From 5bf04c55a0b565cb12039b3955c4b832ede53433 Mon Sep 17 00:00:00 2001
From: Damonamajor <damon.major@cookcountyil.gov>
Date: Wed, 18 Feb 2026 20:59:25 +0000
Subject: [PATCH 01/28] 4 working functions

---
 R/helpers.R | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++-
 params.yaml |  3 +++
 2 files changed, 74 insertions(+), 1 deletion(-)

diff --git a/R/helpers.R b/R/helpers.R
index 8aefbb277..9453f2245 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -199,13 +199,63 @@ extract_num_iterations <- function(x) {
 #   model:      Lightgbm model
 #   leaf_idx:   integer matrix [training data x trees] of leaf indices
 #   init_score: mean value of sale prices in the training data
+#   algorithm:  type of algorithm to use. Set in params.yaml. Possible types
+#               unweighted, unweighted_with_error_reduction, error_reduction,
+#               and prediction_variance
 #   outcome:    Predicted FMV values for each observation in the training data
 # Returns:
 #   weights: numeric matrix [n_obs x n_trees] where each row sums to 1
-extract_tree_weights <- function(model, leaf_idx, init_score, outcome) {
+extract_tree_weights <- function(model,
+                                 leaf_idx,
+                                 init_score = NULL,
+                                 algorithm = "unweighted",
+                                 outcome = NULL) {
   n_obs <- nrow(leaf_idx)
   n_trees <- ncol(leaf_idx)
 
+  # ---------------------------------------------------------
+  # unweighted (vector with 1/n_trees for each tree)
+  # ---------------------------------------------------------
+  if (algorithm == "unweighted") {
+    weights <- rep(1 / n_trees, n_trees)
+
+    return(weights)
+  }
+
+  # ---------------------------------------------------------
+  # prediction_variance:
+  # vector for tree weights based on variance of
+  # leaf values across data
+  # ---------------------------------------------------------
+  if (algorithm == "prediction_variance") {
+    tree_dt <- lgb.model.dt.tree(model)
+    leaf_lookup <- tree_dt[
+      !is.na(leaf_index),
+      c("tree_index", "leaf_index", "leaf_value")
+    ]
+
+    var_per_tree <- numeric(n_trees)
+
+    for (t in seq_len(n_trees)) {
+      # LightGBM is 0-indexed
+      this_tree <- subset(leaf_lookup, tree_index == (t - 1L))
+      m <- match(leaf_idx[, t], this_tree$leaf_index)
+      # incremental outputs for this tree across training rows
+      incr <- this_tree$leaf_value[m]
+      var_per_tree[t] <- stats::var(incr, na.rm = TRUE)
+    }
+
+    var_per_tree[is.na(var_per_tree)] <- 0
+    summed_variance <- sum(var_per_tree)
+    weights <- var_per_tree / summed_variance
+
+    return(weights)
+  }
+
+  # ---------------------------------------------------------
+  # Remaining algorithms require tree-based improvements to the predicted values
+  # ---------------------------------------------------------
+
   init_vec <- rep_len(as.numeric(init_score), n_obs)
 
   # Lookup: leaf_index -> leaf_value for each tree
@@ -236,11 +286,31 @@ extract_tree_weights <- function(model, leaf_idx, init_score, outcome) {
   colnames(tree_predictions) <- NULL
 
   # Absolute errors vs outcome for each prefix
+
   tree_errors <- abs(outcome - tree_predictions)
 
   # Improvement per tree = previous error - next error
   prev_err <- tree_errors[, 1:n_trees, drop = FALSE]
   next_err <- tree_errors[, 2:(n_trees + 1L), drop = FALSE]
+
+  # ---------------------------------------------------------
+  # unweighted_with_error_reduction
+  # (weights are 1/n_improving trees for trees which reduce errors, 0 otherwise)
+  # ---------------------------------------------------------
+  if (algorithm == "unweighted_with_error_reduction") {
+    improving <- prev_err > next_err
+    n_improving <- rowSums(improving)
+
+    weights <- improving / n_improving
+
+    return(weights)
+  }
+
+  # ---------------------------------------------------------
+  # proportional error reduction:
+  # weights are proportional to the reduction in error (prev_err - next_err) for
+  # improving trees, 0 otherwise
+  # ---------------------------------------------------------
   diff_in_errors <- pmax(0, prev_err - next_err)
   dim(diff_in_errors) <- dim(prev_err)
 
diff --git a/params.yaml b/params.yaml
index 2b295d26d..76ce3ca85 100644
--- a/params.yaml
+++ b/params.yaml
@@ -412,6 +412,9 @@ ratio_study:
 comp:
   # Number of comps to generate for each PIN/card
   num_comps: 5
+  # Type of comp run to use. Possible types are unweighted,
+  # unweighted_with_error_reduction, error_reduction, and prediction_variance
+  algorithm: unweighted
 
 # Export -----------------------------------------------------------------------
 

From 22e38e58cd094e99a634e108c1125e80c1dadd33 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 18 Feb 2026 21:46:50 +0000
Subject: [PATCH 02/28] Update comps docs

---
 params.yaml | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/params.yaml b/params.yaml
index 76ce3ca85..5cd8af4e0 100644
--- a/params.yaml
+++ b/params.yaml
@@ -412,8 +412,17 @@ ratio_study:
 comp:
   # Number of comps to generate for each PIN/card
   num_comps: 5
-  # Type of comp run to use. Possible types are unweighted,
-  # unweighted_with_error_reduction, error_reduction, and prediction_variance
+  # Algorithm used to weight trees for the comps similarity score.
+  # Valid options:
+  #   - "unweighted": Equal weight (1/n_trees) for every tree. No
+  #     observation-level variation. Returns a vector of weights.
+  #   - "unweighted_with_error_reduction": Binary 1/0 per tree per observation
+  #     (1 if tree reduces the training sale's prediction error, 0 otherwise),
+  #     then row-normalized. Returns a matrix of weights.
+  #   - "error_reduction": Proportional error reduction for training data sale per
+  #     tree, row-normalized. Returns a matrix of weights.
+  #   - "prediction_variance": Variance of each tree's leaf values across
+  #     training observations, normalized to sum to 1. Returns a vector of weights.
   algorithm: unweighted
 
 # Export -----------------------------------------------------------------------

From 5b655cf2f4fa1d60b7b1e9c21bf0123f6aa25279 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 18 Feb 2026 21:50:11 +0000
Subject: [PATCH 03/28] Add algorithm param for extract_tree_weights

---
 pipeline/04-interpret.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index 9ae810b83..aa65ddce5 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -267,6 +267,7 @@ if (comp_enable) {
     model      = lgbm_final_full_fit$fit,
     leaf_idx   = as.matrix(training_leaf_nodes),
     init_score = mean(training_data$meta_sale_price, na.rm = TRUE),
+    algorithm  = params$comp$algorithm,
     outcome    = training_data$meta_sale_price
   )
 

From 6f0ece521e83ae9efa1c8c51716e7f1e756e8189 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 18 Feb 2026 22:00:50 +0000
Subject: [PATCH 04/28] Add input checking

---
 R/helpers.R | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/R/helpers.R b/R/helpers.R
index 9453f2245..b9b76caa1 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -213,6 +213,19 @@ extract_tree_weights <- function(model,
   n_obs <- nrow(leaf_idx)
   n_trees <- ncol(leaf_idx)
 
+  # Validate algorithm arg
+  valid_algorithms <- c(
+    "unweighted",
+    "prediction_variance",
+    "unweighted_with_error_reduction",
+    "proportional_error_reduction"
+  )
+
+  algorithm <- rlang::arg_match(
+    algorithm,
+    values = valid_algorithms
+  )
+
   # ---------------------------------------------------------
   # unweighted (vector with 1/n_trees for each tree)
   # ---------------------------------------------------------

From 363b108605f6e2b67a47d04a40651bd27be3857e Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 18 Feb 2026 22:06:47 +0000
Subject: [PATCH 05/28] Adjust tree_weights shape checking

