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Handle missing categorical value in CPU evaluator. (#7948)

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This commit is contained in:
Jiaming Yuan
2022-05-27 14:15:47 +08:00
committed by GitHub
parent 2070afea02
commit bde4f25794
7 changed files with 181 additions and 154 deletions
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4
tests/cpp/tree/hist/test_evaluate_splits.cc
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@@ -147,9 +147,9 @@ auto CompareOneHotAndPartition(bool onehot) {
auto dmat =
RandomDataGenerator(kRows, kCols, 0).Seed(3).Type(ft).MaxCategory(n_cats).GenerateDMatrix();
int32_t n_threads = 16;
auto sampler = std::make_shared<common::ColumnSampler>();
auto evaluator = HistEvaluator<CPUExpandEntry>{param, dmat->Info(), n_threads, sampler};
auto evaluator =
HistEvaluator<CPUExpandEntry>{param, dmat->Info(), common::OmpGetNumThreads(0), sampler};
std::vector<CPUExpandEntry> entries(1);
for (auto const &gmat : dmat->GetBatches<GHistIndexMatrix>({32, param.sparse_threshold})) {

5
tests/cpp/tree/test_evaluate_splits.h
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@@ -2,11 +2,14 @@
* Copyright 2022 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/data.h>
#include <algorithm> // next_permutation
#include <numeric> // iota
#include "../../../src/tree/hist/evaluate_splits.h"
#include "../../../src/common/hist_util.h" // HistogramCuts,HistCollection
#include "../../../src/tree/param.h" // TrainParam
#include "../../../src/tree/split_evaluator.h"
#include "../helpers.h"
namespace xgboost {

10
tests/python-gpu/test_gpu_updaters.py
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@@ -77,6 +77,16 @@ class TestGPUUpdaters:
def test_categorical(self, rows, cols, rounds, cats):
self.cputest.run_categorical_basic(rows, cols, rounds, cats, "gpu_hist")
@given(
strategies.integers(10, 400),
strategies.integers(3, 8),
strategies.integers(4, 7)
)
@settings(deadline=None, print_blob=True)
@pytest.mark.skipif(**tm.no_pandas())
def test_categorical_missing(self, rows, cols, cats):
self.cputest.run_categorical_missing(rows, cols, cats, "gpu_hist")
def test_max_cat(self) -> None:
self.cputest.run_max_cat("gpu_hist")

79
tests/python/test_updaters.py
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@@ -1,5 +1,6 @@
from random import choice
from string import ascii_lowercase
from typing import Dict, Any
import testing as tm
import pytest
import xgboost as xgb
@@ -38,6 +39,9 @@ def train_result(param, dmat, num_rounds):
class TestTreeMethod:
USE_ONEHOT = np.iinfo(np.int32).max
USE_PART = 1
@given(exact_parameter_strategy, strategies.integers(1, 20),
tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@@ -213,10 +217,43 @@ class TestTreeMethod:
def test_max_cat(self, tree_method) -> None:
self.run_max_cat(tree_method)
def run_categorical_basic(self, rows, cols, rounds, cats, tree_method):
USE_ONEHOT = np.iinfo(np.int32).max
USE_PART = 1
def run_categorical_missing(
self, rows: int, cols: int, cats: int, tree_method: str
) -> None:
parameters: Dict[str, Any] = {"tree_method": tree_method}
cat, label = tm.make_categorical(
n_samples=256, n_features=4, n_categories=8, onehot=False, sparsity=0.5
)
Xy = xgb.DMatrix(cat, label, enable_categorical=True)
def run(max_cat_to_onehot: int):
# Test with onehot splits
parameters["max_cat_to_onehot"] = max_cat_to_onehot
evals_result: Dict[str, Dict] = {}
booster = xgb.train(
parameters,
Xy,
num_boost_round=16,
evals=[(Xy, "Train")],
evals_result=evals_result
)
assert tm.non_increasing(evals_result["Train"]["rmse"])
y_predt = booster.predict(Xy)
rmse = tm.root_mean_square(label, y_predt)
np.testing.assert_allclose(rmse, evals_result["Train"]["rmse"][-1])
# Test with OHE split
run(self.USE_ONEHOT)
if tree_method == "gpu_hist": # fixme: Test with GPU.
return
# Test with partition-based split
run(self.USE_PART)
def run_categorical_basic(self, rows, cols, rounds, cats, tree_method):
onehot, label = tm.make_categorical(rows, cols, cats, True)
cat, _ = tm.make_categorical(rows, cols, cats, False)
@@ -226,7 +263,7 @@ class TestTreeMethod:
predictor = "gpu_predictor" if tree_method == "gpu_hist" else None
parameters = {"tree_method": tree_method, "predictor": predictor}
# Use one-hot exclusively
parameters["max_cat_to_onehot"] = USE_ONEHOT
parameters["max_cat_to_onehot"] = self.USE_ONEHOT
m = xgb.DMatrix(onehot, label, enable_categorical=False)
xgb.train(
@@ -260,7 +297,7 @@ class TestTreeMethod:
by_grouping: xgb.callback.TrainingCallback.EvalsLog = {}
# switch to partition-based splits
parameters["max_cat_to_onehot"] = USE_PART
parameters["max_cat_to_onehot"] = self.USE_PART
parameters["reg_lambda"] = 0
m = xgb.DMatrix(cat, label, enable_categorical=True)
xgb.train(
@@ -287,27 +324,6 @@ class TestTreeMethod:
)
assert tm.non_increasing(by_grouping["Train"]["rmse"]), by_grouping
# test with missing values
cat, label = tm.make_categorical(
n_samples=256, n_features=4, n_categories=8, onehot=False, sparsity=0.5
)
Xy = xgb.DMatrix(cat, label, enable_categorical=True)
evals_result = {}
# Test with onehot splits
parameters["max_cat_to_onehot"] = USE_ONEHOT
booster = xgb.train(
parameters,
Xy,
num_boost_round=16,
evals=[(Xy, "Train")],
evals_result=evals_result
)
assert tm.non_increasing(evals_result["Train"]["rmse"])
y_predt = booster.predict(Xy)
rmse = tm.root_mean_square(label, y_predt)
np.testing.assert_allclose(rmse, evals_result["Train"]["rmse"][-1])
@given(strategies.integers(10, 400), strategies.integers(3, 8),
strategies.integers(1, 2), strategies.integers(4, 7))
@settings(deadline=None, print_blob=True)
@@ -315,3 +331,14 @@ class TestTreeMethod:
def test_categorical(self, rows, cols, rounds, cats):
self.run_categorical_basic(rows, cols, rounds, cats, "approx")
self.run_categorical_basic(rows, cols, rounds, cats, "hist")
@given(
strategies.integers(10, 400),
strategies.integers(3, 8),
strategies.integers(4, 7)
)
@settings(deadline=None, print_blob=True)
@pytest.mark.skipif(**tm.no_pandas())
def test_categorical_missing(self, rows, cols, cats):
self.run_categorical_missing(rows, cols, cats, "approx")
self.run_categorical_missing(rows, cols, cats, "hist")