Release 4.0.4

README.md

@@ -80,6 +80,7 @@ You can talk to me in advance through e-mails or the [Issues](https://github.com
 
 ## News
 
+* 07/02/2026: `shorttext` 4.0.4 released.
 * 05/26/2026: `shorttext` 4.0.3 released.
 * 05/18/2026: `shorttext` 4.0.2 released.
 * 05/14/2026: `shorttext` 4.0.1 released.

docs/news.rst

@@ -1,6 +1,7 @@
 News
 ====
 
+* 07/02/2026: `shorttext` 4.0.4 released.
 * 05/26/2026: `shorttext` 4.0.3 released.
 * 05/18/2026: `shorttext` 4.0.2 released.
 * 05/14/2026: `shorttext` 4.0.1 released.
@@ -93,6 +94,12 @@ News
 What's New
 ----------
 
+Release 4.0.4 (July 2, 2026)
+----------------------------
+
+* Debugging regarding the update of the library `sparse`.
+* Refactoring.
+
 Release 4.0.3 (May 26, 2026)
 ----------------------------
 

docs/requirements.txt

@@ -13,5 +13,5 @@ numba==0.66.0
 deprecation==2.1.0
 npdict==0.0.11
 orjson==3.11.9
-sparse==0.18.0
+sparse==0.19.0
 loguru==0.7.3

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "shorttext"
-version = "4.0.3"
+version = "4.0.4"
 authors = [
     {name = "Kwan Yuet Stephen Ho", email = "stephenhky@yahoo.com.hk"}
 ]
@@ -40,7 +40,7 @@ dependencies = [
     "snowballstemmer>=3.0.0",
     "numba>=0.57.0",
     "deprecation>=2.0.0",
-    "npdict>=0.0.10",
+    "npdict>=0.0.11",
     "sparse>=0.10.0",
     "orjson>=3.0.0",
     "loguru>=0.6.0"

src/shorttext/utils/dtm.py

@@ -173,7 +173,7 @@ def compute_document_frequency(
     Returns:
         Array of document frequencies for each token.
     """
-    if isinstance(npdtm, npdict.SparseArrayWrappedDict):
+    if npdtm.is_sparse():
         return np.sum(npdtm.to_coo() > 0, axis=0).todense()
     else:
         return np.sum(npdtm.to_numpy() > 0, axis=0)
@@ -194,7 +194,7 @@ def compute_tfidf_document_term_matrix(
     """
     doc_frequencies = compute_document_frequency(npdtm)
     nbdocs = npdtm.dimension_sizes[0]
-    if isinstance(npdtm, npdict.SparseArrayWrappedDict):
+    if npdtm.is_sparse():
         new_dtm_sparray = npdtm.to_coo() * np.log(nbdocs / doc_frequencies)
         return npdtm.generate_dict(new_dtm_sparray, dense=not sparse)
 
@@ -288,11 +288,13 @@ def get_total_termfreq(self, token: str) -> float:
             Total term frequency.
         """
         token_index = self.npdtm._keystrings_to_indices[1][token]
-        if isinstance(self.npdtm, npdict.SparseArrayWrappedDict):
+        if self.npdtm.is_sparse():
             matrix = self.npdtm.to_coo()
+            array = matrix[:, token_index].todense()
         else:
             matrix = self.npdtm.to_numpy()
-        return np.sum(matrix[:, token_index])
+            array = matrix[:, token_index]
+        return np.sum(array)
 
     def get_doc_frequency(self, token) -> int:
         """Get document frequency of a token.
@@ -304,8 +306,8 @@ def get_doc_frequency(self, token) -> int:
             Number of documents containing the token.
         """
         token_index = self.npdtm._keystrings_to_indices[1][token]
-        if isinstance(self.npdtm, npdict.SparseArrayWrappedDict):
-            freq_array = self.npdtm.to_coo()[:, token_index]
+        if self.npdtm.is_sparse():
+            freq_array = self.npdtm.to_coo()[:, token_index].todense()
         else:
             freq_array = self.npdtm.to_numpy()[:, token_index]
         return np.sum(freq_array > 0, axis=0)