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1 # -*- coding: utf-8 -*-
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2 """
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3 Created on Mon Jun 3 19:51:00 2019
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4 @author: Narger
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5 """
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6
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7 import sys
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8 import argparse
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9 import os
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10 import numpy as np
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11 import pandas as pd
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12 from sklearn.datasets import make_blobs
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13 from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
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14 from sklearn.metrics import silhouette_samples, silhouette_score, cluster
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15 import matplotlib
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16 matplotlib.use('agg')
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17 import matplotlib.pyplot as plt
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18 import scipy.cluster.hierarchy as shc
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19 import matplotlib.cm as cm
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20 from typing import Optional, Dict, List
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543
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21 import os
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539
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22
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23 ################################# process args ###############################
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24 def process_args(args_in :List[str] = None) -> argparse.Namespace:
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25 """
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26 Processes command-line arguments.
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27
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28 Args:
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29 args (list): List of command-line arguments.
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30
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31 Returns:
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32 Namespace: An object containing parsed arguments.
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33 """
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34 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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35 description = 'process some value\'s' +
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36 ' genes to create class.')
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37
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38 parser.add_argument('-ol', '--out_log',
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39 help = "Output log")
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40
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41 parser.add_argument('-in', '--input',
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42 type = str,
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43 help = 'input dataset')
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44
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45 parser.add_argument('-cy', '--cluster_type',
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46 type = str,
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47 choices = ['kmeans', 'dbscan', 'hierarchy'],
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48 default = 'kmeans',
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49 help = 'choose clustering algorythm')
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50
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51 parser.add_argument('-sc', '--scaling',
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52 type = str,
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53 choices = ['true', 'false'],
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54 default = 'true',
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55 help = 'choose if you want to scaling the data')
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56
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57 parser.add_argument('-k1', '--k_min',
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58 type = int,
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59 default = 2,
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60 help = 'choose minimun cluster number to be generated')
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61
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62 parser.add_argument('-k2', '--k_max',
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63 type = int,
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64 default = 7,
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65 help = 'choose maximum cluster number to be generated')
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66
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67 parser.add_argument('-el', '--elbow',
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68 type = str,
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69 default = 'false',
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70 choices = ['true', 'false'],
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71 help = 'choose if you want to generate an elbow plot for kmeans')
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72
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73 parser.add_argument('-si', '--silhouette',
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74 type = str,
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75 default = 'false',
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76 choices = ['true', 'false'],
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77 help = 'choose if you want silhouette plots')
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78
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79 parser.add_argument('-td', '--tool_dir',
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80 type = str,
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542
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81 default = os.path.dirname(os.path.abspath(__file__)),
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82 help = 'your tool directory (default: auto-detected package location)')
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539
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83
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84 parser.add_argument('-ms', '--min_samples',
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85 type = int,
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86 help = 'min samples for dbscan (optional)')
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87
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88 parser.add_argument('-ep', '--eps',
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89 type = float,
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90 help = 'eps for dbscan (optional)')
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91
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92 parser.add_argument('-bc', '--best_cluster',
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93 type = str,
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94 help = 'output of best cluster tsv')
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95
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96 parser.add_argument(
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97 '-idop', '--output_path',
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98 type = str,
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99 default='clustering/',
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100 help = 'output path for maps')
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101
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102 args_in = parser.parse_args(args_in)
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103 return args_in
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104
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105 ########################### warning ###########################################
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106 def warning(s :str) -> None:
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107 """
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108 Log a warning message to an output log file and print it to the console.
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109
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110 Args:
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111 s (str): The warning message to be logged and printed.
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112
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113 Returns:
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114 None
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115 """
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116
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117 with open(args.out_log, 'a') as log:
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118 log.write(s + "\n\n")
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119 print(s)
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120
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121 ########################## read dataset ######################################
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122 def read_dataset(dataset :str) -> pd.DataFrame:
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123 """
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124 Read dataset from a CSV file and return it as a Pandas DataFrame.
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125
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126 Args:
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127 dataset (str): the path to the dataset to convert into a DataFrame
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128
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129 Returns:
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130 pandas.DataFrame: The dataset loaded as a Pandas DataFrame.
