skończony projekt
This commit is contained in:
Anna Nowak
parent
ca9cd56b86
commit
c824c34f8c
6
.gitignore
vendored
6
.gitignore
vendored
@ -1,3 +1,5 @@
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train/train.tsv
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.ipynb_checkpoints*
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word2vec.model
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.ipynb_checkpoints/*
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fasttext.model*
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nn.model
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geval
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@ -1,204 +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": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: gensim in c:\\users\\annad\\anaconda3\\lib\\site-packages (3.8.3)\n",
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"Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (5.0.0)\n",
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"Requirement already satisfied: six>=1.5.0 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (1.15.0)\n",
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"Requirement already satisfied: numpy>=1.11.3 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (1.19.2)\n",
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"Requirement already satisfied: scipy>=0.18.1 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (1.5.2)\n",
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"Requirement already satisfied: Cython==0.29.14 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (0.29.14)\n"
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]
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}
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],
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"source": [
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"!pip install gensim"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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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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"from gensim.test.utils import common_texts\n",
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"from gensim.models import Word2Vec\n",
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"import os.path"
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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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"import gzip\n",
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"import shutil\n",
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"with gzip.open('train/train.tsv.gz', 'rb') as f_in:\n",
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" with open('train/train.tsv', 'wb') as f_out:\n",
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" shutil.copyfileobj(f_in, f_out)"
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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": 18,
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"metadata": {},
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||||
"outputs": [
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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",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" 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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"\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>Ball</th>\n",
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" <th>Text</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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" <th>0</th>\n",
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" <td>1</td>\n",
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||||
" <td>Mindaugas Budzinauskas wierzy w odbudowę formy...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>Przyjmujący reprezentacji Polski wrócił do PGE...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0</td>\n",
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" <td>FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
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" <td>Aleksander Filipiak: Czuję się dobrze w nowym ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0</td>\n",
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" <td>Victoria Carl i Aleksiej Czerwotkin mistrzami ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\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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" <th>98127</th>\n",
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" <td>1</td>\n",
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||||
" <td>Kamil Syprzak zaczyna kolekcjonować trofea. FC...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>98128</th>\n",
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" <td>1</td>\n",
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" <td>Holandia: dwa gole Piotra Parzyszka Piotr Parz...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>98129</th>\n",
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" <td>1</td>\n",
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" <td>Sparingowo: Korona gorsza od Stali. Lettieri s...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>98130</th>\n",
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" <td>1</td>\n",
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" <td>Vive - Wisła. Ośmiu debiutantów w tegorocznej ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>98131</th>\n",
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" <td>1</td>\n",
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" <td>WTA Miami: Timea Bacsinszky pokonana, Swietłan...</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>98132 rows × 2 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Ball Text\n",
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"0 1 Mindaugas Budzinauskas wierzy w odbudowę formy...\n",
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"1 1 Przyjmujący reprezentacji Polski wrócił do PGE...\n",
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||||
"2 0 FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...\n",
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"3 1 Aleksander Filipiak: Czuję się dobrze w nowym ...\n",
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"4 0 Victoria Carl i Aleksiej Czerwotkin mistrzami ...\n",
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"... ... ...\n",
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"98127 1 Kamil Syprzak zaczyna kolekcjonować trofea. FC...\n",
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"98128 1 Holandia: dwa gole Piotra Parzyszka Piotr Parz...\n",
