e5 plot fix

E5_cov_real.py

@@ -20,18 +20,15 @@
     'INSECTS-incremental'
     ]
 
-fig, axx = plt.subplots(6,2, figsize=(8,20), sharex=True, sharey=True)
+fig, axx = plt.subplots(2,6, figsize=(20,7.5), sharex=True, sharey=True)
 
-axx[0,0].set_title('ALL')
-axx[0,1].set_title('STD')
+axx[0,0].set_ylabel('ALL')
+axx[1,0].set_ylabel('STD')
 
 for f_id, f in enumerate(real_streams):
 
     res = np.load('results/combined_real_%i.npy' % f_id)
-    print(res.shape) # features+label, drifts, reps, chunks
-
     X = res[indexes]
-    print(X.shape)
 
     # cov entire dataset
     X_all = np.copy(X)
@@ -41,21 +38,25 @@
 
     c = np.abs(np.cov(X_all))
 
-    ax = axx[f_id,0]
+    ax = axx[0, f_id]
     ax.set_ylabel('%s' % (f))
-    ax.imshow(c, cmap='Greys')
+    # print(np.nanmin(c), np.nanmax(c))
+    im = ax.imshow(c, cmap='Greys', vmin=0, vmax=1)
     ax.set_xticks(range(len(labels)), labels, rotation=90)
     ax.set_yticks(range(len(labels)), labels)
 
+    cax_2 = axx[0,-1].inset_axes([1.04, 0.0, 0.05, 1.0])
+    fig.colorbar(im, ax=axx[0,0], cax=cax_2)  
+
     # calculate for metachunk
     collected=[]
     window = 25
 
     for i in range(int(X.shape[-1]/window)):
 
-        print(i*window,(i+1)*window)
+        # print(i*window,(i+1)*window)
         X_w = X[:,i*window:(i+1)*window]
-        print(X_w.shape)
+        # print(X_w.shape)
 
         for a in range(len(labels)):
             m = np.mean(X_w[a])
@@ -69,12 +70,16 @@
 
     std_collected = np.std(np.array(collected), axis=0)
     # std_collected = np.mean(np.array(collected), axis=0)
-    ax = axx[f_id,1]
-    ax.imshow(std_collected, cmap='Greys')
+    ax = axx[1,f_id]
+    im = ax.imshow(std_collected, cmap='Greys',vmin=0,vmax=0.4)
     ax.set_xlim(std_collected.shape[0]-.5,-.5)
 
     ax.set_xticks(range(len(labels)), labels, rotation=90)
     ax.set_yticks(range(len(labels)), labels)
-
+
+    cax_2 = axx[1,-1].inset_axes([1.04, 0.0, 0.05, 1.0])
+    fig.colorbar(im, ax=axx[0,0], cax=cax_2)  
+
 plt.tight_layout()
 plt.savefig('figures/fig_clf/cov_real.png')
+plt.savefig('figures/fig_clf/cov_real.eps')

E5_cov_semi.py

@@ -19,9 +19,9 @@
         ]
 
 
-fig, axx = plt.subplots(6,2, figsize=(8,20), sharex=True, sharey=True)
-axx[0,0].set_title('MEAN ALL')
-axx[0,1].set_title('STD')
+fig, axx = plt.subplots(2,6, figsize=(20,7.5), sharex=True, sharey=True)
+axx[0,0].set_ylabel('MEAN ALL')
+axx[1,0].set_ylabel('STD')
 
 res = np.load('results/combined_semi.npy')
 print(res.shape) # features+label, drifts, reps, chunks
@@ -46,12 +46,17 @@
         covs.append(c)
 
     covs = np.mean(np.array(covs),axis=0)
-    ax = axx[rep,0]
-    ax.set_ylabel('%s' % (streams[rep]))
-    ax.imshow(c, cmap='Greys')
+    ax = axx[0,rep]
+    ax.set_title('%s' % (streams[rep]))
+    print(np.nanmin(c), np.nanmax(c))
+
+    im = ax.imshow(c, cmap='Greys', vmin=0, vmax=1)
     ax.set_xticks(range(len(labels)), labels, rotation=90)
     ax.set_yticks(range(len(labels)), labels)
 
