a1bcd33a3b
* Use typed array for models. * Change the memory snapshot format. * Add new C API for saving to raw format.
484 lines
13 KiB
Python
484 lines
13 KiB
Python
# coding: utf-8
|
|
import os
|
|
import urllib
|
|
import zipfile
|
|
import sys
|
|
from typing import Optional
|
|
from contextlib import contextmanager
|
|
from io import StringIO
|
|
from xgboost.compat import SKLEARN_INSTALLED, PANDAS_INSTALLED
|
|
from xgboost.compat import DASK_INSTALLED
|
|
import pytest
|
|
import gc
|
|
import xgboost as xgb
|
|
import numpy as np
|
|
import platform
|
|
|
|
hypothesis = pytest.importorskip('hypothesis')
|
|
sklearn = pytest.importorskip('sklearn')
|
|
from hypothesis import strategies
|
|
from hypothesis.extra.numpy import arrays
|
|
from joblib import Memory
|
|
from sklearn import datasets
|
|
|
|
try:
|
|
import cupy as cp
|
|
except ImportError:
|
|
cp = None
|
|
|
|
memory = Memory('./cachedir', verbose=0)
|
|
|
|
|
|
def no_ubjson():
|
|
reason = "ubjson is not intsalled."
|
|
try:
|
|
import ubjson # noqa
|
|
return {"condition": False, "reason": reason}
|
|
except ImportError:
|
|
return {"condition": True, "reason": reason}
|
|
|
|
|
|
def no_sklearn():
|
|
return {'condition': not SKLEARN_INSTALLED,
|
|
'reason': 'Scikit-Learn is not installed'}
|
|
|
|
|
|
def no_dask():
|
|
return {'condition': not DASK_INSTALLED,
|
|
'reason': 'Dask is not installed'}
|
|
|
|
|
|
def no_pandas():
|
|
return {'condition': not PANDAS_INSTALLED,
|
|
'reason': 'Pandas is not installed.'}
|
|
|
|
|
|
def no_modin():
|
|
reason = 'Modin is not installed.'
|
|
try:
|
|
import modin.pandas as _ # noqa
|
|
return {'condition': False, 'reason': reason}
|
|
except ImportError:
|
|
return {'condition': True, 'reason': reason}
|
|
|
|
|
|
def no_dt():
|
|
import importlib.util
|
|
spec = importlib.util.find_spec('datatable')
|
|
return {'condition': spec is None,
|
|
'reason': 'Datatable is not installed.'}
|
|
|
|
|
|
def no_matplotlib():
|
|
reason = 'Matplotlib is not installed.'
|
|
try:
|
|
import matplotlib.pyplot as _ # noqa
|
|
return {'condition': False,
|
|
'reason': reason}
|
|
except ImportError:
|
|
return {'condition': True,
|
|
'reason': reason}
|
|
|
|
|
|
def no_dask_cuda():
|
|
reason = 'dask_cuda is not installed.'
|
|
try:
|
|
import dask_cuda as _ # noqa
|
|
return {'condition': False, 'reason': reason}
|
|
except ImportError:
|
|
return {'condition': True, 'reason': reason}
|
|
|
|
|
|
def no_cudf():
|
|
try:
|
|
import cudf # noqa
|
|
CUDF_INSTALLED = True
|
|
except ImportError:
|
|
CUDF_INSTALLED = False
|
|
|
|
return {'condition': not CUDF_INSTALLED,
|
|
'reason': 'CUDF is not installed'}
|
|
|
|
|
|
def no_cupy():
|
|
reason = 'cupy is not installed.'
|
|
try:
|
|
import cupy as _ # noqa
|
|
return {'condition': False, 'reason': reason}
|
|
except ImportError:
|
|
return {'condition': True, 'reason': reason}
|
|
|
|
|
|
def no_dask_cudf():
|
|
reason = 'dask_cudf is not installed.'
