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learn_cat.py
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learn_cat.py
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from tracemalloc import start
import numpy as np
from cluster_stats_new import categoricalClusters
import utils
import time
import argparse
import pickle
import os
import time
from sklearn.metrics.cluster import adjusted_rand_score
class catMM():
"""
Categorical Mixture Model (GMM) with each feature being independent.
This class implements a Gibbs sampler for Bayesian CatMM.
It initializes the model with given data, prior, and initial cluster assignments, and provides
a method to run the Gibbs sampler for a specified number of iterations.
"""
def __init__(self, C: int, alpha: float, gamma, assignments: int, isTrueZ=0):
"""
Initialize the Categorical MM.
Args:
C (np.array, dtype=float): 2D NumPy array of shape (n_samples, n_features) containing the categorical data.
alpha (float): Dirichlet hyperparameter for mixing probabilities, alpha_0.
gamma (float): Dirichlet hyperparameter for catagories
assignments (np.array): 1D NumPy array of shape (n_samples,) containing initial cluster assignments.
"""
if isTrueZ == 1:
self.trueZ = assignments.copy()
else:
self.trueZ = []
self.alpha = alpha
# Initial total number of clusters
K = len(set(assignments))
self.K_max = K
# Total number of samples and categories
self.N, catD = C.shape
# Get number of categories for each feature
self.Ms = np.zeros(catD, int)
for d in range(catD):
self.Ms[d] = len(set(C[d]))
# Setting up the Categorical Cluster object which will track the features and component-wise statistics
self.clusters = categoricalClusters(C, alpha, gamma, K, assignments)
# Initializing the outputs
self.z_map = assignments
self.iter_map = 0
self.log_max_post = -1*np.inf
self.BIC = 0.
self.run_id = -1
def gibbs_sampler(self, n_iter: int, run_id: int, toPrint=True, savePosterior=False, trueAssignments=[]):
"""
Run the Gibbs sampler for the Bayesian GMM.
Args:
n_iter (int): Number of iterations to run the Gibbs sampler.
run_id (int): Unique identifier for the current run.
toPrint (bool, optional): If True, print the results for each iteration. Default is True.
savePosterior (bool, optional): If True, save the posterior score for each data step in each iteration. Default is False.
trueAssignments (list, optional): Ground truth cluster assignments for calculating Adjusted Rand Index (ARI). Default is an empty list.
"""
if len(trueAssignments) > 0:
self.trueZ = trueAssignments
self.run_id = run_id
posteriorList = []
ARI_list = []
# If the posterior is the same for each iteration, a convergence bound can also be set
same_posterior_count = 0
ass_posterior = 0
# Log posterior probability
log_post_Z = np.zeros(self.K_max)
for k in range(self.K_max):
log_post_Z[k] = self.clusters.get_posterior_probability_Z_k(k)
# Print initial information if want to
if toPrint:
if len(self.trueZ) != 0:
print(f"run: {run_id + 1}, iteration:0, K:{self.clusters.K}, posterior:{round(np.sum(log_post_Z), 3)}, ARI: {round(adjusted_rand_score(self.trueZ, self.clusters.assignments), 3)}")
else:
print(f"run: {run_id + 1}, iteration:0, K:{self.clusters.K}, posterior:{round(np.sum(log_post_Z), 3)}")
# Start the Gibbs sampler
for i_iter in range(n_iter):
old_assignments = self.clusters.assignments.copy()
# For each data point
for i in range(self.clusters.N):
# Cache the previous cluster statistics if the same cluster is assigned to the current data point
k_old = self.clusters.assignments[i]
K_old = self.clusters.K
stats_old = self.clusters.cache_cluster_stats(k_old)
# Remove the data point from the data
self.clusters.del_assignment(i)
# Calculate f(z_i = k | z_[-i], alpha)
log_prob_z_k_alpha = np.log(self.clusters.counts + self.alpha / self.clusters.K_max ) - np.log(self.N + self.alpha - 1)
# Calculate f(c_i | C[-i], z_i = k, z_[-i], Gamma)
log_prob_c_i = self.clusters.log_post_pred(i)
# Get f(z_i = k | z_[-i])
log_prob_z_k = log_prob_z_k_alpha + log_prob_c_i
# Sample new cluster identity for the data point using Gumbel-max trick
k = utils.sample_numpy_gumbel(log_prob_z_k)
# k = utils.sample(log_prob_z_k)
# if an empty cluster is sampled
if k >= self.clusters.K:
k = self.clusters.K
# If the sampled cluster is the same as the old one and the cluster didn't become empty
if k==k_old and self.clusters.K == K_old:
self.clusters.restore_cluster_stats(k_old, *stats_old)
self.clusters.assignments[i] = k_old
# Assign a new cluster identity
else:
