I am trying to run
mclp = mclp.solve(GLPK(msg=False))
However, when I do it returns the error:
--------------------------------------------------------------------------- PulpSolverError Traceback (most recent call
last)
C:\Users\J84538~1\AppData\Local\Temp\1/ipykernel_23424/53669721.py in
----> 1 mclp = mclp.solve(GLPK(msg=False))
~\Anaconda3\lib\site-packages\spopt\locate\coverage.py in solve(self,
solver)
579 MCLP object
580 """
--> 581 self.problem.solve(solver)
582 return self
~\Anaconda3\lib\site-packages\pulp\pulp.py in solve(self, solver,
**kwargs) 1888 #time it 1889 self.solutionTime = -clock()
-> 1890 status = solver.actualSolve(self, **kwargs) 1891 self.solutionTime += clock() 1892
self.restoreObjective(wasNone, dummyVar)
~\Anaconda3\lib\site-packages\pulp\apis\glpk_api.py in
actualSolve(self, lp)
57 """Solve a well formulated lp problem"""
58 if not self.executable(self.path):
---> 59 raise PulpSolverError("PuLP: cannot execute "+self.path)
60 tmpLp, tmpSol = self.create_tmp_files(lp.name, 'lp', 'sol')
61 lp.writeLP(tmpLp, writeSOS = 0)
PulpSolverError: PuLP: cannot execute glpsol.exe
I honestly dont know where to even begin with solving this so any help is appreciated.
Related
I am going to parallelizing the function train() to make different clients run in parallel and return the output as an array with object [weight, loss]:
pool = mp.Pool(mp.cpu_count())
a = [pool.apply_async(train, (model[i], remote_torch[i], train_loader_ptr[i], epoch, args, train_data_length[i], clients[i], wglob)) for i in range(cnum)]
output = [out.get() for out in a]
However, it throws the following error when I compile and getting stuck at the output array:
Starting Training
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In [24], line 11
9 pool = mp.Pool(mp.cpu_count())
10 a = [pool.apply_async(train, (model[i], remote_torch[i], train_loader_ptr[i], epoch, args, train_data_length[i], clients[i], wglob)) for i in range(cnum)]
---> 11 output = [out.get() for out in a]
12 for i in range(cnum):
13 for wlocal, loss in output:
Cell In [24], line 11, in <listcomp>(.0)
9 pool = mp.Pool(mp.cpu_count())
10 a = [pool.apply_async(train, (model[i], remote_torch[i], train_loader_ptr[i], epoch, args, train_data_length[i], clients[i], wglob)) for i in range(cnum)]
---> 11 output = [out.get() for out in a]
12 for i in range(cnum):
13 for wlocal, loss in output:
File ~\anaconda\envs\env\lib\multiprocessing\pool.py:771, in ApplyResult.get(self, timeout)
769 return self._value
770 else:
--> 771 raise self._value
File ~\anaconda\envs\env\lib\multiprocessing\pool.py:537, in Pool._handle_tasks(taskqueue, put, outqueue, pool, cache)
535 break
536 try:
--> 537 put(task)
538 except Exception as e:
539 job, idx = task[:2]
File ~\anaconda\envs\env\lib\multiprocessing\connection.py:206, in
_ConnectionBase.send(self, obj)
204 self._check_closed()
205 self._check_writable()
--> 206 self._send_bytes(_ForkingPickler.dumps(obj))
File ~\anaconda\envs\env\lib\multiprocessing\reduction.py:51, in ForkingPickler.dumps(cls, obj, protocol)
48 #classmethod
49 def dumps(cls, obj, protocol=None):
50 buf = io.BytesIO()
---> 51 cls(buf, protocol).dump(obj)
52 return buf.getbuffer()
File stringsource:2, in zmq.backend.cython.socket.Socket.__reduce_cython__()
TypeError: no default __reduce__ due to non-trivial __cinit__
What happen? Does the output get the result too early to cause this problem?
I am stuck in the following error in pymc3 and at a loss.
