I want to convert the results of a Neo4j Data into Graph in Python using py2Neo.
The below link provides a detail program code:
http://nicolewhite.github.io/neo4j-jupyter/hello-world.html
import networkx as nx
%matplotlib inline
results = %cypher MATCH p = (:Person)-[:LIKES]->(:Drink) RETURN p
g = results.get_graph()
nx.draw(g)
The get_graph function throws an error as:
TypeError: error() takes exactly 2 arguments (4 given).
The function expects two arguments. But I do not understand what is wrong with the Cypher Query? Thank you.
Related
I am trying to improve my loop computation speed by using foreach, but there is a simple Rcpp function I defined inside of this loop. I saved the Rcpp function as mproduct.cpp, and I call out the function simply using
sourceCpp("mproduct.cpp")
and the Rcpp function is a simple one, which is to perform matrix product in C++:
// [[Rcpp::depends(RcppArmadillo, RcppEigen)]]
#include <RcppArmadillo.h>
#include <RcppEigen.h>
// [[Rcpp::export]]
SEXP MP(const Eigen::Map<Eigen::MatrixXd> A, Eigen::Map<Eigen::MatrixXd> B){
Eigen::MatrixXd C = A * B;
return Rcpp::wrap(C);
}
So, the function in the Rcpp file is MP, referring to matrix product. I need to perform the following foreach loop (I have simplified the code for illustration):
foreach(j=1:n, .package='Rcpp',.noexport= c("mproduct.cpp"),.combine=rbind)%dopar%{
n=1000000
A<-matrix(rnorm(n,1000,1000))
B<-matrix(rnorm(n,1000,1000))
S<-MP(A,B)
return(S)
}
Since the size of matrix A and B are large, it is why I want to use foreach to alleviate the computational cost.
However, the above code does not work, since it provides me error message:
task 1 failed - "NULL value passed as symbol address"
The reason I added .noexport= c("mproduct.cpp") is to follow some suggestions from people who solved similar issues (Can't run Rcpp function in foreach - "NULL value passed as symbol address"). But somehow this does not solve my issue.
So I tried to install my Rcpp function as a library. I used the following code:
Rcpp.package.skeleton('mp',cpp_files = "<my working directory>")
but it returns me a warning message:
The following packages are referenced using Rcpp::depends attributes however are not listed in the Depends, Imports or LinkingTo fields of the package DESCRIPTION file: RcppArmadillo, RcppEigen
so when I tried to install my package using
install.packages("<my working directory>",repos = NULL,type='source')
I got the warning message:
Error in untar2(tarfile, files, list, exdir, restore_times) :
incomplete block on file
In R CMD INSTALL
Warning in install.packages :
installation of package ‘C:/Users/Lenovo/Documents/mproduct.cpp’ had non-zero exit status
So can someone help me out how to solve 1) using foreach with Rcpp function MP, or 2) install the Rcpp file as a package?
Thank you all very much.
The first step would be making sure that you are optimizing the right thing. For me, this would not be the case as this simple benchmark shows:
set.seed(42)
n <- 1000
A<-matrix(rnorm(n*n), n, n)
B<-matrix(rnorm(n*n), n, n)
MP <- Rcpp::cppFunction("SEXP MP(const Eigen::Map<Eigen::MatrixXd> A, Eigen::Map<Eigen::MatrixXd> B){
Eigen::MatrixXd C = A * B;
return Rcpp::wrap(C);
}", depends = "RcppEigen")
bench::mark(MP(A, B), A %*% B)[1:5]
#> # A tibble: 2 x 5
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 MP(A, B) 277.8ms 278ms 3.60 7.63MB
#> 2 A %*% B 37.4ms 39ms 22.8 7.63MB
So for me the matrix product via %*% is several times faster than the one via RcppEigen. However, I am using Linux with OpenBLAS for matrix operations while you are on Windows, which often means reference BLAS for matrix operations. It might be that RcppEigen is faster on your system. I am not sure how difficult it is for Windows user to get a faster BLAS implementation (https://csgillespie.github.io/efficientR/set-up.html#blas-and-alternative-r-interpreters might contain some pointers), but I would suggest spending some time on investigating this.
Now if you come to the conclusion that you do need RcppEigen or RcppArmadillo in your code and want to put that code into a package, you can do the following. Instead of Rcpp::Rcpp.package.skeleton() use RcppEigen::RcppEigen.package.skeleton() or RcppArmadillo::RcppArmadillo.package.skeleton() to create a starting point for a package based on RcppEigen or RcppArmadillo, respectively.
I am trying to understand Dask delayed more deeply so I decided to work through the examples here. I modified some of the code to reflect how I want to use Dask (see below). But the results are different than what I expected ie. a tuple vs list. When I try to apply '.visualize()' to see what the execution graph looks like I get nothing.
I worked through all the examples in 'delayed.ipynb' and they all work properly including all the visualizations. I then modified the 'for' loop for one example:
for i in range(256):
x = inc(i)
y = dec(x)
z = add(x, y)
zs.append(z)
to a function call the uses a list comprehension. The result is a variation on the original working example.
%%time
import time
import random
from dask import delayed, compute, visualize
zs = []
#delayed
def inc(x):
time.sleep(random.random())
return x + 1
#delayed
def dec(x):
time.sleep(random.random())
return x - 1
#delayed
def add(x, y):
time.sleep(random.random())
return x + y
def myloop(x):
x.append([add(inc(i), dec(inc(i))) for i in range(8)])
return x
result = myloop(zs)
final = compute(*result)
print(final)
I have tried printing out 'result' (function call) which provides the expected list of delay calls but when I print the results of 'compute' I unexpectedly get the desired list as part of a tuple. Why don't I get a just a list?
