def synthesis(prepare_res, slices, job):
dw_passed, _ = prepare_res
# Using set_slice on a dataset that was written in analysis is not
# actually supported, but since it currently works (as long as that
# particular slice wasn't written in analysis) let's test it.
dw_passed.set_slice(0)
dw_passed.write(**{k: v[0] for k, v in test_data.data.items()})
dw_synthesis_split = DatasetWriter(name="synthesis_split", hashlabel="a")
dw_synthesis_split.add("a", "int32")
dw_synthesis_split.add("b", "unicode")
dw_synthesis_split.get_split_write()(1, "a")
dw_synthesis_split.get_split_write_list()([2, "b"])
dw_synthesis_split.get_split_write_dict()({"a": 3, "b": "c"})
dw_synthesis_manual = job.datasetwriter(name="synthesis_manual",
columns={"sliceno": "int32"})
dw_nonetest = job.datasetwriter(name="nonetest",
columns={t: t
for t in test_data.data})
for sliceno in range(slices):
dw_synthesis_manual.set_slice(sliceno)
dw_synthesis_manual.write(sliceno)
dw_nonetest.set_slice(sliceno)
dw_nonetest.write(
**{
k: v[0] if k in test_data.not_none_capable else None
for k, v in test_data.data.items()
})
def synthesis(params):
ds = write(data)
for colname in data[0]:
verify(params.slices, data, ds, hashlabel=colname)
# ok, all the hashing stuff works out, let's test the chaining options.
bonus_ds = write(bonus_data, name="bonus", previous=ds)
# no chaining options - full chain
verify(params.slices, data + bonus_data, bonus_ds, hashlabel="date")
# just the bonus ds
verify(params.slices, bonus_data, bonus_ds, hashlabel="date", length=1)
# built as a chain
verify(params.slices,
data + bonus_data,
bonus_ds,
hashlabel="date",
as_chain=True)
# normal chaining
a = verify(params.slices, data, ds, hashlabel="date")
b = verify(params.slices,
data + bonus_data,
bonus_ds,
hashlabel="date",
previous=a)
assert b.chain() == [
a, b
], "chain of %s is not [%s, %s] as expected" % (b, a, b)
# as_chain sparseness
dw = DatasetWriter(columns=columns, name="empty")
dw.get_split_write()
ds = verify(params.slices, [],
dw.finish(),
hashlabel="date",
as_chain=True)
assert len(
ds.chain()
) == 1, ds + ": dataset_hashpart on empty dataset with as_chain=True did not produce a single dataset"
# two populated slices with the same data, should end up in two datasets.
dw = DatasetWriter(columns=columns, name="0 and 2")
dw.set_slice(0)
dw.write_dict(data[0])
dw.set_slice(1)
dw.set_slice(2)
dw.write_dict(data[0])
for s in range(3, params.slices):
dw.set_slice(s)
ds = verify(params.slices, [data[0]],
dw.finish(),
hashlabel="date",
as_chain=True)
got_slices = len(ds.chain())
assert got_slices == 2, "%s (built with as_chain=True) has %d datasets in chain, expected 2." % (
ds,
got_slices,
)
def synthesis(job):
dw = DatasetWriter(name='empty', columns={'v': 'ascii'})
dw.get_split_write()
empty_ds = dw.finish()
assert empty_ds.min('non-existant column') is empty_ds.max('non-existant column') is None, 'Dataset.min/max() broken for non-existant columns'
for typ, groups in tests.items():
t_ds = subjobs.build('dataset_type', column2type={'v': typ}, source=empty_ds).dataset()
minmax = (t_ds.columns['v'].min, t_ds.columns['v'].max)
if minmax != (None, None):
raise Exception('Typing empty dataset as %s did not give minmax == None, gave %r' % (typ, minmax,))
all_names = list(chain.from_iterable(groupdata[group].keys() for group in groups))
# just 1 and 2, so we don't make way too many
for num_groups in (1, 2,):
for names in combinations(all_names, num_groups):
ds, mn, mx = make_source(names)
t_ds = subjobs.build('dataset_type', column2type={'v': typ}, source=ds).dataset()
got_minmax = (t_ds.columns['v'].min, t_ds.columns['v'].max)
want_minmax = (mn, mx)
chk_minmax(got_minmax, want_minmax, 'Typing %s as %s gave wrong minmax: expected %r, got %r (in %s)' % (ds, typ, want_minmax, got_minmax, t_ds,))
chk_minmax(got_minmax, (t_ds.min('v'), t_ds.max('v')), 'Dataset.min/max() broken on ' + t_ds)
