If I have to classify a new data, how to do it? How to used trained model to classify a new data. I'm kinda new to neural networks. Please help

As far as I know, BCE is customarily used for binary classification problems. Wouldn't it be better to use CrossEntropyLoss

hi,
I tried to run the implementation on colab but I got an error. is it possible to run it on colab?

The code may have a problem. node set is a whole nodes list of a sentence, but to_node is a neighbor of one node. Can you explain the operation?
edge_stat[node_set, to_node] += 1

I run the train.py. Show that error message.
[Errno 2] No such file or directory: 'embeddings/glove.6B.300d.txt'.
Could you please help to confirm? Thanks.

Can you upload those files too

First, very awesome job on this. I'm running into an issue however when training, as each epoch trains to whatever is set, but it seems there is no feed forward going on, as it seems each epoch is training on it's own. Also I'm running your script as standalone, any advice on how to fix this would be very appreciated!

Thank you!

import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, random_split
import numpy as np
from time import time
import argparse

class GloveTokenizer:
def init(self, filename, unk='', pad=''):
self.filename = filename
self.unk = unk
self.pad = pad
self.stoi = dict()
self.itos = dict()
self.embedding_matrix = list()
with open(filename, 'r', encoding='utf8') as f: # Read tokenizer file
for i, line in enumerate(f):
values = line.split()
self.stoi[values[0]] = i
self.itos[i] = values[0]
self.embedding_matrix.append([float(v) for v in values[1:]])
if self.unk is not None: # Add unk token into the tokenizer
i += 1
self.stoi[self.unk] = i
self.itos[i] = self.unk
self.embedding_matrix.append(np.random.rand(len(self.embedding_matrix[0])))
if self.pad is not None: # Add pad token into the tokenizer
i += 1
self.stoi[self.pad] = i
self.itos[i] = self.pad
self.embedding_matrix.append(np.zeros(len(self.embedding_matrix[0])))
self.embedding_matrix = np.array(self.embedding_matrix).astype(np.float32) # Convert if from double to float for efficiency

def encode(self, sentence):
    if type(sentence) == str:
        sentence = sentence.split(' ')
    elif len(sentence): # Convertible to list
        sentence = list(sentence)
    else:
        raise TypeError('sentence should be either a str or a list of str!')
    encoded_sentence = []
    for word in sentence:
        encoded_sentence.append(self.stoi.get(word, self.stoi[self.unk]))
    return encoded_sentence

def decode(self, encoded_sentence):
    try:
        encoded_sentence = list(encoded_sentence)
    except Exception as e:
        print(e)
        raise TypeError('encoded_sentence should be either a str or a data type that is convertible to list type!')
    sentence = []
    for encoded_word in encoded_sentence:
        sentence.append(self.itos[encoded_word])
    return sentence

def embedding(self, encoded_sentence):
    return self.embedding_matrix[np.array(encoded_sentence)]

class TextLevelGNNDataset(Dataset): # For instantiating train, validation and test dataset
def init(self, node_sets, neighbor_sets, public_edge_mask, labels):
super(TextLevelGNNDataset).init()
self.node_sets = node_sets
self.neighbor_sets = neighbor_sets
self.public_edge_mask = public_edge_mask
self.labels = labels

def __getitem__(self, i):
    return torch.LongTensor(self.node_sets[i]), \
           torch.nn.utils.rnn.pad_sequence([torch.LongTensor(neighbor) for neighbor in self.neighbor_sets[i]], batch_first=True, padding_value=1), \
           self.public_edge_mask[torch.LongTensor(self.node_sets[i]).unsqueeze(-1).repeat(1, torch.nn.utils.rnn.pad_sequence([torch.LongTensor(neighbor) for neighbor in self.neighbor_sets[i]], batch_first=True, padding_value=1).shape[-1]), torch.nn.utils.rnn.pad_sequence([torch.LongTensor(neighbor) for neighbor in self.neighbor_sets[i]], batch_first=True, padding_value=1)], \
           torch.FloatTensor(self.labels[i])

def __len__(self):
    return len(self.labels)

class TextLevelGNNDatasetClass: # This class is used to achieve parameters sharing among datasets
def init(self, train_filename, test_filename, tokenizer, MAX_LENGTH=2000, p=4, min_freq=4, train_validation_split=0.8):
self.train_filename = train_filename
self.test_filename = test_filename
self.tokenizer = tokenizer
self.MAX_LENGTH = MAX_LENGTH
self.p = p
self.min_freq = min_freq
self.train_validation_split = train_validation_split

