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sadurska@trui.pl 2021-06-08 23:25:41 +02:00
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "main.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "OY5VomOSCBez"
},
"source": [
"import numpy as np\n",
"import gensim\n",
"import torch\n",
"import pandas as pd\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from torchtext.vocab import Vocab\n",
"from collections import Counter\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"import lzma\n",
"import re\n",
"import itertools"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VXcowLY6HlNC"
},
"source": [
"class NeuralNetworkModel(torch.nn.Module):\n",
"\n",
" def __init__(self, output_size):\n",
" super(NeuralNetworkModel, self).__init__()\n",
" self.fc1 = torch.nn.Linear(10_000,len(train_tokens_ids))\n",
" self.softmax = torch.nn.Softmax(dim=0)\n",
" \n",
"\n",
" def forward(self, x):\n",
" x = self.fc1(x)\n",
" x = self.softmax(x)\n",
" return x"
],
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OXX_vPpTHhOq"
},
"source": [
"class NERModel(torch.nn.Module):\n",
"\n",
" def __init__(self,):\n",
" super(NERModel, self).__init__()\n",
" self.emb = torch.nn.Embedding(23627,200)\n",
" self.fc1 = torch.nn.Linear(600,9)\n",
"\n",
" def forward(self, x):\n",
" x = self.emb(x)\n",
" x = x.reshape(600) \n",
" x = self.fc1(x)\n",
" return x"
],
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NNpGPta9C4TI"
},
"source": [
"def get_dataset(path):\n",
" data = lzma.open(path).read().decode('UTF-8').split('\\n')\n",
" return [line.split('\\t') for line in data][:-1]\n",
"\n",
"train_data = get_dataset('train.tsv.xz')\n",
"\n",
"tokens = []\n",
"ner_tags = []\n",
"\n",
"for i in train_data:\n",
" ner_tags.append(i[0].split())\n",
" tokens.append(i[1].split())\n",
"\n",
"ner_tags_set = list(set(itertools.chain(*ner_tags)))\n",
"\n",
"ner_tags_dictionary = {}\n",
"\n",
"for i in range(len(ner_tags_set)):\n",
" ner_tags_dictionary[ner_tags_set[i]] = i"
],
"execution_count": 46,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "vvOF0opUGEMN"
},
"source": [
"for i in range(len(ner_tags)):\n",
" for j in range(len(ner_tags[i])):\n",
" ner_tags[i][j] = ner_tags_dictionary[ner_tags[i][j]]\n",
"\n",
"def data_preprocessing(data):\n",
" return [ torch.tensor([vocab['<bos>']] +[vocab[token] for token in document ] + [vocab['<eos>']], dtype = torch.long) for document in data ]\n",
"\n",
"def labels_preprocessing(data):\n",
" return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in data ]\n",
"\n",
"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>'])\n",
"\n",
"\n",
"vocab = build_vocab(tokens)\n",
"train_tokens_ids = data_preprocessing(tokens)\n",
"train_labels = labels_preprocessing(ner_tags)"
],
"execution_count": 47,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 437
},
"id": "yoCYSZNeHJeT",
"outputId": "78acbcd4-ca6e-4702-8e91-d1a906b2e252"
},
"source": [
"nn_model = NeuralNetworkModel(len(train_tokens_ids))\n",
"train_tokens_ids[0][1:4]\n",
"\n",
"ner_model = NERModel()\n",
"ner_model(train_tokens_ids[0][1:4])\n",
"\n",
"criterion = torch.nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.Adam(ner_model.parameters())\n",
"\n",
"for epoch in range(2):\n",
" loss_score = 0\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",
" nn_model.train()\n",
" for i in range(100):\n",
" for j in range(1, len(train_labels[i]) - 1):\n",
" \n",
" X = train_tokens_ids[i][j-1: j+2]\n",
" Y = train_labels[i][j: j+1]\n",
"\n",
" Y_predictions = ner_model(X)\n",
" \n",
" \n",
" acc_score += int(torch.argmax(Y_predictions) == Y)\n",
