skończony projekt

This commit is contained in:
Anna Nowak 2021-05-25 22:38:13 +02:00
parent ca9cd56b86
commit c824c34f8c
5 changed files with 11071 additions and 333 deletions

6
.gitignore vendored
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@ -1,3 +1,5 @@
train/train.tsv
.ipynb_checkpoints*
word2vec.model
.ipynb_checkpoints/*
fasttext.model*
nn.model
geval

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@ -1,204 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gensim in c:\\users\\annad\\anaconda3\\lib\\site-packages (3.8.3)\n",
"Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (5.0.0)\n",
"Requirement already satisfied: six>=1.5.0 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (1.15.0)\n",
"Requirement already satisfied: numpy>=1.11.3 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (1.19.2)\n",
"Requirement already satisfied: scipy>=0.18.1 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (1.5.2)\n",
"Requirement already satisfied: Cython==0.29.14 in c:\\users\\annad\\anaconda3\\lib\\site-packages (from gensim) (0.29.14)\n"
]
}
],
"source": [
"!pip install gensim"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from gensim.test.utils import common_texts\n",
"from gensim.models import Word2Vec\n",
"import os.path"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import gzip\n",
"import shutil\n",
"with gzip.open('train/train.tsv.gz', 'rb') as f_in:\n",
" with open('train/train.tsv', 'wb') as f_out:\n",
" shutil.copyfileobj(f_in, f_out)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Mindaugas Budzinauskas wierzy w odbudowę formy...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Przyjmujący reprezentacji Polski wrócił do PGE...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Aleksander Filipiak: Czuję się dobrze w nowym ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>Victoria Carl i Aleksiej Czerwotkin mistrzami ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
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" <th>98127</th>\n",
" <td>1</td>\n",
" <td>Kamil Syprzak zaczyna kolekcjonować trofea. FC...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98128</th>\n",
" <td>1</td>\n",
" <td>Holandia: dwa gole Piotra Parzyszka Piotr Parz...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98129</th>\n",
" <td>1</td>\n",
" <td>Sparingowo: Korona gorsza od Stali. Lettieri s...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98130</th>\n",
" <td>1</td>\n",
" <td>Vive - Wisła. Ośmiu debiutantów w tegorocznej ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98131</th>\n",
" <td>1</td>\n",
" <td>WTA Miami: Timea Bacsinszky pokonana, Swietłan...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>98132 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Ball Text\n",
"0 1 Mindaugas Budzinauskas wierzy w odbudowę formy...\n",
"1 1 Przyjmujący reprezentacji Polski wrócił do PGE...\n",
"2 0 FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...\n",
"3 1 Aleksander Filipiak: Czuję się dobrze w nowym ...\n",
"4 0 Victoria Carl i Aleksiej Czerwotkin mistrzami ...\n",
"... ... ...\n",
"98127 1 Kamil Syprzak zaczyna kolekcjonować trofea. FC...\n",
"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",
"98131 1 WTA Miami: Timea Bacsinszky pokonana, Swietłan...\n",
"\n",
"[98132 rows x 2 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('train/train.tsv', sep='\\t', names=[\"Ball\",\"Text\"])\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"model = None\n",
"if not os.path.isfile('word2vec.model'): \n",
" model = Word2Vec(sentences=data[\"Text\"], window=5, min_count=1, workers=5)\n",
" model.save(\"word2vec.model\")\n",
"else:"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
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"nbformat": 4,
"nbformat_minor": 4
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5452
dev-0/out.tsv Normal file

