[ps1] Add code for Problem 5

2 files changed, 246 insertions, 0 deletions

diff --git a/ps1/code/main.py b/ps1/code/main.py
new file mode 100644
index 0000000..5ddb979
--- /dev/null
+++ b/ps1/code/main.py
@@ -0,0 +1,219 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import networkx as nx
+import random
+
+DATASETS = ["enron", "gpg", "livejournal", "epinions"]
+#DATASETS = ["enron", "gpg", "epinions"]
+
+
+def build_graph(dataset):
+    d = {}
+    with open(dataset + ".txt") as f:
+        for line in f:
+            u, v = map(int, line.strip().split())
+            if u in d:
+                d[u].append(v)
+            else:
+                d[u] = [v]
+            if dataset == "gpg":
+                if v in d:
+                    d[v].append(u)
+                else:
+                    d[v] = [u]
+
+    return d
+
+
+def degree_distributions(dataset):
+    graph = build_graph(dataset)
+    with open(dataset + "_distr.txt", "w") as f:
+        for u in graph:
+            f.write(str(len(graph[u])) + "\n")
+
+
+def plot_degrees(log=False):
+    plt.figure(figsize=(8, 6))
+    for i, dataset in enumerate(DATASETS):
+        values = [int(line.strip()) for line in open(dataset + "_distr.txt")]
+        d = {}
+        for j, value in enumerate(values):
+            if value in d:
+                d[value] += 1
+            else:
+                d[value] = 1
+        ax = plt.subplot(2, 2, i + 1)
+        if log:
+            ax.set_yscale("log")
+            ax.set_xscale("log")
+        a, b = zip(*d.iteritems())
+        plt.plot(a, np.array(b) / float(j), ".")
+        plt.xlabel("Degree")
+        plt.ylabel("Fraction of nodes")
+        plt.title(dataset)
+    plt.tight_layout()
+    plt.savefig("degr.pdf")
+
+
+def centrality(dataset):
+    random.seed()
+    graph = build_graph(dataset)
+    G = nx.from_dict_of_lists(graph)
+    pop = random.sample(graph.keys(), 1000)
+    l = []
+    for i, u in enumerate(pop):
+        print i
+        lengths = nx.single_source_shortest_path_length(G, u)
+        lengths = np.array([v for v in lengths.values() if v != 0])
+        s = np.sum(1. / lengths)
+        l.append(s)
+    with open(dataset + "_centrality.txt", "w") as f:
+        for c in l:
+            f.write(str(c) + "\n")
+
+
+def plot_centrality(log=False):
+    plt.figure(figsize=(8, 6))
+    for i, dataset in enumerate(DATASETS):
+        values = [float(line.strip())
+                  for line in open(dataset + "_centrality.txt")]
+        plt.subplot(2, 2, i + 1)
+        n = len(values)
+        h, b = np.histogram(values, bins=30)
+        width = 0.7 * (b[1] - b[0])
+        plt.bar(b[:-1], h / float(n), width=width)
+        plt.xlabel("Centrality")
+        plt.ylabel("Fraction of nodes")
+        plt.title(dataset)
+    plt.tight_layout()
+    plt.savefig("centrality.pdf")
+
+
+def pagerank(G, alpha=0.85, max_iter=100):
+    if not G.is_directed():
+        D = G.to_directed()
+    else:
+        D = G
+    W = nx.stochastic_graph(D)
+    N = W.number_of_nodes()
+    x = dict.fromkeys(W, 1.0 / N)
+    p = dict.fromkeys(W, 1.0 / N)
+
+    dangling_weights = p
+    dangling_nodes = [n for n in W if W.out_degree(n) == 0.0]
+
+    for i in range(max_iter):
+        print i
+        xlast = x
+        x = dict.fromkeys(xlast.keys(), 0)
+        danglesum = alpha * sum(xlast[n] for n in dangling_nodes)
+        for n in x:
+            for nbr in W[n]:
+                x[nbr] += alpha * xlast[n] * W[n][nbr]["weight"]
+            x[n] += danglesum * dangling_weights[n] + (1.0 - alpha) * p[n]
+    return x
+
+
+def voter(G, max_iter=100):
+    if not G.is_directed():
+        D = G.to_directed()
+    else:
+        D = G
+    W = nx.stochastic_graph(D)