---
 pipeline/04-interpret.R | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index aa65ddce5..a67096990 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -274,13 +274,19 @@ if (comp_enable) {
   if (length(tree_weights) == 0) {
     message("Warning: tree_weights are empty")
   }
-  if (all(rowSums(tree_weights) %in% c(0, 1))) {
-    message("Warning: tree_weights do not sum to 1 or 0 for each row")
-    message("First 5 weights:")
-    print(head(tree_weights, 5))
+  if (is.matrix(tree_weights)) {
+    if (all(rowSums(tree_weights) %in% c(0, 1))) {
+      message("Warning: tree_weights do not sum to 1 or 0 for each row")
+      message("First 5 weights:")
+      print(head(tree_weights, 5))
+    }
+  } else {
+    message(
+      "Tree weights are a vector of length ", length(tree_weights),
+      " (same weights for all training observations)"
+    )
   }
 
-
   # Make sure that the leaf node tibbles are all integers, which is what
   # the comps algorithm expects
   leaf_nodes <- leaf_nodes %>%

From 123dd817fc8a013fd9480b77bcdfc0abce131d42 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 18 Feb 2026 22:11:44 +0000
Subject: [PATCH 06/28] Test vector inclusion

---
 python/comps.py | 164 +++++++++++++++++++++++++++++++++++-------------
 1 file changed, 122 insertions(+), 42 deletions(-)

diff --git a/python/comps.py b/python/comps.py
index aa05148d8..cde640197 100644
--- a/python/comps.py
+++ b/python/comps.py
@@ -17,11 +17,18 @@ def get_comps(
     lightgbm leaf node assignments (`observation_df`) compared to a second
     dataframe of leaf node assignments (`comparison_df`).
 
-    Leaf nodes are weighted according to a tree importance matrix `weights`
-    and used to generate a similarity score. The function returns two
-    dataframes: One containing the indices of the most similar compararables
+    Leaf nodes are weighted according to a tree importance vector or matrix
+    `weights` and used to generate a similarity score. The function returns two
+    dataframes: One containing the indices of the most similar comparables
     and the other containing their corresponding similarity scores.
 
+    Weights can be:
+      - A 1-D array of shape (n_trees,) for algorithms that produce a single
+        weight per tree (e.g. "unweighted", "prediction_variance").
+      - A 2-D matrix of shape (n_training_obs, n_trees) for algorithms that
+        produce per-observation weights (e.g. "error_reduction",
+        "unweighted_with_error_reduction").
+
     More details on the underlying algorithm can be found here:
     https://ccao-data.github.io/lightsnip/articles/finding-comps.html
 
@@ -33,7 +40,7 @@ def get_comps(
             comparables.
         weights (numpy.ndarray):
             Importance weights for leaf nodes, used to compute similarity
-            scores.
+            scores. Either 1-D (n_trees,) or 2-D (n_comparisons, n_trees).
         num_comps (int, optional):
             Number of top comparables to return for each observation.
             Default is 5.
@@ -59,16 +66,38 @@ def get_comps(
             f"({observation_df.shape[1]}) "
             f"must match `comparison_df` ({comparison_df.shape[1]})"
         )
-    if comparison_df.shape != weights.shape:
+
+    if not isinstance(weights, np.ndarray):
+        weights = np.asarray(weights)
+
+    # Determine whether weights are per-tree (1-D vector) or
+    # per-observation (2-D matrix) and validate accordingly
+    if weights.ndim == 1:
+        # Vector weights: one weight per tree, shared across all comparisons.
+        # Used by "unweighted" and "prediction_variance" algorithms.
+        if weights.shape[0] != comparison_df.shape[1]:
+            raise ValueError(
+                f"`weights` length {weights.shape[0]} must equal number of "
+                f"trees {comparison_df.shape[1]}"
+            )
+        weights_arr = weights.astype(np.float32, copy=False)
+        use_matrix_weights = False
+    elif weights.ndim == 2:
+        # Matrix weights: one weight per comparison observation per tree.
+        # Used by "error_reduction" and "unweighted_with_error_reduction".
+        if comparison_df.shape != weights.shape:
+            raise ValueError(
+                f"`comparison_df.shape` {comparison_df.shape} must match "
+                f"`weights.shape` {weights.shape}"
+            )
+        weights_arr = weights.astype(np.float32, copy=False)
+        use_matrix_weights = True
+    else:
         raise ValueError(
-            f"`comparison_df.shape` {comparison_df.shape} must match "
-            f"`weights.shape` {weights.shape}"
+            "`weights` must be a 1-D vector (n_trees,) or 2-D matrix "
+            f"(n_comparisons, n_trees), got {weights.ndim}-D"
         )
 
-    # Convert the weights to a numpy array so that we can take advantage of
-    # numba acceleration later on
-    weights_matrix = np.asarray(weights, dtype=np.float32)
-
     # Chunk the observations so that the script can periodically report progress
     observation_df["chunk"] = pd.cut(
         observation_df.index, bins=num_chunks, labels=False
@@ -98,10 +127,22 @@ def get_comps(
             flush=True,
         )
 
-        # Compute comps for each observation
-        comp_ids, comp_scores = _get_top_n_comps(
-            observation_matrix, possible_comp_matrix, weights_matrix, num_comps
-        )
+        # Compute comps for each observation, dispatching to the appropriate
+        # numba kernel based on the shape of the weights
+        if use_matrix_weights:
+            comp_ids, comp_scores = _get_top_n_comps_matrix_weights(
+                observation_matrix,
+                possible_comp_matrix,
+                weights_arr,
+                num_comps,
+            )
+        else:
+            comp_ids, comp_scores = _get_top_n_comps_vector_weights(
+                observation_matrix,
+                possible_comp_matrix,
+                weights_arr,
+                num_comps,
+            )
 
         observation_ids = observations.index.values
         for obs_idx, comp_idx, comp_score in zip(
@@ -127,46 +168,77 @@ def get_comps(
 
 
 @nb.njit(fastmath=True, parallel=True)
-def _get_top_n_comps(
+def _get_top_n_comps_vector_weights(
+    leaf_node_matrix: np.ndarray,
+    comparison_leaf_node_matrix: np.ndarray,
+    weights_vector: np.ndarray,
+    num_comps: int,
+) -> typing.Tuple[np.ndarray, np.ndarray]:
+    """Compute top-N comps using a shared per-tree weight vector.
+
+    Used by algorithms like "unweighted" and "prediction_variance" where
+    the weight for a tree is the same regardless of which training
+    observation is being compared."""
+    num_observations = len(leaf_node_matrix)
+    num_possible_comparisons = len(comparison_leaf_node_matrix)
+    idx_dtype = np.int32
+    score_dtype = np.float32
+
+    all_top_n_idxs = np.full((num_observations, num_comps), -1, dtype=idx_dtype)
+    all_top_n_scores = np.zeros((num_observations, num_comps), dtype=score_dtype)
+
+    for x_i in nb.prange(num_observations):
+        for y_i in range(num_possible_comparisons):
+            similarity_score = 0.0
+            for tree_idx in range(leaf_node_matrix.shape[1]):
+                if (
+                    leaf_node_matrix[x_i, tree_idx]
+                    == comparison_leaf_node_matrix[y_i, tree_idx]
+                ):
+                    similarity_score += weights_vector[tree_idx]
+
+            if similarity_score > all_top_n_scores[x_i][-1]:
+                for idx, score in enumerate(all_top_n_scores[x_i]):
+                    if similarity_score > score:
+                        insert_at_idx_and_shift(all_top_n_idxs[x_i], y_i, idx)
+                        insert_at_idx_and_shift(
+                            all_top_n_scores[x_i], similarity_score, idx
+                        )
+                        break
+
+    return all_top_n_idxs, all_top_n_scores
+
+
+@nb.njit(fastmath=True, parallel=True)
+def _get_top_n_comps_matrix_weights(
     leaf_node_matrix: np.ndarray,
     comparison_leaf_node_matrix: np.ndarray,
     weights_matrix: np.ndarray,
     num_comps: int,
 ) -> typing.Tuple[np.ndarray, np.ndarray]:
-    """Helper function that takes matrices of leaf node assignments for
-    observations in a tree model, a matrix of weights for each obs/tree, and an
-    integer `num_comps`, and returns a matrix where each observation is scored
-    by similarity to observations in the comparison matrix and the top N scores
-    are returned along with the indexes of the comparison observations."""
+    """Compute top-N comps using a per-observation weight matrix.
+
+    Used by algorithms like "error_reduction" and
+    "unweighted_with_error_reduction" where the weight for a tree
+    depends on which training observation (y_i) is being compared."""
     num_observations = len(leaf_node_matrix)
     num_possible_comparisons = len(comparison_leaf_node_matrix)
     idx_dtype = np.int32
     score_dtype = np.float32
 
