## text_to_word_sequence
“`python
keras.preprocessing.text.text_to_word_sequence(text,
filters=’!”#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n’,
lower=True,
split=” “)
“`
Split a sentence into a list of words.
– __Return__: List of words (str).
– __Arguments__:
– __text__: str.
– __filters__: list (or concatenation) of characters to filter out, such as
punctuation. Default: ‘!”#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n’ , includes
basic punctuation, tabs, and newlines.
– __lower__: boolean. Whether to set the text to lowercase.
– __split__: str. Separator for word splitting.
## one_hot
“`python
keras.preprocessing.text.one_hot(text,
n,
filters=’!”#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n’,
lower=True,
split=” “)
“`
One-hot encodes a text into a list of word indexes in a vocabulary of size n.
This is a wrapper to the `hashing_trick` function using `hash` as the hashing function.
– __Return__: List of integers in [1, n]. Each integer encodes a word (unicity non-guaranteed).
– __Arguments__:
– __text__: str.
– __n__: int. Size of vocabulary.
– __filters__: list (or concatenation) of characters to filter out, such as
punctuation. Default: ‘!”#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n’ , includes
basic punctuation, tabs, and newlines.
– __lower__: boolean. Whether to set the text to lowercase.
– __split__: str. Separator for word splitting.
## hashing_trick
“`python
keras.preprocessing.text.hashing_trick(text,
n,
hash_function=None,
filters=’!”#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n’,
lower=True,
split=’ ‘)
“`
Converts a text to a sequence of indices in a fixed-size hashing space
– __Return__:
A list of integer word indices (unicity non-guaranteed).
– __Arguments__:
– __text__: str.
– __n__: Dimension of the hashing space.
– __hash_function__: defaults to python `hash` function, can be ‘md5’ or
any function that takes in input a string and returns a int.
Note that ‘hash’ is not a stable hashing function, so
it is not consistent across different runs, while ‘md5’
is a stable hashing function.
– __filters__: list (or concatenation) of characters to filter out, such as
punctuation. Default: ‘!”#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n’ , includes
basic punctuation, tabs, and newlines.
– __lower__: boolean. Whether to set the text to lowercase.
– __split__: str. Separator for word splitting.
## Tokenizer
“`python
keras.preprocessing.text.Tokenizer(num_words=None,
filters=’!”#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n’,
lower=True,
split=” “,
char_level=False)
“`
Class for vectorizing texts, or/and turning texts into sequences (=list of word indexes, where the word of rank i in the dataset (starting at 1) has index i).
– __Arguments__: Same as `text_to_word_sequence` above.
– __num_words__: None or int. Maximum number of words to work with (if set, tokenization will be restricted to the top num_words most common words in the dataset).
– __char_level__: if True, every character will be treated as a token.
– __Methods__:
– __fit_on_texts(texts)__:
– __Arguments__:
– __texts__: list of texts to train on.
– __texts_to_sequences(texts)__
– __Arguments__:
– __texts__: list of texts to turn to sequences.
– __Return__: list of sequences (one per text input).
– __texts_to_sequences_generator(texts)__: generator version of the above.
– __Return__: yield one sequence per input text.
– __texts_to_matrix(texts)__:
– __Return__: numpy array of shape `(len(texts), num_words)`.
– __Arguments__:
– __texts__: list of texts to vectorize.
– __mode__: one of “binary”, “count”, “tfidf”, “freq” (default: “binary”).
– __fit_on_sequences(sequences)__:
– __Arguments__:
– __sequences__: list of sequences to train on.
– __sequences_to_matrix(sequences)__:
– __Return__: numpy array of shape `(len(sequences), num_words)`.
– __Arguments__:
– __sequences__: list of sequences to vectorize.
– __mode__: one of “binary”, “count”, “tfidf”, “freq” (default: “binary”).
– __Attributes__:
– __word_counts__: dictionary mapping words (str) to the number of times they appeared on during fit. Only set after fit_on_texts was called.
– __word_docs__: dictionary mapping words (str) to the number of documents/texts they appeared on during fit. Only set after fit_on_texts was called.
– __word_index__: dictionary mapping words (str) to their rank/index (int). Only set after fit_on_texts was called.
– __document_count__: int. Number of documents (texts/sequences) the tokenizer was trained on. Only set after fit_on_texts or fit_on_sequences was called.