Python Agents Example - Real-Time (4.3)
This section provides an example of how you can use the Python processing and Python Connector agents for machine learning.
In this example the classic iris dataset is used. With the help of scikit-learn a model of this dataset is built to predict observations. The iris dataset consists of 3 different types of iris' (Setosa, Versicolor, and Virginica) petal and sepal length, with 50 observations each, totaling 150 observations. The problem is presented as a supervised learning problem, with the aim of predicting the species of an iris using measurements of petal and sepal lengths.Â
There is a production flow, where queries are served via web services requests, and an exploration flow for model validation.
Workflow example with Python processing and Python Connector agents
Workflow Configuration
The sections below provide descriptions of agent configurations for this example workflow.
WS_Handler
The Analysis agent is configured to handle requests from the web services agent. The request is translated into a format required by the machine learned model. Responses from the model are sent back to the web services agent as the result of the prediction.
Example - Code for WS_Handler
import ultra.Iris.UFL_Types;
import ultra.ws.Iris.PRF_WebService.Xtraining;
consume {
debug(input);
if (instanceOf(input, WSCycle_predict)) {
WSCycle_predict cycle = (WSCycle_predict) input;
PredictObservation obs = udrCreate(PredictObservation);
obs.observation = listCreate(double, cycle.param.sepal_length, cycle.param.sepal_width,
cycle.param.petal_length, cycle.param.petal_width);
obs.context = cycle;
udrRoute(obs, "observation");
}
if (instanceOf(input, PredictObservation)) {
PredictObservation obs = (PredictObservation) input;
WSCycle_predict cycle = (WSCycle_predict) obs.context;
Response response = udrCreate(Response);
response.iris = obs.prediction;
cycle.response = response;
udrRoute(cycle, "response");
}
}
Predict
The Python processing agent processes UDRs by defining a consume
block, and uses the selected Interpreter profile that is used to configure the Python executables.
The input is either a UDR of type InstallModel
in which case the current model is replaced by a recently trained model, or a UDR of type PredictObservation
in which case the information in the input is used as input to the model to predict the species of iris.
Example - Code for Predict
import pickle
model = None
targets = None
def consume(input):
if isinstance(input, InstallModel):
global model, targets
model = pickle.loads(input.model)
targets = input.targets
elif isinstance(input, PredictObservation):
if model:
idx = model.predict([input.observation])[0]
input.prediction = targets[idx]
else:
input.prediction = 'please install model'
if input.testing:
debug(input)
udrRoute(input, 'test_prediction')
else:
udrRoute(input, 'prediction')
Python_Connector
The Python Connector agent is configured to bind on port 3810 from which the data will be received. The types accepted for routing are PredictObservation (Iris.UFL_Types)
and InstallModel (Iris.UFL_Types)
 on route r_1.
The Python Connector agent implements a custom API that does all the workflow interaction and provides a high level API to the exploration tool.
Example - Code for Python Connector API
import pickle
from .UFL_Types import InstallModel
from .UFL_Types import PredictObservation
def install_model(model, targets):
udr = InstallModel(
model=pickle.dumps(model),
targets=targets)
udrRoute(udr)
def predict(observation):
udr = PredictObservation(
observation=observation,
testing=True)
udrRoute(udr)
return udrConsume().prediction
__all__ = ['install_model', 'predict']
Exploration Tool
You start the workflow, open the exploration tool of your choice, and run your script.