diff --git a/CHANGELOG.md b/CHANGELOG.md index 9a97fe7..51ba9e0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,14 @@ - Refactored the codebase to allow using the code generation functionality as a library. +- Added support for the `reg:logistic`, `binary:logitraw`, `reg:squarederror`, + `reg:linear`, `reg:absoluteerror`, `reg:pseudohubererror`, and + `reg:quantileerror` objectives. The model's `base_score` is now applied as an + intercept, and the sigmoid is only applied for the logistic objectives + (`binary:logistic` and `reg:logistic`). Unsupported objectives, multi-output + models (such as multi-target regression or multi-quantile + `reg:quantileerror`), and forest models (`num_parallel_tree` greater than one) + trained with early stopping now produce a clear error. ## 0.1.0 (2022-10-06) diff --git a/README.md b/README.md index 1ad324b..44ea336 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,11 @@ Generating code for a model avoids having to call out to a different language ## Model Support -Currently binary classification is supported. +The following XGBoost objectives are supported: + +- Binary classification: `binary:logistic` and `binary:logitraw`. +- Regression: `reg:logistic`, `reg:squarederror`, `reg:linear`, + `reg:absoluteerror`, `reg:pseudohubererror`, and `reg:quantileerror`. ## Supported Languages @@ -38,7 +42,7 @@ Usage of ./xgb2code: ```bash $ ./xgb2code -function-name predict \ - -go-package-name main + -go-package-name main \ -input-json testdata/small-model/model.json \ -language go \ -output-file predict.go @@ -51,6 +55,38 @@ signature: func predict(data []*float32, predMargin bool) float32 { ``` +When `predMargin` is true, the function returns the raw margin (the summed tree +outputs plus the `base_score` intercept). Otherwise, the sigmoid is applied for +the logistic objectives (`binary:logistic` and `reg:logistic`); for all other +objectives the margin is the final prediction, so `predMargin` has no effect. + +## Library Usage + +The code generation functionality is also available as a library via the `gen` +package: + +```go +package main + +import ( + "log" + + "github.com/maxmind/xgb2code/gen" +) + +func main() { + err := gen.GenerateFile( + "testdata/small-model/model.json", // input model JSON + "main", // Go package name + "predict", // function name + "predict.go", // output file + ) + if err != nil { + log.Fatal(err) + } +} +``` + ## Installation [Release binaries and packages](https://github.com/maxmind/xgb2code/releases) diff --git a/gen/generate.go b/gen/generate.go index de1d473..f704c45 100644 --- a/gen/generate.go +++ b/gen/generate.go @@ -16,6 +16,7 @@ func generateSource( packageName, funcName string, trees []*node, + meta modelMeta, r *renderer, ) (string, error) { var treeFunctions []treeFunction @@ -35,7 +36,7 @@ func generateSource( ) } - code, err := r.executeRoot(packageName, funcName, treeFunctions) + code, err := r.executeRoot(packageName, funcName, treeFunctions, meta) if err != nil { return "", err } @@ -85,12 +86,17 @@ func GenerateFile( return err } + meta, err := readModelMeta(x) + if err != nil { + return err + } + r, err := newRenderer() if err != nil { return err } - code, err := generateSource(packageName, funcName, trees, r) + code, err := generateSource(packageName, funcName, trees, meta, r) if err != nil { return err } diff --git a/gen/parse.go b/gen/parse.go index 4ca2c6f..e78dce8 100644 --- a/gen/parse.go +++ b/gen/parse.go @@ -2,11 +2,40 @@ package gen import ( "encoding/json" + "errors" "fmt" + "math" "os" "path/filepath" + "strconv" + "strings" ) +type objective string + +const ( + objectiveBinaryLogistic objective = "binary:logistic" + objectiveBinaryLogitRaw objective = "binary:logitraw" + objectiveRegLogistic