2021-06-09 00:19:16 +02:00
{
2021-06-09 03:01:30 +02:00
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e574fca4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\grzyb\\anaconda3\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
" warnings.warn(msg)\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import csv\n",
"import os.path\n",
"import shutil\n",
"import torch\n",
"from tqdm import tqdm\n",
"from itertools import islice\n",
"from sklearn.model_selection import train_test_split\n",
"from torchtext.vocab import Vocab\n",
"from collections import Counter\n",
"from nltk.tokenize import word_tokenize\n",
"import gensim.downloader as api\n",
"from gensim.models.word2vec import Word2Vec"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b476f295",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting gensim\n",
" Downloading gensim-4.0.1-cp38-cp38-win_amd64.whl (23.9 MB)\n",
"Requirement already satisfied: scipy>=0.18.1 in c:\\users\\grzyb\\anaconda3\\lib\\site-packages (from gensim) (1.6.2)\n",
"Collecting Cython==0.29.21\n",
" Downloading Cython-0.29.21-cp38-cp38-win_amd64.whl (1.7 MB)\n",
"Requirement already satisfied: numpy>=1.11.3 in c:\\users\\grzyb\\anaconda3\\lib\\site-packages (from gensim) (1.20.1)\n",
"Collecting smart-open>=1.8.1\n",
" Downloading smart_open-5.1.0-py3-none-any.whl (57 kB)\n",
"Installing collected packages: smart-open, Cython, gensim\n",
" Attempting uninstall: Cython\n",
" Found existing installation: Cython 0.29.23\n",
" Uninstalling Cython-0.29.23:\n",
" Successfully uninstalled Cython-0.29.23\n",
"Successfully installed Cython-0.29.21 gensim-4.0.1 smart-open-5.1.0\n"
]
}
],
"source": [
"!pip install gensim"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fbe3a657",
"metadata": {},
"outputs": [],
"source": [
"class NERModel(torch.nn.Module):\n",
"\n",
" def __init__(self,):\n",
" super(NERModel, self).__init__()\n",
" self.emb = torch.nn.Embedding(23628,200)\n",
" self.fc1 = torch.nn.Linear(600,9)\n",
" \n",
"\n",
" def forward(self, x):\n",
" x = self.emb(x)\n",
" x = x.reshape(600) \n",
" x = self.fc1(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3497a580",
"metadata": {},
"outputs": [],
"source": [
"def process_output(lines):\n",
" result = []\n",
" for line in lines:\n",
" last_label = None\n",
" new_line = []\n",
" for label in line:\n",
" if(label != \"O\" and label[0:2] == \"I-\"):\n",
" if last_label == None or last_label == \"O\":\n",
" label = label.replace('I-', 'B-')\n",
" else:\n",
" label = \"I-\" + last_label[2:]\n",
" last_label = label\n",
" new_line.append(label)\n",
" x = (\" \".join(new_line))\n",
" result.append(\" \".join(new_line))\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3e78d902",
"metadata": {},
"outputs": [],
"source": [
"def build_vocab(dataset):\n",
" counter = Counter()\n",
" for document in dataset:\n",
" counter.update(document)\n",
" return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ec8537cf",
"metadata": {},
"outputs": [],
"source": [
"def data_process(dt):\n",
" return [ torch.tensor([vocab['<bos>']] +[vocab[token] for token in document ] + [vocab['<eos>']], dtype = torch.long) for document in dt]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "847c958a",
"metadata": {},
"outputs": [],
"source": [
"def labels_process(dt):\n",
" return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "66bee163",
"metadata": {},
"outputs": [],
"source": [
"def predict(input_tokens, labels):\n",
"\n",
" results = []\n",
" \n",
" for i in range(len(input_tokens)):\n",
" line_results = []\n",
" for j in range(1, len(input_tokens[i]) - 1):\n",
" x = input_tokens[i][j-1: j+2].to(device_gpu)\n",
" predicted = ner_model(x.long())\n",
" result = torch.argmax(predicted)\n",
" label = labels[result]\n",
" line_results.append(label)\n",
" results.append(line_results)\n",
"\n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "39046f3f",
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('train/train.tsv.xz', sep='\\t', names=['a', 'b'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9b40a8b6",
"metadata": {},
"outputs": [],
"source": [
"labels = ['O','B-LOC', 'I-LOC','B-MISC', 'I-MISC', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER'] \n",
"train[\"a\"]=train[\"a\"].apply(lambda x: [labels.index(y) for y in x.split()])\n",
"train[\"b\"]=train[\"b\"].apply(lambda x: x.split())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "02a12cbd",
"metadata": {},
"outputs": [],
"source": [
"vocab = build_vocab(train['b'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8cc6d19d",
"metadata": {},
"outputs": [],
"source": [
" tensors = []\n",
"\n",
" for sent in train[\"b\"]:\n",
" sent_tensor = torch.tensor(())\n",
