Merge pull request #582 from terrytangyuan/test

Test (eta decay) and bug fix

R-package/man/predict-xgb.Booster-method.Rd

@@ -5,7 +5,7 @@
 \alias{predict,xgb.Booster-method}
 \title{Predict method for eXtreme Gradient Boosting model}
 \usage{
-\S4method{predict}{xgb.Booster}(object, newdata, missing = NULL,
+\S4method{predict}{xgb.Booster}(object, newdata, missing = NA,
   outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE)
 }
 \arguments{

R-package/man/xgb.DMatrix.Rd

@@ -4,7 +4,7 @@
 \alias{xgb.DMatrix}
 \title{Contruct xgb.DMatrix object}
 \usage{
-xgb.DMatrix(data, info = list(), missing = 0, ...)
+xgb.DMatrix(data, info = list(), missing = NA, ...)
 }
 \arguments{
 \item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character

R-package/man/xgb.cv.Rd

@@ -4,11 +4,10 @@
 \alias{xgb.cv}
 \title{Cross Validation}
 \usage{
-xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
+xgb.cv(params = list(), data, nrounds, nfold, label = NULL, missing = NA,
-  missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
+  prediction = FALSE, showsd = TRUE, metrics = list(), obj = NULL,
-  obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
+  feval = NULL, stratified = TRUE, folds = NULL, verbose = T,
-  verbose = T, print.every.n = 1L, early.stop.round = NULL,
+  print.every.n = 1L, early.stop.round = NULL, maximize = NULL, ...)
-  maximize = NULL, ...)
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:

R-package/man/xgboost.Rd

@@ -4,7 +4,7 @@
 \alias{xgboost}
 \title{eXtreme Gradient Boosting (Tree) library}
 \usage{
-xgboost(data = NULL, label = NULL, missing = NULL, weight = NULL,
+xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
   params = list(), nrounds, verbose = 1, print.every.n = 1L,
   early.stop.round = NULL, maximize = NULL, save_period = 0,
   save_name = "xgboost.model", ...)

R-package/tests/testthat/test_custom_objective.R

@@ -2,11 +2,12 @@ context('Test models with custom objective')
 
 require(xgboost)
 
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
+dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
+
 test_that("custom objective works", {
-  data(agaricus.train, package='xgboost')
-  data(agaricus.test, package='xgboost')
-  dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-  dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
   watchlist <- list(eval = dtest, train = dtrain)
   num_round <- 2

python-package/xgboost/training.py

@@ -98,6 +98,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=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(\
                 evals[-1][1], early_stopping_rounds))
 
@@ -123,7 +124,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
         best_msg = ''
         best_score_i = 0
 
-        if isinstance(learning_rates, list) and len(learning_rates) < 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(num_boost_round):

tests/python/test_models.py

@@ -1,5 +1,6 @@
 import numpy as np
 import xgboost as xgb
+import unittest
 
 dpath = 'demo/data/'
 dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
@@ -7,7 +8,9 @@ dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
 
 rng = np.random.RandomState(1994)
 
-def test_glm():
+class TestModels(unittest.TestCase):
+
+	def test_glm(self):
 		param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
 		watchlist  = [(dtest,'eval'), (dtrain,'train')]
 		num_round = 4
@@ -18,7 +21,22 @@ def test_glm():
 		err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
 		assert err < 0.1
 
-def test_custom_objective():
+	def test_eta_decay(self):
+		param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+		watchlist  = [(dtest,'eval'), (dtrain,'train')]
+		num_round = 2
+		# learning_rates as a list
+		bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.4, 0.3])
+		assert isinstance(bst, xgb.core.Booster)
+
+		# learning_rates as a customized decay function
+		def eta_decay(ithround, num_boost_round):
+			return num_boost_round / ithround
+		bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay)
+		assert isinstance(bst, xgb.core.Booster)
+
+
+	def test_custom_objective(self):
 		param = {'max_depth':2, 'eta':1, 'silent':1 }
 		watchlist  = [(dtest,'eval'), (dtrain,'train')]
 		num_round = 2
@@ -44,7 +62,7 @@ def test_custom_objective():
 		xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
 	       obj = logregobj, feval=evalerror)
 
-def test_fpreproc():
+	def test_fpreproc(self):
 		param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
 		num_round = 2
 		def fpreproc(dtrain, dtest, param):
@@ -55,7 +73,7 @@ def test_fpreproc():
 		xgb.cv(param, dtrain, num_round, nfold=5,
 	       metrics={'auc'}, seed = 0, fpreproc = fpreproc)
 
-def test_show_stdv():
+	def test_show_stdv(self):
 		param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
 		num_round = 2
 		xgb.cv(param, dtrain, num_round, nfold=5,