feat: Changed default behavior for SSD window

src/pyversity/strategies/ssd.py

@@ -10,7 +10,7 @@ def ssd(  # noqa: C901
     k: int,
     diversity: float = 0.5,
     recent_embeddings: np.ndarray | None = None,
-    window: int = 10,
+    window: int | None = None,
     gamma: float = 1.0,
     normalize: bool = True,
     append_bias: bool = True,
@@ -33,7 +33,7 @@ def ssd(  # noqa: C901
                       1.0 = pure diversity, 0.0 = pure relevance.
     :param recent_embeddings: Optional 2D array (m, n_dims), oldest → newest; seeds the sliding window so
                               selection is aware of what was recently shown.
-    :param window: Sliding window size (≥ 1) for Gram-Schmidt bases.
+    :param window: Window size (≥ 1) for Gram-Schmidt bases. If None, defaults to len(recent_embeddings) + k.
     :param gamma: Diversity scale (> 0).
     :param normalize: Whether to normalize embeddings before computing similarity.
     :param append_bias: Append a constant-one bias dimension after normalization.
@@ -44,7 +44,7 @@ def ssd(  # noqa: C901
     # Validate parameters
     if not (0.0 <= float(diversity) <= 1.0):
         raise ValueError("diversity must be in [0, 1]")
-    if window < 1:
+    if window is not None and window < 1:
         raise ValueError("window must be >= 1")
     if gamma <= 0.0:
         raise ValueError("gamma must be > 0")
@@ -65,6 +65,7 @@ def ssd(  # noqa: C901
         )
 
     # Validate recent_embeddings
+    n_recent = 0
     if recent_embeddings is not None and np.size(recent_embeddings) > 0:
         if recent_embeddings.ndim != 2:
             raise ValueError("recent_embeddings must be a 2D array of shape (n_items, n_dims).")
@@ -73,6 +74,10 @@ def ssd(  # noqa: C901
                 f"recent_embeddings has {recent_embeddings.shape[1]} dims; "
                 f"expected {feature_matrix.shape[1]} to match `embeddings` columns."
             )
+        n_recent = int(recent_embeddings.shape[0])
+
+    # Determine effective window size
+    window_size = (n_recent + top_k) if window is None else int(window)
 
     # Pure relevance: select top-k by raw scores
     if float(theta) == 1.0:
@@ -83,7 +88,7 @@ def ssd(  # noqa: C901
             selection_scores=selection_scores,
             strategy=Strategy.SSD,
             diversity=diversity,
-            parameters={"gamma": gamma, "window": window},
+            parameters={"gamma": gamma, "window": window_size},
         )
 
     def _prepare_vectors(matrix: np.ndarray) -> np.ndarray:
@@ -124,7 +129,7 @@ def _prepare_vectors(matrix: np.ndarray) -> np.ndarray:
 
     def _push_basis_vector(basis_vector: np.ndarray) -> None:
         """Add a new basis vector to the sliding window and update residuals/projections."""
-        if len(basis_vectors) == window:
+        if len(basis_vectors) == window_size:
             # Remove oldest basis and restore its contribution to residuals
             oldest_basis = basis_vectors.pop(0)
             oldest_coefficients = projection_coefficients_per_basis.pop(0)
@@ -148,7 +153,7 @@ def _push_basis_vector(basis_vector: np.ndarray) -> None:
     seeded_bases = 0
     if recent_embeddings is not None and np.size(recent_embeddings) > 0:
         context = _prepare_vectors(recent_embeddings.astype(feature_matrix.dtype, copy=False))
-        context = context[-window:]  # keep only the latest `window` items
+        context = context[-window_size:]  # keep only the latest `window_size` items
         for context_vector in context:
             residual_context = context_vector.copy()
             for basis in basis_vectors:
@@ -201,5 +206,5 @@ def _push_basis_vector(basis_vector: np.ndarray) -> None:
         selection_scores=selection_scores.astype(np.float32, copy=False),
         strategy=Strategy.SSD,
         diversity=diversity,
-        parameters={"gamma": gamma, "window": window},
+        parameters={"gamma": gamma, "window": window_size},
     )

tests/test_strategies.py

@@ -247,6 +247,43 @@ def test_ssd_recent_embeddings_window_blocks_multiple_recent() -> None:
     assert res.indices[0] in (2, 3)
 
 
+def test_ssd_window_none_matches_large_window_when_recent_smaller() -> None:
+    """Test that window=None behaves like a large window when recent_embeddings is smaller than k."""
+    emb = np.eye(4, dtype=np.float32)
+    scores = np.ones(4, dtype=np.float32)
+    recent = emb[[0, 1]]
+    k = 3
+
+    res_none = ssd(
+        emb,
+        scores,
+        k=k,
+        window=None,
+        recent_embeddings=recent,
+    )
+    res_big = ssd(
+        emb,
+        scores,
+        k=k,
+        window=10,
+        recent_embeddings=recent,
+    )
+
+    assert np.array_equal(res_none.indices, res_big.indices)
+
+
+def test_ssd_window_none_equals_k_when_no_recent() -> None:
+    """Test that window=None behaves like window=k when recent_embeddings is not provided."""
+    emb = np.eye(5, dtype=np.float32)
+    scores = np.array([0.4, 0.9, 0.1, 0.7, 0.2], dtype=np.float32)
+    k = 3
+
+    res_none = ssd(emb, scores, k=k, window=None, recent_embeddings=None)
+    res_k = ssd(emb, scores, k=k, window=k, recent_embeddings=None)
+
+    assert np.array_equal(res_none.indices, res_k.indices)
+
+
 @pytest.mark.parametrize(
     "strategy, fn, kwargs",
     [