+    x = dict.fromkeys(W, 1.0)
+
+    for i in range(max_iter):
+        print i
+        xlast = x
+        x = dict.fromkeys(xlast.keys(), 0)
+        for n in x:
+            for nbr in W[n]:
+                x[nbr] += xlast[n] * W[n][nbr]["weight"]
+    return x
+
+
+def page_rank(dataset, n=100):
+    graph = build_graph(dataset)
+    G = nx.from_dict_of_lists(graph)
+    x = pagerank(G, max_iter=n)
+    with open(dataset + "_pr.txt", "w") as f:
+        for v in x.itervalues():
+            f.write(str(v) + "\n")
+
+
+def voter_model(dataset, n=100):
+    graph = build_graph(dataset)
+    G = nx.from_dict_of_lists(graph)
+    x = voter(G, max_iter=n)
+    with open(dataset + "_voter.txt", "w") as f:
+        for v in x.itervalues():
+            f.write(str(v) + "\n")
+
+
+def plot_pr():
+    plt.figure(figsize=(8, 6))
+    for i, dataset in enumerate(DATASETS):
+        values = [float(line.strip()) for line in open(dataset + "_pr.txt")]
+        low, high = min(values), max(values)
+        bins = 500
+        thresholds = [low + l * (high - low) / bins for l in xrange(bins)]
+        d = {}
+        for j, value in enumerate(values):
+            for k, t in enumerate(thresholds):
+                if value > t:
+                    u = thresholds[k - 1]
+                    if u in d:
+                        d[u] += 1
+                    else:
+                        d[u] = 1
+
+        ax = plt.subplot(2, 2, i + 1)
+        ax.set_yscale("log")
+        ax.set_xscale("log")
+        a, b = zip(*d.iteritems())
+        plt.plot(a, np.array(b) / float(j), ".")
+        plt.xlabel("PageRank")
+        plt.ylabel("Fraction of nodes")
+        plt.title(dataset)
+    plt.tight_layout()
+    plt.savefig("pr.pdf")
+
+
+def plot_voter():
+    plt.figure(figsize=(8, 6))
+    for i, dataset in enumerate(DATASETS):
+        values = [float(line.strip()) for line in open(dataset + "_voter.txt")]
+        low, high = min(values), max(values)
+        bins = 500
+        thresholds = [low + l * (high - low) / float(bins)
+                      for l in xrange(bins)]
+        d = {}
+        for j, value in enumerate(values):
+            for k, t in enumerate(thresholds):
+                if value > t:
+                    u = thresholds[k - 1]
+                    if u in d:
+                        d[u] += 1
+                    else:
+                        d[u] = 1
+
+        ax = plt.subplot(2, 2, i + 1)
+        ax.set_yscale("log")
+        ax.set_xscale("log")
+        a, b = zip(*d.iteritems())
+        plt.plot(a, np.array(b) / float(j), ".")
+        plt.xlabel("Voter Influence")
+        plt.ylabel("Fraction of nodes")
+        plt.title(dataset)
+    plt.tight_layout()
+    plt.savefig("voter.pdf")
+
+if __name__ == "__main__":
+    #degree_distributions("gpg")
+    #plot_degrees(log=False)
+    #for dataset in DATASETS:
+    #    voter_model(dataset)
+    #plot_centrality()
+    #plot_pr()
+    #page_rank("epinions")
+    plot_voter()
diff --git a/ps1/code/matplotlibrc b/ps1/code/matplotlibrc
new file mode 100644
index 0000000..3bda572
--- /dev/null
+++ b/ps1/code/matplotlibrc
@@ -0,0 +1,27 @@
+font.size : 8
+font.family : sans-serif
+font.sans-serif : Helvetica
+font.serif : Palatino
+text.usetex : True
+mathtext.fontset : custom
+
+
+lines.markersize : 2
+lines.linewidth : 0.75
+lines.linestyle : -
+lines.marker : .
+patch.linewidth : 0.5
+
+axes.linewidth : 0.5
+#axes.color_cycle: 66C2A5, FC8D62, 8DA0CB, E78AC3, A6D854, FFD92F, E5C494, B3B3B3
+
+legend.fontsize : medium
+legend.numpoints : 1
+legend.markerscale : 0.8
+legend.frameon : True
+legend.borderaxespad: 0.6
+
+savefig.dpi : 100
+savefig.bbox : tight
+savefig.pad_inches : 0.05
+figure.figsize: 3.9, 3.5