TypeError: float() argument must be a string or a number, not 'FreeRV'
The below is my code. I pick up this code from here. (Sorry, this is Japanese). However it does not work in my environment:
Google Colab, Python: 3.7.13, pymc3: 3.11.4
import numpy as np
import matplotlib
import matplotlib.pylab as plt
%matplotlib inline
from tqdm import tqdm
import pymc3 as pm
# generate time-series data
np.random.seed(0)
y = np.cumsum(np.random.normal(size=100))
# Infer parameters in time-series data
N = len(y)
T = 1000
with pm.Model() as model:
muZero = pm.Normal(name='muZero', mu=0.0, tau=1.0)
sigmaW = pm.InverseGamma(name='sigmaW', alpha=1.0, beta=1.0)
mu = [0]*N
mu[0] = pm.Normal(name='mu0', mu=muZero, tau=1/sigmaW)
for n in range(1, N):
mu[n] = pm.Normal(name='mu'+str(n), mu=mu[n-1], tau=1/sigmaW)
sigmaV = pm.InverseGamma(name='sigmaV', alpha=1.0, beta=1.0)
y_pre = pm.Normal('y_pre', mu=mu, tau=1/sigmaV, observed=y) # I got error here
start = pm.find_MAP()
step = pm.NUTS()
trace = pm.sample(T, step, start=start)
Here is the full discription of my error.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/pymc3/theanof.py in floatX(X)
82 try:
---> 83 return X.astype(theano.config.floatX)
84 except AttributeError:
AttributeError: 'list' object has no attribute 'astype'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
TypeError: float() argument must be a string or a number, not 'FreeRV'
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
4 frames
<ipython-input-30-c3709f545993> in <module>()
26
27 sigmaV = pm.InverseGamma(name='sigmaV', alpha=1.0, beta=1.0)
---> 28 y_pre = pm.Normal('y_pre', mu=mu, tau=1/sigmaV, observed=y) # I got error here
29 start = pm.find_MAP()
30 step = pm.NUTS()
/usr/local/lib/python3.7/dist-packages/pymc3/distributions/distribution.py in __new__(cls, name, *args, **kwargs)
119 dist = cls.dist(*args, **kwargs, shape=shape)
120 else:
--> 121 dist = cls.dist(*args, **kwargs)
122 return model.Var(name, dist, data, total_size, dims=dims)
123
/usr/local/lib/python3.7/dist-packages/pymc3/distributions/distribution.py in dist(cls, *args, **kwargs)
128 def dist(cls, *args, **kwargs):
129 dist = object.__new__(cls)
--> 130 dist.__init__(*args, **kwargs)
131 return dist
132
/usr/local/lib/python3.7/dist-packages/pymc3/distributions/continuous.py in __init__(self, mu, sigma, tau, sd, **kwargs)
485 self.tau = tt.as_tensor_variable(tau)
486
--> 487 self.mean = self.median = self.mode = self.mu = mu = tt.as_tensor_variable(floatX(mu))
488 self.variance = 1.0 / self.tau
489
/usr/local/lib/python3.7/dist-packages/pymc3/theanof.py in floatX(X)
84 except AttributeError:
85 # Scalar passed
---> 86 return np.asarray(X, dtype=theano.config.floatX)
87
88
ValueError: setting an array element with a sequence.
I have a file of the type:
A B 0.123
A C 0.84
B D 0.52
...
Where the data are tab separated, and the first and second columns are the nodes, and the third is the associated edge weight.
When trying to import this file into cytoscape using py2cytoscape, I'm receiving an error:
from py2cytoscape import cyrest
fileName="/Users/96v/Documents/lco/lcoAllAt25/lcoAll25/lcoAll25_top0.041pct_data/lcoAll25_top0.041pct.txt"
cyclient = cyrest.cyclient()
cyclient.network.import_file(dataTypeList='string,string,double',
afile=fileName,
delimiters='\t',
indexColumnSourceInteraction="0",
indexColumnTargetInteraction="1",
verbose=True)
'http://localhost:1234/v1/commands/network/import file'
TypeError Traceback (most recent call last)
in
----> 1 cyclient.network.import_file(dataTypeList='string,string,double', afile=fileName, delimiters='\t', indexColumnSourceInteraction="0", indexColumnTargetInteraction="1", defaultInteraction="Edge Attribute",verbose=True)
2
~/opt/anaconda3/lib/python3.8/site-packages/py2cytoscape/cyrest/network.py in import_file(self, dataTypeList, defaultInteraction, delimiters, delimitersForDataList, afile, firstRowAsColumnNames, indexColumnSourceInteraction, indexColumnTargetInteraction, indexColumnTypeInteraction, NetworkViewRendererList, RootNetworkList, startLoadRow, TargetColumnList, verbose)
464 afile,firstRowAsColumnNames,indexColumnSourceInteraction,indexColumnTargetInteraction,
465 indexColumnTypeInteraction,NetworkViewRendererList,RootNetworkList,startLoadRow,TargetColumnList])
--> 466 response=api(url=self.__url+"/import file", PARAMS=PARAMS, method="POST", verbose=verbose)
467 return response
468
~/opt/anaconda3/lib/python3.8/site-packages/py2cytoscape/cyrest/base.py in api(namespace, command, PARAMS, body, host, port, version, method, verbose, url, parse_params)
139 sys.stdout.flush()
140 r = requests.post(url = baseurl, json = PARAMS)
--> 141 verbose_=checkresponse(r, verbose=verbose)
142 if (verbose) or (verbose_):
143 verbose=True
~/opt/anaconda3/lib/python3.8/site-packages/py2cytoscape/cyrest/base.py in checkresponse(r, verbose)
43 if 200 <= status < 300:
44 if verbose:
---> 45 print("response status "+status)
46 sys.stdout.flush()
47 res=None
TypeError: can only concatenate str (not "int") to str
The edge weights aren't being recognized, yet the documentation isn't as verbose for this function.
