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External data adapters (#5044)

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* Use external data adapters as lightweight intermediate layer between external data and DMatrix
This commit is contained in:
Rory Mitchell
2019-12-04 10:56:17 +13:00
committed by GitHub
parent f2277e7106
commit e3c34c79be
15 changed files with 1058 additions and 593 deletions
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112
tests/python/test_basic.py
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@@ -64,25 +64,6 @@ class TestBasic(unittest.TestCase):
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
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('root_index', z)
from_view = dmat.get_uint_info('root_index')
dmat = xgb.DMatrix(np.array([[]]))
dmat.set_uint_info('root_index', z + 0)
from_array = dmat.get_uint_info('root_index')
assert (from_view.shape == from_array.shape)
assert (from_view == from_array).all()
def test_record_results(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
@@ -127,72 +108,6 @@ class TestBasic(unittest.TestCase):
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
def test_dmatrix_init(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_dump(self):
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
@@ -250,27 +165,6 @@ class TestBasic(unittest.TestCase):
assert dm.num_row() == row
assert dm.num_col() == cols
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_cv(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
@@ -336,12 +230,6 @@ class TestBasic(unittest.TestCase):
' dtype=float32)]')
assert output == solution
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('root_index')
class TestBasicPathLike(unittest.TestCase):

171
tests/python/test_dmatrix.py Normal file
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@@ -0,0 +1,171 @@
# -*- 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('root_index', z)
from_view = dmat.get_uint_info('root_index')
dmat = xgb.DMatrix(np.array([[]]))
dmat.set_uint_info('root_index', z + 0)
from_array = dmat.get_uint_info('root_index')
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('root_index')
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)

33
tests/python/test_sparse_dmatrix.py
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@@ -1,33 +0,0 @@
import numpy as np
import xgboost as xgb
from scipy.sparse import rand
rng = np.random.RandomState(1)
param = {'max_depth': 3, 'objective': 'binary:logistic', 'verbosity': 0}
def test_sparse_dmatrix_csr():
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')]
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)
def test_sparse_dmatrix_csc():
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')]
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)