When I try to 'visualize' the execution graph I get nothing at all. I was expecting to see as many nodes as are in the generated list.
I did not think I made any significant modifications to the example so what am I not understanding?
The visualize function has the same call signature as compute. So if your compute(*result) call works then try visualize(*result)
I do not understand why I get the error I'm currently getting in Rascal.
|cwd:///loader.rsc|(391,1,<19,33>,<19,34>): subscript not supported on set[Declaration] at |cwd:///loader.rsc|(391,1,<19,33>,<19,34>)
Advice: |http://tutor.rascal-mpl.org/Errors/Static/UnsupportedOperation/UnsupportedOperation.html|
I get this on the following list comprehension:
{asts[astIndexes[i]] | int i <- [0 .. size(astIndexes)]}
If needed, this is the entire file:
module loader
import IO;
import Set;
import List;
import lang::java::m3::Core;
import lang::java::m3::AST;
import String;
set[Declaration] asts = {};
void getAsts(list[loc] partialScanList){
asts = {};
for (loc m <- partialScanList)
asts += createAstFromFile(m, true);
}
void scanMetric(void (set[Declaration]) metricFunction, list[int] astIndexes){
metricFunction({asts[astIndexes[i]] | int i <- [0 .. size(astIndexes)]});
println(0);
}
The answer is that the subscript operator is defined on maps and relations and not on sets. For example on a rel[int,int] x = {<1,2>} you could x[1] and get {2}, and on map[int,int] y = (1:2) you could y[1] and get 2.
A side-note, this codes looks like its computing lookup indexes for AST nodes, but Rascal already has pretty efficient hashes for all ADT constructor trees and those are used to lookup in relations and maps. Since these hash-codes are also integers and their distribution is pretty uniform, it is very hard to increase performance by introducing your own indexing scheme on top of this. Most likely it would degrade performance rather than improve it.
So you if you need a lookup per AST node, you could use a rel[Declaration, Something else]. People often also use loc as references to AST nodes, since they are supposed to be pretty unique. That helps if you can't keep all ASTs in memory all the time.
Consider the following script:
from numba import guvectorize, u1, i8
import numpy as np
#guvectorize([(u1[:],i8)], '(n)->()')
def f(x, res):
res = x.argmax()
x = np.array([1,2,3],dtype=np.uint8)
print(f(x))
print(x.argmax())
print(f(x))
When running it, I get the following:
4382569440205035030
2
2
Why is this happening? Is there a way to get it right?
Python doesn't have references, so res = ... is not actually assigning to the output parameter, but instead rebinding the name res. I believe res is pointing to uninitialized memory, which is why your first run gives a seemingly random value.
Numba works around this using the slice syntax ([:]) which does mutate res- you also need to declare the type as an array. A working function is:
#guvectorize([(u1[:], i8[:])], '(n)->()')
def f(x, res):
res[:] = x.argmax()
I am trying to solve a problem, for example I have a 4 point and each two point has a cost between them. Now I want to find a sequence of nodes which total cost would be less than a bound. I have written a code but it seems not working. The main problem is I have define a python function and trying to call it with in a constraint.
Here is my code: I have a function def getVal(n1,n2): where n1, n2 are Int Sort. The line Nodes = [ Int("n_%s" % (i)) for i in range(totalNodeNumber) ] defines 4 points as Int sort and when I am adding a constraint s.add(getVal(Nodes[0], Nodes[1]) + getVal(Nodes[1], Nodes[2]) < 100) then it calls getVal function immediately. But I want that, when Z3 will decide a value for Nodes[0], Nodes[1], Nodes[2], Nodes[3] then the function should be called for getting the cost between to points.
from z3 import *
import random
totalNodeNumber = 4
Nodes = [ Int("n_%s" % (i)) for i in range(totalNodeNumber) ]
def getVal(n1,n2):
# I need n1 and n2 values those assigned by Z3
cost = random.randint(1,20)
print cost
return IntVal(cost)
s = Solver()
#constraint: Each Nodes value should be distinct
nodes_index_distinct_constraint = Distinct(Nodes)
s.add(nodes_index_distinct_constraint)
#constraint: Each Nodes value should be between 0 and totalNodeNumber
def get_node_index_value_constraint(i):
return And(Nodes[i] >= 0, Nodes[i] < totalNodeNumber)
nodes_index_constraint = [ get_node_index_value_constraint(i) for i in range(totalNodeNumber)]
s.add(nodes_index_constraint)
#constraint: Problem with this constraint
# Here is the problem it's just called python getVal function twice without assiging Nodes[0],Nodes[1],Nodes[2] values
# But I want to implement that - Z3 will call python function during his decission making of variables
s.add(getVal(Nodes[0], Nodes[1]) + getVal(Nodes[1], Nodes[2]) + getVal(Nodes[2], Nodes[3]) < 100)
if s.check() == sat:
print "SAT"
print "Model: "
m = s.model()
nodeIndex = [ m.evaluate(Nodes[i]) for i in range(totalNodeNumber) ]
print nodeIndex
else:
print "UNSAT"
print "No solution found !!"
If this is not a right way to solve the problem then could you please tell me what would be other alternative way to solve it. Can I encode this kind of problem to find optimal sequence of way points using Z3 solver?
I don't understand what problem you need to solve. Definitely, the way getVal is formulated does not make sense. It does not use the arguments n1, n2. If you want to examine values produced by a model, then you do this after Z3 returns from a call to check().
I don't think you can use a python function in your SMT logic. What you could alternatively is define getVal as a Function like this
getVal = Function('getVal',IntSort(),IntSort(),IntSort())
And constraint the edge weights as
s.add(And(getVal(0,1)==1,getVal(1,2)==2,getVal(0,2)==3))
The first two input parameters of getVal represent the node ids and the last integer represents the weight.