Merge branch 'main' into optimize-broadcast

* main:
  Handle min_count=0 (#238)
  Try and fix dtypes on 3.8,3.10 windows
  Preserve input dtypes now that pandas can do it.
  Add pytest-pretty to envs
  factorize early as much as possible
  Bump codecov/codecov-action from 3.1.1 to 3.1.3 (#239)
  [pre-commit.ci] pre-commit autoupdate (#229)

.github/workflows/ci-additional.yaml

@@ -73,7 +73,7 @@ jobs:
         run: |
           python -m pytest --doctest-modules flox --ignore flox/tests --cov=./ --cov-report=xml
       - name: Upload code coverage to Codecov
-        uses: codecov/codecov-action@v3.1.1
+        uses: codecov/codecov-action@v3.1.3
         with:
           file: ./coverage.xml
           flags: unittests
@@ -126,7 +126,7 @@ jobs:
           python -m mypy --install-types --non-interactive --cobertura-xml-report mypy_report
 
       - name: Upload mypy coverage to Codecov
-        uses: codecov/codecov-action@v3.1.1
+        uses: codecov/codecov-action@v3.1.3
         with:
           file: mypy_report/cobertura.xml
           flags: mypy

.github/workflows/ci.yaml

@@ -48,7 +48,7 @@ jobs:
         run: |
           pytest -n auto --cov=./ --cov-report=xml
       - name: Upload code coverage to Codecov
-        uses: codecov/codecov-action@v3.1.1
+        uses: codecov/codecov-action@v3.1.3
         with:
           file: ./coverage.xml
           flags: unittests
@@ -91,7 +91,7 @@ jobs:
         run: |
           python -m pytest -n auto --cov=./ --cov-report=xml
       - name: Upload code coverage to Codecov
-        uses: codecov/codecov-action@v3.1.1
+        uses: codecov/codecov-action@v3.1.3
         with:
           file: ./coverage.xml
           flags: unittests

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 repos:
     - repo: https://github.com/charliermarsh/ruff-pre-commit
       # Ruff version.
-      rev: 'v0.0.246'
+      rev: 'v0.0.260'
       hooks:
         - id: ruff
           args: ["--fix"]
@@ -18,7 +18,7 @@ repos:
         - id: check-docstring-first
 
     - repo: https://github.com/psf/black
-      rev: 23.1.0
+      rev: 23.3.0
       hooks:
         - id: black
 
@@ -31,7 +31,7 @@ repos:
           - mdformat-myst
 
     - repo: https://github.com/nbQA-dev/nbQA
-      rev: 1.6.1
+      rev: 1.7.0
       hooks:
           - id: nbqa-black
           - id: nbqa-ruff
@@ -44,13 +44,13 @@ repos:
           args: [--extra-keys=metadata.kernelspec metadata.language_info.version]
 
     - repo: https://github.com/codespell-project/codespell
-      rev: v2.2.2
+      rev: v2.2.4
       hooks:
         - id: codespell
           additional_dependencies:
             - tomli
 
     - repo: https://github.com/abravalheri/validate-pyproject
-      rev: v0.12.1
+      rev: v0.12.2
       hooks:
         - id: validate-pyproject

ci/environment.yml

@@ -14,6 +14,7 @@ dependencies:
   - pip
   - pytest
   - pytest-cov
+  - pytest-pretty
   - pytest-xdist
   - xarray
   - pre-commit

ci/minimal-requirements.yml

@@ -7,6 +7,7 @@ dependencies:
   - pip
   - pytest
   - pytest-cov
+  - pytest-pretty
   - pytest-xdist
   - numpy==1.20
   - numpy_groupies==0.9.19

ci/no-dask.yml

@@ -9,6 +9,7 @@ dependencies:
   - pip
   - pytest
   - pytest-cov
+  - pytest-pretty
   - pytest-xdist
   - xarray
   - numpydoc

ci/no-xarray.yml

@@ -9,6 +9,7 @@ dependencies:
   - pip
   - pytest
   - pytest-cov
+  - pytest-pretty
   - pytest-xdist
   - dask-core
   - numpydoc

ci/upstream-dev-env.yml

@@ -10,6 +10,7 @@ dependencies:
   - numba
   - pytest
   - pytest-cov
+  - pytest-pretty
   - pytest-xdist
   - pip
   - pip:

flox/aggregations.py

@@ -557,12 +557,15 @@ def _initialize_aggregation(
         assert isinstance(finalize_kwargs, dict)
         agg.finalize_kwargs = finalize_kwargs
 
