Mercurial > repos > bimib > marea

--- a/marea_cluster.py	Mon Nov 25 12:02:28 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,399 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Jun 3 19:51:00 2019
-@author: Narger
-"""
-
-import sys
-import argparse
-import os
-from sklearn.datasets import make_blobs
-from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
-from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score, cluster
-import matplotlib
-matplotlib.use('agg')
-import matplotlib.pyplot as plt
-import scipy.cluster.hierarchy as shc
-import matplotlib.cm as cm
-import numpy as np
-import pandas as pd
-
-################################# process args ###############################
-
-def process_args(args):
-    parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
-                                     description = 'process some value\'s' +
-                                     ' genes to create class.')
-
-    parser.add_argument('-ol', '--out_log',
-                        help = "Output log")
-
-    parser.add_argument('-in', '--input',
-                        type = str,
-                        help = 'input dataset')
-
-    parser.add_argument('-cy', '--cluster_type',
-                        type = str,
-                        choices = ['kmeans', 'dbscan', 'hierarchy'],
-                        default = 'kmeans',
-                        help = 'choose clustering algorythm')
-
-    parser.add_argument('-k1', '--k_min',
-                        type = int,
-                        default = 2,
-                        help = 'choose minimun cluster number to be generated')
-
-    parser.add_argument('-k2', '--k_max',
-                        type = int,
-                        default = 7,
-                        help = 'choose maximum cluster number to be generated')
-
-    parser.add_argument('-el', '--elbow',
-                        type = str,
-                        default = 'false',
-                        choices = ['true', 'false'],
-                        help = 'choose if you want to generate an elbow plot for kmeans')
-
-    parser.add_argument('-si', '--silhouette',
-                        type = str,
-                        default = 'false',
-                        choices = ['true', 'false'],
-                        help = 'choose if you want silhouette plots')
-
-    parser.add_argument('-td', '--tool_dir',
-                        type = str,
-                        required = True,
-                        help = 'your tool directory')
-
-    parser.add_argument('-ms', '--min_samples',
-                        type = float,
-                        help = 'min samples for dbscan (optional)')
-
-    parser.add_argument('-ep', '--eps',
-                        type = float,
-                        help = 'eps for dbscan (optional)')
-
-    parser.add_argument('-bc', '--best_cluster',
-                        type = str,
-                        help = 'output of best cluster tsv')
-
-
-
-    args = parser.parse_args()
-    return args
-
-########################### warning ###########################################
-
-def warning(s):
-    args = process_args(sys.argv)
-    with open(args.out_log, 'a') as log:
-        log.write(s + "\n\n")
-    print(s)
-
-########################## read dataset ######################################
-
-def read_dataset(dataset):
-    try:
-        dataset = pd.read_csv(dataset, sep = '\t', header = 0)
-    except pd.errors.EmptyDataError:
-        sys.exit('Execution aborted: wrong format of dataset\n')
-    if len(dataset.columns) < 2:
-        sys.exit('Execution aborted: wrong format of dataset\n')
-    return dataset
-
-############################ rewrite_input ###################################
-
-def rewrite_input(dataset):
-    #Riscrivo il dataset come dizionario di liste,
-    #non come dizionario di dizionari
-
-    dataset.pop('Reactions', None)
-
-    for key, val in dataset.items():
-        l = []
-        for i in val:
-            if i == 'None':
-                l.append(None)
-            else:
-                l.append(float(i))
-
-        dataset[key] = l
-
-    return dataset
-
-############################## write to csv ##################################
-
-def write_to_csv (dataset, labels, name):
-    #labels = predict
-    predict = [x+1 for x in labels]
-
-    classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
-
-    dest = name
-    classe.to_csv(dest, sep = '\t', index = False,
-                      header = ['Patient_ID', 'Class'])
-
-########################### trova il massimo in lista ########################