Any help would be extremely appreciated!
After looking further at the GUI, I realized:
Columns are not 0 indexed.
Verbose has an error in it.
The below code works fine:
from py2cytoscape import cyrest
fileName="pathToFile"
cyclient = cyrest.cyclient()
collection = cyclient.network.import_file(dataTypeList='string,string,double',
afile=fileName,
delimiters='\t',
indexColumnSourceInteraction="1",
indexColumnTargetInteraction="2",
defaultInteraction="interacts with")
I am trying to experiment with the Ray library for parallel processing some of my functions to get output faster. In my local machine, it works ok in my cloud instance it is showing error
TypeError Traceback (most recent call last)
<ipython-input-14-1941686e1604> in <module>
4 # datalist=f1.result()
5
----> 6 datalist_rayval=Customer_Merchant_value_pass.remote(customerlist)
7 #datalist=ray.get(datalist_rayval)
8
~/anaconda3/lib/python3.7/site-packages/ray/remote_function.py in _remote_proxy(*args, **kwargs)
93 #wraps(function)
94 def _remote_proxy(*args, **kwargs):
---> 95 return self._remote(args=args, kwargs=kwargs)
96
97 self.remote = _remote_proxy
~/anaconda3/lib/python3.7/site-packages/ray/remote_function.py in _remote(self, args, kwargs, num_return_vals, is_direct_call, num_cpus, num_gpus, memory, object_store_memory, resources, max_retries)
168 # first driver. This is an argument for repickling the function,
169 # which we do here.
--> 170 self._pickled_function = pickle.dumps(self._function)
171
172 self._function_descriptor = PythonFunctionDescriptor.from_function(
~/anaconda3/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py in dumps(obj, protocol, buffer_callback)
70 cp = CloudPickler(file, protocol=protocol,
71 buffer_callback=buffer_callback)
---> 72 cp.dump(obj)
73 return file.getvalue()
74
~/anaconda3/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py in dump(self, obj)
615 def dump(self, obj):
616 try:
--> 617 return Pickler.dump(self, obj)
618 except RuntimeError as e:
619 if "recursion" in e.args[0]:
TypeError: can't pickle SSLContext objects
My Ray decorated code is
#ray.remote
def Prefer_Attachment_query2(listval):
customer_wallet=listval[0]
merchant_wallet=listval[1]
#print(x,y)
prefquery="""MATCH (p1:CUSTOMER {WALLETID: '%s'})
MATCH (p2:MERCHANT {WALLETID: '%s'})
RETURN gds.alpha.linkprediction.preferentialAttachment(p1, p2,{relationshipQuery: "PAYMENT"}) as score"""%(customer_wallet,merchant_wallet)
#print(prefquery)
return prefquery
from timeit import default_timer as timer
import itertools
#ray.remote
def Customer_Merchant_value_pass(text):
minicustomer=text
begin=timer()
sum_val=0
list_avg_score=[]
list_category_val=[]
dict_list=[]
#Avg_score=0
with graphdriver.session()as session:
for i in itertools.islice(minicustomer,len(minicustomer)):
for key in list_of_unique_merchants:
print("Here at list_of_unique_merchants customer value is ",i)
print("BMCC_Code",key)
valuelist=list_of_unique_merchants[key]
#print("Uniquelistfor:",key,valuelist)
for j in valuelist:
#print("list len",len(valuelist))
#print("Here the iner of value list ",i)
#print("--------------------------------")
#print([i,j])
pref_attach_score_rayvalue=Prefer_Attachment_query2.remote([i,j])
pref_attach_score=ray.get(pref_attach_score_rayvalue)
#print(pref_attach_score)
result=session.run(pref_attach_score)
for line in result:
#print(line["score"])
sum_val=sum_val+line["score"]
#Avg_score=sum_val/len(valuelist)
Totalsumval=sum_val
print("Totalsum",Totalsumval)
Avg_score=sum_val/len(valuelist)
print("Avg_score",Avg_score)
sum_val=0
list_avg_score.append(Avg_score)
list_category_val.append(key)
avg_score_list=list_avg_score
category_list=list_category_val
#print("sumval is now",sum_val)
#print(result)
max_dictionary =MaxValue_calc(i,category_list,avg_score_list)
#MaxValue_calc(i,category_list,avg_score_list)
print("max_dicitionary",max_dictionary)
dict_list.append(max_dictionary)
rowlist=dict_list
print('appended list',rowlist)
print('process',len(rowlist))
#dict_list=[]
list_avg_score=[]
list_category_val=[]
#print("rowlist", rowlist)
#print("list_category_val is now",list_category_val)
#print("for",i," category AVG scores is now ",category_list)
#print("list_avg_score is now",list_avg_score)
#print("for",i," category AVG scores is now ",avg_score_list)
session.close()
end=timer()
print("Total time :",(end-begin))
return rowlist
datalist_rayval=Customer_Merchant_value_pass.remote(customerlist)
datalist=ray.get(datalist_rayval)
why I am getting this error. and kindly help me to solve this
I want to call R's auto.arima function from Python. I think i have not yet fully understood this interface. Can someone help me here - to send a time series obj to R, call forecast related functions and get back the results?
