I'm trying to do "100 lines of python code" for algorithmic trading and have been stopped early in my tracks with an attribute error.
I'm using sublime text and running it on python 3.7.
Here is the code I used:
import configparser
import oandapy as opy
config = configparser.ConfigParser()
config.read("oanda.cfg")
oanda = opy.API(environment = "practice",
access_token = config["oanda"] ["access_token"] )
Here are the results:
Traceback (most recent call last):
File "100lines.py", line 7, in <module>
oanda = opy.API(environment = "practice",
AttributeError: module 'oandapy' has no attribute 'API'
Try this:
# Print all attributes/functions in module
print(dir(opy))
and check the modules present
I think you'll have to use 'APIv20' rather than just 'API'
The api method is now called APIv20 in the current version (0.0.9).
so changing the last line to:
oanda = opy.APIv20(environment='practice', access_token=config['oanda']['access_token'])
will make solve your issue.
I'm trying to use Hyperopt parallel search with MongoDB, and encountered some issues with Mongotrials, which have been discussed here. I've tried all their methods, and I am still unable to find solutions to my specific problem. The specific model I'm trying to minimize is RadomForestRegressor from sklearn.
I've followed this tutorial. And I'm able to print out the calculated "fmin" with no issue.
Here are my steps so far:
1) Activate a virtual environment called "tensorflow" (I've installed all my libraries there)
2) Start MongoDB:
(tensorflow) bash-3.2$ mongod --dbpath . --port 1234 --directoryperdb --journal --nohttpinterface
3) Initiate workers:
(tensorflow) bash-3.2$ hyperopt-mongo-worker --mongo=localhost:1234/foo_db --poll-interval=0.1
4) Run my python code, and my python code is as follows:
import numpy as np
import pandas as pd
from sklearn.metrics import mean_absolute_error
from hyperopt import hp, fmin, tpe, STATUS_OK, Trials
from hyperopt.mongoexp import MongoTrials
# Preprocessing data
train_xg = pd.read_csv('train.csv')
n_train = len(train_xg)
print "Whole data set size: ", n_train
# Creating columns for features, and categorical features
features_col = [x for x in train_xg.columns if x not in ['id', 'loss', 'log_loss']]
cat_features_col = [x for x in train_xg.select_dtypes(include=['object']).columns if x not in ['id', 'loss', 'log_loss']]
for c in range(len(cat_features_col)):
train_xg[cat_features_col[c]] = train_xg[cat_features_col[c]].astype('category').cat.codes
# Use this to train random forest regressor
train_xg_x = np.array(train_xg[features_col])
train_xg_y = np.array(train_xg['loss'])
space_rf = { 'min_samples_leaf': hp.choice('min_samples_leaf', range(1,100)) }
trials = MongoTrials('mongo://localhost:1234/foo_db/jobs', exp_key='exp1')
def minMe(params):
# Hyperopt tuning for hyperparameters
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
from hyperopt import STATUS_OK
try:
import dill as pickle
print('Went with dill')
except ImportError:
import pickle
def hyperopt_rf(params):
rf = RandomForestRegressor(**params)
return cross_val_score(rf, train_xg_x, train_xg_y).mean()
acc = hyperopt_rf(params)
print 'new acc:', acc, 'params: ', params
return {'loss': -acc, 'status': STATUS_OK}
best = fmin(fn=minMe, space=space_rf, trials=trials, algo=tpe.suggest, max_evals=100)
print "Best: ", best
5) After I run the above Python code, I get the following errors:
INFO:hyperopt.mongoexp:Error while unpickling. Try installing dill via "pip install dill" for enhanced pickling support.
INFO:hyperopt.mongoexp:job exception: 'module' object has no attribute 'minMe'
Traceback (most recent call last):
File "/Users/WernerChao/tensorflow/bin/hyperopt-mongo-worker", line 6, in <module>
sys.exit(hyperopt.mongoexp.main_worker())
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1302, in main_worker
return main_worker_helper(options, args)
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1249, in main_worker_helper
mworker.run_one(reserve_timeout=float(options.reserve_timeout))
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1064, in run_one
domain = pickle.loads(blob)
AttributeError: 'module' object has no attribute 'minMe'
INFO:hyperopt.mongoexp:PROTOCOL mongo
INFO:hyperopt.mongoexp:USERNAME None
INFO:hyperopt.mongoexp:HOSTNAME localhost
INFO:hyperopt.mongoexp:PORT 1234
INFO:hyperopt.mongoexp:PATH /foo_db/jobs
INFO:hyperopt.mongoexp:DB foo_db
INFO:hyperopt.mongoexp:COLLECTION jobs
INFO:hyperopt.mongoexp:PASS None
INFO:hyperopt.mongoexp:Error while unpickling. Try installing dill via "pip install dill" for enhanced pickling support.
