From f94bf128133f8dc3adaa81282ed7ce54b55dd528 Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Fri, 6 Feb 2015 10:48:51 -0500
Subject: changing figures

---
 src/make_plots.py | 40 +++++++++++++++++++++++++++++++++++-----
 1 file changed, 35 insertions(+), 5 deletions(-)

(limited to 'src')

diff --git a/src/make_plots.py b/src/make_plots.py
index 840cc94..c5a50f6 100644
--- a/src/make_plots.py
+++ b/src/make_plots.py
@@ -28,7 +28,7 @@ def compare_greedy_and_lagrange_cs284r():
 
 
 def compute_graph(graph_name, n_cascades, lbda, passed_function, min_proba,
-                max_proba, sparse_edges=False):
+                max_proba, sparse_edges=False, p_init=.05):
     """
     Test running time on different algorithms
     """
@@ -36,7 +36,7 @@ def compute_graph(graph_name, n_cascades, lbda, passed_function, min_proba,
                                         min_proba=min_proba,
                                         sparse_edges=sparse_edges)
     G.import_from_file(graph_name)
-    A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=n_cascades)
+    A = cascade_creation.generate_cascades(G, p_init=p_init, n_cascades=n_cascades)
 
     if passed_function==algorithms.greedy_prediction:
         G_hat = algorithms.greedy_prediction(G, A)
@@ -184,6 +184,7 @@ def plot_ROC_curve(figure_name):
     plt.xlabel("Recall")
     plt.ylabel("Precision")
     plt.grid(color="lightgrey")
+
     ax.plot(recall_lasso_200, precision_lasso_200, 'ko-', 
             color="lightseagreen", label="Lasso-200 cascades")
     ax.plot(recall_sparse_200, precision_sparse_200, 'ko-', 
@@ -196,14 +197,43 @@ def plot_ROC_curve(figure_name):
     plt.savefig("../paper/figures/"+"ROC_curve.pdf")
 
 
+def plot_kronecker_l2norm_nonsparse():
+    plt.clf()
+    fig = plt.figure(1)
+    x = [.01, .05, .1, .15, .2]
+    greedy = [.43, .29, .18, .1, .08]
+    sparse_recov = [.7, .58, .48, .39, .31]
+    max_likel = [.69, .56, .45, .37, .3]
+    lasso = [.66, .55, .46, .38, .3]
+
+    fig, ax = plt.subplots()
+
+    #plt.subplots_adjust(bottom=.2, top=.85)
+    #plt.xticks(ha="right", rotation=45)
+
+    #plt.axis((50, 2000, 0, 145))
+    plt.xlabel("Number of Cascades")
+    plt.ylabel("l2-norm")
+    plt.grid(color="lightgrey")
+    ax.plot(x, greedy, 'ko-', color="lightseagreen", label='Greedy')
+    ax.plot(x, lasso, 'ko-', color="orange", label="Lasso")
+    ax.plot(x, max_likel, 'ko-', color="cornflowerblue", label="MLE")
+    ax.plot(x, sparse_recov, 'ko-', color="k", label="Our Method")
+    plt.legend(loc="upper right")
+    ax.set_xticks(x)
+    ax.set_xticklabels(tuple(x))
+    plt.savefig("../paper/figures/"+"watts_strogatz_p_init.pdf")
+
+
+
 if __name__=="__main__":
     if 1:
         compute_graph("../datasets/watts_strogatz_300_30_point3.txt",
-                    n_cascades=300, lbda=.01, min_proba=.2, max_proba=.7,
+                    n_cascades=300, lbda=.013382, min_proba=.2, max_proba=.7,
                     passed_function=
                     #convex_optimization.sparse_recovery)
-                    #algorithms.greedy_prediction)
-                    convex_optimization.sparse_recovery, p_init=.15)
+                    algorithms.greedy_prediction, p_init=.2)
+                    #convex_optimization.sparse_recovery, p_init=.15)
     if 0:
         compute_graph("../datasets/powerlaw_200_30_point3.txt",
             n_cascades=200, lbda=.01, min_proba=.2, max_proba=.7,
-- 
cgit v1.2.3-70-g09d2