def get_other_results(queries, qml_rankings, num_ranks=None):
document_lookup = read_cache('./doc_lookup.json', get_robust_documents)
document_title_to_id = read_cache('./document_title_to_id.json',
lambda: print('failed'))
document_id_to_title = _.invert(document_title_to_id)
doc_ids = range(len(document_id_to_title))
documents = [
document_lookup[document_id_to_title[doc_id]] for doc_id in doc_ids
]
tokenizer = Tokenizer(
rules=[handle_caps, fix_html, spec_add_spaces, rm_useless_spaces])
tokenized_documents = read_cache('tok_docs.json',
lambda: tokenizer.process_all(documents))
tokenized_queries = tokenizer.process_all(queries)
bm25 = BM25(tokenized_documents)
average_idf = sum(float(val) for val in bm25.idf.values()) / len(bm25.idf)
bm25_rankings = []
glove_rankings = []
rm3_rankings = []
glove = get_glove_lookup(embedding_dim=300, use_large_embed=True)
docs_lms = _calc_docs_lms(bm25.df, bm25.f)
for q, qml_ranking in progressbar(zip(tokenized_queries, qml_rankings)):
bm25_rankings.append(
_get_bm25_ranking(bm25, qml_ranking, q, average_idf=average_idf))
glove_rankings.append(
_get_glove_ranking(glove, tokenized_documents, qml_ranking, q))
rm3_rankings.append(_get_rm3_ranking(docs_lms, bm25.f, qml_ranking, q))
return bm25_rankings, glove_rankings, rm3_rankings
def main():
document_lookup = read_cache('./doc_lookup.json', get_robust_documents)
document_title_to_id = create_id_lookup(document_lookup.keys())
document_id_to_title = _.invert(document_title_to_id)
doc_ids = range(len(document_id_to_title))
documents = [document_lookup[document_id_to_title[doc_id]] for doc_id in doc_ids]
tokenizer = Tokenizer()
tokenized_documents = read_cache('tok_docs.json',
lambda: tokenizer.process_all(documents))
bm25 = BM25(tokenized_documents)
with open('./doc_word_idf.json', 'w+') as fh:
json.dump(bm25.idf, fh)
def get_texts(texts, with_preprocess=False, pre_rules=None):
r"""
Uses fastai's Tokenizer because it does a series of very convenients things
during the tokenization process
See here: https://docs.fast.ai/text.transform.html#Tokenizer
"""
tok_func = Tokenizer()
if with_preprocess:
texts = [" ".join(simple_preprocess(s)) for s in texts]
if pre_rules:
tok_func.pre_rules = pre_rules + tok_func.pre_rules
tokens = tok_func.process_all(texts)
return tokens
def baselines_eval():
rankings_to_eval = read_query_test_rankings()
qrels = parse_qrels()
query_ids = list(qrels.keys())
query_lookup = get_robust_eval_queries()
queries = [query_lookup[query_id] for query_id in query_ids]
k = 10 if len(sys.argv) == 1 else int(sys.argv[1])
document_lookup = read_cache(name('./doc_lookup.json', ['with_titles']),
get_robust_documents_with_titles)
document_title_to_id = read_cache('./document_title_to_id.json',
lambda: print('failed'))
ordered_rankings_to_eval = [[
document_title_to_id[title] for title in rankings_to_eval[query]
] for query in query_ids]
ordered_qrels = [[document_title_to_id[title] for title in qrels[query]]
for query in query_ids]
document_id_to_title = _.invert(document_title_to_id)
doc_ids = range(len(document_id_to_title))
documents = [
document_lookup[document_id_to_title[doc_id]] for doc_id in doc_ids
]
tokenizer = Tokenizer(
rules=[handle_caps, fix_html, spec_add_spaces, rm_useless_spaces])
tokenized_documents = read_cache(
'tok_docs.json',
lambda: tokenizer.process_all(clean_documents(documents)))
tokenized_queries = tokenizer.process_all(clean_documents(queries))
bm25 = gensim_bm25.BM25(tokenized_documents)
# with open('./caches/106756_most_common_doc.json', 'r') as fh:
# doc_token_set = set(json.load(fh))
# corpus, token_lookup = tokens_to_indexes(tokenized_documents,
# None,
# token_set=doc_token_set)
# corpus = [[[token_lookup[term], f] for term, f in doc_fs.items()] for doc_fs in bm25.f]
# tfidf = TfidfModel(corpus)
# lsi = LsiModel(tfidf, id2word=_.invert(token_lookup), num_topics=300)
glove_rankings = []
# lsi_rankings = []
glove = get_glove_lookup(embedding_dim=300, use_large_embed=True)
encoded_docs = torch.stack(
[encode_glove_fs(glove, bm25.idf, doc_fs) for doc_fs in bm25.f])
encoded_docs = encoded_docs / torch.norm(encoded_docs, dim=1).unsqueeze(1)
for q, qml_ranking in progressbar(zip(tokenized_queries,
ordered_rankings_to_eval),
max_value=len(tokenized_queries)):
doc_ids = qml_ranking[:k] if '--rerank' in sys.argv else None
glove_rankings.append(
rank_glove(glove, bm25.idf, encoded_docs, q, doc_ids=doc_ids))
# lsi_rankings.append(rank_lsi(lsi, tfidf, [token_lookup[term] if term in token_lookup else 0 for term in q], doc_ids=doc_ids))
print('indri:', metrics_at_k(ordered_rankings_to_eval, ordered_qrels, k))
print('glove:', metrics_at_k(glove_rankings, ordered_qrels, k))
def get_preds(user_input, vectorizer, m):
tokenizer = Tokenizer()
tok = SpacyTokenizer('en')
toks = tokenizer.process_text(user_input, tok)
term_doc = vectorizer.transform(toks)
preds = m.predict(term_doc)
toks_preds_df = pd.DataFrame()