INFO:hyperopt.mongoexp:job exception: 'module' object has no attribute 'minMe'
Traceback (most recent call last):
File "/Users/WernerChao/tensorflow/bin/hyperopt-mongo-worker", line 6, in <module>
sys.exit(hyperopt.mongoexp.main_worker())
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1302, in main_worker
return main_worker_helper(options, args)
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1249, in main_worker_helper
mworker.run_one(reserve_timeout=float(options.reserve_timeout))
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1064, in run_one
domain = pickle.loads(blob)
AttributeError: 'module' object has no attribute 'minMe'
INFO:hyperopt.mongoexp:PROTOCOL mongo
INFO:hyperopt.mongoexp:USERNAME None
INFO:hyperopt.mongoexp:HOSTNAME localhost
INFO:hyperopt.mongoexp:PORT 1234
INFO:hyperopt.mongoexp:PATH /foo_db/jobs
INFO:hyperopt.mongoexp:DB foo_db
INFO:hyperopt.mongoexp:COLLECTION jobs
INFO:hyperopt.mongoexp:PASS None
INFO:hyperopt.mongoexp:Error while unpickling. Try installing dill via "pip install dill" for enhanced pickling support.
INFO:hyperopt.mongoexp:job exception: 'module' object has no attribute 'minMe'
Traceback (most recent call last):
File "/Users/WernerChao/tensorflow/bin/hyperopt-mongo-worker", line 6, in <module>
sys.exit(hyperopt.mongoexp.main_worker())
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1302, in main_worker
return main_worker_helper(options, args)
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1249, in main_worker_helper
mworker.run_one(reserve_timeout=float(options.reserve_timeout))
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1064, in run_one
domain = pickle.loads(blob)
AttributeError: 'module' object has no attribute 'minMe'
INFO:hyperopt.mongoexp:PROTOCOL mongo
INFO:hyperopt.mongoexp:USERNAME None
INFO:hyperopt.mongoexp:HOSTNAME localhost
INFO:hyperopt.mongoexp:PORT 1234
INFO:hyperopt.mongoexp:PATH /foo_db/jobs
INFO:hyperopt.mongoexp:DB foo_db
INFO:hyperopt.mongoexp:COLLECTION jobs
INFO:hyperopt.mongoexp:PASS None
INFO:hyperopt.mongoexp:no job found, sleeping for 0.7s
INFO:hyperopt.mongoexp:Error while unpickling. Try installing dill via "pip install dill" for enhanced pickling support.
INFO:hyperopt.mongoexp:job exception: 'module' object has no attribute 'minMe'
Traceback (most recent call last):
File "/Users/WernerChao/tensorflow/bin/hyperopt-mongo-worker", line 6, in <module>
sys.exit(hyperopt.mongoexp.main_worker())
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1302, in main_worker
return main_worker_helper(options, args)
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1249, in main_worker_helper
mworker.run_one(reserve_timeout=float(options.reserve_timeout))
File "/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1064, in run_one
domain = pickle.loads(blob)
AttributeError: 'module' object has no attribute 'minMe'
INFO:hyperopt.mongoexp:exiting with N=9223372036854775803 after 4 consecutive exceptions
6) Then Mongo workers would shut off.
Things I've tried:
install "dill" as the error suggested -> didn't work
Put global imports into the objective function so it can pickle -> didn't work
Put try except with "dill" or "pickle" as import -> didn't work
Does anyone have similar issues? I'm running out of ideas to try, and have been working on this for 2 days in vain. I think I am missing something really simple here, just can't seem to find it.
What am I missing?
Any suggestion is welcomed please!
