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Original file line number | Diff line number | Diff line change |
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from __future__ import annotations | ||
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from typing import TYPE_CHECKING, Any, TypedDict | ||
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from hypothesis import Phase, given, settings | ||
from hypothesis import strategies as st | ||
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import polars as pl | ||
from polars.meta import get_index_type | ||
from polars.testing import assert_frame_equal, assert_series_equal | ||
from polars.testing.parametric.strategies import series | ||
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if TYPE_CHECKING: | ||
from collections.abc import Sequence | ||
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class Case(TypedDict): | ||
"""A test case for Skip Batch Predicate.""" | ||
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min: Any | None | ||
max: Any | None | ||
null_count: int | None | ||
len: int | None | ||
can_skip: bool | ||
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def assert_skp_series( | ||
name: str, | ||
dtype: pl.DataType, | ||
expr: pl.Expr, | ||
cases: Sequence[Case], | ||
) -> None: | ||
sbp = expr._skip_batch_predicate({name: dtype}) | ||
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df = pl.DataFrame( | ||
[ | ||
pl.Series(f"{name}_min", [i["min"] for i in cases], dtype), | ||
pl.Series(f"{name}_max", [i["max"] for i in cases], dtype), | ||
pl.Series(f"{name}_nc", [i["null_count"] for i in cases], get_index_type()), | ||
pl.Series("len", [i["len"] for i in cases], get_index_type()), | ||
] | ||
) | ||
mask = pl.Series("can_skip", [i["can_skip"] for i in cases], pl.Boolean) | ||
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out = df.select(can_skip=sbp).to_series() | ||
out = out.replace(None, False) | ||
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try: | ||
assert_series_equal(out, mask) | ||
except AssertionError: | ||
print(sbp) | ||
raise | ||
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def test_equality() -> None: | ||
assert_skp_series( | ||
"a", | ||
pl.Int64, | ||
pl.col("a") == 5, | ||
[ | ||
{"min": 1, "max": 2, "null_count": 0, "len": 42, "can_skip": True}, | ||
{"min": 6, "max": 7, "null_count": 0, "len": 42, "can_skip": True}, | ||
{"min": 1, "max": 7, "null_count": 0, "len": 42, "can_skip": False}, | ||
{"min": None, "max": None, "null_count": 42, "len": 42, "can_skip": True}, | ||
], | ||
) | ||
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assert_skp_series( | ||
"a", | ||
pl.Int64(), | ||
pl.col("a") != 0, | ||
[ | ||
{"min": 0, "max": 0, "null_count": 6, "len": 7, "can_skip": False}, | ||
], | ||
) | ||
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assert_skp_series( | ||
"a", | ||
pl.Struct(), | ||
pl.col("a") != 0, | ||
[ | ||
{"min": None, "max": None, "null_count": 6, "len": 7, "can_skip": False}, | ||
], | ||
) | ||
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CHUNK_SIZE = 7 | ||
NUM_CHUNKS = 13 | ||
TOTAL_SIZE = CHUNK_SIZE * NUM_CHUNKS | ||
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@given( | ||
s=series( | ||
name="x", | ||
min_size=TOTAL_SIZE, | ||
max_size=TOTAL_SIZE, | ||
# allowed_dtypes=[ | ||
# pl.Int64, | ||
# pl.String, | ||
# pl.Date, | ||
# pl.Datetime(time_zone=datetime.timezone.utc), | ||
# pl.Time, | ||
# ], | ||
), | ||
index_a=st.integers(0, TOTAL_SIZE - 1), | ||
index_b=st.integers(0, TOTAL_SIZE - 1), | ||
) | ||
@settings( | ||
report_multiple_bugs=False, | ||
phases=(Phase.explicit, Phase.reuse, Phase.generate, Phase.target, Phase.explain), | ||
) | ||
def test_skip_batch_predicate_parametric( | ||
s: pl.Series, index_a: int, index_b: int | ||
) -> None: | ||
name = "x" | ||
dtype = s.dtype | ||
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value_a = s.slice(index_a, 1) | ||
value_b = s.slice(index_b, 1) | ||
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lit_a = pl.lit(value_a[0], dtype) | ||
lit_b = pl.lit(value_b[0], dtype) | ||
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exprs = [ | ||
pl.col.x == lit_a, | ||
pl.col.x != lit_a, | ||
pl.col.x.eq_missing(lit_a), | ||
pl.col.x.ne_missing(lit_a), | ||
pl.col.x.is_null(), | ||
pl.col.x.is_not_null(), | ||
] | ||
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try: | ||
_ = s > value_a | ||
exprs += [ | ||
pl.col.x > lit_a, | ||
pl.col.x >= lit_a, | ||
pl.col.x < lit_a, | ||
pl.col.x <= lit_a, | ||
pl.col.x.is_between(lit_a, lit_b), | ||
pl.col.x.is_in(pl.Series([value_a[0], value_b[0]], dtype=dtype)), | ||
pl.col.x.is_in(pl.Series([None, value_a[0]], dtype=dtype)), | ||
] | ||
except Exception as _: | ||
pass | ||
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for expr in exprs: | ||
sbp = expr._skip_batch_predicate({name: dtype}) | ||
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mins = [None] * NUM_CHUNKS | ||
try: | ||
mins = [ | ||
s.slice(i * CHUNK_SIZE, CHUNK_SIZE).min() for i in range(NUM_CHUNKS) | ||
] | ||
except Exception as _: | ||
mins = [None] * NUM_CHUNKS | ||
try: | ||
maxs = [ | ||
s.slice(i * CHUNK_SIZE, CHUNK_SIZE).max() for i in range(NUM_CHUNKS) | ||
] | ||
except Exception as _: | ||
maxs = [None] * NUM_CHUNKS | ||
null_counts = [ | ||
s.slice(i * CHUNK_SIZE, CHUNK_SIZE).null_count() for i in range(NUM_CHUNKS) | ||
] | ||
lengths = [CHUNK_SIZE] * NUM_CHUNKS | ||
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df = pl.DataFrame( | ||
[ | ||
pl.Series(f"{name}_min", mins, dtype), | ||
pl.Series(f"{name}_max", maxs, dtype), | ||
pl.Series(f"{name}_nc", null_counts, get_index_type()), | ||
pl.Series("len", lengths, get_index_type()), | ||
] | ||
) | ||
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out = df.select(can_skip=sbp).fill_null(False).to_series() | ||
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included = [] | ||
for i, can_skip in enumerate(out): | ||
if not can_skip: | ||
included += [s.slice(i * CHUNK_SIZE, CHUNK_SIZE).to_frame()] | ||
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skipped_batches_df: pl.DataFrame | ||
if len(included) == 0: | ||
skipped_batches_df = s.head(0).to_frame() | ||
else: | ||
skipped_batches_df = pl.concat(included) | ||
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try: | ||
assert_frame_equal( | ||
s.to_frame().filter(expr), | ||
skipped_batches_df.filter(expr), | ||
) | ||
except Exception as _: | ||
print(expr) | ||
print(sbp) | ||
raise |