diff --git a/Neural graph module/ngm.ipynb b/Neural graph module/ngm.ipynb
index 14d945f4bdc93c70b54d5eb307a085a1777c93db..303e0909ef4f68fccbddc758a6c5d890c1eebfe3 100644
--- a/Neural graph module/ngm.ipynb
+++ b/Neural graph module/ngm.ipynb
@@ -1,999 +1,514 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import datasets\n",
- "import torch\n",
- "import torch.nn as nn\n",
- "import torch.optim as optim\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "from transformers import BertTokenizer, BertModel\n",
- "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
- "from tqdm import tqdm\n",
- "import json\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Use GPU if available\n",
- "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
- "# Print cuda version\n",
- "print(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "class NgmOne(nn.Module):\n",
- " def __init__(self, device):\n",
- " super(NgmOne, self).__init__()\n",
- " self.tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
- " self.bert = BertModel.from_pretrained(\"bert-base-uncased\").to(device)\n",
- " self.linear = nn.Linear(768, 247).to(device)\n",
- " self.softmax = nn.Softmax(dim=1).to(device)\n",
- " self.device = device\n",
- " \n",
- " def forward(self, tokenized_seq, tokenized_mask):\n",
- " tokenized_seq= tokenized_seq.to(self.device)\n",
- " tokenized_mask = tokenized_mask.to(self.device)\n",
- " with torch.no_grad():\n",
- " x = self.bert.forward(tokenized_seq, attention_mask=tokenized_mask)\n",
- " x = x[0][:,0,:].to(self.device)\n",
- " \n",
- " x = self.linear(x)\n",
- " x = self.softmax(x)\n",
- " return x"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# def encode(batch):\n",
- "# return tokenizer(batch, padding=\"max_length\", max_length=256, return_tensors=\"pt\")\n",
- "\n",
- "\n",
- "# def convert_to_features(example_batch):\n",
- "# input_encodings = encode(example_batch['text'])\n",
- "# target_encodings = encode(example_batch['summary'])\n",
- "\n",
- "# labels = target_encodings['input_ids']\n",
- "# decoder_input_ids = shift_tokens_right(\n",
- "# labels, model.config.pad_token_id, model.config.decoder_start_token_id)\n",
- "# labels[labels[:, :] == model.config.pad_token_id] = -100\n",
- "\n",
- "# encodings = {\n",
- "# 'input_ids': input_encodings['input_ids'],\n",
- "# 'attention_mask': input_encodings['attention_mask'],\n",
- "# 'decoder_input_ids': decoder_input_ids,\n",
- "# 'labels': labels,\n",
- "# }\n",
- "\n",
- "# return encodings\n",
- "\n",
- "\n",
- "# def get_dataset(path):\n",
- "# df = pd.read_csv(path, sep=\",\", on_bad_lines='skip')\n",
- "# dataset = datasets.Dataset.from_pandas(df)\n",
- "# dataset = dataset.map(convert_to_features, batched=True)\n",
- "# columns = ['input_ids', 'labels', 'decoder_input_ids', 'attention_mask', ]\n",
- "# dataset.set_format(type='torch', columns=columns)\n",
- "# return dataset\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def make_batch():\n",
- " \"\"\"Triplet is a list of [subject entity, relation, object entity], None if not present\"\"\"\n",
- "\n",
- " # Load predicted data\n",
- " pred = \"../data/qald-9-train-linked.json\"\n",
- "\n",
- " #Load gold data\n",
- " gold = \"../data/qald-9-train-linked.json\"\n",
- " print(\"Beginning making batch\")\n",
- " with open(pred, \"r\") as p, open(gold, \"r\") as g:\n",
- " pred = json.load(p)\n",
- " gold = json.load(g)\n",
- "\n",
- " inputs = []\n",
- " inputs_max_len = 0\n",
- " for d in tqdm(pred[\"questions\"]):\n",
- " question = d[\"question\"][0][\"string\"]\n",
- " query = d[\"query\"][\"sparql\"]\n",
- "\n",
- " #Take the first tripletin query\n",
- " trip = query.split(\"WHERE\")[1]\n",
- " trip = trip.replace(\"{\", \"\").replace(\"}\", \"\")\n",
- " triplet = trip.split(\" \")\n",
- "\n",
- " #remove empty strings\n",
- " triplet = [x for x in triplet if x != \"\"]\n",