    self.train_data = pd.read_csv(self.train_filename, header=None)
    self.test_data = pd.read_csv(self.test_filename, header=None)

    self.stoi = {'<unk>': 0, '<pad>': 1} # Re-index
    self.itos = {0: '<unk>', 1: '<pad>'} # Re-index
    self.vocab_count = len(self.stoi)
    self.embedding_matrix = None
    self.label_dict = dict(zip(self.train_data[0].unique(), pd.get_dummies(self.train_data[0].unique()).values.tolist()))

    self.train_dataset, self.validation_dataset = random_split(self.train_data.to_numpy(), [int(len(self.train_data) * train_validation_split), len(self.train_data) - int(len(self.train_data) * train_validation_split)])
    self.test_dataset = self.test_data.to_numpy()

    self.build_vocab() # Based on train_dataset only. Updates self.stoi, self.itos, self.vocab_count and self.embedding_matrix

    self.train_dataset, self.validation_dataset, self.test_dataset, self.edge_stat, self.public_edge_mask = self.prepare_dataset()

def build_vocab(self):
    vocab_list = [sentence.split(' ') for _, sentence in self.train_dataset]
    unique_vocab = []
    for vocab in vocab_list:
        unique_vocab.extend(vocab)
    unique_vocab = list(set(unique_vocab))
    for vocab in unique_vocab:
        if vocab in self.tokenizer.stoi.keys():
            self.stoi[vocab] = self.vocab_count
            self.itos[self.vocab_count] = vocab
            self.vocab_count += 1
    self.embedding_matrix = self.tokenizer.embedding(self.tokenizer.encode(list(self.stoi.keys())))

def prepare_dataset(self): # will also build self.edge_stat and self.public_edge_mask
    # preparing self.train_dataset
    node_sets = [[self.stoi.get(vocab, 0) for vocab in sentence.strip().split(' ')][:self.MAX_LENGTH] for _, sentence in self.train_dataset] # Only retrieve the first MAX_LENGTH words in each document
    neighbor_sets = [create_neighbor_set(node_set, p=self.p) for node_set in node_sets]
    labels = [self.label_dict[label] for label, _ in self.train_dataset]

    # Construct edge statistics and public edge mask
    edge_stat, public_edge_mask = self.build_public_edge_mask(node_sets, neighbor_sets, min_freq=self.min_freq)
    
    train_dataset = TextLevelGNNDataset(node_sets, neighbor_sets, public_edge_mask, labels)

    # preparing self.validation_dataset
    node_sets = [[self.stoi.get(vocab, 0) for vocab in sentence.strip().split(' ')][:self.MAX_LENGTH] for _, sentence in self.validation_dataset] # Only retrieve the first MAX_LENGTH words in each document
    neighbor_sets = [create_neighbor_set(node_set, p=self.p) for node_set in node_sets]
    labels = [self.label_dict[label] for label, _ in self.validation_dataset]
    validation_dataset = TextLevelGNNDataset(node_sets, neighbor_sets, public_edge_mask, labels)

    # preparing self.test_dataset
    node_sets = [[self.stoi.get(vocab, 0) for vocab in sentence.strip().split(' ')][:self.MAX_LENGTH] for _, sentence in self.test_dataset] # Only retrieve the first MAX_LENGTH words in each document
    neighbor_sets = [create_neighbor_set(node_set, p=self.p) for node_set in node_sets]
    labels = [self.label_dict[label] for label, _ in self.test_dataset]
    test_dataset = TextLevelGNNDataset(node_sets, neighbor_sets, public_edge_mask, labels)

    return train_dataset, validation_dataset, test_dataset, edge_stat, public_edge_mask

def build_public_edge_mask(self, node_sets, neighbor_sets, min_freq=2):
    edge_stat = torch.zeros(self.vocab_count, self.vocab_count)
    for node_set, neighbor_set in zip(node_sets, neighbor_sets):
        for neighbor in neighbor_set:
            for to_node in neighbor:
                edge_stat[node_set, to_node] += 1
    public_edge_mask = edge_stat < min_freq # mark True at uncommon edges
    return edge_stat, public_edge_mask

def create_neighbor_set(node_set, p=2):
if type(node_set[0]) != int:
raise ValueError('node_set should be a 1D list!')
if p < 0:
raise ValueError('p should be an integer >= 0!')
sequence_length = len(node_set)
neighbor_set = []
for i in range(sequence_length):
neighbor = []
for j in range(-p, p+1):
if 0 <= i + j < sequence_length:
neighbor.append(node_set[i+j])
neighbor_set.append(neighbor)
return neighbor_set

