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Merge model compatibility fixes from 1.0rc branch. (#5305)

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* Port test model compatibility.
* Port logit model fix.

https://github.com/dmlc/xgboost/pull/5248
https://github.com/dmlc/xgboost/pull/5281
This commit is contained in:
Jiaming Yuan 2020-02-13 20:41:59 +08:00 committed by GitHub
parent 29eeea709a
commit 911a902835
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GPG Key ID: 4AEE18F83AFDEB23
19 changed files with 550 additions and 106 deletions
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2
CMakeLists.txt
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@ -49,7 +49,7 @@ option(USE_SANITIZER "Use santizer flags" OFF)
option(SANITIZER_PATH "Path to sanitizes.")
set(ENABLED_SANITIZERS "address" "leak" CACHE STRING
"Semicolon separated list of sanitizer names. E.g 'address;leak'. Supported sanitizers are
address, leak and thread.")
address, leak, undefined and thread.")
## Plugins
option(PLUGIN_LZ4 "Build lz4 plugin" OFF)
option(PLUGIN_DENSE_PARSER "Build dense parser plugin" OFF)

2
R-package/R/xgb.Booster.R
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@ -139,6 +139,8 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
#' @param reshape whether to reshape the vector of predictions to a matrix form when there are several
#' prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
#' or predinteraction flags is TRUE.
#' @param training whether is the prediction result used for training. For dart booster,
#' training predicting will perform dropout.
#' @param ... Parameters passed to \code{predict.xgb.Booster}
#'
#' @details

3
R-package/man/predict.xgb.Booster.Rd
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@ -49,6 +49,9 @@ It will use all the trees by default (\code{NULL} value).}
prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
or predinteraction flags is TRUE.}
\item{training}{whether is the prediction result used for training. For dart booster,
training predicting will perform dropout.}
\item{...}{Parameters passed to \code{predict.xgb.Booster}}
}
\value{

2
R-package/tests/testthat/test_custom_objective.R
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@ -31,7 +31,6 @@ num_round <- 2
test_that("custom objective works", {
bst <- xgb.train(param, dtrain, num_round, watchlist)
expect_equal(class(bst), "xgb.Booster")
expect_equal(length(bst$raw), 1100)
expect_false(is.null(bst$evaluation_log))
expect_false(is.null(bst$evaluation_log$eval_error))
expect_lt(bst$evaluation_log[num_round, eval_error], 0.03)
@ -58,5 +57,4 @@ test_that("custom objective using DMatrix attr works", {
param$objective = logregobjattr
bst <- xgb.train(param, dtrain, num_round, watchlist)
expect_equal(class(bst), "xgb.Booster")
expect_equal(length(bst$raw), 1100)
})

79
doc/python/convert_090to100.py Normal file
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@ -0,0 +1,79 @@
'''This is a simple script that converts a pickled XGBoost
Scikit-Learn interface object from 0.90 to a native model. Pickle
format is not stable as it's a direct serialization of Python object.
We advice not to use it when stability is needed.
'''
import pickle
import json
import os
import argparse
import numpy as np
import xgboost
import warnings
def save_label_encoder(le):
'''Save the label encoder in XGBClassifier'''
meta = dict()
for k, v in le.__dict__.items():
if isinstance(v, np.ndarray):
meta[k] = v.tolist()
else:
meta[k] = v
return meta
def xgboost_skl_90to100(skl_model):
'''Extract the model and related metadata in SKL model.'''
model = {}
with open(skl_model, 'rb') as fd:
old = pickle.load(fd)
if not isinstance(old, xgboost.XGBModel):
raise TypeError(
'The script only handes Scikit-Learn interface object')
# Save Scikit-Learn specific Python attributes into a JSON document.
for k, v in old.__dict__.items():
if k == '_le':
model[k] = save_label_encoder(v)
elif k == 'classes_':
model[k] = v.tolist()
elif k == '_Booster':
continue
else:
try:
json.dumps({k: v})
model[k] = v
except TypeError:
warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.')
booster = old.get_booster()
# Store the JSON serialization as an attribute
booster.set_attr(scikit_learn=json.dumps(model))
# Save it into a native model.
i = 0
while True:
path = 'xgboost_native_model_from_' + skl_model + '-' + str(i) + '.bin'
if os.path.exists(path):
i += 1
continue
booster.save_model(path)
break
if __name__ == '__main__':
assert xgboost.__version__ != '1.0.0', ('Please use the XGBoost version'
' that generates this pickle.')
parser = argparse.ArgumentParser(
description=('A simple script to convert pickle generated by'
' XGBoost 0.90 to XGBoost 1.0.0 model (not pickle).'))
parser.add_argument(
'--old-pickle',
type=str,
help='Path to old pickle file of Scikit-Learn interface object. '
'Will output a native model converted from this pickle file',
required=True)
args = parser.parse_args()
xgboost_skl_90to100(args.old_pickle)

