agentMET4FOF - Metrological Agent-based system¶

agentMET4FOF is a Python library developed at the Institute for Manufacturing of the University of Cambridge (UK) as part of the European joint Research Project EMPIR 17IND12 Met4FoF.

For the agentMET4FOF homepage go to GitHub.

agentMET4FOF is written in Python 3.

CircleCI Documentation Status Codecov Badge

Multi-Agent System for Metrology for Factory of the Future (Met4FoF) Code¶

This is supported by European Metrology Programme for Innovation and Research (EMPIR) under the project Metrology for the Factory of the Future (Met4FoF), project number 17IND12. (https://www.ptb.de/empir2018/met4fof/home/)

About¶

How can metrological input be incorporated into an agent-based system for addressing uncertainty of machine learning in future manufacturing?
Includes agent-based simulation and implementation
Readthedocs documentation is available at (https://agentmet4fof.readthedocs.io)

Use agentMET4FOF¶

The easiest way to get started with agentMET4FOF is navigating to the folder in which you want to create a virtual Python environment (venv), create one based on Python 3.6 or later, activate it, first install numpy, then install agentMET4FOF from PyPI.org and then work through the tutorials or examples. To do this, issue the following commands on your Shell:

$ cd /LOCAL/PATH/TO/ENVS
$ python3 -m venv agentMET4FOF_venv
$ source agentMET4FOF_venv/bin/activate
(agentMET4FOF_venv) $ pip install numpy
Collecting numpy
...
Successfully installed numpy-...
(agentMET4FOF_venv) $ pip install agentMET4FOF
Collecting agentMET4FOF
...
Successfully installed agentMET4FOF-... ...
(agentMET4FOF_venv) $ python
Python ... (default, ..., ...) 
[GCC ...] on ...
Type "help", "copyright", "credits" or "license" for more information.
>>> from agentMET4FOF_tutorials import tutorial_1_generator_agent
>>> tutorial_1_generator_agent.demonstrate_generator_agent_use()
Starting NameServer...
Broadcast server running on 0.0.0.0:9091
NS running on 127.0.0.1:3333 (127.0.0.1)
URI = PYRO:Pyro.NameServer@127.0.0.1:3333
INFO [2020-02-21 19:04:26.961014] (AgentController): INITIALIZED
INFO [2020-02-21 19:04:27.032258] (Logger): INITIALIZED
 * Serving Flask app "agentMET4FOF.dashboard.Dashboard" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)
...

Now you can visit http://127.0.0.1:8050/ with any Browser and watch the SineGenerator agent you just spawned.

To get some insights and really get going please visit agentMET4FOF.readthedocs.io .

Get started developing¶

First clone the repository to your local machine as described here. To get started with your present Anaconda installation just go to Anaconda prompt, navigate to your local clone

cd /LOCAL/PATH/TO/agentMET4FOF

and execute

conda env create --file environment.yml

This will create an Anaconda virtual environment with all dependencies satisfied. If you don’t have Anaconda installed already follow this guide first, then create the virtual environment as stated above and then proceed.

Alternatively, for non-conda environments, you can install the dependencies using pip

pip install -r requirements.txt

First take a look at the tutorials and examples or start hacking if you already are familiar with agentMET4FOF and want to customize your agents’ network.

Alternatively, watch the tutorial webinar here

Updates¶

Implemented base class AgentMET4FOF with built-in agent classes DataStreamAgent, MonitorAgent
Implemented class AgentNetwork to start or connect to a agent server
Implemented with ZEMA prognosis of Electromechanical cylinder data set as use case DOI
Implemented interactive web application with user interface

Screenshot of web visualization¶

Web Screenshot

Note¶

In the event of agents not terminating cleanly, run
```
taskkill /f /im python.exe /t
```
in Windows Command Prompt to terminate all background python processes.

Tutorial 1 - A simple pipeline to plot a signal¶

First we define a simple pipeline of two agents, of which one will generate a signal (in our case a SineGeneratorAgent) and the other one plots the signal on the dashboard (this is always a MonitorAgent).

