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Refactor Python tests. (#3897)

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* Deprecate nose tests.
* Format python tests.
This commit is contained in:
Jiaming Yuan
2018-11-15 13:56:33 +13:00
committed by GitHub
parent c76d993681
commit 2ea0f887c1
23 changed files with 302 additions and 225 deletions
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139
tests/python/test_with_sklearn.py
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@@ -4,10 +4,12 @@ import testing as tm
import tempfile
import os
import shutil
from nose.tools import raises
import pytest
rng = np.random.RandomState(1994)
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
class TemporaryDirectory(object):
"""Context manager for tempfile.mkdtemp()"""
@@ -20,7 +22,6 @@ class TemporaryDirectory(object):
def test_binary_classification():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
@@ -38,7 +39,6 @@ def test_binary_classification():
def test_multiclass_classification():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_iris
from sklearn.model_selection import KFold
@@ -59,9 +59,12 @@ def test_multiclass_classification():
xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
preds = xgb_model.predict(X[test_index])
# test other params in XGBClassifier().fit
preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3)
preds2 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False,
ntree_limit=3)
labels = y[test_index]
check_pred(preds, labels, output_margin=False)
@@ -71,7 +74,6 @@ def test_multiclass_classification():
def test_ranking():
tm._skip_if_no_sklearn()
# generate random data
x_train = np.random.rand(1000, 10)
y_train = np.random.randint(5, size=1000)
@@ -105,13 +107,13 @@ def test_ranking():
def test_feature_importances_weight():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
digits = load_digits(2)
y = digits['target']
X = digits['data']
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="weight").fit(X, y)
xgb_model = xgb.XGBClassifier(
random_state=0, importance_type="weight").fit(X, y)
exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.00833333, 0.,
0., 0., 0., 0., 0., 0., 0., 0.025, 0.14166667, 0., 0., 0.,
@@ -127,28 +129,32 @@ def test_feature_importances_weight():
import pandas as pd
y = pd.Series(digits['target'])
X = pd.DataFrame(digits['data'])
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="weight").fit(X, y)
xgb_model = xgb.XGBClassifier(
random_state=0, importance_type="weight").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="weight").fit(X, y)
xgb_model = xgb.XGBClassifier(
random_state=0, importance_type="weight").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
def test_feature_importances_gain():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
digits = load_digits(2)
y = digits['target']
X = digits['data']
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="gain").fit(X, y)
xgb_model = xgb.XGBClassifier(
random_state=0, importance_type="gain").fit(X, y)
exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.00326159, 0., 0., 0.,
0., 0., 0., 0., 0., 0.00297238, 0.00988034, 0., 0., 0., 0.,
0., 0., 0.03512521, 0.41123885, 0., 0., 0., 0., 0.01326332,
0.00160674, 0., 0.4206952, 0., 0., 0., 0., 0.00616747, 0.01237546,
0., 0., 0., 0., 0., 0., 0., 0.08240705, 0., 0., 0., 0.,
0., 0., 0., 0.00100649, 0., 0., 0., 0., 0.], dtype=np.float32)
exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0.00326159, 0., 0., 0., 0., 0., 0., 0., 0.,
0.00297238, 0.00988034, 0., 0., 0., 0., 0., 0.,
0.03512521, 0.41123885, 0., 0., 0., 0.,
0.01326332, 0.00160674, 0., 0.4206952, 0., 0., 0.,
0., 0.00616747, 0.01237546, 0., 0., 0., 0., 0.,
0., 0., 0.08240705, 0., 0., 0., 0., 0., 0., 0.,
0.00100649, 0., 0., 0., 0., 0.], dtype=np.float32)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
@@ -156,15 +162,16 @@ def test_feature_importances_gain():
import pandas as pd
y = pd.Series(digits['target'])
X = pd.DataFrame(digits['data'])
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="gain").fit(X, y)
xgb_model = xgb.XGBClassifier(
random_state=0, importance_type="gain").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
xgb_model = xgb.XGBClassifier(random_state=0, importance_type="gain").fit(X, y)
xgb_model = xgb.XGBClassifier(
random_state=0, importance_type="gain").fit(X, y)
