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from typing import List
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.preprocessing import normalize
from sklearn.utils import check_array
import numpy as np
import scipy.sparse as sp
class ClassTfidfTransformer(TfidfTransformer):
"""
A Class-based TF-IDF procedure using scikit-learns TfidfTransformer as a base.
c-TF-IDF can best be explained as a TF-IDF 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 BM25-inspired idf-weighting procedure instead of the procedure
as defined in the c-TF-IDF 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 bag-of-words 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: float = 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())
# BM25-inspired 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 count-based matrix to c-TF-IDF
Arguments:
X (sparse matrix): A matrix of term/token counts.
Returns:
X (sparse matrix): A c-TF-IDF 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
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