RandomForestClassifier Component of BriCA1

brica1_RandomForestClassifierComponent.py

# coding: utf-8

import numpy as np
import matplotlib.pyplot as plt
from sklearn import ensemble, datasets
 
import brica1


# RandomForeestClassifier Component Definition
class RandomForestClassifierComponent(brica1.Component):
   def __init__(self, n_in):
       super(RandomForestClassifierComponent, self).__init__()
       self.classifier = ensemble.RandomForestClassifier()
       self.make_in_port("in0", n_in)
       self.make_out_port("out0", 1)

   def fire(self):
       x = self.inputs["in0"]
       z = self.classifier.predict([x])
       self.results["out0"] = z

   def fit(self, X, y):
       self.classifier.fit(X, y)


# Load iris dataset
iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target

# Setup data feeder component
feeder = brica1.ConstantComponent()
feeder.make_out_port("out0", 2)

# Setup RandomForestClassfier component
RFC = RandomForestClassifierComponent(2)
RFC.fit(X, y)

# Connect the components
brica1.connect((feeder, "out0"), (RFC, "in0"))

# Add components to module
mod = brica1.Module()
mod.add_component("feeder", feeder)
mod.add_component("RFC", RFC)

# Setup scheduler and agent
s = brica1.VirtualTimeSyncScheduler()
a = brica1.Agent(s)
a.add_submodule("mod", mod)

# Test the classifier
for i in xrange(len(X)):
   feeder.set_state("out0", X[i]) # Set data feeder to training data i

   a.step() # Execute prediction

   print "Actual: {}\tPrediction: {}\t{}".format(y[i], RFC.get_out_port("out0").buffer[0], y[i] == RFC.get_out_port("out0").buffer[0])