projekt
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README.md
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README.md
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Skeptic vs paranormal subreddits
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================================
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Classify a reddit as either from Skeptic subreddit or one of the
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"paranormal" subreddits (Paranormal, UFOs, TheTruthIsHere, Ghosts,
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,Glitch-in-the-Matrix, conspiracytheories).
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Output label is the probability of a paranormal subreddit.
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Sources
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-------
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Data taken from <https://archive.org/details/2015_reddit_comments_corpus>.
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BIN
Raport.docx
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Raport.docx
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130
bayes.ipynb
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bayes.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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.naive_bayes import MultinomialNB\n",
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"from sklearn.pipeline import make_pipeline\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer"
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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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"source": [
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"with open(\"train/in.tsv\") as f:\n",
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" x_train = f.readlines()\n",
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"\n",
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"with open(\"train/expected.tsv\") as f:\n",
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" y_train = f.readlines()"
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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": 20,
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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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"array([1, 0, 0, ..., 0, 0, 1])"
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]
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},
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"execution_count": 20,
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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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"y_train = LabelEncoder().fit_transform(y_train)\n",
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"y_train"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())"
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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": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = pipeline.fit(x_train, y_train)"
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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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"dev-0/in.tsv\") as f:\n",
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" x_dev = f.readlines()"
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction = model.predict(x_dev)\n",
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"np.savetxt(\"dev-0/out.tsv\", prediction, fmt='%d')"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"test-A/in.tsv\") as f:\n",
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" x_test = f.readlines()"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction = model.predict(x_test)\n",
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"np.savetxt(\"test-A/out.tsv\", prediction, fmt='%d')"
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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": 4
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}
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29
bayes.py
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bayes.py
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import numpy as np
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from sklearn.preprocessing import LabelEncoder
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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with open("train/in.tsv") as f:
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x_train = f.readlines()
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with open("train/expected.tsv") as f:
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y_train = f.readlines()
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y_train = LabelEncoder().fit_transform(y_train)
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pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
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model = pipeline.fit(x_train, y_train)
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with open("dev-0/in.tsv") as f:
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x_dev = f.readlines()
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prediction = model.predict(x_dev)
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np.savetxt("dev-0/out.tsv", prediction, fmt='%d')
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with open("test-A/in.tsv") as f:
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x_test = f.readlines()
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prediction = model.predict(x_test)
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np.savetxt("test-A/out.tsv", prediction, fmt='%d')
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1
config.txt
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config.txt
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--metric Likelihood --metric Accuracy --metric F1 --metric F0:N<Precision> --metric F9999999:N<Recall> --precision 4 --in-header in-header.tsv --out-header out-header.tsv
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dev-0/expected.tsv
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dev-0/expected.tsv
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dev-0/in.tsv
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dev-0/in.tsv
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dev-0/out.tsv
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dev-0/out.tsv
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in-header.tsv
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in-header.tsv
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PostText Timestamp
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neural_network.ipynb
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neural_network.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": 2,
