851cba931e
* Define `best_iteration` only if early stopping is used. This is the behavior specified by the document but not honored in the actual code. - Don't set the attributes if there's no early stopping. - Clean up the code for callbacks, and replace assertions with proper exceptions. - Assign the attributes when early stopping `save_best` is used. - Turn the attributes into Python properties. --------- Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
265 lines
8.5 KiB
Python
265 lines
8.5 KiB
Python
"""Tests for running inplace prediction."""
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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import pandas as pd
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import pytest
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from scipy import sparse
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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.data import np_dtypes, pd_dtypes
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from xgboost.testing.shared import validate_leaf_output
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def run_threaded_predict(X, rows, predict_func):
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results = []
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per_thread = 20
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with ThreadPoolExecutor(max_workers=10) as e:
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for i in range(0, rows, int(rows / per_thread)):
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if hasattr(X, "iloc"):
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predictor = X.iloc[i : i + per_thread, :]
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else:
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predictor = X[i : i + per_thread, ...]
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f = e.submit(predict_func, predictor)
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results.append(f)
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for f in results:
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assert f.result()
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def run_predict_leaf(device: str) -> np.ndarray:
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rows = 100
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cols = 4
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classes = 5
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num_parallel_tree = 4
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num_boost_round = 10
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rng = np.random.RandomState(1994)
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X = rng.randn(rows, cols)
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y = rng.randint(low=0, high=classes, size=rows)
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m = xgb.DMatrix(X, y)
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booster = xgb.train(
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{
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"num_parallel_tree": num_parallel_tree,
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"num_class": classes,
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"tree_method": "hist",
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},
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m,
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num_boost_round=num_boost_round,
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)
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booster.set_param({"device": device})
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empty = xgb.DMatrix(np.ones(shape=(0, cols)))
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empty_leaf = booster.predict(empty, pred_leaf=True)
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assert empty_leaf.shape[0] == 0
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leaf = booster.predict(m, pred_leaf=True, strict_shape=True)
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assert leaf.shape[0] == rows
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assert leaf.shape[1] == num_boost_round
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assert leaf.shape[2] == classes
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assert leaf.shape[3] == num_parallel_tree
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validate_leaf_output(leaf, num_parallel_tree)
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n_iters = 2
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sliced = booster.predict(
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m,
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pred_leaf=True,
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iteration_range=(0, n_iters),
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strict_shape=True,
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)
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first = sliced[0, ...]
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assert np.prod(first.shape) == classes * num_parallel_tree * n_iters
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# When there's only 1 tree, the output is a 1 dim vector
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booster = xgb.train({"tree_method": "hist"}, num_boost_round=1, dtrain=m)
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booster.set_param({"device": device})
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assert booster.predict(m, pred_leaf=True).shape == (rows,)
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return leaf
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def test_predict_leaf() -> None:
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run_predict_leaf("cpu")
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def test_predict_shape():
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from sklearn.datasets import fetch_california_housing
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X, y = fetch_california_housing(return_X_y=True)
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reg = xgb.XGBRegressor(n_estimators=1)
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reg.fit(X, y)
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predt = reg.get_booster().predict(xgb.DMatrix(X), strict_shape=True)
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assert len(predt.shape) == 2
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assert predt.shape[0] == X.shape[0]
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assert predt.shape[1] == 1
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contrib = reg.get_booster().predict(
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xgb.DMatrix(X), pred_contribs=True, strict_shape=True
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)
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assert len(contrib.shape) == 3
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assert contrib.shape[1] == 1
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contrib = reg.get_booster().predict(
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xgb.DMatrix(X), pred_contribs=True, approx_contribs=True
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)
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assert len(contrib.shape) == 2
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assert contrib.shape[1] == X.shape[1] + 1
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interaction = reg.get_booster().predict(
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xgb.DMatrix(X), pred_interactions=True, approx_contribs=True
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)
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assert len(interaction.shape) == 3
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assert interaction.shape[1] == X.shape[1] + 1
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assert interaction.shape[2] == X.shape[1] + 1
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interaction = reg.get_booster().predict(
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xgb.DMatrix(X), pred_interactions=True, approx_contribs=True, strict_shape=True
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)
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assert len(interaction.shape) == 4
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assert interaction.shape[1] == 1
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assert interaction.shape[2] == X.shape[1] + 1
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assert interaction.shape[3] == X.shape[1] + 1
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class TestInplacePredict:
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"""Tests for running inplace prediction"""
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@classmethod
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def setup_class(cls):
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cls.rows = 1000