objective = "reg:logistic" + objectiveRegSquaredError objective = "reg:squarederror" + objectiveRegLinear objective = "reg:linear" + objectiveRegAbsoluteError objective = "reg:absoluteerror" + objectiveRegPseudoHuberError objective = "reg:pseudohubererror" + objectiveRegQuantileError objective = "reg:quantileerror" +) + +type modelMeta struct { + objective objective + // intercept is the base score expressed in raw margin (pre-activation) + // space: logit(base_score) for sigmoid objectives, the raw base_score + // otherwise. It is added to the summed tree outputs before any sigmoid. + intercept float64 + // useSigmoid reports whether the summed margin (plus intercept) is passed + // through a sigmoid to produce the final prediction. It is derived from the + // objective in readModelMeta, the single place that classifies objectives. + useSigmoid bool +} + type xgbModel struct { Learner struct { Attributes struct { @@ -14,9 +43,20 @@ type xgbModel struct { } `json:"attributes"` GradientBooster struct { Model struct { + GBTreeModelParam struct { + NumParallelTree string `json:"num_parallel_tree"` + } `json:"gbtree_model_param"` Trees []xgbTree `json:"trees"` } `json:"model"` } `json:"gradient_booster"` + LearnerModelParam struct { + BaseScore string `json:"base_score"` + NumClass string `json:"num_class"` + NumTarget string `json:"num_target"` + } `json:"learner_model_param"` + Objective struct { + Name string `json:"name"` + } `json:"objective"` } `json:"learner"` } @@ -59,6 +99,13 @@ func readTrees(x *xgbModel) ([]*node, error) { trees = append(trees, treeInfo) } + // Models trained without early stopping have no best_iteration; keep all + // trees. Summing every tree is what XGBoost does at inference, so this is + // correct regardless of num_parallel_tree. + if x.Learner.Attributes.BestIteration == "" { + return trees, nil + } + bestIteration, err := x.Learner.Attributes.BestIteration.Int64() if err != nil { return nil, fmt.Errorf( @@ -67,9 +114,42 @@ func readTrees(x *xgbModel) ([]*node, error) { ) } + // Truncating to best_iteration assumes one tree per boosting round, so the + // (bestIteration+1)th tree is the last tree to keep. Models with + // num_parallel_tree > 1 (e.g. boosted random forests) emit several trees + // per round, so this slice would keep the wrong subset; reject them rather + // than silently produce wrong predictions. + if err := checkSingleTreePerIteration(x); err != nil { + return nil, err + } + return trees[:bestIteration+1], nil } +// checkSingleTreePerIteration rejects models that emit more than one tree per +// boosting round (num_parallel_tree > 1), e.g. boosted random forests. The +// best_iteration truncation in readTrees assumes one tree per round, so such +// models cannot be truncated correctly. An empty value means the field is +// absent (one tree per round). +func checkSingleTreePerIteration(x *xgbModel) error { + v := x.Learner.GradientBooster.Model.GBTreeModelParam.NumParallelTree + if v == "" { + return nil + } + n, err := strconv.Atoi(v) + if err != nil { + return fmt.Errorf("error parsing num_parallel_tree: %w", err) + } + if n > 1 { + return fmt.Errorf( + "models with num_parallel_tree > 1 are not supported "+ + "(num_parallel_tree = %d)", + n, + ) + } + return nil +} + type node struct { left *node right *node @@ -114,3 +194,135 @@ func parseTreeInfo(xt xgbTree) (*node, error) { return &nodes[0], nil // Root node } + +func readModelMeta(x *xgbModel) (modelMeta, error) { + obj := objective(x.Learner.Objective.Name) + + // This switch is the single place that classifies objectives: it both + // rejects unsupported objectives and decides whether the margin is passed + // through a sigmoid. Keeping validation and the sigmoid decision together + // means