self.clusters.add_assignment(i,k)
# Save log posterior probability
if savePosterior:
new_assignments = self.clusters.assignments
assignments_change = old_assignments == new_assignments
changed_clusters = []
for i in range(self.N):
if not assignments_change[i]:
changed_clusters.append(old_assignments[i])
changed_clusters.append(new_assignments[i])
changed_clusters = list(set(changed_clusters))
for j in changed_clusters:
log_post_Z[j] = self.clusters.get_posterior_probability_Z_k(j)
posteriorList.append(np.sum(log_post_Z))
# Calculate the ARI if true assignments are provided
if len(self.trueZ) != 0:
ARI_list.append(round(adjusted_rand_score(self.trueZ, self.clusters.assignments), 3))
# Get the list of all changed clusters for the iteration
new_assignments = self.clusters.assignments
assignments_change = old_assignments == new_assignments
changed_clusters = []
for i in range(self.N):
if not assignments_change[i]:
changed_clusters.append(old_assignments[i])
changed_clusters.append(new_assignments[i])
changed_clusters = list(set(changed_clusters))
# Get the posterior score
for j in changed_clusters:
log_post_Z[j] = self.clusters.get_posterior_probability_Z_k(j)
sum_log_post_Z = np.sum(log_post_Z)
# Change the MAP parameters to be updated
if sum_log_post_Z > self.log_max_post:
self.log_max_post = sum_log_post_Z
self.z_map = self.clusters.assignments.copy()
self.iter_map = i_iter + 1
if sum_log_post_Z != ass_posterior:
same_posterior_count = 0
ass_posterior = sum_log_post_Z
else:
same_posterior_count += 1
if toPrint:
if len(self.trueZ) != 0:
print(f"run: {run_id + 1}, iteration:{i_iter + 1}, K:{self.clusters.K}, posterior:{round(sum_log_post_Z, 3)}, ARI: {adjusted_rand_score(self.trueZ, self.clusters.assignments)}")
else:
print(f"run: {run_id + 1}, iteration:{i_iter + 1}, K:{self.clusters.K}, posterior:{round(sum_log_post_Z, 3)}")
if same_posterior_count > n_iter:
break
print(f"{i_iter}/{n_iter} ",end='\r')
self.BIC = self.clusters.K*(self.Ms.sum()) * np.log(self.N) - (2 * self.log_max_post)
print(f"\nRun: {run_id + 1}, K:{len(set(self.z_map))}, BIC: {self.BIC}, logmax post: {self.log_max_post}, max_post_iter: {self.iter_map}")
postData = {
"run":run_id,
"n_iter":n_iter,
"posterior":posteriorList,
"ARI":ARI_list
}
return postData
if __name__ == "__main__":
model_start_time = time.perf_counter()
# Setup argument parser
parser = argparse.ArgumentParser()
# Define the required and optional arguments for the script
parser.add_argument("-f", required=True, type=argparse.FileType('r'), help="Path to the file containing gauusian mixture data")
parser.add_argument("-k", required=True, type=int, help="Known number of clusters and if it's unknown Maximum number of clusters (Or your guess that the number of clusters can't be more than that)")
parser.add_argument("-o", required=False, type=str, help="Output directory")
parser.add_argument("-i", required=False, type=int, help="Collapsed Gibbs sampling iterations")
parser.add_argument("-r", required=False, type=int, help="Number of training runs to run with different initial assignments")
parser.add_argument("-t", required=False, type=argparse.FileType('r'), help="Path to the true parameters file (non-pickle file)")
parser.add_argument("-p", required=False, action="store_true", help="Will print results while Gibbs sampling")
parser.add_argument("-seed", required=False, type=int, help="set a seed value")
# Parse arguments
args = parser.parse_args()
# Set random seed
global_seed = np.random.randint(1, 2**31 - 1) if args.seed == None else args.seed
np.random.seed(global_seed)
################################## Extract data ##################################
# Read data from the input file
C = []
dataFile = args.f
dataFilename = os.path.splitext(os.path.basename(dataFile.name))[0]
for line in dataFile:
C.append(np.array([int(float(i)) for i in line.strip().split(',')]))
C = np.array(C)
N, catD = C.shape
Ms = np.zeros(catD, int)
for d in range(catD):
Ms[d] = len(set(C[d]))
# Model parameters
K_max_BIC = args.k
n_iter = 50 if args.i == None else args.i
training_runs = 1 if args.r == None else args.r
# Print initial setup information
print(f"\nRunning {os.path.basename(__file__)} on {dataFilename} with global seed: {global_seed}")
print(f"N: {N}, K: {K_max_BIC}, Ms: {Ms} Iterations: {n_iter}, Global seed: {global_seed}\n")
################################## Set hyper-parameters ################################## (can we look at the data to set hyperparameters?)
