cfd2a9f872
- Move test files. - Run spark and dask separately to prevent conflicts. - Gather common code into the testing module.
445 lines
16 KiB
Python
445 lines
16 KiB
Python
import json
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from string import ascii_lowercase
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from typing import Any, Dict
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import numpy as np
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import pytest
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from hypothesis import given, note, settings, strategies
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import xgboost as xgb
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from xgboost import testing as tm
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from xgboost.testing.params import (
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exact_parameter_strategy,
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hist_parameter_strategy,
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cat_parameter_strategy,
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)
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def train_result(param, dmat, num_rounds):
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result = {}
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xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
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evals_result=result)
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return result
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class TestTreeMethod:
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USE_ONEHOT = np.iinfo(np.int32).max
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USE_PART = 1
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@given(exact_parameter_strategy, strategies.integers(1, 20),
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tm.dataset_strategy)
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@settings(deadline=None, print_blob=True)
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def test_exact(self, param, num_rounds, dataset):
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if dataset.name.endswith("-l1"):
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return
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param['tree_method'] = 'exact'
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)
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assert tm.non_increasing(result['train'][dataset.metric])
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@given(
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exact_parameter_strategy,
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hist_parameter_strategy,
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strategies.integers(1, 20),
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tm.dataset_strategy,
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)
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@settings(deadline=None, print_blob=True)
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def test_approx(self, param, hist_param, num_rounds, dataset):
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param["tree_method"] = "approx"
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param = dataset.set_params(param)
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param.update(hist_param)
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result = train_result(param, dataset.get_dmat(), num_rounds)
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note(result)
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assert tm.non_increasing(result["train"][dataset.metric])
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_pruner(self):
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import sklearn
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params = {'tree_method': 'exact'}
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cancer = sklearn.datasets.load_breast_cancer()
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X = cancer['data']
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y = cancer["target"]
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dtrain = xgb.DMatrix(X, y)
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booster = xgb.train(params, dtrain=dtrain, num_boost_round=10)
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grown = str(booster.get_dump())
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params = {'updater': 'prune', 'process_type': 'update', 'gamma': '0.2'}
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booster = xgb.train(params, dtrain=dtrain, num_boost_round=10,
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xgb_model=booster)
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after_prune = str(booster.get_dump())
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assert grown != after_prune
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booster = xgb.train(params, dtrain=dtrain, num_boost_round=10,
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xgb_model=booster)
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second_prune = str(booster.get_dump())
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# Second prune should not change the tree
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assert after_prune == second_prune
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@given(exact_parameter_strategy, hist_parameter_strategy, strategies.integers(1, 20),
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tm.dataset_strategy)
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@settings(deadline=None, print_blob=True)
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def test_hist(self, param, hist_param, num_rounds, dataset):
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param['tree_method'] = 'hist'
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param = dataset.set_params(param)
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param.update(hist_param)
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result = train_result(param, dataset.get_dmat(), num_rounds)
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note(result)
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assert tm.non_increasing(result['train'][dataset.metric])
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@given(tm.sparse_datasets_strategy)
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@settings(deadline=None, print_blob=True)
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def test_sparse(self, dataset):
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param = {"tree_method": "hist", "max_bin": 64}
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hist_result = train_result(param, dataset.get_dmat(), 16)
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note(hist_result)
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assert tm.non_increasing(hist_result['train'][dataset.metric])
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param = {"tree_method": "approx", "max_bin": 64}
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approx_result = train_result(param, dataset.get_dmat(), 16)
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note(approx_result)
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assert tm.non_increasing(approx_result['train'][dataset.metric])
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np.testing.assert_allclose(
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hist_result["train"]["rmse"], approx_result["train"]["rmse"]
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)
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def test_hist_categorical(self):
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# hist must be same as exact on all-categorial data
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dpath = 'demo/data/'
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ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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ag_param = {'max_depth': 2,
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'tree_method': 'hist',
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'eta': 1,
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'verbosity': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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hist_res = {}
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exact_res = {}
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xgb.train(ag_param, ag_dtrain, 10,
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[(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=hist_res)
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ag_param["tree_method"] = "exact"
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xgb.train(ag_param, ag_dtrain, 10,
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[(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=exact_res)
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assert hist_res['train']['auc'] == exact_res['train']['auc']
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assert hist_res['test']['auc'] == exact_res['test']['auc']
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_hist_degenerate_case(self):
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# Test a degenerate case where the quantile sketcher won't return any
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# quantile points for a particular feature (the second feature in
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# this example). Source: https://github.com/dmlc/xgboost/issues/2943
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nan = np.nan
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param = {'missing': nan, 'tree_method': 'hist'}
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model = xgb.XGBRegressor(**param)
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X = np.array([[6.18827160e+05, 1.73000000e+02], [6.37345679e+05, nan],
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[6.38888889e+05, nan], [6.28086420e+05, nan]])
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y = [1000000., 0., 0., 500000.]