-def max_index (lista):
-    best = -1
-    best_index = 0
-    for i in range(len(lista)):
-        if lista[i] > best:
-            best = lista [i]
-            best_index = i
-
-    return best_index
-
-################################ kmeans #####################################
-
-def kmeans (k_min, k_max, dataset, elbow, silhouette, best_cluster):
-    if not os.path.exists('clustering'):
-        os.makedirs('clustering')
-
-
-    if elbow == 'true':
-        elbow = True
-    else:
-        elbow = False
-
-    if silhouette == 'true':
-        silhouette = True
-    else:
-        silhouette = False
-
-    range_n_clusters = [i for i in range(k_min, k_max+1)]
-    distortions = []
-    scores = []
-    all_labels = []
-
-    clusterer = KMeans(n_clusters=1, random_state=10)
-    distortions.append(clusterer.fit(dataset).inertia_)
-
-
-    for n_clusters in range_n_clusters:
-        clusterer = KMeans(n_clusters=n_clusters, random_state=10)
-        cluster_labels = clusterer.fit_predict(dataset)
-
-        all_labels.append(cluster_labels)
-        if n_clusters == 1:
-        	silhouette_avg = 0
-        else:
-            silhouette_avg = silhouette_score(dataset, cluster_labels)
-        scores.append(silhouette_avg)
-        distortions.append(clusterer.fit(dataset).inertia_)
-
-    best = max_index(scores) + k_min
-
-    for i in range(len(all_labels)):
-        prefix = ''
-        if (i + k_min == best):
-            prefix = '_BEST'
-
-        write_to_csv(dataset, all_labels[i], 'clustering/kmeans_with_' + str(i + k_min) + prefix + '_clusters.tsv')
-
-
-        if (prefix == '_BEST'):
-            labels = all_labels[i]
-            predict = [x+1 for x in labels]
-            classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
-            classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
-
-
-
-
-        if silhouette:
-            silihouette_draw(dataset, all_labels[i], i + k_min, 'clustering/silhouette_with_' + str(i + k_min) + prefix + '_clusters.png')
-
-
-    if elbow:
-        elbow_plot(distortions, k_min,k_max)
-
-
-
-
-
-############################## elbow_plot ####################################
-
-def elbow_plot (distortions, k_min, k_max):
-    plt.figure(0)
-    x = list(range(k_min, k_max + 1))
-    x.insert(0, 1)
-    plt.plot(x, distortions, marker = 'o')
-    plt.xlabel('Number of clusters (k)')
-    plt.ylabel('Distortion')
-    s = 'clustering/elbow_plot.png'
-    fig = plt.gcf()
-    fig.set_size_inches(18.5, 10.5, forward = True)
-    fig.savefig(s, dpi=100)
-
-
-############################## silhouette plot ###############################
-def silihouette_draw(dataset, labels, n_clusters, path):
-    if n_clusters == 1:
-        return None
-
-    silhouette_avg = silhouette_score(dataset, labels)
-    warning("For n_clusters = " + str(n_clusters) +
-          " The average silhouette_score is: " + str(silhouette_avg))
-
-    plt.close('all')
-    # Create a subplot with 1 row and 2 columns
-    fig, (ax1) = plt.subplots(1, 1)
-
-    fig.set_size_inches(18, 7)
-
-    # The 1st subplot is the silhouette plot
-    # The silhouette coefficient can range from -1, 1 but in this example all
-    # lie within [-0.1, 1]
-    ax1.set_xlim([-1, 1])
-    # The (n_clusters+1)*10 is for inserting blank space between silhouette
-    # plots of individual clusters, to demarcate them clearly.
-    ax1.set_ylim([0, len(dataset) + (n_clusters + 1) * 10])
-
-    # Compute the silhouette scores for each sample
-    sample_silhouette_values = silhouette_samples(dataset, labels)
-
-    y_lower = 10
-    for i in range(n_clusters):
-        # Aggregate the silhouette scores for samples belonging to
-        # cluster i, and sort them
-        ith_cluster_silhouette_values = \
-        sample_silhouette_values[labels == i]
-
-        ith_cluster_silhouette_values.sort()
-
-        size_cluster_i = ith_cluster_silhouette_values.shape[0]
-        y_upper = y_lower + size_cluster_i
-
-        color = cm.nipy_spectral(float(i) / n_clusters)
-        ax1.fill_betweenx(np.arange(y_lower, y_upper),
-                          0, ith_cluster_silhouette_values,
-                                     facecolor=color, edgecolor=color, alpha=0.7)
-
-        # Label the silhouette plots with their cluster numbers at the middle
-        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
-
-        # Compute the new y_lower for next plot
-        y_lower = y_upper + 10  # 10 for the 0 samples
-
-        ax1.set_title("The silhouette plot for the various clusters.")
