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REF: use fused types for groupby_helper #28886

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Merged
merged 5 commits into from
Oct 11, 2019
Merged

REF: use fused types for groupby_helper #28886

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6057696
REF: use fused types for group_last
jbrockmendel Oct 10, 2019
dfcd5ef
REF: use fused types for groupby_helper
jbrockmendel Oct 10, 2019
12c7288
fix whitesapce
jbrockmendel Oct 10, 2019
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jbrockmendel Oct 10, 2019
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jbrockmendel Oct 10, 2019
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237 changes: 145 additions & 92 deletions pandas/_libs/groupby_helper.pxi.in
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Original file line number Diff line number Diff line change
Expand Up @@ -12,39 +12,27 @@ _int64_max = np.iinfo(np.int64).max
# group_nth, group_last, group_rank
# ----------------------------------------------------------------------

{{py:

# name, c_type, nan_val
dtypes = [('float64', 'float64_t', 'NAN'),
('float32', 'float32_t', 'NAN'),
('int64', 'int64_t', 'NPY_NAT'),
('object', 'object', 'NAN')]

def get_dispatch(dtypes):

for name, c_type, nan_val in dtypes:

yield name, c_type, nan_val
}}


{{for name, c_type, nan_val in get_dispatch(dtypes)}}
ctypedef fused rank_t:
float64_t
float32_t
int64_t
object


@cython.wraparound(False)
@cython.boundscheck(False)
def group_last_{{name}}({{c_type}}[:, :] out,
int64_t[:] counts,
{{c_type}}[:, :] values,
const int64_t[:] labels,
Py_ssize_t min_count=-1):
def group_last(rank_t[:, :] out,
int64_t[:] counts,
rank_t[:, :] values,
const int64_t[:] labels,
Py_ssize_t min_count=-1):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{c_type}} val
ndarray[{{c_type}}, ndim=2] resx
rank_t val
ndarray[rank_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs

assert min_count == -1, "'min_count' only used in add and prod"
Expand All @@ -53,19 +41,15 @@ def group_last_{{name}}({{c_type}}[:, :] out,
raise AssertionError("len(index) != len(labels)")

nobs = np.zeros((<object>out).shape, dtype=np.int64)
{{if name == 'object'}}
resx = np.empty((<object>out).shape, dtype=object)
{{else}}
resx = np.empty_like(out)
{{endif}}
if rank_t is object:
resx = np.empty((<object>out).shape, dtype=object)
else:
resx = np.empty_like(out)

N, K = (<object>values).shape

{{if name == "object"}}
if True: # make templating happy
{{else}}
with nogil:
{{endif}}
if rank_t is object:
# TODO: De-duplicate once conditional-nogil is available
for i in range(N):
lab = labels[i]
if lab < 0:
Expand All @@ -76,36 +60,68 @@ def group_last_{{name}}({{c_type}}[:, :] out,
val = values[i, j]

# not nan
if (
{{if not name.startswith("int")}}
val == val and
{{endif}}
val != {{nan_val}}):
if val == val:
nobs[lab, j] += 1
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = {{nan_val}}
out[i, j] = NAN
else:
out[i, j] = resx[i, j]
else:
with nogil:
for i in range(N):
lab = labels[i]
if lab < 0:
continue

counts[lab] += 1
for j in range(K):
val = values[i, j]

# not nan
if rank_t is int64_t:
# need a special notna check
if val != NPY_NAT:
nobs[lab, j] += 1
resx[lab, j] = val
else:
if val == val:
nobs[lab, j] += 1
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
if rank_t is int64_t:
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Do you see any harm in putting this condition in the object block as well? Not sure if this is covered by tests but could see someone mistakenly assuming that the gil and nogil blocks are identical when 0.30 gets released and missing this on refactor

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i like this idea, will do

out[i, j] = NPY_NAT
else:
out[i, j] = NAN
else:
out[i, j] = resx[i, j]

group_last_float64 = group_last["float64_t"]
group_last_float32 = group_last["float32_t"]
group_last_int64 = group_last["int64_t"]
group_last_object = group_last["object"]


@cython.wraparound(False)
@cython.boundscheck(False)
def group_nth_{{name}}({{c_type}}[:, :] out,
int64_t[:] counts,
{{c_type}}[:, :] values,
const int64_t[:] labels, int64_t rank,
Py_ssize_t min_count=-1):
def group_nth(rank_t[:, :] out,
int64_t[:] counts,
rank_t[:, :] values,
const int64_t[:] labels, int64_t rank,
Py_ssize_t min_count=-1):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{c_type}} val
ndarray[{{c_type}}, ndim=2] resx
rank_t val
ndarray[rank_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs

assert min_count == -1, "'min_count' only used in add and prod"
Expand All @@ -114,19 +130,15 @@ def group_nth_{{name}}({{c_type}}[:, :] out,
raise AssertionError("len(index) != len(labels)")

nobs = np.zeros((<object>out).shape, dtype=np.int64)
{{if name=='object'}}
resx = np.empty((<object>out).shape, dtype=object)
{{else}}
resx = np.empty_like(out)
{{endif}}
if rank_t is object:
resx = np.empty((<object>out).shape, dtype=object)
else:
resx = np.empty_like(out)

N, K = (<object>values).shape

{{if name == "object"}}
if True: # make templating happy
{{else}}
with nogil:
{{endif}}
if rank_t is object:
# TODO: De-duplicate once conditional-nogil is available
for i in range(N):
lab = labels[i]
if lab < 0:
Expand All @@ -137,40 +149,73 @@ def group_nth_{{name}}({{c_type}}[:, :] out,
val = values[i, j]