a newly supported objective cannot be silently treated as + // non-sigmoid. For sigmoid objectives base_score is a probability that gets + // converted to a margin-space (logit) intercept; for the rest base_score is + // used directly as the intercept. Note binary:logitraw, despite being a + // logistic objective, outputs the raw margin and (as XGBoost does) uses + // base_score directly, so it is not a sigmoid objective. + var useSigmoid bool + switch obj { + case objectiveBinaryLogistic, objectiveRegLogistic: + useSigmoid = true + case objectiveBinaryLogitRaw, + objectiveRegSquaredError, + objectiveRegLinear, + objectiveRegAbsoluteError, + objectiveRegPseudoHuberError, + objectiveRegQuantileError: + useSigmoid = false + default: + return modelMeta{}, fmt.Errorf( + "unsupported objective: %q", + x.Learner.Objective.Name, + ) + } + + if err := checkSingleOutput(x); err != nil { + return modelMeta{}, err + } + + baseScore, err := parseBaseScore(x.Learner.LearnerModelParam.BaseScore) + if err != nil { + return modelMeta{}, fmt.Errorf( + "error parsing base_score: %w", + err, + ) + } + + // For sigmoid objectives base_score is a probability in (0, 1); convert it + // to the margin-space (logit) intercept the trees operate in. For other + // objectives base_score is already in prediction space and used directly. + var intercept float64 + if useSigmoid { + if baseScore <= 0 || baseScore >= 1 { + return modelMeta{}, fmt.Errorf( + "base_score must be between 0 and 1 (exclusive) for %s, got %v", + obj, + baseScore, + ) + } + intercept = math.Log(baseScore / (1 - baseScore)) + } else { + intercept = baseScore + } + + return modelMeta{ + objective: obj, + intercept: intercept, + useSigmoid: useSigmoid, + }, nil +} + +// checkSingleOutput rejects multi-output models, e.g. multi-target regression +// or multi-quantile reg:quantileerror. The generated code sums all trees into a +// single scalar, so it cannot represent the per-output tree groups that XGBoost +// uses for such models (which would otherwise be silently summed together). +// num_target counts regression/quantile outputs; num_class counts classes. +func checkSingleOutput(x *xgbModel) error { + p := x.Learner.LearnerModelParam + if err := checkOutputCount("num_target", p.NumTarget); err != nil { + return err + } + return checkOutputCount("num_class", p.NumClass) +} + +// checkOutputCount rejects an output-count field (num_target, num_class) that is +// present and greater than one. An empty value means the field is absent. +func checkOutputCount(name, value string) error { + if value == "" { + return nil + } + n, err := strconv.Atoi(value) + if err != nil { + return fmt.Errorf("error parsing %s: %w", name, err) + } + if n > 1 { + return fmt.Errorf( + "multi-output models are not supported (%s = %d)", + name, + n, + ) + } + return nil +} + +// parseBaseScore parses the base_score string from the model JSON. Older +// XGBoost versions store this as a plain number (e.g., "5E-1"), while newer +// versions use a single-element vector format (e.g., "[1.5E2]"). +func parseBaseScore(s string) (float64, error) { + s = strings.TrimSpace(s) + if s == "" { + return 0, errors.New( + "base_score is missing from the model; the model JSON may be " + + "from an unsupported XGBoost version", + ) + } + // Strip the vector brackets only as a matched pair, so a malformed value + // with just one bracket (e.g. "[1.5") is rejected rather than silently + // parsed as the number it surrounds. + if strings.HasPrefix(s, "[") || strings.HasSuffix(s, "]") { + if !strings.HasPrefix(s, "[") || !strings.HasSuffix(s, "]") { + return 0, fmt.Errorf("malformed base_score vector: %q", s) + } + s = strings.TrimSpace(s[1 : len(s)-1]) + } + v, err := strconv.ParseFloat(s, 64) + if