-    # Store scores and indexes in two separate arrays rather than a 3d matrix
-    # for simplicity (array of tuples does not convert to pandas properly).
-    # Indexes default to -1, which is an impossible index and so is a signal
-    # that no comp was found
     all_top_n_idxs = np.full((num_observations, num_comps), -1, dtype=idx_dtype)
     all_top_n_scores = np.zeros((num_observations, num_comps), dtype=score_dtype)
 
     for x_i in nb.prange(num_observations):
-        # TODO: We could probably speed this up by skipping comparisons we've
-        # already made; we just need to do it in a way that will have a
-        # low memory footprint
         for y_i in range(num_possible_comparisons):
             similarity_score = 0.0
-            for tree_idx in range(len(leaf_node_matrix[x_i])):
+            for tree_idx in range(leaf_node_matrix.shape[1]):
                 if (
-                    leaf_node_matrix[x_i][tree_idx]
-                    == comparison_leaf_node_matrix[y_i][tree_idx]
+                    leaf_node_matrix[x_i, tree_idx]
+                    == comparison_leaf_node_matrix[y_i, tree_idx]
                 ):
-                    similarity_score += weights_matrix[y_i][tree_idx]
+                    similarity_score += weights_matrix[y_i, tree_idx]
 
-            # See if the score is higher than any of the top N
-            # comps, and store it in the sorted comps array if it is.
-            # First check if the score is higher than the lowest score,
-            # since otherwise we don't need to bother iterating the scores
             if similarity_score > all_top_n_scores[x_i][-1]:
                 for idx, score in enumerate(all_top_n_scores[x_i]):
                     if similarity_score > score:
@@ -198,18 +270,26 @@ def insert_at_idx_and_shift(
     num_trees = 500
     num_obs = 20001
     num_comparisons = 10000
-    mean_sale_price = 350000
-    std_deviation = 110000
 
-    leaf_nodes = pd.DataFrame(np.random.randint(0, num_obs, size=[num_obs, num_trees]))
+    leaf_nodes = pd.DataFrame(
+        np.random.randint(0, num_obs, size=[num_obs, num_trees])
+    )
     training_leaf_nodes = pd.DataFrame(
         np.random.randint(0, num_comparisons, size=[num_comparisons, num_trees])
     )
-    tree_weights = np.asarray(
+
+    # Test with matrix weights (error_reduction style)
+    tree_weights_matrix = np.asarray(
         [np.random.dirichlet(np.ones(num_trees)) for _ in range(num_comparisons)]
     )
+    start = time.time()
+    get_comps(leaf_nodes, training_leaf_nodes, tree_weights_matrix)
+    end = time.time()
+    print(f"get_comps (matrix weights) runtime: {end - start}s")
 
+    # Test with vector weights (unweighted / prediction_variance style)
+    tree_weights_vector = np.random.dirichlet(np.ones(num_trees))
     start = time.time()
-    get_comps(leaf_nodes, training_leaf_nodes, tree_weights)
+    get_comps(leaf_nodes, training_leaf_nodes, tree_weights_vector)
     end = time.time()
-    print(f"get_comps runtime: {end - start}s")
+    print(f"get_comps (vector weights) runtime: {end - start}s")
\ No newline at end of file

From 4c0d8cceb2e01d1f793f0bec76215dc71b14b0f3 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 18 Feb 2026 22:19:30 +0000
Subject: [PATCH 07/28] Attempt using only 1 get_top_comps function

---
 python/comps.py | 116 +++++++++++++++++-------------------------------
 1 file changed, 40 insertions(+), 76 deletions(-)

diff --git a/python/comps.py b/python/comps.py
index cde640197..9995f3da8 100644
--- a/python/comps.py
+++ b/python/comps.py
@@ -24,7 +24,8 @@ def get_comps(
 
     Weights can be:
       - A 1-D array of shape (n_trees,) for algorithms that produce a single
-        weight per tree (e.g. "unweighted", "prediction_variance").
+        weight per tree (e.g. "unweighted", "prediction_variance"). Will be
+        reshaped to (1, n_trees) before being passed to the numba kernel.
       - A 2-D matrix of shape (n_training_obs, n_trees) for algorithms that
         produce per-observation weights (e.g. "error_reduction",
         "unweighted_with_error_reduction").
@@ -70,28 +71,24 @@ def get_comps(
     if not isinstance(weights, np.ndarray):
         weights = np.asarray(weights)
 
-    # Determine whether weights are per-tree (1-D vector) or
-    # per-observation (2-D matrix) and validate accordingly
+    # Normalize weights to a 2-D matrix so that we can use a single numba
+    # kernel for both vector and matrix weights. A 1-D vector of per-tree
+    # weights is reshaped to (1, n_trees); the numba kernel detects this
+    # shape and broadcasts the single row to all comparison observations.
     if weights.ndim == 1:
-        # Vector weights: one weight per tree, shared across all comparisons.
-        # Used by "unweighted" and "prediction_variance" algorithms.
         if weights.shape[0] != comparison_df.shape[1]:
             raise ValueError(
                 f"`weights` length {weights.shape[0]} must equal number of "
                 f"trees {comparison_df.shape[1]}"
             )
-        weights_arr = weights.astype(np.float32, copy=False)
-        use_matrix_weights = False
+        weights_matrix = weights.reshape(1, -1).astype(np.float32, copy=False)
     elif weights.ndim == 2:
-        # Matrix weights: one weight per comparison observation per tree.
-        # Used by "error_reduction" and "unweighted_with_error_reduction".
         if comparison_df.shape != weights.shape:
             raise ValueError(
                 f"`comparison_df.shape` {comparison_df.shape} must match "
                 f"`weights.shape` {weights.shape}"
             )
-        weights_arr = weights.astype(np.float32, copy=False)
-        use_matrix_weights = True
+        weights_matrix = weights.astype(np.float32, copy=False)
     else:
         raise ValueError(
             "`weights` must be a 1-D vector (n_trees,) or 2-D matrix "
@@ -127,22 +124,10 @@ def get_comps(
             flush=True,
         )
 
-        # Compute comps for each observation, dispatching to the appropriate
-        # numba kernel based on the shape of the weights
-        if use_matrix_weights:
-            comp_ids, comp_scores = _get_top_n_comps_matrix_weights(
-                observation_matrix,
-                possible_comp_matrix,
-                weights_arr,
-                num_comps,
-            )
-        else:
-            comp_ids, comp_scores = _get_top_n_comps_vector_weights(
-                observation_matrix,
-                possible_comp_matrix,
-                weights_arr,
-                num_comps,
-            )
+        # Compute comps for each observation
+        comp_ids, comp_scores = _get_top_n_comps(
+            observation_matrix, possible_comp_matrix, weights_matrix, num_comps
+        )
 
         observation_ids = observations.index.values
         for obs_idx, comp_idx, comp_score in zip(
@@ -168,77 +153,56 @@ def get_comps(
 