def pad_custom_sequence(sequences):
'''
To pad different sequences into a padded tensor for training. The main purpose of this function is to separate different sequence, pad them in different ways and return padded sequences.
Input:
sequences : A sequence with a length of 4, representing the node sets sequence in index 0, neighbor sets sequence in index 1, public edge mask sequence in index 2 and label sequence in index 3.
And the length of each sequences are same as the batch size.
sequences: [node_sets_sequence, neighbor_sets_sequence, public_edge_mask_sequence, label_sequence]
Return:
node_sets_sequence <torch.LongTensor>: The padded node sets sequence (works with batch_size >= 1).
neighbor_sets_sequence <torch.LongTensor>: The padded neighbor sets sequence (works with batch_size >= 1).
public_edge_mask_sequence <torch.BoolTensor>: The padded public edge mask sequence (works with batch_size >= 1).
label_sequence <torch.FloatTensor>: The padded label sequence (works with batch_size >= 1).
'''
node_sets_sequence = []
neighbor_sets_sequence = []
public_edge_mask_sequence = []
label_sequence = []
for node_sets, neighbor_sets, public_edge_mask, label in sequences:
node_sets_sequence.append(node_sets)
neighbor_sets_sequence.append(neighbor_sets)
public_edge_mask_sequence.append(public_edge_mask)
label_sequence.append(label)
node_sets_sequence = torch.nn.utils.rnn.pad_sequence(node_sets_sequence, batch_first=True, padding_value=1)
neighbor_sets_sequence, _ = padding_tensor(neighbor_sets_sequence)
public_edge_mask_sequence, _ = padding_tensor(public_edge_mask_sequence)
label_sequence = torch.nn.utils.rnn.pad_sequence(label_sequence, batch_first=True, padding_value=1)
return node_sets_sequence, neighbor_sets_sequence, public_edge_mask_sequence, label_sequence

def padding_tensor(sequences, padding_idx=1):
'''
To pad tensor of different shape to be of the same shape, i.e. padding [tensor.rand(2, 3), tensor.rand(3, 5)] to a shape (2, 3, 5), where 0th dimension is batch_size, 1st and 2nd dimensions are padded.
Input:
sequences : A list of tensors
padding_idx : The index that corresponds to the padding index
Return:
out_tensor <torch.tensor>: The padded tensor
mask <torch.tensor>: A boolean torch tensor where 1 (represents '') are marked as true
'''
num = len(sequences)
max_len_0 = max([s.shape[0] for s in sequences])
max_len_1 = max([s.shape[1] for s in sequences])
out_dims = (num, max_len_0, max_len_1)
out_tensor = sequences[0].data.new(*out_dims).fill_(padding_idx)
for i, tensor in enumerate(sequences):
len_0 = tensor.size(0)
len_1 = tensor.size(1)
out_tensor[i, :len_0, :len_1] = tensor
mask = out_tensor == padding_idx # Marking all places with padding_idx as mask
return out_tensor, mask

class MessagePassing(nn.Module):
def init(self, vertice_count, input_size, out_size, dropout_rate=0, padding_idx=1):
super(MessagePassing, self).init()
self.vertice_count = vertice_count # |V|
self.input_size = input_size # d
self.out_size = out_size # c
self.dropout_rate = dropout_rate
self.padding_idx = padding_idx
self.information_rate = nn.Parameter(torch.rand(self.vertice_count, 1)) # (|V|, 1), which means it is a column vector
self.linear = nn.Linear(self.input_size, self.out_size) # (d, c)
self.dropout = nn.Dropout(self.dropout_rate)

def forward(self, node_sets, embedded_node, edge_weight, embedded_neighbor_node):

node_sets: (batch_size, l)

embedded_node: (batch_size, l, d)

edge_weight: (batch_size, max_sentence_length, max_neighbor_count)

embedded_neighbor_node: (batch_size, max_sentence_length, max_neighbor_count, d)

