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Fix feature names with special characters. (#9923)

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Jiaming Yuan 2023-12-28 22:45:13 +08:00 committed by GitHub
parent a197899161
commit a7226c0222
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 88 additions and 68 deletions
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12
src/common/common.h
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@ -66,8 +66,20 @@ inline std::vector<std::string> Split(const std::string& s, char delim) {
return ret;
}
/**
* @brief Add escapes for a UTF-8 string.
*/
void EscapeU8(std::string const &string, std::string *p_buffer);
/**
* @brief Add escapes for a UTF-8 string with newly created buffer as return.
*/
inline std::string EscapeU8(std::string const &str) {
std::string buffer;
EscapeU8(str, &buffer);
return buffer;
}
template <typename T>
XGBOOST_DEVICE T Max(T a, T b) {
return a < b ? b : a;

86
src/tree/tree_model.cc
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@ -1,5 +1,5 @@
/**
* Copyright 2015-2023 by Contributors
* Copyright 2015-2023, XGBoost Contributors
* \file tree_model.cc
* \brief model structure for tree
*/
@ -15,9 +15,9 @@
#include <type_traits>
#include "../common/categorical.h"
#include "../common/common.h"
#include "../common/common.h" // for EscapeU8
#include "../predictor/predict_fn.h"
#include "io_utils.h" // GetElem
#include "io_utils.h" // for GetElem
#include "param.h"
#include "xgboost/base.h"
#include "xgboost/data.h"
@ -207,6 +207,7 @@ TreeGenerator* TreeGenerator::Create(std::string const& attrs, FeatureMap const&
__make_ ## TreeGenReg ## _ ## UniqueId ## __ = \
::dmlc::Registry< ::xgboost::TreeGenReg>::Get()->__REGISTER__(Name)
namespace {
std::vector<bst_cat_t> GetSplitCategories(RegTree const& tree, int32_t nidx) {
auto const& csr = tree.GetCategoriesMatrix();
auto seg = csr.node_ptr[nidx];
@ -234,6 +235,15 @@ std::string PrintCatsAsSet(std::vector<bst_cat_t> const &cats) {
return ss.str();
}
std::string GetFeatureName(FeatureMap const& fmap, bst_feature_t split_index) {
CHECK_LE(fmap.Size(), std::numeric_limits<decltype(split_index)>::max());
auto fname = split_index < static_cast<decltype(split_index)>(fmap.Size())
? fmap.Name(split_index)
: ('f' + std::to_string(split_index));
return common::EscapeU8(fname);
}
} // anonymous namespace
class TextGenerator : public TreeGenerator {
using SuperT = TreeGenerator;
@ -263,7 +273,7 @@ class TextGenerator : public TreeGenerator {
std::string result = SuperT::Match(
kIndicatorTemplate,
{{"{nid}", std::to_string(nid)},
{"{fname}", fmap_.Name(split_index)},
{"{fname}", GetFeatureName(fmap_, split_index)},
{"{yes}", std::to_string(nyes)},
{"{no}", std::to_string(tree[nid].DefaultChild())}});
return result;
@ -277,8 +287,7 @@ class TextGenerator : public TreeGenerator {
template_str,
{{"{tabs}", SuperT::Tabs(depth)},
{"{nid}", std::to_string(nid)},
{"{fname}", split_index < fmap_.Size() ? fmap_.Name(split_index) :
std::to_string(split_index)},
{"{fname}", GetFeatureName(fmap_, split_index)},
{"{cond}", cond},
{"{left}", std::to_string(tree[nid].LeftChild())},
{"{right}", std::to_string(tree[nid].RightChild())},
@ -308,7 +317,7 @@ class TextGenerator : public TreeGenerator {
std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
auto cond = tree[nid].SplitCond();
static std::string const kNodeTemplate =
"{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}";
"{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}";
return SplitNodeImpl(tree, nid, kNodeTemplate, SuperT::ToStr(cond), depth);
}
@ -376,7 +385,7 @@ class JsonGenerator : public TreeGenerator {
return result;
}
std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t) const override {
std::string LeafNode(RegTree const& tree, bst_node_t nid, uint32_t) const override {
static std::string const kLeafTemplate =
R"L({ "nodeid": {nid}, "leaf": {leaf} {stat}})L";
static std::string const kStatTemplate =
@ -392,26 +401,22 @@ class JsonGenerator : public TreeGenerator {
return result;
}
std::string Indicator(RegTree const& tree, int32_t nid, uint32_t depth) const override {
std::string Indicator(RegTree const& tree, bst_node_t nid, uint32_t depth) const override {
int32_t nyes = tree[nid].DefaultLeft() ?