7
doc/tutorials/saving_model.rst
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@ -91,7 +91,12 @@ Loading pickled file from different version of XGBoost
As noted, pickled model is neither portable nor stable, but in some cases the pickled
models are valuable. One way to restore it in the future is to load it back with that
specific version of Python and XGBoost, export the model by calling `save_model`.
specific version of Python and XGBoost, export the model by calling `save_model`. To help
easing the mitigation, we created a simple script for converting pickled XGBoost 0.90
Scikit-Learn interface object to XGBoost 1.0.0 native model. Please note that the script
suits simple use cases, and it's advised not to use pickle when stability is needed.
It's located in ``xgboost/doc/python`` with the name ``convert_090to100.py``. See
comments in the script for more details.
********************************************************
Saving and Loading the internal parameters configuration

2
include/xgboost/learner.h
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@ -208,6 +208,8 @@ struct LearnerModelParam {
// As the old `LearnerModelParamLegacy` is still used by binary IO, we keep
// this one as an immutable copy.
LearnerModelParam(LearnerModelParamLegacy const& user_param, float base_margin);
/* \brief Whether this parameter is initialized with LearnerModelParamLegacy. */
bool Initialized() const { return num_feature != 0; }
};
} // namespace xgboost

1
python-package/xgboost/dask.py
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@ -600,6 +600,7 @@ class DaskXGBRegressor(DaskScikitLearnBase):
results = train(self.client, params, dtrain,
num_boost_round=self.get_num_boosting_rounds(),
evals=evals)
# pylint: disable=attribute-defined-outside-init
self._Booster = results['booster']
# pylint: disable=attribute-defined-outside-init
self.evals_result_ = results['history']

