58a6723eb1
* Add num target model parameter, which is configured from input labels. * Change elementwise metric and indexing for weights. * Add demo. * Add tests.
475 lines
13 KiB
Python
475 lines
13 KiB
Python
# coding: utf-8
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import os
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import urllib
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import zipfile
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import sys
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from typing import Optional
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from contextlib import contextmanager
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from io import StringIO
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from xgboost.compat import SKLEARN_INSTALLED, PANDAS_INSTALLED
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from xgboost.compat import DASK_INSTALLED
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import pytest
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import gc
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import xgboost as xgb
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import numpy as np
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import platform
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hypothesis = pytest.importorskip('hypothesis')
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sklearn = pytest.importorskip('sklearn')
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from hypothesis import strategies
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from hypothesis.extra.numpy import arrays
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from joblib import Memory
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from sklearn import datasets
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try:
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import cupy as cp
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except ImportError:
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cp = None
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memory = Memory('./cachedir', verbose=0)
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def no_sklearn():
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return {'condition': not SKLEARN_INSTALLED,
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'reason': 'Scikit-Learn is not installed'}
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def no_dask():
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return {'condition': not DASK_INSTALLED,
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'reason': 'Dask is not installed'}
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def no_pandas():
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return {'condition': not PANDAS_INSTALLED,
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'reason': 'Pandas is not installed.'}
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def no_modin():
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reason = 'Modin is not installed.'
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try:
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import modin.pandas as _ # noqa
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return {'condition': False, 'reason': reason}
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except ImportError:
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return {'condition': True, 'reason': reason}
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def no_dt():
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import importlib.util
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spec = importlib.util.find_spec('datatable')
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return {'condition': spec is None,
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'reason': 'Datatable is not installed.'}
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def no_matplotlib():
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reason = 'Matplotlib is not installed.'
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try:
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import matplotlib.pyplot as _ # noqa
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return {'condition': False,
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'reason': reason}
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except ImportError:
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return {'condition': True,
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'reason': reason}
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def no_dask_cuda():
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reason = 'dask_cuda is not installed.'
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try:
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import dask_cuda as _ # noqa
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return {'condition': False, 'reason': reason}
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except ImportError:
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return {'condition': True, 'reason': reason}
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def no_cudf():
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try:
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import cudf # noqa
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CUDF_INSTALLED = True
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except ImportError:
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CUDF_INSTALLED = False
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return {'condition': not CUDF_INSTALLED,
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'reason': 'CUDF is not installed'}
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def no_cupy():
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reason = 'cupy is not installed.'
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try:
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import cupy as _ # noqa
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return {'condition': False, 'reason': reason}
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except ImportError:
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return {'condition': True, 'reason': reason}
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def no_dask_cudf():
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reason = 'dask_cudf is not installed.'
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try:
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import dask_cudf as _ # noqa
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return {'condition': False, 'reason': reason}
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except ImportError:
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return {'condition': True, 'reason': reason}
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def no_json_schema():
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reason = 'jsonschema is not installed'
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try:
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import jsonschema # noqa
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return {'condition': False, 'reason': reason}
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except ImportError:
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return {'condition': True, 'reason': reason}
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def no_graphviz():
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reason = 'graphviz is not installed'
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try:
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import graphviz # noqa
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return {'condition': False, 'reason': reason}
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except ImportError:
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return {'condition': True, 'reason': reason}
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def no_multiple(*args):
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condition = False
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reason = ''
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for arg in args:
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condition = (condition or arg['condition'])
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if arg['condition']:
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reason = arg['reason']
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break
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return {'condition': condition, 'reason': reason}
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def skip_s390x():
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condition = platform.machine() == "s390x"
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reason = "Known to fail on s390x"
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return {"condition": condition, "reason": reason}
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class IteratorForTest(xgb.core.DataIter):
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def __init__(self, X, y):
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assert len(X) == len(y)
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self.X = X
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self.y = y
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self.it = 0
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super().__init__("./")
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def next(self, input_data):
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if self.it == len(self.X):
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return 0
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# Use copy to make sure the iterator doesn't hold a reference to the data.
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input_data(data=self.X[self.it].copy(), label=self.y[self.it].copy())
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gc.collect() # clear up the copy, see if XGBoost access freed memory.