err != nil { + return 0, fmt.Errorf("not a valid number: %w", err) + } + // ParseFloat accepts "NaN", "Inf", etc. These are never valid base + // scores and would silently produce NaN/Inf predictions (or uncompilable + // Go code for regression objectives), so reject them explicitly. + if math.IsNaN(v) || math.IsInf(v, 0) { + return 0, fmt.Errorf("base_score must be finite, got %v", v) + } + return v, nil +} diff --git a/gen/parse_test.go b/gen/parse_test.go index dd1def6..c883e45 100644 --- a/gen/parse_test.go +++ b/gen/parse_test.go @@ -1,6 +1,8 @@ package gen import ( + "encoding/json" + "math" "path/filepath" "testing" @@ -127,6 +129,301 @@ func TestParseTreeInfo(t *testing.T) { ) } +func TestReadModelMeta(t *testing.T) { + tests := []struct { + name string + objectiveName string + baseScore string + numTarget string + numClass string + expectedObjective objective + expectedIntercept float64 + expectError bool + }{ + { + name: "binary:logistic with default base_score", + objectiveName: "binary:logistic", + baseScore: "5E-1", + expectedObjective: objectiveBinaryLogistic, + expectedIntercept: 0.0, + }, + { + name: "reg:logistic with default base_score", + objectiveName: "reg:logistic", + baseScore: "0.5", + expectedObjective: objectiveRegLogistic, + expectedIntercept: 0.0, + }, + { + // reg:logistic shares the sigmoid path with binary:logistic, so a + // non-default base_score must be logit-transformed, not used raw. + name: "reg:logistic with non-default base_score", + objectiveName: "reg:logistic", + baseScore: "0.7", + expectedObjective: objectiveRegLogistic, + expectedIntercept: math.Log(0.7 / 0.3), + }, + { + name: "reg:squarederror with default base_score", + objectiveName: "reg:squarederror", + baseScore: "0.5", + expectedObjective: objectiveRegSquaredError, + expectedIntercept: 0.5, + }, + { + name: "reg:squarederror with non-default base_score", + objectiveName: "reg:squarederror", + baseScore: "150", + expectedObjective: objectiveRegSquaredError, + expectedIntercept: 150.0, + }, + { + name: "binary:logistic with non-default base_score", + objectiveName: "binary:logistic", + baseScore: "0.7", + expectedObjective: objectiveBinaryLogistic, + expectedIntercept: math.Log(0.7 / 0.3), + }, + { + name: "reg:squarederror with vector-format base_score", + objectiveName: "reg:squarederror", + baseScore: "[1.5E2]", + expectedObjective: objectiveRegSquaredError, + expectedIntercept: 150.0, + }, + { + // Unlike binary:logistic, logitraw outputs the raw margin and uses + // base_score directly (matching XGBoost), so no logit transform. + name: "binary:logitraw uses a raw base_score intercept", + objectiveName: "binary:logitraw", + baseScore: "0.7", + expectedObjective: objectiveBinaryLogitRaw, + expectedIntercept: 0.7, + }, + { + // Because base_score is used raw, values outside (0, 1) that would + // be rejected for binary:logistic are valid for logitraw. + name: "binary:logitraw accepts base_score outside (0,1)", + objectiveName: "binary:logitraw", + baseScore: "1.5", + expectedObjective: objectiveBinaryLogitRaw, + expectedIntercept: 1.5, + }, + { + name: "reg:linear uses a raw base_score intercept", + objectiveName: "reg:linear", + baseScore: "150", + expectedObjective: objectiveRegLinear, + expectedIntercept: 150.0, + }, + { + name: "reg:absoluteerror uses a raw base_score intercept", + objectiveName: "reg:absoluteerror", + baseScore: "2.5", + expectedObjective: objectiveRegAbsoluteError, + expectedIntercept: 2.5, + }, + { + name: "reg:pseudohubererror uses a raw base_score intercept", + objectiveName: "reg:pseudohubererror", + baseScore: "-3", + expectedObjective: objectiveRegPseudoHuberError, + expectedIntercept: -3.0, + }, + { + name: "unsupported objective", + objectiveName: "multi:softprob", + baseScore: "0.5", + expectError: true, + }, + { + name: "logistic with base_score 0", + objectiveName: "binary:logistic", + baseScore: "0", + expectError: true, + }, + { + name: "logistic with base_score 1", + objectiveName: "binary:logistic", + baseScore: "1", + expectError: true, + }, + { + name: "logistic with base_score NaN", + objectiveName: "binary:logistic", + baseScore: "NaN", + expectError: true, + }, + { + name: "logistic with base_score Inf", + objectiveName: "binary:logistic", + baseScore: "Infinity", + expectError: true, + }, + { + name: "reg:squarederror with base_score Inf", + objectiveName: "reg:squarederror", + baseScore: "Infinity", + expectError: true, + }, + { + name: "missing base_score", + objectiveName: "binary:logistic", + baseScore: "", + expectError: true, + }, + { + name: "base_score with unmatched leading bracket", + objectiveName: "reg:squarederror", + baseScore: "[1.5", + expectError: true, + }, + { + name: "base_score with unmatched trailing bracket", + objectiveName: "reg:squarederror", + baseScore: "1.5]", + expectError: true, + }, + { + name: "single-quantile reg:quantileerror uses a raw intercept", + objectiveName: "reg:quantileerror", + baseScore: "[1.405E2]", + numTarget: "1", + expectedObjective: objectiveRegQuantileError, + expectedIntercept: 140.5, + }, + { + name: "multi-quantile reg:quantileerror is rejected", + objectiveName: "reg:quantileerror", + baseScore: "[8.675E1,1.405E2,2.125E2]", + numTarget: "3", + expectError: true, + }, + { + name: "multi-target reg:squarederror is rejected", + objectiveName: "reg:squarederror", + baseScore: "[1.5E2]", + numTarget: "2", + expectError: true, + }, + { + name: "multi-class num_class is rejected", + objectiveName: "reg:squarederror", + baseScore: "0.5", + numClass: "3", + expectError: true, + }, + { + name: "non-numeric num_target is rejected", + objectiveName: "reg:squarederror", + baseScore: "0.5", + numTarget: "not-a-number", + expectError: true, + }, + } + + for _, test := range tests { + t.Run(test.name, func(t *testing.T) { + x := &xgbModel{} + x.Learner.Objective.Name = test.objectiveName + x.Learner.LearnerModelParam.BaseScore = test.baseScore + x.Learner.LearnerModelParam.NumTarget = test.numTarget + x.Learner.LearnerModelParam.NumClass = test.numClass + + meta, err := readModelMeta(x) + if test.expectError { + require.Error(t, err) + return + } + require.NoError(t, err) + assert.Equal(t, test.expectedObjective, meta.objective) + assert.InDelta(t, test.expectedIntercept, meta.intercept, 1e-10) + }) + } +} + +func TestReadTreesNumParallelTree(t *testing.T) { + // makeModel builds a model with a single minimal leaf tree so parseTreeInfo + // succeeds, then sets the fields the num_parallel_tree guard depends on. + makeModel := func(bestIteration, numParallelTree string) *xgbModel { + x := &xgbModel{} + x.Learner.Attributes.BestIteration = json.Number(bestIteration) + x.Learner.GradientBooster.Model.GBTreeModelParam.NumParallelTree = numParallelTree + tree := xgbTree{ + DefaultLeft: []int{0}, + LeftChildren: []int{-1}, + RightChildren: []int{-1}, + SplitConditions: []float64{0}, + SplitIndices: []int{0}, + } + tree.TreeParam.NumNodes = "1" + x.Learner.GradientBooster.Model.Trees = []xgbTree{tree} + return x + } + + tests := []struct { + name string + bestIteration string + numParallelTree string + expectError bool + }{ + { + // Without best_iteration every tree is kept, which is correct even + // for forests, so the guard must not fire. + name: "no best_iteration keeps all trees regardless of num_parallel_tree", + bestIteration: "", + numParallelTree: "2", + }, + { + name: "single tree per iteration is allowed", + bestIteration: "0", + numParallelTree: "1", + }, + { + name: "absent num_parallel_tree