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91 lines
3 KiB
Python
Executable file
91 lines
3 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Oct 03 10:49:22 2018
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@author: doru
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"""
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import sys
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args = sys.argv
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output_folder = args[1]
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n_clusters = int(args[2])
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import os
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os.chdir(output_folder)
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.neighbors import kneighbors_graph
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from sklearn import mixture
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from sklearn import metrics
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import pandas as pd
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import numpy as np
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print("Loading data ...")
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pca_df = pd.read_csv("material/pca.csv", sep = ",", index_col = 0)
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pca = pca_df.values[:, :20]
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ori_labels = pca_df.values[:, 20]
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print("computing agglomerative clustering ... ")
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connectivity_graph = kneighbors_graph(X = pca, n_neighbors = 30)
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clustering = AgglomerativeClustering(n_clusters = n_clusters, affinity = "euclidean",
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connectivity = connectivity_graph,
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linkage = "ward")
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agg_labels = clustering.fit(pca)
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agg_labels = agg_labels.labels_
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print("computing gaussian mixture clustering ... ")
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gmm = mixture.GaussianMixture(n_components = n_clusters, covariance_type = "full",
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random_state = 52).fit(pca)
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gmm_labels = gmm.predict(pca)
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# metrics
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# voted labels
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def voted_labels(true, pred):
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pred_unique = np.unique(pred)
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pred_voted = true.copy()
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for p in pred_unique:
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p_indx = pred == p
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ori_types = true[p_indx]
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ori_unique = np.unique(ori_types, return_counts = True)
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voted = ori_unique[0][np.argmax(ori_unique[1])]
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pred_voted[p_indx] = voted
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return pred_voted
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agg_labels = voted_labels(ori_labels, agg_labels)
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gmm_labels = voted_labels(ori_labels, gmm_labels)
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# compute adjusted Rand Index
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# reference: Comparing partitions - https://link.springer.com/article/10.1007%2FBF01908075
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RandIndex_Louvain_Agg = metrics.adjusted_rand_score(ori_labels, agg_labels)
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RandIndex_Louvain_GMM = metrics.adjusted_rand_score(ori_labels, gmm_labels)
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# ref for mutual information metrics - "Information theoretic measures for clusterings comparison"
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# compute adjusted mutual information
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AdjMutInf_Louvain_Agg = metrics.adjusted_mutual_info_score(ori_labels, agg_labels)
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AdjMutInf_Louvain_GMM = metrics.adjusted_mutual_info_score(ori_labels, gmm_labels)
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RandIndex_Louvain_Agg = str(RandIndex_Louvain_Agg)
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RandIndex_Louvain_GMM = str(RandIndex_Louvain_GMM)
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AdjMutInf_Louvain_Agg = str(AdjMutInf_Louvain_Agg)
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AdjMutInf_Louvain_GMM = str(AdjMutInf_Louvain_GMM)
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# save measures to disk
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result = '\n'.join([RandIndex_Louvain_Agg, RandIndex_Louvain_GMM,
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AdjMutInf_Louvain_Agg, AdjMutInf_Louvain_GMM])
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with open('material/agreement_measures.txt', 'w') as agg_file:
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agg_file.write(result)
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print("saving gaussian mixture clustering labels to disk ...")
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df = pd.DataFrame(gmm_labels)
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df.index = pca_df.index
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df.to_csv("./material/gaussian_mixture.csv")
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print("saving agglomerative clustering labels to disk ...")
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df = pd.DataFrame(agg_labels)
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df.index = pca_df.index
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df.to_csv("./material/agglomerative_clustering.csv")
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