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Support categorical split in tree model dump.
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@trivialfis
trivialfis committed Jun 18, 2021
1 parent 86715e4 commit 8d49052
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Showing 8 changed files with 263 additions and 46 deletions.
6 changes: 4 additions & 2 deletions include/xgboost/feature_map.h
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@@ -1,5 +1,5 @@
/*!
* Copyright 2014 by Contributors
* Copyright 2014-2021 by Contributors
* \file feature_map.h
* \brief Feature map data structure to help visualization and model dump.
* \author Tianqi Chen
Expand All @@ -26,7 +26,8 @@ class FeatureMap {
kIndicator = 0,
kQuantitive = 1,
kInteger = 2,
kFloat = 3
kFloat = 3,
kCategorical = 4
};
/*!
* \brief load feature map from input stream
Expand Down Expand Up @@ -82,6 +83,7 @@ class FeatureMap {
if (!strcmp("q", tname)) return kQuantitive;
if (!strcmp("int", tname)) return kInteger;
if (!strcmp("float", tname)) return kFloat;
if (!strcmp("categorical", tname)) return kCategorical;
LOG(FATAL) << "unknown feature type, use i for indicator and q for quantity";
return kIndicator;
}
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3 changes: 2 additions & 1 deletion python-package/xgboost/plotting.py
View file
Expand Up @@ -3,6 +3,7 @@
# coding: utf-8
"""Plotting Library."""
from io import BytesIO
import json
import numpy as np
from .core import Booster
from .sklearn import XGBModel
Expand Down Expand Up @@ -203,7 +204,7 @@ def to_graphviz(booster, fmap='', num_trees=0, rankdir=None,

if kwargs:
parameters += ':'
parameters += str(kwargs)
parameters += json.dumps(kwargs)
tree = booster.get_dump(
fmap=fmap,
dump_format=parameters)[num_trees]
Expand Down
5 changes: 0 additions & 5 deletions src/predictor/cpu_predictor.cc
View file
Expand Up @@ -52,11 +52,6 @@ bst_float PredValue(const SparsePage::Inst &inst,
if (tree_info[i] == bst_group) {
auto const &tree = *trees[i];
bool has_categorical = tree.HasCategoricalSplit();

auto categories = common::Span<uint32_t const>{tree.GetSplitCategories()};
auto split_types = tree.GetSplitTypes();
auto categories_ptr =
common::Span<RegTree::Segment const>{tree.GetSplitCategoriesPtr()};
auto cats = tree.GetCategoriesMatrix();
bst_node_t nidx = -1;
if (has_categorical) {
Expand Down
182 changes: 146 additions & 36 deletions src/tree/tree_model.cc
View file
@@ -1,5 +1,5 @@
/*!
* Copyright 2015-2020 by Contributors
* Copyright 2015-2021 by Contributors
* \file tree_model.cc
* \brief model structure for tree
*/
Expand Down Expand Up @@ -74,6 +74,7 @@ class TreeGenerator {
int32_t /*nid*/, uint32_t /*depth*/) const {
return "";
}
virtual std::string Categorical(RegTree const&, int32_t, uint32_t) const = 0;
virtual std::string Integer(RegTree const& /*tree*/,
int32_t /*nid*/, uint32_t /*depth*/) const {
return "";
Expand All @@ -92,26 +93,51 @@ class TreeGenerator {
virtual std::string SplitNode(RegTree const& tree, int32_t nid, uint32_t depth) {
auto const split_index = tree[nid].SplitIndex();
std::string result;
auto is_categorical = tree.GetSplitTypes()[nid] == FeatureType::kCategorical;
if (split_index < fmap_.Size()) {
auto check_categorical = [&]() {
CHECK(is_categorical)
<< fmap_.Name(split_index)
<< " in feature map is numerical but tree node is categorical.";
};
auto check_numerical = [&]() {
auto is_numerical = !is_categorical;
CHECK(is_numerical)
<< fmap_.Name(split_index)
<< " in feature map is categorical but tree node is numerical.";
};

