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