187
src/learner.cc
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@ -67,19 +67,26 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
/* \brief global bias */
bst_float base_score;
/* \brief number of features */
unsigned num_feature;
uint32_t num_feature;
/* \brief number of classes, if it is multi-class classification */
int num_class;
int32_t num_class;
/*! \brief Model contain additional properties */
int contain_extra_attrs;
int32_t contain_extra_attrs;
/*! \brief Model contain eval metrics */
int contain_eval_metrics;
int32_t contain_eval_metrics;
/*! \brief the version of XGBoost. */
uint32_t major_version;
uint32_t minor_version;
/*! \brief reserved field */
int reserved[29];
int reserved[27];
/*! \brief constructor */
LearnerModelParamLegacy() {
std::memset(this, 0, sizeof(LearnerModelParamLegacy));
base_score = 0.5f;
major_version = std::get<0>(Version::Self());
minor_version = std::get<1>(Version::Self());
static_assert(sizeof(LearnerModelParamLegacy) == 136,
"Do not change the size of this struct, as it will break binary IO.");
}
// Skip other legacy fields.
Json ToJson() const {
@ -118,7 +125,8 @@ LearnerModelParam::LearnerModelParam(
: base_score{base_margin}, num_feature{user_param.num_feature},
num_output_group{user_param.num_class == 0
? 1
: static_cast<uint32_t>(user_param.num_class)} {}
: static_cast<uint32_t>(user_param.num_class)}
{}
struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// data split mode, can be row, col, or none.
@ -140,7 +148,7 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
.describe("Data split mode for distributed training.");
DMLC_DECLARE_FIELD(disable_default_eval_metric)
.set_default(0)
.describe("flag to disable default metric. Set to >0 to disable");
.describe("Flag to disable default metric. Set to >0 to disable");
DMLC_DECLARE_FIELD(booster)
.set_default("gbtree")
.describe("Gradient booster used for training.");
@ -200,6 +208,7 @@ class LearnerImpl : public Learner {
Args args = {cfg_.cbegin(), cfg_.cend()};
tparam_.UpdateAllowUnknown(args);
auto mparam_backup = mparam_;
mparam_.UpdateAllowUnknown(args);
generic_parameters_.UpdateAllowUnknown(args);
generic_parameters_.CheckDeprecated();
@ -217,17 +226,33 @@ class LearnerImpl : public Learner {
// set seed only before the model is initialized
common::GlobalRandom().seed(generic_parameters_.seed);
// must precede configure gbm since num_features is required for gbm
this->ConfigureNumFeatures();
args = {cfg_.cbegin(), cfg_.cend()}; // renew
this->ConfigureObjective(old_tparam, &args);
this->ConfigureGBM(old_tparam, args);
this->ConfigureMetrics(args);
generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());
// Before 1.0.0, we save `base_score` into binary as a transformed value by objective.
// After 1.0.0 we save the value provided by user and keep it immutable instead. To
// keep the stability, we initialize it in binary LoadModel instead of configuration.
// Under what condition should we omit the transformation:
//
// - base_score is loaded from old binary model.
//
// What are the other possible conditions:
//
// - model loaded from new binary or JSON.
// - model is created from scratch.
// - model is configured second time due to change of parameter
if (!learner_model_param_.Initialized() || mparam_.base_score != mparam_backup.base_score) {
learner_model_param_ = LearnerModelParam(mparam_,
obj_->ProbToMargin(mparam_.base_score));
}
this->ConfigureGBM(old_tparam, args);
generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());
this->ConfigureMetrics(args);
this->need_configuration_ = false;
if (generic_parameters_.validate_parameters) {
@ -337,9 +362,6 @@ class LearnerImpl : public Learner {
cache_));
gbm_->LoadModel(gradient_booster);
