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Convert to a multi-hot index in the CSV, to simplify our DataSets and DataLoaders

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This commit is contained in:
Timothy Allen 2023-12-30 12:30:43 +02:00
parent bedf82d8a1
commit 910e0c9d24
2 changed files with 92 additions and 185 deletions
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22
africat/aa_create_dataset.py
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@ -96,11 +96,7 @@ def parse_and_extract(input_dir, verbose):
cats = list() cats = list()
for cat in doc.findall('./category'): for cat in doc.findall('./category'):
# TODO check against a list of current categories,
# and strip any non-current categories
cats.append(cat.text) cats.append(cat.text)
#entry["categories"] = cats # if you want a list
entry["categories"] = ";".join(cats) # if you want a string
text = list() text = list()
lang = "" lang = ""
@ -115,10 +111,19 @@ def parse_and_extract(input_dir, verbose):
except Exception as e: except Exception as e:
print(f"{xml_file} : {e}") print(f"{xml_file} : {e}")
if text is not None and len(cats) > 1: if text is not None and len(cats) >= 1:
entry["content"] = "\n".join(text)
entry["language"] = lang entry["language"] = lang
entry["content"] = "\n".join(text)
for cat in cats:
entry[cat] = 1
articles.append(entry) articles.append(entry)
else:
if len(cats) < 1:
print(f"No article added for key {key} due to lack of categories")
elif text is None:
print(f"No article added for key {key} due to lack of text")
else:
print(f"No article added for key {key} due to unknown error")
except ET.ParseError as e: except ET.ParseError as e:
if verbose > 1: if verbose > 1:
@ -158,7 +163,10 @@ def scrub_data(articles, verbose):
data['content'] = data.content.parallel_apply(lambda x: x.strip()) data['content'] = data.content.parallel_apply(lambda x: x.strip())
data['content'] = data.content.parallel_apply(lambda x: re.sub(" +", " ", x)) data['content'] = data.content.parallel_apply(lambda x: re.sub(" +", " ", x))
# TODO: lemmas? See spaCy # Any remaining text processing can be done by training/inference step
# sort category columns: lowercase first (key, language, content), then title-cased categories
data.reindex(columns=sorted(data.columns, key=lambda x: (x.casefold(), x.swapcase())))
return data return data

253
africat/categorise.py
View File

@ -19,19 +19,19 @@ import tqdm
import torch import torch
import torchdata.datapipes as dp import torchdata.datapipes as dp
import torchtext.transforms as T import torchtext.transforms as T
import torchtext.vocab as vocab
from torch import nn from torch import nn
from torch.utils.data import Dataset, DataLoader from torch.utils.data import Dataset, DataLoader
from torchtext.vocab import build_vocab_from_iterator
from models.rnn import RNN xlmr_vocab_path = r"https://download.pytorch.org/models/text/xlmr.vocab.pt"
xlmr_spm_model_path = r"https://download.pytorch.org/models/text/xlmr.sentencepiece.bpe.model"
all_categories = list()
# XXX None for all stories # XXX None for all stories
#story_num = 128 story_num = 128
#story_num = 256 #story_num = 256
#story_num = 512 #story_num = 512
#story_num = 1024 #story_num = 1024
story_num = 4096 #story_num = 4096
#story_num = None #story_num = None
def read_csv(input_csv, rows=None, verbose=0): def read_csv(input_csv, rows=None, verbose=0):
@ -42,6 +42,7 @@ def read_csv(input_csv, rows=None, verbose=0):
pd.read_csv(f, pd.read_csv(f,
encoding="utf-8", encoding="utf-8",
quoting=csv.QUOTE_ALL, quoting=csv.QUOTE_ALL,
index_col=0,
nrows=rows, nrows=rows,
chunksize=50, chunksize=50,
), ),
@ -52,10 +53,10 @@ def read_csv(input_csv, rows=None, verbose=0):
data = pd.read_csv(f, data = pd.read_csv(f,
encoding="utf-8", encoding="utf-8",
quoting=csv.QUOTE_ALL, quoting=csv.QUOTE_ALL,
index_col=0,
nrows=rows, nrows=rows,
) )
data.dropna(axis='index', inplace=True)
#print(data) #print(data)
#sys.exit(0) #sys.exit(0)
return data return data
@ -83,9 +84,9 @@ def split_dataset(data, verbose=0):
#print("Length of tests_data: {}".format(len(tests_idx))) #print("Length of tests_data: {}".format(len(tests_idx)))
# Create the training and validation sets, as dataframes # Create the training and validation sets, as dataframes
train_data = data.iloc[train_idx].reset_index().drop('index', axis=1) train_data = data.iloc[train_idx].reset_index()
