7eba285a1e
* Support sklearn cross validation for ranker. - Add a convention for X to include a special `qid` column. sklearn utilities consider only `X`, `y` and `sample_weight` for supervised learning algorithms, but we need an additional qid array for ranking. It's important to be able to support the cross validation function in sklearn since all other tuning functions like grid search are based on cross validation.
164 lines
4.7 KiB
Python
164 lines
4.7 KiB
Python
import json
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import os
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import sys
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import tempfile
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import numpy as np
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import pytest
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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.ranking import run_ranking_qid_df
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sys.path.append("tests/python")
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import test_with_sklearn as twskl # noqa
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pytestmark = pytest.mark.skipif(**tm.no_sklearn())
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rng = np.random.RandomState(1994)
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def test_gpu_binary_classification():
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from sklearn.datasets import load_digits
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from sklearn.model_selection import KFold
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digits = load_digits(n_class=2)
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y = digits['target']
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X = digits['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
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for train_index, test_index in kf.split(X, y):
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xgb_model = cls(
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random_state=42, tree_method='gpu_hist',
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n_estimators=4, gpu_id='0').fit(X[train_index], y[train_index])
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preds = xgb_model.predict(X[test_index])
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labels = y[test_index]
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err = sum(1 for i in range(len(preds))
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if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
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assert err < 0.1
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.skipif(**tm.no_cudf())
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def test_boost_from_prediction_gpu_hist():
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import cudf
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import cupy as cp
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from sklearn.datasets import load_breast_cancer, load_digits
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tree_method = "gpu_hist"
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X, y = load_breast_cancer(return_X_y=True)
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X, y = cp.array(X), cp.array(y)
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twskl.run_boost_from_prediction_binary(tree_method, X, y, None)
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twskl.run_boost_from_prediction_binary(tree_method, X, y, cudf.DataFrame)
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X, y = load_digits(return_X_y=True)
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X, y = cp.array(X), cp.array(y)
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twskl.run_boost_from_prediction_multi_clasas(
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xgb.XGBClassifier, tree_method, X, y, None
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)
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twskl.run_boost_from_prediction_multi_clasas(
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xgb.XGBClassifier, tree_method, X, y, cudf.DataFrame
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)
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def test_num_parallel_tree():
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twskl.run_housing_rf_regression("gpu_hist")
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@pytest.mark.skipif(**tm.no_pandas())
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@pytest.mark.skipif(**tm.no_cudf())
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_categorical():
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import cudf
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import cupy as cp
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import pandas as pd
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from sklearn.datasets import load_svmlight_file
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data_dir = tm.data_dir(__file__)
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X, y = load_svmlight_file(os.path.join(data_dir, "agaricus.txt.train"))
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clf = xgb.XGBClassifier(
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tree_method="gpu_hist",
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enable_categorical=True,
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n_estimators=10,
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)
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X = pd.DataFrame(X.todense()).astype("category")
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clf.fit(X, y)
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with tempfile.TemporaryDirectory() as tempdir:
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model = os.path.join(tempdir, "categorial.json")
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clf.save_model(model)
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with open(model) as fd:
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categorical = json.load(fd)
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categories_sizes = np.array(
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categorical["learner"]["gradient_booster"]["model"]["trees"][0][
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"categories_sizes"
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]
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)
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assert categories_sizes.shape[0] != 0
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np.testing.assert_allclose(categories_sizes, 1)
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def check_predt(X, y):
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reg = xgb.XGBRegressor(
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tree_method="gpu_hist", enable_categorical=True, n_estimators=64
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)
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reg.fit(X, y)
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predts = reg.predict(X)
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booster = reg.get_booster()
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assert "c" in booster.feature_types
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assert len(booster.feature_types) == 1
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inp_predts = booster.inplace_predict(X)
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if isinstance(inp_predts, cp.ndarray):
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inp_predts = cp.asnumpy(inp_predts)
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np.testing.assert_allclose(predts, inp_predts)
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y = [1, 2, 3]
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X = pd.DataFrame({"f0": ["a", "b", "c"]})
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X["f0"] = X["f0"].astype("category")
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check_predt(X, y)
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X = cudf.DataFrame(X)
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check_predt(X, y)
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.skipif(**tm.no_cudf())
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def test_classififer():
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import cudf
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import cupy as cp
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from sklearn.datasets import load_digits
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X, y = load_digits(return_X_y=True)
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y *= 10
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clf = xgb.XGBClassifier(tree_method="gpu_hist", n_estimators=1)
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# numpy
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with pytest.raises(ValueError, match=r"Invalid classes.*"):
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clf.fit(X, y)
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# cupy
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X, y = cp.array(X), cp.array(y)
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with pytest.raises(ValueError, match=r"Invalid classes.*"):
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clf.fit(X, y)
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# cudf
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X, y = cudf.DataFrame(X), cudf.DataFrame(y)
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with pytest.raises(ValueError, match=r"Invalid classes.*"):
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clf.fit(X, y)
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# pandas
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X, y = load_digits(return_X_y=True, as_frame=True)
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y *= 10
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with pytest.raises(ValueError, match=r"Invalid classes.*"):
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clf.fit(X, y)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_ranking_qid_df():
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import cudf
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run_ranking_qid_df(cudf, "gpu_hist")
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