+cax_2 = axx[0,-1].inset_axes([1.04, 0.0, 0.05, 1.0])
+fig.colorbar(im, ax=axx[0,0], cax=cax_2)  
+
 
 # calculate for metachunk
 
@@ -81,14 +86,17 @@
 
     collected = np.array(collected)
     collected_std = np.std(collected, axis=0)
-    ax = axx[rep,1]
-    #ax.imshow(collected_std, cmap='Greys')
-    ax.imshow(collected_std, cmap='Greys')
+    ax = axx[1,rep]
+    # print(np.nanmin(collected_std), np.nanmax(collected_std))
+    im = ax.imshow(collected_std, cmap='Greys', vmin=0, vmax=0.2)
     ax.set_xlim(collected_std.shape[0]-.5,-.5)
     ax.set_xticks(range(len(labels)), labels, rotation=90)
     ax.set_yticks(range(len(labels)), labels)
 
+cax_2 = axx[1,-1].inset_axes([1.04, 0.0, 0.05, 1.0])
+fig.colorbar(im, ax=axx[0,0], cax=cax_2)  
 
 plt.tight_layout()
 plt.savefig('figures/fig_clf/cov_semi.png')
+plt.savefig('figures/fig_clf/cov_semi.eps')
 

E5_cov_syn.py

@@ -18,9 +18,9 @@
 
 drifts = ['Sudden', 'Gradual', 'Incremental']
 
-fig, axx = plt.subplots(3,2, figsize=(8,10), sharex=True, sharey=True)
-axx[0,0].set_title('MEAN ALL')
-axx[0,1].set_title('STD')
+fig, axx = plt.subplots(2,3, figsize=(11,7), sharex=True, sharey=True)
+axx[0,0].set_ylabel('MEAN ALL')
+axx[1,0].set_ylabel('STD')
 
 
 # cov entire dataset
@@ -40,12 +40,15 @@
         covs.append(c)
 
     covs = np.mean(np.array(covs),axis=0)
-    ax = axx[drift,0]
-    ax.set_ylabel('%s' % (drifts[drift]))
-    ax.imshow(c, cmap='Greys')
+    ax = axx[0,drift]
+    ax.set_title('%s' % (drifts[drift]))
+    im = ax.imshow(c, cmap='Greys', vmin=0, vmax=1)
+    # print(np.nanmin(c), np.nanmax(c))
     ax.set_xticks(range(len(labels)), labels, rotation=90)
     ax.set_yticks(range(len(labels)), labels)
 
+cax_2 = axx[0,-1].inset_axes([1.04, 0.0, 0.05, 1.0])
+fig.colorbar(im, ax=axx[0,0], cax=cax_2)  
 
 # calculate for metachunk
 
@@ -75,12 +78,16 @@
 
     collected = np.array(collected)
     collected_std = np.std(collected, axis=0)
-    ax = axx[drift,1]
-    ax.imshow(collected_std, cmap='Greys')
+    ax = axx[1,drift]
+    im = ax.imshow(collected_std, cmap='Greys', vmin=0, vmax=0.1)
     ax.set_xlim(collected_std.shape[0]-.5,-.5)
     ax.set_xticks(range(len(labels)), labels, rotation=90)
     ax.set_yticks(range(len(labels)), labels)
+
+cax_2 = axx[1,-1].inset_axes([1.04, 0.0, 0.05, 1.0])
+fig.colorbar(im, ax=axx[0,0], cax=cax_2)  
 
 
 plt.tight_layout()
-plt.savefig('figures/cov_syn.png')
+plt.savefig('figures/fig_clf/cov_syn.png')
+plt.savefig('figures/fig_clf/cov_syn.eps')