I have written the following code with the help of numpy and I want to improve the performance with numba. I am not sure why it is not working as I have set all the variables as per numba system. I am trying to speed up this code as I would be working with large data sets in the future.
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()
Here is the error I am getting:
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)
Also I am running this code in IDE. If the above code doesn't work with numba then what is the best way to make this code work to get the same result.
The issue is with np.round. It's not entirely clear from the documentation, but you can see from looking at the source, that if you are using the function on an array input, you need to provide all 3 arguments. So the following does not work:
nb.jit(nopython=True)
def func(x):
return np.round(x)
but the following works as expected:
nb.jit(nopython=True)
def func(x):
out = np.empty_like(x)
np.round(x, 0, out)
return out
See the docs for np.around for the full description. I'm going to raise an issue on the numba issue tracker since this isn't obvious from looking at the docs.
来源：https://stackoverflow.com/questions/57435833/why-this-python-class-is-not-working-with-numba-jitclass