[ci] [python] reduce unnecessary data loading in tests

.gitignore

@@ -318,6 +318,8 @@ htmlcov/
 .coverage.*
 .cache
 nosetests.xml
+prof/
+*.prof
 coverage.xml
 *,cover
 .hypothesis/

tests/python_package_test/test_basic.py

@@ -7,9 +7,11 @@
 import numpy as np
 
 from scipy import sparse
-from sklearn.datasets import load_breast_cancer, dump_svmlight_file, load_svmlight_file
+from sklearn.datasets import dump_svmlight_file, load_svmlight_file
 from sklearn.model_selection import train_test_split
 
+from .utils import load_breast_cancer
+
 
 class TestBasic(unittest.TestCase):
 

tests/python_package_test/test_engine.py

@@ -10,8 +10,7 @@
 import lightgbm as lgb
 import numpy as np
 from scipy.sparse import csr_matrix, isspmatrix_csr, isspmatrix_csc
-from sklearn.datasets import (load_boston, load_breast_cancer, load_digits,
-                              load_iris, load_svmlight_file, make_multilabel_classification)
+from sklearn.datasets import load_svmlight_file, make_multilabel_classification
 from sklearn.metrics import log_loss, mean_absolute_error, mean_squared_error, roc_auc_score, average_precision_score
 from sklearn.model_selection import train_test_split, TimeSeriesSplit, GroupKFold
 
@@ -20,6 +19,8 @@
 except ImportError:
     import pickle
 
+from .utils import load_boston, load_breast_cancer, load_digits, load_iris
+
 
 decreasing_generator = itertools.count(0, -1)
 
@@ -2524,6 +2525,7 @@ def test_average_precision_metric(self):
         sklearn_ap = average_precision_score(y, pred)
         self.assertAlmostEqual(ap, sklearn_ap)
         # test that average precision is 1 where model predicts perfectly
+        y = y.copy()
         y[:] = 1
         lgb_X = lgb.Dataset(X, label=y)
         lgb.train(params, lgb_X, num_boost_round=1, valid_sets=[lgb_X], evals_result=res)

tests/python_package_test/test_plotting.py

@@ -3,7 +3,6 @@
 
 import lightgbm as lgb
 from lightgbm.compat import MATPLOTLIB_INSTALLED, GRAPHVIZ_INSTALLED
-from sklearn.datasets import load_breast_cancer
 from sklearn.model_selection import train_test_split
 
 if MATPLOTLIB_INSTALLED:
@@ -12,6 +11,8 @@
 if GRAPHVIZ_INSTALLED:
     import graphviz
 
+from .utils import load_breast_cancer
+
 
 class TestBasic(unittest.TestCase):
 

tests/python_package_test/test_sklearn.py

@@ -10,9 +10,7 @@
 import numpy as np
 from sklearn import __version__ as sk_version
 from sklearn.base import clone
-from sklearn.datasets import (load_boston, load_breast_cancer, load_digits,
-                              load_iris, load_linnerud, load_svmlight_file,
-                              make_multilabel_classification)
+from sklearn.datasets import load_svmlight_file, make_multilabel_classification
 from sklearn.exceptions import SkipTestWarning
 from sklearn.metrics import log_loss, mean_squared_error
 from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split
@@ -22,6 +20,8 @@
                                             check_parameters_default_constructible)
 from sklearn.utils.validation import check_is_fitted
 
+from .utils import load_boston, load_breast_cancer, load_digits, load_iris, load_linnerud
+
 
 decreasing_generator = itertools.count(0, -1)
 

tests/python_package_test/utils.py

@@ -0,0 +1,45 @@
+# coding: utf-8
+import sklearn.datasets
+
+try:
+    from functools import lru_cache
+except ImportError:
+    import warnings
+    warnings.warn("Could not import functools.lru_cache", RuntimeWarning)
+
+    def lru_cache(maxsize=None):
+        cache = {}
+
+        def _lru_wrapper(user_function):
+            def wrapper(*args, **kwargs):
+                arg_key = (args, tuple(kwargs.items()))
+                if arg_key not in cache:
+                    cache[arg_key] = user_function(*args, **kwargs)
+                return cache[arg_key]
+            return wrapper
+        return _lru_wrapper
+
+
+@lru_cache(maxsize=None)
+def load_boston(**kwargs):
+    return sklearn.datasets.load_boston(**kwargs)
+
+
+@lru_cache(maxsize=None)
+def load_breast_cancer(**kwargs):
+    return sklearn.datasets.load_breast_cancer(**kwargs)
+
+
+@lru_cache(maxsize=None)
+def load_digits(**kwargs):
+    return sklearn.datasets.load_digits(**kwargs)
+
+
+@lru_cache(maxsize=None)
+def load_iris(**kwargs):
+    return sklearn.datasets.load_iris(**kwargs)
+
+
+@lru_cache(maxsize=None)
+def load_linnerud(**kwargs):
+    return sklearn.datasets.load_linnerud(**kwargs)