We define a SineGeneratorAgent for which we have to override the functions init_parameters() & agent_loop() to define the new agent’s behaviour.

init_parameters() is used to setup the input data stream and potentially other necessary parameters.
agent_loop() will be endlessly repeated until further notice. It will sample by sample extract the input data stream’s content and push it to all agents connected to SineGeneratorAgent’s output channel by invoking send_output().

The MonitorAgent is connected to the SineGeneratorAgent’s output channel and per default automatically plots the output.

Each agent has an internal current_state which can be used as a switch to change the behaviour of the agent. The available states are listed here.

As soon as all agents are initialized and the connections are set up, the agent network is started by accordingly changing all agents’ state simultaneously.

[1]:

# %load tutorial_1_generator_agent.py
from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
from agentMET4FOF.streams import SineGenerator


class SineGeneratorAgent(AgentMET4FOF):
    """An agent streaming a sine signal

    Takes samples from the :py:mod:`SineGenerator` and pushes them sample by sample
    to connected agents via its output channel.
    """
    _sine_stream: SineGenerator

    def init_parameters(self):
        """Initialize the input data

        Initialize the input data stream as an instance of the
        :py:mod:`SineGenerator` class
        """
        self._sine_stream = SineGenerator()

    def agent_loop(self):
        """Model the agent's behaviour

        On state *Running* the agent will extract sample by sample the input data
        streams content and push it via invoking :py:method:`AgentMET4FOF.send_output`.
        """
        if self.current_state == "Running":
            sine_data = self._sine_stream.next_sample()  # dictionary
            self.send_output(sine_data["x"])


def demonstrate_generator_agent_use():
    # Start agent network server.
    agent_network = AgentNetwork()

    # Initialize agents by adding them to the agent network.
    gen_agent = agent_network.add_agent(agentType=SineGeneratorAgent)
    monitor_agent = agent_network.add_agent(agentType=MonitorAgent)

    # Interconnect agents by either way:
    # 1) by agent network.bind_agents(source, target).
    agent_network.bind_agents(gen_agent, monitor_agent)

    # 2) by the agent.bind_output().
    gen_agent.bind_output(monitor_agent)

    # Set all agents' states to "Running".
    agent_network.set_running_state()

    # Allow for shutting down the network after execution
    return agent_network


if __name__ == "__main__":
    demonstrate_generator_agent_use()

Starting NameServer...
Broadcast server running on 0.0.0.0:9091
NS running on 127.0.0.1:3333 (127.0.0.1)
URI = PYRO:Pyro.NameServer@127.0.0.1:3333
INFO [2020-04-24 09:21:23.002156] (AgentController): INITIALIZED
INFO [2020-04-24 09:21:23.200110] (SineGeneratorAgent_1): INITIALIZED
 * Serving Flask app "agentMET4FOF.dashboard.Dashboard" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off
INFO [2020-04-24 09:21:23.294149] (MonitorAgent_1): INITIALIZED
[2020-04-24 09:21:23.360214] (SineGeneratorAgent_1): Connected output module: MonitorAgent_1
SET STATE:   Running
[2020-04-24 09:21:24.218709] (SineGeneratorAgent_1): Pack time: 0.000897
[2020-04-24 09:21:24.223207] (SineGeneratorAgent_1): Sending: [0.]
[2020-04-24 09:21:24.225895] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:24.226708] (MonitorAgent_1): Tproc: 0.00017
[2020-04-24 09:21:25.217698] (SineGeneratorAgent_1): Pack time: 0.000419
[2020-04-24 09:21:25.220045] (SineGeneratorAgent_1): Sending: [0.47942554]
[2020-04-24 09:21:25.221406] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.47942554]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:25.223795] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0.        , 0.47942554])}
[2020-04-24 09:21:25.224696] (MonitorAgent_1): Tproc: 0.00264
[2020-04-24 09:21:26.217785] (SineGeneratorAgent_1): Pack time: 0.000415
[2020-04-24 09:21:26.218995] (SineGeneratorAgent_1): Sending: [0.84147098]
[2020-04-24 09:21:26.220107] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.84147098]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:26.222038] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0.        , 0.47942554, 0.84147098])}
[2020-04-24 09:21:26.223223] (MonitorAgent_1): Tproc: 0.002481
[2020-04-24 09:21:27.216058] (SineGeneratorAgent_1): Pack time: 0.000131
[2020-04-24 09:21:27.216323] (SineGeneratorAgent_1): Sending: [0.99749499]
[2020-04-24 09:21:27.216876] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.99749499]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:27.217220] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0.        , 0.47942554, 0.84147098, 0.99749499])}
[2020-04-24 09:21:27.217288] (MonitorAgent_1): Tproc: 0.000314
[2020-04-24 09:21:28.215905] (SineGeneratorAgent_1): Pack time: 0.000102
[2020-04-24 09:21:28.216367] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.90929743]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:28.216186] (SineGeneratorAgent_1): Sending: [0.90929743]
[2020-04-24 09:21:28.216623] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0.        , 0.47942554, 0.84147098, 0.99749499, 0.90929743])}
[2020-04-24 09:21:28.216678] (MonitorAgent_1): Tproc: 0.000229

 * Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)

<Figure size 432x288 with 0 Axes>

Tutorial 2 - A simple pipeline with signal postprocessing.¶

Here we demonstrate how to build a MathAgent as an intermediate to process the SineGeneratorAgent’s output before plotting. Subsequently, a MultiMathAgent is built to show the ability to send a dictionary of multiple fields to the recipient. We provide a custom on_received_message() function, which is called every time a message is received from input agents.

The received message is a dictionary of the form:

{
'from':agent_name,
'data': data,
'senderType': agent_class_name,
'channel':'channel_name'
}

By default, 'channel' is set to "default", however a custom channel can be set when needed, which is demonstrated in the next tutorial.

[1]:

# %load tutorial_2_math_agent.py
from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
from agentMET4FOF.streams import SineGenerator


# Define simple math functions.
def divide_by_two(numerator: float) -> float:
    return numerator / 2


def minus(minuend: float, subtrahend: float) -> float:
    return minuend - subtrahend


def plus(summand_1: float, summand_2: float) -> float:
    return summand_1+summand_2


class MathAgent(AgentMET4FOF):
    def on_received_message(self, message):
        data = divide_by_two(message['data'])
        self.send_output(data)

class MultiMathAgent(AgentMET4FOF):
    def init_parameters(self,minus_param=0.5,plus_param=0.5):
        self.minus_param = minus_param
        self.plus_param = plus_param

    def on_received_message(self, message):
        minus_data = minus(message['data'], self.minus_param)
        plus_data = plus(message['data'], self.plus_param)

        self.send_output({'minus':minus_data,'plus':plus_data})

class SineGeneratorAgent(AgentMET4FOF):
    def init_parameters(self):
        self.stream = SineGenerator()

    def agent_loop(self):
        if self.current_state == "Running":
            sine_data = self.stream.next_sample() #dictionary
            self.send_output(sine_data['x'])


def main():
    # start agent network server
    agentNetwork = AgentNetwork()
    # init agents
    gen_agent = agentNetwork.add_agent(agentType=SineGeneratorAgent)
    math_agent = agentNetwork.add_agent(agentType=MathAgent)
    multi_math_agent = agentNetwork.add_agent(agentType=MultiMathAgent)
    monitor_agent = agentNetwork.add_agent(agentType=MonitorAgent)
    # connect agents : We can connect multiple agents to any particular agent
    agentNetwork.bind_agents(gen_agent, math_agent)
    agentNetwork.bind_agents(gen_agent, multi_math_agent)
    # connect
    agentNetwork.bind_agents(gen_agent, monitor_agent)
    agentNetwork.bind_agents(math_agent, monitor_agent)
    agentNetwork.bind_agents(multi_math_agent, monitor_agent)
    # set all agents states to "Running"
    agentNetwork.set_running_state()

    # allow for shutting down the network after execution
    return agentNetwork


if __name__ == '__main__':
    main()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-1-e91ecde3d431> in <module>
      1 # %load tutorial_2_math_agent.py
----> 2 from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
      3 from agentMET4FOF.streams import SineGenerator
      4
      5

ModuleNotFoundError: No module named 'agentMET4FOF'

Tutorial 3 - An advanced pipeline with multichannel signals.¶

We can use different channels for the receiver to handle specifically each channel name. This can be useful for example in splitting train and test channels in machine learning Then, the user will need to implement specific handling of each channel in the receiving agent.