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w = [0, 0, 1, 0]
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model.fit(X, y, sample_weight=w)
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def run_invalid_category(self, tree_method: str) -> None:
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rng = np.random.default_rng()
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# too large
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X = rng.integers(low=0, high=4, size=1000).reshape(100, 10)
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y = rng.normal(loc=0, scale=1, size=100)
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X[13, 7] = np.iinfo(np.int32).max + 1
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# Check is performed during sketching.
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Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
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with pytest.raises(ValueError):
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xgb.train({"tree_method": tree_method}, Xy)
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X[13, 7] = 16777216
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Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
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with pytest.raises(ValueError):
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xgb.train({"tree_method": tree_method}, Xy)
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# mixed positive and negative values
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X = rng.normal(loc=0, scale=1, size=1000).reshape(100, 10)
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y = rng.normal(loc=0, scale=1, size=100)
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Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
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with pytest.raises(ValueError):
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xgb.train({"tree_method": tree_method}, Xy)
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if tree_method == "gpu_hist":
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import cupy as cp
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X, y = cp.array(X), cp.array(y)
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with pytest.raises(ValueError):
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Xy = xgb.DeviceQuantileDMatrix(X, y, feature_types=["c"] * 10)
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def test_invalid_category(self) -> None:
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self.run_invalid_category("approx")
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self.run_invalid_category("hist")
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def run_max_cat(self, tree_method: str) -> None:
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"""Test data with size smaller than number of categories."""
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import pandas as pd
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rng = np.random.default_rng(0)
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n_cat = 100
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n = 5
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X = pd.Series(
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["".join(rng.choice(list(ascii_lowercase), size=3)) for i in range(n_cat)],
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dtype="category",
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)[:n].to_frame()
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reg = xgb.XGBRegressor(
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enable_categorical=True,
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tree_method=tree_method,
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n_estimators=10,
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)
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y = pd.Series(range(n))
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reg.fit(X=X, y=y, eval_set=[(X, y)])
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assert tm.non_increasing(reg.evals_result()["validation_0"]["rmse"])
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@pytest.mark.parametrize("tree_method", ["hist", "approx"])
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@pytest.mark.skipif(**tm.no_pandas())
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def test_max_cat(self, tree_method) -> None:
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self.run_max_cat(tree_method)
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def run_categorical_missing(
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self, rows: int, cols: int, cats: int, tree_method: str
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) -> None:
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parameters: Dict[str, Any] = {"tree_method": tree_method}
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cat, label = tm.make_categorical(
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n_samples=rows, n_features=cols, n_categories=cats, onehot=False, sparsity=0.5
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)
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Xy = xgb.DMatrix(cat, label, enable_categorical=True)
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def run(max_cat_to_onehot: int):
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# Test with onehot splits
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parameters["max_cat_to_onehot"] = max_cat_to_onehot
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evals_result: Dict[str, Dict] = {}
