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 pytest
import pandas as pd

import testing as tm
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)

    @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))

        np.testing.assert_allclose(dmat_predt, arr_predt)
        np.testing.assert_allclose(df_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)