In this example, the MultiGeneratorAgent is used to send two different types of data - Sine and Cosine generator. This is done via specifying send_output (channel="sine") and send_output(channel="cosine").

Then on the receiving end, the on_received_message() function checks for message['channel'] to handle it separately.

Note that by default, MonitorAgent is only subscribed to the "default" channel. Hence it will not respond to the "cosine" and "sine" channel.

[1]:

# %load tutorial_3_multi_channel.py
from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
from agentMET4FOF.streams import SineGenerator, CosineGenerator


def minus(data, minus_val):
    return data-minus_val


def plus(data,plus_val):
    return data+plus_val


class MultiGeneratorAgent(AgentMET4FOF):
    def init_parameters(self):
        self.sine_stream = SineGenerator()
        self.cos_stream = CosineGenerator()

    def agent_loop(self):
        if self.current_state == "Running":
            sine_data = self.sine_stream.next_sample() #dictionary
            cosine_data = self.sine_stream.next_sample() #dictionary
            self.send_output(sine_data['x'], channel="sine")
            self.send_output(cosine_data['x'], channel="cosine")


class MultiOutputMathAgent(AgentMET4FOF):
    def init_parameters(self,minus_param=0.5,plus_param=0.5):
        self.minus_param = minus_param
        self.plus_param = plus_param

    def on_received_message(self, message):
        """
        Checks for message['channel'] and handles them separately
        Acceptable channels are "cosine" and "sine"
        """
        if message['channel'] == "cosine":
            minus_data = minus(message['data'], self.minus_param)
            self.send_output({'cosine_minus':minus_data})
        elif message['channel'] == 'sine':
            plus_data = plus(message['data'], self.plus_param)
            self.send_output({'sine_plus':plus_data})


def main():
    # start agent network server
    agentNetwork = AgentNetwork()
    # init agents
    gen_agent = agentNetwork.add_agent(agentType=MultiGeneratorAgent)
    multi_math_agent = agentNetwork.add_agent(agentType=MultiOutputMathAgent)
    monitor_agent = agentNetwork.add_agent(agentType=MonitorAgent)
    # connect agents : We can connect multiple agents to any particular agent
    # However the agent needs to implement handling multiple inputs
    agentNetwork.bind_agents(gen_agent, multi_math_agent)
    agentNetwork.bind_agents(gen_agent, monitor_agent)
    agentNetwork.bind_agents(multi_math_agent, monitor_agent)
    # set all agents states to "Running"
    agentNetwork.set_running_state()

    # allow for shutting down the network after execution
    return agentNetwork


if __name__ == '__main__':
    main()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-1-90d955fea48f> in <module>
      1 # %load tutorial_3_multi_channel.py
----> 2 from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
      3 from agentMET4FOF.streams import SineGenerator, CosineGenerator
      4
      5

ModuleNotFoundError: No module named 'agentMET4FOF'

agentMET4FOF agents¶

class agentMET4FOF.agents.AgentMET4FOF(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶

Base class for all agents with specific functions to be overridden/supplied by user.

Behavioural functions for users to provide are init_parameters, agent_loop and on_received_message. Communicative functions are bind_output, unbind_output and send_output.

agent_loop()[source]¶

User defined method for the agent to execute for loop_wait seconds specified either in self.loop_wait or explicitly via`init_agent_loop(loop_wait)`

To start a new loop, call init_agent_loop(loop_wait) on the agent Example of usage is to check the current_state of the agent and send data periodically

before_loop()[source]¶: This action is executed before initiating the loop

bind_output(output_agent)[source]¶

Forms Output connection with another agent. Any call on send_output will reach this newly binded agent

Adds the agent to its list of Outputs.

Parameters:	output_agent (AgentMET4FOF or list) – Agent(s) to be binded to this agent’s output channel

handle_process_data(message)[source]¶

Internal method to handle incoming message before calling user-defined on_received_message method.