# Set hyperparameters for the model
alpha = 1.0
gamma = 0.2
################################## Model ##################################
print(f"Total training runs: {training_runs}")
trueFile = args.t
if trueFile:
trueAssignments = np.array([int(line.strip()) for line in trueFile])
# bayesgmm = catMM(C, alpha, gamma, trueAssignments, 1)
# bayesgmm.gibbs_sampler(n_iter, -1)
else:
trueAssignments = []
# Initialize variables to track the best model
max_post = -1*np.inf
least_BIC = 1*np.inf
# Run training with different initial assignments
for i in range(training_runs):
print(f"\nRun: {i+1}")
# Ensure unique initial assignments
starting_assignments = []
while len(set(starting_assignments)) != K_max_BIC:
starting_assignments = np.random.randint(0, K_max_BIC, N)
# Uncomment and modify the following lines if you want to use specific starting assignments
# params_true = pickle.load(open("../data_n1000_d10_k10_m2.0_c2.1_catD0_catM4_seed1616.trueParamPickle", "rb"))
# starting_assignments = params_true['z']
# starting_assignments = pickle.load(open("../data_n1000_d0_k5_m2.1_c2.1_catD1_catM4_seed23.trueParamPickle", "rb"))['z']
# starting_assignments = np.array([3, 0, 2, 0, 0, 3, 2, 2, 3, 3, 0, 2, 2, 3, 0, 0, 0, 2, 2, 2, 3, 0, 3, 0, 2, 0, 0, 3, 3, 0, 2, 1, 2, 2, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 2, 2, 3, 3, 0, 3, 2, 2, 3, 0, 3, 0, 3, 0, 0, 3, 2, 0, 0, 2, 0, 0, 2, 2, 0, 3, 2, 2, 0, 0, 2, 3, 2, 0, 3, 2, 0, 0, 3, 3, 0, 3, 0, 0, 0, 1, 0, 2, 3, 0, 0, 3, 0, 0, 3, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 3, 0, 2, 3, 0, 2, 3, 2, 2, 0, 0, 0, 0, 3, 0, 3, 0, 0, 1, 3, 2, 2, 0, 3, 0, 0, 2, 0, 3, 2, 0, 2, 2, 3, 0, 2, 2, 2, 0, 0, 2, 0, 0, 0, 3, 2, 0, 2, 0, 0, 3, 0, 2, 0, 2, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 2, 3, 0, 0, 0, 3, 0, 3, 0, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 1, 0, 3, 0, 0, 0, 2, 0, 2, 0, 3, 3, 0, 0, 0, 3, 0, 0, 3, 3, 3, 0, 3, 0, 3, 2, 2, 2, 0, 3, 0, 0, 3, 0, 2, 0, 0, 3, 0, 0, 3, 3, 1, 2, 1, 0, 2, 0, 2, 3, 2, 3, 0, 3, 0, 3, 2, 0, 0, 0, 0, 0, 2, 3, 3, 3, 0, 3, 0, 0, 0, 3, 3, 3, 0, 2, 0, 0, 3, 2, 3, 2, 0, 0, 2, 0, 0, 0, 2, 3, 0, 2, 2, 3, 3, 2, 0, 0, 3, 2, 2, 2, 2, 3, 0, 2, 2, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 3, 0, 2, 0, 3, 0, 2, 2, 2, 0, 0, 3, 0, 2, 3, 3, 2, 2, 2, 0, 2, 2, 0, 0, 3, 2, 0, 0, 2, 0, 2, 3, 2, 0, 0, 0, 0, 3, 2, 3, 2, 0, 2, 0, 0, 0, 0, 3, 3, 0, 3, 2, 0, 0, 0, 3, 0, 2, 3, 3, 0, 0, 2, 0, 0, 0, 0, 3, 0, 2, 3, 0, 0, 0, 0, 2, 3, 0, 0, 2, 0, 0, 2, 0, 3, 0, 3, 2, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 3, 2, 0, 2, 0, 0, 3, 3, 0, 2, 0, 2, 2, 0, 3, 0, 0, 3, 2, 2, 0, 2, 3, 0, 3, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 3, 2, 0, 2, 0, 0, 3, 0, 3, 2, 0, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 0, 0, 2, 0, 2, 2, 0, 3, 0, 0, 3, 3, 2, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 2, 2, 0, 0, 2, 0, 1, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 3, 0, 0, 0, 2, 3, 2, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0, 2, 3, 0, 3, 3, 3, 0, 0, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 3, 3, 0, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 2, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 3, 2, 0, 0, 0, 0, 2, 0, 3, 0, 0, 2, 3, 0, 0, 0, 