# not nan
if (
{{if not name.startswith("int")}}
val == val and
{{endif}}
val != {{nan_val}}):
if val == val:
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = {{nan_val}}
out[i, j] = NAN
else:
out[i, j] = resx[i, j]

else:
with nogil:
for i in range(N):
lab = labels[i]
if lab < 0:
continue

counts[lab] += 1
for j in range(K):
val = values[i, j]

# not nan
if rank_t is int64_t:
# need a special notna check
if val != NPY_NAT:
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val
else:
if val == val:
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
if rank_t is int64_t:
out[i, j] = NPY_NAT
else:
out[i, j] = NAN
else:
out[i, j] = resx[i, j]

{{if name != 'object'}}

group_nth_float64 = group_nth["float64_t"]
group_nth_float32 = group_nth["float32_t"]
group_nth_int64 = group_nth["int64_t"]
group_nth_object = group_nth["object"]


@cython.boundscheck(False)
@cython.wraparound(False)
def group_rank_{{name}}(float64_t[:, :] out,
{{c_type}}[:, :] values,
const int64_t[:] labels,
bint is_datetimelike, object ties_method,
bint ascending, bint pct, object na_option):
def group_rank(float64_t[:, :] out,
rank_t[:, :] values,
const int64_t[:] labels,
bint is_datetimelike, object ties_method,
bint ascending, bint pct, object na_option):
"""
Provides the rank of values within each group.

Parameters
----------
out : array of float64_t values which this method will write its results to
values : array of {{c_type}} values to be ranked
values : array of rank_t values to be ranked
labels : array containing unique label for each group, with its ordering
matching up to the corresponding record in `values`
is_datetimelike : bool, default False
Expand Down Expand Up @@ -203,10 +248,13 @@ def group_rank_{{name}}(float64_t[:, :] out,
Py_ssize_t grp_vals_seen=1, grp_na_count=0, grp_tie_count=0
ndarray[int64_t] _as
ndarray[float64_t, ndim=2] grp_sizes
ndarray[{{c_type}}] masked_vals
ndarray[rank_t] masked_vals
ndarray[uint8_t] mask
bint keep_na
{{c_type}} nan_fill_val
rank_t nan_fill_val

if rank_t is object:
raise NotImplementedError("Cant do nogil")

tiebreak = tiebreakers[ties_method]
keep_na = na_option == 'keep'
Expand All @@ -217,25 +265,23 @@ def group_rank_{{name}}(float64_t[:, :] out,
# with mask, without obfuscating location of missing data
# in values array
masked_vals = np.array(values[:, 0], copy=True)
{{if name == 'int64'}}
mask = (masked_vals == {{nan_val}}).astype(np.uint8)
{{else}}
mask = np.isnan(masked_vals).astype(np.uint8)
{{endif}}
if rank_t is int64_t:
mask = (masked_vals == NPY_NAT).astype(np.uint8)
else:
mask = np.isnan(masked_vals).astype(np.uint8)

if ascending ^ (na_option == 'top'):
{{if name == 'int64'}}
nan_fill_val = np.iinfo(np.int64).max
{{else}}
nan_fill_val = np.inf
{{endif}}
if rank_t is int64_t:
nan_fill_val = np.iinfo(np.int64).max
else:
nan_fill_val = np.inf
order = (masked_vals, mask, labels)
else:
{{if name == 'int64'}}
nan_fill_val = np.iinfo(np.int64).min
{{else}}
nan_fill_val = -np.inf
{{endif}}
if rank_t is int64_t:
nan_fill_val = np.iinfo(np.int64).min
else:
nan_fill_val = -np.inf

order = (masked_vals, ~mask, labels)
np.putmask(masked_vals, mask, nan_fill_val)

Expand Down Expand Up @@ -337,8 +383,13 @@ def group_rank_{{name}}(float64_t[:, :] out,
out[i, 0] = NAN
elif grp_sizes[i, 0] != 0:
out[i, 0] = out[i, 0] / grp_sizes[i, 0]
{{endif}}
{{endfor}}


group_rank_float64 = group_rank["float64_t"]
group_rank_float32 = group_rank["float32_t"]
group_rank_int64 = group_rank["int64_t"]
# Note: we do not have a group_rank_object because that would require a
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FWIW I don't think we really even want this #19560

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OK. The comment seemed appropriate since we are using the same fused type. Could add a reference to 19560 in the comment?

# not-nogil implementation.


# ----------------------------------------------------------------------
Expand Down Expand Up @@ -484,7 +535,8 @@ def group_cummin(groupby_t[:, :] out,
const int64_t[:] labels,
int ngroups,
bint is_datetimelike):
"""Cumulative minimum of columns of `values`, in row groups `labels`.
"""
Cumulative minimum of columns of `values`, in row groups `labels`.

Parameters
----------
Expand Down Expand Up @@ -548,9 +600,10 @@ def group_cummin(groupby_t[:, :] out,
def group_cummax(groupby_t[:, :] out,
groupby_t[:, :] values,
const int64_t[:] labels,
int ngroups,
int ngroups,
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weird indenting?

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Looks like it was tabs, just fixed

bint is_datetimelike):
"""Cumulative maximum of columns of `values`, in row groups `labels`.
"""
Cumulative maximum of columns of `values`, in row groups `labels`.

Parameters
----------
Expand Down