toks_preds_df['toks'] = toks
toks_preds_df['preds'] = preds
html_format_list = html_format(toks_preds_df)
html_format_join = ' '.join(html_format_list)
html_format_join = "<div>" + html_format_join + "</span></div>"
return html_format_join
def preprocess_texts(texts,
token_lookup=None,
num_tokens=None,
token_set=None,
drop_if_any_unk=False):
tokenizer = Tokenizer()
tokenized = tokenizer.process_all(texts)
idx_texts, token_lookup = tokens_to_indexes(
tokenized,
token_lookup,
num_tokens=num_tokens,
token_set=token_set,
drop_if_any_unk=drop_if_any_unk)
return idx_texts, token_lookup
def get_texts(df):
labels = df[0].values.astype(np.int64)
texts = f'\n{BOS} {FLD} 1 ' + df[1].astype(str)
texts = list(texts.apply(fixup).values)
tokens = Tokenizer(lang='en_core_web_sm').proc_all_mp(
partition_by_cores(texts), lang='en_core_web_sm')
return tokens, list(labels)
def prepare_clas_dataset(input_path,
output_dir=None,
valid_split=0.2,
tokenizer_lang="xx",
min_freq=2,
seed=42):
"""
Reads a CSV file with texts and labels, splits it into training and validation sets,
tokenizes texts and saves datasets for fine-tuning and for classification.
Attributes:
input_path (str): Path to CSV file with texts in the first and labels in second column.
output_dir (str): Folder where to store the processed dataset.
valid_split (float): A fraction of data used for validation.
tokenizer_lang (str): Language setting for tokenizer.
min_freq (int): Minimal number of occurrences of a word to be conidered for adding to
vocabulary.
seed (int): Random seed that determines the training-validation split.
"""
input_path = Path(input_path)
output_dir = Path(output_dir or input_path.parent)
output_dir.mkdir(parents=True, exist_ok=True)
train_df, valid_df = csv_to_train_valid_df(input_path, valid_split, seed)
data_finetune_lm = TextLMDataBunch.from_df(
output_dir,
train_df,
valid_df,
tokenizer=Tokenizer(lang=tokenizer_lang),
text_cols=0,
min_freq=min_freq)
data_clas = TextClasDataBunch.from_df(
output_dir,
train_df,
valid_df,
tokenizer=Tokenizer(lang=tokenizer_lang),
text_cols=0,
label_cols=1,
vocab=data_finetune_lm.train_ds.vocab,
bs=32,
min_freq=min_freq)
data_finetune_lm.save("data_finetune_lm.pkl")
data_clas.save("data_clas.pkl")
def get_german_db(bs=32):
tokenizer = Tokenizer(lang='de')
tokenizer_proc = TokenizeProcessor(tokenizer=tokenizer)
num_proc = NumericalizeProcessor()
processor = [tokenizer_proc, num_proc]
db = TextList\
.from_csv('/data/10kgerman', 'train.csv', cols=["text"], processor=processor)\
.split_by_rand_pct(0.1)\
.label_from_df('c').databunch()
db.batch_size = bs
return db
def get_texts_and_tokenize(df, n_lbls=1):
labels = df.iloc[:,range(n_lbls)].values
labels = labels.tolist()
labels = list(map(lambda x: ast.literal_eval(x[0]), labels))
texts = f'\n{BOS} ' + df[n_lbls].astype(str)
# perform common fixup to text:
texts = list(texts.apply(fixup).values)
labels = list(labels)
# tokenize texts
tok = Tokenizer().proc_all_mp(partition_by_cores(texts))
return texts, tok, labels
def predict(self, text):
self.model.reset()
self.model.eval()
input_string = 'xbos xfld 1 ' + text
texts = [input_string]
tokens = Tokenizer(lang='en_core_web_sm').proc_all_mp(
partition_by_cores(texts), lang='en_core_web_sm')
encoded_tokens = [self.inspire_data_stoi[p] for p in tokens[0]]
token_array = np.reshape(np.array(encoded_tokens), (-1, 1))
token_array = Variable(torch.from_numpy(token_array))
prediction_scores = self.model(token_array)
prediction_scores_numpy = prediction_scores[0].data.cpu().numpy()
return numpy_softmax(prediction_scores_numpy[0])[0]
def __init__(self, model_name):
super().__init__()
setattr(self, 'model_class', AutoModelForSequenceClassification)
self.model_name = model_name
self.pt_tokenizer, self.pt_config, self.pt_model = [
self.fetch_pretrained(i) for i in
[self.tokenizer_class, self.config_class, self.model_class]
]
self.base_tokenizer = transformer_tokenizer.TransformersBaseTokenizer(
self.model_name, self.pt_tokenizer)
self.tokenizer = Tokenizer(tok_func=self.base_tokenizer,
pre_rules=[],
post_rules=[])
self.vocab = transformer_vocab.TransformersVocab(self.pt_tokenizer)
self.model = CustTransformerModel(self.pt_model)
def __init__(self, path):
texts = pd.read_csv(path + '/jokes_extended_vk_anekdot_preproc.csv',
index_col=0)
texts.dropna(inplace=True)
data = TextList.from_df(texts,
processor=[TokenizeProcessor(tokenizer=Tokenizer(lang="xx")),
NumericalizeProcessor(min_freq=2, max_vocab=60000)])\
.split_by_rand_pct(.1)\
.label_for_lm()\
.databunch(bs=64)
self.learn = language_model_learner(data=data,
arch=AWD_LSTM,
pretrained=None)
self.learn.load_pretrained(path + '/ulmfit/bestmodel_tune.pth',
path + '/ulmfit/bestmodel_tune_itos.pkl')
def evaluate_lm(data_path,
model_dir,
tokenizer_lang="xx",
evaluate_custom_perplexity=False):
"""
Evaluate metrics of a trained language model using any dataset of texts from CSV file.