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
def test_boston_housing_regression():
tm._skip_if_no_sklearn()
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
from sklearn.model_selection import KFold
@@ -178,9 +185,12 @@ def test_boston_housing_regression():
preds = xgb_model.predict(X[test_index])
# test other params in XGBRegressor().fit
preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3)
preds2 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False,
ntree_limit=3)
labels = y[test_index]
assert mean_squared_error(preds, labels) < 25
@@ -190,7 +200,6 @@ def test_boston_housing_regression():
def test_parameter_tuning():
tm._skip_if_no_sklearn()
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
@@ -207,7 +216,6 @@ def test_parameter_tuning():
def test_regression_with_custom_objective():
tm._skip_if_no_sklearn()
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
from sklearn.model_selection import KFold
@@ -241,7 +249,6 @@ def test_regression_with_custom_objective():
def test_classification_with_custom_objective():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
@@ -280,7 +287,6 @@ def test_classification_with_custom_objective():
def test_sklearn_api():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
@@ -298,12 +304,12 @@ def test_sklearn_api():
def test_sklearn_api_gblinear():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target, train_size=120)
tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target,
train_size=120)
classifier = xgb.XGBClassifier(booster='gblinear', n_estimators=100)
classifier.fit(tr_d, tr_l)
@@ -314,8 +320,8 @@ def test_sklearn_api_gblinear():
assert err < 0.5
@pytest.mark.skipif(**tm.no_matplotlib())
def test_sklearn_plotting():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_iris
iris = load_iris()
@@ -344,7 +350,6 @@ def test_sklearn_plotting():
def test_sklearn_nfolds_cv():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
from sklearn.model_selection import StratifiedKFold
@@ -367,14 +372,15 @@ def test_sklearn_nfolds_cv():
skf = StratifiedKFold(n_splits=nfolds, shuffle=True, random_state=seed)
cv1 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, seed=seed)
cv2 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, folds=skf, seed=seed)
cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, stratified=True, seed=seed)
cv2 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds,
folds=skf, seed=seed)
cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds,
stratified=True, seed=seed)
assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0]
assert cv2.iloc[-1, 0] == cv3.iloc[-1, 0]
def test_split_value_histograms():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
digits_2class = load_digits(2)
@@ -383,11 +389,14 @@ def test_split_value_histograms():
y = digits_2class['target']
dm = xgb.DMatrix(X, label=y)
params = {'max_depth': 6, 'eta': 0.01, 'silent': 1, 'objective': 'binary:logistic'}
params = {'max_depth': 6, 'eta': 0.01, 'silent': 1,
'objective': 'binary:logistic'}
gbdt = xgb.train(params, dm, num_boost_round=10)
assert gbdt.get_split_value_histogram("not_there", as_pandas=True).shape[0] == 0
assert gbdt.get_split_value_histogram("not_there", as_pandas=False).shape[0] == 0
assert gbdt.get_split_value_histogram("not_there",
as_pandas=True).shape[0] == 0
assert gbdt.get_split_value_histogram("not_there",
as_pandas=False).shape[0] == 0
assert gbdt.get_split_value_histogram("f28", bins=0).shape[0] == 1
assert gbdt.get_split_value_histogram("f28", bins=1).shape[0] == 1
assert gbdt.get_split_value_histogram("f28", bins=2).shape[0] == 2
@@ -396,8 +405,6 @@ def test_split_value_histograms():
def test_sklearn_random_state():
tm._skip_if_no_sklearn()
clf = xgb.XGBClassifier(random_state=402)
assert clf.get_xgb_params()['seed'] == 402
@@ -406,8 +413,6 @@ def test_sklearn_random_state():
def test_sklearn_n_jobs():
tm._skip_if_no_sklearn()
clf = xgb.XGBClassifier(n_jobs=1)
assert clf.get_xgb_params()['nthread'] == 1
@@ -416,8 +421,6 @@ def test_sklearn_n_jobs():
def test_kwargs():
tm._skip_if_no_sklearn()
params = {'updater': 'grow_gpu', 'subsample': .5, 'n_jobs': -1}
clf = xgb.XGBClassifier(n_estimators=1000, **params)
assert clf.get_params()['updater'] == 'grow_gpu'
@@ -426,7 +429,6 @@ def test_kwargs():
def test_kwargs_grid_search():
tm._skip_if_no_sklearn()