- "\n",
- " if len(triplet) != 3:\n",
- " continue\n",
- "\n",
- " for t in triplet:\n",
- " if not(t.find(\"?\")):\n",
- " triplet[triplet.index(t)] = None\n",
- "\n",
- " #seq = \"[CLS] \" + question + \" [SEP] \"\n",
- " if triplet[0] is not None:\n",
- " #seq += \"[SUB] [SEP] \" + triplet[0]\n",
- " # , padding=True, truncation=True)\n",
- " tokenized_seq = tokenizer(question, \"[SUB]\", triplet[0], padding=True, truncation=True)\n",
- " elif triplet[2] is not None:\n",
- " #seq += \"[OBJ] [SEP] \" + triplet[2]\n",
- " tokenized_seq = tokenizer(question, \"[OBJ]\", triplet[2], padding=True, truncation=True)\n",
- "\n",
- " if inputs_max_len < len(tokenized_seq[\"input_ids\"]):\n",
- " inputs_max_len = len(tokenized_seq[\"input_ids\"])\n",
- " inputs.append(list(tokenized_seq.values())[0])\n",
- "\n",
- " correct_rels_max_len = 0\n",
- " correct_rels = []\n",
- " for d in tqdm(gold[\"questions\"]):\n",
- " question = d[\"question\"][0][\"string\"]\n",
- " query = d[\"query\"][\"sparql\"]\n",
- "\n",
- " #Take the first tripletin query\n",
- " trip = query.split(\"WHERE\")[1]\n",
- " trip = trip.replace(\"{\", \"\").replace(\"}\", \"\")\n",
- " triplet = trip.split(\" \")\n",
- " \n",
- "\n",
- " #remove empty strings\n",
- " triplet = [x for x in triplet if x != \"\"]\n",
- "\n",
- " if len(triplet) != 3:\n",
- " continue\n",
- " # tokenized = tokenizer(triplet[1], padding=True, truncation=True)\n",
- " # if correct_rels_max_len < len(tokenized[\"input_ids\"]):\n",
- " # correct_rels_max_len = len(tokenized[\"input_ids\"])\n",
- "\n",
- " correct_rels.append(triplet[1])#list(tokenized.values())[0])\n",
- "\n",
- " inputs_padded = np.array([i + [0]*(inputs_max_len-len(i)) for i in inputs])\n",
- " #correct_rels_padded = np.array([i + [0]*(correct_rels_max_len-len(i)) for i in correct_rels])\n",
- "\n",
- " inputs_attention_mask = np.where(inputs_padded != 0, 1, 0)\n",
- " #correct_rels_attention_mask = np.where(correct_rels_padded != 0, 1, 0)\n",
- " print(\"Finished with batches\")\n",
- " return torch.LongTensor(inputs_padded), torch.LongTensor(inputs_attention_mask), correct_rels #torch.IntTensor(correct_rels_padded), torch.LongTensor(correct_rels_attention_mask)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# training_args = Seq2SeqTrainingArguments(\n",
- "# output_dir='./models/blackbox',\n",
- "# num_train_epochs=1,\n",
- "# per_device_train_batch_size=1,\n",
- "# per_device_eval_batch_size=1,\n",
- "# warmup_steps=10,\n",
- "# weight_decay=0.01,\n",
- "# logging_dir='./logs',\n",
- "# )\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch.utils.data import Dataset, DataLoader\n",
- "\n",
- "class MyDataset(Dataset):\n",
- " def __init__(self, inputs, attention_mask, correct_rels, relations):\n",
- " self.inputs = inputs\n",
- " self.attention_mask = attention_mask\n",
- " self.correct_rels = correct_rels\n",
- " self.relations = relations\n",
- " \n",
- "\n",
- " def __len__(self):\n",
- " return len(self.inputs)\n",
- "\n",
- " def __getitem__(self, idx):\n",
- " return self.inputs[idx], self.attention_mask[idx], self.relations.index(self.correct_rels[idx])\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "#From scratch json creates data set.\n",
- "# class MyDataset(Dataset):\n",
- "# def __init__(self, json_file, transform=None):\n",
- "# self.qald_data = json.load(json_file)\n",
- "\n",
- "# def __len__(self):\n",
- "# return len(self.qald_data)\n",
- "\n",
- "# def __getitem__(self, idx):\n",
- "# self.inputs[idx], self.attention_mask[idx], self.labels[idx]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
+ "cells": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Beginning making batch\n"
- ]
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\maxbj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "import datasets\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from transformers import BertTokenizer, BertModel\n",