0, 3, 2, 0, 2, 2, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3, 0, 3, 2, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 3, 0, 3, 0, 2, 0, 3, 2, 0, 0, 0, 2, 0, 3, 2, 0, 0, 3, 0, 0, 2, 2, 3, 0, 0, 2, 0, 0, 2, 0, 3, 0, 0, 0, 3, 2, 0, 0, 2, 0, 0, 2, 0, 3, 0, 3, 3, 0, 2, 3, 2, 3, 0, 2, 3, 3, 0, 3, 2, 0, 0, 2, 2, 0, 2, 0, 0, 2, 2, 0, 3, 3, 0, 2, 0, 2, 2, 0, 2, 0, 0, 3, 2, 3, 0, 0, 3, 3, 2, 3, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 2, 3, 0, 3, 3, 0, 2, 3, 3, 2, 0, 2, 2, 0, 0, 0, 1, 2, 2, 3, 0, 3, 0, 3, 0, 3, 0, 0, 3, 0, 3, 0, 3, 2, 2, 2, 2, 2, 0, 0, 2, 2, 0, 2, 2, 3, 0, 3, 0, 3, 3, 2, 0, 0, 0, 3, 0, 2, 2, 0, 2, 3, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 3, 2, 3, 0, 2, 0, 2, 0, 0, 3, 0, 3, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 3, 0, 2, 2, 2, 3, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 3, 0, 2, 2, 2, 3, 0, 2, 3, 0, 0, 2, 3, 0, 3, 2, 0, 0, 0, 2, 3, 0, 3, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 3, 3, 0, 2, 3, 0, 2, 2, 2, 0, 3, 2, 0, 0, 3, 3, 2, 0, 3, 2, 0, 1, 0, 3, 3, 2, 0, 3, 0, 0, 3, 3, 0, 2, 2, 3, 0, 2, 2, 2, 3, 2, 0, 0, 2, 0, 0, 0, 2, 3, 0, 2, 2, 3, 0, 2, 3, 2, 2, 0, 0, 2, 0, 0, 0, 3, 2, 2, 2, 2, 2, 3, 2, 0, 0, 3, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 3, 2, 0, 0, 0])
# starting_assignments = pickle.load(open("catData4d2.p", "rb"))['z']
# Initialize and run the CatMM
catmm = catMM(C, alpha, gamma, starting_assignments)
catmm.gibbs_sampler(n_iter, i, trueAssignments=trueAssignments)
# Track the best model based on BIC score
if catmm.BIC < least_BIC:
least_BIC = catmm.BIC
best_catmm =catmm
################################## Model results ##################################
# Get predictions from the best model
z_pred_map = best_catmm.z_map
predicted_K = len(set(z_pred_map))
# Print results of the best model
print(f"\nBest Model:\nlogmax posterior: {best_catmm.log_max_post}\nPredicted K (MAP): {predicted_K}\nmax post run: {best_catmm.run_id + 1} iteration: {best_catmm.iter_map}")
print(f"Time: {time.perf_counter() - model_start_time}")
# Store predictions
preds = {
"z": z_pred_map,
"time": time.perf_counter() - model_start_time,
"z_last_iter": best_catmm.clusters.assignments
}
################################## Save results ##################################
outDir = "outCat" if args.o == None else args.o
if outDir not in os.listdir():
os.mkdir(outDir)
outputFileName = f"{dataFilename}"
outputFilePath = f"{outDir}/{outputFileName}.txt"
# Save results to text file
with open(outputFilePath, "w") as wFile:
wFile.write(f"N: {N}\n")
wFile.write(f"K: {predicted_K}\n\n")
wFile.write(f"Seed: {global_seed}\n")
wFile.write(f"Iterations: {n_iter}\n")
wFile.write(f"alpha: {alpha}\n")
wFile.write(f"time: {time.perf_counter() - model_start_time}\n")
wFile.write(f"BIC score: {best_catmm.BIC}\n")
wFile.write(f"log max posterior: {best_catmm.log_max_post}\n")
wFile.write(f"MAP assignments: {best_catmm.z_map}\n")
wFile.write(f"Last iteration assignments: {best_catmm.clusters.assignments}\n")
wFile.write(f"gamma:{gamma}")
# Save predictions to pickle file
outputFile = open(f"{outDir}/{outputFileName}.p", "wb")
pickle.dump(preds, outputFile, pickle.HIGHEST_PROTOCOL)
# Save labels
outputFile = open(f"{outDir}/{outputFileName}.labels", "wb")
