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cluster_calculation_random.py
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import json
import math
import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from utils import timer
from distance_calculation import get_mds
def get_dataset(datafile):
with open(datafile, "r") as json_file:
return np.array(json.load(json_file))
def get_labels_highest_score(dataset):
max_score = -math.inf
labels = None
number_of_clusters = 9
for i in range(2, 16):
cluster = KMeans(n_clusters=i, random_state=10)
cluster_labels = cluster.fit_predict(dataset)
score = silhouette_score(dataset, cluster_labels)
if score > max_score:
number_of_clusters = i
max_score = score
labels = cluster_labels
return number_of_clusters, labels
def plot_subplot(labels, unique_labels, dataset, title, index):
ax = plt.subplot(1, 3, index)
for i in unique_labels:
x = dataset[labels == i, 0]
y = dataset[labels == i, 1]
label = f"cluster {i + 1}"
if i == -1:
label = "noise"
ax.scatter(x, y, label=label)
ax.legend()
ax.set_title(title)
plt.tight_layout()
def plot_independent_figure(labels, unique_labels, dataset, title, image_name):
ax = plt.subplot(1, 1, 1)
for i in unique_labels:
x = dataset[labels == i, 0]
y = dataset[labels == i, 1]
label = f"cluster {i + 1}"
if i == -1:
label = "noise"
ax.scatter(x, y, label=label, s=4)
ax.legend()
ax.set_title(title)
plt.tight_layout()
plt.savefig(image_name, dpi=120)
plt.close()
def generate_kmeans_clusters(mx_645_mds_path,
mx_1400_mds_path,
std_2500_mds_path,
output_directory, distance="ws",
single_figure=False):
if not os.path.exists(output_directory):
os.makedirs(output_directory)
dataset_mx_645 = get_dataset(mx_645_mds_path)
dataset_mx_1400 = get_dataset(mx_1400_mds_path)
dataset_std_2500 = get_dataset(std_2500_mds_path)
n_clusters_645, labels_645 = get_labels_highest_score(dataset_mx_645)
unique_labels_645 = np.unique(labels_645)
n_clusters_1400, labels_1400 = get_labels_highest_score(dataset_mx_1400)
unique_labels_1400 = np.unique(labels_1400)
n_clusters_2500, labels_2500 = get_labels_highest_score(dataset_std_2500)
unique_labels_2500 = np.unique(labels_2500)
cluster_info = [
n_clusters_645, n_clusters_1400, n_clusters_2500
]
cluster_file = f"{output_directory}/clusters_{distance}.json"
title = f'mx645: {n_clusters_645} clusters'
image_name = f"{output_directory}/clusters_mx645_{distance}.png"
if single_figure:
plot_subplot(labels_645, unique_labels_645, dataset_mx_645, title, 1)
else:
plot_independent_figure(labels_645, unique_labels_645, dataset_mx_645,
title, image_name)
# print(f"Generated {image_name}")
title = f'mx1400: {n_clusters_1400} clusters'
image_name = f"{output_directory}/clusters_mx1400_{distance}.png"
if single_figure:
plot_subplot(labels_1400, unique_labels_1400, dataset_mx_1400,
title, 2)
else:
plot_independent_figure(labels_1400, unique_labels_1400,
dataset_mx_1400, title, image_name)
# print(f"Generated {image_name}")
title = f'std2500: {n_clusters_2500} clusters'
image_name = f"{output_directory}/clusters_std2500_{distance}.png"
if single_figure:
image_name = f"{output_directory}/clusters_{distance}.png"
plot_subplot(labels_2500, unique_labels_2500, dataset_std_2500,
title, 3)
plt.suptitle(f"Clustering result with {distance}")
plt.tight_layout()
plt.savefig(image_name, dpi=200)
plt.close()
else:
plot_independent_figure(labels_2500, unique_labels_2500,
dataset_std_2500, title, image_name)
# print(f"Generated {image_name}")
with open(cluster_file, "w") as json_file:
json.dump(cluster_info, json_file)
print(f"Generated {cluster_file}")
return cluster_info
def show_clustering_table(labels_645, labels_1400, labels_2500):