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self.it += 1
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return 1
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def reset(self):
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self.it = 0
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def as_arrays(self):
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X = np.concatenate(self.X, axis=0)
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y = np.concatenate(self.y, axis=0)
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return X, y
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# Contains a dataset in numpy format as well as the relevant objective and metric
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class TestDataset:
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def __init__(self, name, get_dataset, objective, metric):
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self.name = name
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self.objective = objective
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self.metric = metric
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self.X, self.y = get_dataset()
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self.w = None
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self.margin: Optional[np.ndarray] = None
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def set_params(self, params_in):
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params_in['objective'] = self.objective
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params_in['eval_metric'] = self.metric
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if self.objective == "multi:softmax":
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params_in["num_class"] = int(np.max(self.y) + 1)
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return params_in
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def get_dmat(self):
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return xgb.DMatrix(self.X, self.y, self.w, base_margin=self.margin)
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def get_device_dmat(self):
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w = None if self.w is None else cp.array(self.w)
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X = cp.array(self.X, dtype=np.float32)
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y = cp.array(self.y, dtype=np.float32)
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return xgb.DeviceQuantileDMatrix(X, y, w, base_margin=self.margin)
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def get_external_dmat(self):
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n_samples = self.X.shape[0]
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n_batches = 10
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per_batch = n_samples // n_batches + 1
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predictor = []
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response = []
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for i in range(n_batches):
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beg = i * per_batch
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end = min((i + 1) * per_batch, n_samples)
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assert end != beg
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X = self.X[beg: end, ...]
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y = self.y[beg: end]
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predictor.append(X)
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response.append(y)
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it = IteratorForTest(predictor, response)
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return xgb.DMatrix(it)
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def __repr__(self):
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return self.name
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@memory.cache
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def get_boston():
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data = datasets.load_boston()
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return data.data, data.target
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@memory.cache
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def get_digits():
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data = datasets.load_digits()
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return data.data, data.target
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@memory.cache
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def get_cancer():
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data = datasets.load_breast_cancer()
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return data.data, data.target
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@memory.cache
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def get_sparse():
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rng = np.random.RandomState(199)
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n = 2000
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sparsity = 0.75
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X, y = datasets.make_regression(n, random_state=rng)
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flag = rng.binomial(1, sparsity, X.shape)
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for i in range(X.shape[0]):
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for j in range(X.shape[1]):
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if flag[i, j]:
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X[i, j] = np.nan
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return X, y
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@memory.cache
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def get_mq2008(dpath):
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from sklearn.datasets import load_svmlight_files
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src = 'https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip'
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target = dpath + '/MQ2008.zip'
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if not os.path.exists(target):
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urllib.request.urlretrieve(url=src, filename=target)
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with zipfile.ZipFile(target, 'r') as f:
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f.extractall(path=dpath)
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(x_train, y_train, qid_train, x_test, y_test, qid_test,
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x_valid, y_valid, qid_valid) = load_svmlight_files(
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(dpath + "MQ2008/Fold1/train.txt",
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dpath + "MQ2008/Fold1/test.txt",
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dpath + "MQ2008/Fold1/vali.txt"),
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query_id=True, zero_based=False)
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return (x_train, y_train, qid_train, x_test, y_test, qid_test,
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x_valid, y_valid, qid_valid)
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@memory.cache
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def make_categorical(
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n_samples: int, n_features: int, n_categories: int, onehot: bool
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):
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import pandas as pd
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rng = np.random.RandomState(1994)
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pd_dict = {}
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for i in range(n_features + 1):
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c = rng.randint(low=0, high=n_categories, size=n_samples)
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pd_dict[str(i)] = pd.Series(c, dtype=np.int64)
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df = pd.DataFrame(pd_dict)
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label = df.iloc[:, 0]
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df = df.iloc[:, 1:]
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for i in range(0, n_features):
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label += df.iloc[:, i]
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label += 1
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df = df.astype("category")
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categories = np.arange(0, n_categories)
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for col in df.columns:
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df[col] = df[col].cat.set_categories(categories)
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if onehot:
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return pd.get_dummies(df), label
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return df, label
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_unweighted_datasets_strategy = strategies.sampled_from(
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[
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TestDataset("boston", get_boston, "reg:squarederror", "rmse"),
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TestDataset("digits", get_digits, "multi:softmax", "mlogloss"),
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TestDataset("cancer", get_cancer, "binary:logistic", "logloss"),
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TestDataset(
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"mtreg",
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lambda: datasets.make_regression(n_samples=128, n_targets=3),
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"reg:squarederror",
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"rmse",
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),
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TestDataset("sparse", get_sparse, "reg:squarederror", "rmse"),