Attributes:
data_path (str): Path to CSV file with texts in the first column.
model_dir (str): Directory with a trained language model.
tokenizer_lang (str): Language setting for tokenizer.
evaluate_custom_perplexity (bool): The perplexity estimated as e^(avg. loss),
but the average loss changes slightly with batch size. To get perplexity computed in
slower but controlled fashion, set `evaluate_custom_perplexity` to True. Discrepancy
between perplexity and custom perplexity is empirically approximately 1%.
"""
model_dir = Path(model_dir)
with open(model_dir / "lm_itos.pkl", "rb") as f:
itos = pickle.load(f)
data_df = pd.read_csv(data_path, header=None)
data = TextLMDataBunch.from_df("",
data_df,
data_df,
text_cols=0,
tokenizer=Tokenizer(lang=tokenizer_lang),
vocab=Vocab(itos))
with open(model_dir / "model_hparams.json", "r") as model_hparams_file:
model_hparams = json.load(model_hparams_file)
learner = lm_learner(data,
AWD_LSTM,
model_dir,
pretrained=True,
config=model_hparams)
loss, acc = learner.validate()
print("Loss: {}, Perplexity: {}, Accuracy: {}".format(
loss, exp(loss), acc))
if evaluate_custom_perplexity:
print(
"Custom perplexity: {}, Fraction OOV: {}, OOV perplexity contribution: {}"
.format(*evaluate_perplexity(learner, data.valid_ds.x)))
def label_linkable_tokens(article_html, tokenizer=Tokenizer(), label_all=True):
parsed_html = BeautifulSoup(article_html, 'html.parser')
link_text = [link.text for link in parsed_html.find_all('a')]
tokenised_links = tokenizer.process_all(link_text)
tokenised_text = tokenizer.process_all([parsed_html.text])[0]
target = np.zeros(len(tokenised_text))
for link in tokenised_links:
start_positions = kmp(tokenised_text, link)
if label_all:
for pos in start_positions:
target[pos : pos + len(link)] = 1
elif label_all == False and len(start_positions) > 0:
pos = start_positions[0]
target[pos : pos + len(link)] = 1
else:
pass
return tokenised_text, target
def prepare_lm_dataset(input_path,
output_dir=None,
valid_split=0.2,
tokenizer_lang="xx",
min_freq=2,
seed=42):
"""
Reads CSV file with texts for training language model, splits it into training and validation
sets, tokenizes and saves the dataset.
Attributes:
input_path (str): Path to CSV file where there are texts in the first column.
output_dir (str): Folder where to store the processed dataset.
valid_split (float): A fraction of data used for validation.
tokenizer_lang (str): Language setting for tokenizer.
min_freq (int): Minimal number of occurrences of a word to be conidered for adding to
vocabulary.
seed (int): Random seed that determines the training-validation split.
