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indexing_engines.py
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"""
Benchmarks in this file depend mostly on code in _libs/
We have to created masked arrays to test the masked engine though. The
array is unpacked on the Cython level.
If a PR does not edit anything in _libs, it is very unlikely that benchmarks
in this file will be affected.
"""
import numpy as np
from pandas._libs import index as libindex
from pandas.core.arrays import BaseMaskedArray
def _get_numeric_engines():
engine_names = [
("Int64Engine", np.int64),
("Int32Engine", np.int32),
("Int16Engine", np.int16),
("Int8Engine", np.int8),
("UInt64Engine", np.uint64),
("UInt32Engine", np.uint32),
("UInt16engine", np.uint16),
("UInt8Engine", np.uint8),
("Float64Engine", np.float64),
("Float32Engine", np.float32),
]
return [
(getattr(libindex, engine_name), dtype)
for engine_name, dtype in engine_names
if hasattr(libindex, engine_name)
]
def _get_masked_engines():
engine_names = [
("MaskedInt64Engine", "Int64"),
("MaskedInt32Engine", "Int32"),
("MaskedInt16Engine", "Int16"),
("MaskedInt8Engine", "Int8"),
("MaskedUInt64Engine", "UInt64"),
("MaskedUInt32Engine", "UInt32"),
("MaskedUInt16engine", "UInt16"),
("MaskedUInt8Engine", "UInt8"),
("MaskedFloat64Engine", "Float64"),
("MaskedFloat32Engine", "Float32"),
]
return [
(getattr(libindex, engine_name), dtype)
for engine_name, dtype in engine_names
if hasattr(libindex, engine_name)
]
class NumericEngineIndexing:
params = [
_get_numeric_engines(),
["monotonic_incr", "monotonic_decr", "non_monotonic"],
[True, False],
[10**5, 2 * 10**6], # 2e6 is above SIZE_CUTOFF
]
param_names = ["engine_and_dtype", "index_type", "unique", "N"]
def setup(self, engine_and_dtype, index_type, unique, N):
engine, dtype = engine_and_dtype
if index_type == "monotonic_incr":
if unique:
arr = np.arange(N * 3, dtype=dtype)
else:
arr = np.array([1, 2, 3], dtype=dtype).repeat(N)
elif index_type == "monotonic_decr":
if unique:
arr = np.arange(N * 3, dtype=dtype)[::-1]
else:
arr = np.array([3, 2, 1], dtype=dtype).repeat(N)
else:
assert index_type == "non_monotonic"
if unique:
arr = np.empty(N * 3, dtype=dtype)
arr[:N] = np.arange(N * 2, N * 3, dtype=dtype)
arr[N:] = np.arange(N * 2, dtype=dtype)
else:
arr = np.array([1, 2, 3], dtype=dtype).repeat(N)
self.data = engine(arr)
# code below avoids populating the mapping etc. while timing.
self.data.get_loc(2)
self.key_middle = arr[len(arr) // 2]
self.key_early = arr[2]
def time_get_loc(self, engine_and_dtype, index_type, unique, N):
self.data.get_loc(self.key_early)
def time_get_loc_near_middle(self, engine_and_dtype, index_type, unique, N):
# searchsorted performance may be different near the middle of a range
# vs near an endpoint
self.data.get_loc(self.key_middle)
class MaskedNumericEngineIndexing:
params = [
_get_masked_engines(),
["monotonic_incr", "monotonic_decr", "non_monotonic"],
[True, False],
[10**5, 2 * 10**6], # 2e6 is above SIZE_CUTOFF
]
param_names = ["engine_and_dtype", "index_type", "unique", "N"]
def setup(self, engine_and_dtype, index_type, unique, N):
engine, dtype = engine_and_dtype
dtype = dtype.lower()
if index_type == "monotonic_incr":
if unique:
arr = np.arange(N * 3, dtype=dtype)
else:
arr = np.array([1, 2, 3], dtype=dtype).repeat(N)
mask = np.zeros(N * 3, dtype=np.bool_)
elif index_type == "monotonic_decr":
if unique:
arr = np.arange(N * 3, dtype=dtype)[::-1]
else:
arr = np.array([3, 2, 1], dtype=dtype).repeat(N)
mask = np.zeros(N * 3, dtype=np.bool_)
else:
assert index_type == "non_monotonic"
if unique:
arr = np.zeros(N * 3, dtype=dtype)
arr[:N] = np.arange(N * 2, N * 3, dtype=dtype)
arr[N:] = np.arange(N * 2, dtype=dtype)
else:
arr = np.array([1, 2, 3], dtype=dtype).repeat(N)
mask = np.zeros(N * 3, dtype=np.bool_)
mask[-1] = True
self.data = engine(BaseMaskedArray(arr, mask))
# code below avoids populating the mapping etc. while timing.
self.data.get_loc(2)
self.key_middle = arr[len(arr) // 2]
self.key_early = arr[2]
def time_get_loc(self, engine_and_dtype, index_type, unique, N):
self.data.get_loc(self.key_early)
def time_get_loc_near_middle(self, engine_and_dtype, index_type, unique, N):
# searchsorted performance may be different near the middle of a range
# vs near an endpoint
self.data.get_loc(self.key_middle)
class ObjectEngineIndexing:
params = [("monotonic_incr", "monotonic_decr", "non_monotonic")]
param_names = ["index_type"]
def setup(self, index_type):
N = 10**5
values = list("a" * N + "b" * N + "c" * N)
arr = {
"monotonic_incr": np.array(values, dtype=object),
"monotonic_decr": np.array(list(reversed(values)), dtype=object),
"non_monotonic": np.array(list("abc") * N, dtype=object),
}[index_type]
self.data = libindex.ObjectEngine(arr)
# code below avoids populating the mapping etc. while timing.
self.data.get_loc("b")
def time_get_loc(self, index_type):
self.data.get_loc("b")