|
|
try:
|
|
import dask_cudf as _ # noqa
|
|
return {'condition': False, 'reason': reason}
|
|
except ImportError:
|
|
return {'condition': True, 'reason': reason}
|
|
|
|
|
|
def no_json_schema():
|
|
reason = 'jsonschema is not installed'
|
|
try:
|
|
import jsonschema # noqa
|
|
return {'condition': False, 'reason': reason}
|
|
except ImportError:
|
|
return {'condition': True, 'reason': reason}
|
|
|
|
|
|
def no_graphviz():
|
|
reason = 'graphviz is not installed'
|
|
try:
|
|
import graphviz # noqa
|
|
return {'condition': False, 'reason': reason}
|
|
except ImportError:
|
|
return {'condition': True, 'reason': reason}
|
|
|
|
|
|
def no_multiple(*args):
|
|
condition = False
|
|
reason = ''
|
|
for arg in args:
|
|
condition = (condition or arg['condition'])
|
|
if arg['condition']:
|
|
reason = arg['reason']
|
|
break
|
|
return {'condition': condition, 'reason': reason}
|
|
|
|
|
|
def skip_s390x():
|
|
condition = platform.machine() == "s390x"
|
|
reason = "Known to fail on s390x"
|
|
return {"condition": condition, "reason": reason}
|
|
|
|
|
|
class IteratorForTest(xgb.core.DataIter):
|
|
def __init__(self, X, y):
|
|
assert len(X) == len(y)
|
|
self.X = X
|
|
self.y = y
|
|
self.it = 0
|
|
super().__init__("./")
|
|
|
|
def next(self, input_data):
|
|
if self.it == len(self.X):
|
|
return 0
|
|
# Use copy to make sure the iterator doesn't hold a reference to the data.
|
|
input_data(data=self.X[self.it].copy(), label=self.y[self.it].copy())
|
|
gc.collect() # clear up the copy, see if XGBoost access freed memory.
|
|
self.it += 1
|
|
return 1
|
|
|
|
def reset(self):
|
|
self.it = 0
|
|
|
|
def as_arrays(self):
|
|
X = np.concatenate(self.X, axis=0)
|
|
y = np.concatenate(self.y, axis=0)
|
|
return X, y
|
|
|
|
|
|
# Contains a dataset in numpy format as well as the relevant objective and metric
|
|
class TestDataset:
|
|
def __init__(self, name, get_dataset, objective, metric):
|
|
self.name = name
|
|
self.objective = objective
|
|
self.metric = metric
|
|
self.X, self.y = get_dataset()
|
|
self.w = None
|
|
self.margin: Optional[np.ndarray] = None
|
|
|
|
def set_params(self, params_in):
|
|
params_in['objective'] = self.objective
|
|
params_in['eval_metric'] = self.metric
|
|
if self.objective == "multi:softmax":
|
|
params_in["num_class"] = int(np.max(self.y) + 1)
|
|
return params_in
|
|
|
|
def get_dmat(self):
|
|
return xgb.DMatrix(self.X, self.y, self.w, base_margin=self.margin)
|
|
|
|
def get_device_dmat(self):
|
|
w = None if self.w is None else cp.array(self.w)
|
|
X = cp.array(self.X, dtype=np.float32)
|
|
y = cp.array(self.y, dtype=np.float32)
|
|
return xgb.DeviceQuantileDMatrix(X, y, w, base_margin=self.margin)
|
|
|
|
def get_external_dmat(self):
|
|
n_samples = self.X.shape[0]
|
|
n_batches = 10
|
|
per_batch = n_samples // n_batches + 1
|
|
|
|
predictor = []
|
|
response = []
|
|
for i in range(n_batches):
|
|
beg = i * per_batch
|
|
end = min((i + 1) * per_batch, n_samples)
|
|
assert end != beg
|
|
X = self.X[beg: end, ...]
