diff --git a/CHANGELOG.md b/CHANGELOG.md index 87aa2fe..dafc6c3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ # CHANGELOG +## 1.1.0 + +- Added support for categorical splits (models trained with + `enable_categorical`). Categorical features are passed as their integer + category codes in the `data` slice, the same encoding XGBoost uses internally. + ## 1.0.0 (2026-06-15) - Refactored the codebase to allow using the code generation functionality as a diff --git a/README.md b/README.md index 44ea336..d541ee5 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,11 @@ The following XGBoost objectives are supported: - Regression: `reg:logistic`, `reg:squarederror`, `reg:linear`, `reg:absoluteerror`, `reg:pseudohubererror`, and `reg:quantileerror`. +Both numeric and categorical splits (models trained with `enable_categorical`) +are supported. Categorical features must be passed to the generated function as +their integer category codes, the same encoding XGBoost uses internally; a +missing feature is represented by a `nil` entry in the `data` slice. + ## Supported Languages Currently `xgb2code` supports generating Go code. diff --git a/gen/parse.go b/gen/parse.go index e78dce8..cfe0cc7 100644 --- a/gen/parse.go +++ b/gen/parse.go @@ -66,7 +66,19 @@ type xgbTree struct { RightChildren []int `json:"right_children"` SplitConditions []float64 `json:"split_conditions"` SplitIndices []int `json:"split_indices"` - TreeParam struct { + // SplitType marks each node's split kind: 0 = numeric, 1 = categorical. It + // is absent in models trained without categorical features, in which case + // every split is numeric. + SplitType []int `json:"split_type"` + // The fields below describe categorical splits. Categories is the flattened + // list of category values across all categorical nodes; for each entry k in + // CategoriesNodes (a node ID), the values that route to that node's right + // child are Categories[CategoriesSegments[k] : CategoriesSegments[k]+CategoriesSizes[k]]. + Categories []int `json:"categories"` + CategoriesNodes []int `json:"categories_nodes"` + CategoriesSegments []int `json:"categories_segments"` + CategoriesSizes []int `json:"categories_sizes"` + TreeParam struct { NumNodes json.Number `json:"num_nodes"` } `json:"tree_param"` } @@ -161,6 +173,11 @@ type nodeData struct { ID int64 SplitCondition float64 SplitIndex int + // Categorical reports whether this is a categorical split. When true, + // Categories holds the category values that route to the right child and + // SplitCondition is unused (XGBoost stores a dummy threshold there). + Categorical bool + Categories []int } func parseTreeInfo(xt xgbTree) (*node, error) { @@ -172,21 +189,48 @@ func parseTreeInfo(xt xgbTree) (*node, error) { ) } + if err := checkNodeArrays(xt, numNodes); err != nil { + return nil, err + } + + categories, err := categorySets(xt, numNodes) + if err != nil { + return nil, err + } + nodes := make([]node, numNodes) for i := range numNodes { + cats, categorical := categories[i] nodes[i].data = nodeData{ DefaultLeft: xt.DefaultLeft[i], ID: i, SplitCondition: xt.SplitConditions[i], SplitIndex: xt.SplitIndices[i], + Categorical: categorical, + Categories: cats, } left := xt.LeftChildren[i] right := xt.RightChildren[i] - if left == -1 { // No child + // A leaf has no children (both -1). Any other combination, including a + // half-wired node with exactly one child, is malformed: the codegen + // treats a node with a nil child as a leaf and would silently drop the + // other subtree, so reject it rather than mispredict. + if left == -1 && right == -1 { continue } + if left < 0 || int64(left) >= numNodes || + right < 0 || int64(right) >= numNodes { + return nil, fmt.Errorf( + "node %d has out-of-range children "+ + "(left=%d, right=%d, num_nodes=%d)", + i, + left, + right, + numNodes, + ) + } nodes[i].left = &nodes[left] nodes[i].right = &nodes[right] @@ -195,6 +239,142 @@ func parseTreeInfo(xt xgbTree) (*node, error) { return &nodes[0], nil // Root node } +// checkNodeArrays verifies that every per-node array has exactly one entry per +// node, so the indexing in parseTreeInfo cannot panic on a truncated or +// inconsistent model. num_nodes comes from a separate JSON field and so is not +// inherently consistent with the arrays it describes. +func checkNodeArrays(xt xgbTree, numNodes int64) error { + arrays := []struct { + name string + n int + }{ + {"default_left", len(xt.DefaultLeft)}, + {"left_children", len(xt.LeftChildren)}, + {"right_children", len(xt.RightChildren)}, + {"split_conditions", len(xt.SplitConditions)}, + {"split_indices", len(xt.SplitIndices)}, + } + for _, a := range arrays { + if int64(a.n) != numNodes { + return fmt.Errorf( + "%s has %d entries but num_nodes is %d", + a.name, + a.n, + numNodes, + ) + } + } + return nil +} + +// categorySets maps each categorical node's ID to the category values that +// route to its right child, decoding XGBoost's flattened categories/segments/ +// sizes representation. It returns an empty map for models trained without +// categorical features. It validates the arrays rather than trusting them: a +// malformed or inconsistent encoding would otherwise cause a categorical node +// to be silently emitted as a numeric split on its dummy threshold, producing +// wrong predictions. +func categorySets(xt xgbTree, numNodes int64) (map[int64][]int, error) { + n := len(xt.CategoriesNodes) + if len(xt.CategoriesSegments) != n || len(xt.CategoriesSizes) != n { + return nil, fmt.Errorf( + "inconsistent categorical arrays: categories_nodes=%d, "+ + "categories_segments=%d, categories_sizes=%d", + n, + len(xt.CategoriesSegments), + len(xt.CategoriesSizes), + ) + } + + sets := make(map[int64][]int, n) + for k := range n { + start := xt.CategoriesSegments[k] + size := xt.CategoriesSizes[k] + if start < 0 || size < 0 || start > len(xt.Categories)-size { + return nil, fmt.Errorf( + "categorical segment [%d:%d+%d] out of range for "+ + "categories of length %d", + start, + start, + size, + len(xt.Categories), + ) + } + nodeID := int64(xt.CategoriesNodes[k]) + if nodeID < 0 || nodeID >= numNodes { + return nil, fmt.Errorf( + "categories_nodes[%d] = %d out of range for num_nodes %d", + k, + nodeID, + numNodes, + ) + } + cats := make([]int, size) + copy(cats, xt.Categories[start:start+size]) + sets[nodeID] = cats + } + + // split_type is the only independent signal of which nodes are categorical, + // so it is what lets us verify that every categorical node was decoded. + // Without it we cannot make that check, and a categorical node missing from + // categories_nodes would be silently emitted as a numeric split on its dummy + // threshold. Real XGBoost models always include split_type when they have + // categorical data, so reject categorical data that lacks it rather than + // risk a wrong prediction. + if len(xt.SplitType) == 0 { + if len(sets) > 0 { + return nil, errors.New( + "model has categorical data (categories_nodes) but no split_type", + ) + } + return sets, nil + } + + if int64(len(xt.SplitType)) != numNodes { + return nil, fmt.Errorf( + "split_type length %d does not match num_nodes %d", + len(xt.SplitType), + numNodes, + ) + } + + // Every node that split_type marks as categorical must have a decoded set, + // and vice versa; a mismatch means we would treat a node as the wrong split + // kind. Any split_type other than 0 (numeric) or 1 (categorical) is an + // encoding we do not understand, so reject it rather than defaulting it to + // numeric. + for i := range numNodes { + switch xt.SplitType[i] { + case 0, 1: + default: + return nil, fmt.Errorf( + "node %d has unsupported split_type %d", + i, + xt.SplitType[i], + ) + } + _, hasSet := sets[i] + isCategorical := xt.SplitType[i] == 1 + if hasSet != isCategorical { + return nil, fmt.Errorf( + "node %d has split_type %d but %s in categories_nodes", + i, + xt.SplitType[i], + presence(hasSet), + ) + } + } + + return sets, nil +} + +func presence(present bool) string { + if present { + return "is present" + } + return "is absent" +} + func readModelMeta(x *xgbModel) (modelMeta, error) { obj := objective(x.Learner.Objective.Name) diff --git a/gen/parse_test.go b/gen/parse_test.go index c883e45..e6e13ed 100644 --- a/gen/parse_test.go +++ b/gen/parse_test.go @@ -129,6 +129,233 @@ func TestParseTreeInfo(t *testing.T) { ) } +func TestParseTreeInfoCategorical(t *testing.T) { + // A three-node tree whose root is a categorical split on feature 1 routing + // categories {0, 2} to the right child. The dummy split_condition XGBoost + // stores for categorical nodes must be ignored. + xt := xgbTree{ + DefaultLeft: []int{1, 0, 0}, + LeftChildren: []int{1, -1, -1}, + RightChildren: []int{2, -1, -1}, + SplitConditions: []float64{1e-45, 0.5, -0.5}, + SplitIndices: []int{1, 0, 0}, + SplitType: []int{1, 0, 0}, + Categories: []int{0, 2}, + CategoriesNodes: []int{0}, + CategoriesSegments: []int{0}, + CategoriesSizes: []int{2}, + } + xt.TreeParam.NumNodes = "3" + + treeInfo, err := parseTreeInfo(xt) + require.NoError(t, err) + + assert.Equal( + t, + &node{ + data: nodeData{ + DefaultLeft: 1, + ID: 0, + SplitCondition: 1e-45, + SplitIndex: 1, + Categorical: true, + Categories: []int{0, 2}, + }, + left: &node{ + data: nodeData{ + ID: 1, + SplitCondition: 0.5, + }, + }, + right: &node{ + data: nodeData{ + ID: 2, + SplitCondition: -0.5, + }, + }, + }, + treeInfo, + ) +} + +func TestCategorySets(t *testing.T) { + // baseTree is a valid two-categorical-node tree the error cases mutate. + baseTree := func() xgbTree { + xt := xgbTree{ + SplitType: []int{1, 1, 0}, + Categories: []int{0, 2, 1, 3, 4}, + CategoriesNodes: []int{0, 1}, + CategoriesSegments: []int{0, 2}, + CategoriesSizes: []int{2, 3}, + } + xt.TreeParam.NumNodes = "3" + return xt + } + + t.Run("valid decoding", func(t *testing.T) { + sets, err := categorySets(baseTree(), 3) + require.NoError(t, err) + assert.Equal( + t, + map[int64][]int{0: {0, 2}, 1: {1, 3, 4}}, + sets, + ) + }) + + t.Run("no categorical splits returns empty", func(t *testing.T) { + xt := xgbTree{SplitType: []int{0, 0, 0}} + xt.TreeParam.NumNodes = "3" + sets, err := categorySets(xt, 3) + require.NoError(t, err) + assert.Empty(t, sets) + }) + + tests := []struct { + name string + mutate func(xt *xgbTree) + }{ + { + name: "mismatched segment length", + mutate: func(xt *xgbTree) { + xt.CategoriesSegments = []int{0} + }, + }, + { + name: "mismatched size length", + mutate: func(xt *xgbTree) { + xt.CategoriesSizes = []int{2} + }, + }, + { + name: "segment out