utils.saveData(outputFile.name, z_pred_map, "labels")
# Print locations of the saved results
print(f"The predicted labels are saved in: {outDir}/{outputFileName}.labels")
print(f"The encoded results are saved in: {outDir}/{outputFileName}.p")
print(f"The readable results are saved in: {outputFilePath}")
################################################################################################################
################## TRASH CODE ############################################ TRASH CODE ##########################
################################################################################################################
# from tracemalloc import start
# import numpy as np
# from cluster_stats_new import categoricalClusters
# import utils
# import time
# import argparse
# import pickle
# import os
# import time
# from sklearn.metrics.cluster import adjusted_rand_score
# import json
# class catMM():
# def __init__(self, C, alpha, gamma, seed, assignments):
# self.alpha = alpha
# K = len(set(assignments))
# self.K_max = K
# self.seed = seed
# self.clusters = categoricalClusters(C, alpha, gamma, K, assignments)
# self.z_map = assignments
# self.iter_map = 0
# self.log_max_post = -1*np.inf
# self.BIC = 0.
# self.run_id = -1
# def gibbs_sampler(self, n_iter, run_id):
# self.run_id = run_id
# same_posterior_count = 0
# ass_posterior = 0
# log_post_Z = np.zeros(self.K_max)
# for k in range(self.K_max):
# log_post_Z[k] = self.clusters.get_posterior_probability_Z_k(k)
# # params_true = json.load(open("../Z_true.json", "rb"))
# # params_true = pickle.load(open("../data_n1000_d0_k5_m2.1_c2.1_catD1_catM4_seed23.trueParamPickle", "rb"))
# params_true = pickle.load(open("catData4.p", "rb"))
# print(f"run: {run_id + 1}, iteration:0, K:{self.clusters.K}, posterior:{np.sum(log_post_Z)}, ARI: {round(adjusted_rand_score(params_true['z'], self.clusters.assignments), 3)}")
# for i_iter in range(n_iter):
# old_assignments = self.clusters.assignments.copy()
# for i in range(self.clusters.N):
# k_old = self.clusters.assignments[i]
# K_old = self.clusters.K
# stats_old = self.clusters.cache_cluster_stats(k_old)
# self.clusters.del_assignment(i)
# log_prob_z_k_alpha = np.log(self.clusters.counts + self.alpha / self.clusters.K_max ) - np.log(N + self.alpha - 1)
# log_prob_c_i = self.clusters.log_post_pred(i)
# log_prob_z_k = log_prob_z_k_alpha + log_prob_c_i
# # k = utils.sample(log_prob_z_k)
# k = utils.sample_numpy_gumbel(log_prob_z_k)
# # if an empty cluster is sampled
# if k >= self.clusters.K:
# k = self.clusters.K
# # breakpoint()
# # if the same old assignment is sampled AND deleting i-th data point didn't make the cluser empty
# if k==k_old and self.clusters.K == K_old:
# self.clusters.restore_cluster_stats(k_old, *stats_old)
# self.clusters.assignments[i] = k_old
# else:
# self.clusters.add_assignment(i,k)
# new_assignments = self.clusters.assignments
# assignments_change = old_assignments == new_assignments
# changed_clusters = []
# for i in range(N):
# if not assignments_change[i]:
# changed_clusters.append(old_assignments[i])
# changed_clusters.append(new_assignments[i])
# changed_clusters = list(set(changed_clusters))
# for j in