Had the same problem in python 3.5. Installing Dill didn't help, nor dir setting workdir in MongoTrials or hyperopt-mongo-worker cli. hyperopt-mongo-worker doesn't seem to have access to __main__ where the function was defined:
AttributeError: Can't get attribute 'minMe' on <module '__main__' from ...hyperopt-mongo-worker
As #jaikumarm suggested, I circumvented the problem by writing a module file with all the required functions. However, instead of soft-linking it into the bin directory, I extended the PYTHONPATH before running hyperopt-mongo-worker:
export PYTHONPATH="${PYTHONPATH}:<dir_with_the_module.py>"
hyperopt-mongo-worker ...
That way, the hyperopt-monogo-worker is able to import the module containing minMe.
I fought with this for several days before coming up with a workable solution. there are two problems:
1. the mongo worker spawns off a separate process to run the optimizer so any context from your original python file is lost and unavailable for this new process.
2. the imports on this new process happen in the context of the hyperopt-mongo-worker scipy, which is in your case will be /Users/WernerChao/tensorflow/bin/.
So my solution is to make this new optimizer function completely self sufficient
optimizer.py
import numpy as np
import pandas as pd
from sklearn.metrics import mean_absolute_error
# Preprocessing data
train_xg = pd.read_csv('train.csv')
n_train = len(train_xg)
print "Whole data set size: ", n_train
# Creating columns for features, and categorical features
features_col = [x for x in train_xg.columns if x not in ['id', 'loss', 'log_loss']]
cat_features_col = [x for x in train_xg.select_dtypes(include=['object']).columns if x not in ['id', 'loss', 'log_loss']]
for c in range(len(cat_features_col)):
train_xg[cat_features_col[c]] = train_xg[cat_features_col[c]].astype('category').cat.codes
# Use this to train random forest regressor
train_xg_x = np.array(train_xg[features_col])
train_xg_y = np.array(train_xg['loss'])
def minMe(params):
# Hyperopt tuning for hyperparameters
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
from hyperopt import STATUS_OK
try:
import dill as pickle
print('Went with dill')
except ImportError:
import pickle
def hyperopt_rf(params):
rf = RandomForestRegressor(**params)
return cross_val_score(rf, train_xg_x, train_xg_y).mean()
acc = hyperopt_rf(params)
print 'new acc:', acc, 'params: ', params
return {'loss': -acc, 'status': STATUS_OK}
wrapper.py
from hyperopt import hp, fmin, tpe, STATUS_OK, Trials
from hyperopt.mongoexp import MongoTrials
import optimizer
space_rf = { 'min_samples_leaf': hp.choice('min_samples_leaf', range(1,100)) }
best = fmin(fn=optimizer.minMe, space=space_rf, trials=trials, algo=tpe.suggest, max_evals=100)
print "Best: ", best
trials = MongoTrials('mongo://localhost:1234/foo_db/jobs', exp_key='exp1')
Once you have this code link the optimizer.py to the bin folder
ln -s /Users/WernerChao/Git/test/optimizer.py /Users/WernerChao/tensorflow/bin/
now run the wrapper.py and then the mongo worker it should be able to import the optimizer from its local context and run the minMe function.
Try to install Dill in the Python environment of your tensorflow (or possibly the worker):
/Users/WernerChao/tensorflow/lib/python2.7/site-packages/hyperopt
Your aim is to get rid of the hyperopt error message:
hyperopt.mongoexp:Error while unpickling. Try installing dill via "pip install dill" for enhanced pickling support.
This is because the Python by default cannot marshal a function. It requires dill library to extend Python's pickling module for serialising/de-serialising Python objects. In your case, it failed to serialise your function minMe().
I made a separate file which calculates the loss and copied it to /anaconda2/bin/
and
/anaconda2/lib/python2.7/site-packages/hyperopt
it is working fine.