+ "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
+ "from tqdm import tqdm\n",
+ "import json\n"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 408/408 [00:00<00:00, 955.51it/s] \n",
- "100%|██████████| 408/408 [00:00<00:00, 136139.70it/s]"
- ]
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Finished with batches\n",
- "features: tensor([[ 101, 2040, 2003, 1996, 3677, 1997, 1996, 4035, 6870, 19247,\n",
- " 1029, 102, 1031, 4942, 1033, 102, 0, 0, 0, 0,\n",
- " 0, 0, 0]]) mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]) label_index tensor(81)\n"
- ]
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Use GPU if available\n",
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+ "# Print cuda version\n",
+ "print(device)"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "#Prepare data\n",
- "\n",
- "def open_json(file):\n",
- " with open(file, \"r\") as f:\n",
- " return json.load(f)\n",
- "\n",
- "relations = open_json(\"../data/relations-query-qald-9-linked.json\")\n",
- "train_set = MyDataset(*make_batch(), relations=relations)\n",
- "train_dataloader = DataLoader(train_set, batch_size=1, shuffle=True)\n",
- "\n",
- "#show first entry\n",
- "train_features, train_mask, train_label = next(iter(train_dataloader))\n",
- "print(\"features:\", train_features, \"mask:\",train_mask,\"label_index\", train_label[0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {},
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "class NgmOne(nn.Module):\n",
+ " def __init__(self, device):\n",
+ " super(NgmOne, self).__init__()\n",
+ " self.tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
+ " self.bert = BertModel.from_pretrained(\"bert-base-uncased\").to(device)\n",
+ " self.linear = nn.Linear(768, 247).to(device)\n",
+ " self.softmax = nn.Softmax(dim=1).to(device)\n",
+ " self.device = device\n",
+ " \n",
+ " def forward(self, tokenized_seq, tokenized_mask):\n",
+ " tokenized_seq= tokenized_seq.to(self.device)\n",
+ " tokenized_mask = tokenized_mask.to(self.device)\n",
+ " with torch.no_grad():\n",
+ " x = self.bert.forward(tokenized_seq, attention_mask=tokenized_mask)\n",
+ " x = x[0][:,0,:].to(self.device)\n",
+ " \n",
+ " x = self.linear(x)\n",
+ " x = self.softmax(x)\n",
+ " return x"
+ ]
+ },
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight']\n",
- "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
- "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
- ]
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def encode(batch):\n",
+ "# return tokenizer(batch, padding=\"max_length\", max_length=256, return_tensors=\"pt\")\n",
+ "\n",
+ "\n",
+ "# def convert_to_features(example_batch):\n",
+ "# input_encodings = encode(example_batch['text'])\n",
+ "# target_encodings = encode(example_batch['summary'])\n",
+ "\n",
+ "# labels = target_encodings['input_ids']\n",
+ "# decoder_input_ids = shift_tokens_right(\n",
+ "# labels, model.config.pad_token_id, model.config.decoder_start_token_id)\n",
+ "# labels[labels[:, :] == model.config.pad_token_id] = -100\n",
+ "\n",
+ "# encodings = {\n",
+ "# 'input_ids': input_encodings['input_ids'],\n",
+ "# 'attention_mask': input_encodings['attention_mask'],\n",
+ "# 'decoder_input_ids': decoder_input_ids,\n",
+ "# 'labels': labels,\n",
+ "# }\n",
+ "\n",
+ "# return encodings\n",
+ "\n",
+ "\n",
+ "# def get_dataset(path):\n",
+ "# df = pd.read_csv(path, sep=\",\", on_bad_lines='skip')\n",
+ "# dataset = datasets.Dataset.from_pandas(df)\n",
+ "# dataset = dataset.map(convert_to_features, batched=True)\n",
+ "# columns = ['input_ids', 'labels', 'decoder_input_ids', 'attention_mask', ]\n",
+ "# dataset.set_format(type='torch', columns=columns)\n",
+ "# return dataset\n"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 0 batch: 0 , loss = 5.509496\n",
