medcat.vocab
Attributes
Classes
The abstract serialisable base class. |
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Vocabulary used to store word embeddings for context similarity |
Module Contents
- class medcat.vocab.AbstractSerialisable
The abstract serialisable base class.
This defines some common defaults.
- get_strategy()
- Return type:
- classmethod get_init_attrs()
- Return type:
list[str]
- classmethod ignore_attrs()
- Return type:
list[str]
- classmethod include_properties()
- Return type:
list[str]
- __eq__(other)
- Parameters:
other (Any)
- Return type:
bool
- medcat.vocab.WordDescriptor
- class medcat.vocab.Vocab
Bases:
medcat.storage.serialisables.AbstractSerialisableVocabulary used to store word embeddings for context similarity calculation. Also used by the spell checker - but not for fixing the spelling only for checking is something correct.
- Properties:
- vocab (dict[str, WordDescriptor]):
- Map from word to attributes, e.g. {‘house’:
{‘vector’: <np.array>, ‘count’: <int>, …}, …}
- index2word (dict[int, str]):
From word to an index - used for negative sampling
- vec_index2word (dict):
Same as index2word but only words that have vectors
- __init__()
- Return type:
None
- vocab: dict[str, WordDescriptor]
- index2word: dict[int, str]
- vec_index2word: dict[int, str]
- cum_probs: numpy.ndarray
- inc_or_add(word, cnt=1, vec=None)
Add a word or increase its count.
- Parameters:
word (str) – Word to be added
cnt (int) – By how much should the count be increased, or to what should it be set if a new word. (Default value = 1)
vec (Optional[np.ndarray]) – Word vector (Default value = None)
- Return type:
None
- remove_all_vectors()
Remove all stored vector representations.
- Return type:
None
- remove_words_below_cnt(cnt)
Remove all words with frequency below cnt.
- Parameters:
cnt (int) – Word count limit.
- Return type:
None
- _rebuild_index()
- inc_wc(word, cnt=1)
Incraese word count by cnt.
- Parameters:
word (str) – For which word to increase the count
cnt (int) – By how muhc to increase the count (Default value = 1)
- Return type:
None
- add_vec(word, vec)
Add vector to a word.
- Parameters:
word (str) – To which word to add the vector.
vec (np.ndarray) – The vector to add.
- Return type:
None
- reset_counts(cnt=1)
Reset the count for all word to cnt.
- Parameters:
cnt (int) – New count for all words in the vocab. (Default value = 1)
- Return type:
None
- update_counts(tokens)
Given a list of tokens update counts for words in the vocab.
- Parameters:
tokens (list[str]) – Usually a large block of text split into tokens/words.
- Return type:
None
- add_word(word, cnt=1, vec=None, replace=True)
Add a word to the vocabulary
- Parameters:
word (str) – The word to be added, it should be lemmatized and lowercased
cnt (int) – Count of this word in your dataset (Default value = 1)
vec (Optional[np.ndarray]) – The vector representation of the word (Default value = None)
replace (bool) – Will replace old vector representation (Default value = True)
- Return type:
None
- add_words(path, replace=True)
Adds words to the vocab from a file, the file is required to have the following format (vec being optional):
<word> <cnt>[ <vec_space_separated>]
- e.g. one line: the word house with 3 dimensional vectors
house 34444 0.3232 0.123213 1.231231
- Parameters:
path (str) – path to the file with words and vectors
replace (bool) – existing words in the vocabulary will be replaced. Defaults to True.
- Return type:
None
- init_cumsums()
Initialise cumulative sums.
This is in place of the unigram table. But similarly to it, this approach allows generating a list of indices that match the probabilistic distribution expected as per the word counts of each word.
- Return type:
None
- get_negative_samples(n=6, ignore_punct_and_num=False)
Get N negative samples.
- Parameters:
n (int) – How many words to return (Default value = 6)
ignore_punct_and_num (bool) – Whether to ignore punctuation and numbers. Defaults to False.
- Raises:
Exception – If no unigram table is present.
- Returns:
list[int] – Indices for words in this vocabulary.
- Return type:
list[int]
- get_vectors(indices)
- Parameters:
indices (list[int])
- Return type:
list[numpy.ndarray]
- __getitem__(word)
- Parameters:
word (str)
- Return type:
int
- vec(word)
- Parameters:
word (str)
- Return type:
Optional[numpy.ndarray]
- count(word)
- Parameters:
word (str)
- Return type:
int
- item(word)
- Parameters:
word (str)
- Return type:
WordDescriptor
- __contains__(word)
- Parameters:
word (str)
- Return type:
bool
- __eq__(other)
- Parameters:
other (Any)
- Return type:
bool
- get_strategy()
- Return type:
- classmethod get_init_attrs()
- Return type:
list[str]
- classmethod ignore_attrs()
- Return type:
list[str]
- classmethod include_properties()
- Return type:
list[str]