valid_data = data.iloc[valid_idx].reset_index().drop('index', axis=1) valid_data = data.iloc[valid_idx].reset_index()
#tests_data = data.iloc[tests_idx].reset_index().drop('index', axis=1) #tests_data = data.iloc[tests_idx].reset_index()
#return(train_data, valid_data, tests_data) #return(train_data, valid_data, tests_data)
return(train_data, valid_data) return(train_data, valid_data)
@ -96,24 +97,25 @@ def split_dataset(data, verbose=0):
''' '''
class TextCategoriesDataset(Dataset): class TextCategoriesDataset(Dataset):
''' Dataset of Text and Categories ''' ''' Dataset of Text and Categories '''
def __init__(self, df, text_column, cats_column, lang_column, transform=None, verbose=0): def __init__(self, df, lang_column, text_column, first_cats_column=0, transform=None, verbose=0):
''' '''
Arguments: Arguments:
df (panda.Dataframe): csv content, loaded as dataframe df (panda.Dataframe): csv content, loaded as dataframe
lang_column (str): the name of the column containing the language
text_column (str): the name of the column containing the text text_column (str): the name of the column containing the text
cats_column (str): the name of the column containing first_cats_column (int): the index of the first column containing
semicolon-separated categories a category
text_column (str): the name of the column containing the language transform (callable, optional): Optional transform to be applied
transform (callable, optional): Optional transform to be on a sample.
applied on a sample.
''' '''
self.df = df self.df = df
self.transform = transform self.transform = transform
self.verbose = verbose self.verbose = verbose
self.text = self.df[text_column]
self.cats = self.df[cats_column]
self.lang = self.df[lang_column] self.lang = self.df[lang_column]
self.text = self.df[text_column]
self.cats = self.df.iloc[:, first_cats_column:].sort_index(axis="columns")
self.cats_vocab = self.cats.columns
# index-to-token dict # index-to-token dict
# <pad> : padding, used for padding the shorter sentences in a batch # <pad> : padding, used for padding the shorter sentences in a batch
@ -126,26 +128,6 @@ class TextCategoriesDataset(Dataset):
# token-to-index dict # token-to-index dict
self.stoi = {k:j for j, k in self.itos.items()} self.stoi = {k:j for j, k in self.itos.items()}
# Create vocabularies upon initialisation
self.text_vocab = build_vocab_from_iterator(
[self.textTokens(text) for i, text in self.df[text_column].items()],
min_freq=2,
specials=self.itos.values(),
special_first=True
)
self.text_vocab.set_default_index(self.text_vocab['<unk>'])
#print(self.text_vocab.get_itos())
self.cats_vocab = build_vocab_from_iterator(
#[self.catTokens(cats) for i, cats in self.df[cats_column].items()],
[self.catTokens(all_categories)],
min_freq=1,
specials=['<unk>'],
special_first=True
)
self.cats_vocab.set_default_index(self.cats_vocab['<unk>'])
#print(self.cats_vocab.get_itos())
def __len__(self): def __len__(self):
return len(self.df) return len(self.df)
@ -158,58 +140,41 @@ class TextCategoriesDataset(Dataset):
idx = idx.tolist() idx = idx.tolist()
# Get the raw data # Get the raw data
text = self.text[idx]
cats = self.cats[idx]
lang = self.lang[idx] lang = self.lang[idx]
text = self.text[idx]
cats = self.cats.iloc[idx]
#print(self.textTransform()(text))
#print(cats)
#print(cats.fillna(0).values)
if self.transform: if self.transform:
text, cats = self.transform(text, cats) text, cats = self.transform(text, cats)
#print(cats) # Numericalise text by applying transforms, and cats by converting
#print(self.catTokens(cats)) # NaN to zeros and stripping the index
#print(self.getTransform(self.cats_vocab, "cats")(self.catTokens(cats)))
# Numericalise by applying transforms
return ( return (
self.getTransform(self.text_vocab, "text")(self.textTokens(text)), self.textTransform()(text),
self.getTransform(self.cats_vocab, "cats")(self.catTokens(cats)), cats.fillna(0).values,
) )
@staticmethod def textTransform(self):
def textTokens(text):
if isinstance(text, str):
return [word for word in text.split()]
@staticmethod
def catTokens(cats):
if isinstance(cats, str):
return [cat for cat in cats.split(';')]
elif isinstance(cats, list):
return [cat for cat in cats]
def getTransform(self, vocab, vType):
''' '''
Create transforms based on given vocabulary. The returned transform Create transforms based on given vocabulary. The returned transform
is applied to a sequence of tokens. is applied to a sequence of tokens.
