Text Processing

class Tokenizer:
    """
    Text tokenization utility class for vectorizing text corpus.
    
    Converts text to sequences of integers or other vectorized representations.
    Maintains internal vocabulary and word-to-index mappings.
    """
    
    def __init__(self, num_words=None, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', 
                 lower=True, split=' ', char_level=False, oov_token=None, 
                 document_count=0, **kwargs):
        """
        Initialize tokenizer.
        
        Parameters:
        - num_words (int, optional): Maximum number of words to keep based on frequency
        - filters (str): Characters to filter out from texts
        - lower (bool): Whether to convert texts to lowercase
        - split (str): Separator for word splitting
        - char_level (bool): Whether to use character-level tokenization
        - oov_token (str, optional): Token to replace out-of-vocabulary words
        - document_count (int): Count of documents processed (for statistics)
        """
    
    def fit_on_texts(self, texts):
        """
        Update internal vocabulary based on a list of texts.
        
        Parameters:
        - texts (list): List of texts to fit on
        """
    
    def texts_to_sequences(self, texts):
        """
        Transform each text to a sequence of integers.
        
        Parameters:
        - texts (list): List of texts to transform
        
        Returns:
        - list: List of sequences (lists of integers)
        """
    
    def texts_to_sequences_generator(self, texts):
        """
        Generator version of texts_to_sequences.
        
        Parameters:
        - texts (list): List of texts to transform
        
        Yields:
        - list: Sequence (list of integers) for each text
        """
    
    def sequences_to_texts(self, sequences):
        """
        Transform sequences back to texts.
        
        Parameters:
        - sequences (list): List of sequences to transform
        
        Returns:
        - list: List of texts
        """
    
    def sequences_to_texts_generator(self, sequences):
        """
        Generator version of sequences_to_texts.
        
        Parameters:
        - sequences (list): List of sequences to transform
        
        Yields:
        - str: Text for each sequence
        """
    
    def texts_to_matrix(self, texts, mode='binary'):
        """
        Convert texts to a matrix representation.
        
        Parameters:
        - texts (list): List of texts to convert
        - mode (str): 'binary', 'count', 'tfidf', 'freq'
        
        Returns:
        - numpy.ndarray: Matrix representation of texts
        """
    
    def sequences_to_matrix(self, sequences, mode='binary'):
        """
        Convert sequences to a matrix representation.
        
        Parameters:
        - sequences (list): List of sequences to convert
        - mode (str): 'binary', 'count', 'tfidf', 'freq'
        
        Returns:
        - numpy.ndarray: Matrix representation of sequences
        """
    
    def fit_on_sequences(self, sequences):
        """
        Update internal vocabulary based on a list of sequences.
        
        Parameters:
        - sequences (list): List of sequences to fit on
        """
    
    def get_config(self):
        """
        Return tokenizer configuration as dictionary.
        
        Returns:
        - dict: Configuration dictionary
        """
    
    def to_json(self, **kwargs):
        """
        Return JSON string containing tokenizer configuration.
        
        Returns:
        - str: JSON string of tokenizer configuration
        """

def text_to_word_sequence(text, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', 
                          lower=True, split=" "):
    """
    Convert text to a sequence of words (or tokens).
    
    Parameters:
    - text (str): Input text
    - filters (str): Characters to filter out (punctuation, etc.)
    - lower (bool): Whether to convert to lowercase
    - split (str): Separator for word splitting
    
    Returns:
    - list: List of words/tokens
    """

def one_hot(text, n, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', 
            lower=True, split=' '):
    """
    One-hot encode text into list of word indexes using hashing.
    
    Parameters:
    - text (str): Input text
    - n (int): Size of vocabulary (hashing space)
    - filters (str): Characters to filter out
    - lower (bool): Whether to convert to lowercase
    - split (str): Separator for word splitting
    
    Returns:
    - list: List of integers (word indexes)
    """

def hashing_trick(text, n, hash_function=None, 
                  filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', 
                  lower=True, split=' '):
    """
    Convert text to sequence of indexes in fixed-size hashing space.
    
    Parameters:
    - text (str): Input text
    - n (int): Size of hashing space
    - hash_function (callable, optional): Hash function to use (default: hash())
    - filters (str): Characters to filter out
    - lower (bool): Whether to convert to lowercase
    - split (str): Separator for word splitting
    
    Returns:
    - list: List of integers (hashed word indexes)
    """

def tokenizer_from_json(json_string):
    """
    Parse JSON tokenizer configuration and return tokenizer instance.
    
    Parameters:
    - json_string (str): JSON string containing tokenizer configuration
    
    Returns:
    - Tokenizer: Tokenizer instance with loaded configuration
    """

from keras_preprocessing.text import Tokenizer

# Create and fit tokenizer
tokenizer = Tokenizer(num_words=1000, oov_token='<OOV>')
texts = [
    'The quick brown fox',
    'jumps over the lazy dog',
    'The dog was lazy'
]

tokenizer.fit_on_texts(texts)

# Convert texts to sequences
sequences = tokenizer.texts_to_sequences(texts)
print(sequences)
# [[1, 4, 5, 6], [7, 8, 1, 2, 3], [1, 3, 9, 2]]

# Get word index
print(tokenizer.word_index)
# {'the': 1, 'lazy': 2, 'dog': 3, 'quick': 4, ...}

# Convert to binary matrix
binary_matrix = tokenizer.texts_to_matrix(texts, mode='binary')
print(binary_matrix.shape)  # (3, 1000)

# Convert to TF-IDF matrix
tfidf_matrix = tokenizer.texts_to_matrix(texts, mode='tfidf')

from keras_preprocessing.text import text_to_word_sequence, one_hot

# Basic word tokenization
words = text_to_word_sequence('Hello, world! How are you?')
print(words)  # ['hello', 'world', 'how', 'are', 'you']

# One-hot encoding with hashing
encoded = one_hot('Hello world', n=1000)
print(encoded)  # [123, 456]  # Hash-based word indexes