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algorithm4.py
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algorithm4.py
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# Name : Ashwin Sai C
# Course : ML - CS6375-003
# Title : Mini Project 4 Part - 2
# Term : Fall 2023
from CLT_class import CLT
from Util import *
from MIXTURE_CLT_org import MIXTURE_CLT
from RandomForest import RandomForest
import numpy as np
import sys
import time
def learn_chow_liu(train_file, valid_file, test_file):
train_dataset=Util.load_dataset(train_file)
valid_dataset=Util.load_dataset(valid_file)
test_dataset=Util.load_dataset(test_file)
concat_train_valid = np.concatenate((train_dataset,valid_dataset))
clt=CLT()
clt.learn(concat_train_valid)
LL = clt.computeLL(test_dataset)/test_dataset.shape[0]
return LL
def tree_bayesian_newtorks():
file_name_list = ['accidents','baudio','bnetflix','jester','kdd','msnbc','nltcs','plants','pumsb_star','tretail']
file_path = '../datasets/dataset/'
for name in file_name_list:
train_file = name+'.ts.data'
val_file = name+'.valid.data'
test_file = name+'.test.data'
LL = learn_chow_liu(file_path+train_file, file_path+val_file, file_path+test_file)
print("Log-Liklihood for ",test_file," : ",LL)
def mixture_tree_bayesian_networks_EM():
file_name_list = ['accidents','baudio','bnetflix','jester','kdd','msnbc','nltcs','plants','pumsb_star','tretail']
# file_name_list = ['accidents','baudio','bnetflix','jester']
file_path = '../datasets/dataset/'
k_list = [2,5,10,15,20]
for index,name in enumerate(file_name_list):
train_file = name+'.ts.data'
val_file = name+'.valid.data'
test_file = name+'.test.data'
train_file_path = file_path+train_file
val_file_path = file_path+val_file
test_file_path = file_path+test_file
train_dataset = Util.load_dataset(train_file_path)
val_dataset = Util.load_dataset(val_file_path)
test_dataset = Util.load_dataset(test_file_path)
print("\nFile name : ",test_file)
mix_clt = MIXTURE_CLT()
# optimal_k = mix_clt.find_optimal_k(train_dataset,val_dataset, k_list)
# print(f"Optimal k based on validation set: {optimal_k}")
num_runs = 5
# optimal_k = 20
optimal_k_list = [20, 20, 20, 20, 2, 2, 20, 20, 20, 20]
# optimal_k_list = [20, 20, 20, 20]
print(train_dataset.shape)
print(val_dataset.shape)
train_dataset = np.concatenate((train_dataset,val_dataset)) # creating train dataset along with val dataset
print(train_dataset.shape)
test_results = mix_clt.run_test_set(train_dataset, test_dataset, optimal_k_list[index], num_runs)
average_test_ll = np.mean(test_results)
std_dev_test_ll = np.std(test_results)
print(f"Average Test Log Likelihood: {average_test_ll}")
print(f"Standard Deviation of Test Log Likelihood: {std_dev_test_ll}")
def mixture_tree_bayesian_networks_RF():
file_name_list = ['accidents','baudio','bnetflix','jester','kdd','msnbc','nltcs','plants','pumsb_star','tretail']
file_path = '../datasets/dataset/'
k_list = [2,5,10,20]
r_list = [5, 75, 130, 200]
for name in file_name_list:
train_file = name+'.ts.data'
val_file = name+'.valid.data'
test_file = name+'.test.data'
train_file_path = file_path+train_file
val_file_path = file_path+val_file
test_file_path = file_path+test_file
train_dataset = Util.load_dataset(train_file_path)
val_dataset = Util.load_dataset(val_file_path)
test_dataset = Util.load_dataset(test_file_path)
print("\nFile name k&r : ",test_file)
rf_clt = RandomForest()
optimal_k,optimal_r = rf_clt.find_optimal_k_r(train_dataset,val_dataset, k_list,r_list)
print(f"Optimal k based on validation set: {optimal_k}")
print(f"Optimal r based on validation set: {optimal_r}")
num_runs = 5
print(train_dataset.shape)
print(val_dataset.shape)
train_dataset = np.concatenate((train_dataset,val_dataset)) # creating train dataset along with val dataset
print(train_dataset.shape)
test_results = rf_clt.run_test_set_k_r(train_dataset, test_dataset, optimal_k, optimal_r, num_runs)
average_test_ll = np.mean(test_results)
std_dev_test_ll = np.std(test_results)
print(f"Average Test Log Likelihood: {average_test_ll}")
print(f"Standard Deviation of Test Log Likelihood: {std_dev_test_ll}")
if __name__ == "__main__":
print("hi")
tree_bayesian_newtorks()
# mixture_tree_bayesian_networks_EM()
# mixture_tree_bayesian_networks_RF()