"""
input_path = Path(input_path)
output_dir = Path(output_dir or input_path.parent)
output_dir.mkdir(parents=True, exist_ok=True)
train_df, valid_df = csv_to_train_valid_df(input_path, valid_split, seed)
data_lm = TextLMDataBunch.from_df(output_dir,
train_df,
valid_df,
text_cols=0,
tokenizer=Tokenizer(lang=tokenizer_lang),
min_freq=min_freq)
data_lm.save("data_lm.pkl")
with open(output_dir / "data_lm_tokenized_train.txt", "w") as f:
f.write("\n".join(map(str, list(data_lm.train_ds.x))))
with open(output_dir / "data_lm_tokenized_valid.txt", "w") as f:
f.write("\n".join(map(str, list(data_lm.valid_ds.x))))
def get_processor(vocab_class_obj):
if conf['use_sentencePiece']:
processor = [
OpenFileProcessor(),
SPProcessor(
sp_model=conf['sentencePiece']['model'],
sp_vocab=conf['sentencePiece']['vocab'],
max_sentence_len=conf['sentencePiece']['max_sentence_len'],
max_vocab_sz=conf['sentencePiece']['max_vocab'])
]
else:
tokenizer = Tokenizer(tok_func=dna_tokenizer_n_char,
pre_rules=[],
post_rules=[],
special_cases=[],
n_cpus=conf['n_workers'])
processor = [
TokenizeProcessor(tokenizer=tokenizer,
include_bos=True,
include_eos=True),
NumericalizeProcessor(vocab=vocab_class_obj,
max_vocab=conf['max_vocab'])
]
return processor, tokenizer
import csv
import multiprocessing as mp
import typing as t
from pathlib import Path
import joblib
from fastai.text import Tokenizer, SpacyTokenizer, Vocab
from tqdm import tqdm
# Create a Tokenizer with the default settings for English
# including punctuation rules and exceptions
tokenizer = Tokenizer()
tok = SpacyTokenizer('en')
def tokenize(line: str) -> str:
doc = ' '.join(tokenizer.process_text(line, tok)) # parse with FastAI tokenizer
return doc + '\n'
def numericalize(doc: str, vocab: Vocab) -> t.List[int]:
return vocab.numericalize(doc)
def build_vocab(docs: t.List[str], max_vocab: int = 10000, min_freq: int = 5) -> Vocab:
return Vocab.create(docs, max_vocab=max_vocab, min_freq=min_freq)
if __name__ == '__main__':
data_path = Path('../data/raw/reviews.csv')
assert data_path.exists()
def get_texts_gensim(texts: List[str]) -> List[List[str]]:
processed_textx = [' '.join(simple_preprocess(t)) for t in texts]
tok = Tokenizer().process_all(processed_textx)
return tok
def get_texts_fastai(texts: List[str]) -> List[List[str]]:
fixed_texts = [fix_html(t) for t in texts]
tok = Tokenizer().process_all(fixed_texts)
return tok
def _apply_tokenizer(self, text):
# Tokenizer creates a list, containing a list of each word pairs, such as
#tokens = [[' ', 't_up', 'bos'], ['senjō'], ['no'], [' ', 't_up', 'eos']]
tokens = Tokenizer(lang=self.lang).proc_all_mp(partition_by_cores(text.split()))
# Flatten nested list
return [word for sublist in tokens for word in sublist]
def get_default_tokenizer():
return Tokenizer()
def main(gpu: Param("GPU to run on", str)=None,
max_cpu_per_dataloader: Param("Max CPU", int, opt=True)=8,
bs: Param("batch size", int)=256,
fp16: Param("mixed precision", int, opt=True)=0,
use_sp_processor: Param("use sentence piece as processor", int) = 0,
sp_model: Param("sentence piece trained model file", str)=None,
sp_vocab: Param("sentence piece trained model file", str)=None,
):
datetime_str = f'{datetime.now():%Y-%m-%d_%H-%M-%S%z}'
random_seed = 0
max_vocab = 30000
print('max_cpu_per_dataloader', max_cpu_per_dataloader, 'bs', bs,
'fp16', fp16, 'sp_processor', use_sp_processor, 'sp_model', sp_model, 'sp_vocab', sp_vocab)
"""## Prepare Dataset"""
local_project_path = './data/sprot_lm/'
#### Distributed
print('gpu', gpu)
gpu = setup_distrib(gpu)
n_gpus = num_distrib()
if n_gpus > 0:
workers = min(max_cpu_per_dataloader, num_cpus()//n_gpus)
else:
workers = min(max_cpu_per_dataloader, num_cpus())
print(gpu, 'n_gpus', n_gpus)
print(gpu, 'workers', workers)
"""## Prepare fastai"""
np.random.seed(random_seed)
if not os.path.exists(local_project_path):
os.makedirs(local_project_path)
print('local_project_path:', local_project_path)
"""## Tokenization"""
tokenizer = Tokenizer(tok_func=dna_tokenizer, pre_rules=[],
post_rules=[], special_cases=[])
processor = [TokenizeProcessor(tokenizer=tokenizer, include_bos=True,
include_eos=True), NumericalizeProcessor(max_vocab=max_vocab)]
df = pickle.load(
open('./data/sprot_lm/sprot_sequence_taxon_anc.pickle', 'rb'))
if use_sp_processor: # './data/sprot_lm/tmp/spm.model', './data/sprot_lm/tmp/spm.vocab'
processor = [OpenFileProcessor(), SPProcessor(sp_model=sp_model, sp_vocab=sp_vocab, max_sentence_len=35826, max_vocab_sz=max_vocab)]
data_lm = (TextList.from_df(df, path=local_project_path, cols='seq_anc_tax', processor=processor)
.split_by_rand_pct(0.1, seed = random_seed)
.label_for_lm()
.databunch(bs=bs, num_workers=workers))
data_lm.vocab.save(local_project_path +
