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import numpy as np
import sys, random
from matplotlib import pyplot as plt
from machineLearning.rf import (
DecisionTree,
Gini, Entropy, MAE, MSE,
Mode, Mean,
CART, ID3, C45,
UsersChoice, Variance, Random, MutualInformation, ANOVA, KendallTau
)
from machineLearning.metric import ConfusionMatrix
from machineLearning.utility import Time
from machineLearning.settings.treeSettings import TreeSettings
from machineLearning.data import Data
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def dataShift(dims):
offSet = [0.25, 0.5, 0.25]
diffLen = abs(len(offSet) - dims)
offSet.extend([0] * diffLen)
random.shuffle(offSet)
return offSet[:dims]
def getImurity(impurity: str):
if impurity == 'gini':
return Gini() # Use Gini index as the impurity measure
elif impurity == 'entropy':
return Entropy() # Use Entropy index as the impurity measure
elif impurity == 'mae':
return MAE() # Use MAE index as the impurity measure
elif impurity == 'mse':
return MSE() # Use MSE index as the impurity measure
def getLeaf(leaf: str):
if leaf == 'mode':
return Mode() # Use mode as the leaf function
elif leaf == 'mean':
return Mean() # Use mean as the leaf function
def getSplit(split: str, percentile: int = None):
if split == 'id3':
return ID3(percentile) # Use ID3 algorithm for splitting
elif split == 'c45':
return C45(percentile) # Use C4.5 algorithm for splitting
elif split == 'cart':
return CART(percentile) # Use CART algorithm for splitting
def getFeatureSelection(selection: str, *args):
if selection == 'choice':
return UsersChoice(*args)
elif selection == 'variance':
return Variance(*args)
elif selection == 'random':
return Random(*args)
elif selection == 'mutual':
return MutualInformation(*args)
elif selection == 'anova':
return ANOVA(*args)
elif selection == 'kendall':
return KendallTau(*args)
if __name__ == "__main__":
settings = TreeSettings()
try:
configFile = sys.argv[1]
settings.getConfig(configFile)
settings.setConfig()
except IndexError:
pass
print(settings)
# Create a timer object to measure execution time
timer = Time()
print("Importing data...\n")
timer.start()
data = Data(trainAmount=settings['trainAmount'], evalAmount=settings['validAmount'], dataPath=settings['dataPath'], normalize=settings['normalize'])
data.inputFeatures(*settings['features'])
data.importData(*settings['dataFiles'])
print(data)
timer.record("Importing Data")
# Create and train a decision tree
timer.start()
print('Setting up tree')
tree = DecisionTree(settings['depth'], settings['minSamples'])
tree.setComponent(getImurity(settings['impurity']))
tree.setComponent(getLeaf(settings['leaf']))
tree.setComponent(getSplit(settings['split'],settings['percentile']))
if settings['featSelection'] is not None:
tree.setFeatureSelection(settings['featSelection'], settings['featParameter']) # Use random feature selection
timer.record("Tree setup")
# Train the tree using the training data
timer.start()
print('begin training...')
#tree.train(trainData,trainLabels)
tree.train(data.trainSet.data,data.trainSet.labels.argmax(1))
timer.record("Training")
# Evaluate the tree on the validation data
timer.start()
print('making predictions...')
#prediction = tree.eval(validData)
prediction = tree.eval(data.evalSet.data)
timer.record("Prediction")
# Print the trained decision tree
print(tree)
print()
# Compute confusion matrix to evaluate the performance of the decision tree
confusion = ConfusionMatrix(2)
#confusion.update(prediction, validLabels)
confusion.update(prediction, data.evalSet.labels.argmax(1))
confusion.percentages()
confusion.calcScores()
# Compute confusion matrix to evaluate the performance of the decision tree
print(confusion)
print()
print(timer)