|
|
y = self.y[beg: end]
|
|
predictor.append(X)
|
|
response.append(y)
|
|
|
|
it = IteratorForTest(predictor, response)
|
|
return xgb.DMatrix(it)
|
|
|
|
def __repr__(self):
|
|
return self.name
|
|
|
|
|
|
@memory.cache
|
|
def get_boston():
|
|
data = datasets.load_boston()
|
|
return data.data, data.target
|
|
|
|
|
|
@memory.cache
|
|
def get_digits():
|
|
data = datasets.load_digits()
|
|
return data.data, data.target
|
|
|
|
|
|
@memory.cache
|
|
def get_cancer():
|
|
data = datasets.load_breast_cancer()
|
|
return data.data, data.target
|
|
|
|
|
|
@memory.cache
|
|
def get_sparse():
|
|
rng = np.random.RandomState(199)
|
|
n = 2000
|
|
sparsity = 0.75
|
|
X, y = datasets.make_regression(n, random_state=rng)
|
|
flag = rng.binomial(1, sparsity, X.shape)
|
|
for i in range(X.shape[0]):
|
|
for j in range(X.shape[1]):
|
|
if flag[i, j]:
|
|
X[i, j] = np.nan
|
|
return X, y
|
|
|
|
|
|
@memory.cache
|
|
def get_mq2008(dpath):
|
|
from sklearn.datasets import load_svmlight_files
|
|
|
|
src = 'https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip'
|
|
target = dpath + '/MQ2008.zip'
|
|
if not os.path.exists(target):
|
|
urllib.request.urlretrieve(url=src, filename=target)
|
|
|
|
with zipfile.ZipFile(target, 'r') as f:
|
|
f.extractall(path=dpath)
|
|
|
|
(x_train, y_train, qid_train, x_test, y_test, qid_test,
|
|
x_valid, y_valid, qid_valid) = load_svmlight_files(
|
|
(dpath + "MQ2008/Fold1/train.txt",
|
|
dpath + "MQ2008/Fold1/test.txt",
|
|
dpath + "MQ2008/Fold1/vali.txt"),
|
|
query_id=True, zero_based=False)
|
|
|
|
return (x_train, y_train, qid_train, x_test, y_test, qid_test,
|
|
x_valid, y_valid, qid_valid)
|
|
|
|
|
|
@memory.cache
|
|
def make_categorical(
|
|
n_samples: int, n_features: int, n_categories: int, onehot: bool
|
|
):
|
|
import pandas as pd
|
|
|
|
rng = np.random.RandomState(1994)
|
|
|
|
pd_dict = {}
|
|
for i in range(n_features + 1):
|
|
c = rng.randint(low=0, high=n_categories, size=n_samples)
|
|
pd_dict[str(i)] = pd.Series(c, dtype=np.int64)
|
|
|
|
df = pd.DataFrame(pd_dict)
|
|
label = df.iloc[:, 0]
|
|
df = df.iloc[:, 1:]
|
|
for i in range(0, n_features):
|
|
label += df.iloc[:, i]
|
|
label += 1
|
|
|
|
df = df.astype("category")
|
|
categories = np.arange(0, n_categories)
|
|
for col in df.columns:
|
|
df[col] = df[col].cat.set_categories(categories)
|
|
|
|
if onehot:
|
|
return pd.get_dummies(df), label
|
|
return df, label
|
|
|
|
|
|
_unweighted_datasets_strategy = strategies.sampled_from(
|
|
[
|
|
TestDataset("boston", get_boston, "reg:squarederror", "rmse"),
|
|
TestDataset("digits", get_digits, "multi:softmax", "mlogloss"),
|
|
TestDataset("cancer", get_cancer, "binary:logistic", "logloss"),
|
|
TestDataset(
|
|
"mtreg",
|
|
lambda: datasets.make_regression(n_samples=128, n_targets=3),
|
|
"reg:squarederror",
|
|
"rmse",
|
|
),
|
|
TestDataset("sparse", get_sparse, "reg:squarederror", "rmse"),
|
|
TestDataset(
|
|
"empty",
|
|
lambda: (np.empty((0, 100)), np.empty(0)),
|
|
"reg:squarederror",
|
|
"rmse",
|
|
),
|
|
]
|
|
)
|
|
|
|
|
|
@strategies.composite
|
|
def _dataset_weight_margin(draw):
|
|
data: TestDataset = draw(_unweighted_datasets_strategy)
|
|
if draw(strategies.booleans()):
|
|
data.w = draw(
|
|
arrays(np.float64, (len(data.y)), elements=strategies.floats(0.1, 2.0))
|
|
)
|
|
if draw(strategies.booleans()):
|
|
num_class = 1
|
|
if data.objective == "multi:softmax":
|
|
num_class = int(np.max(data.y) + 1)
|
|
elif data.name == "mtreg":
|
|
num_class = data.y.shape[1]
|
|
|
|
data.margin = draw(
|
|
arrays(
|
|
np.float64,
|
|
(data.y.shape[0] * num_class),
|
|
elements=strategies.floats(0.5, 1.0),
|
|
)
|
|
)
|
|
if num_class != 1:
|
|
data.margin = data.margin.reshape(data.y.shape[0], num_class)
|
|
|
|
return data
|
|
|
|
|
|
# A strategy for drawing from a set of example datasets
|
|
# May add random weights to the dataset
|
|
dataset_strategy = _dataset_weight_margin()
|
|
|
|
|
|
def non_increasing(L, tolerance=1e-4):
|
|
return all((y - x) < tolerance for x, y in zip(L, L[1:]))
|
|
|
|
|
|
def eval_error_metric(predt, dtrain: xgb.DMatrix):
|
|
"""Evaluation metric for xgb.train"""
|
|
label = dtrain.get_label()
|
|
r = np.zeros(predt.shape)
|
|
gt = predt > 0.5
|
|
if predt.size == 0:
|
|
return "CustomErr", 0
|
|
r[gt] = 1 - label[gt]
|
|
le = predt <= 0.5
|
|
r[le] = label[le]
|
|
return 'CustomErr', np.sum(r)
|
|
|
|
|
|
def eval_error_metric_skl(y_true: np.ndarray, y_score: np.ndarray) -> float:
|
|
"""Evaluation metric that looks like metrics provided by sklearn."""