 
 @nb.njit(fastmath=True, parallel=True)
-def _get_top_n_comps_vector_weights(
-    leaf_node_matrix: np.ndarray,
-    comparison_leaf_node_matrix: np.ndarray,
-    weights_vector: np.ndarray,
-    num_comps: int,
-) -> typing.Tuple[np.ndarray, np.ndarray]:
-    """Compute top-N comps using a shared per-tree weight vector.
-
-    Used by algorithms like "unweighted" and "prediction_variance" where
-    the weight for a tree is the same regardless of which training
-    observation is being compared."""
-    num_observations = len(leaf_node_matrix)
-    num_possible_comparisons = len(comparison_leaf_node_matrix)
-    idx_dtype = np.int32
-    score_dtype = np.float32
-
-    all_top_n_idxs = np.full((num_observations, num_comps), -1, dtype=idx_dtype)
-    all_top_n_scores = np.zeros((num_observations, num_comps), dtype=score_dtype)
-
-    for x_i in nb.prange(num_observations):
-        for y_i in range(num_possible_comparisons):
-            similarity_score = 0.0
-            for tree_idx in range(leaf_node_matrix.shape[1]):
-                if (
-                    leaf_node_matrix[x_i, tree_idx]
-                    == comparison_leaf_node_matrix[y_i, tree_idx]
-                ):
-                    similarity_score += weights_vector[tree_idx]
-
-            if similarity_score > all_top_n_scores[x_i][-1]:
-                for idx, score in enumerate(all_top_n_scores[x_i]):
-                    if similarity_score > score:
-                        insert_at_idx_and_shift(all_top_n_idxs[x_i], y_i, idx)
-                        insert_at_idx_and_shift(
-                            all_top_n_scores[x_i], similarity_score, idx
-                        )
-                        break
-
-    return all_top_n_idxs, all_top_n_scores
-
-
-@nb.njit(fastmath=True, parallel=True)
-def _get_top_n_comps_matrix_weights(
+def _get_top_n_comps(
     leaf_node_matrix: np.ndarray,
     comparison_leaf_node_matrix: np.ndarray,
     weights_matrix: np.ndarray,
     num_comps: int,
 ) -> typing.Tuple[np.ndarray, np.ndarray]:
-    """Compute top-N comps using a per-observation weight matrix.
-
-    Used by algorithms like "error_reduction" and
-    "unweighted_with_error_reduction" where the weight for a tree
-    depends on which training observation (y_i) is being compared."""
+    """Helper function that takes matrices of leaf node assignments for
+    observations in a tree model, a matrix of weights for each obs/tree, and an
+    integer `num_comps`, and returns a matrix where each observation is scored
+    by similarity to observations in the comparison matrix and the top N scores
+    are returned along with the indexes of the comparison observations.
+
+    The weights_matrix is always 2-D. If its first dimension is 1, the single
+    row of weights is broadcast to all comparison observations (i.e. tree-level
+    weights shared across all comparisons). Otherwise, each comparison
+    observation y_i uses its own row of weights."""
     num_observations = len(leaf_node_matrix)
     num_possible_comparisons = len(comparison_leaf_node_matrix)
     idx_dtype = np.int32
     score_dtype = np.float32
 
+    # Detect whether we have shared (vector-style) or per-observation weights
+    shared_weights = weights_matrix.shape[0] == 1
+
+    # Store scores and indexes in two separate arrays rather than a 3d matrix
+    # for simplicity (array of tuples does not convert to pandas properly).
+    # Indexes default to -1, which is an impossible index and so is a signal
+    # that no comp was found
     all_top_n_idxs = np.full((num_observations, num_comps), -1, dtype=idx_dtype)
     all_top_n_scores = np.zeros((num_observations, num_comps), dtype=score_dtype)
 
     for x_i in nb.prange(num_observations):
+        # TODO: We could probably speed this up by skipping comparisons we've
+        # already made; we just need to do it in a way that will have a
+        # low memory footprint
         for y_i in range(num_possible_comparisons):
             similarity_score = 0.0
+            # Use row 0 for shared weights, row y_i for per-obs weights
+            w_i = 0 if shared_weights else y_i
             for tree_idx in range(leaf_node_matrix.shape[1]):
                 if (
                     leaf_node_matrix[x_i, tree_idx]
                     == comparison_leaf_node_matrix[y_i, tree_idx]
                 ):
-                    similarity_score += weights_matrix[y_i, tree_idx]
+                    similarity_score += weights_matrix[w_i, tree_idx]
 
+            # See if the score is higher than any of the top N
+            # comps, and store it in the sorted comps array if it is.
+            # First check if the score is higher than the lowest score,
+            # since otherwise we don't need to bother iterating the scores
             if similarity_score > all_top_n_scores[x_i][-1]:
                 for idx, score in enumerate(all_top_n_scores[x_i]):
                     if similarity_score > score:

From b52fbd88dd76b869d5105ea010bee7cdf89d6b1a Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Fri, 20 Feb 2026 17:24:01 +0000
Subject: [PATCH 08/28] Fix message

---
 pipeline/04-interpret.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index a67096990..dfb57366f 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -26,7 +26,7 @@ purrr::walk(list.files("R/", "\\.R$", full.names = TRUE), source)
 
 
 
-#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+?#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 # 2. Load Data -----------------------------------------------------------------
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 message("Loading model fit and recipe")
@@ -276,7 +276,7 @@ if (comp_enable) {
   }
   if (is.matrix(tree_weights)) {
     if (all(rowSums(tree_weights) %in% c(0, 1))) {
-      message("Warning: tree_weights do not sum to 1 or 0 for each row")
+      message("Warning: tree_weights do sum to 1 or 0 for each row")
       message("First 5 weights:")
       print(head(tree_weights, 5))
     }

From 1923e195a20af4beaf2c23c3cd2671731d9d05cf Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Fri, 20 Feb 2026 17:29:02 +0000
Subject: [PATCH 09/28] Switch boolean check

---
 pipeline/04-interpret.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index dfb57366f..4465b1c5b 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -275,8 +275,8 @@ if (comp_enable) {
     message("Warning: tree_weights are empty")
   }
   if (is.matrix(tree_weights)) {
-    if (all(rowSums(tree_weights) %in% c(0, 1))) {
-      message("Warning: tree_weights do sum to 1 or 0 for each row")
+    if (!all(rowSums(tree_weights) %in% c(0, 1))) {
+      message("Warning: tree_weights do not sum to 1 or 0 for each row")
       message("First 5 weights:")
       print(head(tree_weights, 5))
     }

From 972772ceec3040a6dd2e4724e2af807b3405a16e Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Fri, 20 Feb 2026 19:58:58 +0000
Subject: [PATCH 10/28] Style

---
 pipeline/04-interpret.R | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index 4465b1c5b..e09b7c026 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -24,9 +24,7 @@ reticulate::py_require(
 purrr::walk(list.files("R/", "\\.R$", full.names = TRUE), source)
 
 
-
-
-?#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+? #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 # 2. Load Data -----------------------------------------------------------------
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 message("Loading model fit and recipe")
@@ -84,8 +82,6 @@ if (comp_enable) {
 }
 
 
-
-
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 # 3. Calculate SHAP Values -----------------------------------------------------
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
@@ -151,8 +147,6 @@ if (shap_enable) {
 }
 
 
-
-
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 # 4. Calculate Feature Importance ----------------------------------------------
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
@@ -172,8 +166,6 @@ lightgbm::lgb.importance(lgbm_final_full_fit$fit) %>%
   write_parquet(paths$output$feature_importance$local)
 
 
-
-
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 # 5. Find Comparables  ---------------------------------------------------------
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

From 1f6bf1943021618cd8ba2a689de1100fd7a4ab9f Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Fri, 20 Feb 2026 20:03:49 +0000
Subject: [PATCH 11/28] Format

