cTFIDF
¶
A Classbased TFIDF procedure using scikitlearns TfidfTransformer as a base.
cTFIDF can best be explained as a TFIDF formula adopted for multiple classes by joining all documents per class. Thus, each class is converted to a single document instead of set of documents. The frequency of each word x is extracted for each class c and is l1 normalized. This constitutes the term frequency.
Then, the term frequency is multiplied with IDF which is the logarithm of 1 plus the average number of words per class A divided by the frequency of word x across all classes.
Parameters:
Name  Type  Description  Default 

bm25_weighting 
bool 
Uses BM25inspired idfweighting procedure instead of the procedure
as defined in the cTFIDF formula. It uses the following weighting scheme:

False 
reduce_frequent_words 
bool 
Takes the square root of the bagofwords after normalizing the matrix. Helps to reduce the impact of words that appear too frequently. 
False 
seed_words 
List[str] 
Specific words that will have their idf value increased by
the value of 
None 
seed_multiplier 
bool 
The value with which the idf values of the words in 
2 
Examples:
transformer = ClassTfidfTransformer()
Source code in bertopic\vectorizers\_ctfidf.py
class ClassTfidfTransformer(TfidfTransformer):
"""
A Classbased TFIDF procedure using scikitlearns TfidfTransformer as a base.
![](../algorithm/cTFIDF.svg)
cTFIDF can best be explained as a TFIDF formula adopted for multiple classes
by joining all documents per class. Thus, each class is converted to a single document
instead of set of documents. The frequency of each word **x** is extracted
for each class **c** and is **l1** normalized. This constitutes the term frequency.
Then, the term frequency is multiplied with IDF which is the logarithm of 1 plus
the average number of words per class **A** divided by the frequency of word **x**
across all classes.
Arguments:
bm25_weighting: Uses BM25inspired idfweighting procedure instead of the procedure
as defined in the cTFIDF formula. It uses the following weighting scheme:
`log(1+((avg_nr_samples  df + 0.5) / (df+0.5)))`
reduce_frequent_words: Takes the square root of the bagofwords after normalizing the matrix.
Helps to reduce the impact of words that appear too frequently.
seed_words: Specific words that will have their idf value increased by
the value of `seed_multiplier`.
NOTE: This will only increase the value of words that have an exact match.
seed_multiplier: The value with which the idf values of the words in `seed_words`
are multiplied.
Examples:
```python
transformer = ClassTfidfTransformer()
```
"""
def __init__(self,
bm25_weighting: bool = False,
reduce_frequent_words: bool = False,
seed_words: List[str] = None,
seed_multiplier: bool = 2
):
self.bm25_weighting = bm25_weighting
self.reduce_frequent_words = reduce_frequent_words
self.seed_words = seed_words
self.seed_multiplier = seed_multiplier
super(ClassTfidfTransformer, self).__init__()
def fit(self, X: sp.csr_matrix, multiplier: np.ndarray = None):
"""Learn the idf vector (global term weights).
Arguments:
X: A matrix of term/token counts.
multiplier: A multiplier for increasing/decreasing certain IDF scores
"""
X = check_array(X, accept_sparse=('csr', 'csc'))
if not sp.issparse(X):
X = sp.csr_matrix(X)
dtype = np.float64
if self.use_idf:
_, n_features = X.shape
# Calculate the frequency of words across all classes
df = np.squeeze(np.asarray(X.sum(axis=0)))
# Calculate the average number of samples as regularization
avg_nr_samples = int(X.sum(axis=1).mean())
# BM25inspired weighting procedure
if self.bm25_weighting:
idf = np.log(1+((avg_nr_samples  df + 0.5) / (df+0.5)))
# Divide the average number of samples by the word frequency
# +1 is added to force values to be positive
else:
idf = np.log((avg_nr_samples / df)+1)
# Multiplier to increase/decrease certain idf scores
if multiplier is not None:
idf = idf * multiplier
self._idf_diag = sp.diags(idf, offsets=0,
shape=(n_features, n_features),
format='csr',
dtype=dtype)
return self
def transform(self, X: sp.csr_matrix):
"""Transform a countbased matrix to cTFIDF
Arguments:
X (sparse matrix): A matrix of term/token counts.
Returns:
X (sparse matrix): A cTFIDF matrix
"""
if self.use_idf:
X = normalize(X, axis=1, norm='l1', copy=False)
if self.reduce_frequent_words:
X.data = np.sqrt(X.data)
X = X * self._idf_diag
return X
fit(self, X, multiplier=None)
¶
Learn the idf vector (global term weights).
Parameters:
Name  Type  Description  Default 

X 
csr_matrix 
A matrix of term/token counts. 
required 
multiplier 
ndarray 
A multiplier for increasing/decreasing certain IDF scores 
None 
Source code in bertopic\vectorizers\_ctfidf.py
def fit(self, X: sp.csr_matrix, multiplier: np.ndarray = None):
"""Learn the idf vector (global term weights).
Arguments:
X: A matrix of term/token counts.
multiplier: A multiplier for increasing/decreasing certain IDF scores
"""
X = check_array(X, accept_sparse=('csr', 'csc'))
if not sp.issparse(X):
X = sp.csr_matrix(X)
dtype = np.float64
if self.use_idf:
_, n_features = X.shape
# Calculate the frequency of words across all classes
df = np.squeeze(np.asarray(X.sum(axis=0)))
# Calculate the average number of samples as regularization
avg_nr_samples = int(X.sum(axis=1).mean())
# BM25inspired weighting procedure
if self.bm25_weighting:
idf = np.log(1+((avg_nr_samples  df + 0.5) / (df+0.5)))
# Divide the average number of samples by the word frequency
# +1 is added to force values to be positive
else:
idf = np.log((avg_nr_samples / df)+1)
# Multiplier to increase/decrease certain idf scores
if multiplier is not None:
idf = idf * multiplier
self._idf_diag = sp.diags(idf, offsets=0,
shape=(n_features, n_features),
format='csr',
dtype=dtype)
return self
transform(self, X)
¶
Transform a countbased matrix to cTFIDF
Parameters:
Name  Type  Description  Default 

X 
sparse matrix 
A matrix of term/token counts. 
required 
Returns:
Type  Description 

X (sparse matrix) 
A cTFIDF matrix 
Source code in bertopic\vectorizers\_ctfidf.py
def transform(self, X: sp.csr_matrix):
"""Transform a countbased matrix to cTFIDF
Arguments:
X (sparse matrix): A matrix of term/token counts.
Returns:
X (sparse matrix): A cTFIDF matrix
"""
if self.use_idf:
X = normalize(X, axis=1, norm='l1', copy=False)
if self.reduce_frequent_words:
X.data = np.sqrt(X.data)
X = X * self._idf_diag
return X