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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 numpy as np\n",
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"import torch\n",
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"from nltk.tokenize import word_tokenize\n",
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"import gensim.downloader"
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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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"#wczytywanie danych\n",
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"x_train = pd.read_table('train/in.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])\n",
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"y_train = pd.read_table('train/expected.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['label'])\n",
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"x_dev = pd.read_table('dev-0/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])\n",
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"x_test = pd.read_table('test-A/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = x_train.content.str.lower()\n",
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"x_dev = x_dev.content.str.lower()\n",
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"x_test = x_test.content.str.lower()"
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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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{
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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/tomasz/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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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 15,
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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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"import nltk\n",
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"nltk.download('punkt')"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = [word_tokenize(content) for content in x_train]\n",
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"x_dev = [word_tokenize(content) for content in x_dev]\n",
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"x_test = [word_tokenize(content) for content in x_test]"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"word2vec = gensim.downloader.load(\"word2vec-google-news-300\")"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def document_vector(doc):\n",
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" \"\"\"Create document vectors by averaging word vectors. Remove out-of-vocabulary words.\"\"\"\n",
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" return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = [document_vector(doc) for doc in x_train]\n",
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"x_dev = [document_vector(doc) for doc in x_dev]\n",
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"x_test = [document_vector(doc) for doc in x_test]"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"class NeuralNetwork(torch.nn.Module): \n",
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" def __init__(self, hidden_size):\n",
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" super(NeuralNetwork, self).__init__()\n",
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" self.l1 = torch.nn.Linear(300, hidden_size)\n",
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" self.l2 = torch.nn.Linear(hidden_size, 1)\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.l1(x)\n",
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" x = torch.relu(x)\n",
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" x = self.l2(x)\n",
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" x = torch.sigmoid(x)\n",
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" return x"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"hidden_size = 600\n",
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"epochs = 5\n",
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"batch_size = 15\n",
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"model = NeuralNetwork(hidden_size)\n",
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"criterion = torch.nn.BCELoss()\n",
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"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/tomasz/.local/lib/python3.8/site-packages/torch/autograd/__init__.py:130: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
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" Variable._execution_engine.run_backward(\n"
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]
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}
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],
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"source": [
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"for epoch in range(epochs):\n",
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" model.train()\n",
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" for i in range(0, y_train.shape[0], batch_size):\n",
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" X = x_train[i:i+batch_size]\n",
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" X = torch.tensor(X)\n",
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" y = y_train[i:i+batch_size]\n",
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" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
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" \n",
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" outputs = model(X.float())\n",
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" loss = criterion(outputs, y)\n",
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" \n",
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" optimizer.zero_grad()\n",
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" loss.backward()\n",
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" optimizer.step()"
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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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"source": [
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"y_dev = []\n",
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"y_test = []"
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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": 20,
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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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"NeuralNetwork(\n",
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" (l1): Linear(in_features=300, out_features=600, bias=True)\n",
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" (l2): Linear(in_features=600, out_features=1, bias=True)\n",
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")"
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]