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cls.cols = 10
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cls.missing = 11 # set to integer for testing
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cls.rng = np.random.RandomState(1994)
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cls.X = cls.rng.randn(cls.rows, cls.cols)
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missing_idx = [i for i in range(0, cls.cols, 4)]
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cls.X[:, missing_idx] = cls.missing # set to be missing
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cls.y = cls.rng.randn(cls.rows)
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dtrain = xgb.DMatrix(cls.X, cls.y)
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cls.test = xgb.DMatrix(cls.X[:10, ...], missing=cls.missing)
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cls.num_boost_round = 10
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cls.booster = xgb.train({"tree_method": "hist"}, dtrain, num_boost_round=10)
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def test_predict(self):
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booster = self.booster
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X = self.X
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test = self.test
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predt_from_array = booster.inplace_predict(X[:10, ...], missing=self.missing)
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predt_from_dmatrix = booster.predict(test)
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X_obj = X.copy().astype(object)
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assert X_obj.dtype.hasobject is True
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assert X.dtype.hasobject is False
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np.testing.assert_allclose(
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booster.inplace_predict(X_obj), booster.inplace_predict(X)
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)
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np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)
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predt_from_array = booster.inplace_predict(
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X[:10, ...], iteration_range=(0, 4), missing=self.missing
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)
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predt_from_dmatrix = booster.predict(test, iteration_range=(0, 4))
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np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)
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with pytest.raises(ValueError):
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booster.predict(test, iteration_range=(0, booster.num_boosted_rounds() + 2))
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default = booster.predict(test)
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range_full = booster.predict(test, iteration_range=(0, self.num_boost_round))
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np.testing.assert_allclose(range_full, default)
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range_full = booster.predict(
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test, iteration_range=(0, booster.num_boosted_rounds())
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)
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np.testing.assert_allclose(range_full, default)
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def predict_dense(x):
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inplace_predt = booster.inplace_predict(x)
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d = xgb.DMatrix(x)
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copied_predt = booster.predict(d)
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return np.all(copied_predt == inplace_predt)
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for i in range(10):
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run_threaded_predict(X, self.rows, predict_dense)
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def predict_csr(x):
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inplace_predt = booster.inplace_predict(sparse.csr_matrix(x))
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d = xgb.DMatrix(x)
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copied_predt = booster.predict(d)
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return np.all(copied_predt == inplace_predt)
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for i in range(10):
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run_threaded_predict(X, self.rows, predict_csr)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_predict_pd(self):
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X = self.X
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# construct it in column major style
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df = pd.DataFrame({str(i): X[:, i] for i in range(X.shape[1])})
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booster = self.booster
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df_predt = booster.inplace_predict(df)
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arr_predt = booster.inplace_predict(X)
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dmat_predt = booster.predict(xgb.DMatrix(X))
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X = df.values
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X = np.asfortranarray(X)
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fort_predt = booster.inplace_predict(X)
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np.testing.assert_allclose(dmat_predt, arr_predt)
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np.testing.assert_allclose(df_predt, arr_predt)
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np.testing.assert_allclose(fort_predt, arr_predt)
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def test_base_margin(self):
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booster = self.booster
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base_margin = self.rng.randn(self.rows)
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from_inplace = booster.inplace_predict(data=self.X, base_margin=base_margin)
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dtrain = xgb.DMatrix(self.X, self.y, base_margin=base_margin)
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from_dmatrix = booster.predict(dtrain)
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np.testing.assert_allclose(from_dmatrix, from_inplace)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_dtypes(self) -> None:
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for orig, x in np_dtypes(self.rows, self.cols):
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predt_orig = self.booster.inplace_predict(orig)
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predt = self.booster.inplace_predict(x)
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np.testing.assert_allclose(predt, predt_orig)
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# unsupported types
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for dtype in [
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np.string_,
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np.complex64,
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np.complex128,
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]:
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X: np.ndarray = np.array(orig, dtype=dtype)
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with pytest.raises(ValueError):
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self.booster.inplace_predict(X)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_pd_dtypes(self) -> None:
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from pandas.api.types import is_bool_dtype
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for orig, x in pd_dtypes():
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dtypes = orig.dtypes if isinstance(orig, pd.DataFrame) else [orig.dtypes]
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if isinstance(orig, pd.DataFrame) and is_bool_dtype(dtypes[0]):
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continue
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y = np.arange(x.shape[0])
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Xy = xgb.DMatrix(orig, y, enable_categorical=True)
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booster = xgb.train({"tree_method": "hist"}, Xy, num_boost_round=1)
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predt_orig = booster.inplace_predict(orig)
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predt = booster.inplace_predict(x)
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np.testing.assert_allclose(predt, predt_orig)
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