'vocab_lm_sprot_seq_anc_tax-' + datetime_str + '.pickle')
print('data_cls Training set size', len(data_lm.train_ds))
print('data_cls Validation set size', len(data_lm.valid_ds))
print('vocab size ', len(data_lm.vocab.itos))
learn_lm = language_model_learner(
data_lm, AWD_LSTM, drop_mult=0.1, pretrained=False)
if gpu is None:
print(gpu, 'DataParallel')
learn_lm.model = nn.DataParallel(learn_lm.model)
else:
print(gpu, 'to_distributed')
learn_lm.to_distributed(gpu)
if fp16:
learn_lm.to_fp16()
lr = 3e-3
print(gpu, 'freeze')
learn_lm.freeze()
learn_lm.fit_one_cycle(1, lr, moms=(0.8, 0.7)) # I don't know why multigpu doesn't work without first freezing
print(gpu, 'unfreeze')
learn_lm.unfreeze()
learn_lm.fit_one_cycle(10, lr*10, moms=(0.8, 0.7))
learn_lm.save('lm-sp-anc-v1-1-' + datetime_str)
learn_lm.save_encoder('lm-sp-ans-v1-1-enc-' + datetime_str)
learn_lm.fit_one_cycle(10, lr, moms=(0.8, 0.7))
learn_lm.save('lm-sp-anc-v1-2-' + datetime_str)
learn_lm.save_encoder('lm-sp-ans-v1-2-enc-' + datetime_str)
learn_lm.fit_one_cycle(10, lr/10, moms=(0.8, 0.7))
learn_lm.save('lm-sp-anc-v1-3' + datetime_str)
learn_lm.save_encoder('lm-sp-ans-v1-3-enc-' + datetime_str)
learn_lm.export(file = 'export-lm-sp-ans-v1-3' + datetime_str+ '.pkl')
print('Done')
def main():
global model_to_save
global experiment
global rabbit
rabbit = MyRabbit(args)
if rabbit.model_params.dont_limit_num_uniq_tokens:
raise NotImplementedError()
if rabbit.model_params.frame_as_qa: raise NotImplementedError
if rabbit.run_params.drop_val_loss_calc: raise NotImplementedError
if rabbit.run_params.use_softrank_influence and not rabbit.run_params.freeze_all_but_last_for_influence:
raise NotImplementedError
if rabbit.train_params.weight_influence: raise NotImplementedError
experiment = Experiment(rabbit.train_params + rabbit.model_params +
rabbit.run_params)
print('Model name:', experiment.model_name)
use_pretrained_doc_encoder = rabbit.model_params.use_pretrained_doc_encoder
use_pointwise_loss = rabbit.train_params.use_pointwise_loss
query_token_embed_len = rabbit.model_params.query_token_embed_len
document_token_embed_len = rabbit.model_params.document_token_embed_len
_names = []
if not rabbit.model_params.dont_include_titles:
_names.append('with_titles')
if rabbit.train_params.num_doc_tokens_to_consider != -1:
_names.append('num_doc_toks_' +
str(rabbit.train_params.num_doc_tokens_to_consider))
if not rabbit.run_params.just_caches:
if rabbit.model_params.dont_include_titles:
document_lookup = read_cache(name('./doc_lookup.json', _names),
get_robust_documents)
else:
document_lookup = read_cache(name('./doc_lookup.json', _names),
get_robust_documents_with_titles)
num_doc_tokens_to_consider = rabbit.train_params.num_doc_tokens_to_consider
document_title_to_id = read_cache(
'./document_title_to_id.json',
lambda: create_id_lookup(document_lookup.keys()))
with open('./caches/106756_most_common_doc.json', 'r') as fh:
doc_token_set = set(json.load(fh))
tokenizer = Tokenizer()
tokenized = set(
sum(
tokenizer.process_all(list(
get_robust_eval_queries().values())), []))
doc_token_set = doc_token_set.union(tokenized)
use_bow_model = not any([
rabbit.model_params[attr] for attr in
['use_doc_out', 'use_cnn', 'use_lstm', 'use_pretrained_doc_encoder']
])
use_bow_model = use_bow_model and not rabbit.model_params.dont_use_bow
if use_bow_model:
documents, document_token_lookup = read_cache(
name(f'./docs_fs_tokens_limit_uniq_toks_qrels_and_106756.pkl',
_names),
lambda: prepare_fs(document_lookup,
document_title_to_id,
num_tokens=num_doc_tokens_to_consider,
token_set=doc_token_set))
if rabbit.model_params.keep_top_uniq_terms is not None:
documents = [
dict(
nlargest(rabbit.model_params.keep_top_uniq_terms,
_.to_pairs(doc), itemgetter(1)))
for doc in documents
]
else:
documents, document_token_lookup = read_cache(
name(
f'./parsed_docs_{num_doc_tokens_to_consider}_tokens_limit_uniq_toks_qrels_and_106756.json',
_names), lambda: prepare(document_lookup,
document_title_to_id,
num_tokens=num_doc_tokens_to_consider,
token_set=doc_token_set))
if not rabbit.run_params.just_caches:
train_query_lookup = read_cache('./robust_train_queries.json',
get_robust_train_queries)
train_query_name_to_id = read_cache(
'./train_query_name_to_id.json',
lambda: create_id_lookup(train_query_lookup.keys()))
train_queries, query_token_lookup = read_cache(
'./parsed_robust_queries_dict.json',
lambda: prepare(train_query_lookup,
train_query_name_to_id,
token_lookup=document_token_lookup,
token_set=doc_token_set,
drop_if_any_unk=True))
query_tok_to_doc_tok = {
idx: document_token_lookup.get(query_token)
or document_token_lookup['<unk>']
for query_token, idx in query_token_lookup.items()
}
names = [RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set]]
