[MINOR][MLLIB] Declare size on PySpark Vector

Author: bearomorphism

python/pyspark/mllib/classification.py

@@ -202,9 +202,7 @@ def __init__(
             self._dataWithBiasSize = None
             self._weightsMatrix = None
         else:
-            self._dataWithBiasSize = self._coeff.size // (  # type: ignore[attr-defined]
-                self._numClasses - 1
-            )
+            self._dataWithBiasSize = self._coeff.size // (self._numClasses - 1)
             self._weightsMatrix = self._coeff.toArray().reshape(
                 self._numClasses - 1, self._dataWithBiasSize
             )
@@ -261,11 +259,11 @@ def predict(
 
             best_class = 0
             max_margin = 0.0
-            if x.size + 1 == self._dataWithBiasSize:  # type: ignore[attr-defined]
+            if x.size + 1 == self._dataWithBiasSize:
                 for i in range(0, self._numClasses - 1):
                     margin = (
                         x.dot(self._weightsMatrix[i][0 : x.size])  # type: ignore[attr-defined]
-                        + self._weightsMatrix[i][x.size]  # type: ignore[attr-defined]
+                        + self._weightsMatrix[i][x.size]
                     )
                     if margin > max_margin:
                         max_margin = margin
@@ -936,7 +934,7 @@ def setInitialWeights(
         self._model = LogisticRegressionModel(
             initialWeights,
             0,
-            initialWeights.size,  # type: ignore[attr-defined]
+            initialWeights.size,
             2,
         )
         return self

python/pyspark/mllib/linalg/__init__.py

@@ -306,6 +306,8 @@ class Vector:
     Abstract class for DenseVector and SparseVector
     """
 
+    size: int
+
     def toArray(self) -> np.ndarray:
         """
         Convert the vector into an numpy.ndarray