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booster = xgb.train(
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parameters,
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Xy,
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num_boost_round=16,
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evals=[(Xy, "Train")],
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evals_result=evals_result
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)
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assert tm.non_increasing(evals_result["Train"]["rmse"])
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y_predt = booster.predict(Xy)
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rmse = tm.root_mean_square(label, y_predt)
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np.testing.assert_allclose(rmse, evals_result["Train"]["rmse"][-1])
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# Test with OHE split
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run(self.USE_ONEHOT)
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# Test with partition-based split
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run(self.USE_PART)
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def run_categorical_ohe(self, rows, cols, rounds, cats, tree_method):
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onehot, label = tm.make_categorical(rows, cols, cats, True)
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cat, _ = tm.make_categorical(rows, cols, cats, False)
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by_etl_results = {}
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by_builtin_results = {}
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predictor = "gpu_predictor" if tree_method == "gpu_hist" else None
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parameters = {"tree_method": tree_method, "predictor": predictor}
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# Use one-hot exclusively
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parameters["max_cat_to_onehot"] = self.USE_ONEHOT
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m = xgb.DMatrix(onehot, label, enable_categorical=False)
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xgb.train(
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parameters,
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m,
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num_boost_round=rounds,
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evals=[(m, "Train")],
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evals_result=by_etl_results,
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)
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m = xgb.DMatrix(cat, label, enable_categorical=True)
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xgb.train(
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parameters,
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m,
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num_boost_round=rounds,
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evals=[(m, "Train")],
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evals_result=by_builtin_results,
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)
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# There are guidelines on how to specify tolerance based on considering output as
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# random variables. But in here the tree construction is extremely sensitive to
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# floating point errors. An 1e-5 error in a histogram bin can lead to an entirely
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# different tree. So even though the test is quite lenient, hypothesis can still
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# pick up falsifying examples from time to time.
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np.testing.assert_allclose(
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np.array(by_etl_results["Train"]["rmse"]),
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np.array(by_builtin_results["Train"]["rmse"]),
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rtol=1e-3,
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)
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assert tm.non_increasing(by_builtin_results["Train"]["rmse"])
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by_grouping: xgb.callback.TrainingCallback.EvalsLog = {}
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# switch to partition-based splits
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parameters["max_cat_to_onehot"] = self.USE_PART
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parameters["reg_lambda"] = 0
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m = xgb.DMatrix(cat, label, enable_categorical=True)
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xgb.train(
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parameters,
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m,
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num_boost_round=rounds,
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evals=[(m, "Train")],
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evals_result=by_grouping,
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)
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rmse_oh = by_builtin_results["Train"]["rmse"]
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rmse_group = by_grouping["Train"]["rmse"]
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# always better or equal to onehot when there's no regularization.