changed_clusters:
# log_post_Z[j] = self.clusters.get_posterior_probability_Z_k(j)
# sum_log_post_Z = np.sum(log_post_Z)
# if sum_log_post_Z > self.log_max_post:
# self.log_max_post = sum_log_post_Z
# self.z_map = self.clusters.assignments.copy()
# self.iter_map = i_iter + 1
# if sum_log_post_Z != ass_posterior:
# same_posterior_count = 0
# ass_posterior = sum_log_post_Z
# else:
# same_posterior_count += 1
# # params_true = json.load(open("../Z_true.json", "rb"))
# if (i_iter + 1) % 10 == 0:
# params_true = pickle.load(open("catData4.p", "rb"))
# print(f"run: {run_id + 1}, iteration:{i_iter + 1}, K:{self.clusters.K}, posterior:{sum_log_post_Z}, ARI: {round(adjusted_rand_score(params_true['z'], self.clusters.assignments), 3)}")
# # print(f"run: {run_id + 1}, iteration:{i_iter + 1}, K:{self.clusters.K}, posterior:{sum_log_post_Z}, ARI: {adjusted_rand_score(params_true['z'], self.clusters.assignments)}, ARI max post: {round(adjusted_rand_score(params_true['z'], self.z_map), 2)}")
# if same_posterior_count > n_iter:
# break
# print(f"{i_iter}/{n_iter} ",end='\r')
# # self.BIC = self.clusters.K*(M) * np.log(N) - (2 * self.log_max_post)
# print(f"\nRun: {run_id + 1}, Seed: {self.seed}, K:{len(set(self.z_map))}, BIC: {self.BIC}, logmax post: {self.log_max_post}, max_post_iter: {self.iter_map}")
# if __name__ == "__main__":
# model_start_time = time.perf_counter()
# parser = argparse.ArgumentParser()
# parser.add_argument("-f", required=True, type=argparse.FileType('r'), help="Path to the file containing gauusian mixture data")
# parser.add_argument("-k", required=True, type=int, help="Known number of clusters and if it's unknown Maximum number of clusters (Or your guess that the number of clusters can't be more than that)")
# parser.add_argument("-o", required=False, type=str, help="Output directory")
# parser.add_argument("-i", required=False, type=int, help="Collapsed Gibbs sampling iterations")
# parser.add_argument("-r", required=False, type=int, help="Number of training runs to run with different initial assignments")
# parser.add_argument("-seed", required=False, type=int, help="set a seed value")
# args = parser.parse_args()
# global_seed = np.random.randint(1, 2**31 - 1) if args.seed == None else args.seed
# # seed = 82
# np.random.seed(global_seed) # should not be same as in learn file
# # np.random.seed(np.random.randint(1, 2**31 - 1))
# ################################## Extract data ##################################
# C = []
# dataFile = args.f
# dataFilename = os.path.splitext(os.path.basename(dataFile.name))[0]
# for line in dataFile:
# C.append(np.array([float(i) for i in line.strip().split(',')]))
# N = len(C[0])
# catD = len(C)
# Ms = np.zeros(catD, int)
# for d in catD:
# M[d] = len(set(C[d]))
# # model parameters
# K_max_BIC = args.k
# n_iter = 50 if args.i == None else args.i
# training_runs = 1 if args.r == None else args.r
# print(f"\nRunning {os.path.basename(__file__)} on {dataFilename} with global seed: {global_seed}")
# print(f"N: {N}, K: {K_max_BIC}, M: {Ms.tolist()} Iterations: {n_iter}, Global seed: {global_seed}\n")
# ################################## Set hyper-parameters ################################## (can we look at the data to set hyperparameters?)
