我在numpy的帮助下编写了以下代码，我想提高numba的性能。我不确定为什么它不起作用，因为我已根据 numba 系统设置了所有变量。我正在尝试加快这段代码，因为我将来会处理大型数据集。
import numpy as np
import math
from numba import jitclass
from numba import float64,int64
spec =[
('spacing',float64),
('n_iterations',int64),
('np_emptyhouses',float64[:,:]),
('np_agenthouses',float64[:,:]),
('similarity_threshhold',float64),
('n_changes',int64)
]
@jitclass(spec)
class geo_schelling_update:
def __init__(self,n_iterations,spacing,np_agenthouses,np_emptyhouses,similarity_threshhold):
self.spacing=spacing
self.n_iterations=n_iterations
self.np_emptyhouses=np_emptyhouses
self.np_agenthouses=np_agenthouses
self.similarity_threshhold=similarity_threshhold
def distance_vectorize(self,pointA1, pointA2,agent):
x_square=np.square(pointA1-agent[0])
y_square=np.square(pointA2-agent[1])
dist=np.sqrt(np.array(x_square,dtype=np.float32)+np.array(y_square,dtype=np.float32))
return np.round(dist,4)
def is_unsatisfied_vectorize(self,x,y):
race = np.extract(np.logical_and(np.equal(self.np_agenthouses[:,0],x),np.equal(self.np_agenthouses[:,1],y)),self.np_agenthouses[:,2])[0]
euclid_distance1=round(math.hypot(self.spacing,self.spacing),4)
euclid_distance2=self.spacing
total_agents=np.extract(np.logical_or(np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance1),np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance2)),self.np_agenthouses[:,2])
if total_agents.size ==0:
return False
else:
return total_agents[total_agents==race].size/total_agents.size<self.similarity_threshhold
def move_to_empty(self,x,y):
race = np.extract(np.logical_and(np.equal(self.np_agenthouses[:,0],x),np.equal(self.np_agenthouses[:,1],y)),self.np_agenthouses[:,2])[0]
x_new,y_new=self.np_emptyhouses[np.random.choice(self.np_emptyhouses.shape[0],1),:][0]
self.np_agenthouses=self.np_agenthouses[~(np.logical_and(self.np_agenthouses[:,0]==x, self.np_agenthouses[:,1]==y))]
self.np_agenthouses=np.vstack([self.np_agenthouses,[x_new,y_new,race]])
self.np_emptyhouses=self.np_emptyhouses[~(np.logical_and(self.np_emptyhouses[:,0]==x_new, self.np_emptyhouses[:,1]==y_new))]
self.np_emptyhouses=np.vstack([self.np_emptyhouses,[x,y]])
def update_helper(self,agent):
if self.is_unsatisfied_vectorize(agent[0],agent[1]):
self.move_to_empty(agent[0],agent[1])
return 1
else:
return 0
def update(self):
for i in np.arange(self.n_iterations):
np_oldagenthouses=self.np_agenthouses.copy()
n_changes=0
for row in np_oldagenthouses:
n=self.update_helper(row)
n_changes+=n
print(n_changes)
print(i)
if n_changes == 0:
break
np_agenthouses=np.array([[-71.8, 41.4, 2.0],
[-71.6, 41.4, 2.0],
[-71.6, 41.6, 2.0],
[-71.4, 41.6, 1.0],
[-71.6, 41.8, 1.0],
[-71.4, 41.8, 2.0],
[-71.6, 42.0, 2.0],
[-71.4, 42.0, 1.0],
[-71.4, 41.4, 2.0],
[-71.2, 41.4, 1.0]])
np_emptyhouses=np.array([[-71.8, 41.3],[-71.8, 41.4],[-71.5, 41.5],
[-71.5, 41.6],[-71.7, 41.8],[-71.7, 41.9],
[-71.5, 41.9],[-71.2, 41.4],[-71.6, 41.7]])
spacing=0.1
similarity_threshhold=0.65
n_iterations=100
schelling= geo_schelling_update(n_iterations,
spacing,
np_agenthouses,
np_emptyhouses,similarity_threshhold)
schelling.update()
这是我得到的错误：
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Failed in nopython mode pipeline (step: nopython frontend)
Failed in nopython mode pipeline (step: nopython frontend)
Invalid use of Function(<function round_ at 0x000001909ED270D0>) with argument(s) of type(s): (array(float64, 1d, C), Literal[int](4))
* parameterized
In definition 0:
All templates rejected with literals.
In definition 1:
All templates rejected without literals.
This error is usually caused by passing an argument of a type that is unsupported by the named function.
[1] During: resolving callee type: Function(<function round_ at 0x000001909ED270D0>)
[2] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (42)
File "test2.py", line 42:
def is_unsatisfied_vectorize(self,x,y):
<source elided>
euclid_distance2=self.spacing
total_agents=np.extract(np.logical_or(np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance1),np.equal(np.round(np.hypot((self.np_agenthouses[:,0]-(x)),(self.np_agenthouses[:,1]-(y))),4),euclid_distance2)),self.np_agenthouses[:,2])
^
[1] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'is_unsatisfied_vectorize') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[2] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (57)
[3] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'is_unsatisfied_vectorize') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[4] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (57)
File "test2.py", line 57:
def update_helper(self,agent):
if self.is_unsatisfied_vectorize(agent[0],agent[1]):
^
[1] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update_helper') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[2] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (68)
[3] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update_helper') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[4] During: typing of call at C:/Users/ksharma/Documents/geoschelling/test2.py (68)
File "test2.py", line 68:
def update(self):
<source elided>
for row in np_oldagenthouses:
n=self.update_helper(row)
^
[1] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[2] During: typing of call at <string> (3)
[3] During: resolving callee type: BoundFunction((<class 'numba.types.misc.ClassInstanceType'>, 'update') for instance.jitclass.geo_schelling_update#190b49eec18<spacing:float64,n_iterations:int64,np_emptyhouses:array(float64, 2d, A),np_agenthouses:array(float64, 2d, A),similarity_threshhold:float64,n_changes:int64>)
[4] During: typing of call at <string> (3)
我也在 IDE 中运行此代码。如果上面的代码不适用于 numba，那么使此代码工作以获得相同结果的最佳方法是什么。
问题出在np.round上。从文档中并不完全清楚，但您可以通过查看源代码看到，如果您在数组输入上使用该函数，则需要提供所有 3 个参数。因此，以下内容不起作用：
nb.jit(nopython=True)
def func(x):
return np.round(x)
但以下内容按预期工作：
nb.jit(nopython=True)
def func(x):
out = np.empty_like(x)
np.round(x, 0, out)
return out
有关完整说明，请参阅文档以获取np.around。我将在 numba 问题跟踪器上提出一个问题，因为从查看文档来看这并不明显。