---
 python/comps.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/python/comps.py b/python/comps.py
index 9995f3da8..34b508741 100644
--- a/python/comps.py
+++ b/python/comps.py
@@ -25,10 +25,10 @@ def get_comps(
     Weights can be:
       - A 1-D array of shape (n_trees,) for algorithms that produce a single
         weight per tree (e.g. "unweighted", "prediction_variance"). Will be
-        reshaped to (1, n_trees) before being passed to the numba kernel.
+        reshaped to (1, n_trees) before being passed to numba
       - A 2-D matrix of shape (n_training_obs, n_trees) for algorithms that
         produce per-observation weights (e.g. "error_reduction",
-        "unweighted_with_error_reduction").
+        "unweighted_with_error_reduction")
 
     More details on the underlying algorithm can be found here:
     https://ccao-data.github.io/lightsnip/articles/finding-comps.html
@@ -239,7 +239,11 @@ def insert_at_idx_and_shift(
         np.random.randint(0, num_obs, size=[num_obs, num_trees])
     )
     training_leaf_nodes = pd.DataFrame(
-        np.random.randint(0, num_comparisons, size=[num_comparisons, num_trees])
+        np.random.randint(
+          0,
+          num_comparisons,
+          size=[num_comparisons, num_trees]
+        )
     )
 
     # Test with matrix weights (error_reduction style)
@@ -256,4 +260,4 @@ def insert_at_idx_and_shift(
     start = time.time()
     get_comps(leaf_nodes, training_leaf_nodes, tree_weights_vector)
     end = time.time()
-    print(f"get_comps (vector weights) runtime: {end - start}s")
\ No newline at end of file
+    print(f"get_comps (vector weights) runtime: {end - start}s")

From 365dee4d8b2ebb2d0193e6a08dd6a577b68deb9d Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Fri, 20 Feb 2026 20:34:36 +0000
Subject: [PATCH 12/28] Lint

---
 python/comps.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/python/comps.py b/python/comps.py
index 34b508741..5bd972b00 100644
--- a/python/comps.py
+++ b/python/comps.py
@@ -239,11 +239,7 @@ def insert_at_idx_and_shift(
         np.random.randint(0, num_obs, size=[num_obs, num_trees])
     )
     training_leaf_nodes = pd.DataFrame(
-        np.random.randint(
-          0,
-          num_comparisons,
-          size=[num_comparisons, num_trees]
-        )
+      np.random.randint(0, num_comparisons, size=[num_comparisons, num_trees])
     )
 
     # Test with matrix weights (error_reduction style)

From fa8ce67356bb368a5f8f21319b61131a280d3fb0 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Fri, 20 Feb 2026 20:39:29 +0000
Subject: [PATCH 13/28] Lint

---
 pipeline/04-interpret.R | 4 +++-
 python/comps.py         | 6 ++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index e09b7c026..148603878 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -24,7 +24,7 @@ reticulate::py_require(
 purrr::walk(list.files("R/", "\\.R$", full.names = TRUE), source)
 
 
-? #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 # 2. Load Data -----------------------------------------------------------------
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 message("Loading model fit and recipe")
@@ -82,6 +82,8 @@ if (comp_enable) {
 }
 
 
+
+
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 # 3. Calculate SHAP Values -----------------------------------------------------
 #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
diff --git a/python/comps.py b/python/comps.py
index 5bd972b00..20eefd515 100644
--- a/python/comps.py
+++ b/python/comps.py
@@ -235,11 +235,9 @@ def insert_at_idx_and_shift(
     num_obs = 20001
     num_comparisons = 10000
 
-    leaf_nodes = pd.DataFrame(
-        np.random.randint(0, num_obs, size=[num_obs, num_trees])
-    )
+    leaf_nodes = pd.DataFrame(np.random.randint(0, num_obs, size=[num_obs, num_trees]))
     training_leaf_nodes = pd.DataFrame(
-      np.random.randint(0, num_comparisons, size=[num_comparisons, num_trees])
+        np.random.randint(0, num_comparisons, size=[num_comparisons, num_trees])
     )
 
     # Test with matrix weights (error_reduction style)

From d32eb89bdaac80c0d92d7c77ea069e84a218d4bb Mon Sep 17 00:00:00 2001
From: wagnerlmichael <93889413+wagnerlmichael@users.noreply.github.com>
Date: Mon, 23 Feb 2026 09:01:17 -0600
Subject: [PATCH 14/28] Update R/helpers.R

Co-authored-by: Jean Cochrane <jeancochrane@users.noreply.github.com>
---
 R/helpers.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/R/helpers.R b/R/helpers.R
index b9b76caa1..8774f6676 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -299,7 +299,6 @@ extract_tree_weights <- function(model,
   colnames(tree_predictions) <- NULL
 
   # Absolute errors vs outcome for each prefix
-
   tree_errors <- abs(outcome - tree_predictions)
 
   # Improvement per tree = previous error - next error

From 441c4cded9d7b8cbfdab06ae74c3744e0861b5c6 Mon Sep 17 00:00:00 2001
From: wagnerlmichael <93889413+wagnerlmichael@users.noreply.github.com>
Date: Mon, 23 Feb 2026 09:01:50 -0600
Subject: [PATCH 15/28] Update R/helpers.R

Co-authored-by: Jean Cochrane <jeancochrane@users.noreply.github.com>
---
 R/helpers.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/helpers.R b/R/helpers.R
index 8774f6676..7ef7c9f0b 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -218,7 +218,7 @@ extract_tree_weights <- function(model,
     "unweighted",
     "prediction_variance",
     "unweighted_with_error_reduction",
-    "proportional_error_reduction"
+    "error_reduction"
   )
 
   algorithm <- rlang::arg_match(

From 5ca1ed7df4021477fbd79c003ee09f295249b522 Mon Sep 17 00:00:00 2001
From: wagnerlmichael <93889413+wagnerlmichael@users.noreply.github.com>
Date: Mon, 23 Feb 2026 11:47:22 -0600
Subject: [PATCH 16/28] Update R/helpers.R

Co-authored-by: Jean Cochrane <jeancochrane@users.noreply.github.com>
---
 R/helpers.R | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/R/helpers.R b/R/helpers.R
index 7ef7c9f0b..ee810c00b 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -221,10 +221,12 @@ extract_tree_weights <- function(model,
     "error_reduction"
   )
 
-  algorithm <- rlang::arg_match(
-    algorithm,
-    values = valid_algorithms
-  )
+  if (!algorithm %in% valid_algorithms) {
+    stop(
+      "Invalid algorithm '", algorithm, "'. Must be one of: ",
+      paste0(valid_algorithms, collapse = ", ")
+    )
+  }
 
   # ---------------------------------------------------------
   # unweighted (vector with 1/n_trees for each tree)

From 1e32a952d62c7a755f2cef6c6edb18ab01b973d4 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Mon, 23 Feb 2026 17:55:05 +0000
Subject: [PATCH 17/28] Update tests to accomdate vector weight support