+    if min_count is None:
+        min_count = 0
+
     # This is needed for the dask pathway.
     # Because we use intermediate fill_value since a group could be
     # absent in one block, but present in another block
     # We set it for numpy to get nansum, nanprod tests to pass
     # where the identity element is 0, 1
-    if min_count is not None:
+    if min_count > 0:
         agg.min_count = min_count
         agg.chunk += ("nanlen",)
         agg.numpy += ("nanlen",)
@@ -571,5 +574,7 @@ def _initialize_aggregation(
         agg.fill_value["numpy"] += (0,)
         agg.dtype["intermediate"] += (np.intp,)
         agg.dtype["numpy"] += (np.intp,)
+    else:
+        agg.min_count = 0
 
     return agg

flox/core.py

@@ -354,7 +354,7 @@ def rechunk_for_cohorts(
 def rechunk_for_blockwise(array: DaskArray, axis: T_Axis, labels: np.ndarray) -> DaskArray:
     """
     Rechunks array so that group boundaries line up with chunk boundaries, allowing
-    embarassingly parallel group reductions.
+    embarrassingly parallel group reductions.
 
     This only works when the groups are sequential
     (e.g. labels = ``[0,0,0,1,1,1,1,2,2]``).
@@ -849,7 +849,7 @@ def _finalize_results(
     """
     squeezed = _squeeze_results(results, axis)
 
-    if agg.min_count is not None:
+    if agg.min_count > 0:
         counts = squeezed["intermediates"][-1]
         squeezed["intermediates"] = squeezed["intermediates"][:-1]
 
@@ -860,7 +860,7 @@ def _finalize_results(
     else:
         finalized[agg.name] = agg.finalize(*squeezed["intermediates"], **agg.finalize_kwargs)
 
-    if agg.min_count is not None:
+    if agg.min_count > 0:
         count_mask = counts < agg.min_count
         if count_mask.any():
             # For one count_mask.any() prevents promoting bool to dtype(fill_value) unless
@@ -1598,7 +1598,11 @@ def _lazy_factorize_wrapper(*by: T_By, **kwargs) -> np.ndarray:
 
 
 def _factorize_multiple(
-    by: T_Bys, expected_groups: T_ExpectIndexTuple, any_by_dask: bool, reindex: bool
+    by: T_Bys,
+    expected_groups: T_ExpectIndexTuple,
+    any_by_dask: bool,
+    reindex: bool,
+    sort: bool = True,
 ) -> tuple[tuple[np.ndarray], tuple[np.ndarray, ...], tuple[int, ...]]:
     if any_by_dask:
         import dask.array
@@ -1617,6 +1621,7 @@ def _factorize_multiple(
             expected_groups=expected_groups,
             fastpath=True,
             reindex=reindex,
+            sort=sort,
         )
 
         fg, gs = [], []
@@ -1643,6 +1648,7 @@ def _factorize_multiple(
             expected_groups=expected_groups,
             fastpath=True,
             reindex=reindex,
+            sort=sort,
         )
 
     return (group_idx,), found_groups, grp_shape
@@ -1653,10 +1659,16 @@ def _validate_expected_groups(nby: int, expected_groups: T_ExpectedGroupsOpt) ->
         return (None,) * nby
 
     if nby == 1 and not isinstance(expected_groups, tuple):
-        if isinstance(expected_groups, pd.Index):
+        if isinstance(expected_groups, (pd.Index, np.ndarray)):
             return (expected_groups,)
         else:
-            return (np.asarray(expected_groups),)
+            array = np.asarray(expected_groups)
+            if np.issubdtype(array.dtype, np.integer):
+                # preserve default dtypes
+                # on pandas 1.5/2, on windows
+                # when a list is passed
+                array = array.astype(np.int64)
+            return (array,)
 
     if nby > 1 and not isinstance(expected_groups, tuple):  # TODO: test for list
         raise ValueError(
@@ -1833,21 +1845,28 @@ def groupby_reduce(
     # (pd.IntervalIndex or not)
     expected_groups = _convert_expected_groups_to_index(expected_groups, isbins, sort)
 
-    is_binning = any([isinstance(e, pd.IntervalIndex) for e in expected_groups])
-
-    # TODO: could restrict this to dask-only
-    factorize_early = (nby > 1) or (
-        is_binning and method == "cohorts" and is_duck_dask_array(array)
+    # Don't factorize "early only when
+    # grouping by dask arrays, and not having expected_groups
+    factorize_early = not (
+        # can't do it if we are grouping by dask array but don't have expected_groups
+        any(is_dask and ex_ is None for is_dask, ex_ in zip(by_is_dask, expected_groups))
     )
     if factorize_early:
         bys, final_groups, grp_shape = _factorize_multiple(
-            bys, expected_groups, any_by_dask=any_by_dask, reindex=reindex
+            bys,
+            expected_groups,
+            any_by_dask=any_by_dask,
+            # This is the only way it makes sense I think.
+            # reindex controls what's actually allocated in chunk_reduce
+            # At this point, we care about an accurate conversion to codes.
+            reindex=True,
+            sort=sort,
         )
         expected_groups = (pd.RangeIndex(math.prod(grp_shape)),)
 
     assert len(bys) == 1
-    by_ = bys[0]
-    expected_groups = expected_groups[0]
+    (by_,) = bys
+    (expected_groups,) = expected_groups
 
     if axis is None:
         axis_ = tuple(array.ndim + np.arange(-by_.ndim, 0))
@@ -1898,7 +1917,12 @@ def groupby_reduce(
             min_count = 1
 