# alpha = 1.0
# gamma = 0.2
# ################################## Model ##################################
# seed_l = np.random.randint(1, 2**31 -1, training_runs)
# # seed_l = np.arange(1, training_runs + 1)
# print(f"Total training runs: {training_runs}")
# max_post = -1*np.inf
# least_BIC = 1*np.inf
# for i in range(training_runs):
# seed = seed_l[i]
# print(f"\nRun: {i+1}, seed: {seed}")
# np.random.seed(seed)
# starting_assignments = []
# while len(set(starting_assignments)) != K_max_BIC:
# starting_assignments = np.random.randint(0, K_max_BIC, N)
# # params_true = pickle.load(open("../data_n1000_d10_k10_m2.0_c2.1_catD0_catM4_seed1616.trueParamPickle", "rb"))
# # starting_assignments = params_true['z']
# # starting_assignments = pickle.load(open("../data_n1000_d0_k5_m2.1_c2.1_catD1_catM4_seed23.trueParamPickle", "rb"))['z']
# # starting_assignments = np.array([3, 0, 2, 0, 0, 3, 2, 2, 3, 3, 0, 2, 2, 3, 0, 0, 0, 2, 2, 2, 3, 0, 3, 0, 2, 0, 0, 3, 3, 0, 2, 1, 2, 2, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 2, 2, 3, 3, 0, 3, 2, 2, 3, 0, 3, 0, 3, 0, 0, 3, 2, 0, 0, 2, 0, 0, 2, 2, 0, 3, 2, 2, 0, 0, 2, 3, 2, 0, 3, 2, 0, 0, 3, 3, 0, 3, 0, 0, 0, 1, 0, 2, 3, 0, 0, 3, 0, 0, 3, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 3, 0, 2, 3, 0, 2, 3, 2, 2, 0, 0, 0, 0, 3, 0, 3, 0, 0, 1, 3, 2, 2, 0, 3, 0, 0, 2, 0, 3, 2, 0, 2, 2, 3, 0, 2, 2, 2, 0, 0, 2, 0, 0, 0, 3, 2, 0, 2, 0, 0, 3, 0, 2, 0, 2, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 2, 3, 0, 0, 0, 3, 0, 3, 0, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 1, 0, 3, 0, 0, 0, 2, 0, 2, 0, 3, 3, 0, 0, 0, 3, 0, 0, 3, 3, 3, 0, 3, 0, 3, 2, 2, 2, 0, 3, 0, 0, 3, 0, 2, 0, 0, 3, 0, 0, 3, 3, 1, 2, 1, 0, 2, 0, 2, 3, 2, 3, 0, 3, 0, 3, 2, 0, 0, 0, 0, 0, 2, 3, 3, 3, 0, 3, 0, 0, 0, 3, 3, 3, 0, 2, 0, 0, 3, 2, 3, 2, 0, 0, 2, 0, 0, 0, 2, 3, 0, 2, 2, 3, 3, 2, 0, 0, 3, 2, 2, 2, 2, 3, 0, 2, 2, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 3, 0, 2, 0, 3, 0, 2, 2, 2, 0, 0, 3, 0, 2, 3, 3, 2, 2, 2, 0, 2, 2, 0, 0, 3, 2, 0, 0, 2, 0, 2, 3, 2, 0, 0, 0, 0, 3, 2, 3, 2, 0, 2, 0, 0, 0, 0, 3, 3, 0, 3, 2, 0, 0, 0, 3, 0, 2, 3, 3, 0, 0, 2, 0, 0, 0, 0, 3, 0, 2, 3, 0, 0, 0, 0, 2, 3, 0, 0, 2, 0, 0, 2, 0, 3, 0, 3, 2, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 3, 2, 0, 2, 0, 0, 3, 3, 0, 2, 0, 2, 2, 0, 3, 0, 0, 3, 2, 2, 0, 2, 3, 0, 3, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 3, 2, 0, 2, 0, 0, 3, 0, 3, 2, 0, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 0, 0, 2, 0, 2, 2, 0, 3, 0, 0, 3, 3, 2, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 2, 2, 0, 0, 2, 0, 1, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 3, 0, 0, 0, 2, 3, 2, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0, 2, 3, 0, 3, 3, 3, 0, 0, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 3, 3, 0, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 2, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 3, 2, 0, 0, 0, 0, 2, 0, 3, 0, 0, 2, 3, 0, 0, 0, 0, 3, 2, 0, 2, 2, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3, 0, 3, 2, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 3, 0, 3, 0, 2, 0, 3, 2, 0, 0, 0, 2, 0, 3, 2, 0, 0, 3, 0, 0, 2, 2, 3, 0, 0, 2, 0, 0, 2, 0, 