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131
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132 Raises:
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133 pandas.errors.EmptyDataError: If the dataset file is empty.
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134 sys.exit: If the dataset file has the wrong format (e.g., fewer than 2 columns)
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135 """
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136 try:
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137 dataset = pd.read_csv(dataset, sep = '\t', header = 0)
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138 except pd.errors.EmptyDataError:
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139 sys.exit('Execution aborted: wrong format of dataset\n')
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140 if len(dataset.columns) < 2:
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141 sys.exit('Execution aborted: wrong format of dataset\n')
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142 return dataset
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143
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144 ############################ rewrite_input ###################################
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145 def rewrite_input(dataset :Dict) -> Dict[str, List[Optional[float]]]:
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146 """
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147 Rewrite the dataset as a dictionary of lists instead of as a dictionary of dictionaries.
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148
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149 Args:
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150 dataset (pandas.DataFrame): The dataset to be rewritten.
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151
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152 Returns:
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153 dict: The rewritten dataset as a dictionary of lists.
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154 """
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155 #Riscrivo il dataset come dizionario di liste,
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156 #non come dizionario di dizionari
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157 #dataset.pop('Reactions', None)
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158
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159 for key, val in dataset.items():
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160 l = []
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161 for i in val:
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162 if i == 'None':
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163 l.append(None)
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164 else:
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165 l.append(float(i))
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166
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167 dataset[key] = l
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168
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169 return dataset
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170
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171 ############################## write to csv ##################################
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172 def write_to_csv (dataset :pd.DataFrame, labels :List[str], name :str) -> None:
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173 """
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174 Write dataset and predicted labels to a CSV file.
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175
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176 Args:
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177 dataset (pandas.DataFrame): The dataset to be written.
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178 labels (list): The predicted labels for each data point.
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179 name (str): The name of the output CSV file.
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180
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181 Returns:
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182 None
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183 """
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184 #labels = predict
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185 predict = [x+1 for x in labels]
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186
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187 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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188
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189 dest = name
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190 classe.to_csv(dest, sep = '\t', index = False,
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191 header = ['Patient_ID', 'Class'])
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192
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193 ########################### trova il massimo in lista ########################
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194 def max_index (lista :List[int]) -> int:
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195 """
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196 Find the index of the maximum value in a list.
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197
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198 Args:
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199 lista (list): The list in which we search for the index of the maximum value.
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200
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201 Returns:
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202 int: The index of the maximum value in the list.
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203 """
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204 best = -1
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205 best_index = 0
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206 for i in range(len(lista)):
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207 if lista[i] > best:
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208 best = lista [i]
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209 best_index = i
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210
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211 return best_index
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212
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213 ################################ kmeans #####################################
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214 def kmeans (k_min: int, k_max: int, dataset: pd.DataFrame, elbow: str, silhouette: str, best_cluster: str) -> None:
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215 """
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216 Perform k-means clustering on the given dataset, which is an algorithm used to partition a dataset into groups (clusters) based on their characteristics.
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217 The goal is to divide the data into homogeneous groups, where the elements within each group are similar to each other and different from the elements in other groups.
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218
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219 Args:
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220 k_min (int): The minimum number of clusters to consider.
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221 k_max (int): The maximum number of clusters to consider.
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222 dataset (pandas.DataFrame): The dataset to perform clustering on.
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223 elbow (str): Whether to generate an elbow plot for kmeans ('True' or 'False').
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224 silhouette (str): Whether to generate silhouette plots ('True' or 'False').
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225 best_cluster (str): The file path to save the output of the best cluster.