print(f"Clustering result (645ms, 1400ms, 2500ms):")
for i in range(len(labels_645)):
print(f"{i + 1} & {labels_645[i]} & "
f"{labels_1400[i]} & {labels_2500[i]} \\\\ \\hline")
print("")
def generate_random_distance_dataset(n, seed_value):
# seed for reproducibility
np.random.seed(seed_value)
data = [[0 for j in range(n)] for i in range(n)]
for i in range(n):
for j in range(i):
random_number = np.random.randint(100)
data[i][j] = random_number
data[j][i] = random_number
return data
def get_cluster_subject_group(labels):
cluster = []
max_label = max([int(label) for label in labels]) + 1
for i in range(max_label):
cluster.append([])
for i, label in enumerate(labels):
cluster[label].append(i + 1)
return cluster
def show_matching_between_clusters(output_dir, cluster_645, cluster_1400,
cluster_2500):
cluster_group = {}
for i in range(len(cluster_645)):
for j in range(len(cluster_1400)):
for k in range(len(cluster_2500)):
x = set(cluster_645[i]).intersection(set(cluster_1400[j]))
total_matches = x.intersection(set(cluster_2500[k]))
group_id = f"{i}{j}{k}"
cluster_group[group_id] = total_matches
# for i in cluster_group:
# print(f"Cluster group: {i}: match: {cluster_group[i]}")
# print("")
triplet = {}
print(f"{output_dir}:")
for i in cluster_group:
print(f"Cluster group: {i}: #match: {len(cluster_group[i])}")
triplet[i] = len(cluster_group[i])
triplet_file = f"{output_dir}/clusters_triplet.json"
with open(triplet_file, "w") as json_file:
json.dump(triplet, json_file, indent=4)
print(f"Generated {triplet_file}")
def get_subjects_cluster_id(output_dir, mx_645_mds_path,
mx_1400_mds_path,
std_2500_mds_path):
dataset_mx_645 = get_dataset(mx_645_mds_path)
dataset_mx_1400 = get_dataset(mx_1400_mds_path)
dataset_std_645 = get_dataset(std_2500_mds_path)
n_clusters_645, labels_645 = get_labels_highest_score(dataset_mx_645)
n_clusters_1400, labels_1400 = get_labels_highest_score(dataset_mx_1400)
n_clusters_2500, labels_2500 = get_labels_highest_score(dataset_std_645)
cluster_645 = get_cluster_subject_group(labels_645)
cluster_1400 = get_cluster_subject_group(labels_1400)
cluster_2500 = get_cluster_subject_group(labels_2500)
show_matching_between_clusters(output_dir, cluster_645, cluster_1400,
cluster_2500)
print("\nAdjacency matrix:")
ar = []
for temp in cluster_645:
ar.append(temp)
for temp in cluster_1400:
ar.append(temp)
for temp in cluster_2500:
ar.append(temp)
matrix = [[0 for j in range(len(ar))] for i in range(len(ar))]
for i in range(len(ar)):
for j in range(len(ar)):
matrix[i][j] = len(set(ar[i]).intersection(set(ar[j])))
print(f"{output_dir}:")
print(f"Rows X Columns: [645 clusters, 1400 clusters, 2500 clusters]")
for i in range(len(matrix)):
for j in range(len(matrix)):
print(matrix[i][j], end=" ")
print("")
print("")
adj_matrix_file = f"{output_dir}/clusters_adjancency.json"
with open(adj_matrix_file, "w") as json_file:
json.dump(matrix, json_file)
print(f"Generated {adj_matrix_file}\n")
@timer
def main():
for i in range(51, 150):
experiment_number = i + 1
random_data_dir = f"random_data_" + str(experiment_number)
output_directory = "output_" + random_data_dir
mx_645_mds_ws = f"{output_directory}/mds_mx645_ws.json"
mx_1400_mds_ws = f"{output_directory}/mds_mx1400_ws.json"
std_2500_mds_ws = f"{output_directory}/mds_std2500_ws.json"
cluster_summary = generate_kmeans_clusters(mx_645_mds_ws,
mx_1400_mds_ws,
std_2500_mds_ws,
output_directory,
distance="ws",
single_figure=False)
print(
f"{output_directory}: Number of clusters in 3 cohorts (645ms, 1400ms, 2500ms): {cluster_summary}")
get_subjects_cluster_id(output_directory, mx_645_mds_ws,
mx_1400_mds_ws, std_2500_mds_ws)
if __name__ == "__main__":
main()