learner_model_param_ = LearnerModelParam(mparam_,
obj_->ProbToMargin(mparam_.base_score));
auto const& j_attributes = get<Object const>(learner.at("attributes"));
attributes_.clear();
for (auto const& kv : j_attributes) {
@ -459,6 +481,7 @@ class LearnerImpl : public Learner {
}
if (header[0] == '{') {
// Dispatch to JSON
auto json_stream = common::FixedSizeStream(&fp);
std::string buffer;
json_stream.Take(&buffer);
@ -471,25 +494,10 @@ class LearnerImpl : public Learner {
// read parameter
CHECK_EQ(fi->Read(&mparam_, sizeof(mparam_)), sizeof(mparam_))
<< "BoostLearner: wrong model format";
{
// backward compatibility code for compatible with old model type
// for new model, Read(&name_obj_) is suffice
uint64_t len;
CHECK_EQ(fi->Read(&len, sizeof(len)), sizeof(len));
if (len >= std::numeric_limits<unsigned>::max()) {
int gap;
CHECK_EQ(fi->Read(&gap, sizeof(gap)), sizeof(gap))
<< "BoostLearner: wrong model format";
len = len >> static_cast<uint64_t>(32UL);
}
if (len != 0) {
tparam_.objective.resize(len);
CHECK_EQ(fi->Read(&tparam_.objective[0], len), len)
<< "BoostLearner: wrong model format";
}
}
CHECK(fi->Read(&tparam_.objective)) << "BoostLearner: wrong model format";
CHECK(fi->Read(&tparam_.booster)) << "BoostLearner: wrong model format";
// duplicated code with LazyInitModel
obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
&learner_model_param_, cache_));
@ -508,34 +516,57 @@ class LearnerImpl : public Learner {
}
attributes_ = std::map<std::string, std::string>(attr.begin(), attr.end());
}
if (tparam_.objective == "count:poisson") {
std::string max_delta_step;
fi->Read(&max_delta_step);
cfg_["max_delta_step"] = max_delta_step;
bool warn_old_model { false };
if (attributes_.find("count_poisson_max_delta_step") != attributes_.cend()) {
// Loading model from < 1.0.0, objective is not saved.
cfg_["max_delta_step"] = attributes_.at("count_poisson_max_delta_step");
attributes_.erase("count_poisson_max_delta_step");
warn_old_model = true;
} else {
warn_old_model = false;
}
if (mparam_.contain_eval_metrics != 0) {
std::vector<std::string> metr;
fi->Read(&metr);
for (auto name : metr) {
metrics_.emplace_back(Metric::Create(name, &generic_parameters_));
if (mparam_.major_version >= 1) {
learner_model_param_ = LearnerModelParam(mparam_,
obj_->ProbToMargin(mparam_.base_score));
} else {
// Before 1.0.0, base_score is saved as a transformed value, and there's no version
// attribute in the saved model.
learner_model_param_ = LearnerModelParam(mparam_, mparam_.base_score);
warn_old_model = true;
}
if (attributes_.find("objective") != attributes_.cend()) {
auto obj_str = attributes_.at("objective");
auto j_obj = Json::Load({obj_str.c_str(), obj_str.size()});
obj_->LoadConfig(j_obj);
attributes_.erase("objective");
} else {
warn_old_model = true;
}
if (attributes_.find("metrics") != attributes_.cend()) {
auto metrics_str = attributes_.at("metrics");
std::vector<std::string> names { common::Split(metrics_str, ';') };
attributes_.erase("metrics");
for (auto const& n : names) {
this->SetParam(kEvalMetric, n);
}
}
if (warn_old_model) {
LOG(WARNING) << "Loading model from XGBoost < 1.0.0, consider saving it "
"again for improved compatibility";
}
// Renew the version.
mparam_.major_version = std::get<0>(Version::Self());
mparam_.minor_version = std::get<1>(Version::Self());
cfg_["num_class"] = common::ToString(mparam_.num_class);
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
auto n = tparam_.__DICT__();
cfg_.insert(n.cbegin(), n.cend());
Args args = {cfg_.cbegin(), cfg_.cend()};
generic_parameters_.UpdateAllowUnknown(args);
gbm_->Configure(args);
obj_->Configure({cfg_.begin(), cfg_.end()});
for (auto& p_metric : metrics_) {
p_metric->Configure({cfg_.begin(), cfg_.end()});
}