     # TODO: set in xarray?
-    if min_count is not None and func in ["nansum", "nanprod"] and fill_value is None:
+    if (
+        min_count is not None
+        and min_count > 0
+        and func in ["nansum", "nanprod"]
+        and fill_value is None
+    ):
         # nansum, nanprod have fill_value=0, 1
         # overwrite than when min_count is set
         fill_value = np.nan

flox/xarray.py

@@ -540,7 +540,7 @@ def rechunk_for_cohorts(
 def rechunk_for_blockwise(obj: T_DataArray | T_Dataset, dim: str, labels: T_DataArray):
     """
     Rechunks array so that group boundaries line up with chunk boundaries, allowing
-    embarassingly parallel group reductions.
+    embarrassingly parallel group reductions.
 
     This only works when the groups are sequential
     (e.g. labels = ``[0,0,0,1,1,1,1,2,2]``).

tests/conftest.py

@@ -1,7 +1,7 @@
 import pytest
 
 
-@pytest.fixture(scope="module", params=["flox", "numpy", "numba"])
+@pytest.fixture(scope="module", params=["flox"])
 def engine(request):
     if request.param == "numba":
         try:

tests/test_core.py

@@ -88,7 +88,8 @@ def test_alignment_error():
 
 @pytest.mark.parametrize("dtype", (float, int))
 @pytest.mark.parametrize("chunk", [False, True])
-@pytest.mark.parametrize("expected_groups", [None, [0, 1, 2], np.array([0, 1, 2])])
+# TODO: make this intp when python 3.8 is dropped
+@pytest.mark.parametrize("expected_groups", [None, [0, 1, 2], np.array([0, 1, 2], dtype=np.int64)])
 @pytest.mark.parametrize(
     "func, array, by, expected",
     [
@@ -148,7 +149,12 @@ def test_groupby_reduce(
     )
     # we use pd.Index(expected_groups).to_numpy() which is always int64
     # for the values in this tests
-    g_dtype = by.dtype if expected_groups is None else np.int64
+    if expected_groups is None:
+        g_dtype = by.dtype
+    elif isinstance(expected_groups, np.ndarray):
+        g_dtype = expected_groups.dtype
+    else:
+        g_dtype = np.int64
 
     assert_equal(groups, np.array([0, 1, 2], g_dtype))
     assert_equal(expected_result, result)
@@ -653,7 +659,7 @@ def test_groupby_bins(chunk_labels, kwargs, chunks, engine, method) -> None:
     array = [1, 1, 1, 1, 1, 1]
     labels = [0.2, 1.5, 1.9, 2, 3, 20]
 
-    if method in ["split-reduce", "cohorts"] and chunk_labels:
+    if method == "cohorts" and chunk_labels:
         pytest.xfail()
 
     if chunks:
@@ -784,10 +790,8 @@ def test_dtype_preservation(dtype, func, engine):
 
 
 @requires_dask
-@pytest.mark.parametrize("dtype", [np.int32, np.int64])
-@pytest.mark.parametrize(
-    "labels_dtype", [pytest.param(np.int32, marks=pytest.mark.xfail), np.int64]
-)
+@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.int32, np.int64])
+@pytest.mark.parametrize("labels_dtype", [np.float32, np.float64, np.int32, np.int64])
 @pytest.mark.parametrize("method", ["map-reduce", "cohorts"])
 def test_cohorts_map_reduce_consistent_dtypes(method, dtype, labels_dtype):
     repeats = np.array([4, 4, 12, 2, 3, 4], dtype=np.int32)
@@ -836,10 +840,7 @@ def test_cohorts_nd_by(func, method, axis, engine):
     assert_equal(actual, expected)
 
     actual, groups = groupby_reduce(array, by, sort=False, **kwargs)
-    if method == "map-reduce":
-        assert_equal(groups, np.array([1, 30, 2, 31, 3, 4, 40], dtype=np.int64))
-    else:
-        assert_equal(groups, np.array([1, 30, 2, 31, 3, 40, 4], dtype=np.int64))
+    assert_equal(groups, np.array([1, 30, 2, 31, 3, 4, 40], dtype=np.int64))
     reindexed = reindex_(actual, groups, pd.Index(sorted_groups))
     assert_equal(reindexed, expected)