# verify writing the same data normally also gives the correct result
dw = DatasetWriter(name='rewrite ' + t_ds, columns=t_ds.columns)
write = dw.get_split_write()
for v in t_ds.iterate(None, 'v'):
write(v)
re_ds = dw.finish()
got_minmax = (re_ds.columns['v'].min, re_ds.columns['v'].max)
want_minmax = (mn, mx)
chk_minmax(got_minmax, want_minmax, 'Rewriting %s gave the wrong minmax: expected %r, got %r (in %s)' % (t_ds, want_minmax, got_minmax, re_ds,))
# make sure renaming doesn't mix anything up
dw = DatasetWriter(name='rename', columns={'a': 'ascii', 'b': 'ascii'})
write = dw.get_split_write()
write('5', '3')
write('7', 'oops')
ds = dw.finish()
t_ds = subjobs.build(
'dataset_type',
column2type=dict(num='number', int='int32_10'),
defaults=dict(num='1', int='2'),
rename=dict(a='num', b='int'),
source=ds,
).dataset()
for name, want_minmax in (
('num', (5, 7)),
('int', (2, 3)),
):
got_minmax = (t_ds.columns[name].min, t_ds.columns[name].max)
msg = 'Typing %s gave wrong minmax: expected %r, got %r (in %s)' % (ds, want_minmax, got_minmax, t_ds,)
chk_minmax(got_minmax, want_minmax, msg)
def synthesis():
dw_a = DatasetWriter(name='a', columns={'num': 'int32'})
dw_b = DatasetWriter(name='b', columns={'num': 'int32'}, previous=dw_a)
dw_c = DatasetWriter(name='c', columns={'num': 'int32'}, previous=dw_b)
w = dw_a.get_split_write()
w(3)
w(2)
w = dw_b.get_split_write()
w(2)
w(1)
w = dw_c.get_split_write()
w(0)
a = dw_a.finish()
b = dw_b.finish()
c = dw_c.finish()
opts = dict(
sort_columns='num',
sort_across_slices=True,
)
# sort as a chain
jid = subjobs.build('dataset_sort',
options=opts,
datasets=dict(source=a, previous=None))
assert list(Dataset(jid).iterate(None, 'num')) == [2, 3]
sorted_a = jid
jid = subjobs.build('dataset_sort',
options=opts,
datasets=dict(source=b, previous=jid))
assert list(Dataset(jid).iterate_chain(None, 'num')) == [2, 3, 1, 2]
jid = subjobs.build('dataset_sort',
options=opts,
datasets=dict(source=c, previous=jid))
assert list(Dataset(jid).iterate_chain(None, 'num')) == [2, 3, 1, 2, 0]
# sort all as a single dataset
jid = subjobs.build('dataset_sort',
options=opts,
datasets=dict(source=c, previous=None))
assert list(Dataset(jid).iterate_chain(None, 'num')) == [0, 1, 2, 2, 3]
# merge b and c but not a
jid = subjobs.build('dataset_sort',
options=opts,
datasets=dict(source=c, previous=sorted_a))
# test with new style job.dataset
assert list(jid.dataset().iterate(None, 'num')) == [0, 1, 2]
assert list(jid.dataset().iterate_chain(None, 'num')) == [2, 3, 0, 1, 2]
def mkds(name, columns, data, **kw):
columns = dict.fromkeys(columns, 'int32')
dw = DatasetWriter(name=name, columns=columns, **kw)
write = dw.get_split_write()
for v in data:
write(*v)
return dw.finish()
def synthesis(job):
manual_chain = [Dataset(jobids.selfchain, name) for name in "abcdefgh"]
manual_abf = [manual_chain[0], manual_chain[1], manual_chain[5]]
# build a local abf chain
prev = None
for ix, ds in enumerate(manual_abf):
name = "abf%d" % (ix, )
prev = ds.link_to_here(name, override_previous=prev)
manual_abf_data = list(Dataset.iterate_list(None, None, manual_abf))
local_abf_data = list(Dataset(job, "abf2").iterate_chain(None, None))
assert manual_abf_data == local_abf_data
# disconnect h, verify there is no chain
manual_chain[-1].link_to_here("alone", override_previous=None)
assert len(Dataset(job, "alone").chain()) == 1
# check that the original chain is unhurt
assert manual_chain == manual_chain[-1].chain()
# So far so good, now make a chain long enough to have a cache.
prev = None
ix = 0
going = True
while going:
if prev and "cache" in prev._data:
going = False
name = "longchain%d" % (ix, )
dw = DatasetWriter(name=name, previous=prev)
dw.add("ix", "number")
dw.get_split_write()(ix)
prev = dw.finish()
ix += 1
# we now have a chain that goes one past the first cache point
full_chain = Dataset(prev).chain()
assert "cache" in full_chain[
-2]._data # just to check the above logic is correct
assert "cache" not in full_chain[-1]._data # just to be sure..
