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[PYTHON] Simplify training logic, update rabit lib

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This commit is contained in:
tqchen 2016-02-27 19:56:29 -08:00
parent 90bc7f8f6b
commit 4a16b729fc
10 changed files with 108 additions and 77 deletions
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2
Makefile
View File

@ -118,7 +118,7 @@ lib/libxgboost.a: $(ALL_DEP)
lib/libxgboost.dll lib/libxgboost.so: $(ALL_DEP)
@mkdir -p $(@D)
$(CXX) $(CFLAGS) -shared -o $@ $(filter %.o %.a, $^) $(LDFLAGS)
$(CXX) $(CFLAGS) -shared -o $@ $(filter %.o %a, $^) $(LDFLAGS)
java/libxgboost4j.so: java/xgboost4j_wrapper.cpp $(ALL_DEP)
$(CXX) $(CFLAGS) $(JAVAINCFLAGS) -shared -o $@ $(filter %.cpp %.o %.a, $^) $(LDFLAGS)

3
R-package/src/Makevars
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@ -14,4 +14,5 @@ PKG_CPPFLAGS= -I$(PKGROOT)/include -I$(PKGROOT)/dmlc-core/include -I$(PKGROOT)/
PKG_CXXFLAGS= $(SHLIB_OPENMP_CFLAGS) $(SHLIB_PTHREAD_FLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS) $(SHLIB_PTHREAD_FLAGS)
OBJECTS= ./xgboost_R.o ./xgboost_custom.o ./xgboost_assert.o\
$(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o $(PKGROOT)/rabit/src/engine_empty.o
$(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o\
$(PKGROOT)/rabit/src/engine_empty.o $(PKGROOT)/rabit/src/c_api.o

3
R-package/src/Makevars.win
View File

@ -26,6 +26,7 @@ PKG_CPPFLAGS= -I$(PKGROOT)/include -I$(PKGROOT)/dmlc-core/include -I$(PKGROOT)/
PKG_CXXFLAGS= $(SHLIB_OPENMP_CFLAGS) $(SHLIB_PTHREAD_FLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS) $(SHLIB_PTHREAD_FLAGS)
OBJECTS= ./xgboost_R.o ./xgboost_custom.o ./xgboost_assert.o\
$(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o $(PKGROOT)/rabit/src/engine_empty.o
$(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o\
$(PKGROOT)/rabit/src/engine_empty.o $(PKGROOT)/rabit/src/c_api.o
$(OBJECTS) : xgblib

25
include/xgboost/c_api.h
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@ -11,6 +11,9 @@
#define XGB_EXTERN_C extern "C"
#endif
// XGBoost C API will include APIs in Rabit C API
#include <rabit/c_api.h>
#if defined(_MSC_VER) || defined(_WIN32)
#define XGB_DLL XGB_EXTERN_C __declspec(dllexport)
#else
@ -221,6 +224,7 @@ XGB_DLL int XGBoosterFree(BoosterHandle handle);
XGB_DLL int XGBoosterSetParam(BoosterHandle handle,
const char *name,
const char *value);
/*!
* \brief update the model in one round using dtrain
* \param handle handle
@ -282,6 +286,7 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
unsigned ntree_limit,
bst_ulong *out_len,
const float **out_result);
/*!
* \brief load model from existing file
* \param handle handle
@ -353,4 +358,24 @@ XGB_DLL int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
bst_ulong *out_len,
const char ***out_models);
// --- Distributed training API----
// NOTE: functions in rabit/c_api.h will be also available in libxgboost.so
/*!
* \brief Initialize the booster from rabit checkpoint.
* This is used in distributed training API.
* \param handle handle
* \param version The output version of the model.
* \return 0 when success, -1 when failure happens
*/
XGB_DLL int XGBoosterLoadRabitCheckpoint(
BoosterHandle handle,
int* version);
/*!
* \brief Save the current checkpoint to rabit.
* \param handle handle
* \return 0 when success, -1 when failure happens
*/
XGB_DLL int XGBoosterSaveRabitCheckPoint(BoosterHandle handle);
#endif // XGBOOST_C_API_H_

2
include/xgboost/learner.h
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@ -8,7 +8,7 @@
#ifndef XGBOOST_LEARNER_H_
#define XGBOOST_LEARNER_H_
#include <rabit.h>
#include <rabit/rabit.h>
#include <utility>
#include <string>
#include <vector>

