Dissertation: final update

2018-09-10 11:27:11 +02:00 · 2018-09-10 11:27:11 +02:00 · aedd94d4af
commit aedd94d4af
parent baec57c51d
30 changed files with 17967 additions and 243 deletions
--- a/dissertation/clustering.py
+++ b/dissertation/clustering.py
@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+
+import numpy as np
+
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.svm import OneClassSVM
+
+import dill
+
+import matplotlib
+matplotlib.use("PDF")
+import matplotlib.pyplot as plt
+plt.style.use("fivethirtyeight")
+plt.rcParams["figure.figsize"] = (10, 6)
+
+
+N_CLUSTERS = 10
+
+GENERATIVE = True
+SOCIOPATTERNS = True
+
+if __name__=="__main__":
+    if GENERATIVE:
+        print("==== Generative model ====")
+        zz_dgms = dill.load(open("generative/zz_dgms.dill", "rb"))
+        wrcf_dgms = dill.load(open("generative/wrcf_dgms.dill", "rb"))
+        zz_gram1 = dill.load(open("generative/zz_gram1.dill", "rb"))
+        wrcf_gram1 = dill.load(open("generative/wrcf_gram1.dill", "rb"))
+        zz_distmat = dill.load(open("generative/zz_distmat.dill", "rb"))
+        wrcf_distmat = dill.load(open("generative/wrcf_distmat.dill", "rb"))
+
+        print("Zigzag + kernel")
+        clf = AgglomerativeClustering(
+            n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(zz_gram1)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/gen_zz_k.pdf", transparent=True,
+                    pad_inches=0.3, bbox_inches="tight")
+
+        print("WRCF + kernel")
+        clf = AgglomerativeClustering(
+            n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(wrcf_gram1)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/gen_wrcf_k.pdf", transparent=True,
+                    pad_inches=0.3, bbox_inches="tight")
+        
+        print("Zigzag + bottleneck")
+        clf = AgglomerativeClustering(
+            n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(zz_distmat)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/gen_zz_b.pdf", transparent=True,
+                    pad_inches=0.3, bbox_inches="tight")
+
+        print("WRCF + bottleneck")
+        clf = AgglomerativeClustering(
+            n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(wrcf_distmat)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/gen_wrcf_b.pdf", transparent=True,
+                    pad_inches=0.3, bbox_inches="tight")
+
+    if SOCIOPATTERNS:
+        print("==== SocioPatterns dataset ====")
+        zz_dgms = dill.load(open("sociopatterns/zz_dgms.dill", "rb"))
+        wrcf_dgms = dill.load(open("sociopatterns/wrcf_dgms.dill", "rb"))
+        zz_gram1 = dill.load(open("sociopatterns/zz_gram1.dill", "rb"))
+        wrcf_gram1 = dill.load(open("sociopatterns/wrcf_gram1.dill", "rb"))
+        zz_distmat = dill.load(open("sociopatterns/zz_distmat.dill", "rb"))
+        wrcf_distmat = dill.load(open("sociopatterns/wrcf_distmat.dill", "rb"))
+        
+        print("Zigzag + kernel")
+        clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(zz_gram1)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/sp_zz_k.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight")
+        
+        print("WRCF + kernel")
+        clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(wrcf_gram1)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/sp_wrcf_k.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight")
+        
+        print("Zigzag + bottleneck")
+        clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(zz_distmat)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/sp_zz_b.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight")
+        
+        print("WRCF + bottleneck")
+        clf = AgglomerativeClustering(n_clusters=N_CLUSTERS, affinity='precomputed', linkage='average')
+        clf.fit(wrcf_distmat)
+        fig, ax = plt.subplots()
+        ax.step(range(len(clf.labels_)), clf.labels_, where='post')
+        ax.set_xlabel("Subnetwork")
+        ax.set_ylabel("Cluster")
+        fig.savefig("fig/sp_wrcf_b.pdf", transparent=True, pad_inches=0.3, bbox_inches="tight")