Added test for eta decay (+3 squashed commits)
Squashed commits: [9109887] Added test for eta decay(+1 squashed commit) Squashed commits: [1336bd4] Added tests for eta decay (+2 squashed commit) Squashed commits: [91aac2d] Added tests for eta decay (+1 squashed commit) Squashed commits: [3ff48e7] Added test for eta decay [6bb1eed] Rewrote Rd files [bf0dec4] Added learning_rates for diff eta in each boosting round
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terrytangyuan
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@ -5,7 +5,7 @@
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\alias{predict,xgb.Booster-method}
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\title{Predict method for eXtreme Gradient Boosting model}
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\usage{
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\S4method{predict}{xgb.Booster}(object, newdata, missing = NULL,
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\S4method{predict}{xgb.Booster}(object, newdata, missing = NA,
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outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE)
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}
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\arguments{
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@ -4,7 +4,7 @@
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\alias{xgb.DMatrix}
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\title{Contruct xgb.DMatrix object}
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\usage{
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xgb.DMatrix(data, info = list(), missing = 0, ...)
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xgb.DMatrix(data, info = list(), missing = NA, ...)
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}
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\arguments{
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\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character
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@ -4,11 +4,10 @@
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\alias{xgb.cv}
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\title{Cross Validation}
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\usage{
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xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
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missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
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obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
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verbose = T, print.every.n = 1L, early.stop.round = NULL,
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maximize = NULL, ...)
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xgb.cv(params = list(), data, nrounds, nfold, label = NULL, missing = NA,
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prediction = FALSE, showsd = TRUE, metrics = list(), obj = NULL,
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feval = NULL, stratified = TRUE, folds = NULL, verbose = T,
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print.every.n = 1L, early.stop.round = NULL, maximize = NULL, ...)
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}
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\arguments{
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\item{params}{the list of parameters. Commonly used ones are:
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@ -4,7 +4,7 @@
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\alias{xgboost}
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\title{eXtreme Gradient Boosting (Tree) library}
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\usage{
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xgboost(data = NULL, label = NULL, missing = NULL, weight = NULL,
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xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
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params = list(), nrounds, verbose = 1, print.every.n = 1L,
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early.stop.round = NULL, maximize = NULL, save_period = 0,
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save_name = "xgboost.model", ...)
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@ -2,11 +2,12 @@ context('Test models with custom objective')
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require(xgboost)
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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test_that("custom objective works", {
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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watchlist <- list(eval = dtest, train = dtrain)
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num_round <- 2
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@ -45,3 +46,13 @@ test_that("custom objective works", {
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expect_equal(class(bst), "xgb.Booster")
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expect_equal(length(bst$raw), 1064)
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})
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test_that("different eta for each boosting round works", {
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num_round <- 2
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watchlist <- list(eval = dtest, train = dtrain)
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param <- list(max.depth=2, eta=1, nthread = 2, silent=1)
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bst <- xgb.train(param, dtrain, num_round, watchlist, learning_rates = c(0.2, 0.3))
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})
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@ -1,5 +1,6 @@
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import numpy as np
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import xgboost as xgb
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import unittest
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dpath = 'demo/data/'
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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@ -7,56 +8,76 @@ dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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rng = np.random.RandomState(1994)
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def test_glm():
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param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 4
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bst = xgb.train(param, dtrain, num_round, watchlist)
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assert isinstance(bst, xgb.core.Booster)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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assert err < 0.1
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class TestModels(unittest.TestCase):
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def test_custom_objective():
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param = {'max_depth':2, 'eta':1, 'silent':1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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def logregobj(preds, dtrain):
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labels = dtrain.get_label()
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preds = 1.0 / (1.0 + np.exp(-preds))
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grad = preds - labels
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hess = preds * (1.0-preds)
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return grad, hess
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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def test_glm(self):
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param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 4
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bst = xgb.train(param, dtrain, num_round, watchlist)
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assert isinstance(bst, xgb.core.Booster)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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assert err < 0.1
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# test custom_objective in training
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bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
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assert isinstance(bst, xgb.core.Booster)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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assert err < 0.1
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def test_eta_decay(self):
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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# learning_rates as a list
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bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.4, 0.3])
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assert isinstance(bst, xgb.core.Booster)
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# different length
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num_round = 4
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self.assertRaises(ValueError, xgb.train, param, dtrain, num_round, watchlist, learning_rates=[0.4, 0.3, 0.2])
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# test custom_objective in cross-validation
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xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
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obj = logregobj, feval=evalerror)
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# learning_rates as a customized decay function
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def eta_decay(ithround, num_boost_round):
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return num_boost_round / ithround
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bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay)
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assert isinstance(bst, xgb.core.Booster)
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def test_fpreproc():
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
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num_round = 2
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def fpreproc(dtrain, dtest, param):
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label = dtrain.get_label()
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ratio = float(np.sum(label == 0)) / np.sum(label==1)
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param['scale_pos_weight'] = ratio
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return (dtrain, dtest, param)
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'auc'}, seed = 0, fpreproc = fpreproc)
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def test_show_stdv():
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
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num_round = 2
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'error'}, seed = 0, show_stdv = False)
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def test_custom_objective(self):
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param = {'max_depth':2, 'eta':1, 'silent':1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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def logregobj(preds, dtrain):
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labels = dtrain.get_label()
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preds = 1.0 / (1.0 + np.exp(-preds))
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grad = preds - labels
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hess = preds * (1.0-preds)
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return grad, hess
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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# test custom_objective in training
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bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
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assert isinstance(bst, xgb.core.Booster)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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assert err < 0.1
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# test custom_objective in cross-validation
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xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
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obj = logregobj, feval=evalerror)
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def test_fpreproc(self):
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
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num_round = 2
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def fpreproc(dtrain, dtest, param):
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label = dtrain.get_label()
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ratio = float(np.sum(label == 0)) / np.sum(label==1)
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param['scale_pos_weight'] = ratio
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return (dtrain, dtest, param)
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'auc'}, seed = 0, fpreproc = fpreproc)
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def test_show_stdv(self):
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
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num_round = 2
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'error'}, seed = 0, show_stdv = False)
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