// copy dsplit from config since it will not run again during restore
if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
@ -552,15 +583,8 @@ class LearnerImpl : public Learner {
void SaveModel(dmlc::Stream* fo) const override {
LearnerModelParamLegacy mparam = mparam_; // make a copy to potentially modify
std::vector<std::pair<std::string, std::string> > extra_attr;
// extra attributed to be added just before saving
if (tparam_.objective == "count:poisson") {
auto it = cfg_.find("max_delta_step");
if (it != cfg_.end()) {
// write `max_delta_step` parameter as extra attribute of booster
mparam.contain_extra_attrs = 1;
extra_attr.emplace_back("count_poisson_max_delta_step", it->second);
}
}
{
std::vector<std::string> saved_params;
// check if rabit_bootstrap_cache were set to non zero before adding to checkpoint
@ -577,6 +601,24 @@ class LearnerImpl : public Learner {
}
}
}
{
// Similar to JSON model IO, we save the objective.
Json j_obj { Object() };
obj_->SaveConfig(&j_obj);
std::string obj_doc;
Json::Dump(j_obj, &obj_doc);
extra_attr.emplace_back("objective", obj_doc);
}
// As of 1.0.0, JVM Package and R Package uses Save/Load model for serialization.
// Remove this part once they are ported to use actual serialization methods.
if (mparam.contain_eval_metrics != 0) {
std::stringstream os;
for (auto& ev : metrics_) {
os << ev->Name() << ";";
}
extra_attr.emplace_back("metrics", os.str());
}
fo->Write(&mparam, sizeof(LearnerModelParamLegacy));
fo->Write(tparam_.objective);
fo->Write(tparam_.booster);
@ -589,25 +631,6 @@ class LearnerImpl : public Learner {
fo->Write(std::vector<std::pair<std::string, std::string>>(
attr.begin(), attr.end()));
}
if (tparam_.objective == "count:poisson") {
auto it = cfg_.find("max_delta_step");
if (it != cfg_.end()) {
fo->Write(it->second);
} else {
// recover value of max_delta_step from extra attributes
auto it2 = attributes_.find("count_poisson_max_delta_step");
const std::string max_delta_step
= (it2 != attributes_.end()) ? it2->second : kMaxDeltaStepDefaultValue;
fo->Write(max_delta_step);
}
}
if (mparam.contain_eval_metrics != 0) {
std::vector<std::string> metr;
for (auto& ev : metrics_) {
metr.emplace_back(ev->Name());
}
fo->Write(metr);
}
}
void Save(dmlc::Stream* fo) const override {
@ -661,11 +684,13 @@ class LearnerImpl : public Learner {
If you are loading a serialized model (like pickle in Python) generated by older
XGBoost, please export the model by calling `Booster.save_model` from that version
first, then load it back in current version. See:
first, then load it back in current version. There's a simple script for helping
the process. See:
https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
for more details about differences between saving model and serializing.
for reference to the script, and more details about differences between saving model and
serializing.
)doc";
int64_t sz {-1};
@ -854,7 +879,8 @@ class LearnerImpl : public Learner {
void ConfigureObjective(LearnerTrainParam const& old, Args* p_args) {
// Once binary IO is gone, NONE of these config is useful.
if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0") {
if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0" &&
tparam_.objective != "multi:softprob") {
cfg_["num_output_group"] = cfg_["num_class"];
if (atoi(cfg_["num_class"].c_str()) > 1 && cfg_.count("objective") == 0) {
tparam_.objective = "multi:softmax";
@ -919,7 +945,6 @@ class LearnerImpl : public Learner {
}
CHECK_NE(mparam_.num_feature, 0)
<< "0 feature is supplied. Are you using raw Booster interface?";
learner_model_param_.num_feature = mparam_.num_feature;
// Remove these once binary IO is gone.
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
cfg_["num_class"] = common::ToString(mparam_.num_class);