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226
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227 Returns:
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228 None
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229 """
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230 if not os.path.exists(args.output_path):
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231 os.makedirs(args.output_path)
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232
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233
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234 if elbow == 'true':
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235 elbow = True
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236 else:
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237 elbow = False
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238
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239 if silhouette == 'true':
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240 silhouette = True
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241 else:
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242 silhouette = False
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243
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244 range_n_clusters = [i for i in range(k_min, k_max+1)]
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245 distortions = []
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246 scores = []
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247 all_labels = []
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248
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249 clusterer = KMeans(n_clusters=1, random_state=10)
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250 distortions.append(clusterer.fit(dataset).inertia_)
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251
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252
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253 for n_clusters in range_n_clusters:
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254 clusterer = KMeans(n_clusters=n_clusters, random_state=10)
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255 cluster_labels = clusterer.fit_predict(dataset)
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256
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257 all_labels.append(cluster_labels)
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258 if n_clusters == 1:
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259 silhouette_avg = 0
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260 else:
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261 silhouette_avg = silhouette_score(dataset, cluster_labels)
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262 scores.append(silhouette_avg)
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263 distortions.append(clusterer.fit(dataset).inertia_)
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264
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265 best = max_index(scores) + k_min
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266
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267 for i in range(len(all_labels)):
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268 prefix = ''
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269 if (i + k_min == best):
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270 prefix = '_BEST'
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271
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272 write_to_csv(dataset, all_labels[i], f'{args.output_path}/kmeans_with_' + str(i + k_min) + prefix + '_clusters.tsv')
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273
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274
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275 if (prefix == '_BEST'):
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276 labels = all_labels[i]
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277 predict = [x+1 for x in labels]
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278 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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279 classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
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280
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281
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282
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283
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284 if silhouette:
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285 silhouette_draw(dataset, all_labels[i], i + k_min, f'{args.output_path}/silhouette_with_' + str(i + k_min) + prefix + '_clusters.png')
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286
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287
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288 if elbow:
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289 elbow_plot(distortions, k_min,k_max)
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290
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291
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292
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293
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294
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295 ############################## elbow_plot ####################################
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296 def elbow_plot (distortions: List[float], k_min: int, k_max: int) -> None:
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297 """
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298 Generate an elbow plot to visualize the distortion for different numbers of clusters.
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299 The elbow plot is a graphical tool used in clustering analysis to help identifying the appropriate number of clusters by looking for the point where the rate of decrease
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300 in distortion sharply decreases, indicating the optimal balance between model complexity and clustering quality.
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301
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302 Args:
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303 distortions (list): List of distortion values for different numbers of clusters.
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304 k_min (int): The minimum number of clusters considered.
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305 k_max (int): The maximum number of clusters considered.
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306
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307 Returns:
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308 None
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309 """
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310 plt.figure(0)
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311 x = list(range(k_min, k_max + 1))
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312 x.insert(0, 1)
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313 plt.plot(x, distortions, marker = 'o')
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314 plt.xlabel('Number of clusters (k)')
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315 plt.ylabel('Distortion')
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316 s = f'{args.output_path}/elbow_plot.png'
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317 fig = plt.gcf()
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318 fig.set_size_inches(18.5, 10.5, forward = True)
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319 fig.savefig(s, dpi=100)
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320
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321
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322 ############################## silhouette plot ###############################
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323 def silhouette_draw(dataset: pd.DataFrame, labels: List[str], n_clusters: int, path:str) -> None:
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324 """
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325 Generate a silhouette plot for the clustering results.
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326 The silhouette coefficient is a measure used to evaluate the quality of clusters obtained from a clustering algorithmand it quantifies how similar an object is to its own cluster compared to other clusters.
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327 The silhouette coefficient ranges from -1 to 1, where:
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328 - A value close to +1 indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. This implies that the object is in a dense, well-separated cluster.
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329 - A value close to 0 indicates that the object is close to the decision boundary between two neighboring clusters.
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330 - A value close to -1 indicates that the object may have been assigned to the wrong cluster.
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331
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332 Args:
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333 dataset (pandas.DataFrame): The dataset used for clustering.
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334 labels (list): The cluster labels assigned to each data point.
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335 n_clusters (int): The number of clusters.
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336 path (str): The path to save the silhouette plot image.
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337
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338 Returns:
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339 None
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340 """
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341 if n_clusters == 1:
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342 return None
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343
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344 silhouette_avg = silhouette_score(dataset, labels)
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345 warning("For n_clusters = " + str(n_clusters) +
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346 " The average silhouette_score is: " + str(silhouette_avg))
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347
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348 plt.close('all')
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349 # Create a subplot with 1 row and 2 columns
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350 fig, (ax1) = plt.subplots(1, 1)
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351
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352 fig.set_size_inches(18, 7)
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353
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354 # The 1st subplot is the silhouette plot
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355 # The silhouette coefficient can range from -1, 1 but in this example all
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356 # lie within [-0.1, 1]
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357 ax1.set_xlim([-1, 1])
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358 # The (n_clusters+1)*10 is for inserting blank space between silhouette
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359 # plots of individual clusters, to demarcate them clearly.