switch (fmap_.TypeOf(split_index)) {
case FeatureMap::kIndicator: {
result = this->Indicator(tree, nid, depth);
break;
}
case FeatureMap::kInteger: {
result = this->Integer(tree, nid, depth);
break;
}
case FeatureMap::kFloat:
case FeatureMap::kQuantitive: {
result = this->Quantitive(tree, nid, depth);
break;
}
default:
LOG(FATAL) << "Unknown feature map type.";
case FeatureMap::kCategorical: {
check_categorical();
result = this->Categorical(tree, nid, depth);
break;
}
case FeatureMap::kIndicator: {
check_numerical();
result = this->Indicator(tree, nid, depth);
break;
}
case FeatureMap::kInteger: {
check_numerical();
result = this->Integer(tree, nid, depth);
break;
}
case FeatureMap::kFloat:
case FeatureMap::kQuantitive: {
check_numerical();
result = this->Quantitive(tree, nid, depth);
break;
}
default:
LOG(FATAL) << "Unknown feature map type.";
}
} else {
result = this->PlainNode(tree, nid, depth);
if (is_categorical) {
result = this->Categorical(tree, nid, depth);
} else {
result = this->PlainNode(tree, nid, depth);
}
}
return result;
}
Expand Down Expand Up @@ -179,6 +205,32 @@ TreeGenerator* TreeGenerator::Create(std::string const& attrs, FeatureMap const&
__make_ ## TreeGenReg ## _ ## UniqueId ## __ = \
::dmlc::Registry< ::xgboost::TreeGenReg>::Get()->__REGISTER__(Name)

std::vector<bst_cat_t> GetSplitCategories(RegTree const &tree, int32_t nidx) {
auto const &csr = tree.GetCategoriesMatrix();
auto seg = csr.node_ptr[nidx];
auto split = common::KCatBitField{csr.categories.subspan(seg.beg, seg.size)};

std::vector<bst_cat_t> cats;
for (size_t i = 0; i < split.Size(); ++i) {
if (split.Check(i)) {
cats.push_back(static_cast<bst_cat_t>(i));
}
}
return cats;
}

std::string PrintCatsAsSet(std::vector<bst_cat_t> const &cats) {
std::stringstream ss;
ss << "{";
for (size_t i = 0; i < cats.size(); ++i) {
ss << cats[i];
if (i != cats.size() - 1) {
ss << ",";
}
}
ss << "}";
return ss.str();
}

class TextGenerator : public TreeGenerator {
using SuperT = TreeGenerator;
Expand Down Expand Up @@ -258,6 +310,17 @@ class TextGenerator : public TreeGenerator {
return SplitNodeImpl(tree, nid, kNodeTemplate, SuperT::ToStr(cond), depth);
}

std::string Categorical(RegTree const &tree, int32_t nid,
uint32_t depth) const override {
auto cats = GetSplitCategories(tree, nid);
std::string cats_str = PrintCatsAsSet(cats);
static std::string const kNodeTemplate =
"{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}";
std::string const result =
SplitNodeImpl(tree, nid, kNodeTemplate, cats_str, depth);
return result;
}

std::string NodeStat(RegTree const& tree, int32_t nid) const override {
static std::string const kStatTemplate = ",gain={loss_chg},cover={sum_hess}";
std::string const result = SuperT::Match(
Expand Down Expand Up @@ -343,6 +406,24 @@ class JsonGenerator : public TreeGenerator {
return result;
}

std::string Categorical(RegTree const& tree, int32_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"
R"I("split_condition": {cond}, "yes": {right}, "no": {left}, )I"
R"I("missing": {missing})I";
std::string cats_ptr = "[";
for (size_t i = 0; i < cats.size(); ++i) {
cats_ptr += std::to_string(cats[i]);
if (i != cats.size() - 1) {
cats_ptr += ", ";
}
}
cats_ptr += "]";
auto results = SplitNodeImpl(tree, nid, kCategoryTemplate, cats_ptr, depth);
return results;
}