- "Epoch 1 batch: 0 , loss = 5.507990\n",
- "Epoch 2 batch: 0 , loss = 5.506450\n",
- "Epoch 3 batch: 0 , loss = 5.504800\n",
- "Epoch 4 batch: 0 , loss = 5.502756\n",
- "Epoch 5 batch: 0 , loss = 5.500911\n",
- "Epoch 6 batch: 0 , loss = 5.498587\n",
- "Epoch 7 batch: 0 , loss = 5.496222\n",
- "Epoch 8 batch: 0 , loss = 5.493753\n",
- "Epoch 9 batch: 0 , loss = 5.492003\n",
- "Epoch 10 batch: 0 , loss = 5.489583\n",
- "Epoch 11 batch: 0 , loss = 5.487563\n",
- "Epoch 12 batch: 0 , loss = 5.485127\n",
- "Epoch 13 batch: 0 , loss = 5.481408\n",
- "Epoch 14 batch: 0 , loss = 5.479365\n",
- "Epoch 15 batch: 0 , loss = 5.476476\n",
- "Epoch 16 batch: 0 , loss = 5.475063\n",
- "Epoch 17 batch: 0 , loss = 5.473976\n",
- "Epoch 18 batch: 0 , loss = 5.471124\n",
- "Epoch 19 batch: 0 , loss = 5.472827\n",
- "Epoch 20 batch: 0 , loss = 5.466723\n",
- "Epoch 21 batch: 0 , loss = 5.464960\n",
- "Epoch 22 batch: 0 , loss = 5.466041\n",
- "Epoch 23 batch: 0 , loss = 5.462078\n",
- "Epoch 24 batch: 0 , loss = 5.460968\n",
- "Epoch 25 batch: 0 , loss = 5.463361\n",
- "Epoch 26 batch: 0 , loss = 5.462362\n",
- "Epoch 27 batch: 0 , loss = 5.459974\n",
- "Epoch 28 batch: 0 , loss = 5.462227\n",
- "Epoch 29 batch: 0 , loss = 5.457432\n",
- "Epoch 30 batch: 0 , loss = 5.456643\n",
- "Epoch 31 batch: 0 , loss = 5.455560\n",
- "Epoch 32 batch: 0 , loss = 5.454978\n",
- "Epoch 33 batch: 0 , loss = 5.452355\n",
- "Epoch 34 batch: 0 , loss = 5.449059\n",
- "Epoch 35 batch: 0 , loss = 5.450156\n",
- "Epoch 36 batch: 0 , loss = 5.443484\n",
- "Epoch 37 batch: 0 , loss = 5.441142\n",
- "Epoch 38 batch: 0 , loss = 5.438660\n",
- "Epoch 39 batch: 0 , loss = 5.437134\n",
- "Epoch 40 batch: 0 , loss = 5.434308\n",
- "Epoch 41 batch: 0 , loss = 5.431943\n",
- "Epoch 42 batch: 0 , loss = 5.429959\n",
- "Epoch 43 batch: 0 , loss = 5.428108\n",
- "Epoch 44 batch: 0 , loss = 5.426363\n",
- "Epoch 45 batch: 0 , loss = 5.428246\n",
- "Epoch 46 batch: 0 , loss = 5.424921\n",
- "Epoch 47 batch: 0 , loss = 5.425705\n",
- "Epoch 48 batch: 0 , loss = 5.424184\n",
- "Epoch 49 batch: 0 , loss = 5.423537\n",
- "Epoch 50 batch: 0 , loss = 5.424012\n",
- "Epoch 51 batch: 0 , loss = 5.419333\n",
- "Epoch 52 batch: 0 , loss = 5.414816\n",
- "Epoch 53 batch: 0 , loss = 5.418826\n",
- "Epoch 54 batch: 0 , loss = 5.418625\n",
- "Epoch 55 batch: 0 , loss = 5.419236\n",
- "Epoch 56 batch: 0 , loss = 5.421367\n",
- "Epoch 57 batch: 0 , loss = 5.418901\n",
- "Epoch 58 batch: 0 , loss = 5.416471\n",
- "Epoch 59 batch: 0 , loss = 5.414352\n",
- "Epoch 60 batch: 0 , loss = 5.416772\n",
- "Epoch 61 batch: 0 , loss = 5.412184\n",
- "Epoch 62 batch: 0 , loss = 5.407087\n",
- "Epoch 63 batch: 0 , loss = 5.404146\n",
- "Epoch 64 batch: 0 , loss = 5.405499\n",
- "Epoch 65 batch: 0 , loss = 5.404672\n",
- "Epoch 66 batch: 0 , loss = 5.399035\n",
- "Epoch 67 batch: 0 , loss = 5.407500\n",
- "Epoch 68 batch: 0 , loss = 5.403162\n",
- "Epoch 69 batch: 0 , loss = 5.397717\n",
- "Epoch 70 batch: 0 , loss = 5.398447\n",
- "Epoch 71 batch: 0 , loss = 5.398669\n",
- "Epoch 72 batch: 0 , loss = 5.392782\n",
- "Epoch 73 batch: 0 , loss = 5.392102\n",
- "Epoch 74 batch: 0 , loss = 5.386912\n",
- "Epoch 75 batch: 0 , loss = 5.384616\n",
- "Epoch 76 batch: 0 , loss = 5.382891\n",
- "Epoch 77 batch: 0 , loss = 5.381707\n",
- "Epoch 78 batch: 0 , loss = 5.376785\n",
- "Epoch 79 batch: 0 , loss = 5.374002\n",
- "Epoch 80 batch: 0 , loss = 5.376191\n",
- "Epoch 81 batch: 0 , loss = 5.381535\n",
- "Epoch 82 batch: 0 , loss = 5.376332\n",
- "Epoch 83 batch: 0 , loss = 5.372051\n",
- "Epoch 84 batch: 0 , loss = 5.367785\n",
- "Epoch 85 batch: 0 , loss = 5.367027\n",
- "Epoch 86 batch: 0 , loss = 5.366450\n",
- "Epoch 87 batch: 0 , loss = 5.364227\n",
- "Epoch 88 batch: 0 , loss = 5.364299\n",
- "Epoch 89 batch: 0 , loss = 5.363710\n",
