172 lines
6.1 KiB
Python
172 lines
6.1 KiB
Python
# -*- coding: utf-8 -*-
|
|
import numpy as np
|
|
import xgboost as xgb
|
|
import unittest
|
|
import scipy.sparse
|
|
from scipy.sparse import rand
|
|
|
|
rng = np.random.RandomState(1)
|
|
|
|
dpath = 'demo/data/'
|
|
rng = np.random.RandomState(1994)
|
|
|
|
|
|
class TestDMatrix(unittest.TestCase):
|
|
def test_dmatrix_numpy_init(self):
|
|
data = np.random.randn(5, 5)
|
|
dm = xgb.DMatrix(data)
|
|
assert dm.num_row() == 5
|
|
assert dm.num_col() == 5
|
|
|
|
data = np.array([[1, 2], [3, 4]])
|
|
dm = xgb.DMatrix(data)
|
|
assert dm.num_row() == 2
|
|
assert dm.num_col() == 2
|
|
|
|
# 0d array
|
|
self.assertRaises(ValueError, xgb.DMatrix, np.array(1))
|
|
# 1d array
|
|
self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3]))
|
|
# 3d array
|
|
data = np.random.randn(5, 5, 5)
|
|
self.assertRaises(ValueError, xgb.DMatrix, data)
|
|
# object dtype
|
|
data = np.array([['a', 'b'], ['c', 'd']])
|
|
self.assertRaises(ValueError, xgb.DMatrix, data)
|
|
|
|
def test_csr(self):
|
|
indptr = np.array([0, 2, 3, 6])
|
|
indices = np.array([0, 2, 2, 0, 1, 2])
|
|
data = np.array([1, 2, 3, 4, 5, 6])
|
|
X = scipy.sparse.csr_matrix((data, indices, indptr), shape=(3, 3))
|
|
dtrain = xgb.DMatrix(X)
|
|
assert dtrain.num_row() == 3
|
|
assert dtrain.num_col() == 3
|
|
|
|
def test_csc(self):
|
|
row = np.array([0, 2, 2, 0, 1, 2])
|
|
col = np.array([0, 0, 1, 2, 2, 2])
|
|
data = np.array([1, 2, 3, 4, 5, 6])
|
|
X = scipy.sparse.csc_matrix((data, (row, col)), shape=(3, 3))
|
|
dtrain = xgb.DMatrix(X)
|
|
assert dtrain.num_row() == 3
|
|
assert dtrain.num_col() == 3
|
|
|
|
def test_np_view(self):
|
|
# Sliced Float32 array
|
|
y = np.array([12, 34, 56], np.float32)[::2]
|
|
from_view = xgb.DMatrix(np.array([[]]), label=y).get_label()
|
|
from_array = xgb.DMatrix(np.array([[]]), label=y + 0).get_label()
|
|
assert (from_view.shape == from_array.shape)
|
|
assert (from_view == from_array).all()
|
|
|
|
# Sliced UInt array
|
|
z = np.array([12, 34, 56], np.uint32)[::2]
|
|
dmat = xgb.DMatrix(np.array([[]]))
|
|
dmat.set_uint_info('group', z)
|
|
from_view = dmat.get_uint_info('group_ptr')
|
|
dmat = xgb.DMatrix(np.array([[]]))
|
|
dmat.set_uint_info('group', z + 0)
|
|
from_array = dmat.get_uint_info('group_ptr')
|
|
assert (from_view.shape == from_array.shape)
|
|
assert (from_view == from_array).all()
|
|
|
|
def test_feature_names(self):
|
|
data = np.random.randn(5, 5)
|
|
|
|
# different length
|
|
self.assertRaises(ValueError, xgb.DMatrix, data,
|
|
feature_names=list('abcdef'))
|
|
# contains duplicates
|
|
self.assertRaises(ValueError, xgb.DMatrix, data,
|
|
feature_names=['a', 'b', 'c', 'd', 'd'])
|
|
# contains symbol
|
|
self.assertRaises(ValueError, xgb.DMatrix, data,
|
|
feature_names=['a', 'b', 'c', 'd', 'e<1'])
|
|
|
|
dm = xgb.DMatrix(data)
|
|
dm.feature_names = list('abcde')
|
|
assert dm.feature_names == list('abcde')
|
|
|
|
assert dm.slice([0, 1]).feature_names == dm.feature_names
|
|
|
|
dm.feature_types = 'q'
|
|
assert dm.feature_types == list('qqqqq')
|
|
|
|
dm.feature_types = list('qiqiq')
|
|
assert dm.feature_types == list('qiqiq')
|
|
|
|
def incorrect_type_set():
|
|
dm.feature_types = list('abcde')
|
|
|
|
self.assertRaises(ValueError, incorrect_type_set)
|
|
|
|
# reset
|
|
dm.feature_names = None
|
|
self.assertEqual(dm.feature_names, ['f0', 'f1', 'f2', 'f3', 'f4'])
|
|
assert dm.feature_types is None
|
|
|
|
def test_feature_names(self):
|
|
data = np.random.randn(100, 5)
|
|
target = np.array([0, 1] * 50)
|
|
|
|
cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'],
|
|
[u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']]
|
|
|
|
for features in cases:
|
|
dm = xgb.DMatrix(data, label=target,
|
|
feature_names=features)
|
|
assert dm.feature_names == features
|
|
assert dm.num_row() == 100
|
|
assert dm.num_col() == 5
|
|
|
|
params = {'objective': 'multi:softprob',
|
|
'eval_metric': 'mlogloss',
|
|
'eta': 0.3,
|
|
'num_class': 3}
|
|
|
|
bst = xgb.train(params, dm, num_boost_round=10)
|
|
scores = bst.get_fscore()
|
|
assert list(sorted(k for k in scores)) == features
|
|
|
|
dummy = np.random.randn(5, 5)
|
|
dm = xgb.DMatrix(dummy, feature_names=features)
|
|
bst.predict(dm)
|
|
|
|
# different feature name must raises error
|
|
dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
|
|
self.assertRaises(ValueError, bst.predict, dm)
|
|
|
|
def test_get_info(self):
|
|
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
|
|
dtrain.get_float_info('label')
|
|
dtrain.get_float_info('weight')
|
|
dtrain.get_float_info('base_margin')
|
|
dtrain.get_uint_info('group_ptr')
|
|
|
|
def test_sparse_dmatrix_csr(self):
|
|
nrow = 100
|
|
ncol = 1000
|
|
x = rand(nrow, ncol, density=0.0005, format='csr', random_state=rng)
|
|
assert x.indices.max() < ncol - 1
|
|
x.data[:] = 1
|
|
dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
|
|
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
|
|
watchlist = [(dtrain, 'train')]
|
|
param = {'max_depth': 3, 'objective': 'binary:logistic', 'verbosity': 0}
|
|
bst = xgb.train(param, dtrain, 5, watchlist)
|
|
bst.predict(dtrain)
|
|
|
|
def test_sparse_dmatrix_csc(self):
|
|
nrow = 1000
|
|
ncol = 100
|
|
x = rand(nrow, ncol, density=0.0005, format='csc', random_state=rng)
|
|
assert x.indices.max() < nrow - 1
|
|
x.data[:] = 1
|
|
dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
|
|
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
|
|
watchlist = [(dtrain, 'train')]
|
|
param = {'max_depth': 3, 'objective': 'binary:logistic', 'verbosity': 0}
|
|
bst = xgb.train(param, dtrain, 5, watchlist)
|
|
bst.predict(dtrain)
|