This was my Traceback
Traceback (most recent call last):
File "/home/greatskull/anaconda2/bin/hyperopt-mongo-worker", line 6, in <module>
sys.exit(hyperopt.mongoexp.main_worker())
File "/home/greatskull/anaconda2/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1302, in main_worker
return main_worker_helper(options, args)
File "/home/greatskull/anaconda2/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1249, in main_worker_helper
mworker.run_one(reserve_timeout=float(options.reserve_timeout))
File "/home/greatskull/anaconda2/lib/python2.7/site-packages/hyperopt/mongoexp.py", line 1073, in run_one
with temp_dir(workdir, erase_created_workdir), working_dir(workdir):
File "/home/greatskull/anaconda2/lib/python2.7/contextlib.py", line 17, in __enter__
return self.gen.next()
File "/home/greatskull/anaconda2/lib/python2.7/site-packages/hyperopt/utils.py", line 229, in temp_dir
os.makedirs(dir)
File "/home/greatskull/anaconda2/lib/python2.7/os.py", line 150, in makedirs
makedirs(head, mode)
File "/home/greatskull/anaconda2/lib/python2.7/os.py", line 157, in makedirs
mkdir(name, mode)
My goal is to run http server on python3 using tornado (http://www.tornadoweb.org/).
I started with running example code found on their webpage:
import tornado.httpserver
import tornado.ioloop
from tornado import httputil
def handle_request(request):
message = "You requested %s\n" % request.uri
request.connection.write_headers(
httputil.ResponseStartLine('HTTP/1.1', 200, 'OK'),
{"Content-Length": str(len(message))})
request.connection.write(message)
request.connection.finish()
http_server = tornado.httpserver.HTTPServer(handle_request)
http_server.listen(8080)
tornado.ioloop.IOLoop.instance().start()
This code throws following exception when receiving any http request:
ERROR:tornado.application:Uncaught exception
Traceback (most recent call last):
File "/usr/local/lib/python3.4/dist-packages/tornado/http1connection.py", line 234, in _read_message
delegate.finish()
File "/usr/local/lib/python3.4/dist-packages/tornado/httpserver.py", line 280, in finish
self.server.request_callback(self.request)
File "test.py", line 10, in handle_request
{"Content-Length": str(len(message))})
File "/usr/local/lib/python3.4/dist-packages/tornado/http1connection.py", line 367, in write_headers
lines.extend([utf8(n) + b": " + utf8(v) for n, v in headers.get_all()])
AttributeError: 'dict' object has no attribute 'get_all'
Whats wrong ? This is sample code, so it should work and problem is somewhere in my enviroment ?
I tried this both on latest Ubuntu and Win 7 and it gave me same error.
Thanks in advance
The documentation had a bug, fixed recently. For any version of Python, you can't pass a raw dict to write_headers. Instead you must wrap the dict in an HTTPHeaders object. For Python 3, the message must be a bytes instance. With both fixes:
def handle_request(request):
message = ("You requested %s\n" % request.uri).encode('ascii')
request.connection.write_headers(
httputil.ResponseStartLine('HTTP/1.1', 200, 'OK'),
httputil.HTTPHeaders({"Content-Length": str(len(message))}))
request.connection.write(message)
request.connection.finish()
I 've tried to use tornado.platform.twisted to integrate txyam memcached client, but when I try to check it for functioning, next error is thrown:
Traceback (most recent call last):
File "swcomet/tx_memcache_helper.py", line 32, in <module>
mem_helper = MemcacheHelper()
File "swcomet/tx_memcache_helper.py", line 19, in __init__
self.add(4)
File "/home/rustem/work/sw.services.swcomet.python/venv/local/lib/python2.7/site-packages/tornado/gen.py", line 117, in wrapper
gen = func(*args, **kwargs)
File "swcomet/tx_memcache_helper.py", line 25, in add
self.mem.getPickled(user_id, decompress=True)
File "/home/rustem/work/sw.services.swcomet.python/venv/lib/python2.7/site-packages/txyam/client.py", line 133, in getPickled
return self.get(key, **kwargs).addCallback(handleResult, uncompress)
File "/home/rustem/work/sw.services.swcomet.python/venv/lib/python2.7/site-packages/txyam/client.py", line 27, in wrapper
func = getattr(self.getClient(key), cmd)
File "/home/rustem/work/sw.services.swcomet.python/venv/lib/python2.7/site-packages/txyam/client.py", line 48, in getClient
raise NoServerError, "No connected servers remaining."
txyam.client.NoServerError: No connected servers remaining.
