Added 'Memory' class

sklearn2pmml/cross_reference/__init__.py

@@ -5,6 +5,31 @@
 
 import numpy
 
+class Memory(object):
+
+	def __init__(self):
+		self.data = dict()
+
+	def __getitem__(self, key):
+		return self.data[key]
+
+	def __setitem__(self, key, value):
+		self.data[key] = value
+
+	def clear(self):
+		self.data.clear()
+
+	def __len__(self):
+		return len(self.data)
+
+	def __getstate__(self):
+		state = self.__dict__.copy()
+		state["data"] = dict()
+		return state
+
+	def __setstate__(self, state):
+		self.__dict__.update(state)
+
 def make_memorizer_union(memory, names):
 	return FeatureUnion([
 		("memorizer", Memorizer(memory, names)),

sklearn2pmml/cross_reference/tests/__init__.py

@@ -1,8 +1,30 @@
 from pandas import DataFrame
-from sklearn2pmml.cross_reference import make_memorizer_union, make_recaller_union, Memorizer, Recaller
+from sklearn.pipeline import make_pipeline
+from sklearn2pmml.cross_reference import make_memorizer_union, make_recaller_union, Memory, Memorizer, Recaller
 from unittest import TestCase
 
 import numpy
+import pickle
+
+class MemoryTest(TestCase):
+
+	def test_item_assignment(self):
+		memory = Memory()
+		self.assertEqual(0, len(memory))
+		memory["int"] = [1, 2, 3]
+		self.assertEqual(1, len(memory))
+		self.assertEqual([1, 2, 3], memory["int"])
+		with self.assertRaises(KeyError):
+			memory["float"]
+		memory.clear()
+		self.assertEqual(0, len(memory))
+
+	def test_pickle(self):
+		memory = Memory()
+		memory["int"] = [1, 2, 3]
+		self.assertEqual(1, len(memory))
+		memory_clone = pickle.loads(pickle.dumps(memory))
+		self.assertEqual(0, len(memory_clone))
 
 class MemorizerTest(TestCase):
 
@@ -11,7 +33,9 @@ def test_transform(self):
 		self.assertEqual(0, len(memory))
 		memorizer = Memorizer(memory, ["int"])
 		X = numpy.asarray([[-1], [1]])
-		Xt = memorizer.fit_transform(X)
+		memorizer.fit(X)
+		self.assertEquals(0, len(memory))
+		Xt = memorizer.transform(X)
 		self.assertEqual((2, 0), Xt.shape)
 		self.assertEqual(1, len(memory))
 		self.assertEqual([-1, 1], memory["int"].tolist())
@@ -20,7 +44,8 @@ def test_transform(self):
 		self.assertEqual((0, 0), memory.shape)
 		memorizer = Memorizer(memory, ["int", "float", "str"])
 		X = numpy.asarray([[1, 1.0, "one"], [2, 2.0, "two"], [3, 3.0, "three"]])
-		Xt = memorizer.fit_transform(X)
+		pipeline = make_pipeline(memorizer)
+		Xt = pipeline.fit_transform(X)
 		self.assertEqual((3, 0), Xt.shape)
 		self.assertEqual((3, 3), memory.shape)
 		self.assertEqual(["1", "2", "3"], memory["int"].tolist())
@@ -37,19 +62,23 @@ def test_transform(self):
 		}
 		recaller = Recaller(memory, ["int"])
 		X = numpy.empty((2, 1), dtype = str)
-		Xt = recaller.fit_transform(X)
+		recaller.fit(X)
+		self.assertEqual(1, len(memory))
+		Xt = recaller.transform(X)
 		self.assertEqual((2, 1), Xt.shape)
 		self.assertEqual([-1, 1], Xt[:, 0].tolist())
 
 		memory = DataFrame([[1, 1.0, "one"], [2, 2.0, "two"], [3, 3.0, "three"]], columns = ["int", "float", "str"])
 		self.assertEqual((3, 3), memory.shape)
 		recaller = Recaller(memory, ["int"])
+		pipeline = make_pipeline(recaller)
 		X = numpy.empty((3, 5), dtype = str)
-		Xt = recaller.fit_transform(X)
+		Xt = pipeline.fit_transform(X)
 		self.assertEqual((3, 1), Xt.shape)
 		self.assertEqual([1, 2, 3], Xt[:, 0].tolist())
 		recaller = Recaller(memory, ["int", "float", "str"])
-		Xt = recaller.fit_transform(X)
+		pipeline = make_pipeline(recaller)
+		Xt = pipeline.fit_transform(X)
 		self.assertEqual((3, 3), Xt.shape)
 		self.assertEqual([1, 2, 3], Xt[:, 0].tolist())
 		self.assertEqual([1.0, 2.0, 3.0], Xt[:, 1].tolist())