"""Tests for running inplace prediction.""" from concurrent.futures import ThreadPoolExecutor import numpy as np import pandas as pd import pytest from scipy import sparse import xgboost as xgb from xgboost import testing as tm from xgboost.testing.data import np_dtypes, pd_dtypes from xgboost.testing.shared import validate_leaf_output def run_threaded_predict(X, rows, predict_func): results = [] per_thread = 20 with ThreadPoolExecutor(max_workers=10) as e: for i in range(0, rows, int(rows / per_thread)): if hasattr(X, "iloc"): predictor = X.iloc[i : i + per_thread, :] else: predictor = X[i : i + per_thread, ...] f = e.submit(predict_func, predictor) results.append(f) for f in results: assert f.result() def run_predict_leaf(device: str) -> np.ndarray: rows = 100 cols = 4 classes = 5 num_parallel_tree = 4 num_boost_round = 10 rng = np.random.RandomState(1994) X = rng.randn(rows, cols) y = rng.randint(low=0, high=classes, size=rows) m = xgb.DMatrix(X, y) booster = xgb.train( { "num_parallel_tree": num_parallel_tree, "num_class": classes, "tree_method": "hist", }, m, num_boost_round=num_boost_round, ) booster.set_param({"device": device}) empty = xgb.DMatrix(np.ones(shape=(0, cols))) empty_leaf = booster.predict(empty, pred_leaf=True) assert empty_leaf.shape[0] == 0 leaf = booster.predict(m, pred_leaf=True, strict_shape=True) assert leaf.shape[0] == rows assert leaf.shape[1] == num_boost_round assert leaf.shape[2] == classes assert leaf.shape[3] == num_parallel_tree validate_leaf_output(leaf, num_parallel_tree) n_iters = np.int32(2) sliced = booster.predict( m, pred_leaf=True, iteration_range=(0, n_iters), strict_shape=True, ) first = sliced[0, ...] assert np.prod(first.shape) == classes * num_parallel_tree * n_iters # When there's only 1 tree, the output is a 1 dim vector booster = xgb.train({"tree_method": "hist"}, num_boost_round=1, dtrain=m) booster.set_param({"device": device}) assert booster.predict(m, pred_leaf=True).shape == (rows,) return leaf def test_predict_leaf() -> None: run_predict_leaf("cpu") def test_predict_shape(): from sklearn.datasets import fetch_california_housing X, y = fetch_california_housing(return_X_y=True) reg = xgb.XGBRegressor(n_estimators=1) reg.fit(X, y) predt = reg.get_booster().predict(xgb.DMatrix(X), strict_shape=True) assert len(predt.shape) == 2 assert predt.shape[0] == X.shape[0] assert predt.shape[1] == 1 contrib = reg.get_booster().predict( xgb.DMatrix(X), pred_contribs=True, strict_shape=True ) assert len(contrib.shape) == 3 assert contrib.shape[1] == 1 contrib = reg.get_booster().predict( xgb.DMatrix(X), pred_contribs=True, approx_contribs=True ) assert len(contrib.shape) == 2 assert contrib.shape[1] == X.shape[1] + 1 interaction = reg.get_booster().predict( xgb.DMatrix(X), pred_interactions=True, approx_contribs=True ) assert len(interaction.shape) == 3 assert interaction.shape[1] == X.shape[1] + 1 assert interaction.shape[2] == X.shape[1] + 1 interaction = reg.get_booster().predict( xgb.DMatrix(X), pred_interactions=True, approx_contribs=True, strict_shape=True ) assert len(interaction.shape) == 4 assert interaction.shape[1] == 1 assert interaction.shape[2] == X.shape[1] + 1 assert interaction.shape[3] == X.shape[1] + 1 class TestInplacePredict: """Tests for running inplace prediction""" @classmethod def setup_class(cls): cls.rows = 1000 cls.cols = 10 cls.missing = 11 # set to integer for testing cls.rng = np.random.RandomState(1994) cls.X = cls.rng.randn(cls.rows, cls.cols) missing_idx = [i for i in range(0, cls.cols, 4)] cls.X[:, missing_idx] = cls.missing # set to be missing cls.y = cls.rng.randn(cls.rows) dtrain = xgb.DMatrix(cls.X, cls.y) cls.test = xgb.DMatrix(cls.X[:10, ...], missing=cls.missing) cls.num_boost_round = 10 cls.booster = xgb.train({"tree_method": "hist"}, dtrain, num_boost_round=10) def test_predict(self): booster = self.booster X = self.X test = self.test predt_from_array = booster.inplace_predict(X[:10, ...], missing=self.missing) predt_from_dmatrix = booster.predict(test) X_obj = X.copy().astype(object) assert X_obj.dtype.hasobject is True assert X.dtype.hasobject is False np.testing.assert_allclose( booster.inplace_predict(X_obj), booster.inplace_predict(X) ) np.testing.assert_allclose(predt_from_dmatrix, predt_from_array) predt_from_array = booster.inplace_predict( X[:10, ...], iteration_range=(0, 4), missing=self.missing ) predt_from_dmatrix = booster.predict(test, iteration_range=(0, 4)) np.testing.assert_allclose(predt_from_dmatrix, predt_from_array) with pytest.raises(ValueError): booster.predict(test, iteration_range=(0, booster.num_boosted_rounds() + 2)) default = booster.predict(test) range_full = booster.predict(test, iteration_range=(0, self.num_boost_round)) np.testing.assert_allclose(range_full, default) range_full = booster.predict( test, iteration_range=(0, booster.num_boosted_rounds()) ) np.testing.assert_allclose(range_full, default) def predict_dense(x): inplace_predt = booster.inplace_predict(x) d = xgb.DMatrix(x) copied_predt = booster.predict(d) return np.all(copied_predt == inplace_predt) for i in range(10): run_threaded_predict(X, self.rows, predict_dense) def predict_csr(x): inplace_predt = booster.inplace_predict(sparse.csr_matrix(x)) d = xgb.DMatrix(x) copied_predt = booster.predict(d) return np.all(copied_predt == inplace_predt) for i in range(10): run_threaded_predict(X, self.rows, predict_csr) @pytest.mark.skipif(**tm.no_pandas()) def test_predict_pd(self): X = self.X # construct it in column major style df = pd.DataFrame({str(i): X[:, i] for i in range(X.shape[1])}) booster = self.booster df_predt = booster.inplace_predict(df) arr_predt = booster.inplace_predict(X) dmat_predt = booster.predict(xgb.DMatrix(X)) X = df.values X = np.asfortranarray(X) fort_predt = booster.inplace_predict(X) np.testing.assert_allclose(dmat_predt, arr_predt) np.testing.assert_allclose(df_predt, arr_predt) np.testing.assert_allclose(fort_predt, arr_predt) def test_base_margin(self): booster = self.booster base_margin = self.rng.randn(self.rows) from_inplace = booster.inplace_predict(data=self.X, base_margin=base_margin) dtrain = xgb.DMatrix(self.X, self.y, base_margin=base_margin) from_dmatrix = booster.predict(dtrain) np.testing.assert_allclose(from_dmatrix, from_inplace) @pytest.mark.skipif(**tm.no_pandas()) def test_dtypes(self) -> None: for orig, x in np_dtypes(self.rows, self.cols): predt_orig = self.booster.inplace_predict(orig) predt = self.booster.inplace_predict(x) np.testing.assert_allclose(predt, predt_orig) # unsupported types for dtype in [ np.bytes_, np.complex64, np.complex128, ]: X: np.ndarray = np.array(orig, dtype=dtype) with pytest.raises(ValueError): self.booster.inplace_predict(X) @pytest.mark.skipif(**tm.no_pandas()) def test_pd_dtypes(self) -> None: from pandas.api.types import is_bool_dtype for orig, x in pd_dtypes(): dtypes = orig.dtypes if isinstance(orig, pd.DataFrame) else [orig.dtypes] if isinstance(orig, pd.DataFrame) and is_bool_dtype(dtypes[0]): continue y = np.arange(x.shape[0]) Xy = xgb.DMatrix(orig, y, enable_categorical=True) booster = xgb.train({"tree_method": "hist"}, Xy, num_boost_round=1) predt_orig = booster.inplace_predict(orig) predt = booster.inplace_predict(x) np.testing.assert_allclose(predt, predt_orig)