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@ -9,8 +9,12 @@
"import pandas as pd\n",
"import numpy as np\n",
"from gensim.test.utils import common_texts\n",
"from gensim.models import Word2Vec\n",
"import os.path"
"from gensim.models import FastText\n",
"import os.path\n",
"import gzip\n",
"import shutil\n",
"import torch\n",
"import torch.optim as optim"
]
},
{
@ -19,8 +23,10 @@
"metadata": {},
"outputs": [],
"source": [
"import gzip\n",
"import shutil\n",
"features = 100\n",
"batch_size = 16\n",
"criterion = torch.nn.BCELoss()\n",
"\n",
"with gzip.open('train/train.tsv.gz', 'rb') as f_in:\n",
" with open('train/train.tsv', 'wb') as f_out:\n",
" shutil.copyfileobj(f_in, f_out)"
@ -33,105 +39,19 @@
"outputs": [
{
"data": {
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ball</th>\n",
" <th>Text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Mindaugas Budzinauskas wierzy w odbudowę formy...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Przyjmujący reprezentacji Polski wrócił do PGE...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Aleksander Filipiak: Czuję się dobrze w nowym ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>Victoria Carl i Aleksiej Czerwotkin mistrzami ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98127</th>\n",
" <td>1</td>\n",
" <td>Kamil Syprzak zaczyna kolekcjonować trofea. FC...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98128</th>\n",
" <td>1</td>\n",
" <td>Holandia: dwa gole Piotra Parzyszka Piotr Parz...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98129</th>\n",
" <td>1</td>\n",
" <td>Sparingowo: Korona gorsza od Stali. Lettieri s...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98130</th>\n",
" <td>1</td>\n",
" <td>Vive - Wisła. Ośmiu debiutantów w tegorocznej ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98131</th>\n",
" <td>1</td>\n",
" <td>WTA Miami: Timea Bacsinszky pokonana, Swietłan...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>98132 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Ball Text\n",
"0 1 Mindaugas Budzinauskas wierzy w odbudowę formy...\n",
"1 1 Przyjmujący reprezentacji Polski wrócił do PGE...\n",
"2 0 FEN 9: Zapowiedź walki Róża Gumienna vs Katarz...\n",
"3 1 Aleksander Filipiak: Czuję się dobrze w nowym ...\n",
"4 0 Victoria Carl i Aleksiej Czerwotkin mistrzami ...\n",
"... ... ...\n",
"98127 1 Kamil Syprzak zaczyna kolekcjonować trofea. FC...\n",
"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",
"98131 1 WTA Miami: Timea Bacsinszky pokonana, Swietłan...\n",
"\n",
"[98132 rows x 2 columns]"
"0 [mindaugas, budzinauskas, wierzy, w, odbudowę,...\n",
"1 [przyjmujący, reprezentacji, polski, wrócił, d...\n",
"2 [fen, 9:, zapowiedź, walki, róża, gumienna, vs...\n",
"3 [aleksander, filipiak:, czuję, się, dobrze, w,...\n",
"4 [victoria, carl, i, aleksiej, czerwotkin, mist...\n",
" ... \n",
"98127 [kamil, syprzak, zaczyna, kolekcjonować, trofe...\n",
"98128 [holandia:, dwa, gole, piotra, parzyszka, piot...\n",
"98129 [sparingowo:, korona, gorsza, od, stali., lett...\n",
"98130 [vive, -, wisła., ośmiu, debiutantów, w, tegor...\n",
"98131 [wta, miami:, timea, bacsinszky, pokonana,, sw...\n",
"Name: Text, Length: 98132, dtype: object"
]
},
"execution_count": 3,
@ -141,7 +61,8 @@
],
"source": [
"data = pd.read_csv('train/train.tsv', sep='\\t', names=[\"Ball\",\"Text\"])\n",
"data"
"data[\"Text\"] = data[\"Text\"].str.lower().str.split()\n",
"data[\"Text\"]"
]
},
{
@ -150,42 +71,162 @@
"metadata": {},
"outputs": [],
"source": [
"model = None\n",
"sentences = [x.split() for x in data[\"Text\"]]\n",
"if not os.path.isfile('word2vec.model'):\n",
" model = Word2Vec(sentences=data[\"Text\"])\n",
" model.save(\"word2vec.model\")\n",
" model.train(sentences, total_examples=len(sentences), epochs=10)\n",
"ft_model = None\n",
"if not os.path.isfile('fasttext.model'):\n",
" ft_model = FastText(size=features, window=3, min_count=1)\n",
" ft_model.build_vocab(sentences=data[\"Text\"])\n",
" ft_model.train(data[\"Text\"], total_examples=len(data[\"Text\"]), epochs=10)\n",
" ft_model.save(\"fasttext.model\")\n",
"else:\n",
" model = Word2Vec.load(\"word2vec.model\")"
" ft_model = FastText.load(\"fasttext.model\")\n",
" \n",
"def document_vector(doc):\n",
" result = ft_model.wv[doc]\n",
" return np.max(result, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"X = [document_vector(x) for x in data[\"Text\"]]\n",
"Y = data[\"Ball\"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"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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