FIX: ensure compatibility between sklearn and spark tfidf vectors for…

… skl>=1.5

Change for TfidfTransformer of sklearn v1.5 in order to ensure compatibility between the
pandas and spark version of emm. In sklearn v1.5+ TfidfTransformer no longer has the _idf_diag attribute,
needed for setting the compatibility.

emm/indexing/pandas_normalized_tfidf.py

@@ -76,13 +76,25 @@ def fit(self, X: pd.Series | pd.DataFrame) -> TfidfVectorizer:
             super().fit(X)
 
             timer.label("normalize")
-            idf_diag = self._tfidf._idf_diag
+            n_features = self.idf_.shape[0]
+
+            # 1. this max_idf_square value is used in normalization step for simulating out-of-vocabulary tokens
+            idf_diag = scipy.sparse.diags(
+                self.idf_, offsets=0, shape=(n_features, n_features), format="csr", dtype=self.dtype
+            )
             idf_diag = idf_diag - scipy.sparse.diags(np.ones(idf_diag.shape[0]), shape=idf_diag.shape, dtype=self.dtype)
-            self._tfidf._idf_diag = idf_diag
-            assert self._tfidf._idf_diag.dtype == self.dtype
-            # this value is used in normalization step for simulating out-of-vocabulary tokens
             self.max_idf_square = idf_diag.max() ** 2
 
+            # 2. ensure compatibility between sklearn and spark tfidf vectors
+            if hasattr(self._tfidf, "_idf_diag"):
+                # sklearn < 1.5
+                self._tfidf._idf_diag = idf_diag
+                assert self._tfidf._idf_diag.dtype == self.dtype
+            else:
+                # sklearn >= 1.5
+                self.idf_ = self.idf_ - np.ones(n_features, dtype=self.dtype)
+                assert self.idf_.dtype == self.dtype
+
             timer.log_params({"n": len(X), "n_features": idf_diag.shape[0]})
 
         return self

pyproject.toml

@@ -18,7 +18,7 @@ dependencies = [
   # Fix for error ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject.
   "numpy>=1.20.1",
   "scipy",
-  "scikit-learn<1.5.0",
+  "scikit-learn>=1.0.0",
   "pandas>=1.1.0,!=1.5.0",
   "jinja2", # for pandas https://pandas.pydata.org/docs/getting_started/install.html#visualization
   "rapidfuzz<3.0.0",