5
tests/ci_build/Dockerfile.cpu
View File

@ -21,8 +21,9 @@ ENV GOSU_VERSION 1.10
# Install Python packages
RUN \
pip install pyyaml cpplint pylint astroid sphinx numpy scipy pandas matplotlib sh recommonmark guzzle_sphinx_theme mock \
breathe matplotlib graphviz pytest scikit-learn wheel kubernetes urllib3 jsonschema && \
pip install pyyaml cpplint pylint astroid sphinx numpy scipy pandas matplotlib sh \
recommonmark guzzle_sphinx_theme mock breathe matplotlib graphviz \
pytest scikit-learn wheel kubernetes urllib3 jsonschema boto3 && \
pip install https://h2o-release.s3.amazonaws.com/datatable/stable/datatable-0.7.0/datatable-0.7.0-cp37-cp37m-linux_x86_64.whl && \
pip install "dask[complete]"

35
tests/cpp/test_learner.cc
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@ -183,6 +183,41 @@ TEST(Learner, JsonModelIO) {
delete pp_dmat;
}
TEST(Learner, BinaryModelIO) {
size_t constexpr kRows = 8;
int32_t constexpr kIters = 4;
auto pp_dmat = CreateDMatrix(kRows, 10, 0);
std::shared_ptr<DMatrix> p_dmat {*pp_dmat};
p_dmat->Info().labels_.Resize(kRows);
std::unique_ptr<Learner> learner{Learner::Create({p_dmat})};
learner->SetParam("eval_metric", "rmsle");
learner->Configure();
for (int32_t iter = 0; iter < kIters; ++iter) {
learner->UpdateOneIter(iter, p_dmat);
}
dmlc::TemporaryDirectory tempdir;
std::string const fname = tempdir.path + "binary_model_io.bin";
{
// Make sure the write is complete before loading.
std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
learner->SaveModel(fo.get());
}
learner.reset(Learner::Create({p_dmat}));
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
learner->LoadModel(fi.get());
learner->Configure();
Json config { Object() };
learner->SaveConfig(&config);
std::string config_str;
Json::Dump(config, &config_str);
ASSERT_NE(config_str.find("rmsle"), std::string::npos);
ASSERT_EQ(config_str.find("WARNING"), std::string::npos);
delete pp_dmat;
}
#if defined(XGBOOST_USE_CUDA)
// Tests for automatic GPU configuration.
TEST(Learner, GPUConfiguration) {

148
tests/python/generate_models.py Normal file
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@ -0,0 +1,148 @@
import xgboost
import numpy as np
import os
kRounds = 2
kRows = 1000
kCols = 4
kForests = 2
kMaxDepth = 2
kClasses = 3
X = np.random.randn(kRows, kCols)
w = np.random.uniform(size=kRows)
version = xgboost.__version__
np.random.seed(1994)
target_dir = 'models'
def booster_bin(model):
return os.path.join(target_dir,
'xgboost-' + version + '.' + model + '.bin')
def booster_json(model):
return os.path.join(target_dir,
'xgboost-' + version + '.' + model + '.json')
def skl_bin(model):
return os.path.join(target_dir,
'xgboost_scikit-' + version + '.' + model + '.bin')
def skl_json(model):
return os.path.join(target_dir,
'xgboost_scikit-' + version + '.' + model + '.json')
def generate_regression_model():
print('Regression')
y = np.random.randn(kRows)
data = xgboost.DMatrix(X, label=y, weight=w)
booster = xgboost.train({'tree_method': 'hist',
'num_parallel_tree': kForests,
'max_depth': kMaxDepth},
num_boost_round=kRounds, dtrain=data)
booster.save_model(booster_bin('reg'))
booster.save_model(booster_json('reg'))
reg = xgboost.XGBRegressor(tree_method='hist',
num_parallel_tree=kForests,
max_depth=kMaxDepth,
n_estimators=kRounds)
reg.fit(X, y, w)
reg.save_model(skl_bin('reg'))
reg.save_model(skl_json('reg'))
def generate_logistic_model():
print('Logistic')
y = np.random.randint(0, 2, size=kRows)
assert y.max() == 1 and y.min() == 0
data = xgboost.DMatrix(X, label=y, weight=w)
booster = xgboost.train({'tree_method': 'hist',
'num_parallel_tree': kForests,
'max_depth': kMaxDepth,
'objective': 'binary:logistic'},
num_boost_round=kRounds, dtrain=data)
booster.save_model(booster_bin('logit'))
booster.save_model(booster_json('logit'))
reg = xgboost.XGBClassifier(tree_method='hist',
num_parallel_tree=kForests,
max_depth=kMaxDepth,
n_estimators=kRounds)
reg.fit(X, y, w)
reg.save_model(skl_bin('logit'))
reg.save_model(skl_json('logit'))
def generate_classification_model():
print('Classification')
y = np.random.randint(0, kClasses, size=kRows)
data = xgboost.DMatrix(X, label=y, weight=w)
booster = xgboost.train({'num_class': kClasses,
'tree_method': 'hist',
'num_parallel_tree': kForests,
'max_depth': kMaxDepth},
num_boost_round=kRounds, dtrain=data)
booster.save_model(booster_bin('cls'))
booster.save_model(booster_json('cls'))
cls = xgboost.XGBClassifier(tree_method='hist',
num_parallel_tree=kForests,
max_depth=kMaxDepth,
n_estimators=kRounds)
cls.fit(X, y, w)
cls.save_model(skl_bin('cls'))
cls.save_model(skl_json('cls'))
def generate_ranking_model():
print('Learning to Rank')
y = np.random.randint(5, size=kRows)
w = np.random.uniform(size=20)
g = np.repeat(50, 20)
data = xgboost.DMatrix(X, y, weight=w)
data.set_group(g)
booster = xgboost.train({'objective': 'rank:ndcg',
'num_parallel_tree': kForests,
'tree_method': 'hist',
'max_depth': kMaxDepth},
num_boost_round=kRounds,
dtrain=data)
booster.save_model(booster_bin('ltr'))
booster.save_model(booster_json('ltr'))
ranker = xgboost.sklearn.XGBRanker(n_estimators=kRounds,
tree_method='hist',
objective='rank:ndcg',
max_depth=kMaxDepth,
num_parallel_tree=kForests)
ranker.fit(X, y, g, sample_weight=w)
ranker.save_model(skl_bin('ltr'))
ranker.save_model(skl_json('ltr'))
def write_versions():
versions = {'numpy': np.__version__,
'xgboost': version}
with open(os.path.join(target_dir, 'version'), 'w') as fd:
fd.write(str(versions))
if __name__ == '__main__':
if not os.path.exists(target_dir):
os.mkdir(target_dir)
generate_regression_model()
generate_logistic_model()
generate_classification_model()
generate_ranking_model()
write_versions()