    tmp_tensor = (edge_weight.view(-1, 1) * embedded_neighbor_node.view(-1, self.input_size)).view(embedded_neighbor_node.shape) # (batch_size, max_sentence_length, max_neighbor_count, d)
    tmp_tensor = tmp_tensor.masked_fill(tmp_tensor == 0, -1e8) # -1e18 switched (batch_size, max_sentence_length, max_neighbor_count, d), mask for M such that masked places are marked as -1e18
    tmp_tensor = self.dropout(tmp_tensor)
    M = tmp_tensor.max(dim=2)[0] # (batch_size, max_sentence_length, d), which is same shape as embedded_node (batch_size, l, d)
    information_rate = self.information_rate[node_sets] # (batch_size, l, 1)
    information_rate = information_rate.masked_fill((node_sets == self.padding_idx).unsqueeze(-1), 1) # (batch_size, l, 1), Fill the information rate of the padding index as 1, such that new e_n = (1-i_r) * M + i_r * e_n = (1-1) * 0 + 1 * e_n = e_n (no update)
    embedded_node = (1 - information_rate) * M + information_rate * embedded_node # (batch_size, l, d)
    sum_embedded_node = embedded_node.sum(dim=1) # (batch_size, d)
    x = F.relu(self.linear(sum_embedded_node)) # (batch_size, c)
    x = self.dropout(x) # if putting dropout with p=0.5 here, it is equivalent to wiping 4 choices out of 8 choices on the question sheet, which does not make sense. If a dropout layer is placed at here, it works the best when p=0 (disabled), followed by p=0.05, ..., p=0.5 (worst and does not even converge).
    y = F.softmax(x, dim=1) # (batch_size, c) along the c dimension
    return y

class TextLevelGNN(nn.Module):
def init(self, pretrained_embeddings, out_size=4, dropout_rate=0, padding_idx=1):
super(TextLevelGNN, self).init()
self.out_size = out_size # c
self.padding_idx = padding_idx
self.weight_matrix = nn.Parameter(torch.randn(pretrained_embeddings.shape[0], pretrained_embeddings.shape[0])) # (|V|, |V|)
self.embedding = nn.Embedding.from_pretrained(pretrained_embeddings, freeze=False, padding_idx=self.padding_idx) # (|V|, d)
self.message_passing = MessagePassing(vertice_count=pretrained_embeddings.shape[0], input_size=pretrained_embeddings.shape[1], out_size=self.out_size, dropout_rate=dropout_rate, padding_idx=self.padding_idx) # input_size: (d,); out_size: (c,)
self.public_edge_weight = nn.Parameter(torch.randn(1, 1)) # (1, 1)

def forward(self, node_sets, neighbor_sets, public_edge_mask):

node_sets: (batch_size, l)

neighbor_sets: (batch_size, max_sentence_length, max_neighbor_count)

neighbor_sets_mask: (batch_size, max_sentence_length, max_neighbor_count)

public_edge_mask: (batch_size, max_sentence_length, max_neighbor_count)

    embedded_node = self.embedding(node_sets) # (batch_size, l, d)
    edge_weight = model.weight_matrix[node_sets.unsqueeze(2).repeat(1, 1, neighbor_sets.shape[-1]), neighbor_sets] # (batch_size, max_sentence_length, max_neighbor_count), neighbor_sets.shape[-1]: eg p=2, this expression=5; p=3, this expression=7. This is to first make node_sets to have same shape with neighbor_sets, then just do 1 query instead of 32*100 queries to speed up performance
    a = edge_weight * ~public_edge_mask # (batch_size, max_sentence_length, max_neighbor_count)
    b = self.public_edge_weight.unsqueeze(2).expand(1, public_edge_mask.shape[-2], public_edge_mask.shape[-1]) * public_edge_mask # (batch_size, max_sentence_length, max_neighbor_count)
    edge_weight = a + b # (batch_size, max_sentence_length, max_neighbor_count)
    embedded_neighbor_node = self.embedding(neighbor_sets) # (batch_size, max_sentece_length, max_neighbor_count, d)

    # Apply mask to edge_weight, to mask and cut-off any relationships to the padding nodes
    edge_weight = edge_weight.masked_fill((node_sets.unsqueeze(2).repeat(1, 1, neighbor_sets.shape[-1]) == self.padding_idx) | (neighbor_sets == self.padding_idx), 0) # (batch_size, max_sentence_length, max_neighbor_count)
    x = self.message_passing(node_sets, embedded_node, edge_weight, embedded_neighbor_node) # (batch_size, c)
    return x