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"98129 1 Sparingowo: Korona gorsza od Stali. Lettieri s...\n",
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"98130 1 Vive - Wisła. Ośmiu debiutantów w tegorocznej ...\n",
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"98131 1 WTA Miami: Timea Bacsinszky pokonana, Swietłan...\n",
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"\n",
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"[98132 rows x 2 columns]"
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]
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},
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"execution_count": 18,
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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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"data = pd.read_csv('train/train.tsv', sep='\\t', names=[\"Ball\",\"Text\"])\n",
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"data"
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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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"model = None\n",
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"if not os.path.isfile('word2vec.model'): \n",
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" model = Word2Vec(sentences=data[\"Text\"], window=5, min_count=1, workers=5)\n",
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" model.save(\"word2vec.model\")\n",
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"else:"
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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": "3.8.5"
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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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5452
dev-0/out.tsv
Normal file
5452
dev-0/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
@ -9,8 +9,12 @@
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"import pandas as pd\n",
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"import numpy as np\n",
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"from gensim.test.utils import common_texts\n",
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"from gensim.models import Word2Vec\n",
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"import os.path"
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"from gensim.models import FastText\n",
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"import os.path\n",
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"import gzip\n",
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"import shutil\n",
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"import torch\n",
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"import torch.optim as optim"
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]
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},
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{
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@ -19,8 +23,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import gzip\n",
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"import shutil\n",
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"features = 100\n",
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"batch_size = 16\n",
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"criterion = torch.nn.BCELoss()\n",
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"\n",
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"with gzip.open('train/train.tsv.gz', 'rb') as f_in:\n",
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" with open('train/train.tsv', 'wb') as f_out:\n",
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" shutil.copyfileobj(f_in, f_out)"
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@ -33,105 +39,19 @@
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"outputs": [
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{
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"data": {
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"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
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||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Ball</th>\n",
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" <th>Text</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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" <th>0</th>\n",
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||||
" <td>1</td>\n",
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||||
" <td>Mindaugas Budzinauskas wierzy w odbudowę formy...</td>\n",
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" </tr>\n",
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||||
" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>Przyjmujący reprezentacji Polski wrócił do PGE...</td>\n",
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" </tr>\n",
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||||
" <tr>\n",
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" <th>2</th>\n",
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" <td>0</td>\n",
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||||
" <td>FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...</td>\n",
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||||
" </tr>\n",
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||||
" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
|
||||
" <td>Aleksander Filipiak: Czuję się dobrze w nowym ...</td>\n",
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||||
" </tr>\n",
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||||
" <tr>\n",
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||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
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||||
" <td>Victoria Carl i Aleksiej Czerwotkin mistrzami ...</td>\n",
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||||
" </tr>\n",
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||||
" <tr>\n",
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||||
" <th>...</th>\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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||||
" <th>98127</th>\n",
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||||
" <td>1</td>\n",
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" <td>Kamil Syprzak zaczyna kolekcjonować trofea. FC...</td>\n",
|
||||
" </tr>\n",
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||||
" <tr>\n",
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||||
" <th>98128</th>\n",
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||||
" <td>1</td>\n",
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||||
" <td>Holandia: dwa gole Piotra Parzyszka Piotr Parz...</td>\n",
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||||
" </tr>\n",
