astonzhang commented on a change in pull request #8763: Add mxnet.text APIs URL: https://github.com/apache/incubator-mxnet/pull/8763#discussion_r159170154
########## File path: python/mxnet/text/embedding.py ########## @@ -0,0 +1,722 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# coding: utf-8 +# pylint: disable=consider-iterating-dictionary + +"""Read text files and load embeddings.""" +from __future__ import absolute_import +from __future__ import print_function + +from collections import Counter +import io +import logging +import os +import tarfile +import warnings +import zipfile + +from ..gluon.utils import check_sha1 +from ..gluon.utils import download +from .. import ndarray as nd + + +class TextIndexer(object): + """Indexing for text tokens. + + + Build indices for the unknown token, reserved tokens, and input counter + keys. Indexed tokens can be used by instances of + :func:`~mxnet.text.embeddings.TextEmbed`, such as instances of + :func:`~mxnet.text.glossary.Glossary`. + + + Parameters + ---------- + counter : collections.Counter or None, default None + Counts text token frequencies in the text data. Its keys will be indexed + according to frequency thresholds such as `most_freq_count` and + `min_freq`. + most_freq_count : None or int, default None + The maximum possible number of the most frequent tokens in the keys of + `counter` that can be indexed. Note that this argument does not count + any token from `reserved_tokens`. If this argument is None or larger + than its largest possible value restricted by `counter` and + `reserved_tokens`, this argument becomes positive infinity. + min_freq : int, default 1 + The minimum frequency required for a token in the keys of `counter` to + be indexed. + unknown_token : str, default '<unk>' + The string representation for any unknown token. In other words, any + unknown token will be indexed as the same string representation. This + string representation cannot be any token to be indexed from the keys of + `counter` or from `reserved_tokens`. + reserved_tokens : list of strs or None, default None + A list of reserved tokens that will always be indexed. It cannot contain + `unknown_token`, or duplicate reserved tokens. + + + Properties + ---------- + token_to_idx : dict mapping str to int + A dict mapping each token to its index integer. + idx_to_token : list of strs + A list of indexed tokens where the list indices and the token indices + are aligned. + unknown_token : str + The string representation for any unknown token. In other words, any + unknown token will be indexed as the same string representation. + reserved_tokens : list of strs or None + A list of reserved tokens that will always be indexed. + unknown_idx : int + The index for `unknown_token`. + """ + def __init__(self, counter=None, most_freq_count=None, min_freq=1, + unknown_token='<unk>', reserved_tokens=None): + # Sanity checks. + assert min_freq > 0, '`min_freq` must be set to a positive value.' + + if reserved_tokens is not None: + for reserved_token in reserved_tokens: + assert reserved_token != unknown_token, \ + '`reserved_token` cannot contain `unknown_token`.' + assert len(set(reserved_tokens)) == len(reserved_tokens), \ + '`reserved_tokens` cannot contain duplicate reserved tokens.' + + self._index_unknown_and_reserved_tokens(unknown_token, reserved_tokens) + + if counter is not None: + self._index_counter_keys(counter, unknown_token, reserved_tokens, + most_freq_count, min_freq) + + def _index_unknown_and_reserved_tokens(self, unknown_token, + reserved_tokens): + """Indexes unknown and reserved tokens.""" + self._unknown_token = unknown_token + self._idx_to_token = [unknown_token] + + if reserved_tokens is None: + self._reserved_tokens = None + else: + # Python 2 does not support list.copy(). + self._reserved_tokens = reserved_tokens[:] + self._idx_to_token.extend(reserved_tokens) + + self._token_to_idx = {token: idx for idx, token in + enumerate(self._idx_to_token)} + + def _index_counter_keys(self, counter, unknown_token, reserved_tokens, + most_freq_count, min_freq): + """Indexes keys of `counter`. + + + Indexes keys of `counter` according to frequency thresholds such as + `most_freq_count` and `min_freq`. + """ + assert isinstance(counter, Counter), \ + '`counter` must be an instance of collections.Counter.' + + if reserved_tokens is not None: + reserved_tokens = set(reserved_tokens) + else: + reserved_tokens = set() + + token_freqs = sorted(counter.items(), key=lambda x: x[0]) + token_freqs.sort(key=lambda x: x[1], reverse=True) + + if most_freq_count is None: + # 1 is the unknown token count. + token_cap = 1 + len(reserved_tokens) + len(counter) + else: + token_cap = 1 + len(reserved_tokens) + most_freq_count + + for token, freq in token_freqs: + if freq < min_freq or len(self._idx_to_token) == token_cap: + break + assert token != unknown_token, \ + 'Keys of `counter` cannot contain `unknown_token`. Set ' \ + '`unknown_token` to another string representation.' + if token not in reserved_tokens: + self._idx_to_token.append(token) + self._token_to_idx[token] = len(self._idx_to_token) - 1 + + def __len__(self): + return len(self.idx_to_token) + + @property + def