''' '''
if vType == "text": return T.Sequential(
return T.Sequential( # converts the sentences to indices based on given vocabulary using SentencePiece
# converts the sentences to indices based on given vocabulary T.SentencePieceTokenizer(xlmr_spm_model_path),
T.VocabTransform(vocab=vocab), T.VocabTransform(torch.hub.load_state_dict_from_url(xlmr_vocab_path)),
# Add <sos> at beginning of each sentence. 1 because the index # Add <sos> at beginning of each sentence. 1 because the index
# for <sos> in vocabulary is 1 as seen in previous section # for <sos> in vocabulary is 1 as seen in previous section
T.AddToken(self.text_vocab['<sos>'], begin=True), T.AddToken(self.stoi['<sos>'], begin=True),
# Add <eos> at end of each sentence. 2 because the index # Add <eos> at end of each sentence. 2 because the index
# for <eos> in vocabulary is 2 as seen in previous section # for <eos> in vocabulary is 2 as seen in previous section
T.AddToken(self.text_vocab['<eos>'], begin=False) T.AddToken(self.stoi['<eos>'], begin=False)
) )
elif vType == "cats":
return T.Sequential(
# converts the sentences to indices based on given vocabulary
T.VocabTransform(vocab=vocab),
)
else:
raise Exception('wrong transformation type')
''' '''
@ -223,12 +188,11 @@ class CollateBatch:
in a batch of equal length. We can do this a collate_fn callback class, in a batch of equal length. We can do this a collate_fn callback class,
which returns a tensor which returns a tensor
''' '''
def __init__(self, pad_idx, cats): def __init__(self, pad_idx):
''' '''
pad_idx (int): the index of the "<pad>" token in the vocabulary. pad_idx (int): the index of the "<pad>" token in the vocabulary.
''' '''
self.pad_idx = pad_idx self.pad_idx = pad_idx
self.cats = cats
def __call__(self, batch): def __call__(self, batch):
''' '''
@ -236,13 +200,6 @@ class CollateBatch:
is a list of tokens is a list of tokens
''' '''
batch_text, batch_cats = zip(*batch) batch_text, batch_cats = zip(*batch)
#for i in range(len(batch)):
# print(batch[i])
#max_text_len = len(max(batch_text, key=len))
#max_cats_len = len(max(batch_cats, key=len))
#text_tensor = T.ToTensor(self.pad_idx)(batch_text)
#cats_tensor = T.ToTensor(self.pad_idx)(batch_cats)
# Pad text to the longest # Pad text to the longest
text_tensor = nn.utils.rnn.pad_sequence( text_tensor = nn.utils.rnn.pad_sequence(
@ -251,44 +208,7 @@ class CollateBatch:
) )
text_lengths = torch.tensor([t.shape[0] for t in text_tensor]) text_lengths = torch.tensor([t.shape[0] for t in text_tensor])
#cats_tensor = torch.nn.utils.rnn.pad_sequence( cats_tensor = torch.tensor(batch_cats, dtype=torch.float32)
# [torch.LongTensor(s) for s in batch_cats],
# batch_first=True, padding_value=self.pad_idx
#)
#cats_lengths = torch.LongTensor(list(map(len, batch_cats)))
'''
# Pad cats_tensor to all possible categories
num_cats = len(all_categories)
# Convert cats to multi-label one-hot representation
cats_tensor = torch.full((len(batch_cats), num_cats), self.pad_idx).float()
cats_lengths = torch.LongTensor(list(map(len, batch_cats)))
for idx, cats in enumerate(batch_cats):
#print("\nsample", idx, cats)
for c in cats:
#print(c)
cats_tensor[idx][c] = 1
#print(cats_tensor[idx])
'''
# Convert cats to multi-label one-hot representation
# add one to all_categories to account for <unk>
cats_tensor = torch.full((len(batch_cats), len(all_categories)+1), self.pad_idx).float()
for idx, cats in enumerate(batch_cats):
#print("\nsample", idx, cats)
for c in cats:
cats_tensor[idx][c] = 1
#print(cats_tensor[idx])
#sys.exit(0)
'''
