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sinhrks
2016-04-24 16:34:46 +09:00
parent b3c9e6a0db
commit 8fc2456c87
19 changed files with 282 additions and 199 deletions
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148
tests/python/test_basic_models.py
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@@ -8,82 +8,94 @@ dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
rng = np.random.RandomState(1994)
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
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
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
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)
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 + 1)
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay)
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 + 1)
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
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)
def test_custom_objective(self):
param = {'max_depth': 2, 'eta': 1, 'silent': 1}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2
# 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 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
# test custom_objective in cross-validation
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
obj = logregobj, feval=evalerror)
def evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
# test maximize parameter
def neg_evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels == (preds > 0.0))) / len(labels)
bst2 = xgb.train(param, dtrain, num_round, watchlist, logregobj, neg_evalerror, maximize=True)
preds2 = bst2.predict(dtest)
err2 = sum(1 for i in range(len(preds2)) if int(preds2[i]>0.5)!=labels[i]) / float(len(preds2))
assert err == err2
# 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_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)
# test custom_objective in cross-validation
xgb.cv(param, dtrain, num_round, nfold=5, seed=0,
obj=logregobj, feval=evalerror)
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)
# test maximize parameter
def neg_evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels == (preds > 0.0))) / len(labels)
bst2 = xgb.train(param, dtrain, num_round, watchlist, logregobj, neg_evalerror, maximize=True)
preds2 = bst2.predict(dtest)
err2 = sum(1 for i in range(len(preds2))
if int(preds2[i] > 0.5) != labels[i]) / float(len(preds2))
assert err == err2
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)