xgboost/tests/python/test_predict.py

'''Tests for running inplace prediction.'''
from concurrent.futures import ThreadPoolExecutor
import numpy as np
from scipy import sparse

import xgboost as xgb


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 verify_leaf_output(leaf: np.ndarray, num_parallel_tree: int):
    for i in range(leaf.shape[0]):     # n_samples
        for j in range(leaf.shape[1]):  # n_rounds
            for k in range(leaf.shape[2]):    # n_classes
                tree_group = leaf[i, j, k, :]
                assert tree_group.shape[0] == num_parallel_tree
                # No sampling, all trees within forest are the same
                assert np.all(tree_group == tree_group[0])


def run_predict_leaf(predictor):
    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,
            "predictor": predictor,
            "tree_method": "hist",
        },
        m,
        num_boost_round=num_boost_round,
    )

    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

    verify_leaf_output(leaf, num_parallel_tree)

    ntree_limit = 2
    sliced = booster.predict(
        m, pred_leaf=True, ntree_limit=num_parallel_tree * ntree_limit, strict_shape=True
    )
    first = sliced[0, ...]

    assert np.prod(first.shape) == classes * num_parallel_tree * ntree_limit
    return leaf


def test_predict_leaf():
    run_predict_leaf('cpu_predictor')


def test_predict_shape():
    from sklearn.datasets import load_boston
    X, y = load_boston(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


class TestInplacePredict:
    '''Tests for running inplace prediction'''
    @classmethod
    def setup_class(cls):
        cls.rows = 100
        cls.cols = 10

        cls.rng = np.random.RandomState(1994)

        cls.X = cls.rng.randn(cls.rows, cls.cols)
        cls.y = cls.rng.randn(cls.rows)

        dtrain = xgb.DMatrix(cls.X, cls.y)

        cls.booster = xgb.train({'tree_method': 'hist'}, dtrain, num_boost_round=10)

        cls.test = xgb.DMatrix(cls.X[:10, ...])

    def test_predict(self):
        booster = self.booster
        X = self.X
        test = self.test

        predt_from_array = booster.inplace_predict(X[:10, ...])
        predt_from_dmatrix = booster.predict(test)

        np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)

        predt_from_array = booster.inplace_predict(X[:10, ...], iteration_range=(0, 4))
        predt_from_dmatrix = booster.predict(test, ntree_limit=4)

        np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)

        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)

    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)