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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": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sklearn\n",
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"from sklearn.pipeline import make_pipeline\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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"from sklearn.naive_bayes import MultinomialNB\n",
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"from sklearn.preprocessing import LabelEncoder "
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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": 47,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def getInput(path):\n",
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" with open(path,encoding='utf-8') as f:\n",
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" return 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": 48,
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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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"/c/Users/mkoci/Desktop/naiwny_bayes\n"
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]
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}
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],
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"source": [
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"!pwd"
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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": 49,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_in=getInput('./train/in.tsv')\n",
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"train_expected=getInput('./train/expected.tsv')\n",
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"test_in=getInput('./test-A/in.tsv')\n",
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"dev_in=getInput('./dev-0/in.tsv')\n",
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"dev_expected=getInput('./dev-0/expected.tsv')"
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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": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())\n",
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"encTransform = LabelEncoder().fit_transform(train_expected)\n",
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"model = pipeline.fit(train_in, encTransform)\n",
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"dev_predicted = model.predict(dev_in)\n",
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"test_predicted = model.predict(test_in)\n"
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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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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('./dev-0/out.tsv', \"w\") as result:\n",
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" for out in dev_predicted:\n",
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" result.write(str(out) + '\\n')\n",
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"with open('./test-A/out.tsv', \"w\") as result:\n",
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" for out in test_predicted:\n",
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" result.write(str(out) + '\\n') "
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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": 55,
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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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"[NbConvertApp] Converting notebook Naiwny_bayes.ipynb to script\n",
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"[NbConvertApp] Writing 1337 bytes to Naiwny_bayes.py\n"
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]
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}
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],
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"source": [
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"!jupyter nbconvert --to script Naiwny_bayes.ipynb"
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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": "3.9.1"
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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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@ -1,76 +0,0 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[46]:
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import sklearn
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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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import numpy as np
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.preprocessing import LabelEncoder
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# In[47]:
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def getInput(path):
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with open(path,encoding='utf-8') as f:
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return f.readlines()
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# In[48]:
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get_ipython().system('pwd')
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# In[49]:
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train_in=getInput('./train/in.tsv')
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train_expected=getInput('./train/expected.tsv')
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test_in=getInput('./test-A/in.tsv')
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dev_in=getInput('./dev-0/in.tsv')
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dev_expected=getInput('./dev-0/expected.tsv')
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# In[50]:
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pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
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encTransform = LabelEncoder().fit_transform(train_expected)
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model = pipeline.fit(train_in, encTransform)
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dev_predicted = model.predict(dev_in)
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test_predicted = model.predict(test_in)
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# In[ ]:
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# In[54]:
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with open('./dev-0/out.tsv', "w") as result:
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for out in dev_predicted:
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result.write(str(out) + '\n')
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with open('./test-A/out.tsv', "w") as result:
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for out in test_predicted:
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result.write(str(out) + '\n')
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# In[55]:
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get_ipython().system('jupyter nbconvert --to script Naiwny_bayes.ipynb')
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# In[ ]:
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155