2
tests/python/test_basic.py
View File

@ -39,7 +39,7 @@ class TestBasic(unittest.TestCase):
def test_basic(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
param = {'max_depth': 2, 'eta': 1,
'objective': 'binary:logistic'}
# specify validations set to watch performance
watchlist = [(dtest, 'eval'), (dtrain, 'train')]

23
tests/python/test_basic_models.py
View File

@ -284,16 +284,31 @@ class TestModels(unittest.TestCase):
self.assertRaises(ValueError, bst.predict, dm1)
bst.predict(dm2) # success
def test_model_binary_io(self):
model_path = 'test_model_binary_io.bin'
parameters = {'tree_method': 'hist', 'booster': 'gbtree',
'scale_pos_weight': '0.5'}
X = np.random.random((10, 3))
y = np.random.random((10,))
dtrain = xgb.DMatrix(X, y)
bst = xgb.train(parameters, dtrain, num_boost_round=2)
bst.save_model(model_path)
bst = xgb.Booster(model_file=model_path)
os.remove(model_path)
config = json.loads(bst.save_config())
assert float(config['learner']['objective'][
'reg_loss_param']['scale_pos_weight']) == 0.5
def test_model_json_io(self):
model_path = './model.json'
model_path = 'test_model_json_io.json'
parameters = {'tree_method': 'hist', 'booster': 'gbtree'}
j_model = json_model(model_path, parameters)
assert isinstance(j_model['learner'], dict)
bst = xgb.Booster(model_file='./model.json')
bst = xgb.Booster(model_file=model_path)
bst.save_model(fname=model_path)
with open('./model.json', 'r') as fd:
with open(model_path, 'r') as fd:
j_model = json.load(fd)
assert isinstance(j_model['learner'], dict)
@ -302,7 +317,7 @@ class TestModels(unittest.TestCase):
@pytest.mark.skipif(**tm.no_json_schema())
def test_json_schema(self):
import jsonschema
model_path = './model.json'
model_path = 'test_json_schema.json'
path = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
doc = os.path.join(path, 'doc', 'model.schema')

