NgramHash Class

Inheritance

nimbusml.internal.core.feature_extraction.text.extractor._ngramhash.NgramHash

NgramHash

Constructor

NgramHash(number_of_bits=16, ngram_length=1, skip_length=0, all_lengths=True, seed=314489979, ordered=True, maximum_number_of_inverts=0, **params)

Parameters

Examples

   ###############################################################################
   # NGramFeaturizer
   from nimbusml import FileDataStream
   from nimbusml.datasets import get_dataset
   from nimbusml.feature_extraction.text import NGramFeaturizer
   from nimbusml.feature_extraction.text.extractor import NgramHash

   # data input (as a FileDataStream)
   path = get_dataset('wiki_detox_train').as_filepath()

   data = FileDataStream.read_csv(path, sep='\t')
   print(data.head())
   #   Sentiment                                      SentimentText
   # 0          1  ==RUDE== Dude, you are rude upload that carl p...
   # 1          1  == OK! ==  IM GOING TO VANDALIZE WILD ONES WIK...
   # 2          1  Stop trolling, zapatancas, calling me a liar m...
   # 3          1  ==You're cool==  You seem like a really cool g...
   # 4          1  ::::: Why are you threatening me? I'm not bein...

   # transform usage
   xf = NGramFeaturizer(word_feature_extractor=NgramHash(),
                        columns=['SentimentText'])

   # fit and transform
   features = xf.fit_transform(data)

   # print features
   print(features.head())

   #   Sentiment  SentimentText.0    ...   entimentText.65534  SentimentText.65535
   # 0          1             0.0    ...                  0.0                  0.0
   # 1          1             0.0    ...                  0.0                  0.0
   # 2          1             0.0    ...                  0.0                  0.0
   # 3          1             0.0    ...                  0.0                  0.0
   # 4          1             0.0    ...                  0.0                  0.0

Remarks

The NGramFeaturizer transform produces a bag of counts of sequences of consecutive words, called n-grams, from a given corpus of text. There are two ways it can do this:

• build a dictionary of n-grams and use the id in the dictionary as

the index in the bag;

• hash each n-gram and use the hash value as the index in the bag.

This class provide the text extractor that implement the second. In NGramFeaturizer, users should specify which text extractor to use as the argument.

The purpose of hashing is to convert variable-length text documents into equal-length numeric feature vectors, to support dimensionality reduction and to make the lookup of feature weights faster.

The n-grams are represented as count vectors, with vector slots corresponding to their hashes. Embedding ngrams in a vector space allows their contents to be compared in an efficient manner. The slot values in the vector can be weighted by the following factors:

• term frequency - The number of occurrences of the slot in the

text

• inverse document frequency - A ratio (the logarithm of

  inverse relative slot frequency) that measures the information a

slot provides by determining how common or rare it is across the entire

text.

• term frequency-inverse document frequency - the product

  term frequency and the inverse document frequency.

Methods

get_params

get_params(deep=False)