is allowed", + bestIteration: "0", + numParallelTree: "", + }, + { + name: "num_parallel_tree > 1 with best_iteration is rejected", + bestIteration: "0", + numParallelTree: "2", + expectError: true, + }, + { + name: "non-numeric num_parallel_tree is rejected", + bestIteration: "0", + numParallelTree: "not-a-number", + expectError: true, + }, + } + + for _, test := range tests { + t.Run(test.name, func(t *testing.T) { + trees, err := readTrees( + makeModel(test.bestIteration, test.numParallelTree), + ) + if test.expectError { + require.Error(t, err) + return + } + require.NoError(t, err) + assert.Len(t, trees, 1) + }) + } +} + +func TestReadModelMetaFromJSON(t *testing.T) { + x, err := readModel(filepath.Join("testdata", "small-model", "model.json")) + require.NoError(t, err) + + meta, err := readModelMeta(x) + require.NoError(t, err) + + assert.Equal(t, objectiveBinaryLogistic, meta.objective) + assert.InDelta(t, 0.0, meta.intercept, 1e-10) +} + func BenchmarkParseTreeInfo(b *testing.B) { x, err := readModel(filepath.Join("testdata", "small-model", "model.json")) require.NoError(b, err) diff --git a/gen/templates.go b/gen/templates.go index af44e05..3d33a45 100644 --- a/gen/templates.go +++ b/gen/templates.go @@ -98,14 +98,17 @@ func (r *renderer) executeDecisionNode( type rootParams struct { FuncName string + Intercept float64 PackageName string TreeFunctions []treeFunction + UseSigmoid bool } func (r *renderer) executeRoot( packageName, funcName string, treeFunctions []treeFunction, + meta modelMeta, ) (string, error) { var buf bytes.Buffer err := r.template.ExecuteTemplate( @@ -113,8 +116,10 @@ func (r *renderer) executeRoot( rootTemplateName, rootParams{ FuncName: funcName, + Intercept: meta.intercept, PackageName: packageName, TreeFunctions: treeFunctions, + UseSigmoid: meta.useSigmoid, }, ) if err != nil { diff --git a/gen/templates/root.tmpl b/gen/templates/root.tmpl index 211b4ae..22b5f98 100644 --- a/gen/templates/root.tmpl +++ b/gen/templates/root.tmpl @@ -1,21 +1,31 @@ // Code generated by xgb2code DO NOT EDIT. package {{.PackageName}} +{{- if .UseSigmoid}} import ( "math" ) +{{- end}} func {{.FuncName}}(data []*float32, predMargin bool) float32 { var sum float32 {{range .TreeFunctions }} sum += {{.Name}}(data) {{- end}} +{{- if ne .Intercept 0.0}} + + sum += {{.Intercept}} +{{- end}} if predMargin { return sum } +{{- if .UseSigmoid}} return 1.0 / (1.0 + float32(math.Exp(float64(-sum)))) // sigmoid +{{- else}} + return sum +{{- end}} } {{range .TreeFunctions}} func {{.Name}}(data []*float32) float32 { diff --git a/gen/testdata/breast-cancer/generate-model.py b/gen/testdata/binary-logistic/generate-model.py similarity index 86% rename from gen/testdata/breast-cancer/generate-model.py rename to gen/testdata/binary-logistic/generate-model.py index c91da81..aefef75 100644 --- a/gen/testdata/breast-cancer/generate-model.py +++ b/gen/testdata/binary-logistic/generate-model.py @@ -24,7 +24,7 @@ stratify=data.target, # pylint: disable=no-member ) -XGB_MISSING = np.NaN +XGB_MISSING = np.nan NUM_ROUNDS = 500 DEFAULT_XGB_PARAMS = { "objective": "binary:logistic", @@ -38,11 +38,14 @@ "colsample_bylevel": 0.9, "colsample_bytree": 0.9, "colsample_bynode": 0.9, + # Use a non-default base_score so the generated code exercises a nonzero + # (logit-transformed) intercept combined with the sigmoid activation. + "base_score": 0.7, } # Set feature names rather than leave them as the defaults of "0", "1", etc, to # test that it will work with xgb2code. -feature_names = data.feature_names # pylint: disable=no-member +feature_names = list(data.feature_names) # pylint: disable=no-member dtrain = xgb.DMatrix( data=X_train, diff --git a/gen/testdata/binary-logistic/model.json b/gen/testdata/binary-logistic/model.json