<filename>libraries/nalp/nalp/encoders/word2vec.py
import multiprocessing

import numpy as np
from gensim.models.word2vec import Word2Vec as W2V

import nalp.utils.logging as l
from nalp.core.encoder import Encoder

logger = l.get_logger(__name__)


class Word2vecEncoder(Encoder):
    """A Word2vecEncoder class is responsible for learning a Word2Vec encode and
    further encoding new data.

    """

    def __init__(self):
        """Initizaliation method.

        """

        logger.info('Overriding class: Encoder -> Word2vecEncoder.')

        # Overrides its parent class with any custom arguments if needed
        super(Word2vecEncoder, self).__init__()

        logger.info('Class overrided.')

    def learn(self, tokens, max_features=128, window_size=5, min_count=1, algorithm=0, learning_rate=0.01, iterations=1000):
        """Learns a Word2Vec representation based on the its methodology.

        One can use CBOW or Skip-gram algorithm for the learning procedure.

        Args:
            tokens (list): A list of tokens.
            max_features (int): Maximum number of features to be fitted.
            window_size (int): Maximum distance between current and predicted word.
            min_count (int): Minimum count of words for its use.
            algorithm (bool): 1 for skip-gram, while 0 for CBOW.
            learning_rate (float): Starting value of the learning procedure learning rate.
            iterations (int): Number of iterations.

        """

        logger.debug('Learning how to encode ...')

        # Creates a Word2Vec model
        self.encoder = W2V(sentences=[tokens], size=max_features, window=window_size, min_count=min_count,
                           sg=algorithm, alpha=learning_rate, iter=iterations, workers=multiprocessing.cpu_count())

    def encode(self, tokens):
        """Encodes the data into a Word2Vec representation.

        Args:
            tokens (list): A list of tokens to be encoded.

        """

        logger.debug('Encoding new tokens ...')

        # Checks if enconder actually exists, if not raises an error
        if not self.encoder:
            # Creates the error
            e = 'You need to call learn() prior to encode() method.'

            # Logs the error
            logger.error(e)

            raise RuntimeError(e)

        # Gets the actual word vectors from Word2Vec class
        wv = self.encoder.wv

        # Creates an encoded tokens variable to hold encoded data
        encoded_tokens = np.zeros((len(tokens), self.encoder.vector_size))

        # Iterate through all tokens
        for i, token in enumerate(tokens):
            # Else, store its word vector value to a new variable
            encoded_tokens[i, :] = wv[token]

        return encoded_tokens

    def decode(self, encoded_tokens):
        """Decodes the encoding back to tokens.

        Args:
            encoded_tokens (np.array): A numpy array containing the encoded tokens.

        Returns:
            A list of decoded tokens.

        """

        logger.debug('Decoding encoded tokens ...')

        # Checks if decoder actually exists, if not raises an error
        if not self.encoder:
            # Creates the error
            e = 'You need to call learn() prior to decode() method.'

            # Logs the error
            logger.error(e)

            raise RuntimeError(e)

        # Decoding the tokens
        decoded_tokens = [self.encoder.wv.most_similar(
            positive=[t])[0][0] for t in encoded_tokens]

        return decoded_tokens

<filename>libraries/nalp/nalp/encoders/word2vec.py
import multiprocessing

import numpy as np
from gensim.models.word2vec import Word2Vec as W2V

import nalp.utils.logging as l
from nalp.core.encoder import Encoder

logger = l.get_logger(__name__)


class Word2vecEncoder(Encoder):
    """A Word2vecEncoder class is responsible for learning a Word2Vec encode and
    further encoding new data.

    """

    def __init__(self):
        """Initizaliation method.

        """

        logger.info('Overriding class: Encoder -> Word2vecEncoder.')

        # Overrides its parent class with any custom arguments if needed
        super(Word2vecEncoder, self).__init__()

        logger.info('Class overrided.')

    def learn(self, tokens, max_features=128, window_size=5, min_count=1, algorithm=0, learning_rate=0.01, iterations=1000):
        """Learns a Word2Vec representation based on the its methodology.

        One can use CBOW or Skip-gram algorithm for the learning procedure.

        Args:
            tokens (list): A list of tokens.
            max_features (int): Maximum number of features to be fitted.
            window_size (int): Maximum distance between current and predicted word.
            min_count (int): Minimum count of words for its use.
            algorithm (bool): 1 for skip-gram, while 0 for CBOW.
            learning_rate (float): Starting value of the learning procedure learning rate.
            iterations (int): Number of iterations.

        """

        logger.debug('Learning how to encode ...')

        # Creates a Word2Vec model
        self.encoder = W2V(sentences=[tokens], size=max_features, window=window_size, min_count=min_count,
                           sg=algorithm, alpha=learning_rate, iter=iterations, workers=multiprocessing.cpu_count())

    def encode(self, tokens):
        """Encodes the data into a Word2Vec representation.

        Args:
            tokens (list): A list of tokens to be encoded.

        """

        logger.debug('Encoding new tokens ...')

        # Checks if enconder actually exists, if not raises an error
        if not self.encoder:
            # Creates the error
            e = 'You need to call learn() prior to encode() method.'

            # Logs the error
            logger.error(e)

            raise RuntimeError(e)

        # Gets the actual word vectors from Word2Vec class
        wv = self.encoder.wv

        # Creates an encoded tokens variable to hold encoded data
        encoded_tokens = np.zeros((len(tokens), self.encoder.vector_size))

        # Iterate through all tokens
        for i, token in enumerate(tokens):
            # Else, store its word vector value to a new variable
            encoded_tokens[i, :] = wv[token]

        return encoded_tokens

    def decode(self, encoded_tokens):
        """Decodes the encoding back to tokens.

        Args:
            encoded_tokens (np.array): A numpy array containing the encoded tokens.

        Returns:
            A list of decoded tokens.

        """

        logger.debug('Decoding encoded tokens ...')

        # Checks if decoder actually exists, if not raises an error
        if not self.encoder:
            # Creates the error
            e = 'You need to call learn() prior to decode() method.'

            # Logs the error
            logger.error(e)

            raise RuntimeError(e)

        # Decoding the tokens
        decoded_tokens = [self.encoder.wv.most_similar(
            positive=[t])[0][0] for t in encoded_tokens]

        return decoded_tokens

A Word2vecEncoder class is responsible for learning a Word2Vec encode and     further encoding new data. Initizaliation method. # Overrides its parent class with any custom arguments if needed Learns a Word2Vec representation based on the its methodology.          One can use CBOW or Skip-gram algorithm for the learning procedure.          Args:             tokens (list): A list of tokens.             max_features (int): Maximum number of features to be fitted.             window_size (int): Maximum distance between current and predicted word.             min_count (int): Minimum count of words for its use.             algorithm (bool): 1 for skip-gram, while 0 for CBOW.             learning_rate (float): Starting value of the learning procedure learning rate.             iterations (int): Number of iterations. # Creates a Word2Vec model Encodes the data into a Word2Vec representation.          Args:             tokens (list): A list of tokens to be encoded. # Checks if enconder actually exists, if not raises an error # Creates the error # Logs the error # Gets the actual word vectors from Word2Vec class # Creates an encoded tokens variable to hold encoded data # Iterate through all tokens # Else, store its word vector value to a new variable Decodes the encoding back to tokens.          Args:             encoded_tokens (np.array): A numpy array containing the encoded tokens.          Returns:             A list of decoded tokens. # Checks if decoder actually exists, if not raises an error # Creates the error # Logs the error # Decoding the tokens