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bench_join.py
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bench_join.py
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from pandas.compat import range, lrange
import numpy as np
import pandas._libs.lib as lib
from pandas import *
from copy import deepcopy
import time
n = 1000000
K = 1
pct_overlap = 0.2
a = np.arange(n, dtype=np.int64)
b = np.arange(n * pct_overlap, n * (1 + pct_overlap), dtype=np.int64)
dr1 = DatetimeIndex('1/1/2000', periods=n, offset=offsets.Minute())
dr2 = DatetimeIndex(
dr1[int(pct_overlap * n)], periods=n, offset=offsets.Minute(2))
aobj = a.astype(object)
bobj = b.astype(object)
av = np.random.randn(n)
bv = np.random.randn(n)
avf = np.random.randn(n, K)
bvf = np.random.randn(n, K)
a_series = Series(av, index=a)
b_series = Series(bv, index=b)
a_frame = DataFrame(avf, index=a, columns=lrange(K))
b_frame = DataFrame(bvf, index=b, columns=lrange(K, 2 * K))
def do_left_join(a, b, av, bv):
out = np.empty((len(a), 2))
lib.left_join_1d(a, b, av, bv, out)
return out
def do_outer_join(a, b, av, bv):
result_index, aindexer, bindexer = lib.outer_join_indexer(a, b)
result = np.empty((2, len(result_index)))
lib.take_1d(av, aindexer, result[0])
lib.take_1d(bv, bindexer, result[1])
return result_index, result
def do_inner_join(a, b, av, bv):
result_index, aindexer, bindexer = lib.inner_join_indexer(a, b)
result = np.empty((2, len(result_index)))
lib.take_1d(av, aindexer, result[0])
lib.take_1d(bv, bindexer, result[1])
return result_index, result
from line_profiler import LineProfiler
prof = LineProfiler()
from pandas.util.testing import set_trace
def do_left_join_python(a, b, av, bv):
indexer, mask = lib.ordered_left_join_int64(a, b)
n, ak = av.shape
_, bk = bv.shape
result_width = ak + bk
result = np.empty((result_width, n), dtype=np.float64)
result[:ak] = av.T
bchunk = result[ak:]
_take_multi(bv.T, indexer, bchunk)
np.putmask(bchunk, np.tile(mask, bk), np.nan)
return result
def _take_multi(data, indexer, out):
if not data.flags.c_contiguous:
data = data.copy()
for i in range(data.shape[0]):
data[i].take(indexer, out=out[i])
def do_left_join_multi(a, b, av, bv):
n, ak = av.shape
_, bk = bv.shape
result = np.empty((n, ak + bk), dtype=np.float64)
lib.left_join_2d(a, b, av, bv, result)
return result
def do_outer_join_multi(a, b, av, bv):
n, ak = av.shape
_, bk = bv.shape
result_index, rindexer, lindexer = lib.outer_join_indexer(a, b)
result = np.empty((len(result_index), ak + bk), dtype=np.float64)
lib.take_join_contiguous(av, bv, lindexer, rindexer, result)
# result = np.empty((ak + bk, len(result_index)), dtype=np.float64)
# lib.take_axis0(av, rindexer, out=result[:ak].T)
# lib.take_axis0(bv, lindexer, out=result[ak:].T)
return result_index, result
def do_inner_join_multi(a, b, av, bv):
n, ak = av.shape
_, bk = bv.shape
result_index, rindexer, lindexer = lib.inner_join_indexer(a, b)
result = np.empty((len(result_index), ak + bk), dtype=np.float64)
lib.take_join_contiguous(av, bv, lindexer, rindexer, result)
# result = np.empty((ak + bk, len(result_index)), dtype=np.float64)
# lib.take_axis0(av, rindexer, out=result[:ak].T)
# lib.take_axis0(bv, lindexer, out=result[ak:].T)
return result_index, result
def do_left_join_multi_v2(a, b, av, bv):
indexer, mask = lib.ordered_left_join_int64(a, b)
bv_taken = bv.take(indexer, axis=0)
np.putmask(bv_taken, mask.repeat(bv.shape[1]), np.nan)
return np.concatenate((av, bv_taken), axis=1)
def do_left_join_series(a, b):
return b.reindex(a.index)
def do_left_join_frame(a, b):
a.index._indexMap = None
b.index._indexMap = None
return a.join(b, how='left')
# a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
# b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
# print(lib.inner_join_indexer(a, b))
out = np.empty((10, 120000))
def join(a, b, av, bv, how="left"):
func_dict = {'left': do_left_join_multi,
'outer': do_outer_join_multi,
'inner': do_inner_join_multi}
f = func_dict[how]
return f(a, b, av, bv)
def bench_python(n=100000, pct_overlap=0.20, K=1):
import gc
ns = [2, 3, 4, 5, 6]
iterations = 200
pct_overlap = 0.2
kinds = ['outer', 'left', 'inner']
all_results = {}
for logn in ns:
n = 10 ** logn
a = np.arange(n, dtype=np.int64)
b = np.arange(n * pct_overlap, n * pct_overlap + n, dtype=np.int64)
avf = np.random.randn(n, K)
bvf = np.random.randn(n, K)
a_frame = DataFrame(avf, index=a, columns=lrange(K))
b_frame = DataFrame(bvf, index=b, columns=lrange(K, 2 * K))
all_results[logn] = result = {}
for kind in kinds:
gc.disable()
elapsed = 0
_s = time.clock()
for i in range(iterations):
if i % 10 == 0:
elapsed += time.clock() - _s
gc.collect()
_s = time.clock()
a_frame.join(b_frame, how=kind)
# join(a, b, avf, bvf, how=kind)
elapsed += time.clock() - _s
gc.enable()
result[kind] = (elapsed / iterations) * 1000
return DataFrame(all_results, index=kinds)
def bench_xts(n=100000, pct_overlap=0.20):
from pandas.rpy.common import r
r('a <- 5')
xrng = '1:%d' % n
start = n * pct_overlap + 1
end = n + start - 1
yrng = '%d:%d' % (start, end)
r('library(xts)')
iterations = 500
kinds = ['left', 'outer', 'inner']
result = {}
for kind in kinds:
r('x <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, xrng))
r('y <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, yrng))
stmt = 'for (i in 1:%d) merge(x, y, join="%s")' % (iterations, kind)
elapsed = r('as.list(system.time(%s, gcFirst=F))$elapsed' % stmt)[0]
result[kind] = (elapsed / iterations) * 1000
return Series(result)