full_chain[-2].link_to_here("nocache", override_previous=None)
full_chain[-1].link_to_here("withcache", override_previous=full_chain[-3])
assert "cache" not in Dataset(job, "nocache")._data
assert "cache" in Dataset(job, "withcache")._data
# And make sure they both get the right data too.
assert list(Dataset(prev).iterate_chain(None, "ix")) == list(range(ix))
assert list(Dataset(job, "nocache").iterate_chain(None, "ix")) == [ix - 2]
assert list(Dataset(job, "withcache").iterate_chain(
None, "ix")) == list(range(ix - 2)) + [ix - 1]
def make_source(names):
names = sorted(names)
dsname = '+'.join(names)
if dsname not in sources:
dw = DatasetWriter(name=dsname, columns={'v': 'ascii'})
write = dw.get_split_write()
for name in names:
for value in data[name][0]:
write(value)
sources[dsname] = (
dw.finish(),
min(unnan(data[name][1] for name in names)),
max(unnan(data[name][2] for name in names)),
)
return sources[dsname]
def test_column_discarding():
dw = DatasetWriter(name='column discarding')
dw.add('a', 'bytes')
dw.add('b', 'bytes')
dw.add('c', 'bytes')
w = dw.get_split_write()
w(b'a', b'b', b'c')
source = dw.finish()
# Discard b because it's not typed
ac_implicit = subjobs.build(
'dataset_type',
source=source,
column2type=dict(a='ascii', c='ascii'),
discard_untyped=True,
).dataset()
assert sorted(ac_implicit.columns) == ['a', 'c'], '%s: %r' % (ac_implicit, sorted(ac_implicit.columns),)
assert list(ac_implicit.iterate(None)) == [('a', 'c',)], ac_implicit
# Discard b explicitly
ac_explicit = subjobs.build(
'dataset_type',
source=source,
column2type=dict(a='ascii', c='ascii'),
rename=dict(b=None),
).dataset()
assert sorted(ac_explicit.columns) == ['a', 'c'], '%s: %r' % (ac_explicit, sorted(ac_explicit.columns),)
assert list(ac_explicit.iterate(None)) == [('a', 'c',)], ac_explicit
# Discard c by overwriting it with b. Keep untyped b.
ac_bASc = subjobs.build(
'dataset_type',
source=source,
column2type=dict(a='ascii', c='ascii'),
rename=dict(b='c'),
).dataset()
assert sorted(ac_bASc.columns) == ['a', 'b', 'c'], '%s: %r' % (ac_bASc, sorted(ac_bASc.columns),)
assert list(ac_bASc.iterate(None)) == [('a', b'b', 'b',)], ac_bASc
# Discard c by overwriting it with b. Also type b as a different type.
abc_bASc = subjobs.build(
'dataset_type',
source=source,
column2type=dict(a='ascii', b='strbool', c='ascii'),
rename=dict(b='c'),
).dataset()
assert sorted(abc_bASc.columns) == ['a', 'b', 'c'], '%s: %r' % (abc_bASc, sorted(abc_bASc.columns),)
assert list(abc_bASc.iterate(None)) == [('a', True, 'b',)], abc_bASc
def test_rehash_with_empty_slices():
dw = DatasetWriter(name='rehash with empty slices', hashlabel='a')
dw.add('a', 'ascii')
dw.add('b', 'ascii')
w = dw.get_split_write()
w('a', '42')
w('42', 'b')
source = dw.finish()
hashfunc = typed_writer('int32').hash
def verify_hashing(caption, want_values, **kw):
ds = subjobs.build('dataset_type',
source=source,
column2type=dict(a='int32_10'),
caption=caption,
**kw).dataset()
got_values = set()
for sliceno in range(g.slices):
for got in ds.iterate(sliceno):
assert hashfunc(got[0]) % g.slices == sliceno
assert got not in got_values
got_values.add(got)
assert want_values == got_values
verify_hashing('with discard', {(
42,
'b',
)}, filter_bad=True)
# using defaults uses some different code paths
verify_hashing('with default=0 (probably two slices)', {(
0,
'42',
), (
42,
'b',
)},
defaults=dict(a='0'))
verify_hashing('with default=42 (one slice)', {(
42,
'42',
), (
42,
'b',
)},
defaults=dict(a='42'))
def prepare():
dw = DatasetWriter(columns={"data": "ascii"})
write = dw.get_split_write()
write("foo")
write("bar")