3, 0, 0, 0, 3, 2, 0, 0, 2, 0, 0, 2, 0, 3, 0, 3, 3, 0, 2, 3, 2, 3, 0, 2, 3, 3, 0, 3, 2, 0, 0, 2, 2, 0, 2, 0, 0, 2, 2, 0, 3, 3, 0, 2, 0, 2, 2, 0, 2, 0, 0, 3, 2, 3, 0, 0, 3, 3, 2, 3, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 2, 3, 0, 3, 3, 0, 2, 3, 3, 2, 0, 2, 2, 0, 0, 0, 1, 2, 2, 3, 0, 3, 0, 3, 0, 3, 0, 0, 3, 0, 3, 0, 3, 2, 2, 2, 2, 2, 0, 0, 2, 2, 0, 2, 2, 3, 0, 3, 0, 3, 3, 2, 0, 0, 0, 3, 0, 2, 2, 0, 2, 3, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 3, 2, 3, 0, 2, 0, 2, 0, 0, 3, 0, 3, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 3, 0, 2, 2, 2, 3, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 3, 0, 2, 2, 2, 3, 0, 2, 3, 0, 0, 2, 3, 0, 3, 2, 0, 0, 0, 2, 3, 0, 3, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 3, 3, 0, 2, 3, 0, 2, 2, 2, 0, 3, 2, 0, 0, 3, 3, 2, 0, 3, 2, 0, 1, 0, 3, 3, 2, 0, 3, 0, 0, 3, 3, 0, 2, 2, 3, 0, 2, 2, 2, 3, 2, 0, 0, 2, 0, 0, 0, 2, 3, 0, 2, 2, 3, 0, 2, 3, 2, 2, 0, 0, 2, 0, 0, 0, 3, 2, 2, 2, 2, 2, 3, 2, 0, 0, 3, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 3, 2, 0, 0, 0])
# # starting_assignments = pickle.load(open("catData4.p", "rb"))['z']
# catmm = catMM(C, alpha, gamma, seed, starting_assignments)
# catmm.gibbs_sampler(n_iter, i)
# # if catmm.BIC < least_BIC:
# # least_BIC = catmm.BIC
# # best_catmm =catmm
# if catmm.log_max_post > max_post:
# max_post = catmm.log_max_post
# best_catmm = catmm
# ################################## Model results ##################################
# z_pred_map = best_catmm.z_map
# predicted_K = len(set(z_pred_map))
# print(f"\nBest Model:\nlogmax posterior: {best_catmm.log_max_post}\nPredicted K (MAP): {predicted_K}\nmax post run: {best_catmm.run_id + 1} iteration: {best_catmm.iter_map}")
# print(f"Time: {time.perf_counter() - model_start_time}")
# mu_pred = []
# sigma_pred = []
# preds = {
# "mu": mu_pred,
# "sigma": np.array(sigma_pred),
# "z": z_pred_map,
# "time": time.perf_counter() - model_start_time,
# "z_last_iter": best_catmm.clusters.assignments
# }
# ################################## Save results ##################################
# outDir = "outputs_result" if args.o == None else args.o
# if outDir not in os.listdir():
# os.mkdir(outDir)
# outputFileName = f"{dataFilename}"
# outputFilePath = f"{outDir}/{outputFileName}.txt"
# with open(outputFilePath, "w") as wFile:
# wFile.write(f"N: {N}\n")
# wFile.write(f"Ms: {Ms}\n")
# wFile.write(f"K: {predicted_K}\n\n")
# wFile.write(f"Seed: {catmm.seed}\n")
# wFile.write(f"Iterations: {n_iter}\n")
# wFile.write(f"alpha: {alpha}\n")
# wFile.write(f"time: {time.perf_counter() - model_start_time}\n")
# wFile.write(f"BIC score: {best_catmm.BIC}\n")
# wFile.write(f"log max posterior: {best_catmm.log_max_post}\n")
# wFile.write(f"MAP assignments: {best_catmm.z_map}\n")
# wFile.write(f"Last iteration assignments: {best_catmm.clusters.assignments}\n")
# wFile.write(f"gamma:{gamma}")
# outputFile = open(f"{outDir}/{outputFileName}.predParamPickle", "wb")
# pickle.dump(preds, outputFile, pickle.HIGHEST_PROTOCOL)
# print(f"The encoded results are saved in: {outDir}/{outputFileName}.predParamPickle\n")
# print(f"The readable results are saved in: {outputFilePath}\n")