- "Epoch 90 batch: 0 , loss = 5.356105\n",
- "Epoch 91 batch: 0 , loss = 5.353946\n",
- "Epoch 92 batch: 0 , loss = 5.356158\n",
- "Epoch 93 batch: 0 , loss = 5.355119\n",
- "Epoch 94 batch: 0 , loss = 5.348075\n",
- "Epoch 95 batch: 0 , loss = 5.351038\n",
- "Epoch 96 batch: 0 , loss = 5.349224\n",
- "Epoch 97 batch: 0 , loss = 5.344652\n",
- "Epoch 98 batch: 0 , loss = 5.341354\n",
- "Epoch 99 batch: 0 , loss = 5.342059\n",
- "Epoch 100 batch: 0 , loss = 5.344870\n",
- "Epoch 101 batch: 0 , loss = 5.335486\n",
- "Epoch 102 batch: 0 , loss = 5.339808\n",
- "Epoch 103 batch: 0 , loss = 5.330384\n",
- "Epoch 104 batch: 0 , loss = 5.333795\n",
- "Epoch 105 batch: 0 , loss = 5.336934\n",
- "Epoch 106 batch: 0 , loss = 5.334551\n",
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- "Epoch 498 batch: 0 , loss = 5.007164\n",
- "Epoch 499 batch: 0 , loss = 5.007099\n"
- ]
- }
- ],
- "source": [
- "# Train with data loader.\n",
- "\n",
- "\n",
- "\n",
- "model = NgmOne(device)\n",
- "criterion = nn.CrossEntropyLoss()\n",
- "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
- "\n",
- "epoch = 500\n",
- "batch_size = 200\n",
- "for e in range(epoch):\n",
- " train_dataloader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
- " for i_batch, sample_batched in enumerate(train_dataloader):\n",
- " optimizer.zero_grad()\n",
- " train = sample_batched[0]\n",
- " train_mask = sample_batched[1]\n",
- " label_index = sample_batched[2].to(device)\n",
- "\n",
- " # Forward pass\n",
- " output = model(train, train_mask)\n",
- " loss = criterion(output, label_index)\n",
- "\n",
- " # backward and optimize\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " if i_batch % batch_size == 0:\n",
- " print(\"Epoch\", e, \"batch:\",i_batch, ', loss =', '{:.6f}'.format(loss))\n",
- " \n",
- " \n",
- " \n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model = NgmOne(device)\n",
- "\n",
- "EPOCHS = 1500\n",
- "criterion = nn.CrossEntropyLoss()\n",
- "optimizer = optim.Adam(model.parameters(), lr=0.007)\n",
- "\n",
- "with open(\"../data/relations-query-qald-9-linked.json\", \"r\") as f:\n",
- " relations = json.load(f)\n",
- "\n",
- "train, train_mask, corr_rels = make_batch()\n",
- "corr_indx = torch.LongTensor([relations.index(r) for r in corr_rels]).to(device)\n",
- "\n",
- "for epoch in range(EPOCHS):\n",
- " optimizer.zero_grad()\n",
- "\n",
- " # Forward pass\n",
- " output = model(train, train_mask)\n",
- " loss = criterion(output, corr_indx)\n",
- "\n",
- " if (epoch + 1) % 1 == 0:\n",
- " print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))\n",
- " # Backward pass\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {},
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_batch():\n",
+ " \"\"\"Triplet is a list of [subject entity, relation, object entity], None if not present\"\"\"\n",
+ "\n",
+ " # Load predicted data\n",
+ " pred = \"../data/qald-9-train-linked.json\"\n",
+ "\n",
+ " #Load gold data\n",
+ " gold = \"../data/qald-9-train-linked.json\"\n",
+ " print(\"Beginning making batch\")\n",
+ " with open(pred, \"r\") as p, open(gold, \"r\") as g:\n",
+ " pred = json.load(p)\n",
+ " gold = json.load(g)\n",
+ "\n",
+ " inputs = []\n",
+ " correct_rels = []\n",
+ " inputs_max_len = 0\n",
+ " for d in tqdm(pred[\"questions\"]):\n",
+ " question = d[\"question\"][0][\"string\"]\n",
+ " query = d[\"query\"][\"sparql\"]\n",
+ "\n",
+ " #Take the first tripletin query\n",
+ " trip = query.split(\"WHERE {\")[1]\n",
+ " trip = trip.split(\"}\")[0]\n",
+ " trip = trip.split(\"FILTER \")[0]\n",
+ " trip = trip.replace(\"{\", \"\").replace(\"}\", \"\")\n",
+ " trip = trip.replace(\".\", \"\")\n",
+ " trip = trip.replace(\";\", \"\")\n",
+ " triplet = trip.split(\" \")\n",
+ "\n",
+ " #remove empty strings\n",
+ " triplet = [x for x in triplet if x != \"\"]\n",
+ "\n",
