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46 changes: 35 additions & 11 deletions asv_bench/benchmarks/indexing_engines.py
Original file line number Diff line number Diff line change
Expand Up @@ -35,25 +35,49 @@ 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"]
param_names = ["engine_and_dtype", "index_type", "unique", "N"]

def setup(self, engine_and_dtype, index_type):
def setup(self, engine_and_dtype, index_type, unique, N):
engine, dtype = engine_and_dtype
N = 10 ** 5
values = list([1] * N + [2] * N + [3] * N)
arr = {
"monotonic_incr": np.array(values, dtype=dtype),
"monotonic_decr": np.array(list(reversed(values)), dtype=dtype),
"non_monotonic": np.array([1, 2, 3] * N, dtype=dtype),
}[index_type]

if index_type == "monotonic_incr":
if unique:
arr = np.arange(N * 3, dtype=dtype)
else:
values = list([1] * N + [2] * N + [3] * N)
arr = np.array(values, dtype=dtype)
elif index_type == "monotonic_decr":
if unique:
arr = np.arange(N * 3, dtype=dtype)[::-1]
else:
values = list([1] * N + [2] * N + [3] * N)
arr = np.array(values, dtype=dtype)[::-1]
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] * N, dtype=dtype)

self.data = engine(arr)
# code belows avoids populating the mapping etc. while timing.
self.data.get_loc(2)

def time_get_loc(self, engine_and_dtype, index_type):
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:
Expand Down