tree[nid].RightChild() : tree[nid].LeftChild();
static std::string const kIndicatorTemplate =
R"ID( "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no})ID";
auto split_index = tree[nid].SplitIndex();
auto fname = fmap_.Name(split_index);
std::string qfname; // quoted
common::EscapeU8(fname, &qfname);
auto result = SuperT::Match(
kIndicatorTemplate,
{{"{nid}", std::to_string(nid)},
auto result =
SuperT::Match(kIndicatorTemplate, {{"{nid}", std::to_string(nid)},
{"{depth}", std::to_string(depth)},
{"{fname}", qfname},
{"{fname}", GetFeatureName(fmap_, split_index)},
{"{yes}", std::to_string(nyes)},
{"{no}", std::to_string(tree[nid].DefaultChild())}});
return result;
}
std::string Categorical(RegTree const& tree, int32_t nid, uint32_t depth) const override {
std::string Categorical(RegTree const& tree, bst_node_t nid, uint32_t depth) const override {
auto cats = GetSplitCategories(tree, nid);
static std::string const kCategoryTemplate =
R"I( "nodeid": {nid}, "depth": {depth}, "split": "{fname}", )I"
@ -429,18 +434,13 @@ class JsonGenerator : public TreeGenerator {
return results;
}
std::string SplitNodeImpl(RegTree const &tree, int32_t nid,
std::string const &template_str, std::string cond,
uint32_t depth) const {
std::string SplitNodeImpl(RegTree const& tree, bst_node_t nid, std::string const& template_str,
std::string cond, uint32_t depth) const {
auto split_index = tree[nid].SplitIndex();
auto fname = split_index < fmap_.Size() ? fmap_.Name(split_index) : std::to_string(split_index);
std::string qfname; // quoted
common::EscapeU8(fname, &qfname);
std::string const result = SuperT::Match(
template_str,
{{"{nid}", std::to_string(nid)},
std::string const result =
SuperT::Match(template_str, {{"{nid}", std::to_string(nid)},
{"{depth}", std::to_string(depth)},
{"{fname}", qfname},
{"{fname}", GetFeatureName(fmap_, split_index)},
{"{cond}", cond},
{"{left}", std::to_string(tree[nid].LeftChild())},
{"{right}", std::to_string(tree[nid].RightChild())},
@ -605,9 +605,8 @@ class GraphvizGenerator : public TreeGenerator {
auto const& extra = kwargs["graph_attrs"];
static std::string const kGraphTemplate = " graph [ {key}=\"{value}\" ]\n";
for (auto const& kv : extra) {
param_.graph_attrs += SuperT::Match(kGraphTemplate,
{{"{key}", kv.first},
{"{value}", kv.second}});
param_.graph_attrs +=
SuperT::Match(kGraphTemplate, {{"{key}", kv.first}, {"{value}", kv.second}});
}
kwargs.erase("graph_attrs");
@ -646,17 +645,15 @@ class GraphvizGenerator : public TreeGenerator {
// Only indicator is different, so we combine all different node types into this
// function.
std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t) const override {
auto split = tree[nid].SplitIndex();
auto split_index = tree[nid].SplitIndex();
auto cond = tree[nid].SplitCond();
static std::string const kNodeTemplate =
" {nid} [ label=\"{fname}{<}{cond}\" {params}]\n";
static std::string const kNodeTemplate = " {nid} [ label=\"{fname}{<}{cond}\" {params}]\n";
// Indicator only has fname.
bool has_less = (split >= fmap_.Size()) || fmap_.TypeOf(split) != FeatureMap::kIndicator;
std::string result = SuperT::Match(kNodeTemplate, {
{"{nid}", std::to_string(nid)},
{"{fname}", split < fmap_.Size() ? fmap_.Name(split) :
'f' + std::to_string(split)},
bool has_less =
(split_index >= fmap_.Size()) || fmap_.TypeOf(split_index) != FeatureMap::kIndicator;
std::string result =
SuperT::Match(kNodeTemplate, {{"{nid}", std::to_string(nid)},
{"{fname}", GetFeatureName(fmap_, split_index)},
{"{<}", has_less ? "<" : ""},
{"{cond}", has_less ? SuperT::ToStr(cond) : ""},
{"{params}", param_.condition_node_params}});
@ -672,12 +669,11 @@ class GraphvizGenerator : public TreeGenerator {
" {nid} [ label=\"{fname}:{cond}\" {params}]\n";
auto cats = GetSplitCategories(tree, nid);
auto cats_str = PrintCatsAsSet(cats);
auto split = tree[nid].SplitIndex();
std::string result = SuperT::Match(
kLabelTemplate,
{{"{nid}", std::to_string(nid)},
{"{fname}", split < fmap_.Size() ? fmap_.Name(split)
: 'f' + std::to_string(split)},
auto split_index = tree[nid].SplitIndex();
std::string result =
SuperT::Match(kLabelTemplate, {{"{nid}", std::to_string(nid)},
{"{fname}", GetFeatureName(fmap_, split_index)},
{"{cond}", cats_str},
{"{params}", param_.condition_node_params}});

