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Python bindings for AutoMan Runtime. Software is current in development, and not properly tested.

Project description


See AutoMan This package is currently in development.

System Requirements

To use this package you must be running Python 2.7.15 or 3.2+, and Scala 2.11.7+. This package relies on ScalaPB and gRPC. If you use SBT to build this project, all Scala dependencies will be downloaded. To install gRPC for Python (needed for the Python client), follow these instructions.

How to Build

The easiest way to build this project is by using SBT. To build this project, from the /PyAutoman directory, run

sbt clean compile pack

SBT will also compile the necessary .proto into Scala classes automatically. To generate the the python files needed, grpcio-tools and googleapis-common-protos need to be installed. These python dependencies are automatically installed by pip if this package is installed from the provided tarball (). To install the necessary packages manually, run the following two commands:

pip install grpcio-tools
pip install googleapis-common-protos

To use gRPC generate the python files needed for interacting with the RPC service, from the /PyAutoman directory, run the following command:

python -m grpc_tools.protoc -I src/main/protobuf/ --python_out=src/main/pyautoman/pyautoman/core/grpc_gen_classes --grpc_python_out=src/main/pyautoman/pyautoman/core/grpc_gen_classes src/main/protobuf/automanlib_rpc.proto src/main/protobuf/automanlib_classes.proto src/main/protobuf/automanlib_wrappers.proto

Move the files compiled by sbt into the correct directory by copying them:

cp -r target/pack/ src/main/pyautoman/pyautoman/core/rpc/server/

Then change to the directory containing and run it from there:

cd src/main/pyautoman/
python sdist

Alternaltively, you can run ./ located in the root directory, to do the steps outlined above.

How to Install

To install this package without building, use pip install to install the tarball in the directory PyAutoMan/src/main/pyautoman/dist/. There are currently multiple development versions, current latest version is pyautoman-0.2.0.dev0.

pip install pyautoman-0.2.0.dev0.tar.gz

How to Use

To run tasks, first create an Automan object. The constructor for Automan objects requires an adapter, and take optional parameters for the RPC server address and port number (default is 'localhost' and 50051). The adapter we pass to the constructor is simply a dictionary with the following required fields:

  • access_id - the login id for the crowdsource backend
  • access_key - the login access key for the crowdsource backend
  • type - the type of crowdsource backend. currently only "mturk" is an accepted type
  • any optional arguments for the adapter (currently only "sandbox_mode" for MTurk adapter)

First, import the Automan and EstimateOutcome classes from pyautoman.automan, then create an adapter

Python 2.7.15 |Anaconda, Inc.| (default, May  1 2018, 18:37:05) 
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from pyautoman.automan import Automan, EstimateOutcome
>>> adapter = {
...     "access_id" : "access id here",
...     "access_key" : "access key here",
...     "sandbox_mode" : "true",
...     "type" : "MTurk"
... }

When an Automan object is being initialized, if the server_addr is 'localhost' it will start a local AutoMan RPC server as a new process, configured to listen on the provided port number. Future functionality will allow users to connect to remote RPC servers. We can now use the Automan object to submit tasks to the crowdsource back-end. Currently, only the estimate function of Automan is available. See example code for usage.

>>> a = Automan(adapter, server_addr='localhost',port=50051)
python client is starting server...
Server Started on port 50051 ...
>>> photo_url = ""
>>> estim = a.estimate(text = "How many cars are in this parking lot?",budget = 6.00, title = "Car Counting",image_url = photo_url)

Each type of task has fields that are required. All tasks require text (a text description of the task), and budget (desired upper limit of cost of task). We specify the tasks we would like AutoMan to carry out, and either when the question has timed out and budget is exceeded (resulting in a low confidence or overbudget outcome respectively) or the desired confidence level has been met.

The outcome can be either:

  • Confident estimate
  • Low Confidence estimate
  • Overbudget

If the task went overbudget, the need and have fields of the returned EstimateOutcome are initialized, otherwise high, low, est, cost, and conf are initialized. PyAutoman uses gRPC's implementation of Futures. To ensure the future is resolved before values are accessed, only try to access respective values within code blocks that ensure those values are set. See methods isConfident(), isLowConfidence(), and isOverBudget() below. To see more example code, and an example for posting multiple tasks, see PyAutoMan/examples

To simply print the result of the task, use printOutcome().

>>> estim.printOutcome()
Outcome: Low Confidence Estimate
Estimate low: 62.000000 high:62.000000 est:62.000000


Example Code

from pyautoman.automan import Automan, EstimateOutcome

# make mechanical turk adapter
adapter = {
	"access_id" : "access id here",
	"access_key" : "access key here",
	"sandbox_mode" : "true",
	"type" : "MTurk"

# image to submit with our task
photo_url = ""

# make AutoMan object 
# 'suppress_output' sets the how much output from the RPC server to print to stdout. current valid values are
# 		"all" 	- suppress all output
# 		"none "	- show all output 

