From 1900587707bd4818672ab9bbc97e63c8639a7ba2 Mon Sep 17 00:00:00 2001
From: Harry Slatyer <harry.slatyer@gmail.com>
Date: Fri, 9 Dec 2016 17:05:38 +1100
Subject: [PATCH] Plot NN surface when there are 2 params

---
 mloop/visualizations.py | 28 +++++++++++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/mloop/visualizations.py b/mloop/visualizations.py
index 494a458..8219fbc 100644
--- a/mloop/visualizations.py
+++ b/mloop/visualizations.py
@@ -12,6 +12,8 @@
 import matplotlib.pyplot as plt
 import matplotlib as mpl
 
+from mpl_toolkits.mplot3d import Axes3D
+
 figure_counter = 0
 cmap = plt.get_cmap('hsv')
 run_label = 'Run number'
@@ -570,6 +572,7 @@ def create_neural_net_learner_visualizations(filename,
     visualization = NeuralNetVisualizer(filename, file_type=file_type)
     if plot_cross_sections:
         visualization.plot_cross_sections()
+        visualization.plot_surface()
 
             
 class NeuralNetVisualizer(mll.NeuralNetLearner):
@@ -659,7 +662,7 @@ def return_cross_sections(self, points=100, cross_section_center=None):
             cross_parameter_arrays = np.array(cross_parameter_arrays)
         cost_arrays = self.cost_scaler.inverse_transform(np.array(cost_arrays))
         return (cross_parameter_arrays,cost_arrays) 
-    
+
     def plot_cross_sections(self):
         '''
         Produce a figure of the cross section about best cost and parameters
@@ -686,3 +689,26 @@ def plot_cross_sections(self):
         for ind in range(self.num_params):
             artists.append(plt.Line2D((0,1),(0,0), color=self.param_colors[ind], linestyle='-'))
         plt.legend(artists,[str(x) for x in range(1,self.num_params+1)],loc=legend_loc)    
+
+    def plot_surface(self):
+        '''
+        Produce a figure of the cost surface (only works when there are 2 parameters)
+        '''
+        if self.num_params != 2:
+            return
+        global figure_counter
+        figure_counter += 1
+        fig = plt.figure(figure_counter)
+        ax = fig.add_subplot(111, projection='3d')
+
+        points = 50
+        param_set = [ np.linspace(min_p, max_p, points) for (min_p,max_p) in zip(self.min_boundary,self.max_boundary)]
+        params = [(x,y) for x in param_set[0] for y in param_set[1]]
+        costs = self.predict_costs_from_param_array(params)
+        ax.scatter([param[0] for param in params], [param[1] for param in params], costs)
+        ax.set_zlim(top=100)
+        ax.set_xlabel('x')
+        ax.set_ylabel('y')
+        ax.set_zlabel('cost')
+
+        ax.scatter(self.all_params[:,0], self.all_params[:,1], self.all_costs, c='r')