-        ax1.set_xlabel("The silhouette coefficient values")
-        ax1.set_ylabel("Cluster label")
-
-        # The vertical line for average silhouette score of all the values
-        ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
-
-        ax1.set_yticks([])  # Clear the yaxis labels / ticks
-        ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
-
-
-        plt.suptitle(("Silhouette analysis for clustering on sample data "
-                          "with n_clusters = " + str(n_clusters) + "\nAverage silhouette_score = " + str(silhouette_avg)), fontsize=12, fontweight='bold')
-
-
-        plt.savefig(path, bbox_inches='tight')
-
-######################## dbscan ##############################################
-
-def dbscan(dataset, eps, min_samples, best_cluster):
-    if not os.path.exists('clustering'):
-        os.makedirs('clustering')
-
-    if eps is not None:
-    	clusterer = DBSCAN(eps = eps, min_samples = min_samples)
-    else:
-    	clusterer = DBSCAN()
-
-    clustering = clusterer.fit(dataset)
-
-    core_samples_mask = np.zeros_like(clustering.labels_, dtype=bool)
-    core_samples_mask[clustering.core_sample_indices_] = True
-    labels = clustering.labels_
-
-    # Number of clusters in labels, ignoring noise if present.
-    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
-
-
-    labels = labels
-    predict = [x+1 for x in labels]
-    classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
-    classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
-
-
-########################## hierachical #######################################
-
-def hierachical_agglomerative(dataset, k_min, k_max, best_cluster, silhouette):
-
-    if not os.path.exists('clustering'):
-        os.makedirs('clustering')
-
-    plt.figure(figsize=(10, 7))
-    plt.title("Classes Dendogram")
-    shc.dendrogram(shc.linkage(dataset, method='ward'), labels=dataset.index.values.tolist())
-    fig = plt.gcf()
-    fig.savefig('clustering/dendogram.png', dpi=200)
-
-    range_n_clusters = [i for i in range(k_min, k_max+1)]
-
-    scores = []
-    labels = []
-
-    for n_clusters in range_n_clusters:
-        cluster = AgglomerativeClustering(n_clusters=n_clusters, affinity='euclidean', linkage='ward')
-        cluster.fit_predict(dataset)
-        cluster_labels = cluster.labels_
-        labels.append(cluster_labels)
-        write_to_csv(dataset, cluster_labels, 'clustering/hierarchical_with_' + str(n_clusters) + '_clusters.tsv')
-
-    best = max_index(scores) + k_min
-
-    for i in range(len(labels)):
-        prefix = ''
-        if (i + k_min == best):
-            prefix = '_BEST'
-        if silhouette == 'true':
-            silihouette_draw(dataset, labels[i], i + k_min, 'clustering/silhouette_with_' + str(i + k_min) + prefix + '_clusters.png')
-
-    for i in range(len(labels)):
-        if (i + k_min == best):
-            labels = labels[i]
-            predict = [x+1 for x in labels]
-            classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
-            classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
-
-
-############################# main ###########################################
-
-
-def main():
-    if not os.path.exists('clustering'):
-        os.makedirs('clustering')
-
-    args = process_args(sys.argv)
-
-    #Data read
-
-    X = read_dataset(args.input)
-    X = pd.DataFrame.to_dict(X, orient='list')
-    X = rewrite_input(X)
-    X = pd.DataFrame.from_dict(X, orient = 'index')
-
-    for i in X.columns:
-        tmp = X[i][0]
-        if tmp == None:
-            X = X.drop(columns=[i])
-
-
-    if args.cluster_type == 'kmeans':
-        kmeans(args.k_min, args.k_max, X, args.elbow, args.silhouette, args.best_cluster)
-
-    if args.cluster_type == 'dbscan':
-        dbscan(X, args.eps, args.min_samples, args.best_cluster)
-
-    if args.cluster_type == 'hierarchy':
-        hierachical_agglomerative(X, args.k_min, args.k_max, args.best_cluster, args.silhouette)
-
-##############################################################################
-
-if __name__ == "__main__":
-    main()