If current_state is either Stop or Reset, it will terminate early before entering on_received_message

init_agent_loop(loop_wait: Optional[int] = 1.0)[source]¶

Initiates the agent loop, which iterates every loop_wait seconds

Stops every timers and initiate a new loop.

Parameters:	loop_wait (int, optional) – The wait between each iteration of the loop

init_parameters()[source]¶: User provided function to initialize parameters of choice.

log_info(message)[source]¶

Prints logs to be saved into logfile with Logger Agent

Parameters:	message (str) – Message to be logged to the internal Logger Agent

on_init()[source]¶

Internal initialization to setup the agent: mainly on setting the dictionary of Inputs, Outputs, PubAddr.

Calls user-defined init_parameters() upon finishing.

Inputs¶

Dictionary of Agents connected to its input channels. Messages will arrive from agents in this dictionary. Automatically updated when bind_output() function is called

Type:	dict

Outputs¶

Dictionary of Agents connected to its output channels. Messages will be sent to agents in this dictionary. Automatically updated when bind_output() function is called

Type:	dict

PubAddr_alias¶

Name of Publish address socket

Type:	str

PubAddr¶

Publish address socket handle

Type:	str

AgentType¶

Name of class

Type:	str

current_state¶

Current state of agent. Can be used to define different states of operation such as “Running”, “Idle, “Stop”, etc.. Users will need to define their own flow of handling each type of self.current_state in the agent_loop

Type:	str

loop_wait¶

The interval to wait between loop. Call init_agent_loop to restart the timer or set the value of loop_wait in init_parameters when necessary.

Type:	int

memory_buffer_size¶

The total number of elements to be stored in the agent memory When total elements exceeds this number, the latest elements will be replaced with the incoming data elements

Type:	int

on_received_message(message)[source]¶

User-defined method and is triggered to handle the message passed by Input.

Parameters:	message (Dictionary) – The message received is in form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name} agent_name is the name of the Input agent which sent the message data is the actual content of the message

pack_data(data, channel='default')[source]¶

Internal method to pack the data content into a dictionary before sending out.

Special case : if the data is already a message, then the from and senderType will be altered to this agent, without altering the data and channel within the message this is used for more succinct data processing and passing.

Parameters:	data (argument) – Data content to be packed before sending out to agents. channel (str) – Key of dictionary which stores data
Returns:	Packed message data
Return type:	dict of the form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name}.

reset()[source]¶

This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.

Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.

send_output(data, channel='default')[source]¶

Sends message data to all connected agents in self.Outputs.

Output connection can first be formed by calling bind_output. By default calls pack_data(data) before sending out. Can specify specific channel as opposed to ‘default’ channel.

Parameters:	data (argument) – Data content to be sent out channel (str) – Key of message dictionary which stores data
Returns:	message
Return type:	dict of the form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name}.

send_plot(fig: Union[matplotlib.figure.Figure, Dict[str, matplotlib.figure.Figure]], mode: str = 'image')[source]¶

Sends plot to agents connected to this agent’s Output channel.

This method is different from send_output which will be sent to through the ‘plot’ channel to be handled.

Tradeoffs between “image” and “plotly” modes are that “image” are more stable and “plotly” are interactive. Note not all (complicated) matplotlib figures can be converted into a plotly figure.

Parameters:	fig (matplotlib.figure.Figure or dict of matplotlib.figure.Figure) – Alternatively, multiple figures can be nested in a dict (with any preferred keys) e.g {“Temperature”:matplotlib.Figure, “Acceleration”:matplotlib.Figure} mode (str) – “image” - converts into image via encoding at base64 string. “plotly” - converts into plotly figure using mpl_to_plotly Default: “image”
Returns:	graph
Return type:	str or plotly figure or dict of one of those converted figure(s)

stop_agent_loop()[source]¶: Stops agent_loop from running. Note that the agent will still be responding to messages

unbind_output(output_agent)[source]¶

Remove existing output connection with another agent. This reverses the bind_output method