+ " if len(triplet) % 3 == 0 and \" \".join(triplet).find(\"rdf\") == -1:\n",
+ " for i in range(len(triplet)//3):\n",
+ " triplet_i = triplet[i*3:i*3+3]\n",
+ " for t in triplet_i:\n",
+ " if not(t.find(\"?\")):\n",
+ " triplet_i[triplet_i.index(t)] = None\n",
+ "\n",
+ " #seq = \"[CLS] \" + question + \" [SEP] \"\n",
+ " if triplet_i[0] is not None and triplet_i[1] is not None:\n",
+ " #seq += \"[SUB] [SEP] \" + triplet[0]\n",
+ " # , padding=True, truncation=True)\n",
+ " tokenized_seq = tokenizer(question, \"[SUB]\", triplet_i[0], padding=True, truncation=True)\n",
+ " elif triplet_i[2] is not None and triplet_i[1] is not None:\n",
+ " #seq += \"[OBJ] [SEP] \" + triplet[2]\n",
+ " tokenized_seq = tokenizer(question, \"[OBJ]\", triplet_i[2], padding=True, truncation=True)\n",
+ " else:\n",
+ " continue\n",
+ "\n",
+ " if inputs_max_len < len(tokenized_seq[\"input_ids\"]):\n",
+ " inputs_max_len = len(tokenized_seq[\"input_ids\"])\n",
+ " inputs.append(list(tokenized_seq.values())[0])\n",
+ " correct_rels.append(triplet_i[1])\n",
+ "\n",
+ " inputs_padded = np.array([i + [0]*(inputs_max_len-len(i)) for i in inputs])\n",
+ " #correct_rels_padded = np.array([i + [0]*(correct_rels_max_len-len(i)) for i in correct_rels])\n",
+ "\n",
+ " inputs_attention_mask = np.where(inputs_padded != 0, 1, 0)\n",
+ " #correct_rels_attention_mask = np.where(correct_rels_padded != 0, 1, 0)\n",
+ " print(\"Finished with batches\")\n",
+ " return torch.LongTensor(inputs_padded), torch.LongTensor(inputs_attention_mask), correct_rels #torch.IntTensor(correct_rels_padded), torch.LongTensor(correct_rels_attention_mask)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# training_args = Seq2SeqTrainingArguments(\n",
+ "# output_dir='./models/blackbox',\n",
+ "# num_train_epochs=1,\n",
+ "# per_device_train_batch_size=1,\n",
+ "# per_device_eval_batch_size=1,\n",
+ "# warmup_steps=10,\n",
+ "# weight_decay=0.01,\n",
+ "# logging_dir='./logs',\n",
+ "# )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "\n",
+ "class MyDataset(Dataset):\n",
+ " def __init__(self, inputs, attention_mask, correct_rels, relations):\n",
+ " self.inputs = inputs\n",
+ " self.attention_mask = attention_mask\n",
+ " self.correct_rels = correct_rels\n",
+ " self.relations = relations\n",
+ " \n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.inputs)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " return self.inputs[idx], self.attention_mask[idx], self.relations.index(self.correct_rels[idx])\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "#From scratch json creates data set.\n",
+ "# class MyDataset(Dataset):\n",
+ "# def __init__(self, json_file, transform=None):\n",
+ "# self.qald_data = json.load(json_file)\n",
+ "\n",
+ "# def __len__(self):\n",
+ "# return len(self.qald_data)\n",
+ "\n",
+ "# def __getitem__(self, idx):\n",
+ "# self.inputs[idx], self.attention_mask[idx], self.labels[idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Beginning making batch\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias']\n",
+ "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finished with batches\n",
+ "features: tensor([[ 101, 2040, 2003, 1996, 3677, 1997, 1996, 4035, 6870, 19247,\n",
+ " 1029, 102, 1031, 4942, 1033, 102, 0, 0, 0, 0,\n",
+ " 0, 0, 0]]) mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]) label_index tensor(81)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Prepare data\n",
+ "\n",
+ "def open_json(file):\n",
+ " with open(file, \"r\") as f:\n",
+ " return json.load(f)\n",
+ "\n",
+ "relations = open_json(\"../data/relations-query-qald-9-linked.json\")\n",
+ "train_set = MyDataset(*make_batch(), relations=relations)\n",
+ "train_dataloader = DataLoader(train_set, batch_size=1, shuffle=True)\n",
+ "\n",
+ "#show first entry\n",
+ "train_features, train_mask, train_label = next(iter(train_dataloader))\n",