---
 python/tests/test_comps.py | 126 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 125 insertions(+), 1 deletion(-)

diff --git a/python/tests/test_comps.py b/python/tests/test_comps.py
index fc61e1ef1..17f239cce 100644
--- a/python/tests/test_comps.py
+++ b/python/tests/test_comps.py
@@ -8,6 +8,8 @@
 @pt.mark.parametrize(
     "leaf_nodes,training_leaf_nodes,tree_weights,num_comps,num_chunks,expected_comps,expected_scores",
     [
+        # Matrix weights tests:
+        # - - - - - - - - - - -
         # Simple example that tests one input observation with uniform weights
         # to make sure that the algorithm correctly prioritizes comparison
         # observations with the highest number of leaf node matches
@@ -203,6 +205,118 @@
             ),
             id="more_possible_comps_than_num_comps",
         ),
+        # Vector weights tests:
+        # - - - - - - - - - - -
+        # Test one observation with uniform 1-D weights (vector). With a 1-D
+        # weight vector, every tree gets the same weight and that weight is
+        # broadcast identically to every comparison observation. This should
+        # produce the same results as the matrix case with identical rows.
+        pt.param(
+            pd.DataFrame([[1, 1, 1]]),
+            pd.DataFrame([[1, 1, 0], [1, 0, 0], [0, 0, 0]]),
+            np.array([0.333, 0.333, 0.333]),
+            3,
+            1,
+            pd.DataFrame(
+                {
+                    "comp_idx_1": [0],
+                    "comp_idx_2": [1],
+                    "comp_idx_3": [-1],
+                },
+                dtype=np.int32,
+            ),
+            pd.DataFrame(
+                {
+                    "comp_score_1": [0.333 * 2],
+                    "comp_score_2": [0.333 * 1],
+                    "comp_score_3": [0.333 * 0],
+                },
+                dtype=np.float32,
+            ),
+            id="one_observation_1d_weights",
+        ),
+        # Test two observations with uniform 1-D weights. The 1-D vector is
+        # used for all comparison rows, so every comparison uses the same
+        # per-tree weight. Results should mirror the 2-D uniform-weight case.
+        pt.param(
+            pd.DataFrame([[1, 1, 1], [2, 2, 2]]),
+            pd.DataFrame([[1, 1, 1], [1, 1, 2], [1, 2, 2], [2, 2, 2]]),
+            np.array([0.333, 0.333, 0.333]),
+            4,
+            1,
+            pd.DataFrame(
+                {
+                    "comp_idx_1": [0, 3],
+                    "comp_idx_2": [1, 2],
+                    "comp_idx_3": [2, 1],
+                    "comp_idx_4": [-1, -1],
+                },
+                dtype=np.int32,
+            ),
+            pd.DataFrame(
+                {
+                    "comp_score_1": [0.333 * 3] * 2,
+                    "comp_score_2": [0.333 * 2] * 2,
+                    "comp_score_3": [0.333 * 1] * 2,
+                    "comp_score_4": [0.333 * 0] * 2,
+                },
+                dtype=np.float32,
+            ),
+            id="two_observations_1d_weights",
+        ),
+        # Test 1-D weights with non-uniform per-tree weights. With weights
+        pt.param(
+            pd.DataFrame([[1, 1, 1]]),
+            pd.DataFrame([[1, 1, 0], [1, 0, 0], [0, 0, 0]]),
+            np.array([0.5, 0.3, 0.2]),
+            3,
+            1,
+            pd.DataFrame(
+                {"comp_idx_1": [0], "comp_idx_2": [1], "comp_idx_3": [-1]},
+                dtype=np.int32,
+            ),
+            pd.DataFrame(
+                {
+                    "comp_score_1": [0.5 + 0.3],
+                    "comp_score_2": [0.5],
+                    "comp_score_3": [0.0],
+                },
+                dtype=np.float32,
+            ),
+            id="non_uniform_1d_weights",
+        ),
+        # Test that 1-D vector weights produce the same result as an
+        # equivalent 2-D matrix where every row is the same weight vector.
+        # This confirms the logic is correct: a (3,) vector
+        # reshaped to (1, 3) should behave identically to a (3, 3) matrix
+        # with all rows equal to [0.5, 0.3, 0.2].
+        pt.param(
+            pd.DataFrame([[1, 1, 1]]),
+            pd.DataFrame([[1, 1, 0], [1, 1, 0], [1, 1, 0]]),
+            np.array([0.5, 0.3, 0.2]),
+            3,
+            1,
+            pd.DataFrame(
+                {
+                    # All comparison obs match on trees 1 & 2 with the same
+                    # weight, so they all tie and tiebreak by index order
+                    "comp_idx_1": [0],
+                    "comp_idx_2": [1],
+                    "comp_idx_3": [2],
+                },
+                dtype=np.int32,
+            ),
+            pd.DataFrame(
+                {
+                    # All comparison obs get the same score: 0.5 + 0.3 = 0.8
+                    "comp_score_1": [0.5 + 0.3],
+                    "comp_score_2": [0.5 + 0.3],
+                    "comp_score_3": [0.5 + 0.3],
+                },
+                dtype=np.float32,
+            ),
+            id="1d_weights_tiebreak_same_as_uniform_matrix",
+        ),
     ],
 )
 def test_get_comps(
@@ -244,6 +358,16 @@ def test_get_comps(
             "`comparison_df.shape` (3, 3) must match `weights.shape` (2, 2)",
             id="comparison_weights_shapes_match",
         ),
+        # Vector weights validation tests:
+        # 1-D weights with wrong length: 2 weights for 3 trees
+        pt.param(
+            pd.DataFrame([[1, 1, 1]]),
+            pd.DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]]),
+            np.array([0.333, 0.333]),
+            ValueError,
+            "`weights` length 2 must equal number of trees 3",
+            id="1d_weights_wrong_length",
+        ),
     ],
 )
 def test_get_comps_raises_on_invalid_inputs(
@@ -271,4 +395,4 @@ def test_get_comps_raises_on_invalid_inputs(
 )
 def test_insert_at_idx_and_shift(arr, elem, idx, expected):
     result = comps_module.insert_at_idx_and_shift(np.array(arr), elem, idx)
-    np.testing.assert_array_equal(result, np.array(expected))
+    np.testing.assert_array_equal(result, np.array(expected))
\ No newline at end of file

From 899450c6ab6a002277b656fa89a5e14e8580bcf7 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Mon, 23 Feb 2026 21:06:06 +0000
Subject: [PATCH 18/28] Remove redundant test

---
 python/tests/test_comps.py | 35 ++---------------------------------
 1 file changed, 2 insertions(+), 33 deletions(-)

diff --git a/python/tests/test_comps.py b/python/tests/test_comps.py
index 17f239cce..faeb8802a 100644
--- a/python/tests/test_comps.py
+++ b/python/tests/test_comps.py
@@ -264,7 +264,7 @@
             ),
             id="two_observations_1d_weights",
         ),
-        # Test 1-D weights with non-uniform per-tree weights. With weights
+        # Test 1-D weights with non-uniform per-tree weights.
         pt.param(
             pd.DataFrame([[1, 1, 1]]),
             pd.DataFrame([[1, 1, 0], [1, 0, 0], [0, 0, 0]]),
@@ -285,38 +285,6 @@
             ),
             id="non_uniform_1d_weights",
         ),
-        # Test that 1-D vector weights produce the same result as an
-        # equivalent 2-D matrix where every row is the same weight vector.
-        # This confirms the logic is correct: a (3,) vector
-        # reshaped to (1, 3) should behave identically to a (3, 3) matrix
-        # with all rows equal to [0.5, 0.3, 0.2].
-        pt.param(
-            pd.DataFrame([[1, 1, 1]]),
-            pd.DataFrame([[1, 1, 0], [1, 1, 0], [1, 1, 0]]),
-            np.array([0.5, 0.3, 0.2]),
-            3,
-            1,
-            pd.DataFrame(
-                {
-                    # All comparison obs match on trees 1 & 2 with the same
-                    # weight, so they all tie and tiebreak by index order
-                    "comp_idx_1": [0],
-                    "comp_idx_2": [1],
-                    "comp_idx_3": [2],
-                },
-                dtype=np.int32,
-            ),
-            pd.DataFrame(
-                {
-                    # All comparison obs get the same score: 0.5 + 0.3 = 0.8
-                    "comp_score_1": [0.5 + 0.3],
-                    "comp_score_2": [0.5 + 0.3],
-                    "comp_score_3": [0.5 + 0.3],
-                },
-                dtype=np.float32,
-            ),
-            id="1d_weights_tiebreak_same_as_uniform_matrix",
-        ),
     ],
 )
 def test_get_comps(
@@ -352,6 +320,7 @@ def test_get_comps(
         ),
         pt.param(
             pd.DataFrame([[1, 1, 1]]),
+
             pd.DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]]),
             np.array([0.333] * 4).reshape(2, 2),
             ValueError,

