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Added test for eta decay (+3 squashed commits)

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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
This commit is contained in:
terrytangyuan
2015-10-31 10:35:01 -04:00
parent c817efbd8a
commit 888edba03f
6 changed files with 93 additions and 62 deletions
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119
tests/python/test_models.py
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@@ -1,5 +1,6 @@
import numpy as np
import xgboost as xgb
import unittest
dpath = 'demo/data/'
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
@@ -7,56 +8,76 @@ dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
rng = np.random.RandomState(1994)
def test_glm():
param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 4
bst = xgb.train(param, dtrain, num_round, watchlist)
assert isinstance(bst, xgb.core.Booster)
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
assert err < 0.1
class TestModels(unittest.TestCase):
def test_custom_objective():
param = {'max_depth':2, 'eta':1, 'silent':1 }
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 2
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0-preds)
return grad, hess
def evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
# test custom_objective in training
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
assert isinstance(bst, xgb.core.Booster)
preds = bst.predict(dtest)
labels = dtest.get_label()
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_glm(self):
param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 4
bst = xgb.train(param, dtrain, num_round, watchlist)
assert isinstance(bst, xgb.core.Booster)
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
assert err < 0.1
# test custom_objective in cross-validation
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
obj = logregobj, feval=evalerror)
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)
# different length
num_round = 4
self.assertRaises(ValueError, xgb.train, param, dtrain, num_round, watchlist, learning_rates=[0.4, 0.3, 0.2])
def test_fpreproc():
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
num_round = 2
def fpreproc(dtrain, dtest, param):
label = dtrain.get_label()
ratio = float(np.sum(label == 0)) / np.sum(label==1)
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'auc'}, seed = 0, fpreproc = fpreproc)
# 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_show_stdv():
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
num_round = 2
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'error'}, seed = 0, show_stdv = False)
def test_custom_objective(self):
param = {'max_depth':2, 'eta':1, 'silent':1 }
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 2
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0-preds)
return grad, hess
def evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
# test custom_objective in training
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
assert isinstance(bst, xgb.core.Booster)
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
assert err < 0.1
# test custom_objective in cross-validation
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
obj = logregobj, feval=evalerror)
def test_fpreproc(self):
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
num_round = 2
def fpreproc(dtrain, dtest, param):
label = dtrain.get_label()
ratio = float(np.sum(label == 0)) / np.sum(label==1)
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'auc'}, seed = 0, fpreproc = fpreproc)
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,
metrics={'error'}, seed = 0, show_stdv = False)