+ "print(\"features:\", train_features, \"mask:\",train_mask,\"label_index\", train_label[0])"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Beginning making batch\n"
- ]
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight']\n",
+ "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finished with batches\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "'<http://dbpediaorg/property/launchPad>' is not in list",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn [43], line 11\u001b[0m\n\u001b[0;32m 8\u001b[0m relations \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(f)\n\u001b[0;32m 10\u001b[0m train, train_mask, corr_rels \u001b[39m=\u001b[39m make_batch()\n\u001b[1;32m---> 11\u001b[0m corr_indx \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mLongTensor([relations\u001b[39m.\u001b[39mindex(r) \u001b[39mfor\u001b[39;00m r \u001b[39min\u001b[39;00m corr_rels])\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m 13\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(EPOCHS):\n\u001b[0;32m 14\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n",
+ "Cell \u001b[1;32mIn [43], line 11\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 8\u001b[0m relations \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(f)\n\u001b[0;32m 10\u001b[0m train, train_mask, corr_rels \u001b[39m=\u001b[39m make_batch()\n\u001b[1;32m---> 11\u001b[0m corr_indx \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mLongTensor([relations\u001b[39m.\u001b[39;49mindex(r) \u001b[39mfor\u001b[39;00m r \u001b[39min\u001b[39;00m corr_rels])\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m 13\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(EPOCHS):\n\u001b[0;32m 14\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n",
+ "\u001b[1;31mValueError\u001b[0m: '<http://dbpediaorg/property/launchPad>' is not in list"
+ ]
+ }
+ ],
+ "source": [
+ "# Train with data loader.\n",
+ "\n",
+ "\n",
+ "\n",
+ "model = NgmOne(device)\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+ "\n",
+ "epoch = 500\n",
+ "batch_size = 200\n",
+ "for e in range(epoch):\n",
+ " train_dataloader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
+ " for i_batch, sample_batched in enumerate(train_dataloader):\n",
+ " optimizer.zero_grad()\n",
+ " train = sample_batched[0]\n",
+ " train_mask = sample_batched[1]\n",
+ " label_index = sample_batched[2].to(device)\n",
+ "\n",
+ " # Forward pass\n",
+ " output = model(train, train_mask)\n",
+ " loss = criterion(output, label_index)\n",
+ "\n",
+ " # backward and optimize\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " if i_batch % batch_size == 0:\n",
+ " print(\"Epoch\", e, \"batch:\",i_batch, ', loss =', '{:.6f}'.format(loss))\n",
+ " \n",
+ " \n",
+ " \n",
+ "\n"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 408/408 [00:00<00:00, 962.29it/s] \n",
- "100%|██████████| 408/408 [00:00<00:00, 81687.72it/s]\n"
- ]
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = NgmOne(device)\n",
+ "\n",
+ "EPOCHS = 1500\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "optimizer = optim.Adam(model.parameters(), lr=0.007)\n",
+ "\n",
+ "with open(\"../data/relations-query-qald-9-linked.json\", \"r\") as f:\n",
+ " relations = json.load(f)\n",
+ "\n",
+ "train, train_mask, corr_rels = make_batch()\n",
+ "corr_indx = torch.LongTensor([relations.index(r) for r in corr_rels]).to(device)\n",
+ "\n",
+ "for epoch in range(EPOCHS):\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " # Forward pass\n",
+ " output = model(train, train_mask)\n",
+ " loss = criterion(output, corr_indx)\n",
+ "\n",
+ " if (epoch + 1) % 1 == 0:\n",
+ " print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))\n",
+ " # Backward pass\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " \n"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Finished with batches\n",
