
travel, 305
|
||||
video, 1790
|
||||
dark, 384
|
||||
bed, 328
|
||||
reports, 95
|
||||
walking, 138
|
||||
beings, 233
|
||||
ET, 562
|
||||
shadow, 449
|
||||
nThe, 0
|
||||
Looks, 36
|
||||
board, 151
|
||||
scared, 322
|
||||
night, 1327
|
||||
bright, 348
|
||||
house, 1054
|
||||
spirits, 369
|
||||
photos, 511
|
||||
Very, 42
|
||||
sitting, 42
|
||||
lived, 51
|
||||
story, 324
|
||||
thats, 127
|
||||
video, 1790
|
||||
speed, 101
|
||||
window, 366
|
||||
plane, 258
|
||||
creepy, 444
|
||||
shape, 397
|
||||
cameras, 302
|
||||
wake, 180
|
||||
sighting, 1073
|
||||
passed, 24
|
||||
eye, 58
|
||||
woke, 267
|
||||
activity, 64
|
||||
dad, 89
|
||||
film, 479
|
||||
Sounds, 5
|
||||
feet, 43
|
||||
fake, 698
|
||||
standing, 33
|
||||
happened, 569
|
||||
UFO, 3604
|
||||
fly, 648
|
||||
ufo, 721
|
||||
voice, 95
|
||||
night, 1327
|
||||
circles, 122
|
||||
lol, 310
|
||||
seconds, 135
|
||||
extraterrestrial, 267
|
||||
experience, 342
|
||||
paralysis, 332
|
||||
aircraft, 247
|
||||
room, 273
|
||||
brother, 29
|
||||
haunted, 335
|
||||
youtube, 30
|
||||
story, 324
|
||||
Ghost, 238
|
||||
spot, 79
|
||||
paranormal, 1871
|
||||
house, 1054
|
||||
scary, 136
|
||||
distance, 176
|
||||
nIf, 0
|
||||
witness, 495
|
||||
freaked, 236
|
||||
witnesses, 224
|
||||
music, 34
|
||||
weather, 9
|
||||
images, 125
|
||||
cant, 78
|
||||
NASA, 60
|
||||
walked, 52
|
||||
sky, 769
|
||||
floating, 168
|
||||
noise, 251
|
||||
disclosure, 254
|
||||
miles, 78
|
||||
civilization, 125
|
||||
Ouija, 175
|
||||
record, 133
|
||||
visit, 217
|
||||
audio, 113
|
||||
appeared, 103
|
||||
incident, 91
|
||||
slowly, 24
|
||||
stars, 84
|
||||
glitch, 602
|
||||
corner, 141
|
||||
orbs, 254
|
||||
lens, 282
|
||||
visiting, 83
|
||||
town, 36
|
||||
camera, 1611
|
||||
location, 205
|
||||
hoax, 380
|
||||
visited, 97
|
||||
aliens, 794
|
||||
light, 1910
|
||||
ship, 144
|
||||
recording, 248
|
||||
abduction, 239
|
||||
experience, 342
|
||||
UFOs, 1097
|
||||
floor, 32
|
||||
driving, 19
|
||||
didnt, 119
|
||||
UFO, 3604
|
||||
project, 19
|
||||
communicate, 29
|
||||
radar, 77
|
||||
visible, 54
|
||||
ball, 480
|
||||
planes, 75
|
||||
street, 30
|
||||
flash, 377
|
||||
room, 273
|
||||
sharing, 271
|
||||
balloon, 539
|
||||
presence, 26
|
||||
entity, 140
|
||||
filmed, 193
|
||||
sleeping, 70
|
||||
witnessed, 138
|
||||
Aliens, 95
|
||||
reflection, 260
|
||||
lucid, 135
|
||||
digital, 138
|
||||
light, 1910
|
||||
entities, 172
|
||||
recorded, 74
|
||||
fake, 698
|
||||
memories, 51
|
||||
aliens, 794
|
||||
flight, 51
|
||||
Loading…
Reference in New Issue
Block a user