std::string SplitNodeImpl(RegTree const &tree, int32_t nid,
std::string const &template_str, std::string cond,
uint32_t depth) const {
Expand Down Expand Up @@ -534,6 +615,27 @@ class GraphvizGenerator : public TreeGenerator {
}

protected:
template <bool is_categorical>
std::string BuildEdge(RegTree const &tree, bst_node_t nid, int32_t child, bool left) const {
static std::string const kEdgeTemplate =
" {nid} -> {child} [label=\"{branch}\" color=\"{color}\"]\n";
// Is this the default child for missing value?
bool is_missing = tree[nid].DefaultChild() == child;
std::string branch;
if (is_categorical) {
branch = std::string{left ? "no" : "yes"} + std::string{is_missing ? ", missing" : ""};
} else {
branch = std::string{left ? "yes" : "no"} + std::string{is_missing ? ", missing" : ""};
}
std::string buffer =
SuperT::Match(kEdgeTemplate,
{{"{nid}", std::to_string(nid)},
{"{child}", std::to_string(child)},
{"{color}", is_missing ? param_.yes_color : param_.no_color},
{"{branch}", branch}});
return buffer;
}

// 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 {
Expand All @@ -552,27 +654,32 @@ class GraphvizGenerator : public TreeGenerator {
{"{cond}", has_less ? SuperT::ToStr(cond) : ""},
{"{params}", param_.condition_node_params}});

static std::string const kEdgeTemplate =
" {nid} -> {child} [label=\"{branch}\" color=\"{color}\"]\n";
auto MatchFn = SuperT::Match; // mingw failed to capture protected fn.
auto BuildEdge =
[&tree, nid, MatchFn, this](int32_t child, bool left) {
// Is this the default child for missing value?
bool is_missing = tree[nid].DefaultChild() == child;
std::string branch = std::string {left ? "yes" : "no"} +
std::string {is_missing ? ", missing" : ""};
std::string buffer = MatchFn(kEdgeTemplate, {
{"{nid}", std::to_string(nid)},
{"{child}", std::to_string(child)},
{"{color}", is_missing ? param_.yes_color : param_.no_color},
{"{branch}", branch}});
return buffer;
};
result += BuildEdge(tree[nid].LeftChild(), true);
result += BuildEdge(tree[nid].RightChild(), false);
result += BuildEdge<false>(tree, nid, tree[nid].LeftChild(), true);
result += BuildEdge<false>(tree, nid, tree[nid].RightChild(), false);

return result;
};

std::string Categorical(RegTree const& tree, int32_t nid, uint32_t) const override {
static std::string const kLabelTemplate =
" {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)},
{"{cond}", cats_str},
{"{params}", param_.condition_node_params}});

result += BuildEdge<true>(tree, nid, tree[nid].LeftChild(), true);
result += BuildEdge<true>(tree, nid, tree[nid].RightChild(), false);

return result;
}

std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t) const override {
static std::string const kLeafTemplate =
" {nid} [ label=\"leaf={leaf-value}\" {params}]\n";
Expand All @@ -588,9 +695,12 @@ class GraphvizGenerator : public TreeGenerator {
return this->LeafNode(tree, nid, depth);
}
static std::string const kNodeTemplate = "{parent}\n{left}\n{right}";
auto node = tree.GetSplitTypes()[nid] == FeatureType::kCategorical
? this->Categorical(tree, nid, depth)
: this->PlainNode(tree, nid, depth);
auto result = SuperT::Match(
kNodeTemplate,
{{"{parent}", this->PlainNode(tree, nid, depth)},
{{"{parent}", node},
{"{left}", this->BuildTree(tree, tree[nid].LeftChild(), depth+1)},
{"{right}", this->BuildTree(tree, tree[nid].RightChild(), depth+1)}});
return result;
Expand Down

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