new file mode 100644 index 0000000..0b75652 --- /dev/null +++ b/gen/testdata/binary-logistic/model.json @@ -0,0 +1 @@ +{"learner":{"attributes":{},"feature_names":["mean radius","mean texture","mean perimeter","mean area","mean smoothness","mean compactness","mean concavity","mean concave points","mean symmetry","mean fractal dimension","radius error","texture error","perimeter error","area error","smoothness error","compactness error","concavity error","concave points error","symmetry error","fractal dimension error","worst radius","worst texture","worst perimeter","worst area","worst smoothness","worst compactness","worst concavity","worst concave points","worst symmetry","worst fractal 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+import sklearn.datasets +import xgboost as xgb + +RANDOM_SEED = 0 +np.random.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) + +data = sklearn.datasets.load_breast_cancer(return_X_y=False) +df = pd.DataFrame(data.data) # pylint: disable=no-member +# make some columns NaN at random. +df = df.mask(np.random.random(df.shape) < 0.2) + +X_train, X_test, y_train, y_test = train_test_split( + df, + data.target, # pylint: disable=no-member + test_size=0.30, + stratify=data.target, # pylint: disable=no-member +) + +XGB_MISSING = np.nan +NUM_ROUNDS = 500 +DEFAULT_XGB_PARAMS = { + # binary:logitraw is a logistic objective but outputs the raw margin with no + # sigmoid, and (matching XGBoost) uses base_score directly as the raw + # intercept rather than as a probability. 0.7 is an arbitrary non-default + # value that exercises a nonzero raw intercept on the no-sigmoid output path. + "objective": "binary:logitraw", + "eta": 0.1, + "eval_metric": "auc", + "nthread": 1, + "seed": RANDOM_SEED, + "tree_method": "hist", + "max_depth": 12, + "subsample": 0.9, + "colsample_bylevel": 0.9, + "colsample_bytree": 0.9, + "colsample_bynode": 0.9, + "base_score": 0.7, +} + +# Set feature names rather than leave them as the defaults of "0", "1", etc, to +# test that it will work with xgb2code. +feature_names = list(data.feature_names) # pylint: disable=no-member + +dtrain = xgb.DMatrix( + data=X_train, + label=y_train, + missing=XGB_MISSING, + feature_names=feature_names, +) +dtest = xgb.DMatrix( + data=X_test, + label=y_test, + missing=XGB_MISSING, + feature_names=feature_names, +) +evallist = [(dtrain, "train"), (dtest, "eval")] + +booster = xgb.train( + DEFAULT_XGB_PARAMS, + dtrain, + NUM_ROUNDS, + evals=evallist, +) + +booster.save_model("model.json") +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/binary-logitraw/model.json b/gen/testdata/binary-logitraw/model.json new 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Regression objectives can produce large-magnitude + // outputs where a tight absolute bound is unrealistic, so accept the + // prediction if either the absolute or the relative error is small. The + // relative denominator is floored at 1.0 so that for small-magnitude + // outputs (the [0, 1] range of logistic objectives, or near-zero + // regression targets) the check stays effectively absolute, rather than a + // target near zero blowing the relative error up to infinity. + const tolerance = 0.00001 + absDelta := math.Abs(float64(gotPrediction - expectedPrediction)) + relDelta := absDelta / math.Max(math.Abs(float64(expectedPrediction)), 1.0) + if absDelta > tolerance && relDelta > tolerance { return fmt.Errorf("got %f, expected %f", gotPrediction, expectedPrediction) } diff --git a/gen/testdata/reg-quantileerror/generate-model.py b/gen/testdata/reg-quantileerror/generate-model.py new file mode 100644 index 0000000..6a9463d --- /dev/null +++ b/gen/testdata/reg-quantileerror/generate-model.