Parameters:	output_agent (AgentMET4FOF) – Agent binded to this agent’s output channel

update_data_memory(agent_from, data=None)[source]¶

Updates data stored in self.memory with the received message

Checks if sender agent has sent any message before If it did,then append, otherwise create new entry for it

Parameters:	agent_from (dict \| str) – if type is dict, we expect it to be the agentMET4FOF dict message to be compliant with older code otherwise, we expect it to be name of agent sender and data will need to be passed as parameter data – optional if agent_from is a dict. Otherwise this parameter is compulsory. Any supported data which can be stored in dict as buffer.

class agentMET4FOF.agents.AgentNetwork(ip_addr='127.0.0.1', port=3333, connect=False, log_filename='log_file.csv', dashboard_modules=True, dashboard_update_interval=3, dashboard_max_monitors=10, dashboard_port=8050)[source]¶

Object for starting a new Agent Network or connect to an existing Agent Network specified by ip & port

Provides function to add agents, (un)bind agents, query agent network state, set global agent states Interfaces with an internal _AgentController which is hidden from user

add_agent(name=' ', agentType=<class 'agentMET4FOF.agents.AgentMET4FOF'>, log_mode=True, memory_buffer_size=1000000, ip_addr=None)[source]¶

Instantiates a new agent in the network.

Parameters:	str (name) – with the same name. Defaults to the agent’s class name. AgentMET4FOF (agentType) – network. Defaults to `AgentMET4FOF` bool (log_mode) – Logger Agent. Defaults to True.
Returns:	AgentMET4FOF
Return type:	Newly instantiated agent

agents(filter_agent=None)[source]¶

Returns all agent names connected to Agent Network.

Returns:	list
Return type:	names of all agents

bind_agents(source, target)[source]¶

Binds two agents communication channel in a unidirectional manner from source Agent to target Agent

Any subsequent calls of source.send_output() will reach target Agent’s message queue.

Parameters:	source (AgentMET4FOF) – Source agent whose Output channel will be binded to target target (AgentMET4FOF) – Target agent whose Input channel will be binded to source

connect(ip_addr='127.0.0.1', port=3333, verbose=True)[source]¶

Parameters:	ip_addr (str) – IP Address of server to connect to port (int) – Port of server to connect to

get_agent(agent_name)[source]¶

Returns a particular agent connected to Agent Network.

Parameters:	agent_name (str) – Name of agent to search for in the network

set_agents_state(filter_agent=None, state='Idle')[source]¶

Blanket operation on all agents to set their current_state attribute to given state

Can be used to define different states of operation such as “Running”, “Idle, “Stop”, etc.. Users will need to define their own flow of handling each type of self.current_state in the agent_loop

Parameters:	filter_agent (str) – (Optional) Filter name of agents to set the states state (str) – State of agents to set

set_running_state(filter_agent=None)[source]¶

Blanket operation on all agents to set their current_state attribute to “Running”

Users will need to define their own flow of handling each type of self.current_state in the agent_loop

Parameters:	filter_agent (str) – (Optional) Filter name of agents to set the states

set_stop_state(filter_agent=None)[source]¶

Blanket operation on all agents to set their current_state attribute to “Stop”

Users will need to define their own flow of handling each type of self.current_state in the agent_loop

Parameters:	filter_agent (str) – (Optional) Filter name of agents to set the states

shutdown()[source]¶: Shutdowns the entire agent network and all agents

start_server(ip_addr='127.0.0.1', port=3333)[source]¶

Parameters:	ip_addr (str) – IP Address of server to start port (int) – Port of server to start

unbind_agents(source, target)[source]¶

Unbinds two agents communication channel in a unidirectional manner from source Agent to target Agent

This is the reverse of bind_agents()

Parameters:	source (AgentMET4FOF) – Source agent whose Output channel will be unbinded from target target (AgentMET4FOF) – Target agent whose Input channel will be unbinded from source

class agentMET4FOF.agents.DataStreamAgent(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶

Able to simulate generation of datastream by loading a given DataStreamMET4FOF object.