parser = argparse.ArgumentParser()
parser.add_argument('--cuda', default='0', type=str, required=False,
help='Choosing which cuda to use')
parser.add_argument('--embedding_size', default=2000, type=int, required=False,
help='Number of hidden units in each layer of the graph embedding part')
parser.add_argument('--p', default=4, type=int, required=False,
help='The window size')
parser.add_argument('--min_freq', default=4, type=int, required=False,
help='The minimum no. of occurrence for a word to be considered as a meaningful word. Words with less than this occurrence will be mapped to a globally shared embedding weight (to the token). It corresponds to the parameter k in the original paper.')
parser.add_argument('--max_length', default=2000, type=int, required=False,
help='The max length of each document to be processed')
parser.add_argument('--dropout', default=0, type=float, required=False,
help='Dropout rate')
parser.add_argument('--lr', default=1e-1, type=float, required=False,
help='Initial learning rate')
parser.add_argument('--lr_decay_factor', default=0, type=float, required=False,
help='Multiplicative factor of learning rate decays')
parser.add_argument('--lr_decay_every', default=5, type=int, required=False,
help='Decaying learning rate every ? epochs')
parser.add_argument('--weight_decay', default=1e-4, type=float, required=False,
help='Weight decay (L2 penalty)')
parser.add_argument('--warm_up_epoch', default=0, type=int, required=False,
help='Pretraining for ? epochs before early stopping to be in effect')
parser.add_argument('--early_stopping_patience', default=300, type=int, required=False,
help='Waiting for ? more epochs after the best epoch to see any further improvements')
parser.add_argument('--early_stopping_criteria', default='loss', type=str, required=False,
choices=['accuracy', 'loss'],
help='Early stopping according to validation accuracy or validation loss')
parser.add_argument("--epoch", default=400, type=int, required=False,
help='Number of epochs to train')
args = parser.parse_args()

Fetch data (Run this part to download data for the first time) THESE ARE NO LONGER ACTIVE LINKS

#train_url = 'https://www.cs.umb.edu/~smimarog/textmining/datasets/r8-train-all-terms.txt'
#test_url = 'https://www.cs.umb.edu/~smimarog/textmining/datasets/r8-test-all-terms.txt'

pd.read_csv(r'MyTrainDataset')
pd.read_csv(r'MyTestDataset')

tokenizer = GloveTokenizer(r'C:\Users\nickc\Desktop\Datasets\Glove\glove.6B.300d.txt')
dataset = TextLevelGNNDatasetClass(train_filename=r'MyTrainDataset',
test_filename=r'MyTestDataset',
train_validation_split=0.8,
tokenizer=tokenizer,
p=args.p,
min_freq=args.min_freq,
MAX_LENGTH=args.max_length)
train_loader = DataLoader(dataset.train_dataset, batch_size=32, shuffle=True, collate_fn=pad_custom_sequence)
validation_loader = DataLoader(dataset.validation_dataset, batch_size=32, shuffle=True, collate_fn=pad_custom_sequence)
test_loader = DataLoader(dataset.test_dataset, batch_size=32, shuffle=True, collate_fn=pad_custom_sequence)

device = torch.device(f'cuda:{args.cuda}') if torch.cuda.is_available() else torch.device('cpu')
print(device)
model = TextLevelGNN(pretrained_embeddings=torch.tensor(dataset.embedding_matrix), dropout_rate=args.dropout).to(device)
criterion = nn.BCELoss()

lr = args.lr
lr_decay_factor = args.lr_decay_factor
lr_decay_every = args.lr_decay_every
weight_decay = args.weight_decay

warm_up_epoch = args.warm_up_epoch
early_stopping_patience = args.early_stopping_patience
early_stopping_criteria = args.early_stopping_criteria
best_epoch = 0 # Initialize

training = {}
validation = {}
testing = {}
training['accuracy'] = []
training['loss'] = []
validation['accuracy'] = []
validation['loss'] = []
testing['accuracy'] = []
testing['loss'] = []

for epoch in range(args.epoch):
model.train()
train_loss = 0
train_correct_items = 0
previous_epoch_timestamp = time()

if epoch % lr_decay_every == 0: # Update optimizer for every lr_decay_every epochs
    if epoch != 0: # When it is the first epoch, disable the lr_decay_factor
        lr *= lr_decay_factor
    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)

for i, (node_sets, neighbor_sets, public_edge_masks, labels) in enumerate(train_loader):

print('Finished batch:', i)