# XXX why??
## SORT YOUR TENSORS BY LENGTH!
text_lengths, perm_idx = text_lengths.sort(0, descending=True)
text_tensor = text_tensor[perm_idx]
cats_tensor = cats_tensor[perm_idx]
'''
#print("text", text_tensor) #print("text", text_tensor)
#print("text shape:", text_tensor.shape) #print("text shape:", text_tensor.shape)
@ -296,7 +216,6 @@ class CollateBatch:
#print("cats shape:", cats_tensor.shape) #print("cats shape:", cats_tensor.shape)
#print(text_lengths) #print(text_lengths)
#print("text_lengths shape:", text_lengths.shape) #print("text_lengths shape:", text_lengths.shape)
#sys.exit(0) #sys.exit(0)
return ( return (
@ -305,48 +224,27 @@ class CollateBatch:
text_lengths, text_lengths,
) )
def cat2tensor(label_vocab, labels, pad_idx: int): def tensor2cat(dataset, tensor):
all_labels = vocab.get_itos() cats = dataset.cats_vocab
num_labels = len(all_labels)
# add <unk>
if 0 not in all_labels:
num_labels += 1
labels_tensor = torch.full((len(labels), num_labels), pad_idx).float()
labels_lengths = torch.LongTensor(list(map(len, labels)))
for idx, labels in enumerate(labels):
#print("\nsample", idx, labels)
for l in labels:
labels_tensor[idx][l] = 1
#print(labels_tensor[idx])
return labels_tensor
def tensor2cat(vocab, tensor):
all_cats = vocab.get_itos()
if tensor.ndimension() == 2: if tensor.ndimension() == 2:
batch = list() batch = list()
for result in tensor: for result in tensor:
chance = dict() chance = dict()
for idx, pred in enumerate(result): for idx, pred in enumerate(result):
if pred > 0: # XXX if pred > 0: # XXX
chance[all_cats[idx]] = pred.item() chance[cats[idx]] = pred.item()
#print(chance)
batch.append(chance) batch.append(chance)
return batch return batch
elif tensor.ndimension() == 1: elif tensor.ndimension() == 1:
chance = dict() chance = dict()
for idx, pred in enumerate(tensor): for idx, pred in enumerate(tensor):
if idx >= len(all_cats): if idx >= len(cats):
print(f"Idx {idx} not in {len(all_cats)} categories") print(f"Idx {idx} not in {len(cats)} categories")
#elif pred > 0: # XXX elif pred > 0: # XXX
#print(idx, len(all_cats)) chance[cats[idx]] = pred.item()
chance[all_cats[idx]] = pred.item()
#print(chance)
return chance return chance
else: else:
raise ValueError("Only tensors with 2 dimensions are supported") raise ValueError("Only tensors with 1 dimension or batches with 2 dimensions are supported")
return vocab.get_itos(cat)
def train(dataloader, dataset, model, optimizer, criterion, epoch=0): def train(dataloader, dataset, model, optimizer, criterion, epoch=0):
@ -452,6 +350,17 @@ def evaluate(dataloader, dataset, model, criterion, epoch=0):
}) })
return total_acc / total_count return total_acc / total_count
# TODO seeding:
def seed_everything(seed=42):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Some cudnn methods can be random even after fixing the seed
# unless you tell it to be deterministic
torch.backends.cudnn.deterministic = True
def main(): def main():
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(
@ -487,37 +396,26 @@ def main():
data = read_csv(input_csv=args.input, rows=story_num, verbose=args.verbose) data = read_csv(input_csv=args.input, rows=story_num, verbose=args.verbose)
# create list of all categories
global all_categories
for cats in data.categories:
for c in cats.split(";"):
if c not in all_categories:
all_categories.append(c)
all_categories = sorted(all_categories)
#print(all_categories)
#print(len(all_categories))