if rabbit.train_params.use_pointwise_loss or not rabbit.run_params.just_caches:
train_data = read_cache(
name('./robust_train_query_results_tokens_qrels_and_106756.json',
names), lambda: read_query_result(
train_query_name_to_id,
document_title_to_id,
train_queries,
path='./indri/query_result' + RANKER_NAME_TO_SUFFIX[
rabbit.train_params.ranking_set]))
else:
train_data = []
q_embed_len = rabbit.model_params.query_token_embed_len
doc_embed_len = rabbit.model_params.document_token_embed_len
if rabbit.model_params.append_difference or rabbit.model_params.append_hadamard:
assert q_embed_len == doc_embed_len, 'Must use same size doc and query embeds when appending diff or hadamard'
if q_embed_len == doc_embed_len:
glove_lookup = get_glove_lookup(
embedding_dim=q_embed_len,
use_large_embed=rabbit.model_params.use_large_embed,
use_word2vec=rabbit.model_params.use_word2vec)
q_glove_lookup = glove_lookup
doc_glove_lookup = glove_lookup
else:
q_glove_lookup = get_glove_lookup(
embedding_dim=q_embed_len,
use_large_embed=rabbit.model_params.use_large_embed,
use_word2vec=rabbit.model_params.use_word2vec)
doc_glove_lookup = get_glove_lookup(
embedding_dim=doc_embed_len,
use_large_embed=rabbit.model_params.use_large_embed,
use_word2vec=rabbit.model_params.use_word2vec)
num_query_tokens = len(query_token_lookup)
num_doc_tokens = len(document_token_lookup)
doc_encoder = None
if use_pretrained_doc_encoder or rabbit.model_params.use_doc_out:
doc_encoder, document_token_embeds = get_doc_encoder_and_embeddings(
document_token_lookup, rabbit.model_params.only_use_last_out)
if rabbit.model_params.use_glove:
query_token_embeds_init = init_embedding(q_glove_lookup,
query_token_lookup,
num_query_tokens,
query_token_embed_len)
else:
query_token_embeds_init = from_doc_to_query_embeds(
document_token_embeds, document_token_lookup,
query_token_lookup)
if not rabbit.train_params.dont_freeze_pretrained_doc_encoder:
dont_update(doc_encoder)
if rabbit.model_params.use_doc_out:
doc_encoder = None
else:
document_token_embeds = init_embedding(doc_glove_lookup,
document_token_lookup,
num_doc_tokens,
document_token_embed_len)
if rabbit.model_params.use_single_word_embed_set:
query_token_embeds_init = document_token_embeds
else:
query_token_embeds_init = init_embedding(q_glove_lookup,
query_token_lookup,
num_query_tokens,
query_token_embed_len)
if not rabbit.train_params.dont_freeze_word_embeds:
dont_update(document_token_embeds)
dont_update(query_token_embeds_init)
else:
do_update(document_token_embeds)
do_update(query_token_embeds_init)
if rabbit.train_params.add_rel_score:
query_token_embeds, additive = get_additive_regularized_embeds(
query_token_embeds_init)
rel_score = RelScore(query_token_embeds, document_token_embeds,
rabbit.model_params, rabbit.train_params)
else:
query_token_embeds = query_token_embeds_init
additive = None
rel_score = None
eval_query_lookup = get_robust_eval_queries()
eval_query_name_document_title_rels = get_robust_rels()
test_query_names = []
val_query_names = []
for query_name in eval_query_lookup:
if len(val_query_names) >= 50: test_query_names.append(query_name)
else: val_query_names.append(query_name)
test_query_name_document_title_rels = _.pick(
eval_query_name_document_title_rels, test_query_names)
test_query_lookup = _.pick(eval_query_lookup, test_query_names)
test_query_name_to_id = create_id_lookup(test_query_lookup.keys())
test_queries, __ = prepare(test_query_lookup,
test_query_name_to_id,
token_lookup=query_token_lookup)
eval_ranking_candidates = read_query_test_rankings(
'./indri/query_result_test' +
RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set])
test_candidates_data = read_query_result(
test_query_name_to_id,
document_title_to_id,
dict(zip(range(len(test_queries)), test_queries)),
path='./indri/query_result_test' +
RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set])
test_ranking_candidates = process_raw_candidates(test_query_name_to_id,
test_queries,
document_title_to_id,
test_query_names,
eval_ranking_candidates)
test_data = process_rels(test_query_name_document_title_rels,
document_title_to_id, test_query_name_to_id,
test_queries)
val_query_name_document_title_rels = _.pick(
eval_query_name_document_title_rels, val_query_names)
val_query_lookup = _.pick(eval_query_lookup, val_query_names)
val_query_name_to_id = create_id_lookup(val_query_lookup.keys())
val_queries, __ = prepare(val_query_lookup,
val_query_name_to_id,
token_lookup=query_token_lookup)
val_candidates_data = read_query_result(
val_query_name_to_id,
document_title_to_id,
dict(zip(range(len(val_queries)), val_queries)),
path='./indri/query_result_test' +
RANKER_NAME_TO_SUFFIX[rabbit.train_params.ranking_set])
val_ranking_candidates = process_raw_candidates(val_query_name_to_id,