    node_sets = node_sets.to(device)
    neighbor_sets = neighbor_sets.to(device)
    public_edge_masks = public_edge_masks.to(device)
    labels = labels.to(device)
    prediction = model(node_sets, neighbor_sets, public_edge_masks)
    loss = criterion(prediction, labels).to(device)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    train_loss += loss.item()
    train_correct_items += (prediction.argmax(dim=1) == labels.argmax(dim=1)).sum().item()
train_accuracy = train_correct_items / len(dataset.train_dataset)

model.eval()
validation_loss = 0
validation_correct_items = 0
for i, (node_sets, neighbor_sets, public_edge_masks, labels) in enumerate(validation_loader):
    node_sets = node_sets.to(device)
    neighbor_sets = neighbor_sets.to(device)
    public_edge_masks = public_edge_masks.to(device)
    labels = labels.to(device)
    prediction = model(node_sets, neighbor_sets, public_edge_masks)
    loss = criterion(prediction, labels).to(device)
    validation_loss += loss.item()
    validation_correct_items += (prediction.argmax(dim=1) == labels.argmax(dim=1)).sum().item()
validation_accuracy = validation_correct_items / len(dataset.validation_dataset)

model.eval()
test_loss = 0
test_correct_items = 0
for i, (node_sets, neighbor_sets, public_edge_masks, labels) in enumerate(test_loader):
    node_sets = node_sets.to(device)
    neighbor_sets = neighbor_sets.to(device)
    public_edge_masks = public_edge_masks.to(device)
    labels = labels.to(device)
    prediction = model(node_sets, neighbor_sets, public_edge_masks)
    loss = criterion(prediction, labels).to(device)
    test_loss += loss.item()
    test_correct_items += (prediction.argmax(dim=1) == labels.argmax(dim=1)).sum().item()
test_accuracy = test_correct_items / len(dataset.test_dataset)
print(f'Epoch: {epoch+1}, Training Loss: {train_loss:.4f}, Validation Loss: {validation_loss:.4f}, Testing Loss: {test_loss:.4f}, Training Accuracy: {train_accuracy:.4f}, Validation Accuracy: {validation_accuracy:.4f}, Testing Accuracy: {test_accuracy:.4f}, Time Used: {time()-previous_epoch_timestamp:.2f}s')
training['accuracy'].append(train_accuracy)
training['loss'].append(train_loss)
validation['accuracy'].append(validation_accuracy)
validation['loss'].append(validation_loss)
testing['accuracy'].append(test_accuracy)
testing['loss'].append(test_loss)

# add warmup mechanism for warm_up_epoch epochs
if epoch >= warm_up_epoch:
    best_epoch = warm_up_epoch
    # early stopping
    if early_stopping_criteria == 'accuracy':
        if validation['accuracy'][epoch] > validation['accuracy'][best_epoch]:
            best_epoch = epoch
        elif epoch >= best_epoch + early_stopping_patience:
            print(f'Early stopping... (No further increase in validation accuracy) for consecutive {early_stopping_patience} epochs.')
            break
    if early_stopping_criteria == 'loss':
        if validation['loss'][epoch] < validation['loss'][best_epoch]:
            best_epoch = epoch
        elif epoch >= best_epoch + early_stopping_patience:
            print(f'Early stopping... (No further decrease in validation loss) for consecutive {early_stopping_patience} epochs.')
            break
elif epoch + 1 == warm_up_epoch:
    print('--- Warm up finished ---')

df = pd.concat([pd.DataFrame(training), pd.DataFrame(validation), pd.DataFrame(testing)], axis=1)
df.columns = ['Training Accuracy', 'Training Loss', 'Validation Accuracy', 'Validation Loss', 'Testing Accuracy', 'Testing Loss']
df.to_csv(f'embedding_size={args.embedding_size},p={args.p},min_freq={args.min_freq},max_length={args.max_length},dropout={args.dropout},lr={args.lr},lr_decay_factor={args.lr_decay_factor},lr_decay_every={args.lr_decay_every},weight_decay={args.weight_decay},warm_up_epoch={args.warm_up_epoch},early_stopping_patience={args.early_stopping_patience},early_stopping_criteria={args.early_stopping_criteria},epoch={args.epoch}.csv') # Logging

import matplotlib.pyplot as plt

plt.plot(training['loss'], label='Training Loss')
plt.plot(validation['loss'], label='Validation Loss')
plt.plot(testing['loss'], label='Testing Loss')
plt.legend()
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()

plt.plot(training['accuracy'], label='Training Accuracy')
plt.plot(validation['accuracy'], label='Validation Accuracy')
plt.plot(testing['accuracy'], label='Testing Accuracy')
plt.legend()
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.show()