" \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",
" \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",
"\n",
" \n",
" optimizer.zero_grad()\n",
" loss = criterion(Y_predictions.unsqueeze(0), Y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
"\n",
" loss_score += loss.item() \n",
" \n",
" precision = prec_score / selected_items\n",
" recall = recall_score / relevant_items\n",
" f1_score = (2*precision * recall) / (precision + recall)\n",
" display('epoch: ', epoch)\n",
" display('loss: ', loss_score / items_total)\n",
" display('acc: ', acc_score / items_total)\n",
" display('prec: ', precision)\n",
" display('recall: : ', recall)\n",
" display('f1: ', f1_score)"
],
"execution_count": 27,
"outputs": [
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{
"cell_type": "code",
"metadata": {
"id": "KOVSTjGWVuq9"
},
"source": [
"with open('dev-0/in.tsv', \"r\", encoding=\"utf-8\") as f:\n",
" dev_0_data = [line.rstrip() for line in f]\n",
" \n",
"dev_0_data = [i.split() for i in dev_0_data]\n",
"with open('dev-0/expected.tsv', \"r\", encoding=\"utf-8\") as f:\n",
" dev_0_tags = [line.rstrip() for line in f]\n",
" \n",
"dev_0_tags = [i.split() for i in dev_0_tags]\n",
"for i in range(len(dev_0_tags)):\n",
" for j in range(len(dev_0_tags[i])):\n",
" dev_0_tags[i][j] = ner_tags_dictionary[dev_0_tags[i][j]]\n",
"test_tokens_ids = data_preprocessing(dev_0_data)\n",
"test_labels = labels_preprocessing(dev_0_tags)\n"
],
"execution_count": 41,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 192
},
"id": "Pt7sVRdhWCqC",
"outputId": "acd75e73-e26f-48f6-f36c-a96668822968"
},
"source": [
"result = []\n",
"\n",
"loss_score = 0\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",
"nn_model.eval()\n",
"\n",
"for i in range(len(test_tokens_ids)):\n",
" result.append([])\n",
" for j in range(1, len(test_labels[i]) - 1):\n",
"\n",
" X = test_tokens_ids[i][j-1: j+2]\n",
" Y = test_labels[i][j: j+1]\n",
"\n",
" Y_predictions = ner_model(X)\n",
"\n",
"\n",
" acc_score += int(torch.argmax(Y_predictions) == Y)\n",
"\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",
"\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",
" loss = criterion(Y_predictions.unsqueeze(0), Y)\n",
" loss_score += loss.item() \n",
" \n",
" result[i].append(int(torch.argmax(Y_predictions)))\n",
"\n",
"precision = prec_score / selected_items\n",
"recall = recall_score / relevant_items\n",
"f1_score = (2*precision * recall) / (precision + recall)\n",
"display('loss: ', loss_score / items_total)\n",
"display('acc: ', acc_score / items_total)\n",
"display('prec: ', precision)\n",
"display('recall: : ', recall)\n",
"display('f1: ', f1_score)"
],
"execution_count": 42,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
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},
"text/plain": [
"'loss: '"
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{
"cell_type": "code",
"metadata": {
"id": "d-QCHMrycKwH"
},
"source": [
"with open('test-A/in.tsv', \"r\", encoding=\"utf-8\") as f:\n",
" test_data = [line.rstrip() for line in f]\n",
" \n",
"test_data = [i.split() for i in test_data]\n",
"test_tokens_ids = data_preprocessing(test_data)\n",
"result = []\n",
"\n",
"loss_score = 0\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",
"nn_model.eval()\n",
"\n",
"test_tokens_length = len(test_tokens_ids)\n",
"\n",
"for i in range(test_tokens_length):\n",
" result.append([])\n",
" for j in range(1, len(test_tokens_ids[i]) - 1):\n",
"\n",
" X = test_tokens_ids[i][j-1: j + 2]\n",
"\n",
" Y_predictions = ner_model(X)\n",
" result[i].append(int(torch.argmax(Y_predictions)))"
],
"execution_count": 49,
"outputs": []
}
]
}