diff --git a/benchmarks/rf_intelex/bench_rf_fit.py b/benchmarks/rf_intelex/bench_rf_fit.py new file mode 100644 index 0000000000000..c6ce5b434a36d --- /dev/null +++ b/benchmarks/rf_intelex/bench_rf_fit.py @@ -0,0 +1,584 @@ +"""Benchmark RandomForest fit time against scikit-learn-intelex. + +The suite is intentionally compact: every retained case should run in less than +10 seconds per estimator/backend timing, and the full suite should run in less +than 3 minutes on the development machine used for this investigation. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import os +import platform +import subprocess +import sys +import time +from dataclasses import dataclass +from pathlib import Path +from statistics import median + +import numpy as np +from threadpoolctl import threadpool_info, threadpool_limits + +N_ESTIMATORS = 20 +DEFAULT_N_JOBS = 1 +MAX_CASE_SECONDS = 10.0 + + +@dataclass(frozen=True) +class DatasetSpec: + name: str + task: str + n_samples: int + n_features: int + dtype: str + signal: str + n_classes: int = 2 + + +@dataclass(frozen=True) +class CaseSpec: + name: str + dataset: DatasetSpec + params: dict + + +DATASETS = { + "reg_1f_deep_f32": DatasetSpec( + "reg_1f_deep_f32", "regression", 60_000, 1, "float32", "deep" + ), + "reg_12f_signal_f32": DatasetSpec( + "reg_12f_signal_f32", "regression", 24_000, 12, "float32", "signal" + ), + "reg_80f_wide_f32": DatasetSpec( + "reg_80f_wide_f32", "regression", 9_000, 80, "float32", "wide" + ), + "reg_12f_signal_f64": DatasetSpec( + "reg_12f_signal_f64", "regression", 16_000, 12, "float64", "signal" + ), + "clf_12f_signal_f32": DatasetSpec( + "clf_12f_signal_f32", "classification", 28_000, 12, "float32", "signal", 3 + ), + "clf_96f_sqrt_f32": DatasetSpec( + "clf_96f_sqrt_f32", "classification", 10_000, 96, "float32", "wide", 4 + ), + "reg_24f_low_card_f32": DatasetSpec( + "reg_24f_low_card_f32", "regression", 30_000, 24, "float32", "low_card" + ), + "clf_24f_low_card_f32": DatasetSpec( + "clf_24f_low_card_f32", "classification", 30_000, 24, "float32", "low_card", 4 + ), +} + + +RETAINED_CASES = [ + CaseSpec( + "reg_1f_deep_full", + DATASETS["reg_1f_deep_f32"], + {"bootstrap": False, "max_features": 1.0, "max_depth": None}, + ), + CaseSpec( + "reg_12f_full_deep", + DATASETS["reg_12f_signal_f32"], + {"bootstrap": False, "max_features": 1.0, "max_depth": None}, + ), + CaseSpec( + "reg_12f_shallow_bootstrap", + DATASETS["reg_12f_signal_f32"], + {"bootstrap": True, "max_features": 1.0, "max_depth": 10}, + ), + CaseSpec( + "reg_80f_sqrt_leaf8", + DATASETS["reg_80f_wide_f32"], + { + "bootstrap": False, + "max_features": "sqrt", + "max_depth": None, + "min_samples_leaf": 8, + }, + ), + CaseSpec( + "reg_12f_full_f64", + DATASETS["reg_12f_signal_f64"], + {"bootstrap": False, "max_features": 1.0, "max_depth": None}, + ), + CaseSpec( + "clf_12f_full_deep", + DATASETS["clf_12f_signal_f32"], + {"bootstrap": False, "max_features": 1.0, "max_depth": None}, + ), + CaseSpec( + "clf_12f_shallow_bootstrap", + DATASETS["clf_12f_signal_f32"], + {"bootstrap": True, "max_features": 1.0, "max_depth": 10}, + ), + CaseSpec( + "clf_96f_sqrt_leaf8", + DATASETS["clf_96f_sqrt_f32"], + { + "bootstrap": False, + "max_features": "sqrt", + "max_depth": None, + "min_samples_leaf": 8, + }, + ), + CaseSpec( + "reg_24f_low_card", + DATASETS["reg_24f_low_card_f32"], + {"bootstrap": False, "max_features": 1.0, "max_depth": None}, + ), + CaseSpec( + "clf_24f_low_card", + DATASETS["clf_24f_low_card_f32"], + {"bootstrap": False, "max_features": 1.0, "max_depth": None}, + ), +] + + +PILOT_CASES = [ + *RETAINED_CASES, + CaseSpec( + "reg_80f_full_depth12", + DATASETS["reg_80f_wide_f32"], + {"bootstrap": False, "max_features": 1.0, "max_depth": 12}, + ), + CaseSpec( + "clf_96f_full_depth12", + DATASETS["clf_96f_sqrt_f32"], + {"bootstrap": False, "max_features": 1.0, "max_depth": 12}, + ), +] + + +def make_data(spec: DatasetSpec): + rng = np.random.default_rng(stable_seed(spec.name)) + X = rng.standard_normal((spec.n_samples, spec.n_features), dtype=np.float64) + + if spec.signal == "deep": + x0 = X[:, 0] + base = np.sin(9 * x0) + 0.5 * np.sign(x0) * np.sqrt(np.abs(x0) + 0.01) + base += 0.15 * rng.standard_normal(spec.n_samples) + elif spec.signal == "wide": + informative = min(8, spec.n_features) + coefs = np.linspace(1.5, -0.6, informative) + base = X[:, :informative] @ coefs + base += 0.35 * (X[:, 0] > 0) * X[:, 1] + base += 0.15 * rng.standard_normal(spec.n_samples) + elif spec.signal == "low_card": + bins = np.array([3, 4, 5, 7, 9, 11], dtype=np.float64) + for feature_idx in range(spec.n_features): + n_bins = bins[feature_idx % bins.size] + X[:, feature_idx] = np.floor((X[:, feature_idx] + 3.0) * n_bins / 6.0) + X[:, feature_idx] = np.clip(X[:, feature_idx], 0, n_bins - 1) + informative = min(8, spec.n_features) + coefs = np.linspace(1.0, -0.7, informative) + base = X[:, :informative] @ coefs + base += 0.4 * (X[:, 0] == X[:, 1]) + base += 0.05 * rng.standard_normal(spec.n_samples) + else: + informative = min(6, spec.n_features) + coefs = np.linspace(2.0, -1.0, informative) + base = X[:, :informative] @ coefs + base += 0.5 * np.sin(X[:, 0] * X[:, 1]) + base += 0.15 * rng.standard_normal(spec.n_samples) + + X = X.astype(spec.dtype, copy=False) + if spec.task == "regression": + return X, base.astype(np.float64, copy=False) + + quantiles = np.linspace(0, 1, spec.n_classes + 1)[1:-1] + thresholds = np.quantile(base, quantiles) + y = np.searchsorted(thresholds, base).astype(np.int64, copy=False) + return X, y + + +def stable_seed(text: str) -> int: + seed = 0x5EED + for char in text: + seed = ((seed * 131) + ord(char)) % (2**32) + return seed + + +def get_case(case_name: str, suite: str = "retained") -> CaseSpec: + cases = PILOT_CASES if suite == "pilot" else RETAINED_CASES + for case in cases: + if case.name == case_name: + return case + valid = ", ".join(case.name for case in cases) + raise SystemExit(f"Unknown case {case_name!r}. Valid cases: {valid}") + + +def make_estimator(case: CaseSpec, backend: str, n_jobs: int): + params = { + "n_estimators": N_ESTIMATORS, + "n_jobs": n_jobs, + "random_state": stable_seed(case.name), + **case.params, + } + if backend == "sklearn": + if case.dataset.task == "regression": + from sklearn.ensemble import RandomForestRegressor + + return RandomForestRegressor(**params) + from sklearn.ensemble import RandomForestClassifier + + return RandomForestClassifier(**params) + + params["max_bins"] = case.dataset.n_samples + if case.dataset.task == "regression": + from sklearnex.ensemble import RandomForestRegressor + + return RandomForestRegressor(**params) + from sklearnex.ensemble import RandomForestClassifier + + return RandomForestClassifier(**params) + + +def sklearnex_support(estimator, X, y): + status = estimator._onedal_cpu_supported("fit", X, y, None) + return status.get_status(), list(getattr(status, "messages", ())) + + +def summarize_forest(estimator): + summary = {} + try: + trees = estimator.estimators_ + except Exception as exc: + # sklearnex lazy conversion can fail on unsupported cases. + summary["tree_summary_error"] = repr(exc) + return summary + + depths = np.array([tree.tree_.max_depth for tree in trees], dtype=np.int64) + leaves = np.array([tree.tree_.n_leaves for tree in trees], dtype=np.int64) + summary.update( + { + "depth_median": float(np.median(depths)), + "depth_max": int(depths.max()), + "leaves_median": float(np.median(leaves)), + "leaves_max": int(leaves.max()), + } + ) + return summary + + +def time_fit(case: CaseSpec, backend: str, n_jobs: int, repeat: int): + X, y = make_data(case.dataset) + estimator = make_estimator(case, backend, n_jobs) + + support_ok = True + support_messages = [] + if backend == "sklearnex": + support_ok, support_messages = sklearnex_support(estimator, X, y) + if not support_ok: + return { + "case": case.name, + "backend": backend, + "repeat": repeat, + "included": False, + "reason": "sklearnex fallback preflight", + "support_messages": support_messages, + **case_metadata(case, n_jobs), + } + + with threadpool_limits(limits=n_jobs): + start = time.perf_counter() + estimator.fit(X, y) + fit_time = time.perf_counter() - start + + included = True + reason = "" + if backend == "sklearnex" and not hasattr(estimator, "_onedal_estimator"): + included = False + reason = "sklearnex fitted without _onedal_estimator" + + result = { + "case": case.name, + "backend": backend, + "repeat": repeat, + "fit_time": fit_time, + "included": included, + "reason": reason, + "support_messages": support_messages, + **case_metadata(case, n_jobs), + } + result.update(summarize_forest(estimator)) + return result + + +def case_metadata(case: CaseSpec, n_jobs: int): + return { + "task": case.dataset.task, + "dataset": case.dataset.name, + "n_samples": case.dataset.n_samples, + "n_features": case.dataset.n_features, + "dtype": case.dataset.dtype, + "signal": case.dataset.signal, + "n_classes": case.dataset.n_classes, + "n_estimators": N_ESTIMATORS, + "n_jobs": n_jobs, + "params": json.dumps(case.params, sort_keys=True), + "max_bins_sklearnex": case.dataset.n_samples, + } + + +def environment_metadata(): + import sklearnex + + import sklearn + + try: + import onedal + + onedal_version = getattr(onedal, "__version__", None) + except ImportError: + onedal_version = None + + return { + "python": sys.version.replace("\n", " "), + "executable": sys.executable, + "platform": platform.platform(), + "cpu_count": os.cpu_count(), + "sklearn": sklearn.