Can be used in incremental training or batch training mode. To simulate batch training mode, set pretrain_size=-1 , otherwise, set pretrain_size and batch_size for the respective See DataStreamMET4FOF on loading your own data set as a data stream.

agent_loop()[source]¶

User defined method for the agent to execute for loop_wait seconds specified either in self.loop_wait or explicitly via`init_agent_loop(loop_wait)`

To start a new loop, call init_agent_loop(loop_wait) on the agent Example of usage is to check the current_state of the agent and send data periodically

init_parameters(stream=<agentMET4FOF.streams.DataStreamMET4FOF object>, pretrain_size=None, batch_size=1, loop_wait=1, randomize=False)[source]¶

Parameters:

stream (DataStreamMET4FOF) – A DataStreamMET4FOF object which provides the sample data
pretrain_size (int) – The number of sample data to send through in the first loop cycle, and subsequently, the batch_size will be used
batch_size (int) – The number of sample data to send in every loop cycle
loop_wait (int) – The duration to wait (seconds) at the end of each loop cycle before going into the next cycle
randomize (bool) – Determines if the dataset should be shuffled before streaming

reset()[source]¶

This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.

Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.

class agentMET4FOF.agents.MonitorAgent(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶

Unique Agent for storing plots and data from messages received from input agents.

The dashboard searches for Monitor Agents’ memory and plots to draw the graphs “plot” channel is used to receive base64 images from agents to plot on dashboard

memory¶

Dictionary of format {agent1_name : agent1_data, agent2_name : agent2_data}

Type:	dict

plots¶

Dictionary of format {agent1_name : agent1_plot, agent2_name : agent2_plot}

Type:	dict

plot_filter¶

List of keys to filter the ‘data’ upon receiving message to be saved into memory Used to specifically select only a few keys to be plotted

Type:	list of str

init_parameters(plot_filter=[], custom_plot_function=-1, **kwargs)[source]¶: User provided function to initialize parameters of choice.

on_received_message(message)[source]¶

Handles incoming data from ‘default’ and ‘plot’ channels.

Stores ‘default’ data into self.memory and ‘plot’ data into self.plots

Parameters:	message (dict) – Acceptable channel values are ‘default’ or ‘plot’

reset()[source]¶

This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.

Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.

update_plot_memory(message)[source]¶

Updates plot figures stored in self.plots with the received message

Parameters:	message (dict) – Standard message format specified by AgentMET4FOF class Message[‘data’] needs to be base64 image string and can be nested in dictionary for multiple plots Only the latest plot will be shown kept and does not keep a history of the plots.

class agentMET4FOF.agents.TransformerAgent(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶

init_parameters(method=None, **kwargs)[source]¶: User provided function to initialize parameters of choice.

on_received_message(message)[source]¶

User-defined method and is triggered to handle the message passed by Input.

Parameters:	message (Dictionary) – The message received is in form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name} agent_name is the name of the Input agent which sent the message data is the actual content of the message

agentMET4FOF streams¶

class agentMET4FOF.streams.CosineGenerator(num_cycles=1000)[source]¶

class agentMET4FOF.streams.DataStreamMET4FOF[source]¶

Class for creating finite datastream for ML with x as inputs and y as target Data can be fetched sequentially using next_sample() or all at once all_samples()

For sensors data: The format shape for 2D data stream (num_samples, n_sensors) The format shape for 3D data stream (num_samples, sample_length , n_sensors)

all_samples()[source]¶

Returns all the samples in the data stream

Returns:	samples
Return type:	dict of the form {‘x’: current_sample_x, ‘y’: current_sample_y}

next_sample(batch_size=1)[source]¶

Fetches the samples from the data stream and advances the internal pointer current_idx

Parameters:	batch_size (int) – number of batches to get from data stream
Returns:	samples
Return type:	dict of the form {‘x’: current_sample_x, ‘y’: current_sample_y}

class agentMET4FOF.streams.SineGenerator(num_cycles=1000)[source]¶

agentMET4FOF.streams.extract_x_y(message)[source]¶

Extracts features & target from message[‘data’] with expected structure such as :

tuple - (x,y)
dict - {‘x’:x_data,’y’:y_data}

Handle data structures of dictionary to extract features & target

Indices and tables¶

References¶

[Bang2019]

Bang X. Yong, A. Brintrup Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System, 9th Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future