def train_distributed_knn(
hash_and_embedding: dbag.Bag,
batch_size: int,
num_centroids: int,
num_probes: int,
num_quantizers: int,
bits_per_quantizer: int,
training_sample_prob: float,
shared_scratch_dir: Path,
final_index_path: Path,
id_field: str = "id",
embedding_field: str = "embedding",
) -> Path:
"""
Computing all of the embeddings and then performing a KNN is a problem for memory.
So, what we need to do instead is compute batches of embeddings, and use them in Faiss
to reduce their dimensionality and process the appropriatly.
I'm so sorry this one function has to do so much...
@param hash_and_embedding: bag of hash value and embedding values
@param text_field: input text field that we embed.
@param id_field: output id field we use to store number hashes
@param batch_size: number of sentences per batch
@param num_centroids: number of voronoi cells in approx nn
@param num_probes: number of cells to consider when querying
@param num_quantizers: number of sub-vectors to discritize
@param bits_per_quantizer: bits per sub-vector
@param shared_scratch_dir: location to store intermediate results.
@param training_sample_prob: chance a point is trained on
@return The path you can load the resulting FAISS index
"""
init_index_path = shared_scratch_dir.joinpath("init.index")
if not init_index_path.is_file():
print("\t- Constructing initial index:", init_index_path)
# First off, we need to get a representative sample for faiss training
training_data = hash_and_embedding.random_sample(
prob=training_sample_prob).pluck(embedding_field)
# Train initial index, store result in init_index_path
init_index_path = dask.compute(
dask.delayed(train_initial_index)(
training_data=training_data,
num_centroids=num_centroids,
num_probes=num_probes,
num_quantizers=num_quantizers,
bits_per_quantizer=bits_per_quantizer,
output_path=init_index_path,
))
else:
print("\t- Using initial index:", init_index_path)
# For each partition, load embeddings to idx
partial_idx_paths = []
for part_idx, part in enumerate(hash_and_embedding.to_delayed()):
part_path = shared_scratch_dir.joinpath(f"part-{part_idx}.index")
if part_path.is_file(): # rudimentary ckpt
partial_idx_paths.append(dask.delayed(part_path))
else:
partial_idx_paths.append(
dask.delayed(add_points_to_index)(
records=part,
init_index_path=init_index_path,
output_path=part_path,
batch_size=batch_size,
))
return dask.delayed(merge_index)(
init_index_path=init_index_path,
partial_idx_paths=partial_idx_paths,
final_index_path=final_index_path,
)
def get_frequent_ngrams(analyzed_sentences: dbag.Bag,
max_ngram_length: int,
min_ngram_support: int,
min_ngram_support_per_partition: int,
ngram_sample_rate: float,
token_field: str = "tokens",
ngram_field: str = "ngrams") -> dbag.Bag:
"""
Adds a new field containing a list of all mined n-grams. N-grams are tuples
of strings such that at least one string is not a stopword. Strings are
collected from the lemmas of sentences. To be counted, an ngram must occur
in at least `min_ngram_support` sentences.
"""
def part_to_ngram_counts(
records: Iterable[Record]) -> Iterable[Dict[Tuple[str], int]]:
ngram2count = {}
for rec in records:
def interesting(idx):
t = rec[token_field][idx]
return not t["stop"] and t["pos"] in INTERESTING_POS_TAGS
# beginning of ngram
for start_tok_idx in range(len(rec[token_field])):
# ngrams must begin with an interesting word
if not interesting(start_tok_idx):
continue
# for each potential n-gram size
for ngram_len in range(2, max_ngram_length):
end_tok_idx = start_tok_idx + ngram_len
# ngrams cannot extend beyond the sentence
if end_tok_idx > len(rec[token_field]):
continue
# ngrams must end with an interesting word
if not interesting(end_tok_idx - 1):
continue
# the ngram is an ordered tuple of lemmas
ngram = tuple(
rec[token_field][tok_idx]["lemma"]
for tok_idx in range(start_tok_idx, end_tok_idx))
if ngram in ngram2count:
ngram2count[ngram] += 1
else:
ngram2count[ngram] = 1
# filter out all low-occurrence ngrams in this partition
return [{
n: c
for n, c in ngram2count.items()
if c >= min_ngram_support_per_partition
}]
def valid_ngrams(ngram2count: Dict[str, int]) -> Set[Tuple[str]]:
ngrams = {n for n, c in ngram2count.items() if c >= min_ngram_support}
return ngrams
def parse_ngrams(record: Record, ngram_model: Set[Tuple[str]]):
record[ngram_field] = []
start_tok_idx = 0
while start_tok_idx < len(record[token_field]):
incr = 1 # amount to move start_tok_idx
# from max -> 2. Match longest
for ngram_len in range(max_ngram_length, 1, -1):
# get bounds of ngram and make sure its within sentence
end_tok_idx = start_tok_idx + ngram_len
if end_tok_idx > len(record[token_field]):
continue
ngram = tuple(record[token_field][tok_idx]["lemma"]
for tok_idx in range(start_tok_idx, end_tok_idx))
# if match
if ngram in ngram_model:
record[ngram_field].append("_".join(ngram))
# skip over matched terms
incr = ngram_len
break
start_tok_idx += incr
return record
# Begin the actual function
if max_ngram_length < 1:
# disable, record empty field for all ngrams
def init_nothing(rec: Record) -> Record:
rec[ngram_field] = []
return rec
return analyzed_sentences.map(init_nothing)
else:
ngram2count = (analyzed_sentences.random_sample(
ngram_sample_rate).map_partitions(part_to_ngram_counts).fold(
misc_util.merge_counts, initial={}))
ngram_model = delayed(valid_ngrams)(ngram2count)
return analyzed_sentences.map(parse_ngrams, ngram_model=ngram_model)
