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
convert_to_plotly(matplotlib_fig)[source]

Internal method to convert matplotlib figure to plotly figure

Parameters:matplotlib_fig (plt.Figure) – Matplotlib figure to be converted
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=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) – 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=<Figure size 640x480 with 0 Axes>)[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.

Parameters:fig (Figure) – Can be either matplotlib figure or plotly figure
Returns:The message format is {‘from’
Return type:agent_name, ‘plot’: data, ‘senderType’: agent_class}.
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(message)[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:message (dict) – Standard message format specified by AgentMET4FOF class
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:
  • name (str) – Unique name of agent. If left empty, the name will be automatically set to its class name. There cannot be more than one agent with the same name.
  • agentType (AgentMET4FOF) – Agent class to be instantiated in the network.
  • log_mode (bool) – Default is True. Determines if messages will be logged to background Logger Agent.
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