__version__, + "sklearnex": getattr(sklearnex, "__version__", None), + "onedal": onedal_version, + "threadpool_info": threadpool_info(), + "n_estimators": N_ESTIMATORS, + "max_case_seconds": MAX_CASE_SECONDS, + } + + +def run_suite(args): + cases = PILOT_CASES if args.suite == "pilot" else RETAINED_CASES + backends = ["sklearn", "sklearnex"] + rows = [] + start = time.perf_counter() + for case in cases: + for backend in backends: + for repeat in range(args.repeats): + row = time_fit(case, backend, args.n_jobs, repeat) + rows.append(row) + print(json.dumps(row, sort_keys=True), flush=True) + elapsed = time.perf_counter() - start + write_outputs(rows, args.output_dir, args.suite, elapsed) + print(f"elapsed={elapsed:.3f}s", file=sys.stderr) + return 0 if elapsed <= args.max_suite_seconds or args.suite == "pilot" else 2 + + +def write_outputs(rows, output_dir: Path, suite: str, elapsed: float): + output_dir.mkdir(parents=True, exist_ok=True) + jsonl_path = output_dir / f"{suite}_raw.jsonl" + csv_path = output_dir / f"{suite}_summary.csv" + env_path = output_dir / "environment.json" + + jsonl_path.write_text( + "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), + encoding="utf-8", + ) + env = environment_metadata() + env["suite_elapsed"] = elapsed + env_path.write_text(json.dumps(env, indent=2, sort_keys=True), encoding="utf-8") + + summaries = summarize_rows(rows) + with csv_path.open("w", newline="", encoding="utf-8") as f: + fieldnames = [ + "case", + "backend", + "included", + "n_samples", + "n_features", + "dtype", + "task", + "n_jobs", + "median_fit_time", + "min_fit_time", + "max_fit_time", + "repeats", + "max_case_ok", + "reason", + ] + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(summaries) + + +def summarize_rows(rows): + grouped = {} + for row in rows: + key = (row["case"], row["backend"]) + grouped.setdefault(key, []).append(row) + + summaries = [] + for (case, backend), items in grouped.items(): + included_items = [item for item in items if item.get("included")] + times = [item["fit_time"] for item in included_items if "fit_time" in item] + first = items[0] + reason = "; ".join( + sorted({item.get("reason", "") for item in items if item.get("reason")}) + ) + summaries.append( + { + "case": case, + "backend": backend, + "included": bool(times), + "n_samples": first["n_samples"], + "n_features": first["n_features"], + "dtype": first["dtype"], + "task": first["task"], + "n_jobs": first["n_jobs"], + "median_fit_time": median(times) if times else "", + "min_fit_time": min(times) if times else "", + "max_fit_time": max(times) if times else "", + "repeats": len(times), + "max_case_ok": max(times) <= MAX_CASE_SECONDS if times else False, + "reason": reason, + } + ) + return summaries + + +def run_one(args): + case = get_case(args.case, args.suite) + for repeat in range(args.repeats): + row = time_fit(case, args.backend, args.n_jobs, repeat) + print(json.dumps(row, sort_keys=True), flush=True) + + +def preflight(args): + failures = [] + for case in RETAINED_CASES: + X, y = make_data(case.dataset) + estimator = make_estimator(case, "sklearnex", args.n_jobs) + support_ok, messages = sklearnex_support(estimator, X, y) + row = { + "case": case.name, + "support_ok": support_ok, + "messages": messages, + **case_metadata(case, args.n_jobs), + } + print(json.dumps(row, sort_keys=True)) + if not support_ok: + failures.append(row) + return 1 if failures else 0 + + +def run_unsupported_checks(args): + from scipy import sparse + + checks = [] + case = RETAINED_CASES[0] + X, y = make_data(case.dataset) + + unsupported = [ + ("sparse_input", sparse.csr_matrix(X), y, {}), + ("warm_start", X, y, {"warm_start": True}), + ("multi_output", X, np.column_stack([y, y]), {}), + ] + for name, Xi, yi, extra_params in unsupported: + probe = CaseSpec(name, case.dataset, {**case.params, **extra_params}) + estimator = make_estimator(probe, "sklearnex", args.n_jobs) + support_ok, messages = sklearnex_support(estimator, Xi, yi) + row = {"check": name, "support_ok": support_ok, "messages": messages} + checks.append(row) + print(json.dumps(row, sort_keys=True)) + + return 0 if all(not row["support_ok"] for row in checks) else 1 + + +def profile_command(args): + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + cmd = [ + sys.executable, + __file__, + "one", + "--case", + args.case, + "--backend", + args.backend, + "--suite", + args.suite, + "--n-jobs", + str(args.n_jobs), + "--repeats", + "1", + ] + py_spy_cmd = [ + "py-spy", + "record", + "--rate", + str(args.rate), + "--format", + args.format, + "--output", + str(output), + "--", + *cmd, + ] + if args.native: + py_spy_cmd.insert(2, "--native") + print(" ".join(py_spy_cmd)) + completed = subprocess.run(py_spy_cmd, check=False) + return completed.returncode + + +def main(argv=None): + parser = argparse.ArgumentParser() + subparsers = parser.add_subparsers(dest="command", required=True) + + run_parser = subparsers.add_parser("run") + run_parser.add_argument( + "--suite", choices=["retained", "pilot"], default="retained" + ) + run_parser.add_argument("--repeats", type=int, default=3) + run_parser.add_argument("--n-jobs", type=int, default=DEFAULT_N_JOBS) + run_parser.add_argument( + "--output-dir", type=Path, default=Path("reports/rf_intelex/results") + ) + run_parser.add_argument("--max-suite-seconds", type=float, default=180.0) + run_parser.set_defaults(func=run_suite) + + one_parser = subparsers.add_parser("one") + one_parser.add_argument("--case", required=True) + one_parser.add_argument( + "--backend", choices=["sklearn", "sklearnex"], required=True + ) + one_parser.add_argument( + "--suite", choices=["retained", "pilot"], default="retained" + ) + one_parser.add_argument("--n-jobs", type=int, default=DEFAULT_N_JOBS) + one_parser.add_argument("--repeats", type=int, default=1) + one_parser.set_defaults(func=run_one) + + preflight_parser = subparsers.add_parser("preflight") + preflight_parser.add_argument("--n-jobs", type=int, default=DEFAULT_N_JOBS) + preflight_parser.set_defaults(func=preflight) + + unsupported_parser = subparsers.add_parser("unsupported-checks") + unsupported_parser.add_argument("--n-jobs", type=int, default=DEFAULT_N_JOBS) + unsupported_parser.set_defaults(func=run_unsupported_checks) + + profile_parser = subparsers.add_parser("profile") + profile_parser.add_argument("--case", required=True) + profile_parser.add_argument( + "--backend", choices=["sklearn", "sklearnex"], required=True + ) + profile_parser.add_argument( + "--suite", choices=["retained", "pilot"], default="retained" + ) + profile_parser.add_argument("--n-jobs", type=int, default=DEFAULT_N_JOBS) + profile_parser.add_argument("--output", required=True) + profile_parser.add_argument( + "--format", choices=["flamegraph", "speedscope"], default="speedscope" + ) + profile_parser.add_argument("--rate", type=int, default=200) + profile_parser.add_argument("--native", action="store_true") + profile_parser.set_defaults(func=profile_command) + + args = parser.parse_args(argv) + return args.func(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/reports/rf_intelex/benchmark_report.md b/reports/rf_intelex/benchmark_report.md new file mode 100644 index 0000000000000..99dfe84f02696 --- /dev/null +++ b/reports/rf_intelex/benchmark_report.md @@ -0,0 +1,116 @@ +# RandomForest fit benchmark: scikit-learn vs sklearnex + +Date: 2026-05-18 + +## Methodology + +The benchmark compares fit time for local scikit-learn RandomForest estimators +against `sklearnex.ensemble` RandomForest estimators on CPU. All benchmarked +forests use `n_estimators=20`. For sklearnex, every retained case sets +`max_bins=n_samples` to avoid intentionally benchmarking low-bin approximate +histogram splitting against scikit-learn's exact splitter. + +The retained suite uses dense, finite, single-output synthetic datasets only. +Before each sklearnex timing the harness calls +`_onedal_cpu_supported("fit", X, y, None)`, and after fit it requires +`_onedal_estimator` to be present. Unsupported cases are excluded from timing +summaries. + +Artifacts: + +- Harness: `benchmarks/rf_intelex/bench_rf_fit.py` +- Raw timings: `reports/rf_intelex/results/retained_raw.jsonl` +- Summary CSV: `reports/rf_intelex/results/retained_summary.csv` +- Environment: `reports/rf_intelex/results/environment.json` +- Native profiles: `reports/rf_intelex/profiles/*_native.speedscope.json` + +Environment: + +- Python: 3.14.3 +- scikit-learn: 1.9.dev0 +- sklearnex: 2199.9.9 +- oneDAL: 2021.6 +- CPU count: 16 logical, 12 physical reported by `psutil` +- Primary timing setting: `n_jobs=1` and `threadpool_limits(1)` + +## Retained suite + +The retained suite completed in 134.5s, under the 3 minute target. The slowest +individual retained timing was 5.62s, under the 10s per-benchmark target. + +| Case | Task | Shape | HP focus | sklearn s | sklearnex s | speedup | +|---|---:|---:|---|---:|---:|---:| +| `reg_1f_deep_full` | regression | 60000 x 1 float32 | bootstrap=False, max_depth=None, max_features=1.0 | 1.681 | 0.669 | 2.51x | +| `reg_12f_full_deep` | regression | 24000 x 12 float32 | bootstrap=False, max_depth=None, max_features=1.0 | 4.557 | 2.751 | 1.66x | +| `reg_12f_shallow_bootstrap` | regression | 24000 x 12 float32 | bootstrap=True, max_depth=10, max_features=1.0 | 2.060 | 1.323 | 1.56x | +| `reg_80f_sqrt_leaf8` | regression | 9000 x 80 float32 | bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=8 | 1.044 | 0.481 | 2.17x | +| `reg_12f_full_f64` | regression | 16000 x 12 float64 | bootstrap=False, max_depth=None, max_features=1.0 | 2.843 | 1.766 | 1.61x | +| `clf_12f_full_deep` | classification | 28000 x 12 float32 | bootstrap=False, max_depth=None, max_features=1.0 | 5.616 | 2.824 | 1.99x | +| `clf_12f_shallow_bootstrap` | classification | 28000 x 12 float32 | bootstrap=True, max_depth=10, max_features=1.0 | 3.108 | 1.911 | 1.63x | +| `clf_96f_sqrt_leaf8` | classification | 10000 x 96 float32 | bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=8 | 1.685 | 0.930 | 1.81x | +| `reg_24f_low_card` | regression | 30000 x 24 float32 | bootstrap=False, max_depth=None, max_features=1.0 | 4.044 | 1.270 | 3.18x | +| `clf_24f_low_card` | classification | 30000 x 24 float32 | bootstrap=False, max_depth=None, max_features=1.0 | 2.450 | 0.488 | 5.02x | + +Pilot cases removed from the retained suite: + +- `clf_96f_full_depth12`: scikit-learn timing was 13.4s, above the 10s target. +- `reg_80f_full_depth12`: retained as an exploratory pilot only; it was useful but + less distinct than the final wide-feature `sqrt` case. + +The low-cardinality cases are dense numerical arrays with many repeated values, +intended to represent the common real-world situation where numerical columns +are effectively discrete or quantized. + +Fallback validation: + +- All retained sklearnex cases passed oneDAL CPU preflight. +- Explicit negative checks rejected sparse input, `warm_start=True`, and + multi-output targets with the expected sklearnex support messages. + +## Profiling findings + +Profiles were collected with `py-spy record --native --format speedscope`. +Some `py-spy` invocations returned `No child process` after writing the file, +but the profile artifacts were produced and reported sample counts with zero or +near-zero sampling errors. + +Main scikit-learn finding: exact tree fitting is dominated by repeated sorting +of feature values in the dense splitter. + +Representative native samples: + +- `reg_12f_shallow_bootstrap` scikit-learn: + `_sorting_introsort_3way` was the top exclusive frame at about 36% of sampled + time. The tree build path goes through `_parallel_build_trees`, + `DepthFirstTreeBuilder_build`, `BestSplitter_node_split`, and + `DensePartitioner_sort_samples_and_feature_values`. +- `clf_96f_sqrt_leaf8` scikit-learn: + `_sorting_introsort_3way` was again the top exclusive frame at about 23% of + sampled time, followed by classification criterion update and Gini impurity + computations. +- `reg_1f_deep_full` scikit-learn: + sorting remained the largest identifiable cost, with `_sorting_introsort_3way` + and `_sorting_heapsort` prominent, plus criterion initialization and updates. + +Main sklearnex finding: the oneDAL path spends most sampled native time inside +decision forest training kernels, with split search and internal sorting visible +as `findSplit...