# 'loglevel' sets the the logging level for Automan. valid values are
#		'debug' - debug level 
#		'info' 	- information level 
#		'warn' 	- warnings only
#		'fatal' - fatal messages only (default)

a = Automan(adapter, server_addr='localhost',port=50051,suppress_output="none", loglevel='fatal')

estim = a.estimate(text = "How many cars are in this parking lot?",
	budget = 6.00,
	title = "Car Counting",
	confidence_int = 10,
#	question_timeout_multiplier = 40,# uncomment to set question to timeout on mturk, good for testing purposes, set no less than 40. see docs for more detail
	image_url = photo_url)

	print("Outcome: Estimate")
	print("Estimate low: %f high:%f est:%f "%(estim.low, estim.high, estim.est))

	print("Outcome: Low Confidence Estimate")
	print("Estimate low: %f high:%f est:%f "%(estim.low, estim.high, estim.est))

	print("Outcome: Over Budget")
	print(" need: %f have:%f"%(estim.need, estim.have))

# this is temporary, in future client will automatically handle shutdown
# to be safe, only call _shutdown() after the future has resolved,
# or else the server will kill itself before the computation is finished 

You can run this code on MTurk with the 'sandbox_mode' option set to 'true' to submit dummy worker responses (need to create requester developer sandbox and worker sandbox accounts). Here is what the output would look like if a single worker submitted a response of 62.

Outcome: Low Confidence Estimate
Estimate low: 62.000000 high:62.000000 est:62.000000 


AutoMan Class


Automan(self, adapter, server_addr = 'localhost', port = 50051, suppress_output = 'all', loglevel='info', logging='none')
  • adapter - a dictionary storing adapter credentials to use to connect to the crowdsource backend. Must contain necessary adapter fields
  • server_addr - the string hostname address of the gRPC Automan server to connect to
  • port - the port number to connect to the gRPC Automan server
  • supress_output - the level of output to show from the gRPC Automan server. "none" displays all output, "all" supresses all output from server
  • loglevel - Specifies the AutoMan worker log verbosity, for setting the level of logging output directly from AutoMan Worker. values *'debug' - debug level *'info' - information level *'warn' - warnings only *'fatal' - fatal messages only (default)
  • logging - Specifies the AutoMan worker logging behaviour, for setting the logging behaviour of AutoMan worker. values *'none' - no logging *'t' - log trace only *'tm' - log trace and use for memoization *'tv' - log trace and output debug information *'tmv' - log trace, use for memoization and output debug info

AutoMan Class Methods

Automan.estimate(self, text, budget, image_url ="", title = "", confidence = 0.95, confidence_int = -1, img_alt_txt = "",  
sample_size = -1,dont_reject = True, pay_all_on_failure = True, dry_run = False, wage = 11.00,  
max_value = sys.float_info.max, min_value = sys.float_info.min, question_timeout_multiplier = 500,  
initial_worker_timeout_in_s = 30)

Description :

Provides AutoMan's estimate functionality. Uses the crowdsource backend to obtain a quality-controlled  
estimate of a single real value.     
*Note*: Be careful when setting 'question_timeout_multiplier' and 'initial_worker_timeout_in_s' in tasks.  
Setting too low can cause the question to timeout too soon and result in failure to get results.  
Use, at minimum, values 40 or higher for `question_timeout_multiplier` and 30 or higher for `initial_worker_timeout_in_s`. 

Returns : EstimateOutcome

  • text - the text description of the task to display to the worker (required)
  • budget - the threshold cost for the task (required)
  • image_url - an image url to be associated with the task
  • title - title of the task, displayed to worker
  • confidence - desired confidence level
  • confidence_int - desired confidence interval
  • img_alt_txt - alternative image text, for generated webpage displayed to worker
  • sample_size - desired sample size, default of -1 indicates to use default samp. size of 30
  • dont_reject - indicate whether to accept all answers automatically or not (?)
  • pay_all_on_failure - indicate whether to pay all workers on task failure or note (?)
  • dry_run - indicate whether to do a dry run or not
  • wage - minimum wage to pay the worker, in USD/hr
  • max_value - min value for dimension being estimated
  • min_value - max value for dimension being estimated
  • question_timeout_multiplier - multiplier to calculate question timeout on MTurk. Question timeout = question_timeout_multiplier * initial_worker_timeout_in_s
  • initial_worker_timeout_in_s - timeout in seconds for the worker thread in the RPC server

EstimateOutcome Class

Instances of this class will always be created for the user. This class will never need to be instantiated manually.

EstimateOutcome Class Attributes

This class contains a Future, representing the outcome of the task. Value attributes in this class (e.g. high, est, cost, need, etc) are initially set to NaN so that they're values cannot be accidentally used unless the future has resolved to a case where those values are valid (e.g., if the outcome_type was OVERBUDGET then need and have are the only valid attributes). To ensure that the future is always resolved first and the respective attributes are initialized, always use attributes of an EstimateOutcome in a code block that ensures those values are set. Attributes are as follows:
For Confident and LowConfidence outcomes:

  • high - the highest value a worker reported, set to NaN intially
  • low - the lowest value a worker reported, set to NaN intially
  • est - AutoMan's estimated value, set to NaN intially
  • cost - the cost to complete the task, set to NaN intially
  • conf - the confidence interval of the estimate, set to NaN intially

For OverBudget outcomes:

  • need - the amount needed for AutoMan to continue attempting to obtain an estimate
  • have - the current amount budgeted for the task

EstimateOutcome Class Methods


Description :

Indicates if the outcome of the task is a confident estimate

Returns : boolean - True if the outcome met the desired confidence level and interval, False otherwise


Description :

Indicates if the outcome of the task is a low confidence estimate  

Returns : boolean - True if the outcome was a low confidence estimate, False otherwise


Description :

Indicates if the outcome of the task is over budget or not  

Returns : boolean - True if the outcome was over budget, False otherwise

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