Bag of words a N-gramy

Feature Engineering for Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

Problémy s bag of words

 

Kladný význam

Jedno slovo: happy

Záporný význam

Bi-gram: not happy

Kladný význam

Tri-gram: never not happy

Feature Engineering for Machine Learning in Python

Použití N-gramů

tv_bi_gram_vec = TfidfVectorizer(ngram_range = (2,2))

# Fit and apply bigram vectorizer
tv_bi_gram = tv_bi_gram_vec\
               .fit_transform(speech_df['text'])

# Print the bigram features
print(tv_bi_gram_vec.get_feature_names())

[u'american people', u'best ability ',
 u'beloved country', u'best interests' ... ]
Feature Engineering for Machine Learning in Python

Hledání nejčastějších slov

# Create a DataFrame with the Counts features
tv_df = pd.DataFrame(tv_bi_gram.toarray(),
                     columns=tv_bi_gram_vec.get_feature_names())\
                        .add_prefix('Counts_')

tv_sums = tv_df.sum()
print(tv_sums.head())

Counts_administration government    12
Counts_almighty god                 15
Counts_american people              36
Counts_beloved country               8
Counts_best ability                  8
dtype: int64
Feature Engineering for Machine Learning in Python

Hledání nejčastějších slov

print(tv_sums.sort_values(ascending=False)).head()

Counts_united states         152
Counts_fellow citizens        97
Counts_american people        36
Counts_federal government     35
Counts_self government        30
dtype: int64
Feature Engineering for Machine Learning in Python

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Feature Engineering for Machine Learning in Python

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