`, `qSort`, and `OrderedRespHelperBest` / +`UnorderedRespHelperBest` frames. Even with `max_bins=n_samples`, the native +symbols still include `decision_forest::method::hist`; the important fairness +control here is that sklearnex was not using a small bin count. + +## Improvement hypotheses + +1. Dense feature sorting is still the first scikit-learn target, but any change + must preserve duplicate-heavy performance. Real-world datasets often contain + low-cardinality numerical features, so continuous-only synthetic speedups are + not enough evidence. +2. The low-cardinality cases show sklearnex has a particularly large advantage + on repeated-value data: 3.18x for regression and 5.02x for classification in + this suite. +3. Bootstrap and shallow-depth overheads still matter, but they are secondary in + these profiles: sklearnex speedups are smaller in shallow bootstrap cases, + while sorting remains visible. + +See `reports/rf_intelex/optimization_experiments.md` for the first branch-level +experiment. diff --git a/reports/rf_intelex/optimization_experiments.md b/reports/rf_intelex/optimization_experiments.md new file mode 100644 index 0000000000000..9de726c74697b --- /dev/null +++ b/reports/rf_intelex/optimization_experiments.md @@ -0,0 +1,338 @@ +# RandomForest optimization experiments + +## `rfopt/disable_three_way_sort` + +Branch commit: `6319799c7b` (`RFOPT experiment disabling three-way dense sort`) + +Status: rejected as a general direction. + +Change: + +- In `sklearn/tree/_partitioner.pyx`, call `simultaneous_sort(..., + use_three_way_partition=False)` for dense splitter feature sorting. + +Hypothesis: + +- The benchmark data is mostly continuous, so equal feature values are rare. + The three-way partition variant appears expensive in native profiles. Disabling + it might reduce sorting overhead in the exact splitter for this subset. + +Focused benchmark results, scikit-learn backend only, `n_estimators=20`, +`n_jobs=1`: + +| Case | Baseline median s | Branch median s | Speedup | +|---|---:|---:|---:| +| `reg_1f_deep_full` | 1.662 | 1.339 | 1.24x | +| `reg_12f_shallow_bootstrap` | 2.057 | 1.740 | 1.18x | +| `clf_96f_sqrt_leaf8` | 1.566 | 1.348 | 1.16x | + +Validation: + +- `pytest sklearn/tree/tests/test_tree.py -q` +- Result: `525 passed, 98 warnings in 4.02s` + +Assessment: + +- This is a cautionary experiment, not a candidate optimization. It improves the + continuous-focused synthetic subset, but that subset is too narrow. +- Prior investigation found this change detrimental on real-world datasets, + where many numerical columns have low cardinality or many tied values. +- The benchmark suite now includes retained low-cardinality numerical cases so + future sort-related experiments do not overfit to continuous synthetic data. +- Do not pursue this branch as a general scikit-learn change without a new + algorithm that keeps the duplicate-heavy benefit of three-way partitioning. + +## Global sorted sample index + +Branches: + +- `rfopt/global-sorted-index-intp`, commit `c2f99937cf` +- `rfopt/global-sorted-index-uint32`, commit `5bc4d0e362` +- `rfopt/global-sorted-index-uint16`, commit `bf4c55f14d` + +Status: promising for continuous / high-cardinality dense data, but still a +prototype. + +Change: + +- At splitter initialization, precompute a per-feature global sorted sample + order for dense data when `max_features == n_features`. +- At large nodes, filter that global order through a node membership marker + instead of sorting the node-local sample values again. +- Skip the fast path for sampled-feature cases, low-cardinality-looking + features, small nodes, and missing-valued features. +- The three variants differ only in the dtype used to store the global sample + order: `intp`, `uint32`, or `uint16`. + +Focused benchmark results, scikit-learn backend only, retained suite, +`n_estimators=20`, `n_jobs=1`, 3 repeats: + +| Case | Baseline median s | intp s | uint16 s | uint32 s | Best speedup | +|---|---:|---:|---:|---:|---:| +| `reg_1f_deep_full` | 1.681 | 1.336 | 1.314 | 1.300 | 1.29x | +| `reg_12f_full_deep` | 4.557 | 3.628 | 3.620 | 3.619 | 1.26x | +| `reg_12f_shallow_bootstrap` | 2.060 | 1.683 | 1.673 | 1.678 | 1.23x | +| `reg_80f_sqrt_leaf8` | 1.044 | 1.016 | 1.017 | 1.019 | 1.03x | +| `reg_12f_full_f64` | 2.843 | 2.288 | 2.296 | 2.308 | 1.24x | +| `clf_12f_full_deep` | 5.616 | 3.967 | 3.975 | 3.955 | 1.42x | +| `clf_12f_shallow_bootstrap` | 3.108 | 2.328 | 2.296 | 2.298 | 1.35x | +| `clf_96f_sqrt_leaf8` | 1.685 | 1.547 | 1.539 | 1.530 | 1.10x | +| `reg_24f_low_card` | 4.044 | 3.966 | 3.994 | 3.998 | 1.02x | +| `clf_24f_low_card` | 2.450 | 2.485 | 2.492 | 2.475 | 0.99x | + +Suite timings: + +- `intp`: 119.0s +- `uint16`: 116.8s +- `uint32`: 116.7s + +Validation: + +- `pytest sklearn/tree/tests/test_tree.py -q` +- `intp`: `525 passed, 98 warnings in 3.72s` +- `uint16`: `525 passed, 98 warnings in 3.76s` +- `uint32`: `525 passed, 98 warnings in 3.83s` + +Assessment: + +- The main win comes from avoiding repeated node-local sorting on continuous + features, especially for full-feature forests. That is consistent with the + sklearnex / oneDAL strategy of building indexed feature representations and + scanning those representations at training time. +- Narrowing the sample-index dtype does not materially improve CPU time on this + suite. `uint16` and `uint32` are mostly useful to reduce temporary memory + traffic and footprint. `uint16` is only valid for `n_samples <= 65535`. +- `uint8` was not implemented as a retained-suite variant because it can only + encode datasets up to 255 samples if used for global sample ids. That would + not exercise the RandomForest fit bottleneck measured here. +- This prototype is not yet a candidate patch: it precomputes the sorted order + per tree instead of per forest, uses NumPy `argsort` during splitter + initialization, and needs a better memory policy. It is still useful evidence + that a scikit-learn-portable top-node indexed path can recover a substantial + part of sklearnex's advantage on high-cardinality dense data. + +## `rfopt/hist-best-splitter` + +Branch status: implemented prototype. + +Change: + +- Add opt-in `max_bins=None` to dense `DecisionTreeClassifier`, + `DecisionTreeRegressor`, `RandomForestClassifier`, and + `RandomForestRegressor`. +- When `max_bins` is set, supported dense trees use a hybrid + `HistBestSplitter`. +- Each feature is binned into at most `max_bins` ordered bins. Features with at + most `max_bins` unique non-missing values keep exact value bins; + higher-cardinality features use quantile-derived bin thresholds. +- In `RandomForest*`, the bin codes are now precomputed once per forest fit and + shared by all newly grown trees instead of being recomputed per tree. +- Hot histogram loops now keep the owning arrays as memoryviews but use local C + pointers for codes, samples, weights, histogram workspaces, and class counts. +- The small-node fallback now follows the oneDAL-inspired + `node_size <= 0.02 * n_bins` rule instead of an absolute 512-sample cutoff. +- When `max_bins=None`, the default behavior is unchanged. A possible future + improvement is to automatically detect low-cardinality features and use the + binned path only for those features, while keeping exact sorting for the rest. +- Supported criteria: `gini`, `entropy` / `log_loss`, `squared_error`. +- Supported: sample weights and missing values. +- Explicitly unsupported: sparse input, random splitter, multi-output, + monotonic constraints, `absolute_error`, `poisson`, and `friedman_mse`. + +Focused retained low-cardinality RF timings, `n_estimators=20`, `n_jobs=1`, +3 repeats: + +| Case | sklearn sort median s | sklearn `max_bins=n_samples` median s | sklearn speedup | sklearnex median s | +|---|---:|---:|---:|---:| +| `reg_24f_low_card` | 4.027 | 1.697 | 2.37x | 1.431 | +| `clf_24f_low_card` | 2.520 | 0.759 | 3.32x | 0.552 | + +Validation: + +- `pytest sklearn/tree/tests/test_tree.py -q` +- Result: `534 passed, 98 warnings in 4.18s` +- `pytest sklearn/tree/tests/test_tree.py -k 'hist_best_splitter' -q` +- Result: `9 passed` +- `pytest sklearn/ensemble/tests/test_forest.py -k 'max_bins' -q` +- Result: `1 passed` +- `pre-commit run cython-lint --files sklearn/tree/_splitter.pyx` +- Result: passed +- `pre-commit run ruff-check --files sklearn/ensemble/_forest.py + sklearn/tree/_classes.py` +- Result: passed +- `pre-commit run ruff-format --files sklearn/ensemble/_forest.py + sklearn/tree/_classes.py` +- Result: passed + +Assessment: + +- This now recovers most of sklearnex's low-cardinality advantage in these two + retained cases. sklearnex remains faster, especially for classification. +- With the updated `max_bins` semantics, high-cardinality features are no longer + exact unless `max_bins` is large enough to keep every unique value. This makes + `max_bins=255` comparable in spirit to sklearnex's binned training mode. +- Further speedups likely require more oneDAL-like specialization: compact + `uint8` / `uint16` codes, sparse workspace clearing, incremental child + histogram reuse, and a less generic tree-building loop around the histogram + splitter. + +### `max_bins=255` branch vs sklearnex retained-suite run + +The retained suite was rerun with both this branch and sklearnex configured with +`max_bins=255`, `n_estimators=20`, `n_jobs=1`, and 3 repeats. All sklearnex +cases passed the oneDAL preflight and fitted with `_onedal_estimator`. + +In this run both implementations use `max_bins=255` as a binning parameter. +Low-cardinality features are still exact in this branch when their unique +non-missing values fit within 255 bins; high-cardinality features are quantile +binned. + +Raw outputs: + +- `reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex/retained_raw.jsonl` +- `reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex/retained_summary.csv` +- `reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex/environment.json` + +Suite elapsed time: 57.1s. Every individual fit remained below 10s. + +| Case | branch `max_bins=255` median s | sklearnex `max_bins=255` median s | sklearnex speedup | +|---|---:|---:|---:| +| `clf_12f_full_deep` | 1.556 | 0.595 | 2.61x | +| `clf_12f_shallow_bootstrap` | 0.999 | 0.308 | 3.25x | +| `clf_24f_low_card` | 0.920 | 0.588 | 1.57x | +| `clf_96f_sqrt_leaf8` | 0.565 | 0.304 | 1.86x | +| `reg_12f_full_deep` | 2.714 | 1.301 | 2.09x | +| `reg_12f_full_f64` | 1.765 | 0.860 | 2.05x | +| `reg_12f_shallow_bootstrap` | 0.881 | 0.293 | 3.01x | +| `reg_1f_deep_full` | 0.269 | 0.105 | 2.55x | +| `reg_24f_low_card` | 2.067 | 1.673 | 1.24x | +| `reg_80f_sqrt_leaf8` | 0.366 | 0.170 | 2.15x | + +Takeaways: + +- The updated semantics close a large part of the previous high-cardinality gap + because the branch no longer falls back to exact sorting for those features. +- sklearnex remains faster across the retained suite, by roughly 1.2x to 3.3x in + this run. The remaining gap is likely implementation polish: compact code + dtypes, tighter histogram memory management, less generic criterion and tree + builder control flow, and oneDAL's native parallel/runtime code. + +### Profiling and cleanup toward the 2x target + +Follow-up profiling used native `py-spy` speedscope recordings under +`reports/rf_intelex/profiles_max_bins_255/`. + +Main finding: + +- `HistBestSplitter` inherited `BestSplitter.init`, which still precomputed the + exact