" for word in sent:\n",
" temp = torch.tensor([word[0].isupper(), word[0].isdigit()])\n",
" sent_tensor = torch.cat((sent_tensor, temp))\n",
"\n",
" tensors.append(sent_tensor)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "690085f6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'NVIDIA GeForce RTX 2060'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.get_device_name(0)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "64b2d751",
"metadata": {},
"outputs": [],
"source": [
"device_gpu = torch.device(\"cuda:0\")\n",
"ner_model = NERModel().to(device_gpu)\n",
"criterion = torch.nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.Adam(ner_model.parameters())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "094d7e69",
"metadata": {},
"outputs": [],
"source": [
"train_labels = labels_process(train['a'])\n",
"train_tokens_ids = data_process(train['b'])\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "17291b41",
"metadata": {},
"outputs": [],
"source": [
"train_tensors = [torch.cat((token, tensors[i])) for i, token in enumerate(train_tokens_ids)]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "045b7186",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0\n",
"f1: 0.6373470953763748\n",
"acc: 0.9116419913061858\n",
"epoch: 1\n",
"f1: 0.7973076923076923\n",
"acc: 0.9540771782783307\n",
"epoch: 2\n",
"f1: 0.8640167364016735\n",
"acc: 0.9702287410511612\n",
"epoch: 3\n",
"f1: 0.9038441719055962\n",
"acc: 0.9793820591289644\n",
"epoch: 4\n",
"f1: 0.928903400400047\n",
"acc: 0.9850890978100043\n"
]
}
],
"source": [
"for epoch in range(5):\n",
" acc_score = 0\n",
" prec_score = 0\n",
" selected_items = 0\n",
" recall_score = 0\n",
" relevant_items = 0\n",
" items_total = 0\n",
" ner_model.train()\n",
" for i in range(len(train_labels)):\n",
" for j in range(1, len(train_labels[i]) - 1):\n",
" X = train_tensors[i][j - 1: j + 2].to(device_gpu)\n",
"\n",
" Y = train_labels[i][j: j + 1].to(device_gpu)\n",
"\n",
" Y_predictions = ner_model(X.long())\n",
"\n",
" acc_score += int(torch.argmax(Y_predictions) == Y)\n",
" if torch.argmax(Y_predictions) != 0:\n",
" selected_items += 1\n",
" if torch.argmax(Y_predictions) != 0 and torch.argmax(Y_predictions) == Y.item():\n",
" prec_score += 1\n",
" if Y.item() != 0:\n",
" relevant_items += 1\n",
" if Y.item() != 0 and torch.argmax(Y_predictions) == Y.item():\n",
" recall_score += 1\n",
"\n",
" items_total += 1\n",
" optimizer.zero_grad()\n",
" loss = criterion(Y_predictions.unsqueeze(0), Y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" precision = prec_score / selected_items\n",
" recall = recall_score / relevant_items\n",
" f1_score = (2 * precision * recall) / (precision + recall)\n",
" print(f'epoch: {epoch}')\n",
" print(f'f1: {f1_score}')\n",
" print(f'acc: {acc_score / items_total}')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "f75aa5e2",
"metadata": {},
"outputs": [],
"source": [
"def create_tensors_list(data):\n",
" tensors = []\n",
"\n",
" for sent in data[\"a\"]:\n",
" sent_tensor = torch.tensor(())\n",
" for word in sent:\n",
" temp = torch.tensor([word[0].isupper(), word[0].isdigit()])\n",
" sent_tensor = torch.cat((sent_tensor, temp))\n",
"\n",
" tensors.append(sent_tensor)\n",
"\n",
" return tensors"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "49215802",
"metadata": {},
"outputs": [],
"source": [
"dev = pd.read_csv('dev-0/in.tsv', sep='\\t', names=['a'])\n",
"dev[\"a\"] = dev[\"a\"].apply(lambda x: x.split())\n",
"\n",
"dev_tokens_ids = data_process(dev[\"a\"])\n",
"\n",
"dev_extra_tensors = create_tensors_list(dev)\n",
"\n",
"dev_tensors = [torch.cat((token, dev_extra_tensors[i])) for i, token in enumerate(dev_tokens_ids)]\n",
"\n",
"results = predict(dev_tensors, labels)\n",
"results_processed = process_output(results)\n",
"\n",
"with open(\"dev-0/out.tsv\", \"w\") as f:\n",
" for line in results_processed:\n",
" f.write(line + \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "8c5b007e",
"metadata": {},
"outputs": [],
"source": [
"test = pd.read_csv('test-A/in.tsv', sep='\\t', names=['a'])\n",
"test[\"a\"] = test[\"a\"].apply(lambda x: x.split())\n",
"\n",
"test_tokens_ids = data_process(test[\"a\"])\n",
"\n",
"test_extra_tensors = create_tensors_list(test)\n",
"\n",
"test_tensors = [torch.cat((token, test_extra_tensors[i])) for i, token in enumerate(test_tokens_ids)]\n",
"\n",
"results = predict(test_tensors, labels)\n",
"results_processed = process_output(results)\n",
"\n",
"with open(\"test-A/out.tsv\", \"w\") as f:\n",
" for line in results_processed:\n",
" f.write(line + \"\\n\")"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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