- "lowest confidence 0.14628477\n",
- "Accuracy: 0.5245098039215687\n"
- ]
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Beginning making batch\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 408/408 [00:00<00:00, 962.29it/s] \n",
+ "100%|██████████| 408/408 [00:00<00:00, 81687.72it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finished with batches\n",
+ "lowest confidence 0.14628477\n",
+ "Accuracy: 0.5245098039215687\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict\n",
+ "train, train_mask, corr_rels = make_batch()\n",
+ "with torch.no_grad():\n",
+ " output = model(train, train_mask)\n",
+ "\n",
+ "output = output.detach().cpu().numpy()\n",
+ "\n",
+ "prediction = [relations[np.argmax(pred).item()]for pred in output]\n",
+ "probability = [pred[np.argmax(pred)] for pred in output]\n",
+ "correct_pred = [corr_rels[i] for i in range(len(output))]\n",
+ "\n",
+ "\n",
+ "\n",
+ "preds = [\n",
+ " {\"pred\": relations[np.argmax(pred).item()], \n",
+ " \"prob\": pred[np.argmax(pred)],\n",
+ " \"correct\": corr_rels[count]} \n",
+ " for count, pred in enumerate(output)\n",
+ " ]\n",
+ "\n",
+ "\n",
+ "\n",
+ "# for pred in preds:\n",
+ "# print(\"pred\", pred[\"pred\"], \"prob\", pred[\"prob\"], \"correct\", pred[\"correct\"])\n",
+ "\n",
+ "# for pred, prob, correct_pred in zip(prediction, probability, correct_pred):\n",
+ "# print(\"pred:\", pred, \"prob:\", prob, \"| correct\", correct_pred)\n",
+ "\n",
+ "print(\"lowest confidence\", min(probability))\n",
+ "\n",
+ "def accuracy_score(y_true, y_pred):\n",
+ " corr_preds=0\n",
+ " wrong_preds=0\n",
+ " for pred, correct in zip(y_pred, y_true):\n",
+ " if pred == correct:\n",
+ " corr_preds += 1\n",
+ " else:\n",
+ " wrong_preds += 1\n",
+ " return corr_preds/(corr_preds+wrong_preds)\n",
+ "\n",
+ "print(\"Accuracy:\", accuracy_score(correct_pred, prediction))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.9.11 64-bit",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.11"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "64e7cd3b4b88defe39dd61a4584920400d6beb2615ab2244e340c2e20eecdfe9"
+ }
}
- ],
- "source": [
- "# Predict\n",
- "train, train_mask, corr_rels = make_batch()\n",
- "with torch.no_grad():\n",
- " output = model(train, train_mask)\n",
- "\n",
- "output = output.detach().cpu().numpy()\n",
- "\n",
- "prediction = [relations[np.argmax(pred).item()]for pred in output]\n",
- "probability = [pred[np.argmax(pred)] for pred in output]\n",
- "correct_pred = [corr_rels[i] for i in range(len(output))]\n",
- "\n",
- "\n",
- "\n",
- "preds = [\n",
- " {\"pred\": relations[np.argmax(pred).item()], \n",
- " \"prob\": pred[np.argmax(pred)],\n",
- " \"correct\": corr_rels[count]} \n",
- " for count, pred in enumerate(output)\n",
- " ]\n",
- "\n",
- "\n",
- "\n",
- "# for pred in preds:\n",
- "# print(\"pred\", pred[\"pred\"], \"prob\", pred[\"prob\"], \"correct\", pred[\"correct\"])\n",
- "\n",
- "# for pred, prob, correct_pred in zip(prediction, probability, correct_pred):\n",
- "# print(\"pred:\", pred, \"prob:\", prob, \"| correct\", correct_pred)\n",
- "\n",
- "print(\"lowest confidence\", min(probability))\n",
- "\n",
- "def accuracy_score(y_true, y_pred):\n",
- " corr_preds=0\n",
- " wrong_preds=0\n",
- " for pred, correct in zip(y_pred, y_true):\n",
- " if pred == correct:\n",
- " corr_preds += 1\n",
- " else:\n",
- " wrong_preds += 1\n",
- " return corr_preds/(corr_preds+wrong_preds)\n",
- "\n",
- "print(\"Accuracy:\", accuracy_score(correct_pred, prediction))\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.10.4 ('tdde19')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.4"
},
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "8e4aa0e1a1e15de86146661edda0b2884b54582522f7ff2b916774ba6b8accb1"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}