def scatter_diag(props,ps,os,x_diag,y_diag,plot=True):
from matplotlib.colors import Colormap as cmap
imp = 'knn'
xi = props.index(x_diag)
yi = props.index(y_diag)
p_x = ps[imp][xi,:,:].ravel() # Unravel all predictions of test data over all cv splits.
p_y = ps[imp][yi,:,:].ravel() # Unravel all test data ground truth over all cv splits.
o_x = os[imp][xi,:,:].ravel() # Unravel all predictions of test data over all cv splits.
o_y = os[imp][yi,:,:].ravel() # Unravel all test data ground truth over all cv splits.
mask = o_x.mask + o_y.mask
p_x = p_x[mask==False]
p_y = p_y[mask==False]
o_x = o_x[mask==False]
o_y = o_y[mask==False]
colors = np.vstack((o_x.data,np.zeros(len(o_x)),o_y.data)).T
colors[colors==0] = 0.2
if plot:
plt.figure(figsize=(10,10))
plt.scatter(p_x+0.02*np.random.rand(p_pd.shape[0]),
p_y+0.02*np.random.rand(p_pd.shape[0]),
s=15,
c=colors)
plt.xlabel(x_diag)
plt.ylabel(y_diag)
plt.xlim(0,p_x.max()*1.05)
plt.ylim(0,p_y.max()*1.05)
plt.legend()
return p_x,p_y,o_x,o_y
def plot_total_correct_cumul(X_total_correct,ctrl):
X_ctrl = X_total_correct[ctrl == True,0]
X_pd = X_total_correct[ctrl == False,0]
plt.plot(sorted(X_ctrl),np.linspace(0,1,len(X_ctrl)),'k',label='Control')
plt.plot(sorted(X_pd),np.linspace(0,1,len(X_pd)),'r',label='PD')
plt.xlabel('Total Correct')
plt.ylabel('Cumulative Probability')
plt.legend(loc=2)
def plot_cumul(X,Y,label):
X_pos = X[Y == True]
X_neg = X[Y == False]
plt.plot(sorted(X_neg),np.linspace(0,1,len(X_neg)),'k',label='-')
plt.plot(sorted(X_pos),np.linspace(0,1,len(X_pos)),'r',label='+')
plt.xlabel(label)
plt.ylabel('Cumulative Probability')
plt.ylim(0,1)
plt.legend(loc=2)
def plot_roc_curve(Y,p_parks_tc,p_parks_r):
fpr_tc,tpr_tc,roc_auc_tc = get_roc_curve(Y,p_parks_tc)
fpr_r_mnb,tpr_r_mnb,roc_auc_r_mnb = get_roc_curve(Y,p_parks_r)
plt.plot(fpr_tc, tpr_tc, lw=2, color='gray', label='AUC using Total Correct = %0.2f' % (roc_auc_tc))
#plot(fpr_r_bnb, tpr_r_bnb, lw=2, color='r', label='Responses area = %0.2f' % (roc_auc_r_bnb))
plt.plot(fpr_r_mnb, tpr_r_mnb, lw=2, color='g', label='AUC using individual responses = %0.2f' % (roc_auc_r_mnb))
plt.xlabel('False Positive Rate')#, fontsize='large', fontweight='bold')
plt.ylabel('True Positive Rate')#, fontsize='large', fontweight='bold')
plt.title('ROC curves')#, fontsize='large', fontweight='bold')
plt.xticks()#fontsize='large', fontweight='bold')
plt.yticks()#fontsize='large', fontweight='bold')
plt.legend(loc="lower right")
def plot_roc_curve(Y,n0=None,n1=None,smooth=False,no_plot=False,**ps):
aucs = []
aucs_sd = []
if n0 is None:
n0 = sum(Y==0)
if n1 is None:
n1 = sum(Y>0)
for i,(title,p) in enumerate(sorted(ps.items())):
fpr,tpr,auc = get_roc_curve(Y,p,smooth=smooth)
aucs.append(auc)
# Confidence Intervals for the Area under the ROC Curve
# Cortes and Mohri
# http://www.cs.nyu.edu/~mohri/pub/area.pdf
m = n1
n = n0
A = auc
Pxxy = 0
Pxyy = 0
iters = 10000
for j in range(iters):
index = np.arange(len(Y))
np.random.shuffle(index)
p_shuff = p[index]
Y_shuff = Y[index]
pa,pb = p_shuff[Y_shuff>0][0:2]
na,nb = p_shuff[Y_shuff==0][0:2]
Pxxy += ((pa>na) and (pb>na))
Pxyy += ((na<pa) and (nb<pa))
Pxxy/=iters
Pxyy/=iters
#print(A,Pxxy,Pxyy,m,n)
var = (A*(1-A)+(m-1)*(Pxxy-(A**2))+(n-1)*(Pxyy-(A**2)))/(m*n)
sd = np.sqrt(var)
aucs_sd.append(sd)
if not no_plot:
plt.plot(fpr, tpr, lw=2, color=get_colors(i), label='%s = %0.2f' % (title,auc))
else:
print('%s = %0.3f +/- %0.3f' % (title,auc,sd))
if not no_plot:
plt.xlabel('False Positive Rate')#, fontsize='large', fontweight='bold')
plt.ylabel('True Positive Rate')#, fontsize='large', fontweight='bold')
plt.title('ROC curves')#, fontsize='large', fontweight='bold')
plt.xticks()#fontsize='large', fontweight='bold')
plt.yticks()#fontsize='large', fontweight='bold')
plt.xlim(-0.01,1.01)
plt.ylim(-0.01,1.01)
plt.legend(loc="lower right",fontsize=17)
return aucs,aucs_sd