|
|
r = np.zeros(y_score.shape)
|
|
gt = y_score > 0.5
|
|
r[gt] = 1 - y_true[gt]
|
|
le = y_score <= 0.5
|
|
r[le] = y_true[le]
|
|
return np.sum(r)
|
|
|
|
|
|
def softmax(x):
|
|
e = np.exp(x)
|
|
return e / np.sum(e)
|
|
|
|
|
|
def softprob_obj(classes):
|
|
def objective(labels, predt):
|
|
rows = labels.shape[0]
|
|
grad = np.zeros((rows, classes), dtype=float)
|
|
hess = np.zeros((rows, classes), dtype=float)
|
|
eps = 1e-6
|
|
for r in range(predt.shape[0]):
|
|
target = labels[r]
|
|
p = softmax(predt[r, :])
|
|
for c in range(predt.shape[1]):
|
|
assert target >= 0 or target <= classes
|
|
g = p[c] - 1.0 if c == target else p[c]
|
|
h = max((2.0 * p[c] * (1.0 - p[c])).item(), eps)
|
|
grad[r, c] = g
|
|
hess[r, c] = h
|
|
|
|
grad = grad.reshape((rows * classes, 1))
|
|
hess = hess.reshape((rows * classes, 1))
|
|
return grad, hess
|
|
|
|
return objective
|
|
|
|
|
|
class DirectoryExcursion:
|
|
def __init__(self, path: os.PathLike, cleanup=False):
|
|
'''Change directory. Change back and optionally cleaning up the directory when exit.
|
|
|
|
'''
|
|
self.path = path
|
|
self.curdir = os.path.normpath(os.path.abspath(os.path.curdir))
|
|
self.cleanup = cleanup
|
|
self.files = {}
|
|
|
|
def __enter__(self):
|
|
os.chdir(self.path)
|
|
if self.cleanup:
|
|
self.files = {
|
|
os.path.join(root, f)
|
|
for root, subdir, files in os.walk(self.path) for f in files
|
|
}
|
|
|
|
def __exit__(self, *args):
|
|
os.chdir(self.curdir)
|
|
if self.cleanup:
|
|
files = {
|
|
os.path.join(root, f)
|
|
for root, subdir, files in os.walk(self.path) for f in files
|
|
}
|
|
diff = files.difference(self.files)
|
|
for f in diff:
|
|
os.remove(f)
|
|
|
|
|
|
@contextmanager
|
|
def captured_output():
|
|
"""Reassign stdout temporarily in order to test printed statements
|
|
Taken from:
|
|
https://stackoverflow.com/questions/4219717/how-to-assert-output-with-nosetest-unittest-in-python
|
|
|
|
Also works for pytest.
|
|
|
|
"""
|
|
new_out, new_err = StringIO(), StringIO()
|
|
old_out, old_err = sys.stdout, sys.stderr
|
|
try:
|
|
sys.stdout, sys.stderr = new_out, new_err
|
|
yield sys.stdout, sys.stderr
|
|
finally:
|
|
sys.stdout, sys.stderr = old_out, old_err
|
|
|
|
|
|
try:
|
|
# Python 3.7+
|
|
from contextlib import nullcontext as noop_context
|
|
except ImportError:
|
|
# Python 3.6
|
|
from contextlib import suppress as noop_context
|
|
|
|
|
|
CURDIR = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
|
|
PROJECT_ROOT = os.path.normpath(
|
|
os.path.join(CURDIR, os.path.pardir, os.path.pardir))
|