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||||
" <tr>\n",
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||||
" <th>98129</th>\n",
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||||
" <td>1</td>\n",
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||||
" <td>Sparingowo: Korona gorsza od Stali. Lettieri s...</td>\n",
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||||
" </tr>\n",
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||||
" <tr>\n",
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||||
" <th>98130</th>\n",
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||||
" <td>1</td>\n",
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" <td>Vive - Wisła. Ośmiu debiutantów w tegorocznej ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>98131</th>\n",
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" <td>1</td>\n",
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" <td>WTA Miami: Timea Bacsinszky pokonana, Swietłan...</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>98132 rows × 2 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Ball Text\n",
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"0 1 Mindaugas Budzinauskas wierzy w odbudowę formy...\n",
|
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"1 1 Przyjmujący reprezentacji Polski wrócił do PGE...\n",
|
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"2 0 FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...\n",
|
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"3 1 Aleksander Filipiak: Czuję się dobrze w nowym ...\n",
|
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"4 0 Victoria Carl i Aleksiej Czerwotkin mistrzami ...\n",
|
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"... ... ...\n",
|
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"98127 1 Kamil Syprzak zaczyna kolekcjonować trofea. FC...\n",
|
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"98128 1 Holandia: dwa gole Piotra Parzyszka Piotr Parz...\n",
|
||||
"98129 1 Sparingowo: Korona gorsza od Stali. Lettieri s...\n",
|
||||
"98130 1 Vive - Wisła. Ośmiu debiutantów w tegorocznej ...\n",
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"98131 1 WTA Miami: Timea Bacsinszky pokonana, Swietłan...\n",
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"\n",
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"[98132 rows x 2 columns]"
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"0 [mindaugas, budzinauskas, wierzy, w, odbudowę,...\n",
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"1 [przyjmujący, reprezentacji, polski, wrócił, d...\n",
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"2 [fen, 9:, zapowiedź, walki, róża, gumienna, vs...\n",
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"3 [aleksander, filipiak:, czuję, się, dobrze, w,...\n",
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"4 [victoria, carl, i, aleksiej, czerwotkin, mist...\n",
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" ... \n",
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"98127 [kamil, syprzak, zaczyna, kolekcjonować, trofe...\n",
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"98128 [holandia:, dwa, gole, piotra, parzyszka, piot...\n",
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||||
"98129 [sparingowo:, korona, gorsza, od, stali., lett...\n",
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||||
"98130 [vive, -, wisła., ośmiu, debiutantów, w, tegor...\n",
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"98131 [wta, miami:, timea, bacsinszky, pokonana,, sw...\n",
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"Name: Text, Length: 98132, dtype: object"
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]
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},
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"execution_count": 3,
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],
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"source": [
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"data = pd.read_csv('train/train.tsv', sep='\\t', names=[\"Ball\",\"Text\"])\n",
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"data"
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"data[\"Text\"] = data[\"Text\"].str.lower().str.split()\n",
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"data[\"Text\"]"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"model = None\n",
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"sentences = [x.split() for x in data[\"Text\"]]\n",
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"if not os.path.isfile('word2vec.model'):\n",
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" model = Word2Vec(sentences=data[\"Text\"])\n",
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" model.save(\"word2vec.model\")\n",
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" model.train(sentences, total_examples=len(sentences), epochs=10)\n",
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"ft_model = None\n",
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"if not os.path.isfile('fasttext.model'):\n",
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" ft_model = FastText(size=features, window=3, min_count=1)\n",
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" ft_model.build_vocab(sentences=data[\"Text\"])\n",
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" ft_model.train(data[\"Text\"], total_examples=len(data[\"Text\"]), epochs=10)\n",
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" ft_model.save(\"fasttext.model\")\n",
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"else:\n",
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" model = Word2Vec.load(\"word2vec.model\")"
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" ft_model = FastText.load(\"fasttext.model\")\n",
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" \n",
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"def document_vector(doc):\n",
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" result = ft_model.wv[doc]\n",
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" return np.max(result, 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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = [document_vector(x) for x in data[\"Text\"]]\n",
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"Y = data[\"Ball\"]"
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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": [
|
||||
"class NeuralNetworkModel(torch.nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(NeuralNetworkModel, self).__init__()\n",
|
||||
" self.fc1 = torch.nn.Linear(features,200)\n",
|
||||
" self.fc2 = torch.nn.Linear(200,150)\n",
|
||||
" self.fc3 = torch.nn.Linear(150,1)\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" x = self.fc1(x)\n",
|