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360 ax1.set_ylim([0, len(dataset) + (n_clusters + 1) * 10])
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361
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362 # Compute the silhouette scores for each sample
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363 sample_silhouette_values = silhouette_samples(dataset, labels)
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364
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365 y_lower = 10
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366 for i in range(n_clusters):
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367 # Aggregate the silhouette scores for samples belonging to
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368 # cluster i, and sort them
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369 ith_cluster_silhouette_values = \
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370 sample_silhouette_values[labels == i]
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371
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372 ith_cluster_silhouette_values.sort()
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373
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374 size_cluster_i = ith_cluster_silhouette_values.shape[0]
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375 y_upper = y_lower + size_cluster_i
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376
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377 color = cm.nipy_spectral(float(i) / n_clusters)
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378 ax1.fill_betweenx(np.arange(y_lower, y_upper),
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379 0, ith_cluster_silhouette_values,
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380 facecolor=color, edgecolor=color, alpha=0.7)
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381
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382 # Label the silhouette plots with their cluster numbers at the middle
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383 ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
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384
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385 # Compute the new y_lower for next plot
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386 y_lower = y_upper + 10 # 10 for the 0 samples
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387
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388 ax1.set_title("The silhouette plot for the various clusters.")
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389 ax1.set_xlabel("The silhouette coefficient values")
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390 ax1.set_ylabel("Cluster label")
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391
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392 # The vertical line for average silhouette score of all the values
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393 ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
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394
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395 ax1.set_yticks([]) # Clear the yaxis labels / ticks
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396 ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
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397
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398
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399 plt.suptitle(("Silhouette analysis for clustering on sample data "
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400 "with n_clusters = " + str(n_clusters) + "\nAverage silhouette_score = " + str(silhouette_avg)), fontsize=12, fontweight='bold')
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401
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402
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403 plt.savefig(path, bbox_inches='tight')
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404
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405 ######################## dbscan ##############################################
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406 def dbscan(dataset: pd.DataFrame, eps: float, min_samples: float, best_cluster: str) -> None:
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407 """
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408 Perform DBSCAN clustering on the given dataset, which is a clustering algorithm that groups together closely packed points based on the notion of density.
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409
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410 Args:
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411 dataset (pandas.DataFrame): The dataset to be clustered.
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412 eps (float): The maximum distance between two samples for one to be considered as in the neighborhood of the other.
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413 min_samples (float): The number of samples in a neighborhood for a point to be considered as a core point.
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414 best_cluster (str): The file path to save the output of the best cluster.
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415
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416 Returns:
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417 None
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418 """
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419 if not os.path.exists(args.output_path):
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420 os.makedirs(args.output_path)
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421
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422 if eps is not None:
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423 clusterer = DBSCAN(eps = eps, min_samples = min_samples)
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424 else:
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425 clusterer = DBSCAN()
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426
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427 clustering = clusterer.fit(dataset)
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428
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429 core_samples_mask = np.zeros_like(clustering.labels_, dtype=bool)
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430 core_samples_mask[clustering.core_sample_indices_] = True
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431 labels = clustering.labels_
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432
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433 # Number of clusters in labels, ignoring noise if present.
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434 n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
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435
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436
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437 labels = labels
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438 predict = [x+1 for x in labels]
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439 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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440 classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
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441
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442
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443 ########################## hierachical #######################################
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444 def hierachical_agglomerative(dataset: pd.DataFrame, k_min: int, k_max: int, best_cluster: str, silhouette: str) -> None:
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445 """
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446 Perform hierarchical agglomerative clustering on the given dataset.
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447
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448 Args:
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449 dataset (pandas.DataFrame): The dataset to be clustered.
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450 k_min (int): The minimum number of clusters to consider.