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},
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"execution_count": 20,
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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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"model.eval()"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"with torch.no_grad():\n",
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" for i in range(0, len(x_dev), batch_size):\n",
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" X = x_dev[i:i+batch_size]\n",
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" X = torch.tensor(X)\n",
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" outputs = model(X.float()) \n",
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" prediction = (outputs > 0.5)\n",
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" y_dev.extend(prediction)\n",
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"\n",
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" for i in range(0, len(x_test), batch_size):\n",
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" X = x_test[i:i+batch_size]\n",
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" X = torch.tensor(X)\n",
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" outputs = model(X.float())\n",
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" y = (outputs > 0.5)\n",
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" y_test.extend(prediction)"
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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": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
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"y_test = np.asarray(y_test, dtype=np.int32)\n",
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"\n",
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"y_dev = pd.DataFrame({'label':y_dev})\n",
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"y_test = pd.DataFrame({'label':y_test})\n",
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"\n",
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"y_dev.to_csv(r'dev-0/out.tsv', sep='\\t', index=False, header=False)\n",
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"y_test.to_csv(r'test-A/out.tsv', sep='\\t', index=False, header=False)"
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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": 4
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}
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95
nural_network.py
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95
nural_network.py
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import pandas as pd
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import numpy as np
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import torch
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from nltk.tokenize import word_tokenize
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import gensim.downloader
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x_train = pd.read_table('train/in.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])
|
||||
y_train = pd.read_table('train/expected.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['label'])
|
||||
x_dev = pd.read_table('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
|
||||
x_test = pd.read_table('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
|
||||
|
||||
x_train = x_train.content.str.lower()
|
||||
x_dev = x_dev.content.str.lower()
|
||||
x_test = x_test.content.str.lower()
|
||||
|
||||
x_train = [word_tokenize(content) for content in x_train]
|
||||
x_dev = [word_tokenize(content) for content in x_dev]
|
||||
x_test = [word_tokenize(content) for content in x_test]
|
||||
|
||||
word2vec = gensim.downloader.load("word2vec-google-news-300")
|
||||
|
||||
def document_vector(doc):
|
||||
"""Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
|
||||
return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)
|
||||
|
||||
x_train = [document_vector(doc) for doc in x_train]
|
||||
x_dev = [document_vector(doc) for doc in x_dev]
|
||||
x_test = [document_vector(doc) for doc in x_test]
|
||||
|
||||
class NeuralNetwork(torch.nn.Module):
|
||||
def __init__(self, hidden_size):
|
||||
super(NeuralNetwork, self).__init__()
|
||||
self.l1 = torch.nn.Linear(300, hidden_size)
|
||||
self.l2 = torch.nn.Linear(hidden_size, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.l1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.l2(x)
|
||||
x = torch.sigmoid(x)
|
||||
return x
|
||||
|
||||
|
||||
hidden_size = 600
|
||||
epochs = 5
|
||||
batch_size = 15
|
||||
model = NeuralNetwork(hidden_size)
|
||||
criterion = torch.nn.BCELoss()
|
||||
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
|
||||
|
||||
for epoch in range(epochs):
|
||||
model.train()
|
||||
for i in range(0, y_train.shape[0], batch_size):
|
||||
X = x_train[i:i+batch_size]
|
||||
X = torch.tensor(X)
|
||||
y = y_train[i:i+batch_size]
|
||||
y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
|
||||
|
||||
outputs = model(X.float())
|
||||
loss = criterion(outputs, y)
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
y_dev = []
|
||||
y_test = []
|
||||
|
||||
model.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
for i in range(0, len(x_dev), batch_size):
|
||||
X = x_dev[i:i+batch_size]
|
||||
X = torch.tensor(X)
|
||||
outputs = model(X.float())
|
||||
prediction = (outputs > 0.5)
|
||||
y_dev.extend(prediction)
|
||||
|
||||
for i in range(0, len(x_test), batch_size):
|
||||
X = x_test[i:i+batch_size]
|
||||
X = torch.tensor(X)
|
||||
outputs = model(X.float())
|
||||
y = (outputs > 0.5)
|
||||
y_test.extend(prediction)
|
||||
|
||||
|
||||
y_dev = np.asarray(y_dev, dtype=np.int32)
|
||||
y_test = np.asarray(y_test, dtype=np.int32)
|
||||
|
||||
y_dev = pd.DataFrame({'label':y_dev})
|
||||
y_test = pd.DataFrame({'label':y_test})
|
||||
|
||||
y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
|
||||
y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
|
||||
1
out-header.tsv
Normal file
1
out-header.tsv
Normal file
@ -0,0 +1 @@
|
||||
Label
|
||||
|
5152
test-A/in.tsv
Normal file
5152
test-A/in.tsv
Normal file
File diff suppressed because one or more lines are too long
2408
test-A/out.tsv
Normal file
2408
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
289579
train/expected.tsv
Normal file
289579
train/expected.tsv
Normal file
File diff suppressed because it is too large
Load Diff
289579
train/in.tsv
Normal file
289579
train/in.tsv
Normal file
File diff suppressed because one or more lines are too long
14
wyniki.txt
Normal file
14
wyniki.txt
Normal file
@ -0,0 +1,14 @@
|
||||
Bayes:
|
||||
Likelihood 0.0000
|
||||
Accuracy 0.7367
|
||||
F1.0 0.4367
|
||||
Precision 0.8997
|
||||
Recall 0.2883
|
||||
|
||||
Logistic Regression:
|
||||
Likelihood 0.0000
|
||||
Accuracy 0.7523
|
||||
F1.0 0.6143
|
||||
Precision 0.6842
|
||||
Recall 0.5573
|
||||
|
||||
Loading…
Reference in New Issue
Block a user