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python +"""Generate a single-quantile reg:quantileerror model to test""" + +import random +import numpy as np +import pandas as pd # type: ignore +from sklearn.model_selection import train_test_split +import sklearn.datasets +import xgboost as xgb + +RANDOM_SEED = 0 +np.random.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) + +data = sklearn.datasets.load_diabetes(return_X_y=False) +df = pd.DataFrame(data.data) # pylint: disable=no-member +# make some columns NaN at random. +df = df.mask(np.random.random(df.shape) < 0.2) + +X_train, X_test, y_train, y_test = train_test_split( + df, + data.target, # pylint: disable=no-member + test_size=0.30, +) + +XGB_MISSING = np.nan +NUM_ROUNDS = 500 +DEFAULT_XGB_PARAMS = { + # A single quantile_alpha produces a single-output model. base_score is + # left unset so XGBoost estimates it (the target's median for alpha=0.5), + # exercising the raw, auto-derived intercept on the identity output path. + "objective": "reg:quantileerror", + "quantile_alpha": 0.5, + "eta": 0.1, + "nthread": 1, + "seed": RANDOM_SEED, + "tree_method": "hist", + "max_depth": 6, + "subsample": 0.9, + "colsample_bylevel": 0.9, + "colsample_bytree": 0.9, + "colsample_bynode": 0.9, +} + +# Set feature names rather than leave them as the defaults of "0", "1", etc, to +# test that it will work with xgb2code. +feature_names = data.feature_names # pylint: disable=no-member + +dtrain = xgb.DMatrix( + data=X_train, + label=y_train, + missing=XGB_MISSING, + feature_names=feature_names, +) +dtest = xgb.DMatrix( + data=X_test, + label=y_test, + missing=XGB_MISSING, + feature_names=feature_names, +) +evallist = [(dtrain, "train"), (dtest, "eval")] + +booster = xgb.train( + DEFAULT_XGB_PARAMS, + dtrain, + NUM_ROUNDS, + evals=evallist, +) + +booster.save_model("model.json") +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/reg-quantileerror/model.json b/gen/testdata/reg-quantileerror/model.json new file mode 100644 index 0000000..831de82 --- /dev/null +++ b/gen/testdata/reg-quantileerror/model.json @@ -0,0 +1 @@ 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+random.seed(RANDOM_SEED) + +data = sklearn.datasets.load_diabetes(return_X_y=False) +df = pd.DataFrame(data.data) # pylint: disable=no-member +# make some columns NaN at random. +df = df.mask(np.random.random(df.shape) < 0.2) + +X_train, X_test, y_train, y_test = train_test_split( + df, + data.target, # pylint: disable=no-member + test_size=0.30, +) + +XGB_MISSING = np.nan +NUM_ROUNDS = 500 +DEFAULT_XGB_PARAMS = { + "objective": "reg:squarederror", + "eta": 0.1, + "eval_metric": "rmse", + "nthread": 1, + "seed": RANDOM_SEED, + "tree_method": "hist", + "max_depth": 6, + "subsample": 0.9, + "colsample_bylevel": 0.9, + "colsample_bytree": 0.9, + "colsample_bynode": 0.9, + "base_score": 150.0, +} + +# Set feature names rather than leave them as the defaults of "0", "1", etc, to +# test that it will work with xgb2code. +feature_names = data.feature_names # pylint: disable=no-member + +dtrain = xgb.DMatrix( + data=X_train, + label=y_train, + missing=XGB_MISSING, + feature_names=feature_names, +) +dtest = xgb.DMatrix( + data=X_test, + label=y_test, + missing=XGB_MISSING, + feature_names=feature_names, +) +evallist = [(dtrain, "train"), (dtest, "eval")] + +booster = xgb.train( + DEFAULT_XGB_PARAMS, + dtrain, + NUM_ROUNDS, + evals=evallist, +) + +booster.save_model("model.json") +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/reg-squarederror/model.json b/gen/testdata/reg-squarederror/model.json new file mode 100644 index 0000000..6c24d64 --- /dev/null +++ b/gen/testdata/reg-squarederror/model.json @@ -0,0 +1 @@ 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-const version = "0.1.0" +const version = "1.0.0"