token_to_idx(self): + return self._token_to_idx + + @property + def idx_to_token(self): + return self._idx_to_token + + @property + def unknown_token(self): + return self._unknown_token + + @property + def reserved_tokens(self): + return self._reserved_tokens + + @property + def unknown_idx(self): + return 0 + + +class TextEmbed(TextIndexer): + """Text embedding base class. + + + To load text embeddings from an externally hosted pre-trained text embedding + file, such as those of GloVe and FastText, use + `TextEmbed.create(embed_name, pretrain_file)`. To get all the + available `embed_name` and `pretrain_file`, use + `TextEmbed.get_embed_names_and_pretrain_files()`. + + Alternatively, to load embedding vectors from a custom pre-trained text + embedding file, use :func:`~mxnet.text.embeddings.CustomEmbed`. + + For the same token, its index and embedding vector may vary across different + instances of :func:`~mxnet.text.glossary.TextEmbed`. + + + Properties + ---------- + vec_len : int + The length of the embedding vector for each token. + idx_to_vec : mxnet.ndarray.NDArray + For all the indexed tokens in this embedding, this NDArray maps each + token's index to an embedding vector. The largest valid index maps + to the initialized embedding vector for every reserved token, such as an + unknown_token token and a padding token. + """ + + # Key-value pairs for text embedding name in lower case and text embedding + # class. + embed_registry = {} + + def __init__(self, **kwargs): + super(TextEmbed, self).__init__(**kwargs) + + @staticmethod + def _get_pretrain_file_path_from_url(url, embed_root, embed_name, + pretrain_file): + """Get the local path to the pre-trained text embedding file from url. + + + The pretrained embedding file will be downloaded from url if it has not + been downloaded yet or the existing file fails to match its expected + SHA-1 hash. + """ + embed_root = os.path.expanduser(embed_root) + + embed_dir = os.path.join(embed_root, embed_name) + pretrain_file_path = os.path.join(embed_dir, pretrain_file) + download_file = os.path.basename(url) + download_file_path = os.path.join(embed_dir, download_file) + + embed_cls = TextEmbed.embed_registry[embed_name] + expected_file_hash = embed_cls.pretrain_file_sha1[pretrain_file] + + if hasattr(embed_cls, 'pretrain_archive_sha1'): + expected_download_hash = \ + embed_cls.pretrain_archive_sha1[download_file] + else: + expected_download_hash = expected_file_hash + + # The specified pretrained embedding file does not exist or fails to + # match its expected SHA-1 hash. + if not os.path.isfile(pretrain_file_path) or \ + not check_sha1(pretrain_file_path, expected_file_hash): + # If download_file_path exists and matches + # expected_download_hash, there is no need to download. + download(url, download_file_path, + sha1_hash=expected_download_hash) + + # If the downloaded file does not match its expected SHA-1 hash, + # we do not encourage to load embeddings from it in case that its + # data format is changed. + assert check_sha1(download_file_path, expected_download_hash), \ + 'The downloaded file %s does not match its expected SHA-1 ' \ + 'hash. This is caused by the changes at the externally ' \ + 'hosted pretrained embedding file(s). Since its data format ' \ + 'may also be changed, it is discouraged to continue to use ' \ + 'mxnet.text.glossary.TextEmbed.create(%s, **kwargs) ' \ + 'to load the pretrained embedding %s. If you still wish to load ' \ + 'the changed embedding file, please specify its path %s via ' \ + 'pretrain_file of mxnet.text.glossary.TextEmbed(). It will be ' \ + 'loaded only if its data format remains unchanged.' % \ + (download_file_path, embed_name, embed_name, download_file_path) + + ext = os.path.splitext(download_file)[1] + if ext == '.zip': + with zipfile.ZipFile(download_file_path, 'r') as zf: + zf.extractall(embed_dir) + elif ext == '.gz': + with tarfile.open(download_file_path, 'r:gz') as tar: + tar.extractall(path=embed_dir) + return pretrain_file_path + + def _load_embedding(self, pretrain_file_path, elem_delim, unknown_vec): + """Load embedding vectors from the pre-trained text embedding file. + + + Index 0 of `self.idx_to_vec` maps to the initialized embedding vector + for every unknown token whose string representation is + `self.unknown_token`. For duplicate tokens, only the first-encountered + embedding vector will be loaded and the rest will be skipped. + """ + pretrain_file_path = os.path.expanduser(pretrain_file_path) + + if not os.path.isfile(pretrain_file_path): + raise ValueError('`pretrain_file_path` must be a valid path to ' + 'the pre-trained text embedding file.') + + with io.open(pretrain_file_path, 'r', encoding='utf8') as f: + lines = f.readlines() + + logging.info('Loading pretrained embedding vectors from %s', + pretrain_file_path) + + vec_len = None + all_elems = [] + tokens = set() + for line in lines: + elems = line.rstrip().split(elem_delim) + + assert len(elems) > 1, 'The data format of the pre-trained ' \ + 'text embedding file %s is unexpected.' \ + % pretrain_file_path Review comment: resolved. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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