Improve icolumn.py cc 1/4 test_numerical_column (#407)

Summary:
Pull Request resolved: #407

Improves test coverage for icolumn.py through test_numerical_column. This stack gets icolumn.py to 86% coverage.

Reviewed By: OswinC

Differential Revision: D37492648

fbshipit-source-id: a90267d2372677722a8cdcae55d43fd146c4b9b3

Author: andrewaikens87

torcharrow/icolumn.py

@@ -269,18 +269,21 @@ def __len__(self):
     # printing ----------------------------------------------------------------
 
     def __str__(self):
-        return f"Column([{', '.join(str(i) for i in self)}], id = {self.id})"
+        item_padding = "'" if dt.is_string(self.dtype) else ""
+        return f"Column([{', '.join(f'{item_padding}{i}{item_padding}' for i in self)}], id = {self.id})"
 
     def __repr__(self):
-        rows = [[l if l is not None else "None"] for l in self]
+        item_padding = "'" if dt.is_string(self.dtype) else ""
+        rows = [
+            [f"{item_padding}{l}{item_padding}" if l is not None else "None"]
+            for l in self
+        ]
         tab = tabulate(
             rows,
             tablefmt="plain",
             showindex=True,
         )
-        typ = (
-            f"dtype: {self._dtype}, length: {len(self)}, null_count: {self.null_count}"
-        )
+        typ = f"dtype: {self._dtype}, length: {len(self)}, null_count: {self.null_count}, device: {self.device}"
         return tab + dt.NL + typ
 
     # selectors/getters -------------------------------------------------------

torcharrow/test/test_numerical_column.py

@@ -4,13 +4,16 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import operator
 import statistics
 import typing as ty
 import unittest
+from collections import defaultdict
 from math import ceil, floor, isnan, log
 
 import numpy as np
 import numpy.testing
+import pandas as pd
 import torcharrow as ta
 import torcharrow.dtypes as dt
 from torcharrow.icolumn import Column
@@ -32,7 +35,7 @@ def base_test_empty(self):
         return empty_i64_column
 
     def base_test_full(self):
-        col = ta.column([i for i in range(4)], dtype=dt.int64, device=self.device)
+        col = ta.column(list(range(4)), dtype=dt.int64, device=self.device)
 
         # self.assertEqual(col._offset, 0)
         self.assertEqual(len(col), 4)
@@ -43,7 +46,7 @@ def base_test_full(self):
         return col
 
     def base_test_is_immutable(self):
-        col = ta.column([i for i in range(4)], dtype=dt.int64, device=self.device)
+        col = ta.column(list(range(4)), dtype=dt.int64, device=self.device)
         with self.assertRaises(AttributeError):
             # AssertionError: can't append a finalized list
             col._append(None)
@@ -164,6 +167,13 @@ def base_test_map_where_filter(self):
         # Values that are not found in the dict are converted to None
         self.assertEqual(list(col.map({3: 33})), [None, None, None, 33, None, None])
 
+        # maps default dict
+        d_dict = defaultdict(lambda: 1, {None: 2})
+        self.assertEqual(
+            list(col.map(arg=d_dict)),
+            [2, 2, 2, 1, 1, 1],
+        )
+
         # maps None
         self.assertEqual(
             list(col.map({None: 1, 3: 33})),
@@ -196,6 +206,18 @@ def base_test_map_where_filter(self):
         # filter
         self.assertEqual(list(col.filter([True, False] * 3)), [None, None, 4])
 
+        with self.assertRaisesRegex(
+            expected_exception=TypeError,
+            expected_regex="columns parameter for flat columns not supported",
+        ):
+            col.filter([True, False], columns=["test", "test2"])
+
+        with self.assertRaisesRegex(
+            expected_exception=TypeError,
+            expected_regex="predicate must be a unary boolean predicate or iterable of booleans",
+        ):
+            col.filter(123)
+
     @staticmethod
     def _accumulate(col, val):
         if len(col) == 0:
@@ -217,6 +239,26 @@ def base_test_reduce(self):
         )
         self.assertEqual(list(d), [1, 3, 6])
 
+        col_no_init = c.reduce(
+            fun=operator.add,
+        )
+        self.assertEqual(sum(c), col_no_init)
+
+        c_empty = ta.column(dtype=dt.int64, device=self.device)
+        result = c_empty.reduce(
+            fun=TestNumericalColumn._accumulate,
+            initializer=c,
+        )
+        self.assertTrue(all(c == result))
+
+        with self.assertRaisesRegex(
+            expected_exception=TypeError,
+            expected_regex="reduce of empty sequence with no initial value",
+        ):
+            c_empty.reduce(
+                fun=TestNumericalColumn._accumulate,
+            )
+
     def base_test_sort_stuff(self):
         col = ta.column([2, 1, 3], device=self.device)
 
@@ -795,6 +837,46 @@ def base_test_batch_collate(self):
         it = c.batch(2)
         self.assertEqual(list(Column.unbatch(it)), [1, 2, 3, 4, 5, 6, 7])
 
+    def base_test_str(self):
+        c = ta.column(list(range(5)), device=self.device)
+        c.id = 123
+
+        expected = "Column([0, 1, 2, 3, 4], id = 123)"
+        self.assertEqual(expected, str(c))
+
+    def base_test_repr(self):
+        c = ta.column(list(range(5)), device=self.device)
+        expected_repr = (
+            "0  0\n"
+            "1  1\n"
+            "2  2\n"
+            "3  3\n"
+            "4  4\n"
+            f"dtype: int64, length: 5, null_count: 0, device: {self.device}"
+        )
+
+        self.assertEqual(expected_repr, repr(c))
+
+    def base_test_to_pandas(self):
+        c_repr = list(range(10))
+        c = ta.column(c_repr, device=self.device)
+        expected = pd.Series(c_repr)
+        self.assertTrue(all(expected == c.to_pandas()))
+
+    def base_test_transform(self):
+        c_repr = list(range(10))
+        c = ta.column(c_repr, device=self.device)
+
+        result = c.transform(lambda x: x * 10)
+
+        self.assertEqual([x * 10 for x in c_repr], list(result))
+
+        with self.assertRaisesRegex(
+            expected_exception=TypeError,
+            expected_regex="columns parameter for flat columns not supported",
+        ):
+            c.transform(lambda x: x * 10, columns=["test"])
+
 
 if __name__ == "__main__":
 

torcharrow/test/test_numerical_column_cpu.py

@@ -83,6 +83,18 @@ def test_batch_collate(self):
     def test_cast(self):
         return self.base_test_cast()
 
+    def test_str(self):
+        self.base_test_str()
+
+    def test_repr(self):
+        self.base_test_repr()
+
+    def test_to_pandas(self):
+        self.base_test_to_pandas()
+
+    def test_transform(self):
+        self.base_test_transform()
+
 
 if __name__ == "__main__":
     unittest.main()