val_queries,
document_title_to_id,
val_query_names,
eval_ranking_candidates)
val_data = process_rels(val_query_name_document_title_rels,
document_title_to_id, val_query_name_to_id,
val_queries)
train_normalized_score_lookup = read_cache(
name('./train_normalized_score_lookup.pkl', names),
lambda: get_normalized_score_lookup(train_data))
test_normalized_score_lookup = get_normalized_score_lookup(
test_candidates_data)
val_normalized_score_lookup = get_normalized_score_lookup(
val_candidates_data)
if use_pointwise_loss:
normalized_train_data = read_cache(
name('./normalized_train_query_data_qrels_and_106756.json', names),
lambda: normalize_scores_query_wise(train_data))
collate_fn = lambda samples: collate_query_samples(
samples,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
train_dl = build_query_dataloader(
documents,
normalized_train_data,
rabbit.train_params,
rabbit.model_params,
cache=name('train_ranking_qrels_and_106756.json', names),
limit=10,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=train_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=False)
test_dl = build_query_dataloader(
documents,
test_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=test_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
val_dl = build_query_dataloader(
documents,
val_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
model = PointwiseScorer(query_token_embeds, document_token_embeds,
doc_encoder, rabbit.model_params,
rabbit.train_params)
else:
if rabbit.train_params.use_noise_aware_loss:
ranker_query_str_to_rankings = get_ranker_query_str_to_rankings(
train_query_name_to_id,
document_title_to_id,
train_queries,
limit=rabbit.train_params.num_snorkel_train_queries)
query_names = reduce(
lambda acc, query_to_ranking: acc.intersection(
set(query_to_ranking.keys()))
if len(acc) != 0 else set(query_to_ranking.keys()),
ranker_query_str_to_rankings.values(), set())
all_ranked_lists_by_ranker = _.map_values(
ranker_query_str_to_rankings, lambda query_to_ranking:
[query_to_ranking[query] for query in query_names])
ranker_query_str_to_pairwise_bins = get_ranker_query_str_to_pairwise_bins(
train_query_name_to_id,
document_title_to_id,
train_queries,
limit=rabbit.train_params.num_train_queries)
snorkeller = Snorkeller(ranker_query_str_to_pairwise_bins)
snorkeller.train(all_ranked_lists_by_ranker)
calc_marginals = snorkeller.calc_marginals
else:
calc_marginals = None
collate_fn = lambda samples: collate_query_pairwise_samples(
samples,
use_bow_model=use_bow_model,
calc_marginals=calc_marginals,
use_dense=rabbit.model_params.use_dense)
if rabbit.run_params.load_influences:
try:
with open(rabbit.run_params.influences_path) as fh:
pairs_to_flip = defaultdict(set)
for pair, influence in json.load(fh):
if rabbit.train_params.use_pointwise_loss:
condition = True
else:
condition = influence < rabbit.train_params.influence_thresh
if condition:
query = tuple(pair[1])
pairs_to_flip[query].add(tuple(pair[0]))
except FileNotFoundError:
pairs_to_flip = None
else:
pairs_to_flip = None
train_dl = build_query_pairwise_dataloader(
documents,
train_data,
rabbit.train_params,
rabbit.model_params,
pairs_to_flip=pairs_to_flip,
cache=name('train_ranking_qrels_and_106756.json', names),
limit=10,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=train_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=False)
test_dl = build_query_pairwise_dataloader(
documents,
test_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=test_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
val_dl = build_query_pairwise_dataloader(
documents,
val_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True)
val_rel_dl = build_query_pairwise_dataloader(
documents,
val_data,
rabbit.train_params,
rabbit.model_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
collate_fn=collate_fn,
is_test=True,
rel_vs_irrel=True,
candidates=val_ranking_candidates,
num_to_rank=rabbit.run_params.num_to_rank)
model = PairwiseScorer(query_token_embeds,
document_token_embeds,
doc_encoder,
rabbit.model_params,
rabbit.train_params,
use_bow_model=use_bow_model)
train_ranking_dataset = RankingDataset(
documents,
train_dl.dataset.rankings,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
query_tok_to_doc_tok=query_tok_to_doc_tok,
normalized_score_lookup=train_normalized_score_lookup,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
test_ranking_dataset = RankingDataset(
documents,
test_ranking_candidates,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
relevant=test_dl.dataset.rankings,
query_tok_to_doc_tok=query_tok_to_doc_tok,
cheat=rabbit.run_params.cheat,
normalized_score_lookup=test_normalized_score_lookup,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
