BUG: fix label array code dtype condense

tests/test_labelarray.py

@@ -341,25 +341,30 @@ class LabelArrayTestCase(ZiplineTestCase):
         arr[:] = orig_arr
         check_arrays(arr, orig_arr)
 
-    def test_narrow_code_storage(self):
-        def check_roundtrip(arr):
-            assert_equal(
+    @staticmethod
+    def check_roundtrip(arr):
+        assert_equal(
+            arr.as_string_array(),
+            LabelArray(
                 arr.as_string_array(),
-                LabelArray(
-                    arr.as_string_array(),
-                    arr.missing_value,
-                ).as_string_array(),
-            )
+                arr.missing_value,
+            ).as_string_array(),
+        )
 
-        def create_categories(width, plus_one):
-            length = int(width / 8) + plus_one
-            return [
-                ''.join(cs)
-                for cs in take(
-                    2 ** width + plus_one,
-                    product([chr(c) for c in range(256)], repeat=length),
-                )
-            ]
+    @staticmethod
+    def create_categories(width, plus_one):
+        length = int(width / 8) + plus_one
+        return [
+            ''.join(cs)
+            for cs in take(
+                2 ** width + plus_one,
+                product([chr(c) for c in range(256)], repeat=length),
+            )
+        ]
+
+    def test_narrow_code_storage(self):
+        create_categories = self.create_categories
+        check_roundtrip = self.check_roundtrip
 
         # uint8
         categories = create_categories(8, plus_one=False)
@@ -386,11 +391,6 @@ class LabelArrayTestCase(ZiplineTestCase):
         self.assertEqual(arr.itemsize, 2)
         check_roundtrip(arr)
 
-        # uint16 inference
-        arr = LabelArray(categories, missing_value=categories[0])
-        self.assertEqual(arr.itemsize, 2)
-        check_roundtrip(arr)
-
         # fits in uint16
         categories = create_categories(16, plus_one=False)
         arr = LabelArray(
@@ -422,3 +422,26 @@ class LabelArrayTestCase(ZiplineTestCase):
 
         # NOTE: we could do this for 32 and 64; however, no one has enough RAM
         # or time for that.
+
+    def test_narrow_condense_back_to_valid_size(self):
+        categories = ['a'] * (2 ** 8 + 1)
+        arr = LabelArray(categories, missing_value=categories[0])
+        assert_equal(arr.itemsize, 1)
+        self.check_roundtrip(arr)
+
+        # longer than int16 but still fits when deduped
+        categories = self.create_categories(16, plus_one=False)
+        categories.append(categories[0])
+        arr = LabelArray(categories, missing_value=categories[0])
+        assert_equal(arr.itemsize, 2)
+        self.check_roundtrip(arr)
+
+    def manual_narrow_condense_back_to_valid_size_slow(self):
+        """This test is really slow so we don't want it run by default.
+        """
+        # tests that we don't try to create an 'int24' (which is meaningless)
+        categories = self.create_categories(24, plus_one=False)
+        categories.append(categories[0])
+        arr = LabelArray(categories, missing_value=categories[0])
+        assert_equal(arr.itemsize, 4)
+        self.check_roundtrip(arr)

zipline/lib/_factorize.pyx

@@ -1,7 +1,7 @@
 """
 Factorization algorithms.
 """
-from libc.math cimport floor, log
+from libc.math cimport log
 cimport numpy as np
 import numpy as np
 
@@ -144,6 +144,9 @@ cdef factorize_strings_impl(np.ndarray[object] values,
     return codes, categories_array, reverse_categories
 
 
+cdef list _int_sizes = [1, 1, 2, 4, 4, 8, 8, 8, 8]
+
+
 cpdef factorize_strings(np.ndarray[object] values,
                         object missing_value,
                         int sort):
@@ -209,11 +212,22 @@ cpdef factorize_strings(np.ndarray[object] values,
         # unreachable
         raise ValueError('nvalues larger than uint64')
 
-    if len(categories_array) < 2 ** codes.dtype.itemsize:
-        # if there are a lot of duplicates in the values we may need to shrink
-        # the width of the ``codes`` array
-        codes = codes.astype(unsigned_int_dtype_with_size_in_bytes(
-            floor(log2(len(categories_array))),
-        ))
+    length = len(categories_array)
+    if length < 1:
+        # lim x -> 0 log2(x) == -infinity so we floor at uint8
+        narrowest_dtype = np.uint8
+    else:
+        # The number of bits required to hold the codes up to ``length`` is
+        # log2(length). The number of bits per bytes is 8. We cannot have
+        # fractional bytes so we need to round up. Finally, we can only have
+        # integers with widths 1, 2, 4, or 8 so so we need to round up to the
+        # next value by looking up the next largest size in ``_int_sizes``.
+        narrowest_dtype = unsigned_int_dtype_with_size_in_bytes(
+            _int_sizes[int(np.ceil(log2(length) / 8))]
+        )
+
+    if codes.dtype != narrowest_dtype:
+        # condense the codes down to the narrowest dtype possible
+        codes = codes.astype(narrowest_dtype)
 
     return codes, categories_array, reverse_categories