splitter's global sorted index. This was dead work for the histogram + path. In `clf_12f_shallow_bootstrap`, py-spy showed large NumPy sort/merge + time from `_global_sorted_index` before the tree fitting work. + +Fixes: + +- `HistBestSplitter.init` now calls `Splitter.init` directly and creates only + the dense partitioner needed for final sample partitioning. +- Histogram workspace clearing now uses `memset` and only clears buffers needed + by the active criterion. Classification no longer clears regression-only + sum/squared-sum workspaces; regression no longer clears class-count + workspaces. + +After these changes, the retained `max_bins=255` suite was rerun against +sklearnex with the same settings. All sklearnex cases passed oneDAL preflight. + +Raw outputs: + +- `reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex_under2x/retained_raw.jsonl` +- `reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex_under2x/retained_summary.csv` +- `reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex_under2x/environment.json` + +Suite elapsed time: 45.3s. Every individual fit remained below 10s. + +| Case | branch `max_bins=255` median s | sklearnex `max_bins=255` median s | branch / sklearnex | +|---|---:|---:|---:| +| `clf_12f_full_deep` | 0.808 | 0.594 | 1.36x | +| `clf_12f_shallow_bootstrap` | 0.404 | 0.311 | 1.30x | +| `clf_24f_low_card` | 0.820 | 0.608 | 1.35x | +| `clf_96f_sqrt_leaf8` | 0.475 | 0.327 | 1.45x | +| `reg_12f_full_deep` | 1.800 | 1.350 | 1.33x | +| `reg_12f_full_f64` | 1.173 | 0.864 | 1.36x | +| `reg_12f_shallow_bootstrap` | 0.398 | 0.293 | 1.36x | +| `reg_1f_deep_full` | 0.150 | 0.108 | 1.39x | +| `reg_24f_low_card` | 1.801 | 1.484 | 1.21x | +| `reg_80f_sqrt_leaf8` | 0.337 | 0.172 | 1.96x | + +Current remaining gap: + +- The largest remaining gap is `reg_80f_sqrt_leaf8` at 1.96x. Profiling shows + most branch time is now real histogram work: `_build_feature_histogram` and + `_node_split_hist`. sklearnex still has lower constant factors from compact + `unsigned char` bins, specialized oneDAL split kernels, and tighter data + layout / runtime integration. + +### Final warm-up + 10-repeat retained-suite run + +The retained `max_bins=255` branch-vs-sklearnex suite was rerun one last time +with a 30s untimed warm-up followed by 10 timed repeats per case/backend. + +Raw outputs: + +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10/retained_raw.jsonl` +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10/retained_summary.csv` +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10/warmup_raw.jsonl` +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10/environment.json` + +Warm-up elapsed time: 30.2s, with 44 untimed fits. Timed elapsed time: 138.3s. +The variability column is `(max - min) / median` across the 10 timed repeats. + +| Case | branch median s | sklearnex median s | branch / sklearnex | branch variability | sklearnex variability | +|---|---:|---:|---:|---:|---:| +| `clf_12f_full_deep` | 0.768 | 0.576 | 1.33x | 0.043 | 0.049 | +| `clf_12f_shallow_bootstrap` | 0.386 | 0.304 | 1.27x | 0.075 | 0.034 | +| `clf_24f_low_card` | 0.747 | 0.511 | 1.46x | 0.110 | 0.163 | +| `clf_96f_sqrt_leaf8` | 0.477 | 0.287 | 1.66x | 0.161 | 0.040 | +| `reg_12f_full_deep` | 1.772 | 1.196 | 1.48x | 0.039 | 0.118 | +| `reg_12f_full_f64` | 1.154 | 0.789 | 1.46x | 0.034 | 0.047 | +| `reg_12f_shallow_bootstrap` | 0.390 | 0.287 | 1.36x | 0.047 | 0.028 | +| `reg_1f_deep_full` | 0.144 | 0.104 | 1.38x | 0.010 | 0.016 | +| `reg_24f_low_card` | 1.765 | 1.319 | 1.34x | 0.215 | 0.127 | +| `reg_80f_sqrt_leaf8` | 0.310 | 0.165 | 1.88x | 0.149 | 0.128 | + +The 10-repeat run keeps every retained case below the 2x target. The highest +observed ratio is `reg_80f_sqrt_leaf8` at 1.88x. Some low-cardinality rows have +noticeable repeat-to-repeat variability, especially `reg_24f_low_card`, so small +changes in those rows should be interpreted with that noise level in mind. + +### Float64-input warm-up + 10-repeat retained-suite run + +The same final protocol was rerun after forcing every generated `X` to +`float64` before fitting both implementations. This checks whether sklearnex's +dtype handling gives it an unfair advantage when the benchmark datasets are +mixed `float32` / `float64`. + +Raw outputs: + +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10_xfloat64/retained_raw.jsonl` +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10_xfloat64/retained_summary.csv` +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10_xfloat64/warmup_raw.jsonl` +- `reports/rf_intelex/results_max_bins_255_warmup30_repeats10_xfloat64/environment.json` + +Warm-up elapsed time: 30.1s, with 39 untimed fits. Timed elapsed time: 139.3s. +The variability column is `(max - min) / median` across the 10 timed repeats. + +| Case | branch median s | sklearnex median s | branch / sklearnex | branch variability | sklearnex variability | +|---|---:|---:|---:|---:|---:| +| `clf_12f_full_deep` | 0.771 | 0.572 | 1.35x | 0.028 | 0.048 | +| `clf_12f_shallow_bootstrap` | 0.389 | 0.306 | 1.27x | 0.125 | 0.025 | +| `clf_24f_low_card` | 0.742 | 0.510 | 1.46x | 0.148 | 0.172 | +| `clf_96f_sqrt_leaf8` | 0.418 | 0.289 | 1.45x | 0.047 | 0.071 | +| `reg_12f_full_deep` | 1.792 | 1.241 | 1.44x | 0.046 | 0.218 | +| `reg_12f_full_f64` | 1.170 | 0.795 | 1.47x | 0.027 | 0.054 | +| `reg_12f_shallow_bootstrap` | 0.448 | 0.308 | 1.45x | 0.225 | 0.475 | +| `reg_1f_deep_full` | 0.146 | 0.096 | 1.52x | 0.170 | 0.319 | +| `reg_24f_low_card` | 1.768 | 1.272 | 1.39x | 0.097 | 0.173 | +| `reg_80f_sqrt_leaf8` | 0.311 | 0.170 | 1.84x | 0.155 | 0.140 | + +Forcing `X` to `float64` does not materially change the conclusion: every +retained case remains below the 2x target. The largest ratio is still the +wide/sqrt regression case, now at 1.84x. diff --git a/reports/rf_intelex/results/environment.json b/reports/rf_intelex/results/environment.json new file mode 100644 index 0000000000000..b9a07a3207042 --- /dev/null +++ b/reports/rf_intelex/results/environment.json @@ -0,0 +1,42 @@ +{ + "cpu_count": 16, + "executable": "/home/arthur/open-source/skl/rf-intelex/sklearn-env/bin/python", + "max_case_seconds": 10.0, + "n_estimators": 20, + "onedal": "2021.6", + "platform": "Linux-6.17.0-23-generic-x86_64-with-glibc2.39", + "python": "3.14.3 (main, Feb 12 2026, 00:42:54) [Clang 21.1.4 ]", + "sklearn": "1.9.dev0", + "sklearnex": "2199.9.9", + "suite_elapsed": 134.52146856900072, + "threadpool_info": [ + { + "architecture": "Haswell", + "filepath": "/home/arthur/open-source/skl/rf-intelex/sklearn-env/lib/python3.14/site-packages/numpy.libs/libscipy_openblas64_-32a4b2a6.so", + "internal_api": "openblas", + "num_threads": 16, + "prefix": "libscipy_openblas", + 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\"max_features\": 1.0}", "reason": "", "repeat": 2, "signal": "low_card", "support_messages": [], "task": "classification"} diff --git a/reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex_no_sorted_init/retained_summary.csv b/reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex_no_sorted_init/retained_summary.csv new file mode 100644 index 0000000000000..c586c57e6e67e --- /dev/null +++ b/reports/rf_intelex/results_max_bins_255_branch_vs_sklearnex_no_sorted_init/retained_summary.csv @@ -0,0 +1,21 @@ +case,backend,included,n_samples,n_features,dtype,task,n_jobs,median_fit_time,min_fit_time,max_fit_time,repeats,max_case_ok,reason +clf_12f_full_deep,sklearn_hist_255,True,28000,12,float32,classification,1,1.0122431460185908,1.008742222969886,1.0163109899731353,3,True, +clf_12f_full_deep,sklearnex_255,True,28000,12,float32,classification,1,0.5808527520275675,0.5568180899717845,0.6206058550160378,3,True, 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0.47080481197917834, "included": true, "leaves_max": 3669, "leaves_median": 3661.0, "max_bins_sklearnex": 30000, "n_classes": 4, "n_estimators": 20, "n_features": 24, "n_jobs": 1, "n_samples": 30000, "params": "{\"bootstrap\": false, \"max_depth\": null, \"max_features\": 1.0}", "reason": "", "repeat": 2, "signal": "low_card", "support_messages": [], "task": "classification"} diff --git a/reports/rf_intelex/results_uint32/retained_summary.csv b/reports/rf_intelex/results_uint32/retained_summary.csv new file mode 100644 index 0000000000000..98a8654c0c42b --- /dev/null +++ b/reports/rf_intelex/results_uint32/retained_summary.csv @@ -0,0 +1,21 @@ +case,backend,included,n_samples,n_features,dtype,task,n_jobs,median_fit_time,min_fit_time,max_fit_time,repeats,max_case_ok,reason +reg_1f_deep_full,sklearn,True,60000,1,float32,regression,1,1.299809091957286,1.2988185790018179,1.332524322962854,3,True, +reg_1f_deep_full,sklearnex,True,60000,1,float32,regression,1,0.6105931729543954,0.606891445000656,0.6149197539780289,3,True, +reg_12f_full_deep,sklearn,True,24000,12,float32,regression,1,3.6187034029862843,3.6058034999878146,3.666488383023534,3,True, +reg_12f_full_deep,sklearnex,True,24000,12,float32,regression,1,2.556739996012766,2.5548221690114588,2.680918395984918,3,True, +reg_12f_shallow_bootstrap,sklearn,True,24000,12,float32,regression,1,1.6784980159718543,1.669716342992615,1.681956340034958,3,True, +reg_12f_shallow_bootstrap,sklearnex,True,24000,12,float32,regression,1,1.2970849979901686,1.2938648049603216,1.2983584229950793,3,True, +reg_80f_sqrt_leaf8,sklearn,True,9000,80,float32,regression,1,1.018770080001559,1.0170130690094084,1.0201508839963935,3,True, +reg_80f_sqrt_leaf8,sklearnex,True,9000,80,float32,regression,1,0.46587761503178626,0.4624467630055733,0.46604678401490673,3,True, +reg_12f_full_f64,sklearn,True,16000,12,float64,regression,1,2.3075999270076863,2.286107269988861,2.321792265982367,3,True, +reg_12f_full_f64,sklearnex,True,16000,12,float64,regression,1,1.6607528449967504,1.6568896459648386,1.6620212029665709,3,True, +clf_12f_full_deep,sklearn,True,28000,12,float32,classification,1,3.9546919090207666,3.9358363409992307,3.956205588008743,3,True, +clf_12f_full_deep,sklearnex,True,28000,12,float32,classification,1,2.7214673890266567,2.6956737299915403,2.729142952011898,3,True, +clf_12f_shallow_bootstrap,sklearn,True,28000,12,float32,classification,1,2.298425071989186,2.2925172050017864,2.298936221981421,3,True, +clf_12f_shallow_bootstrap,sklearnex,True,28000,12,float32,classification,1,1.7791172589641064,1.778762870002538,1.7875194759690203,3,True, +clf_96f_sqrt_leaf8,sklearn,True,10000,96,float32,classification,1,1.5299080869881436,1.5238942939904518,1.5339811070007272,3,True, +clf_96f_sqrt_leaf8,sklearnex,True,10000,96,float32,classification,1,0.8474417200195603,0.8443197399610654,0.8496425240300596,3,True, +reg_24f_low_card,sklearn,True,30000,24,float32,regression,1,3.9978069130447693,3.9903743489994667,4.104758576024324,3,True, +reg_24f_low_card,sklearnex,True,30000,24,float32,regression,1,1.237533273990266,1.2244339530006982,1.238558323995676,3,True, +clf_24f_low_card,sklearn,True,30000,24,float32,classification,1,2.475167834025342,2.4747051439480856,2.4856789450277574,3,True, +clf_24f_low_card,sklearnex,True,30000,24,float32,classification,1,0.47182634501950815,0.47080481197917834,0.49460726603865623,3,True, diff --git a/reports/rf_intelex/sklearnex_strategy_notes.md b/reports/rf_intelex/sklearnex_strategy_notes.md new file mode 100644 index 0000000000000..7e96b79d7ef65 --- /dev/null +++ b/reports/rf_intelex/sklearnex_strategy_notes.md @@ -0,0 +1,251 @@ +# What sklearnex / oneDAL does faster + +Date: 2026-05-18 + +Reference sources inspected: + +- `external-src/scikit-learn-intelex` at `7e3972a` +- `external-src/oneDAL` at `8f6aa2b` + +## Short answer + +The sklearnex speedup is not just a more polished implementation of the same +node-local sort loop. It uses a different representation strategy in oneDAL: +features are indexed/binned globally, compact integer index types are selected +when possible, and many split searches operate over per-feature index/bin +histograms. Scikit-learn's exact dense splitter instead repeatedly copies and +comparison-sorts node-local feature values for each candidate feature. + +With `max_bins=n_samples`, the sklearnex benchmark is still not using a small +approximate bin count, but the oneDAL code path still goes through its +`hist`/indexed-feature machinery. When a feature has few distinct values, this +is a major advantage. + +## Evidence from source + +sklearnex passes scikit-learn parameters into oneDAL, including: + +- `features_per_node` +- `tree_count` +- `bootstrap` +- `observations_per_tree_fraction` +- `max_bins` +- `min_bin_size` + +Relevant files: + +- `external-src/scikit-learn-intelex/sklearnex/ensemble/_forest.py` +- `external-src/scikit-learn-intelex/onedal/ensemble/forest.cpp` + +oneDAL constructs an `IndexedFeatures` representation: + +- `external-src/oneDAL/cpp/daal/src/algorithms/dtrees/dtrees_feature_type_helper.h` +- `external-src/oneDAL/cpp/daal/src/algorithms/dtrees/dtrees_feature_type_helper.i` + +The header describes the structure as creating and storing an index of every +feature, mapping feature values to indices in the sorted unique-value array. +The implementation initializes those indices per column in parallel. + +In decision forest training, oneDAL chooses compact index types based on the +maximum number of indices: + +- `uint8_t` when `maxNumIndices() <= 256` +- `uint16_t` when `maxNumIndices() <= 65536` +- otherwise the default index type + +Relevant file: + +- `external-src/oneDAL/cpp/daal/src/algorithms/dtrees/forest/regression/df_regression_train_dense_default_impl.i` + +The split loop decides whether to use indexed features: + +```text +bUseIndexedFeatures = + !_memorySavingMode + && n > qMax * indexedFeatures.numIndices(iFeature) +``` + +If enabled, oneDAL calls `findSplitForFeatureSorted`, which builds histograms +over indexed values and scans `nDiffFeatMax`, the number of distinct indices, +not the raw number of samples. + +Scikit-learn's dense exact path is different: + +- `sklearn/tree/_splitter.pyx` calls + `partitioner.sort_samples_and_feature_values(current_split.feature)` for each + candidate feature at each node. +- `sklearn/tree/_partitioner.pyx` copies the node-local feature values and calls + `simultaneous_sort(..., use_three_way_partition=True)`. +- The criterion then scans the sorted sample order. + +This is exact and general, but duplicate-heavy and low-cardinality features +still pay for repeated comparison sorting. + +## Evidence from profiles + +Profiles were collected with `py-spy record --native --format speedscope`. +The percentages below are from exclusive native samples in the generated +speedscope files. They are approximate, but the pattern is stable. + +| Case | Backend | Timing in suite | Main exclusive costs | +|---|---:|---:|---| +| `reg_12f_shallow_bootstrap` | sklearn | 2.060s | sort/index 36.9%, criterion/hist 7.5%, split-search 2.3% | +| `reg_12f_shallow_bootstrap` | sklearnex | 1.323s | split-search 14.7%, sort/index 11.9%, alloc/libc 12.8% | +| `clf_96f_sqrt_leaf8` | sklearn | 1.685s | sort/index 25.2%, criterion/hist 12.2%, split-search 2.2% | +| `clf_96f_sqrt_leaf8` | sklearnex | 0.930s | split-search 15.3%, sort/index 7.0%, alloc/libc 16.2% | +| `clf_24f_low_card` | sklearn | 2.450s | sort/index 35.4%, criterion/hist 10.3%, split-search 0.9% | +| `clf_24f_low_card` | sklearnex | 0.488s | split-search 21.3%, sort/index 2.2%, alloc/libc 13.0% | + +Representative top frames: + +- scikit-learn: + - `_sorting_introsort_3way` + - `DensePartitioner_sort_samples_and_feature_values` + - `ClassificationCriterion_update` + - `RegressionCriterion_update` + - `Gini_children_impurity` +- sklearnex / oneDAL: + - `decision_forest::...findBestSplitSerial` + - `findSplitForFeatureSorted` + - `findSplitFewClassesDispatch` + - `ColIndexTask::getSorted` + - `qSort` + +The low-cardinality classification case is the strongest proof in this suite: +scikit-learn spends about 35% of native samples in sorting/indexing and takes +2.45s, while sklearnex spends about 2% in sort/index work and takes 0.49s. + +## Fundamental differences + +### 1. Global feature indexing + +oneDAL builds a global mapping from feature values to ordered integer indices or +bins. This can amortize sorting/indexing and exposes the number of distinct +values per feature. + +scikit-learn only keeps raw `X` plus node-local sample arrays. It repeatedly +copies and sorts values for the current node and feature. + +### 2. Histogram over unique values + +For indexed features, oneDAL can build per-node histograms over feature indices: + +- regression: sums / weights per indexed value +- classification: class counts per indexed value + +It then scans the ordered unique values to find the best split. + +This is still exact for low-cardinality numerical features when the indices +represent true unique values. With `max_bins=n_samples`, it also avoids small-bin +approximation in the benchmarked cases. + +### 3. Compact index types + +oneDAL specializes on `uint8_t`, `uint16_t`, or larger index types depending on +the maximum number of indexed values. This reduces memory bandwidth for +low-cardinality features. + +### 4. Native whole-forest training kernel + +oneDAL trains the forest inside a native decision forest kernel with its own +threading and per-tree buffers. scikit-learn dispatches trees through joblib and +then uses Cython for each tree. In these `n_jobs=1` measurements, this is less +important than the split representation, but it still reduces Python boundary +surface. + +## Ideas worth porting to scikit-learn + +### 1. Low-cardinality counting-sort path in `DensePartitioner` + +Most practical first step. + +Instead of replacing three-way sort, add a special path that detects globally +low-cardinality dense numerical features and sorts node samples by precomputed +integer codes using counting sort. This preserves the existing sorted-sample +criterion loop, so it is less invasive than a full histogram splitter. + +Sketch: + +- During dense splitter initialization, optionally precompute per-feature ordered + codes for features with `n_unique <= threshold`, e.g. 256 or 1024. +- Store compact codes (`uint8` / `uint16`) for those features. +- In `sort_samples_and_feature_values`, if the feature has codes, counting-sort + `samples[start:end]` by code and fill `feature_values` from the bin borders or + representative values. +- Then reuse the existing `next_p`, criterion updates, missing handling, and + split record machinery. + +Why this is promising: + +- It targets the exact area where sklearnex is strongest on real-world-like + repeated-value data. +- It avoids the rejected `disable_three_way_sort` mistake: duplicate-heavy + features should get faster, not slower. +- It is localized mostly to dense partitioning/splitter state. + +Risks: + +- Extra memory can be large if codes are stored for many features. +- Need careful handling for sample weights, missing values, float32 equality, + monotonic constraints, and multi-output targets. +- The precompute cost must be amortized. This is likely more attractive in + forests than in a single small tree. + +### 2. Constant / single-value feature detection before sorting + +Lower effort but smaller upside. + +oneDAL can use indexed features to detect when a node has no differing values for +a feature before doing expensive split work. scikit-learn currently sorts then +checks whether the sorted range is constant. + +Possible scikit-learn experiment: + +- For dense features, add a cheap pre-sort min/max or code-based + `has_different_values` check for known low-cardinality / indexed features. +- Skip sorting entirely when all node samples share one value. + +This helps datasets with many constant-in-node features, but it adds a scan for +cases that would not be constant. It is best paired with the indexed/coded path. + +### 3. Exact histogram split path for low-cardinality features + +Larger effort. + +Implement a true low-cardinality exact histogram splitter that computes class +counts or regression sums per unique value and scans those bins directly. This is +closer to oneDAL and can avoid sorting entirely, but it duplicates criterion +logic and has a larger compatibility surface. + +This may be worthwhile long term, but the counting-sort path is the safer first +experiment because it reuses existing criterion code. + +## Ideas not worth pursuing as-is + +### Disable three-way sort globally + +This was measured as faster on continuous synthetic cases, but prior experience +found it detrimental on real-world data with many ties. The current report now +keeps it as a rejected experiment. It should not be used as a general +optimization. + +## Recommended next experiment + +Create `rfopt/low_card_counting_sort`: + +1. Add a dense-only, no-missing first prototype. +2. Detect low-cardinality float32 features at splitter initialization. +3. Precompute ordered integer codes for features with `n_unique <= 256`. +4. Use counting sort by code in `DensePartitioner.sort_samples_and_feature_values`. +5. Reuse the existing criterion scan over sorted samples. +6. Benchmark at least: + - `clf_24f_low_card` + - `reg_24f_low_card` + - `clf_96f_sqrt_leaf8` + - one continuous case to confirm no regression from detection overhead + +Acceptance bar: + +- Clear speedup on low-cardinality cases. +- Neutral within noise on continuous cases. +- Tree tests pass, plus targeted duplicate-heavy RandomForest tests. diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index 28b9d0cbdf63f..b4100a89bfcc4 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -63,6 +63,7 @@ class calls the ``fit`` method of each sub-estimator on random samples DecisionTreeRegressor, ExtraTreeClassifier, ExtraTreeRegressor, + _splitter, ) from sklearn.utils import ( check_random_state, @@ -458,6 +459,14 @@ def fit(self, X, y, sample_weight=None): self._make_estimator(append=False, random_state=random_state) for i in range(n_more_estimators) ] + precomputed_hist_bins = None + max_bins = getattr(self, "max_bins", None) + if max_bins is not None and not issparse(X): + precomputed_hist_bins = _splitter._hist_binned_features( + X, max_bins, missing_values_in_feature_mask + ) + for tree in trees: + tree._max_bins_precomputed = precomputed_hist_bins # Parallel loop: we prefer the threading backend as the Cython code # for fitting the trees is internally releasing the Python GIL @@ -465,26 +474,32 @@ def fit(self, X, y, sample_weight=None): # that case. However, for joblib 0.12+ we respect any # parallel_backend contexts set at a higher level, # since correctness does not rely on using threads. - trees = Parallel( - n_jobs=self.n_jobs, - verbose=self.verbose, - prefer="threads", - )( - delayed(_parallel_build_trees)( - t, - self.bootstrap, - X, - y, - _sample_weight, - i, - len(trees), + try: + trees = Parallel( + n_jobs=self.n_jobs, verbose=self.verbose, - class_weight=self.class_weight, - n_samples_bootstrap=n_samples_bootstrap, - missing_values_in_feature_mask=missing_values_in_feature_mask, + prefer="threads", + )( + delayed(_parallel_build_trees)( + t, + self.bootstrap, + X, + y, + _sample_weight, + i, + len(trees), + verbose=self.verbose, + class_weight=self.class_weight, + n_samples_bootstrap=n_samples_bootstrap, + missing_values_in_feature_mask=missing_values_in_feature_mask, + ) + for i, t in enumerate(trees) ) - for i, t in enumerate(trees) - ) + finally: + if precomputed_hist_bins is not None: + for tree in trees: + if hasattr(tree, "_max_bins_precomputed"): + del tree._max_bins_precomputed # Collect newly grown trees self.estimators_.extend(trees) @@ -1534,6 +1549,7 @@ def __init__( ccp_alpha=0.0, max_samples=None, monotonic_cst=None, + max_bins=None, ): super().