from sklearn.model_selection import GridSearchCV
from sklearn import datasets
@@ -446,17 +448,14 @@ def test_kwargs_grid_search():
assert len(means) == len(set(means))
@raises(TypeError)
def test_kwargs_error():
tm._skip_if_no_sklearn()
params = {'updater': 'grow_gpu', 'subsample': .5, 'n_jobs': -1}
clf = xgb.XGBClassifier(n_jobs=1000, **params)
assert isinstance(clf, xgb.XGBClassifier)
with pytest.raises(TypeError):
clf = xgb.XGBClassifier(n_jobs=1000, **params)
assert isinstance(clf, xgb.XGBClassifier)
def test_sklearn_clone():
tm._skip_if_no_sklearn()
from sklearn.base import clone
clf = xgb.XGBClassifier(n_jobs=2, nthread=3)
@@ -465,7 +464,6 @@ def test_sklearn_clone():
def test_validation_weights_xgbmodel():
tm._skip_if_no_sklearn()
from sklearn.datasets import make_hastie_10_2
# prepare training and test data
@@ -489,7 +487,8 @@ def test_validation_weights_xgbmodel():
# evaluate logloss metric on test set *without* using weights
evals_result_without_weights = clf.evals_result()
logloss_without_weights = evals_result_without_weights["validation_0"]["logloss"]
logloss_without_weights = evals_result_without_weights[
"validation_0"]["logloss"]
# now use weights for the test set
np.random.seed(0)
@@ -503,13 +502,13 @@ def test_validation_weights_xgbmodel():
evals_result_with_weights = clf.evals_result()
logloss_with_weights = evals_result_with_weights["validation_0"]["logloss"]
# check that the logloss in the test set is actually different when using weights
# than when not using them
assert all((logloss_with_weights[i] != logloss_without_weights[i] for i in [0, 1]))
# check that the logloss in the test set is actually different when using
# weights than when not using them
assert all((logloss_with_weights[i] != logloss_without_weights[i]
for i in [0, 1]))
def test_validation_weights_xgbclassifier():
tm._skip_if_no_sklearn()
from sklearn.datasets import make_hastie_10_2
# prepare training and test data
@@ -533,7 +532,8 @@ def test_validation_weights_xgbclassifier():
# evaluate logloss metric on test set *without* using weights
evals_result_without_weights = clf.evals_result()
logloss_without_weights = evals_result_without_weights["validation_0"]["logloss"]
logloss_without_weights = evals_result_without_weights[
"validation_0"]["logloss"]
# now use weights for the test set
np.random.seed(0)
@@ -547,13 +547,13 @@ def test_validation_weights_xgbclassifier():
evals_result_with_weights = clf.evals_result()
logloss_with_weights = evals_result_with_weights["validation_0"]["logloss"]
# check that the logloss in the test set is actually different when using weights
# than when not using them
assert all((logloss_with_weights[i] != logloss_without_weights[i] for i in [0, 1]))
# check that the logloss in the test set is actually different
# when using weights than when not using them
assert all((logloss_with_weights[i] != logloss_without_weights[i]
for i in [0, 1]))
def test_save_load_model():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
@@ -576,7 +576,6 @@ def test_save_load_model():
def test_RFECV():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_boston
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_iris
@@ -587,21 +586,25 @@ def test_RFECV():
bst = xgb.XGBClassifier(booster='gblinear', learning_rate=0.1,
n_estimators=10, n_jobs=1, objective='reg:linear',
random_state=0, silent=True)
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='neg_mean_squared_error')
rfecv = RFECV(
estimator=bst, step=1, cv=3, scoring='neg_mean_squared_error')
rfecv.fit(X, y)
# Binary classification
X, y = load_breast_cancer(return_X_y=True)
bst = xgb.XGBClassifier(booster='gblinear', learning_rate=0.1,
n_estimators=10, n_jobs=1, objective='binary:logistic',
n_estimators=10, n_jobs=1,
objective='binary:logistic',
random_state=0, silent=True)
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='roc_auc')
rfecv.fit(X, y)
# Multi-class classification
X, y = load_iris(return_X_y=True)
bst = xgb.XGBClassifier(base_score=0.4, booster='gblinear', learning_rate=0.1,
n_estimators=10, n_jobs=1, objective='multi:softprob',
bst = xgb.XGBClassifier(base_score=0.4, booster='gblinear',
learning_rate=0.1,
n_estimators=10, n_jobs=1,
objective='multi:softprob',
random_state=0, reg_alpha=0.001, reg_lambda=0.01,
scale_pos_weight=0.5, silent=True)
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='neg_log_loss')