Tf-Idf Representation
Feature Engineering for Machine Learning in Python
Robert O'Callaghan
Director of Data Science, Ordergroove
Introducing TF-IDF
print(speech_df['Counts_the'].head())
0 21
1 13
2 29
3 22
4 20
TF-IDF
Importing the vectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
tv = TfidfVectorizer()
print(tv)
TfidfVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
dtype=<type 'numpy.float64'>, encoding=u'utf-8', input=u'content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), norm=u'l2', preprocessor=None, smooth_idf=True,
stop_words=None, strip_accents=None, sublinear_tf=False,
token_pattern=u'(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True,
vocabulary=None)
Max features and stopwords
tv = TfidfVectorizer(max_features=100,
stop_words='english')
max_features: Maximum number of columns created from TF-IDF
stop_words: List of common words to omit e.g. "and", "the" etc.
Fitting your text
tv.fit(train_speech_df['text'])
train_tv_transformed = tv.transform(train_speech_df['text'])
Putting it all together
train_tv_df = pd.DataFrame(train_tv_transformed.toarray(),
columns=tv.get_feature_names())\
.add_prefix('TFIDF_')
train_speech_df = pd.concat([train_speech_df, train_tv_df],
axis=1, sort=False)
Inspecting your transforms
examine_row = train_tv_df.iloc[0]
print(examine_row.sort_values(ascending=False))
TFIDF_government 0.367430
TFIDF_public 0.333237
TFIDF_present 0.315182
TFIDF_duty 0.238637
TFIDF_citizens 0.229644
Name: 0, dtype: float64
Applying the vectorizer to new data
test_tv_transformed = tv.transform(test_df['text_clean'])
test_tv_df = pd.DataFrame(test_tv_transformed.toarray(),
columns=tv.get_feature_names())\
.add_prefix('TFIDF_')
test_speech_df = pd.concat([test_speech_df, test_tv_df],
axis=1, sort=False)
Let's practice!
Feature Engineering for Machine Learning in Python