of range", + mutate: func(xt *xgbTree) { + xt.CategoriesSizes = []int{2, 99} + }, + }, + { + name: "negative segment start", + mutate: func(xt *xgbTree) { + xt.CategoriesSegments = []int{-1, 2} + }, + }, + { + name: "split_type length mismatch", + mutate: func(xt *xgbTree) { + xt.SplitType = []int{1, 1} + }, + }, + { + name: "split_type categorical but no set", + mutate: func(xt *xgbTree) { + xt.SplitType = []int{1, 1, 1} + }, + }, + { + name: "set present but split_type numeric", + mutate: func(xt *xgbTree) { + xt.SplitType = []int{0, 1, 0} + }, + }, + { + name: "node ID out of range", + mutate: func(xt *xgbTree) { + xt.CategoriesNodes = []int{0, 99} + }, + }, + { + name: "negative node ID", + mutate: func(xt *xgbTree) { + xt.CategoriesNodes = []int{0, -1} + }, + }, + { + name: "categorical data but no split_type", + mutate: func(xt *xgbTree) { + xt.SplitType = nil + }, + }, + { + name: "unsupported split_type value", + mutate: func(xt *xgbTree) { + xt.SplitType = []int{2, 1, 0} + }, + }, + } + for _, test := range tests { + t.Run(test.name, func(t *testing.T) { + xt := baseTree() + test.mutate(&xt) + _, err := categorySets(xt, 3) + require.Error(t, err) + }) + } +} + +func TestParseTreeInfoValidation(t *testing.T) { + // baseTree is a valid numeric three-node tree (root with two leaves) that + // the error cases mutate. + baseTree := func() xgbTree { + xt := xgbTree{ + DefaultLeft: []int{0, 0, 0}, + LeftChildren: []int{1, -1, -1}, + RightChildren: []int{2, -1, -1}, + SplitConditions: []float64{0.5, 0, 0}, + SplitIndices: []int{0, 0, 0}, + } + xt.TreeParam.NumNodes = "3" + return xt + } + + t.Run("valid tree parses", func(t *testing.T) { + _, err := parseTreeInfo(baseTree()) + require.NoError(t, err) + }) + + tests := []struct { + name string + mutate func(xt *xgbTree) + }{ + { + name: "per-node array too short", + mutate: func(xt *xgbTree) { + xt.SplitIndices = []int{0, 0} + }, + }, + { + name: "per-node array too long", + mutate: func(xt *xgbTree) { + xt.DefaultLeft = []int{0, 0, 0, 0} + }, + }, + { + name: "child index out of range", + mutate: func(xt *xgbTree) { + xt.LeftChildren = []int{5, -1, -1} + }, + }, + { + name: "negative child index", + mutate: func(xt *xgbTree) { + xt.RightChildren = []int{-2, -1, -1} + }, + }, + { + name: "half-wired node with one missing child", + mutate: func(xt *xgbTree) { + xt.RightChildren = []int{-1, -1, -1} + }, + }, + } + for _, test := range tests { + t.Run(test.name, func(t *testing.T) { + xt := baseTree() + test.mutate(&xt) + _, err := parseTreeInfo(xt) + require.Error(t, err) + }) + } +} + func TestReadModelMeta(t *testing.T) { tests := []struct { name string diff --git a/gen/templates.go b/gen/templates.go index 3d33a45..f0bc477 100644 --- a/gen/templates.go +++ b/gen/templates.go @@ -54,6 +54,24 @@ func newRenderer() (*renderer, error) { return &r, nil } +// categoryTest builds the "value not in category set" predicate for a +// categorical split, e.g. "(*data[3] != 0 && *data[3] != 2)". It returns the +// empty string for numeric splits. A categorical node with an empty set routes +// every present value left, so the predicate is the constant "true". +func categoryTest(d nodeData) string { + if !d.Categorical { + return "" + } + if len(d.Categories) == 0 { + return "true" + } + parts := make([]string, len(d.Categories)) + for i, c := range d.Categories { + parts[i] = fmt.Sprintf("*data[%d] != %d", d.SplitIndex, c) + } + return "(" + strings.Join(parts, " && ") + ")" +} + func indent(level int) string { var sb strings.Builder for range level + 1 { @@ -68,6 +86,10 @@ type decisionNodeParams struct { Left string Right string Level int + // CategoryTest is the Go expression, set only for categorical splits, that + // is true when the feature value is *not* in the node's category set (and so + // routes left). It mirrors the numeric "*data[i] < threshold" predicate. + CategoryTest string } func (r *renderer) executeDecisionNode( @@ -81,10 +103,11 @@ func (r *renderer) executeDecisionNode( &buf, decisionNodeTemplateName, decisionNodeParams{ - Left: left, - Level: level, - nodeData: tree.data, - Right: right, + Left: left, + Level: level, + nodeData: tree.data, + Right: right, + CategoryTest: categoryTest(tree.data), }, ) if err != nil { diff --git a/gen/templates/decision_node.tmpl b/gen/templates/decision_node.tmpl index 2e8357e..a5b49bd 100644 --- a/gen/templates/decision_node.tmpl +++ b/gen/templates/decision_node.tmpl @@ -1,7 +1,7 @@ {{if .DefaultLeft -}} -{{indent .Level}}if data[{{.SplitIndex}}] == nil || *data[{{.SplitIndex}}] < {{.SplitCondition}} { +{{indent .Level}}if data[{{.SplitIndex}}] == nil || {{if .Categorical}}{{.CategoryTest}}{{else}}*data[{{.SplitIndex}}] < {{.SplitCondition}}{{end}} { {{else -}} -{{indent .Level}}if data[{{.SplitIndex}}] != nil && *data[{{.SplitIndex}}] < {{.SplitCondition}} { +{{indent .Level}}if data[{{.SplitIndex}}] != nil && {{if .Categorical}}{{.CategoryTest}}{{else}}*data[{{.SplitIndex}}] < {{.SplitCondition}}{{end}} { {{end}} {{- .Left -}} {{indent .Level}}} else { diff --git a/gen/templates_test.go b/gen/templates_test.go new file mode 100644 index 0000000..0597275 --- /dev/null +++ b/gen/templates_test.go @@ -0,0 +1,44 @@ +package gen + +import ( + "testing" + + "github.com/stretchr/testify/assert" +) + +func TestCategoryTest(t *testing.T) { + tests := []struct { + name string + data nodeData + want string + }{ + { + name: "numeric split has no category test", + data: nodeData{SplitCondition: 1.5}, + want: "", + }, + { + // An empty set means no value routes right, so every present value + // routes left; the predicate must be the constant "true". + name: "empty category set", + data: nodeData{Categorical: true, SplitIndex: 3}, + want: "true", + }, + { + name: "single category", + data: nodeData{Categorical: true, SplitIndex: 1, Categories: []int{2}}, + want: "(*data[1] != 2)", + }, + { + name: "multiple categories", + data: nodeData{Categorical: true, SplitIndex: 3, Categories: []int{0, 2, 5}}, + want: "(*data[3] != 0 && *data[3] != 2 && *data[3] != 5)", + }, + } + + for _, test := range tests { + t.Run(test.name, func(t *testing.T) { + assert.Equal(t, test.want, categoryTest(test.data)) + }) + } +} diff --git a/gen/testdata/categorical/generate-model.py b/gen/testdata/categorical/generate-model.py new file mode 100644 index 0000000..214caa5 --- /dev/null +++ b/gen/testdata/categorical/generate-model.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python +"""Generate a model with categorical features to test. + +Categorical features are passed as integer category codes with per-feature +types ("c" for categorical, "q" for numeric) so the values written to xtest.csv +are exactly the codes the model splits on. Missing values are included so the +generated code's missing-value handling (default_left) is exercised for both +numeric and categorical splits. +""" + +import random +import numpy as np +import pandas as pd # type: ignore +from sklearn.model_selection import train_test_split +import xgboost as xgb + +RANDOM_SEED = 0 +np.random.