130
tests/python/test_model_compatibility.py Normal file
View File

@ -0,0 +1,130 @@
import xgboost
import os
import generate_models as gm
import json
import zipfile
import pytest
def run_model_param_check(config):
assert config['learner']['learner_model_param']['num_feature'] == str(4)
assert config['learner']['learner_train_param']['booster'] == 'gbtree'
def run_booster_check(booster, name):
config = json.loads(booster.save_config())
run_model_param_check(config)
if name.find('cls') != -1:
assert (len(booster.get_dump()) == gm.kForests * gm.kRounds *
gm.kClasses)
assert float(
config['learner']['learner_model_param']['base_score']) == 0.5
assert config['learner']['learner_train_param'][
'objective'] == 'multi:softmax'
elif name.find('logit') != -1:
assert len(booster.get_dump()) == gm.kForests * gm.kRounds
assert config['learner']['learner_model_param']['num_class'] == str(0)
assert config['learner']['learner_train_param'][
'objective'] == 'binary:logistic'
elif name.find('ltr') != -1:
assert config['learner']['learner_train_param'][
'objective'] == 'rank:ndcg'
else:
assert name.find('reg') != -1
assert len(booster.get_dump()) == gm.kForests * gm.kRounds
assert float(
config['learner']['learner_model_param']['base_score']) == 0.5
assert config['learner']['learner_train_param'][
'objective'] == 'reg:squarederror'
def run_scikit_model_check(name, path):
if name.find('reg') != -1:
reg = xgboost.XGBRegressor()
reg.load_model(path)
config = json.loads(reg.get_booster().save_config())
if name.find('0.90') != -1:
assert config['learner']['learner_train_param'][
'objective'] == 'reg:linear'
else:
assert config['learner']['learner_train_param'][
'objective'] == 'reg:squarederror'
assert (len(reg.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
run_model_param_check(config)
elif name.find('cls') != -1:
cls = xgboost.XGBClassifier()
cls.load_model(path)
if name.find('0.90') == -1:
assert len(cls.classes_) == gm.kClasses
assert len(cls._le.classes_) == gm.kClasses
assert cls.n_classes_ == gm.kClasses
assert (len(cls.get_booster().get_dump()) ==
gm.kRounds * gm.kForests * gm.kClasses), path
config = json.loads(cls.get_booster().save_config())
assert config['learner']['learner_train_param'][
'objective'] == 'multi:softprob', path
run_model_param_check(config)
elif name.find('ltr') != -1:
ltr = xgboost.XGBRanker()
ltr.load_model(path)
assert (len(ltr.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
config = json.loads(ltr.get_booster().save_config())
assert config['learner']['learner_train_param'][
'objective'] == 'rank:ndcg'
run_model_param_check(config)
elif name.find('logit') != -1:
logit = xgboost.XGBClassifier()
logit.load_model(path)
assert (len(logit.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
config = json.loads(logit.get_booster().save_config())
assert config['learner']['learner_train_param'][
'objective'] == 'binary:logistic'
else:
assert False
@pytest.mark.ci
def test_model_compatibility():
'''Test model compatibility, can only be run on CI as others don't
have the credentials.
'''
path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(path, 'models')
try:
import boto3
import botocore
except ImportError:
pytest.skip(
'Skiping compatibility tests as boto3 is not installed.')
try:
s3_bucket = boto3.resource('s3').Bucket('xgboost-ci-jenkins-artifacts')
zip_path = 'xgboost_model_compatibility_test.zip'
s3_bucket.download_file(zip_path, zip_path)
except botocore.exceptions.NoCredentialsError:
pytest.skip(
'Skiping compatibility tests as running on non-CI environment.')
with zipfile.ZipFile(zip_path, 'r') as z:
z.extractall(path)
models = [
os.path.join(root, f) for root, subdir, files in os.walk(path)
for f in files
if f != 'version'
]
assert models
for path in models:
name = os.path.basename(path)
if name.startswith('xgboost-'):
booster = xgboost.Booster(model_file=path)
run_booster_check(booster, name)
elif name.startswith('xgboost_scikit'):
run_scikit_model_check(name, path)
else:
assert False

4
tests/python/test_ranking.py
View File

@ -115,7 +115,6 @@ class TestRanking(unittest.TestCase):
# model training parameters
cls.params = {'objective': 'rank:pairwise',
'booster': 'gbtree',
'silent': 0,
'eval_metric': ['ndcg']
}
@ -153,7 +152,8 @@ class TestRanking(unittest.TestCase):
Test cross-validation with a group specified
"""
cv = xgboost.cv(self.params, self.dtrain, num_boost_round=2500,
early_stopping_rounds=10, shuffle=False, nfold=10, as_pandas=False)
early_stopping_rounds=10, shuffle=False, nfold=10,
as_pandas=False)
assert isinstance(cv, dict)
assert len(cv) == 4