__init__( estimator=DecisionTreeClassifier(), @@ -1550,6 +1566,7 @@ def __init__( "random_state", "ccp_alpha", "monotonic_cst", + "max_bins", ), bootstrap=bootstrap, oob_score=oob_score, @@ -1571,6 +1588,7 @@ def __init__( self.min_impurity_decrease = min_impurity_decrease self.monotonic_cst = monotonic_cst self.ccp_alpha = ccp_alpha + self.max_bins = max_bins class RandomForestRegressor(ForestRegressor): @@ -1910,6 +1928,7 @@ def __init__( ccp_alpha=0.0, max_samples=None, monotonic_cst=None, + max_bins=None, ): super().__init__( estimator=DecisionTreeRegressor(), @@ -1926,6 +1945,7 @@ def __init__( "random_state", "ccp_alpha", "monotonic_cst", + "max_bins", ), bootstrap=bootstrap, oob_score=oob_score, @@ -1937,6 +1957,10 @@ def __init__( ) if isinstance(criterion, str) and criterion == "friedman_mse": + if max_bins is not None: + raise ValueError( + "max_bins does not support the friedman_mse regression criterion." + ) # TODO(1.11): remove support of "friedman_mse" criterion. criterion = "squared_error" warn( @@ -1956,6 +1980,7 @@ def __init__( self.min_impurity_decrease = min_impurity_decrease self.ccp_alpha = ccp_alpha self.monotonic_cst = monotonic_cst + self.max_bins = max_bins class ExtraTreesClassifier(ForestClassifier): diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py index ca2a4d102a844..2bc874634150b 100644 --- a/sklearn/ensemble/tests/test_forest.py +++ b/sklearn/ensemble/tests/test_forest.py @@ -119,6 +119,25 @@ REG_CRITERIONS = ("squared_error", "absolute_error", "friedman_mse", "poisson") +def test_random_forest_max_bins_propagates_to_trees(): + rng = np.random.RandomState(0) + X = rng.randint(0, 5, size=(1200, 4)).astype(np.float32) + y_clf = (X[:, 0] > 2).astype(np.intp) + y_reg = X[:, 0] + 0.5 * X[:, 1] + + clf = RandomForestClassifier( + n_estimators=3, max_bins=8, max_depth=4, random_state=0, n_jobs=1 + ).fit(X, y_clf) + reg = RandomForestRegressor( + n_estimators=3, max_bins=8, max_depth=4, random_state=0, n_jobs=1 + ).fit(X, y_reg) + + assert [tree.max_bins for tree in clf.estimators_] == [8, 8, 8] + assert [tree.max_bins for tree in reg.estimators_] == [8, 8, 8] + assert clf.predict(X[:5]).shape == (5,) + assert reg.predict(X[:5]).shape == (5,) + + @pytest.mark.parametrize("name", FOREST_CLASSIFIERS) def test_classification_toy(name): """Check classification on a toy dataset.""" diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py index 313072da4a9cc..ad2a2df42ea4a 100644 --- a/sklearn/tree/_classes.py +++ b/sklearn/tree/_classes.py @@ -140,6 +140,7 @@ def __init__( class_weight=None, ccp_alpha=0.0, monotonic_cst=None, + max_bins=None, ): self.criterion = criterion self.splitter = splitter @@ -154,6 +155,7 @@ def __init__( self.class_weight = class_weight self.ccp_alpha = ccp_alpha self.monotonic_cst = monotonic_cst + self.max_bins = max_bins def get_depth(self): """Return the depth of the decision tree. @@ -384,6 +386,7 @@ def _fit( SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS splitter = self.splitter + max_bins = getattr(self, "max_bins", None) if self.monotonic_cst is None: monotonic_cst = None else: @@ -423,8 +426,34 @@ def _fit( # *positive class*, all signs must be flipped. monotonic_cst *= -1 + if max_bins is not None: + if issparse(X): + raise ValueError("max_bins is only supported for dense input.") + if splitter != "best": + raise ValueError("max_bins is only supported with splitter='best'.") + if not isinstance(self.criterion, str): + raise ValueError("max_bins does not support custom Criterion objects.") + if self.n_outputs_ != 1: + raise ValueError("max_bins does not support multi-output trees.") + if monotonic_cst is not None: + raise ValueError("max_bins does not support monotonic constraints.") + if is_classification: + if self.criterion not in ("gini", "entropy", "log_loss"): + raise ValueError( + "max_bins only supports gini, entropy and log_loss " + "classification criteria." + ) + elif self.criterion != "squared_error": + raise ValueError( + "max_bins only supports the squared_error regression criterion." + ) + if not isinstance(self.splitter, Splitter): - splitter = SPLITTERS[self.splitter]( + splitter = ( + _splitter.HistBestSplitter + if max_bins is not None + else SPLITTERS[self.splitter] + )( criterion, self.max_features_, min_samples_leaf, @@ -432,6 +461,11 @@ def _fit( random_state, monotonic_cst, ) + if max_bins is not None: + splitter.max_bins = max_bins + precomputed_bins = getattr(self, "_max_bins_precomputed", None) + if precomputed_bins is not None: + splitter.precomputed_bins = precomputed_bins if is_classifier(self): self.tree_ = Tree(self.n_features_in_, self.n_classes_, self.n_outputs_) @@ -949,6 +983,7 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree): **BaseDecisionTree._parameter_constraints, "criterion": [StrOptions({"gini", "entropy", "log_loss"}), Hidden(Criterion)], "class_weight": [dict, list, StrOptions({"balanced"}), None], + "max_bins": [Interval(Integral, 2, None, closed="left"), None], } def __init__( @@ -967,6 +1002,7 @@ def __init__( class_weight=None, ccp_alpha=0.0, monotonic_cst=None, + max_bins=None, ): super().__init__( criterion=criterion, @@ -982,6 +1018,7 @@ def __init__( min_impurity_decrease=min_impurity_decrease, monotonic_cst=monotonic_cst, ccp_alpha=ccp_alpha, + max_bins=max_bins, ) @_fit_context(prefer_skip_nested_validation=True) @@ -1327,6 +1364,7 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): StrOptions({"squared_error", "absolute_error", "poisson"}), Hidden(Criterion), ], + "max_bins": [Interval(Integral, 2, None, closed="left"), None], } def __init__( @@ -1344,8 +1382,13 @@ def __init__( min_impurity_decrease=0.0, ccp_alpha=0.0, monotonic_cst=None, + max_bins=None, ): if isinstance(criterion, str) and criterion == "friedman_mse": + if max_bins is not None: + raise ValueError( + "max_bins does not support the friedman_mse regression criterion." + ) # TODO(1.11): remove support of "friedman_mse" criterion. criterion = "squared_error" warnings.warn( @@ -1368,6 +1411,7 @@ def __init__( min_impurity_decrease=min_impurity_decrease, ccp_alpha=ccp_alpha, monotonic_cst=monotonic_cst, + max_bins=max_bins, ) @_fit_context(prefer_skip_nested_validation=True) diff --git a/sklearn/tree/_partitioner.pxd b/sklearn/tree/_partitioner.pxd index d36ffc799c4d8..c889311bd3ab1 100644 --- a/sklearn/tree/_partitioner.pxd +++ b/sklearn/tree/_partitioner.pxd @@ -73,6 +73,10 @@ cdef class DensePartitioner: cdef intp_t n_missing cdef const uint8_t[::1] missing_values_in_feature_mask cdef char[::1] swap_buffer + cdef const intp_t[:, ::1] global_sorted_samples + cdef intp_t[::1] feature_n_unique + cdef intp_t[::1] sample_marker + cdef intp_t marker cdef void sort_samples_and_feature_values( self, intp_t current_feature diff --git a/sklearn/tree/_partitioner.pyx b/sklearn/tree/_partitioner.pyx index 266928b1055aa..182f8d5d19089 100644 --- a/sklearn/tree/_partitioner.pyx +++ b/sklearn/tree/_partitioner.pyx @@ -40,6 +40,8 @@ cdef class DensePartitioner: intp_t[::1] samples, float32_t[::1] feature_values, const uint8_t[::1] missing_values_in_feature_mask, + const intp_t[:, ::1] global_sorted_samples=None, + intp_t[::1] feature_n_unique=None, ): self.X = X self.samples = samples @@ -47,6 +49,10 @@ cdef class DensePartitioner: self.missing_values_in_feature_mask = missing_values_in_feature_mask buffer_size = samples.size * max(samples.itemsize, feature_values.itemsize) self.swap_buffer = np.empty(buffer_size, dtype=np.uint8) + self.global_sorted_samples = global_sorted_samples + self.feature_n_unique = feature_n_unique + self.sample_marker = np.zeros(X.shape[0], dtype=np.intp) + self.marker = 0 # TODO: As optimization we could make `swap_array_slices` always pick the smallest side # to get copied in the buffer, which would allow to use a buffer twice smaller. @@ -66,12 +72,15 @@ cdef class DensePartitioner: in self.n_missing. """ cdef: - intp_t i, current_end + intp_t i, current_end, node_size, sample_idx, out, n_total_samples float32_t[::1] feature_values = self.feature_values const float32_t[:, :] X = self.X intp_t[::1] samples = self.samples intp_t n_missing = 0 const uint8_t[::1] missing_values_in_feature_mask = self.missing_values_in_feature_mask + const intp_t[:, ::1] global_sorted_samples = self.global_sorted_samples + intp_t[::1] feature_n_unique = self.feature_n_unique + intp_t[::1] sample_marker = self.sample_marker # Sort samples along that feature; by copying the values into an array and # sorting the array in a manner which utilizes the cache more effectively. @@ -95,6 +104,28 @@ cdef class DensePartitioner: feature_values[i] = X[samples[i], current_feature] i += 1 else: + node_size = self.end - self.start + n_total_samples = X.shape[0] + if ( + global_sorted_samples is not None + and node_size > 512 + and feature_n_unique[current_feature] > 1024 + and node_size * 50 > feature_n_unique[current_feature] + ): + self.marker += 1 + for i in range(self.start, self.end): + sample_marker[samples[i]] = self.marker + + out = self.start + for i in range(n_total_samples): + sample_idx = global_sorted_samples[current_feature, i] + if sample_marker[sample_idx] == self.marker: + samples[out] = sample_idx + feature_values[out] = X[sample_idx, current_feature] + out += 1 + self.n_missing = 0 + return + # When there are no missing values, we only need to copy the data into # feature_values for i in range(self.start, self.end): diff --git a/sklearn/tree/_splitter.pyx b/sklearn/tree/_splitter.pyx index bf4de11b2517a..3d56c138af544 100644 --- a/sklearn/tree/_splitter.pyx +++ b/sklearn/tree/_splitter.pyx @@ -20,10 +20,14 @@ of splitting strategies: # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause -from libc.math cimport INFINITY -from libc.string cimport memcpy +from libc.math cimport INFINITY, log +from libc.string cimport memcpy, memset -from sklearn.tree._criterion cimport Criterion +from sklearn.tree._criterion cimport ( + ClassificationCriterion, + Criterion, + RegressionCriterion, +) from sklearn.tree._partitioner cimport ( FEATURE_THRESHOLD, DensePartitioner, SparsePartitioner, ) @@ -41,6 +45,97 @@ ctypedef fused Partitioner: SparsePartitioner +def _global_sorted_index(X, intp_t max_features): + """Precompute feature-wise global sorted sample indices. + + Experimental RandomForest optimization: large dense nodes can filter this + order instead of comparison-sorting node-local feature values. + """ + X_array = np.asarray(X) + n_samples, n_features = X_array.shape + if max_features != n_features: + return None, None + + sample_size = min(n_samples, 2048) + sorted_samples = np.empty((n_features, n_samples), dtype=np.intp) + feature_n_unique = np.zeros(n_features, dtype=np.intp) + any_feature = False + for feature_idx in range(X_array.shape[1]): + # Cheap guard: the global sorted-index path is intended for continuous + # or near-continuous features. Low-cardinality features are better left + # to the current three-way sorter until they get a dedicated coded path. + sample_values = X_array[:sample_size, feature_idx] + if np.unique(sample_values).size < sample_size // 2: + continue + + order = np.argsort(X_array[:, feature_idx], kind="mergesort") + sorted_samples[feature_idx] = order + ordered_values = X_array[order, feature_idx] + if ordered_values.size == 0: + feature_n_unique[feature_idx] = 0 + else: + feature_n_unique[feature_idx] = 1 + np.count_nonzero( + np.diff(ordered_values) > FEATURE_THRESHOLD + ) + any_feature = True + if not any_feature: + return None, None + return sorted_samples, feature_n_unique + + +def _hist_binned_features(X, intp_t max_bins, missing_values_in_feature_mask): + """Precompute ordered feature bin codes for histogram splitting.""" + X_array = np.asarray(X) + n_samples, n_features = X_array.shape + codes = np.full((n_features, n_samples), -1, dtype=np.int32) + bin_thresholds = np.empty((n_features, max_bins), dtype=np.float32) + n_bins = np.zeros(n_features, dtype=np.intp) + all_features_binned = True + + for feature_idx in range(n_features): + values = X_array[:, feature_idx] + if ( + missing_values_in_feature_mask is not None + and missing_values_in_feature_mask[feature_idx] + ): + non_missing = ~np.isnan(values) + feature_values = values[non_missing] + else: + non_missing = None + feature_values = values + + uniques = np.unique(feature_values) + if uniques.size == 0: + n_bins[feature_idx] = 0 + continue + + if uniques.size <= max_bins: + n_feature_bins = uniques.size + thresholds = uniques[:-1] / 2.0 + uniques[1:] / 2.0 + else: + percentile_ranks = np.linspace(0.0, 100.0, max_bins + 1)[1:-1] + thresholds = np.percentile( + feature_values, percentile_ranks, method="midpoint" + ) + thresholds = np.unique(thresholds.astype(np.float32, copy=False)) + n_feature_bins = thresholds.size + 1 + + thresholds = thresholds.astype(np.float32, copy=False) + n_bins[feature_idx] = n_feature_bins + if thresholds.size: + bin_thresholds[feature_idx, : thresholds.size] = thresholds + if non_missing is None: + codes[feature_idx] = np.searchsorted(thresholds, values).astype( + np.int32, copy=False + ) + else: + codes[feature_idx, non_missing] = np.searchsorted( + thresholds, values[non_missing] + ).astype(np.int32, copy=False) + + return codes, bin_thresholds, n_bins, all_features_binned + + cdef inline void _init_split(SplitRecord* self, intp_t start_pos) noexcept nogil: self.impurity_left = INFINITY self.impurity_right = INFINITY @@ -739,6 +834,8 @@ cdef inline int node_split_random( cdef class BestSplitter(Splitter): """Splitter for finding the best split on dense data.""" cdef DensePartitioner partitioner + cdef object global_sorted_samples + cdef object feature_n_unique cdef int init( self, object X, @@ -747,8 +844,16 @@ cdef class BestSplitter(Splitter): const uint8_t[::1] missing_values_in_feature_mask, ) except -1: Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask) + self.global_sorted_samples, self.feature_n_unique = _global_sorted_index( + X, self.max_features + ) self.partitioner = DensePartitioner( - X, self.samples, self.feature_values, missing_values_in_feature_mask + X, + self.samples, + self.feature_values, + missing_values_in_feature_mask, + self.global_sorted_samples, + self.feature_n_unique, ) cdef int node_split( @@ -764,6 +869,473 @@ cdef class BestSplitter(Splitter): parent_record, ) + +cdef class HistBestSplitter(BestSplitter): + """Histogram splitter for dense data binned into at most ``max_bins`` bins.""" + cdef public intp_t max_bins + cdef public object precomputed_bins + cdef const int32_t[:, ::1] bin_codes + cdef float32_t[:, ::1] bin_thresholds + cdef intp_t[::1] n_bins + cdef intp_t[::1] hist_counts + cdef intp_t[::1] active_bins + cdef float64_t[::1] hist_weights + cdef float64_t[::1] hist_sums + cdef float64_t[::1] hist_sq_sums + cdef float64_t[:, ::1] hist_class_counts + cdef float64_t[::1] missing_class_counts + cdef float64_t[::1] left_class_counts + cdef intp_t criterion_kind + cdef intp_t n_classes + cdef bint all_features_binned + + cdef int init( + self, + object X, + const float64_t[:, ::1] y, + const float64_t[:] sample_weight, + const uint8_t[::1] missing_values_in_feature_mask, + ) except -1: + cdef object codes + cdef object bin_thresholds + cdef object n_bins + cdef object all_features_binned + cdef intp_t workspace_bins + cdef str criterion_name + + Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask) + self.global_sorted_samples = None + self.feature_n_unique = None + self.partitioner = DensePartitioner( + X, + self.samples, + self.feature_values, + missing_values_in_feature_mask, + self.global_sorted_samples, + self.feature_n_unique, + ) + if self.max_bins <= 0: + self.all_features_binned = False + return 0 + + if self.precomputed_bins is None: + codes, bin_thresholds, n_bins, all_features_binned = _hist_binned_features( + X, self.max_bins, missing_values_in_feature_mask + ) + else: + codes, bin_thresholds, n_bins, all_features_binned = self.precomputed_bins + self.bin_codes = codes + self.bin_thresholds = bin_thresholds + self.n_bins = n_bins + self.all_features_binned = all_features_binned + + workspace_bins = self.max_bins + self.hist_counts = np.empty(workspace_bins, dtype=np.intp) + self.active_bins = np.empty(workspace_bins, dtype=np.intp) + self.hist_weights = np.empty(workspace_bins, dtype=np.float64) + self.hist_sums = np.empty(workspace_bins, dtype=np.float64) + self.hist_sq_sums = np.empty(workspace_bins, dtype=np.float64) + + criterion_name = type(self.criterion).__name__ + if criterion_name == "Gini": + self.criterion_kind = 1 + elif criterion_name == "Entropy": + self.criterion_kind = 2 + elif criterion_name == "MSE": + self.criterion_kind = 3 + else: + self.criterion_kind = 0 + + if self.criterion_kind in (1, 2): + self.n_classes = ( self.criterion).n_classes[0] + self.hist_class_counts = np.empty( + (workspace_bins, self.n_classes), dtype=np.float64 + ) + self.missing_class_counts = np.empty(self.n_classes, dtype=np.float64) + self.left_class_counts = np.empty(self.n_classes, dtype=np.float64) + else: + self.n_classes = 0 + self.hist_class_counts = np.empty((1, 1), dtype=np.float64) + self.missing_class_counts = np.empty(1, dtype=np.float64) + self.left_class_counts = np.empty(1, dtype=np.float64) + return 0 + + cdef int node_split( + self, + ParentInfo* parent_record, + SplitRecord* split, + ) except -1 nogil: + cdef uint32_t saved_rand_r_state = self.rand_r_state + cdef int hist_status + + if ( + not self.all_features_binned + or self.criterion_kind == 0 + or self.with_monotonic_cst + ): + return node_split_best( + self, + self.partitioner, + self.criterion, + split, + parent_record, + ) + + hist_status = self._node_split_hist(parent_record, split) + if hist_status == 1: + self.rand_r_state = saved_rand_r_state + return node_split_best( + self, + self.partitioner, + self.criterion, + split, + parent_record, + ) + return hist_status + + cdef int _node_split_hist( + self, + ParentInfo* parent_record, + SplitRecord* split, + ) except -1 nogil: + cdef: + SplitRecord best_split, current_split + float64_t best_improvement = -INFINITY + float64_t current_improvement + float64_t impurity = parent_record.impurity + intp_t start = self.start + intp_t end = self.end + intp_t node_size = end - start + intp_t max_features = self.max_features + intp_t min_samples_leaf = self.min_samples_leaf + float64_t min_weight_leaf = self.min_weight_leaf + uint32_t* random_state = &self.rand_r_state + intp_t[::1] features = self.features + intp_t[::1] constant_features = self.constant_features + intp_t n_features = self.n_features + intp_t n_known_constants = parent_record.n_constant_features + intp_t n_total_constants = n_known_constants + intp_t n_found_constants = 0 + intp_t n_drawn_constants = 0 + intp_t n_visited_features = 0 + intp_t f_i = n_features + intp_t f_j + intp_t current_feature + intp_t n_feature_bins + intp_t p + intp_t i, j, c + intp_t active_count + intp_t missing_count + intp_t left_count, right_count + float64_t missing_weight + float64_t missing_sum + float64_t missing_sq_sum + float64_t left_weight, right_weight + float64_t left_sum, right_sum + float64_t left_sq_sum, right_sq_sum + float64_t impurity_left, impurity_right + float64_t weighted_n_node_samples = ( + self.criterion.weighted_n_node_samples + ) + float64_t weighted_n_samples = self.weighted_n_samples + bint missing_go_to_left + intp_t* n_bins_ptr = &self.n_bins[0] + intp_t* hist_counts_ptr = &self.hist_counts[0] + intp_t* active_bins_ptr = &self.active_bins[0] + float64_t* hist_weights_ptr = &self.hist_weights[0] + float64_t* hist_sums_ptr = &self.hist_sums[0] + float64_t* hist_sq_sums_ptr = &self.hist_sq_sums[0] + float64_t* hist_class_counts_ptr = &self.hist_class_counts[0, 0] + float64_t* missing_class_counts_ptr = &self.missing_class_counts[0] + float64_t* left_class_counts_ptr = &self.left_class_counts[0] + const float32_t* bin_thresholds_ptr + + _init_split(&best_split, end) + _init_split(¤t_split, start) + self.partitioner.init_node_split(start, end) + + while ( + f_i > n_total_constants + and (n_visited_features < max_features or + n_visited_features <= n_found_constants + n_drawn_constants) + ): + n_visited_features += 1 + f_j = rand_int(n_drawn_constants, f_i - n_found_constants, random_state) + + if f_j < n_known_constants: + features[n_drawn_constants], features[f_j] = ( + features[f_j], features[n_drawn_constants] + ) + n_drawn_constants += 1 + continue + + f_j += n_found_constants + current_feature = features[f_j] + n_feature_bins = n_bins_ptr[current_feature] + if n_feature_bins <= 0: + return 1 + if node_size <= (0.02 * n_feature_bins): + return 1 + + active_count = self._build_feature_histogram( + current_feature, + n_feature_bins, + &missing_count, + &missing_weight, + &missing_sum, + &missing_sq_sum, + ) + + if active_count == 0: + features[f_j], features[n_total_constants] = ( + features[n_total_constants], features[f_j] + ) + n_found_constants += 1 + n_total_constants += 1 + continue + + if active_count == 1 and missing_count == 0: + features[f_j], features[n_total_constants] = ( + features[n_total_constants], features[f_j] + ) + n_found_constants += 1 + n_total_constants += 1 + continue + + f_i -= 1 + features[f_i], features[f_j] = features[f_j], features[f_i] + current_split.feature = current_feature + bin_thresholds_ptr = &self.bin_thresholds[current_feature, 0] + + for i in range(2 if missing_count > 0 else 1): + missing_go_to_left = i == 1 + left_count = missing_count if missing_go_to_left else 0 + left_weight = missing_weight if missing_go_to_left else 0.0 + left_sum = missing_sum if missing_go_to_left else 0.0 + left_sq_sum = missing_sq_sum if missing_go_to_left else 0.0 + + if self.criterion_kind in (1, 2): + for c in range(self.n_classes): + if missing_go_to_left: + left_class_counts_ptr[c] = missing_class_counts_ptr[c] + else: + left_class_counts_ptr[c] = 0.0 + + for j in range(active_count): + p = active_bins_ptr[j] + left_count += hist_counts_ptr[p] + left_weight += hist_weights_ptr[p] + + if self.criterion_kind == 3: + left_sum += hist_sums_ptr[p] + left_sq_sum += hist_sq_sums_ptr[p] + else: + for c in range(self.n_classes): + left_class_counts_ptr[c] += ( + hist_class_counts_ptr[p * self.n_classes + c] + ) + + if j == active_count - 1 and not ( + missing_count > 0 and not missing_go_to_left + ): + continue + + right_count = node_size - left_count + if left_count < min_samples_leaf or right_count < min_samples_leaf: + continue + + right_weight = weighted_n_node_samples - left_weight + if left_weight < min_weight_leaf or right_weight < min_weight_leaf: + continue + + if self.criterion_kind == 3: + right_sum = ( self.criterion).sum_total[0] - left_sum + right_sq_sum = ( self.criterion).sq_sum_total - left_sq_sum + impurity_left = left_sq_sum / left_weight - ( + left_sum / left_weight + ) * (left_sum / left_weight) + impurity_right = right_sq_sum / right_weight - ( + right_sum / right_weight + ) * (right_sum / right_weight) + else: + self._hist_children_class_impurity( + left_weight, + right_weight, + &impurity_left, + &impurity_right, + ) + + current_improvement = ( + weighted_n_node_samples / weighted_n_samples + ) * ( + impurity + - left_weight / weighted_n_node_samples * impurity_left + - right_weight / weighted_n_node_samples * impurity_right + ) + + if current_improvement > best_improvement: + best_improvement = current_improvement + current_split.pos = start + left_count + current_split.improvement = current_improvement + current_split.impurity_left = impurity_left + current_split.impurity_right = impurity_right + if j == active_count - 1: + current_split.threshold = INFINITY + else: + current_split.threshold = bin_thresholds_ptr[p] + if missing_count == 0: + current_split.missing_go_to_left = left_count > right_count + else: + current_split.missing_go_to_left = missing_go_to_left + best_split = current_split + + if best_split.pos < end: + self.partitioner.partition_samples_final(&best_split) + self.criterion.reset() + self.criterion.update(best_split.pos) + self.criterion.children_impurity( + &best_split.impurity_left, &best_split.impurity_right + ) + best_split.improvement = self.criterion.impurity_improvement( + impurity, best_split.impurity_left, best_split.impurity_right + ) + + memcpy(&features[0], &constant_features[0], sizeof(intp_t) * n_known_constants) + memcpy(&constant_features[n_known_constants], + &features[n_known_constants], + sizeof(intp_t) * n_found_constants) + + parent_record.n_constant_features = n_total_constants + split[0] = best_split + return 0 + + cdef intp_t _build_feature_histogram( + self, + intp_t current_feature, + intp_t n_feature_bins, + intp_t* missing_count, + float64_t* missing_weight, + float64_t* missing_sum, + float64_t* missing_sq_sum, + ) noexcept nogil: + cdef: + intp_t i, p, sample_idx, c + int32_t code + float64_t w + float64_t y_value + intp_t active_count = 0 + intp_t* samples_ptr = &self.samples[0] + const int32_t* codes_ptr = &self.bin_codes[current_feature, 0] + intp_t* hist_counts_ptr = &self.hist_counts[0] + intp_t* active_bins_ptr = &self.active_bins[0] + float64_t* hist_weights_ptr = &self.hist_weights[0] + float64_t* hist_sums_ptr = &self.hist_sums[0] + float64_t* hist_sq_sums_ptr = &self.hist_sq_sums[0] + float64_t* hist_class_counts_ptr = &self.hist_class_counts[0, 0] + float64_t* missing_class_counts_ptr = &self.missing_class_counts[0] + const float64_t* y_ptr = &self.y[0, 0] + const float64_t* sample_weight_ptr = NULL + + if self.sample_weight is not None: + sample_weight_ptr = &self.sample_weight[0] + + missing_count[0] = 0 + missing_weight[0] = 0.0 + missing_sum[0] = 0.0 + missing_sq_sum[0] = 0.0 + + memset(hist_counts_ptr, 0, n_feature_bins * sizeof(intp_t)) + memset(hist_weights_ptr, 0, n_feature_bins * sizeof(float64_t)) + + if self.criterion_kind == 3: + memset(hist_sums_ptr, 0, n_feature_bins * sizeof(float64_t)) + memset(hist_sq_sums_ptr, 0, n_feature_bins * sizeof(float64_t)) + elif self.criterion_kind in (1, 2): + memset( + hist_class_counts_ptr, + 0, + n_feature_bins * self.n_classes * sizeof(float64_t), + ) + memset(missing_class_counts_ptr, 0, self.n_classes * sizeof(float64_t)) + + for p in range(self.start, self.end): + sample_idx = samples_ptr[p] + code = codes_ptr[sample_idx] + w = 1.0 + if sample_weight_ptr != NULL: + w = sample_weight_ptr[sample_idx] + + if code < 0: + missing_count[0] += 1 + missing_weight[0] += w + if self.criterion_kind == 3: + y_value = y_ptr[sample_idx] + missing_sum[0] += w * y_value + missing_sq_sum[0] += w * y_value * y_value + else: + c = y_ptr[sample_idx] + missing_class_counts_ptr[c] += w + continue + + hist_counts_ptr[code] += 1 + hist_weights_ptr[code] += w + if self.criterion_kind == 3: + y_value = y_ptr[sample_idx] + hist_sums_ptr[code] += w * y_value + hist_sq_sums_ptr[code] += w * y_value * y_value + else: + c = y_ptr[sample_idx] + hist_class_counts_ptr[code * self.n_classes + c] += w + + for i in range(n_feature_bins): + if hist_counts_ptr[i] > 0: + active_bins_ptr[active_count] = i + active_count += 1 + + return active_count + + cdef void _hist_children_class_impurity( + self, + float64_t left_weight, + float64_t right_weight, + float64_t* impurity_left, + float64_t* impurity_right, + ) noexcept nogil: + cdef: + intp_t c + float64_t count_left + float64_t count_right + float64_t sq_left = 0.0 + float64_t sq_right = 0.0 + float64_t value + float64_t* left_class_counts_ptr = &self.left_class_counts[0] + const float64_t* total_class_counts_ptr = ( + &( self.criterion).sum_total[0, 0] + ) + + if self.criterion_kind == 1: + for c in range(self.n_classes): + count_left = left_class_counts_ptr[c] + count_right = total_class_counts_ptr[c] - count_left + sq_left += count_left * count_left + sq_right += count_right * count_right + impurity_left[0] = 1.0 - sq_left / (left_weight * left_weight) + impurity_right[0] = 1.0 - sq_right / (right_weight * right_weight) + else: + impurity_left[0] = 0.0 + impurity_right[0] = 0.0 + for c in range(self.n_classes): + count_left = left_class_counts_ptr[c] + if count_left > 0.0: + value = count_left / left_weight + impurity_left[0] -= value * log(value) + + count_right = total_class_counts_ptr[c] - count_left + if count_right > 0.0: + value = count_right / right_weight + impurity_right[0] -= value * log(value) + + cdef class BestSparseSplitter(Splitter): """Splitter for finding the best split, using the sparse data.""" cdef SparsePartitioner partitioner @@ -795,6 +1367,8 @@ cdef class BestSparseSplitter(Splitter): cdef class RandomSplitter(Splitter): """Splitter for finding the best random split on dense data.""" cdef DensePartitioner partitioner + cdef object global_sorted_samples + cdef object feature_n_unique cdef int init( self, object X, @@ -803,8 +1377,16 @@ cdef class RandomSplitter(Splitter): const uint8_t[::1] missing_values_in_feature_mask, ) except -1: Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask) + self.global_sorted_samples, self.feature_n_unique = _global_sorted_index( + X, self.max_features + ) self.partitioner = DensePartitioner( - X, self.samples, self.feature_values, missing_values_in_feature_mask + X, + self.samples, + self.feature_values, + missing_values_in_feature_mask, + self.global_sorted_samples, + self.feature_n_unique, ) cdef int node_split( diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index eb6d52c49a037..dc7597cd22e3c 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -2570,6 +2570,80 @@ def test_missing_values_best_splitter_missing_both_classes_has_nan(criterion): assert_array_equal(y_pred, [1, 0, 1]) +@pytest.mark.parametrize("criterion", ["gini", "entropy", "log_loss"]) +def test_hist_best_splitter_classifier_low_cardinality(criterion): + """Check the histogram splitter path on low-cardinality classification data.""" + rng = np.random.RandomState(0) + X = rng.randint(0, 5, size=(1200, 4)).astype(np.float32) + X[::17, 0] = np.nan + y = ((X[:, 1] > 2) | np.isnan(X[:, 0])).astype(np.intp) + sample_weight = rng.uniform(0.1, 2.0, size=X.shape[0]) + + tree = DecisionTreeClassifier( + criterion=criterion, max_bins=8, max_depth=4, random_state=0 + ) + tree.fit(X, y, sample_weight=sample_weight) + + assert tree.tree_.node_count > 1 + assert_array_equal(tree.predict(X[:10]), y[:10]) + + +def test_hist_best_splitter_regressor_low_cardinality(): + """Check the histogram splitter path on low-cardinality regression data.""" + rng = np.random.RandomState(0) + X = rng.randint(0, 5, size=(1200, 4)).astype(np.float32) + X[::17, 0] = np.nan + y = np.nan_to_num(X[:, 0], nan=4.0) + 0.5 * X[:, 2] + sample_weight = rng.uniform(0.1, 2.0, size=X.shape[0]) + + tree = DecisionTreeRegressor( + criterion="squared_error", max_bins=8, max_depth=4, random_state=0 + ) + tree.fit(X, y, sample_weight=sample_weight) + + assert tree.tree_.node_count > 1 + assert_allclose(tree.predict(X[:5]), y[:5], atol=0.6) + + +@pytest.mark.parametrize( + "estimator, params, err_msg", + [ + ( + DecisionTreeRegressor(), + {"criterion": "absolute_error"}, + "squared_error regression criterion", + ), + ( + DecisionTreeRegressor(), + {"criterion": "poisson"}, + "squared_error regression criterion", + ), + ( + DecisionTreeRegressor(), + {"criterion": "friedman_mse"}, + "friedman_mse", + ), + ( + DecisionTreeRegressor(), + {"monotonic_cst": [1]}, + "monotonic constraints", + ), + ( + DecisionTreeClassifier(), + {"splitter": "random"}, + "splitter='best'", + ), + ], +) +def test_hist_best_splitter_unsupported_cases(estimator, params, err_msg): + X = np.arange(20, dtype=np.float32).reshape(-1, 1) + y = np.arange(20) % 2 + + estimator.set_params(max_bins=4, **params) + with pytest.raises(ValueError, match=err_msg): + estimator.fit(X, y) + + @pytest.mark.parametrize("sparse_container", CSR_CONTAINERS) @pytest.mark.parametrize( "tree",