This is what I have done so far:
from rpy2.robjects import r
from rpy2.robjects import pandas2ri
#create a python time series
count = range(1, 51)
df['count'] = count
df['date'] = pd.date_range('2016-01-01', '2016-02-19')
df.set_index('date', inlace = True)
df.sort_index(inplace = True)
pandas2ri.activate()
r_timeseries = pandas2ri.py2ri(df)
r('fit <- auto.arima(r_timeseries)')
I think I have to import some R packages (like forecast). Not sure how to go about doing that in Python, properly pass the python time series object to R etc.
In [63]: r_ts = pandas2ri.py2ri(df)
In [64]: r_ts
Out[64]:
<DataFrame - Python:0x1126a93f8 / R:0x7ff7bfa51bc8>
[IntVector]
X0: <class 'rpy2.robjects.vectors.IntVector'>
<IntVector - Python:0x1126a96c8 / R:0x7ff7be1af1c0>
[ 1, 2, 3, ..., 48, 49, 50]
And, when I attempt to call forecast
In [83]: x = r('forecast(r_ts)')
/Library/Python/2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: Error in forecast(r_ts) : object 'r_ts' not found
res = super(Function, self).__call__(*new_args, **new_kwargs)
---------------------------------------------------------------------------
RRuntimeError Traceback (most recent call last)
<ipython-input-83-0765ffc30741> in <module>()
----> 1 x = r('forecast(r_ts)')
/Library/Python/2.7/site-packages/rpy2/robjects/__init__.pyc in __call__(self, string)
319 def __call__(self, string):
320 p = _rparse(text=StrSexpVector((string,)))
--> 321 res = self.eval(p)
322 return conversion.ri2py(res)
323
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
176 v = kwargs.pop(k)
177 kwargs[r_k] = v
--> 178 return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
179
180 pattern_link = re.compile(r'\\link\{(.+?)\}')
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
104 for k, v in kwargs.items():
105 new_kwargs[k] = conversion.py2ri(v)
--> 106 res = super(Function, self).__call__(*new_args, **new_kwargs)
107 res = conversion.ri2ro(res)
108 return res
RRuntimeError: Error in forecast(r_ts) : object 'r_ts' not found
I tried the following as well:
In [99]: f = r('forecast.auto.arima(r_ts)')
---------------------------------------------------------------------------
RRuntimeError Traceback (most recent call last)
<ipython-input-99-1c4610d2740d> in <module>()
----> 1 f = r('forecast.auto.arima(r_ts)')
/Library/Python/2.7/site-packages/rpy2/robjects/__init__.pyc in __call__(self, string)
319 def __call__(self, string):
320 p = _rparse(text=StrSexpVector((string,)))
--> 321 res = self.eval(p)
322 return conversion.ri2py(res)
323
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
176 v = kwargs.pop(k)
177 kwargs[r_k] = v
--> 178 return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
179
180 pattern_link = re.compile(r'\\link\{(.+?)\}')
/Library/Python/2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
104 for k, v in kwargs.items():
105 new_kwargs[k] = conversion.py2ri(v)
--> 106 res = super(Function, self).__call__(*new_args, **new_kwargs)
107 res = conversion.ri2ro(res)
108 return res
RRuntimeError: Error in eval(expr, envir, enclos) :
could not find function "forecast.auto.arima"
you could try what I do
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
pandas2ri.activate()
ro.r('library(forecast)')
rdf = pandas2ri.py2ri(df)
ro.globalenv['r_timeseries'] = rdf
pred = ro.r('as.data.frame(forecast(auto.arima(r_timeseries),h=5))')
this way, you can handle pred as a data frame like this
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
51 51 51 51 51 51
52 52 52 52 52 52
53 53 53 53 53 53
54 54 54 54 54 54
55 55 55 55 55 55
In the first attempt you are telling R to use a variable r_ts that it does not now much about (the name r_ts is defined in your Python namespace), and in the second attempt you are added to this a function name R does not know anything about. Both error message are precisely reporting this as a problem.
Your first attempt could be rewritten as:
x = r('forecast')(r_ts)