Included decision boundary in ROC and P-R curves

Modules.py

@@ -720,6 +720,8 @@ def Assess_Model(Data,Target,N2,K2,modelTest,n_classes,Plot,Save,SaveName):
 
     ## ROC, Precision-Recall curves, confusion matrix
     curve_i = np.linspace(0,1,100)
+    #curve_i = np.linspace(0,1,101)
+    
     Curves = np.zeros((N2,K2,2,100))
     CM = np.zeros((N2,K2,n_classes**2,2))
 
@@ -784,7 +786,7 @@ def Assess_Model(Data,Target,N2,K2,modelTest,n_classes,Plot,Save,SaveName):
     # Plot mean confusion matrix and curves
     if Plot == True:
         pl.CM(CM_means[:,0],C,n_classes,Save,SaveName)
-        pl.Curves(N2,K2,Curves,curve_i,Model_Mean[0:2,0],Model_Std[0:2,0],C,Save,SaveName)
+        pl.Curves(N2,K2,Curves,curve_i,Model_Mean[2:7,0],Model_Mean[0:2,0],Model_Std[0:2,0],C,Save,SaveName)
 
     return Model_Mean, Model_Std
 ###

Plots.py

@@ -86,8 +86,8 @@ def IndexDist(Time,YearsIndex,Storms,Kp_all,KpHours_all,HMC,Save):
     HMC_step = 7.78
     HMC_range = np.arange(10,480+HMC_step,HMC_step)
     
-    Index_range = HMC_range #Kp_range
-    step = HMC_step #Kp_step
+    Index_range = Kp_range #HMC_range
+    step = Kp_step #HMC_step
     
     years = np.arange(1932,2016,1)
     Percentages = np.zeros((len(years),len(Index_range)))
@@ -123,11 +123,11 @@ def IndexDist(Time,YearsIndex,Storms,Kp_all,KpHours_all,HMC,Save):
 
         HMCValues = -HMC[Storms[StormsBlock]]  
 
-        #KpValuesAll.extend(KpValues.tolist())
-        #Hist[l,:], bin_edges = np.histogram(KpValues, bins = Index_range)
+        KpValuesAll.extend(KpValues.tolist())
+        Hist[l,:], bin_edges = np.histogram(KpValues, bins = Index_range)
 
-        KpValuesAll.extend(HMCValues.tolist())
-        Hist[l,:], bin_edges = np.histogram(HMCValues, bins = Index_range)
+        #KpValuesAll.extend(HMCValues.tolist())
+        #Hist[l,:], bin_edges = np.histogram(HMCValues, bins = Index_range)
 
         Hist[l,:] *= 100/len(Storms1932)
         
@@ -173,13 +173,13 @@ def IndexDist(Time,YearsIndex,Storms,Kp_all,KpHours_all,HMC,Save):
     ax2.legend(loc=4,ncol=1,frameon=False,fontsize=10)
     ax2.set_ylabel('Occurrence [%]')
 
-    #ax2.set_xticklabels(np.arange(0,10,1))
-    #ax2.set_xlabel('Kp')
-    #ax2.set_ylim([0,10])
+    ax2.set_xticklabels(np.arange(0,10,1))
+    ax2.set_xlabel('Kp')
+    ax2.set_ylim([0,10])
 
-    ax2.set_xticklabels(np.around(Index_range[::3]/100,1))
-    ax2.set_xlabel('-HMC/100 [nT]')
-    ax2.set_ylim([0,20])
+    #ax2.set_xticklabels(np.around(Index_range[::3]/100,1))
+    #ax2.set_xlabel('-HMC/100 [nT]')
+    #ax2.set_ylim([0,20])
 
     #ax3.fill_between(np.arange(0,len(Kp_range)-1,1)+0.5,CumSum,CumSum2,color='maroon')
     #ax3.set_ylim([0,105])
@@ -389,7 +389,7 @@ def CM(CM,Condition,n_classes,Save,SaveName):
     plt.show()
 ###
 ###
-def Curves(N2,K2,Curves,curve_i,Model_Mean,Model_Std,C,Save,SaveName):
+def Curves(N2,K2,Curves,curve_i,Scorers,Model_Mean,Model_Std,C,Save,SaveName):
 
     fig, axs = plt.subplots(1,2,facecolor='w',edgecolor='k',sharey=True)
     fig.set_size_inches([14,6], forward=True)
@@ -426,6 +426,9 @@ def Curves(N2,K2,Curves,curve_i,Model_Mean,Model_Std,C,Save,SaveName):
     axs[0].plot(curve_i,ROC_outer,color='maroon',label='Total mean',zorder=2)
     axs[1].plot(curve_i,PR_outer,color='maroon',label='Total mean',zorder=2)
 
+    axs[0].scatter(Scorers[2],Scorers[0],s=40,color='blue',label='Decision boundary',zorder=3)
+    axs[1].scatter(Scorers[0],Scorers[4],s=40,color='blue',label='Decision boundary',zorder=3)
+    
     P = C[1,0]; PP = C[1,1]; N = C[0,0]; PN = C[0,1]; POP = sum(C[:,0])
     #P,PP,N,PN = C
     axs[0].plot([0,1],[0,1],linestyle='--',color='midnightblue',label='Chance')