tests: add a test suite for sklearn objects

Add the test recipe from #155 to demonstrate that
sklearn DecisionTreeClassifier objects can be properly
serialized.

This behavior was originally fixed in #170.

Closes #155
Signed-off-by: David Aguilar <davvid@gmail.com>

requirements-dev.txt

@@ -13,6 +13,7 @@ pytest-black-multipy
 pytest-cov
 pytest-flake8
 simplejson
+sklearn
 sqlalchemy
 ujson; python_version < '3.8'
 yajl; sys_platform != 'win32' and python_version < '3.8'

setup.cfg

@@ -57,6 +57,7 @@ testing =
 	numpy
 	pandas
 	pymongo
+	sklearn
 	sqlalchemy
 
 testing.libs =

tests/sklearn_test.py

@@ -0,0 +1,57 @@
+from __future__ import absolute_import, division, unicode_literals
+
+import pytest
+
+try:
+    import numpy as np
+    from sklearn.tree import DecisionTreeClassifier
+except ImportError:
+    pytest.skip('sklearn is not available', allow_module_level=True)
+
+import jsonpickle
+import jsonpickle.ext.numpy
+
+
+@pytest.fixture(scope='module', autouse=True)
+def numpy_extension():
+    """Initialize the numpy extension for this test module"""
+    jsonpickle.ext.numpy.register_handlers()
+    yield  # control to the test function.
+    jsonpickle.ext.numpy.unregister_handlers()
+
+
+def test_decision_tree():
+    #  Create data.
+    np.random.seed(13)
+    x_values = np.random.randint(low=0, high=10, size=12)
+    x = x_values.reshape(4, 3)
+    y_values = np.random.randint(low=0, high=2, size=4)
+    y = y_values.reshape(-1, 1)
+
+    # train model
+    classifier = DecisionTreeClassifier(max_depth=1)
+    classifier.fit(x, y)
+
+    # freeze and thaw
+    pickler = jsonpickle.pickler.Pickler()
+    unpickler = jsonpickle.unpickler.Unpickler()
+    actual = unpickler.restore(pickler.flatten(classifier))
+
+    assert isinstance(actual, classifier.__class__)
+    if hasattr(classifier, 'tree_'):
+        assert isinstance(actual.tree_, classifier.tree_.__class__)
+
+    # predict from thawed
+    array_values = np.array([1, 2, 3])
+    array = array_values.reshape(1, -1)
+    prediction = actual.predict(array)
+    assert prediction[0] == 1
+
+    assert actual.max_depth == classifier.max_depth
+    assert actual.score(x, y) == classifier.score(x, y)
+    if hasattr(classifier, 'get_depth'):
+        assert actual.get_depth() == classifier.get_depth()
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])