2
tests/cpp/tree/test_tree_model.cc
View File

@ -404,7 +404,7 @@ TEST(Tree, DumpText) {
}
ASSERT_EQ(n_conditions, 3ul);
ASSERT_NE(str.find("[f0<0]"), std::string::npos);
ASSERT_NE(str.find("[f0<0]"), std::string::npos) << str;
ASSERT_NE(str.find("[f1<1]"), std::string::npos);
ASSERT_NE(str.find("[f2<2]"), std::string::npos);

24
tests/python/test_basic_models.py
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@ -28,10 +28,11 @@ def json_model(model_path: str, parameters: dict) -> dict:
if model_path.endswith("ubj"):
import ubjson
with open(model_path, "rb") as ubjfd:
model = ubjson.load(ubjfd)
else:
with open(model_path, 'r') as fd:
with open(model_path, "r") as fd:
model = json.load(fd)
return model
@ -439,25 +440,34 @@ class TestModels:
'objective': 'multi:softmax'}
validate_model(parameters)
def test_special_model_dump_characters(self):
def test_special_model_dump_characters(self) -> None:
params = {"objective": "reg:squarederror", "max_depth": 3}
feature_names = ['"feature 0"', "\tfeature\n1", "feature 2"]
feature_names = ['"feature 0"', "\tfeature\n1", """feature "2"."""]
X, y, w = tm.make_regression(n_samples=128, n_features=3, use_cupy=False)
Xy = xgb.DMatrix(X, label=y, feature_names=feature_names)
booster = xgb.train(params, Xy, num_boost_round=3)
json_dump = booster.get_dump(dump_format="json")
assert len(json_dump) == 3
def validate(obj: dict) -> None:
def validate_json(obj: dict) -> None:
for k, v in obj.items():
if k == "split":
assert v in feature_names
elif isinstance(v, dict):
validate(v)
validate_json(v)
for j_tree in json_dump:
loaded = json.loads(j_tree)
validate(loaded)
validate_json(loaded)
dot_dump = booster.get_dump(dump_format="dot")
for d in dot_dump:
assert d.find(r"feature \"2\"") != -1
text_dump = booster.get_dump(dump_format="text")
for d in text_dump:
assert d.find(r"feature \"2\"") != -1
def test_categorical_model_io(self):
X, y = tm.make_categorical(256, 16, 71, False)
@ -485,6 +495,7 @@ class TestModels:
@pytest.mark.skipif(**tm.no_sklearn())
def test_attributes(self):
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
cls = xgb.XGBClassifier(n_estimators=2)
cls.fit(X, y, early_stopping_rounds=1, eval_set=[(X, y)])
@ -674,6 +685,7 @@ class TestModels:
@pytest.mark.skipif(**tm.no_pandas())
def test_feature_info(self):
import pandas as pd
rows = 100
cols = 10
X = rng.randn(rows, cols)