109
python-package/xgboost/training.py
View File

@ -24,7 +24,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
Data to be trained.
num_boost_round: int
Number of boosting iterations.
watchlist (evals): list of pairs (DMatrix, string)
evals: list of pairs (DMatrix, string)
List of items to be evaluated during training, this allows user to watch
performance on the validation set.
obj : function
@ -117,48 +117,13 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
evals_result.clear()
evals_result.update(dict([(key, {}) for key in evals_name]))
if not early_stopping_rounds:
for i in range(nboost, nboost + num_boost_round):
bst.update(dtrain, i, obj)
nboost += 1
if len(evals) != 0:
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, STRING_TYPES):
msg = bst_eval_set
else:
msg = bst_eval_set.decode()
if verbose_eval:
if verbose_eval_every_line:
if i % verbose_eval_every_line == 0 or i == num_boost_round - 1:
sys.stderr.write(msg + '\n')
else:
sys.stderr.write(msg + '\n')
if evals_result is not None:
res = re.findall("([0-9a-zA-Z@]+[-]*):-?([0-9.]+).", msg)
for key in evals_name:
evals_idx = evals_name.index(key)
res_per_eval = len(res) // len(evals_name)
for r in range(res_per_eval):
res_item = res[(evals_idx*res_per_eval) + r]
res_key = res_item[0]
res_val = res_item[1]
if res_key in evals_result[key]:
evals_result[key][res_key].append(res_val)
else:
evals_result[key][res_key] = [res_val]
bst.best_iteration = (nboost - 1)
bst.best_ntree_limit = nboost * num_parallel_tree
return bst
else:
# early stopping
# early stopping
if early_stopping_rounds is not None:
if len(evals) < 1:
raise ValueError('For early stopping you need at least one set in evals.')
if verbose_eval:
sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(\
sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(
evals[-1][1], early_stopping_rounds))
# is params a list of tuples? are we using multiple eval metrics?
@ -166,7 +131,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
if len(params) != len(dict(params).items()):
params = dict(params)
sys.stderr.write("Multiple eval metrics have been passed: " \
"'{0}' will be used for early stopping.\n\n".format(params['eval_metric']))
"'{0}' will be used for early stopping.\n\n".format(params['eval_metric']))
else:
params = dict(params)
@ -184,20 +149,23 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
else:
best_score = float('inf')
best_msg = ''
best_score_i = (nboost - 1)
best_msg = ''
best_score_i = (nboost - 1)
if isinstance(learning_rates, list) and len(learning_rates) != num_boost_round:
raise ValueError("Length of list 'learning_rates' has to equal 'num_boost_round'.")
if isinstance(learning_rates, list) and len(learning_rates) != num_boost_round:
raise ValueError("Length of list 'learning_rates' has to equal 'num_boost_round'.")
for i in range(nboost, nboost + num_boost_round):
if learning_rates is not None:
if isinstance(learning_rates, list):
bst.set_param({'eta': learning_rates[i]})
else:
bst.set_param({'eta': learning_rates(i, num_boost_round)})
bst.update(dtrain, i, obj)
nboost += 1
for i in range(nboost, nboost + num_boost_round):
if learning_rates is not None:
if isinstance(learning_rates, list):
bst.set_param({'eta': learning_rates[i]})
else:
bst.set_param({'eta': learning_rates(i, num_boost_round)})
bst.update(dtrain, i, obj)
nboost += 1
# check evaluation result.
if len(evals) != 0:
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, STRING_TYPES):
@ -226,22 +194,28 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
else:
evals_result[key][res_key] = [res_val]
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
best_score = score
best_score_i = (nboost - 1)
best_msg = msg
elif i - best_score_i >= early_stopping_rounds:
if verbose_eval:
sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg))
bst.best_score = best_score
bst.best_iteration = best_score_i
break
bst.best_score = best_score
if early_stopping_rounds:
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
best_score = score
best_score_i = (nboost - 1)
best_msg = msg
elif i - best_score_i >= early_stopping_rounds:
if verbose_eval:
sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg))
# best iteration will be assigned in the end.
bst.best_score = best_score
bst.best_iteration = best_score_i
break
if early_stopping_rounds:
best_score = best_score
bst.best_iteration = best_score_i
bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
return bst
else:
bst.best_iteration = nboost - 1
bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
return bst
class CVPack(object):
@ -486,4 +460,3 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
results = np.array(results)
return results

2
rabit

@ -1 +1 @@
Subproject commit 112d866dc92354304c0891500374fe40cdf13a50
Subproject commit 56ec4263f9a70a315c1f153dc5897b7c1b58250c

31
src/c_api/c_api.cc
View File

@ -3,6 +3,8 @@
#include <xgboost/data.h>
#include <xgboost/learner.h>
#include <xgboost/c_api.h>
#include <xgboost/logging.h>
#include <rabit/rabit.h>
#include <cstdio>
#include <vector>
#include <string>
@ -84,6 +86,10 @@ int XGDMatrixCreateFromFile(const char *fname,
int silent,
DMatrixHandle *out) {
API_BEGIN();
if (rabit::IsDistributed()) {
LOG(CONSOLE) << "XGBoost distributed mode detected, "
<< "will split data among workers";
}
*out = DMatrix::Load(
fname, silent != 0, false);
API_END();
@ -526,3 +532,28 @@ int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
XGBoostDumpModelImpl(handle, featmap, with_stats, len, out_models);
API_END();
}
int XGBoosterLoadRabitCheckpoint(BoosterHandle handle,
int* version) {
API_BEGIN();
Booster* bst = static_cast<Booster*>(handle);
*version = rabit::LoadCheckPoint(bst->learner());
if (version != 0) {
bst->initialized_ = true;
}
API_END();
}
int XGBoosterSaveRabitCheckPoint(BoosterHandle handle) {
API_BEGIN();
Booster* bst = static_cast<Booster*>(handle);
if (bst->learner()->AllowLazyCheckPoint()) {
rabit::LazyCheckPoint(bst->learner());
} else {
rabit::CheckPoint(bst->learner());
}
API_END();
}
// force link rabit
static int XGBOOST_LINK_RABIT_C_API_ = RabitLinkTag();

2
src/common/sync.h
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@ -8,6 +8,6 @@
#ifndef XGBOOST_COMMON_SYNC_H_
#define XGBOOST_COMMON_SYNC_H_
#include <rabit.h>
#include <rabit/rabit.h>
#endif // XGBOOST_COMMON_SYNC_H_

2
tests/python/test_models.py
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@ -31,7 +31,7 @@ class TestModels(unittest.TestCase):
# learning_rates as a customized decay function
def eta_decay(ithround, num_boost_round):
return num_boost_round / ithround
return num_boost_round / (ithround + 1)
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay)
assert isinstance(bst, xgb.core.Booster)