From 4e269fc0ccba5c3759bad63fede001af10df83bc Mon Sep 17 00:00:00 2001
From: Jean Cochrane <jean@jeancochrane.com>
Date: Mon, 23 Feb 2026 15:20:09 -0600
Subject: [PATCH 19/28] Fix failing Python test infrastructure on CI

---
 .github/workflows/test.yaml |   9 +-
 .gitignore                  |   3 -
 pipeline/04-interpret.R     |   6 +-
 python/.gitignore           |   7 +-
 python/pyproject.toml       |  13 ++
 python/uv.lock              | 382 ++++++++++++++++++++++++++++++++++++
 6 files changed, 409 insertions(+), 11 deletions(-)
 create mode 100644 python/uv.lock

diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 6e2947deb..868eeb2cf 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -20,8 +20,10 @@ jobs:
         uses: astral-sh/setup-uv@v4
         with:
           enable-cache: true
-          cache-dependency-glob: requirements.txt
           cache-suffix: pytest
+          cache-dependency-glob: |
+            python/pyproject.toml
+            python/uv.lock
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -29,10 +31,9 @@ jobs:
           python-version: 3.12
 
       - name: Install dependencies
+        working-directory: python
         shell: bash
-        run: |
-          uv pip install -r requirements.txt
-          uv pip install pytest~=8.3.5
+        run: uv pip install .
 
       - name: Run Python tests
         shell: bash
diff --git a/.gitignore b/.gitignore
index 9de0fa307..033b11087 100644
--- a/.gitignore
+++ b/.gitignore
@@ -30,7 +30,4 @@ cache/
 # Ignore scratch documents
 scratch*.*
 
-# Python files
-__pycache__
-
 /.quarto/
diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index 9ae810b83..f5b59e519 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -14,7 +14,11 @@ tictoc::tic("Interpret")
 # configures reticulate to use uv to install those dependencies. Reticulate
 # will install the dependencies when we import them. In this particular script,
 # we don't import Python dependencies directly, but rather we import the
-# comps module which then imports these dependencies
+# comps module which then imports these dependencies.
+#
+# Because the reticulate uv integration is not very sophisticated, this
+# dependency list is duplicated in `pipeline/04-interpret.R`. If you add or
+# change any dependencies in this list, make sure to change them there too
 reticulate::py_require(
   packages = c("numpy==2.2.*", "numba==0.62.*", "pandas==2.3.*"),
   python_version = "3.10"
diff --git a/python/.gitignore b/python/.gitignore
index d04bdcda0..659d1195d 100644
--- a/python/.gitignore
+++ b/python/.gitignore
@@ -1,3 +1,4 @@
-# Ignore uv lockfile because we use requirements.txt for this project, in order
-# to make it compatible with reticulate
-uv.lock
+# Python artifacts
+__pycache__
+build/
+*.egg-info
diff --git a/python/pyproject.toml b/python/pyproject.toml
index 1f643ed16..f0f3154e2 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -3,6 +3,19 @@ name = "model-res-avm-python"
 version = "0.0.1"
 description = "Python code for the CCAO residential model AVM"
 requires-python = ">=3.10"
+# Because the reticulate uv integration is not very sophisticated, this
+# dependency list is duplicated in `pipeline/04-interpret.R`. If you add or
+# change any dependencies in this list, make sure to change them there too
+dependencies = [
+  "numpy==2.2.*",
+  "numba==0.62.*",
+  "pandas==2.3.*"
+]
+
+[project.optional-dependencies]
+tests = [
+  "pytest==8.3.*"
+]
 
 [tool.pytest.ini_options]
 minversion = "7.0.0"
diff --git a/python/uv.lock b/python/uv.lock
new file mode 100644
index 000000000..8973d9bfd
--- /dev/null
+++ b/python/uv.lock
@@ -0,0 +1,382 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
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+sdist = { url = "https://files.pythonhosted.org/packages/5e/a7/c202b344c5ca7daf398f3b8a477eeb205cf3b6f32e7ec3a6bac0629ca975/tzdata-2025.3.tar.gz", hash = "sha256:de39c2ca5dc7b0344f2eba86f49d614019d29f060fc4ebc8a417896a620b56a7", size = 196772, upload-time = "2025-12-13T17:45:35.667Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/b0/003792df09decd6849a5e39c28b513c06e84436a54440380862b5aeff25d/tzdata-2025.3-py2.py3-none-any.whl", hash = "sha256:06a47e5700f3081aab02b2e513160914ff0694bce9947d6b76ebd6bf57cfc5d1", size = 348521, upload-time = "2025-12-13T17:45:33.889Z" },
+]

From 3396b13e80a4ba48bcb315a1d3217275af693587 Mon Sep 17 00:00:00 2001
From: Damonamajor <damon.major@cookcountyil.gov>
Date: Mon, 23 Feb 2026 21:21:06 +0000
Subject: [PATCH 20/28] improve commenting

---
 R/helpers.R                | 35 +++++++++++++++++------------------
 python/tests/test_comps.py |  3 +--
 2 files changed, 18 insertions(+), 20 deletions(-)

diff --git a/R/helpers.R b/R/helpers.R
index ee810c00b..12fa6be48 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -184,16 +184,11 @@ extract_num_iterations <- function(x) {
 }
 
 # Helper function to return weights for comps
-# Computes per-tree weights from cumulative leaf node values.
 
-# Basic Steps
-# For every observation, map its assigned leaf index in
-# each tree to the corresponding leaf value.
-# Compute the row-wise cumulative sums of these
-# leaf values (stand-in for training data predictions).
-# Calculate the absolute prediction error.
-# Compute the reduction in error.
-# Normalize these improvements so that row-weights sum to 1.
+# The `extract_tree_weights` function allows the user to return weights
+# for each tree in a LightGBM model. Based on internal testing, we currently
+# default to an unweighted value of 1 / n_trees for each tree. This returns
+# a single vector with a length of the number of trees.
 
 # Inputs:
 #   model:      Lightgbm model
@@ -229,7 +224,9 @@ extract_tree_weights <- function(model,
   }
 
   # ---------------------------------------------------------
-  # unweighted (vector with 1/n_trees for each tree)
+  # Unweighted:
+  # Vector with 1/n_trees for each tree. This is the default input.
+  # This returns a vector with the length of the number of trees.
   # ---------------------------------------------------------
   if (algorithm == "unweighted") {
     weights <- rep(1 / n_trees, n_trees)
@@ -238,9 +235,9 @@ extract_tree_weights <- function(model,
   }
 
   # ---------------------------------------------------------
-  # prediction_variance:
-  # vector for tree weights based on variance of
-  # leaf values across data
+  # Prediction_variance:
+  # Vector for tree weights based on variance of leaf values across data.
+  # This returns a vector with the length of the number of trees.
   # ---------------------------------------------------------
   if (algorithm == "prediction_variance") {
     tree_dt <- lgb.model.dt.tree(model)
@@ -308,8 +305,9 @@ extract_tree_weights <- function(model,
   next_err <- tree_errors[, 2:(n_trees + 1L), drop = FALSE]
 
   # ---------------------------------------------------------
-  # unweighted_with_error_reduction
-  # (weights are 1/n_improving trees for trees which reduce errors, 0 otherwise)
+  # Unweighted_with_error_reduction:
+  # Weights are 1/n_improving trees for trees which reduce errors, 0 otherwise.
+  # This returns a matrix with dimensions of observations x trees.
   # ---------------------------------------------------------
   if (algorithm == "unweighted_with_error_reduction") {
     improving <- prev_err > next_err
@@ -321,9 +319,10 @@ extract_tree_weights <- function(model,
   }
 