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for a, b in zip(rmse_oh, rmse_group):
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assert a >= b
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parameters["reg_lambda"] = 1.0
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by_grouping = {}
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xgb.train(
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parameters,
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m,
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num_boost_round=32,
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evals=[(m, "Train")],
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evals_result=by_grouping,
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)
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assert tm.non_increasing(by_grouping["Train"]["rmse"]), by_grouping
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@given(strategies.integers(10, 400), strategies.integers(3, 8),
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strategies.integers(1, 2), strategies.integers(4, 7))
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@settings(deadline=None, print_blob=True)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical_ohe(self, rows, cols, rounds, cats):
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self.run_categorical_ohe(rows, cols, rounds, cats, "approx")
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self.run_categorical_ohe(rows, cols, rounds, cats, "hist")
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@given(
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tm.categorical_dataset_strategy,
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exact_parameter_strategy,
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hist_parameter_strategy,
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cat_parameter_strategy,
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strategies.integers(4, 32),
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strategies.sampled_from(["hist", "approx"]),
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)
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@settings(deadline=None, print_blob=True)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical(
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self,
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dataset: tm.TestDataset,
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exact_parameters: Dict[str, Any],
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hist_parameters: Dict[str, Any],
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cat_parameters: Dict[str, Any],
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n_rounds: int,
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tree_method: str,
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) -> None:
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cat_parameters.update(exact_parameters)
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cat_parameters.update(hist_parameters)
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cat_parameters["tree_method"] = tree_method
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results = train_result(cat_parameters, dataset.get_dmat(), n_rounds)
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tm.non_increasing(results["train"]["rmse"])
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@given(
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hist_parameter_strategy,
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cat_parameter_strategy,
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strategies.sampled_from(["hist", "approx"]),
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)
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@settings(deadline=None, print_blob=True)
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def test_categorical_ames_housing(
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self,
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hist_parameters: Dict[str, Any],
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cat_parameters: Dict[str, Any],
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tree_method: str,
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) -> None:
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cat_parameters.update(hist_parameters)
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dataset = tm.TestDataset(
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"ames_housing", tm.get_ames_housing, "reg:squarederror", "rmse"
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)
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cat_parameters["tree_method"] = tree_method
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results = train_result(cat_parameters, dataset.get_dmat(), 16)
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tm.non_increasing(results["train"]["rmse"])
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@given(
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strategies.integers(10, 400),
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strategies.integers(3, 8),
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strategies.integers(4, 7)
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)
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@settings(deadline=None, print_blob=True)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical_missing(self, rows, cols, cats):
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self.run_categorical_missing(rows, cols, cats, "approx")
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self.run_categorical_missing(rows, cols, cats, "hist")
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def run_adaptive(self, tree_method, weighted) -> None:
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rng = np.random.RandomState(1994)
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from sklearn.datasets import make_regression
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from sklearn.utils import stats
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n_samples = 256
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X, y = make_regression(n_samples, 16, random_state=rng)
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if weighted:
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w = rng.normal(size=n_samples)
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w -= w.min()
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Xy = xgb.DMatrix(X, y, weight=w)
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base_score = stats._weighted_percentile(y, w, percentile=50)
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else:
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Xy = xgb.DMatrix(X, y)
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base_score = np.median(y)
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booster_0 = xgb.train(
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{
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"tree_method": tree_method,
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"base_score": base_score,
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"objective": "reg:absoluteerror",
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},
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Xy,
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num_boost_round=1,
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)
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booster_1 = xgb.train(
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{"tree_method": tree_method, "objective": "reg:absoluteerror"},
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Xy,
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num_boost_round=1,
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)
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config_0 = json.loads(booster_0.save_config())
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config_1 = json.loads(booster_1.save_config())
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def get_score(config: Dict) -> float:
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return float(config["learner"]["learner_model_param"]["base_score"])
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assert get_score(config_0) == get_score(config_1)
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raw_booster = booster_1.save_raw(raw_format="deprecated")
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booster_2 = xgb.Booster(model_file=raw_booster)
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config_2 = json.loads(booster_2.save_config())
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assert get_score(config_1) == get_score(config_2)
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raw_booster = booster_1.save_raw(raw_format="ubj")
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booster_2 = xgb.Booster(model_file=raw_booster)
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config_2 = json.loads(booster_2.save_config())
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assert get_score(config_1) == get_score(config_2)
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booster_0 = xgb.train(
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{
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"tree_method": tree_method,
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"base_score": base_score + 1.0,
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"objective": "reg:absoluteerror",
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},
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Xy,
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num_boost_round=1,
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)
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config_0 = json.loads(booster_0.save_config())
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np.testing.assert_allclose(get_score(config_0), get_score(config_1) + 1)
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@pytest.mark.skipif(**tm.no_sklearn())
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@pytest.mark.parametrize(
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"tree_method,weighted", [
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("approx", False), ("hist", False), ("approx", True), ("hist", True)
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]
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)
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def test_adaptive(self, tree_method, weighted) -> None:
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self.run_adaptive(tree_method, weighted)
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