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451 k_max (int): The maximum number of clusters to consider.
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452 best_cluster (str): The file path to save the output of the best cluster.
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453 silhouette (str): Whether to generate silhouette plots ('True' or 'False').
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454
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455 Returns:
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456 None
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457 """
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458 if not os.path.exists(args.output_path):
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459 os.makedirs(args.output_path)
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460
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461 plt.figure(figsize=(10, 7))
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462 plt.title("Customer Dendograms")
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463 shc.dendrogram(shc.linkage(dataset, method='ward'), labels=dataset.index.values.tolist())
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464 fig = plt.gcf()
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465 fig.savefig(f'{args.output_path}/dendogram.png', dpi=200)
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466
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467 range_n_clusters = [i for i in range(k_min, k_max+1)]
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468
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469 scores = []
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470 labels = []
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471
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472 n_classi = dataset.shape[0]
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473
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474 for n_clusters in range_n_clusters:
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475 cluster = AgglomerativeClustering(n_clusters=n_clusters, affinity='euclidean', linkage='ward')
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476 cluster.fit_predict(dataset)
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477 cluster_labels = cluster.labels_
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478 labels.append(cluster_labels)
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479 write_to_csv(dataset, cluster_labels, f'{args.output_path}/hierarchical_with_' + str(n_clusters) + '_clusters.tsv')
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480
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481 best = max_index(scores) + k_min
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482
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483 for i in range(len(labels)):
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484 prefix = ''
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485 if (i + k_min == best):
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486 prefix = '_BEST'
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487 if silhouette == 'true':
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488 silhouette_draw(dataset, labels[i], i + k_min, f'{args.output_path}/silhouette_with_' + str(i + k_min) + prefix + '_clusters.png')
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489
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490 for i in range(len(labels)):
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491 if (i + k_min == best):
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492 labels = labels[i]
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493 predict = [x+1 for x in labels]
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494 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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495 classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
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496
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497
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498 ############################# main ###########################################
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499 def main(args_in:List[str] = None) -> None:
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500 """
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501 Initializes everything and sets the program in motion based on the fronted input arguments.
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502
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503 Returns:
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504 None
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505 """
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506 global args
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507 args = process_args(args_in)
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508
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509 if not os.path.exists(args.output_path):
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510 os.makedirs(args.output_path)
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511
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512 #Data read
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513
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514 X = read_dataset(args.input)
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515 X = X.iloc[:, 1:]
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516 X = pd.DataFrame.to_dict(X, orient='list')
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517 X = rewrite_input(X)
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518 X = pd.DataFrame.from_dict(X, orient = 'index')
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519
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520 for i in X.columns:
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521 if any(val is None or np.isnan(val) for val in X[i]):
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522 X = X.drop(columns=[i])
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523
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524 if args.scaling == "true":
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525 list_to_remove = []
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526 toll_std=1e-8
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527 for i in X.columns:
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528 mean_i = X[i].mean()
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529 std_i = X[i].std()
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530 if std_i >toll_std:
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531 #scaling with mean 0 and std 1
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532 X[i] = (X[i]-mean_i)/std_i
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533 else:
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534 #remove feature because std = 0 during clustering
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535 list_to_remove.append(i)
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536 if len(list_to_remove)>0:
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537 X = X.drop(columns=list_to_remove)
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538
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539 if args.k_max != None:
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540 numero_classi = X.shape[0]
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541 while args.k_max >= numero_classi:
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542 err = 'Skipping k = ' + str(args.k_max) + ' since it is >= number of classes of dataset'
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543 warning(err)
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544 args.k_max = args.k_max - 1
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545
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546
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547 if args.cluster_type == 'kmeans':
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548 kmeans(args.k_min, args.k_max, X, args.elbow, args.silhouette, args.best_cluster)
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549
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550 if args.cluster_type == 'dbscan':
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551 dbscan(X, args.eps, args.min_samples, args.best_cluster)
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552
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553 if args.cluster_type == 'hierarchy':
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554 hierachical_agglomerative(X, args.k_min, args.k_max, args.best_cluster, args.silhouette)
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555
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556 ##############################################################################
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557 if __name__ == "__main__":
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558 main()
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