The source code which dumps that error:
import tornado.ioloop
import tornado.gen
from txyam.client import YamClient
from swtools.date import _ts
import tornado.platform.twisted
MEMHOSTS = ['127.0.0.1111']
USER_EXPIRATION_TIME = 61
class MemcacheHelper(object):
def __init__(self, *a, **kw):
try:
self.mem = YamClient(["127.0.0.1"])
except Exception, e:
print "ERror", e
self.clients = set()
self.add(4)
#tornado.gen.engine
def add(self, user_id, expire=None):
self.clients.add(user_id)
expire = expire or USER_EXPIRATION_TIME
self.mem.getPickled(user_id, decompress=True)
print "hmmm"
if __name__ == '__main__':
print "trying to start on top of IOLOOP"
ioloop = tornado.ioloop.IOLoop.instance()
#reactor = TornadoReactor(ioloop)
mem_helper = MemcacheHelper()
#mem_helper.add(4)
ioloop.start()
Please, help me to solve this problem!
txyam appears not to let you perform any memcache operations until after at least one connection has been established:
def getActiveConnections(self):
return [factory.client for factory in self.factories if not factory.client is None]
def getClient(self, key):
hosts = self.getActiveConnections()
log.msg("Using %i active hosts" % len(hosts))
if len(hosts) == 0:
raise NoServerError, "No connected servers remaining."
return hosts[ketama(key) % len(hosts)]
It attempts to set up these connections right away:
def __init__(self, hosts):
"""
#param hosts: A C{list} of C{tuple}s containing hosts and ports.
"""
self.connect(hosts)
But connection setup is asynchronous, and it doesn't expose an event to indicate when at least one connection has been established.
So your code fails because you call add right away, before any connections exist. A good long-term fix would be to file a bug report against txyam, because this isn't a very nice interface. YamClient could have a whenReady method that returns a Deferred that fires when you are actually allowed to use the YamClient instance. Or there could be an alternate constructor that returns a Deferred that fires with the YamClient instance, but only after it can be used.
I'm trying to validate a transaction receipt from an inApp purchase with the Apple store server from my Twisted server. I have sent the (SKPaymentTransaction *)transaction.transactionReceipt from my app to my server.
But now, sending the JSON object to the Apple server, I keep getting an unhandled error in Deferred from my Agent.request(). I suspect this is because I'm not listening on port 443 for response from Apple store, but I don't want my app to communicate with my Twisted server on port 443 also. Here is my code:
from twisted.application import internet, service
from twisted.internet import protocol, reactor
from zope.interface import implements
from twisted.web.iweb import IBodyProducer
from twisted.internet import defer
from twisted.web.client import Agent
from twisted.web.http_headers import Headers
import json
import base64
class StringProducer(object):
implements(IBodyProducer)
def __init__(self, body):
self.body = body
self.length = len(body)
def startProducing(self, consumer):
consumer.write(self.body)
return succeed(None)
def pauseProducing(self):
pass
def stopProducing(self):
pass
def printResponse(response):
print response # just testing to see what I have
def httpRequest(url, values, headers={}, method='POST'):
agent = Agent(reactor)
d = agent.request(method,
url,
Headers(headers),
StringProducer(values)
)
d.addCallback(printResponse)
class storeServer(protocol.Protocol):
def dataReceived(self, data):
receiptBase64 = base64.standard_b64encode(data)
jsonReceipt = json.dumps({'receipt-data':receiptBase64})
print jsonReceipt # verified that my data is correct
d = httpRequest(
"https://buy.itunes.apple.com/verifyReceipt",
jsonReceipt,
{'Content-Type': ['application/x-www-form-urlencoded']}
)
factory = protocol.Factory()
factory.protocol = storeServer
tcpServer = internet.TCPServer(30000, factory)
tcpServer.setServiceParent(application)
How can I fix this error? Do I have to create another service listening on port 443? If so, how might I have the service connecting to my app communicate with the service connecting through https?
The comment style in your code sample is incorrect. Python uses # for comments, not //.
After fixing that and running the snippet through pyflakes, I see these errors:
program.py:1: 'service' imported but unused
program.py:6: 'defer' imported but unused
program.py:21: undefined name 'succeed'
program.py:48: local variable 'd' is assigned to but never used
program.py:57: undefined name 'application'
It seems likely that the undefined name on line 21 is the cause of the NameError you've encountered. NameError is how Python signals this sort of bug:
x = y
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'y' is not defined