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) + +N = 600 +# Three numeric features and three categorical features of differing +# cardinality (3, 5, 8), interleaved so split_indices mix the two kinds. +num0 = np.random.normal(size=N) +cat0 = np.random.randint(0, 3, size=N).astype(float) +num1 = np.random.normal(size=N) +cat1 = np.random.randint(0, 5, size=N).astype(float) +num2 = np.random.normal(size=N) +cat2 = np.random.randint(0, 8, size=N).astype(float) + +X = np.column_stack([num0, cat0, num1, cat1, num2, cat2]) +feature_types = ["q", "c", "q", "c", "q", "c"] +feature_names = ["num0", "cat0", "num1", "cat1", "num2", "cat2"] + +# The label depends on the categorical features (so categorical splits are +# learned) plus a numeric contribution. +y = ( + ((cat0 == 1) | (cat1 >= 3) | (np.isin(cat2, [0, 2, 5, 7]))) + & (num0 + num1 > -0.5) +).astype(int) + +# Make some values NaN at random, across both numeric and categorical columns. +mask = np.random.random(X.shape) < 0.15 +X[mask] = np.nan + +X_train, X_test, y_train, y_test = train_test_split( + X, + y, + test_size=0.30, + stratify=y, +) + +XGB_MISSING = np.nan +NUM_ROUNDS = 50 +DEFAULT_XGB_PARAMS = { + "objective": "binary:logistic", + "eta": 0.2, + "eval_metric": "auc", + "nthread": 1, + "seed": RANDOM_SEED, + "tree_method": "hist", + "max_depth": 6, + # Set max_cat_to_onehot explicitly (rather than relying on the XGBoost + # default, which could change) so this single fixture deterministically + # covers both categorical split encodings. With cardinalities 3, 5, and 8, + # the threshold of 4 means cat0 (3 categories) is split one-hot style + # (single-category sets) while cat1 and cat2 use partition-based, + # multi-category sets. + "max_cat_to_onehot": 4, +} + +dtrain = xgb.DMatrix( + data=X_train, + label=y_train, + missing=XGB_MISSING, + feature_names=feature_names, + feature_types=feature_types, + enable_categorical=True, +) +dtest = xgb.DMatrix( + data=X_test, + label=y_test, + missing=XGB_MISSING, + feature_names=feature_names, + feature_types=feature_types, + enable_categorical=True, +) +evallist = [(dtrain, "train"), (dtest, "eval")] + +booster = xgb.train( + DEFAULT_XGB_PARAMS, + dtrain, + NUM_ROUNDS, + evals=evallist, +) + +booster.save_model("model.json") +pd.DataFrame(X_test).to_csv("xtest.csv", header=False, index=False) +pd.Series(booster.predict(dtest)).to_csv("preds.csv", header=False, index=False) diff --git a/gen/testdata/categorical/model.json b/gen/testdata/categorical/model.json new file mode 100644 index 0000000..c0b5c29 --- /dev/null +++ b/gen/testdata/categorical/model.json @@ -0,0 +1 @@ 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a/main_test.go +++ b/main_test.go @@ -22,6 +22,7 @@ func TestGenerateAndRunModels(t *testing.T) { {model: "reg-squarederror"}, {model: "binary-logitraw"}, {model: "reg-quantileerror"}, + {model: "categorical"}, } for _, test := range tests { diff --git a/mise.lock b/mise.lock index 8af2965..4fc3927 100644 --- a/mise.lock +++ b/mise.lock @@ -1,4 +1,4 @@ -# @generated - this file is auto-generated by `mise lock` https://mise.en.dev/dev-tools/mise-lock.html +# @generated - this file is auto-generated by `mise lock` https://mise.jdx.dev/dev-tools/mise-lock.html [[tools."github:golangci/golangci-lint"]] version = "2.11.3" diff --git a/version.go b/version.go index 6e3701a..8e54e85 100644 --- a/version.go +++ b/version.go @@ -1,3 +1,3 @@ package main -const version = "1.0.0" +const version = "1.1.0"