val_ranking_dataset = RankingDataset(
documents,
val_ranking_candidates,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
relevant=val_dl.dataset.rankings,
query_tok_to_doc_tok=query_tok_to_doc_tok,
cheat=rabbit.run_params.cheat,
normalized_score_lookup=val_normalized_score_lookup,
use_bow_model=use_bow_model,
use_dense=rabbit.model_params.use_dense)
if rabbit.train_params.memorize_test:
train_dl = test_dl
train_ranking_dataset = test_ranking_dataset
model_data = DataBunch(train_dl,
val_rel_dl,
test_dl,
collate_fn=collate_fn,
device=torch.device('cuda') if
torch.cuda.is_available() else torch.device('cpu'))
multi_objective_model = MultiObjective(model, rabbit.train_params,
rel_score, additive)
model_to_save = multi_objective_model
if rabbit.train_params.memorize_test:
try:
del train_data
except:
pass
if not rabbit.run_params.just_caches:
del document_lookup
del train_query_lookup
del query_token_lookup
del document_token_lookup
del train_queries
try:
del glove_lookup
except UnboundLocalError:
del q_glove_lookup
del doc_glove_lookup
if rabbit.run_params.load_model:
try:
multi_objective_model.load_state_dict(
torch.load(rabbit.run_params.load_path))
except RuntimeError:
dp = nn.DataParallel(multi_objective_model)
dp.load_state_dict(torch.load(rabbit.run_params.load_path))
multi_objective_model = dp.module
else:
train_model(multi_objective_model, model_data, train_ranking_dataset,
val_ranking_dataset, test_ranking_dataset,
rabbit.train_params, rabbit.model_params,
rabbit.run_params, experiment)
if rabbit.train_params.fine_tune_on_val:
fine_tune_model_data = DataBunch(
val_rel_dl,
val_rel_dl,
test_dl,
collate_fn=collate_fn,
device=torch.device('cuda')
if torch.cuda.is_available() else torch.device('cpu'))
train_model(multi_objective_model,
fine_tune_model_data,
val_ranking_dataset,
val_ranking_dataset,
test_ranking_dataset,
rabbit.train_params,
rabbit.model_params,
rabbit.run_params,
experiment,
load_path=rabbit.run_params.load_path)
multi_objective_model.eval()
device = model_data.device
gpu_multi_objective_model = multi_objective_model.to(device)
if rabbit.run_params.calc_influence:
if rabbit.run_params.freeze_all_but_last_for_influence:
last_layer_idx = _.find_last_index(
multi_objective_model.model.pointwise_scorer.layers,
lambda layer: isinstance(layer, nn.Linear))
to_last_layer = lambda x: gpu_multi_objective_model(
*x, to_idx=last_layer_idx)
last_layer = gpu_multi_objective_model.model.pointwise_scorer.layers[
last_layer_idx]
diff_wrt = [p for p in last_layer.parameters() if p.requires_grad]
else:
diff_wrt = None
test_hvps = calc_test_hvps(
multi_objective_model.loss,
gpu_multi_objective_model,
DeviceDataLoader(train_dl, device, collate_fn=collate_fn),
val_rel_dl,
rabbit.run_params,
diff_wrt=diff_wrt,
show_progress=True,
use_softrank_influence=rabbit.run_params.use_softrank_influence)
influences = []
if rabbit.train_params.use_pointwise_loss:
num_real_samples = len(train_dl.dataset)
else:
num_real_samples = train_dl.dataset._num_pos_pairs
if rabbit.run_params.freeze_all_but_last_for_influence:
_sampler = SequentialSamplerWithLimit(train_dl.dataset,
num_real_samples)
_batch_sampler = BatchSampler(_sampler,
rabbit.train_params.batch_size,
False)
_dl = DataLoader(train_dl.dataset,
batch_sampler=_batch_sampler,
collate_fn=collate_fn)
sequential_train_dl = DeviceDataLoader(_dl,
device,
collate_fn=collate_fn)
influences = calc_dataset_influence(gpu_multi_objective_model,
to_last_layer,
sequential_train_dl,
test_hvps,
sum_p=True).tolist()
else:
for i in progressbar(range(num_real_samples)):
train_sample = train_dl.dataset[i]
x, labels = to_device(collate_fn([train_sample]), device)
device_train_sample = (x, labels.squeeze())
influences.append(
calc_influence(multi_objective_model.loss,
gpu_multi_objective_model,
device_train_sample,
test_hvps,
diff_wrt=diff_wrt).sum().tolist())
with open(rabbit.run_params.influences_path, 'w+') as fh:
json.dump([[train_dl.dataset[idx][1], influence]
for idx, influence in enumerate(influences)], fh)
import pandas as pd import numpy as np import os import pickle from pathlib import Path from sklearn.utils import Bunch from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer from fastai.text import Tokenizer from eda import EDA tok = Tokenizer() def extract_features(df, out_path, max_features=30000, vectorizer=None): reviews = df['reviewText'].tolist() tok_reviews = tok.process_all(reviews) if vectorizer is None: vectorizer = TfidfVectorizer(max_features=max_features, preprocessor=lambda x: x, tokenizer = lambda x: x, min_df=5) X = vectorizer.fit_transform(tok_reviews) else: X = vectorizer.transform(tok_reviews) featset = Bunch(X=X, y=df.overall.values) pickle.dump(featset, open(out_path, 'wb')) return vectorizer