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TestDataset(
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"empty",
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lambda: (np.empty((0, 100)), np.empty(0)),
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"reg:squarederror",
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"rmse",
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),
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]
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)
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@strategies.composite
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def _dataset_weight_margin(draw):
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data: TestDataset = draw(_unweighted_datasets_strategy)
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if draw(strategies.booleans()):
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data.w = draw(
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arrays(np.float64, (len(data.y)), elements=strategies.floats(0.1, 2.0))
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)
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if draw(strategies.booleans()):
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num_class = 1
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if data.objective == "multi:softmax":
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num_class = int(np.max(data.y) + 1)
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elif data.name == "mtreg":
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num_class = data.y.shape[1]
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data.margin = draw(
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arrays(
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np.float64,
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(data.y.shape[0] * num_class),
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elements=strategies.floats(0.5, 1.0),
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)
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)
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if num_class != 1:
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data.margin = data.margin.reshape(data.y.shape[0], num_class)
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return data
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# A strategy for drawing from a set of example datasets
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# May add random weights to the dataset
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dataset_strategy = _dataset_weight_margin()
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def non_increasing(L, tolerance=1e-4):
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return all((y - x) < tolerance for x, y in zip(L, L[1:]))
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def eval_error_metric(predt, dtrain: xgb.DMatrix):
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"""Evaluation metric for xgb.train"""
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label = dtrain.get_label()
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r = np.zeros(predt.shape)
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gt = predt > 0.5
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if predt.size == 0:
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return "CustomErr", 0
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r[gt] = 1 - label[gt]
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le = predt <= 0.5
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r[le] = label[le]
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return 'CustomErr', np.sum(r)
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def eval_error_metric_skl(y_true: np.ndarray, y_score: np.ndarray) -> float:
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"""Evaluation metric that looks like metrics provided by sklearn."""
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r = np.zeros(y_score.shape)
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gt = y_score > 0.5
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r[gt] = 1 - y_true[gt]
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le = y_score <= 0.5
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r[le] = y_true[le]
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return np.sum(r)
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def softmax(x):
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e = np.exp(x)
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return e / np.sum(e)
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def softprob_obj(classes):
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def objective(labels, predt):
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rows = labels.shape[0]
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grad = np.zeros((rows, classes), dtype=float)
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hess = np.zeros((rows, classes), dtype=float)
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eps = 1e-6
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for r in range(predt.shape[0]):
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target = labels[r]
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p = softmax(predt[r, :])
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for c in range(predt.shape[1]):
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assert target >= 0 or target <= classes
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g = p[c] - 1.0 if c == target else p[c]
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h = max((2.0 * p[c] * (1.0 - p[c])).item(), eps)
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grad[r, c] = g
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hess[r, c] = h
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grad = grad.reshape((rows * classes, 1))
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hess = hess.reshape((rows * classes, 1))
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return grad, hess
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return objective
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class DirectoryExcursion:
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def __init__(self, path: os.PathLike, cleanup=False):
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'''Change directory. Change back and optionally cleaning up the directory when exit.
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'''
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self.path = path
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self.curdir = os.path.normpath(os.path.abspath(os.path.curdir))
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self.cleanup = cleanup
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self.files = {}
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def __enter__(self):
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os.chdir(self.path)
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if self.cleanup:
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self.files = {
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os.path.join(root, f)
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for root, subdir, files in os.walk(self.path) for f in files
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}
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def __exit__(self, *args):
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os.chdir(self.curdir)
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if self.cleanup:
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files = {
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os.path.join(root, f)
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for root, subdir, files in os.walk(self.path) for f in files
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}
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diff = files.difference(self.files)
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for f in diff:
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os.remove(f)
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@contextmanager
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def captured_output():
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"""Reassign stdout temporarily in order to test printed statements
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Taken from:
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https://stackoverflow.com/questions/4219717/how-to-assert-output-with-nosetest-unittest-in-python
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Also works for pytest.
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"""
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new_out, new_err = StringIO(), StringIO()
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old_out, old_err = sys.stdout, sys.stderr
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try:
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sys.stdout, sys.stderr = new_out, new_err
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yield sys.stdout, sys.stderr
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finally:
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sys.stdout, sys.stderr = old_out, old_err
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try:
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# Python 3.7+
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from contextlib import nullcontext as noop_context
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except ImportError:
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# Python 3.6
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from contextlib import suppress as noop_context
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CURDIR = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
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PROJECT_ROOT = os.path.normpath(
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os.path.join(CURDIR, os.path.pardir, os.path.pardir))
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