   # ---------------------------------------------------------
-  # proportional error reduction:
-  # weights are proportional to the reduction in error (prev_err - next_err) for
-  # improving trees, 0 otherwise
+  # Proportional error reduction:
+  # Weights are proportional to the reduction in error (prev_err - next_err) for
+  # improving trees, 0 otherwise. This returns a matrix with dimensions of
+  # observations x trees.
   # ---------------------------------------------------------
   diff_in_errors <- pmax(0, prev_err - next_err)
   dim(diff_in_errors) <- dim(prev_err)
diff --git a/python/tests/test_comps.py b/python/tests/test_comps.py
index faeb8802a..ab4427e75 100644
--- a/python/tests/test_comps.py
+++ b/python/tests/test_comps.py
@@ -320,7 +320,6 @@ def test_get_comps(
         ),
         pt.param(
             pd.DataFrame([[1, 1, 1]]),
-
             pd.DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]]),
             np.array([0.333] * 4).reshape(2, 2),
             ValueError,
@@ -364,4 +363,4 @@ def test_get_comps_raises_on_invalid_inputs(
 )
 def test_insert_at_idx_and_shift(arr, elem, idx, expected):
     result = comps_module.insert_at_idx_and_shift(np.array(arr), elem, idx)
-    np.testing.assert_array_equal(result, np.array(expected))
\ No newline at end of file
+    np.testing.assert_array_equal(result, np.array(expected))

From 71368088683fdce9a8d9b23cc597a97b2726f342 Mon Sep 17 00:00:00 2001
From: Jean Cochrane <jean@jeancochrane.com>
Date: Mon, 23 Feb 2026 15:28:21 -0600
Subject: [PATCH 21/28] Make sure to install test dependencies in `test`
 workflow

---
 .github/workflows/test.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 868eeb2cf..34ec0dfe7 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -33,7 +33,7 @@ jobs:
       - name: Install dependencies
         working-directory: python
         shell: bash
-        run: uv pip install .
+        run: uv pip install .[tests]
 
       - name: Run Python tests
         shell: bash

From 82605f85ee4ef530b2a25db545efd7c846d47ec5 Mon Sep 17 00:00:00 2001
From: Jean Cochrane <jean@jeancochrane.com>
Date: Mon, 23 Feb 2026 15:33:35 -0600
Subject: [PATCH 22/28] Fix incorrect path reference in interpret stage

---
 pipeline/04-interpret.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index f5b59e519..cc1036295 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -17,7 +17,7 @@ tictoc::tic("Interpret")
 # comps module which then imports these dependencies.
 #
 # Because the reticulate uv integration is not very sophisticated, this
-# dependency list is duplicated in `pipeline/04-interpret.R`. If you add or
+# dependency list is duplicated in `python/pyproject.toml`. If you add or
 # change any dependencies in this list, make sure to change them there too
 reticulate::py_require(
   packages = c("numpy==2.2.*", "numba==0.62.*", "pandas==2.3.*"),

From 1200ea3d3913afd7f9c5048145a0c9eb225bdfc1 Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Mon, 23 Feb 2026 21:35:41 +0000
Subject: [PATCH 23/28] CHeck to make sure weights sum to 1

---
 pipeline/04-interpret.R | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/pipeline/04-interpret.R b/pipeline/04-interpret.R
index 148603878..491f03299 100644
--- a/pipeline/04-interpret.R
+++ b/pipeline/04-interpret.R
@@ -275,6 +275,13 @@ if (comp_enable) {
       print(head(tree_weights, 5))
     }
   } else {
+    tree_weights_sum <- sum(tree_weights)
+    if (!isTRUE(all.equal(tree_weights_sum, 1))) {
+      stop(
+        "Tree weights vector does not sum to 1 (got ", tree_weights_sum, "). ",
+        "All sales would have a score of 0 if weights sum to 0."
+      )
+    }
     message(
       "Tree weights are a vector of length ", length(tree_weights),
       " (same weights for all training observations)"

From 1cfb285d52847fc90c8639d6659ce3ce4f6949a4 Mon Sep 17 00:00:00 2001
From: Damonamajor <56321109+Damonamajor@users.noreply.github.com>
Date: Mon, 23 Feb 2026 15:54:55 -0600
Subject: [PATCH 24/28] Update helpers.R

---
 R/helpers.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/helpers.R b/R/helpers.R
index 12fa6be48..7766ec237 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -319,7 +319,7 @@ extract_tree_weights <- function(model,
   }
 
   # ---------------------------------------------------------
-  # Proportional error reduction:
+  # Proportional_error_reduction:
   # Weights are proportional to the reduction in error (prev_err - next_err) for
   # improving trees, 0 otherwise. This returns a matrix with dimensions of
   # observations x trees.

From dc091472df60798e0012442a8dbd01507de53c9e Mon Sep 17 00:00:00 2001
From: Damonamajor <56321109+Damonamajor@users.noreply.github.com>
Date: Tue, 24 Feb 2026 15:34:23 -0600
Subject: [PATCH 25/28] Add param to finalize.R

---
 pipeline/05-finalize.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pipeline/05-finalize.R b/pipeline/05-finalize.R
index 369434cd1..c57898605 100644
--- a/pipeline/05-finalize.R
+++ b/pipeline/05-finalize.R
@@ -93,6 +93,7 @@ metadata <- tibble::tibble(
   shap_enable = shap_enable,
   comp_enable = comp_enable,
   comp_num_comps = params$comp$num_comps,
+  comp_algorithm = params$comp$algorithm,
   cv_enable = cv_enable,
   cv_num_folds = params$cv$num_folds,
   cv_fold_overlap = params$cv$fold_overlap,

From 2215b3d69fbfe62baac38f5de525ac41ecd3834c Mon Sep 17 00:00:00 2001
From: Damonamajor <56321109+Damonamajor@users.noreply.github.com>
Date: Tue, 24 Feb 2026 15:35:39 -0600
Subject: [PATCH 26/28] alphabetize

---
 pipeline/05-finalize.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pipeline/05-finalize.R b/pipeline/05-finalize.R
index c57898605..1341b004f 100644
--- a/pipeline/05-finalize.R
+++ b/pipeline/05-finalize.R
@@ -91,9 +91,9 @@ metadata <- tibble::tibble(
   ratio_study_near_column = params$ratio_study$near_column,
   ratio_study_num_quantile = list(params$ratio_study$num_quantile),
   shap_enable = shap_enable,
+  comp_algorithm = params$comp$algorithm,
   comp_enable = comp_enable,
   comp_num_comps = params$comp$num_comps,
-  comp_algorithm = params$comp$algorithm,
   cv_enable = cv_enable,
   cv_num_folds = params$cv$num_folds,
   cv_fold_overlap = params$cv$fold_overlap,

From 0d55dc1f72a9829ca7b0e6a338aafcc69ed720fd Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 25 Feb 2026 15:35:53 +0000
Subject: [PATCH 27/28] Add in-place adustment

---
 python/comps.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/comps.py b/python/comps.py
index 20eefd515..6891ed64f 100644
--- a/python/comps.py
+++ b/python/comps.py
@@ -95,6 +95,9 @@ def get_comps(
             f"(n_comparisons, n_trees), got {weights.ndim}-D"
         )
 
+    # Avoid editing the df in-place
+    observation_df = observation_df.copy()
+    
     # Chunk the observations so that the script can periodically report progress
     observation_df["chunk"] = pd.cut(
         observation_df.index, bins=num_chunks, labels=False

From ffa4f1bdc4e089c7760fee6d55b293c977b2a04c Mon Sep 17 00:00:00 2001
From: Michael Wagner <wagnerleemichael@gmail.com>
Date: Wed, 25 Feb 2026 15:43:52 +0000
Subject: [PATCH 28/28] Remove space

---
 python/comps.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/comps.py b/python/comps.py
index 6891ed64f..9cc93e1a9 100644
--- a/python/comps.py
+++ b/python/comps.py
@@ -97,7 +97,7 @@ def get_comps(
 
     # Avoid editing the df in-place
     observation_df = observation_df.copy()
-    
+
     # Chunk the observations so that the script can periodically report progress
     observation_df["chunk"] = pd.cut(
         observation_df.index, bins=num_chunks, labels=False