Untitled.ipynb
155
Untitled.ipynb
@ -1,155 +0,0 @@
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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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"def getInput(path):\n",
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" with open(path,encoding='utf-8') as f:\n",
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" return 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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gensim.downloader as gensim\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import torch\n",
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"from nltk.tokenize import word_tokenize"
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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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"word2vec = gensim.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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# train_in=getInput('./train/in.tsv')\n",
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"# train_expected=getInput('./train/expected.tsv')\n",
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"# test_in=getInput('./test-A/in.tsv')\n",
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"# dev_in=getInput('./dev-0/in.tsv')\n",
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"# dev_expected=getInput('./dev-0/expected.tsv')"
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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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"class NeuralNetworkModel(torch.nn.Module):\n",
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" def __init__(self):\n",
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" super(NeuralNetworkModel, self).__init__()\n",
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" self.l01 = torch.nn.Linear(300, 300)\n",
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" self.l02 = torch.nn.Linear(300, 1)\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.l01(x)\n",
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" x = torch.relu(x)\n",
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" x = self.l02(x)\n",
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" x = torch.sigmoid(x)\n",
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" return x\n",
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"\n",
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"def d2v(doc):\n",
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" return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)\n",
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"x_train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\\t', header=None, error_bad_lines=False, quoting=3)\n",
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"x_train = x_train[0].str.lower()\n",
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"x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n",
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"x_dev = x_dev[0].str.lower()\n",
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"x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n",
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"x_test = x_test[0].str.lower()\n",
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"y_train = pd.read_table('train/expected.tsv', sep='\\t', header=None, quoting=3)\n",
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"y_train = y_train[0]\n",
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"x_train = [word_tokenize(x) for x in x_train]\n",
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"x_dev = [word_tokenize(x) for x in x_dev]\n",
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"x_test = [word_tokenize(x) for x in x_test]\n",
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"x_train = [d2v(doc) for doc in x_train]\n",
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"x_dev = [d2v(doc) for doc in x_dev]\n",
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"x_test = [d2v(doc) for doc in x_test]\n",
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"model = NeuralNetworkModel()\n",
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"BATCH_SIZE = 10\n",
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"criterion = torch.nn.BCELoss()\n",
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"optimizer = torch.optim.Adam(model.parameters())\n",
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"for epoch in range(BATCH_SIZE):\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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" optimizer.zero_grad()\n",
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" outputs = model(X.float())\n",
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" loss = criterion(outputs, y)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"y_dev = []\n",
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"y_test = []\n",
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"model.eval()\n",
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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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" y = (outputs > 0.5)\n",
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" y_dev.extend(y)\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(y)\n",
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"\n",
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"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
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"Y_dev = pd.DataFrame({'label': y_dev})\n",
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"y_test = np.asarray(y_test, dtype=np.int32)\n",
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"Y_test = pd.DataFrame({'label': y_test})\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)\n"
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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": {
|
||||
"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",
|
||||
"mimetype": "text/x-python",
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"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
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"version": "3.9.1"
|
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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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116
Untitled.py
116
Untitled.py
@ -1,116 +0,0 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[2]:
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def getInput(path):
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with open(path,encoding='utf-8') as f:
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return f.readlines()
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# In[6]:
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import gensim.downloader as gensim
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import numpy as np
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import pandas as pd
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import torch
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from nltk.tokenize import word_tokenize
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# In[8]:
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word2vec = gensim.load('word2vec-google-news-300')
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# In[5]:
|
||||
|
||||
|
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# train_in=getInput('./train/in.tsv')
|
||||
# train_expected=getInput('./train/expected.tsv')
|
||||
# test_in=getInput('./test-A/in.tsv')
|
||||
# dev_in=getInput('./dev-0/in.tsv')
|
||||
# dev_expected=getInput('./dev-0/expected.tsv')
|
||||
|
||||
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# In[14]:
|
||||
|
||||
|
||||
class NeuralNetworkModel(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(NeuralNetworkModel, self).__init__()
|
||||
self.l01 = torch.nn.Linear(300, 300)
|
||||
self.l02 = torch.nn.Linear(300, 1)
|
||||
|
||||
def forward(self, x):
|
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x = self.l01(x)
|
||||
x = torch.relu(x)
|
||||
x = self.l02(x)
|
||||
x = torch.sigmoid(x)
|
||||
return x
|
||||
|
||||
def d2v(doc):
|
||||
return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)
|
||||
x_train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\t', header=None, error_bad_lines=False, quoting=3)
|
||||
x_train = x_train[0].str.lower()
|
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x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
|
||||
x_dev = x_dev[0].str.lower()
|
||||
x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
|
||||
x_test = x_test[0].str.lower()
|
||||
y_train = pd.read_table('train/expected.tsv', sep='\t', header=None, quoting=3)
|
||||
y_train = y_train[0]
|
||||
x_train = [word_tokenize(x) for x in x_train]
|
||||
x_dev = [word_tokenize(x) for x in x_dev]
|
||||
x_test = [word_tokenize(x) for x in x_test]
|
||||
x_train = [d2v(doc) for doc in x_train]
|
||||
x_dev = [d2v(doc) for doc in x_dev]
|
||||
x_test = [d2v(doc) for doc in x_test]
|
||||
model = NeuralNetworkModel()
|
||||
BATCH_SIZE = 10
|
||||
criterion = torch.nn.BCELoss()
|
||||
optimizer = torch.optim.Adam(model.parameters())
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||||
for epoch in range(BATCH_SIZE):
|
||||
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)
|
||||
optimizer.zero_grad()
|
||||
outputs = model(X.float())
|
||||
loss = criterion(outputs, y)
|
||||
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())
|
||||
y = (outputs > 0.5)
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||||
y_dev.extend(y)
|
||||
|
||||
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(y)
|
||||
|
||||
y_dev = np.asarray(y_dev, dtype=np.int32)
|
||||
Y_dev = pd.DataFrame({'label': y_dev})
|
||||
y_test = np.asarray(y_test, dtype=np.int32)
|
||||
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)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,222 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf-8
|
||||
|
||||
# In[1]:
|
||||
|
||||
|
||||
import csv
|
||||
|
||||
|
||||
# In[2]:
|
||||
|
||||
|
||||
get_ipython().system('pip install gensim')
|
||||
|
||||
|
||||
# In[17]:
|
||||
|
||||
|
||||
import nltk
|
||||
nltk.download('punkt')
|
||||
|
||||
|
||||
# In[9]:
|
||||
|
||||
|
||||
get_ipython().system('pip install nltk')
|
||||
|
||||
|
||||
# In[3]:
|
||||
|
||||
|
||||
get_ipython().system('pip install torch')
|
||||
|
||||
|
||||
# In[4]:
|
||||
|
||||
|
||||
import gensim.downloader
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
|
||||
|
||||
# In[5]:
|
||||
|
||||
|
||||
import torch.nn as nn
|
||||
from nltk import word_tokenize
|
||||
|
||||
|
||||
# In[13]:
|
||||
|
||||
|
||||
header_names = ["content", "id", "label"]
|
||||
|
||||
|
||||
# In[23]:
|
||||
|
||||
|
||||
class FF(nn.Module):
|
||||
def __init__(self, input_dim, hidden_dim, output_dim):
|
||||
super(FF, self).__init__()
|
||||
self.fc1 = nn.Linear(input_dim, hidden_dim)
|
||||
self.relu1 = nn.ReLU()
|
||||
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
|
||||
self.relu2 = nn.ReLU()
|
||||
self.fc3 = nn.Linear(hidden_dim, output_dim)
|
||||
|
||||
def forward(self, x):
|
||||
out = self.fc1(x)
|
||||
out = self.relu1(out)
|
||||
out = self.relu2(out)
|
||||
out = self.fc3(out)
|
||||
return torch.sigmoid(out)
|
||||
|
||||
train_set_labels = pd.read_table(
|
||||
"train/expected.tsv",
|
||||
error_bad_lines=False,
|
||||
quoting=csv.QUOTE_NONE,
|
||||
header=None,
|
||||
names=header_names[2:],
|
||||
)
|
||||
|
||||
train_set_features = pd.read_table(
|
||||
"train/in.tsv.xz",
|
||||
error_bad_lines=False,
|
||||
quoting=csv.QUOTE_NONE,
|
||||
header=None,
|
||||
names=header_names[:2],
|
||||
)
|
||||
|
||||
|
||||
test_set = pd.read_table(
|
||||
"test-A/in.tsv.xz",
|
||||
error_bad_lines=False,
|
||||
header=None,
|
||||
quoting=csv.QUOTE_NONE,
|
||||
names=header_names[:2],
|
||||
)
|
||||
|
||||
dev_set = pd.read_table(
|
||||
"dev-0/in.tsv.xz",
|
||||
error_bad_lines=False,
|
||||
header=None,
|
||||
quoting=csv.QUOTE_NONE,
|
||||
names=header_names[:2],
|
||||
)
|
||||
X_train = train_set_features["content"].str.lower()
|
||||
y_train = train_set_labels["label"]
|
||||
|
||||
X_dev = dev_set["content"].str.lower()
|
||||
X_test = test_set["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")
|
||||
|
||||
|
||||
# In[24]:
|
||||
|
||||
|
||||
X_train = [
|
||||
np.mean(
|
||||
[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
|
||||
axis=0,
|
||||
)
|
||||
for content in X_train
|
||||
]
|
||||
X_dev = [
|
||||
np.mean(
|
||||
[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
|
||||
axis=0,
|
||||
)
|
||||
for content in X_dev
|
||||
]
|
||||
|
||||
X_test = [
|
||||
np.mean(
|
||||
[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
|
||||
axis=0,
|
||||
)
|
||||
for content in X_test
|
||||
]
|
||||
hidden_layer = 650
|
||||
epochs = 15
|
||||
batch_size = 10
|
||||
|
||||
|
||||
# In[27]:
|
||||
|
||||
|
||||
output_dim = 1
|
||||
|
||||
|
||||
input_dim =300
|
||||
model = FF(input_dim, hidden_layer, output_dim)
|
||||
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
|
||||
criterion = torch.nn.BCELoss()
|
||||
|
||||
|
||||
# In[28]:
|
||||
|
||||
|
||||
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()
|
||||
test_prediction = []
|
||||
dev_prediction = []
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
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())
|
||||
|
||||
prediction = outputs > 0.5
|
||||
test_prediction += prediction.tolist()
|
||||
|
||||
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
|
||||
dev_prediction += prediction.tolist()
|
||||
|
||||
test_prediction = np.asarray(test_prediction, dtype=np.int32)
|
||||
dev_prediction = np.asarray(dev_prediction, dtype=np.int32)
|
||||
test_prediction.tofile("./test-A/out.tsv", sep="\n")
|
||||
dev_prediction.tofile("./dev-0/out.tsv", sep="\n")
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
5272
dev-0/in.tsv
5272
dev-0/in.tsv
File diff suppressed because one or more lines are too long
5272
dev-0/out.tsv
5272
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
5152
test-A/in.tsv
5152
test-A/in.tsv
File diff suppressed because one or more lines are too long
5152
test-A/out.tsv
5152
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
289579
train/in.tsv
289579
train/in.tsv
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user