||||
" x = torch.relu(x)\n",
|
||||
" x = self.fc2(x)\n",
|
||||
" x = torch.sigmoid(x)\n",
|
||||
" x = self.fc3(x)\n",
|
||||
" x = torch.sigmoid(x)\n",
|
||||
" return x\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"def get_loss_acc(model, X_dataset, Y_dataset):\n",
|
||||
" loss_score = 0\n",
|
||||
" acc_score = 0\n",
|
||||
" items_total = 0\n",
|
||||
" model.eval()\n",
|
||||
" for i in range(0, Y_dataset.shape[0], batch_size):\n",
|
||||
" x = X_dataset[i:i+batch_size]\n",
|
||||
" x = torch.tensor(x)\n",
|
||||
" y = Y_dataset[i:i+batch_size]\n",
|
||||
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)\n",
|
||||
" y_predictions = model(x)\n",
|
||||
" acc_score += torch.sum((y_predictions >= 0.5) == y).item()\n",
|
||||
" items_total += y.shape[0] \n",
|
||||
"\n",
|
||||
" loss = criterion(y_predictions, y)\n",
|
||||
"\n",
|
||||
" loss_score += loss.item() * y.shape[0] \n",
|
||||
" return (loss_score / items_total), (acc_score / items_total)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model_path = 'nn.model'\n",
|
||||
"nn_model = NeuralNetworkModel()\n",
|
||||
" \n",
|
||||
"if not os.path.isfile(model_path):\n",
|
||||
" optimizer = optim.SGD(nn_model.parameters(), lr=0.1)\n",
|
||||
"\n",
|
||||
" display(get_loss_acc(nn_model, X, Y))\n",
|
||||
" for epoch in range(5):\n",
|
||||
" nn_model.train()\n",
|
||||
" for i in range(0, len(X), batch_size):\n",
|
||||
" x = X[i:i+batch_size]\n",
|
||||
" x = torch.tensor(x)\n",
|
||||
"\n",
|
||||
" y = Y[i:i+batch_size]\n",
|
||||
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)\n",
|
||||
"\n",
|
||||
" y_predictions = nn_model(x)\n",
|
||||
" loss = criterion(y_predictions, y)\n",
|
||||
"\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
" display(get_loss_acc(nn_model, X, Y))\n",
|
||||
" torch.save(nn_model.state_dict(), model_path)\n",
|
||||
"else:\n",
|
||||
" nn_model.load_state_dict(torch.load(model_path))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_dev = pd.read_csv('dev-0/in.tsv', sep='\\t', names=[\"Text\"])[\"Text\"]\n",
|
||||
"y_dev = pd.read_csv('dev-0/expected.tsv', sep='\\t', names=[\"Ball\"])[\"Ball\"]\n",
|
||||
"x_dev = [document_vector(x) for x in x_dev.str.lower().str.split()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "KeyError",
|
||||
"evalue": "\"word 'Mindaugas' not in vocabulary\"",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-6-dec2e93bf676>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprepared_training_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Text'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprepared_training_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Text'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[0;32m 4198\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4199\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4200\u001b[1;33m \u001b[0mmapped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4202\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mpandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;32m<ipython-input-6-dec2e93bf676>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprepared_training_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Text'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprepared_training_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Text'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\gensim\\models\\keyedvectors.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, entities)\u001b[0m\n\u001b[0;32m 353\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mentities\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mentity\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mentity\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentities\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 356\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__contains__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mentity\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\gensim\\models\\keyedvectors.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 353\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mentities\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mentity\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mentity\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentities\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 356\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__contains__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mentity\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\gensim\\models\\keyedvectors.py\u001b[0m in \u001b[0;36mget_vector\u001b[1;34m(self, word)\u001b[0m\n\u001b[0;32m 469\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 470\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 471\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 472\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 473\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mwords_closer_than\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\gensim\\models\\keyedvectors.py\u001b[0m in \u001b[0;36mword_vec\u001b[1;34m(self, word, use_norm)\u001b[0m\n\u001b[0;32m 466\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 467\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 468\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"word '%s' not in vocabulary\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 469\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 470\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mKeyError\u001b[0m: \"word 'Mindaugas' not in vocabulary\""
|
||||
]
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(0.45761072419184756, 0.7694424064563463)"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"prepared_training_data['Text'] = prepared_training_data['Text'].apply(lambda x: model.wv[x.split()])"
|
||||
"get_loss_acc(nn_model, x_dev, y_dev)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_dev_prediction = nn_model(torch.tensor(x_dev))\n",
|
||||
"y_dev_prediction = np.array([round(y) for y in y_dev_prediction.flatten().tolist()])\n",
|
||||
"np.savetxt(\"dev-0/out.tsv\", y_dev_prediction, fmt='%d')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_test = pd.read_csv('test-A/in.tsv', sep='\\t', names=[\"Text\"])[\"Text\"]\n",
|
||||
"x_test = [document_vector(x) for x in x_test.str.lower().str.split()]\n",
|
||||
"y_test_prediction = nn_model(torch.tensor(x_test))\n",
|
||||
"y_test_prediction = np.array([round(y) for y in y_test_prediction.flatten().tolist()])\n",
|
||||
"np.savetxt(\"test-A/out.tsv\", y_test_prediction, fmt='%d')"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
5447
test-A/out.tsv
Normal file
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test-A/out.tsv
Normal file
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