#sys.exit(0)
train_data, valid_data, = split_dataset(data, verbose=args.verbose) train_data, valid_data, = split_dataset(data, verbose=args.verbose)
''' '''
dataset = TextCategoriesDataset(df=data, dataset = TextCategoriesDataset(df=data,
text_column="content",
cats_column="categories",
lang_column="language", lang_column="language",
text_column="content",
first_cats_column=data.columns.get_loc("content")+1,
verbose=args.verbose, verbose=args.verbose,
) )
''' '''
train_dataset = TextCategoriesDataset(df=train_data, train_dataset = TextCategoriesDataset(df=train_data,
text_column="content",
cats_column="categories",
lang_column="language", lang_column="language",
text_column="content",
first_cats_column=train_data.columns.get_loc("content")+1,
verbose=args.verbose, verbose=args.verbose,
) )
valid_dataset = TextCategoriesDataset(df=valid_data, valid_dataset = TextCategoriesDataset(df=valid_data,
text_column="content",
cats_column="categories",
lang_column="language", lang_column="language",
text_column="content",
first_cats_column=valid_data.columns.get_loc("content")+1,
verbose=args.verbose, verbose=args.verbose,
) )
#for text, cat in enumerate(train_dataset): #for text, cat in enumerate(train_dataset):
@ -525,6 +423,7 @@ def main():
#print("-" * 20) #print("-" * 20)
#for text, cat in enumerate(valid_dataset): #for text, cat in enumerate(valid_dataset):
# print(text, cat) # print(text, cat)
#print(tensor2cat(train_dataset, torch.tensor([0, 0, 0, 1., 0.9])))
#sys.exit(0) #sys.exit(0)
# Get cpu, gpu or mps device for training. # Get cpu, gpu or mps device for training.
@ -565,7 +464,7 @@ def main():
drop_last=True, drop_last=True,
shuffle=True, shuffle=True,
num_workers=0, num_workers=0,
collate_fn=CollateBatch(cats=train_dataset.cats_vocab.get_stoi(), pad_idx=train_dataset.stoi['<pad>']), collate_fn=CollateBatch(pad_idx=train_dataset.stoi['<pad>']),
) )
''' '''
train_dataloader = DataLoader(train_dataset, train_dataloader = DataLoader(train_dataset,
@ -573,20 +472,20 @@ def main():
drop_last=True, drop_last=True,
shuffle=True, shuffle=True,
num_workers=0, num_workers=0,
collate_fn=CollateBatch(cats=train_dataset.cats_vocab.get_stoi(), pad_idx=train_dataset.stoi['<pad>']), collate_fn=CollateBatch(pad_idx=train_dataset.stoi['<pad>']),
) )
valid_dataloader = DataLoader(valid_dataset, valid_dataloader = DataLoader(valid_dataset,
batch_size=batch_size, batch_size=batch_size,
drop_last=True, drop_last=True,
shuffle=True, shuffle=True,
num_workers=0, num_workers=0,
collate_fn=CollateBatch(cats=train_dataset.cats_vocab.get_stoi(), pad_idx=train_dataset.stoi['<pad>']), collate_fn=CollateBatch(pad_idx=train_dataset.stoi['<pad>']),
) )
#for i_batch, sample_batched in enumerate(dataloader): #for i_batch, sample_batched in enumerate(dataloader):
# print(i_batch, sample_batched[0], sample_batched[1]) # print(i_batch, sample_batched[0], sample_batched[1])
#for i_batch, sample_batched in enumerate(train_dataloader): for i_batch, sample_batched in enumerate(train_dataloader):
# print(i_batch, sample_batched[0], sample_batched[1]) print(i_batch, sample_batched[0], sample_batched[1])
#sys.exit(0) sys.exit(0)
input_size = len(train_dataset.text_vocab) input_size = len(train_dataset.text_vocab)
output_size = len(train_dataset.cats_vocab) # every output item is the likelihood of a particular category output_size = len(train_dataset.cats_vocab) # every output item is the likelihood of a particular category