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 violin_roc(data):
plt.figure(figsize=(15, 15))
sns.set_context("notebook", font_scale=2.5,
rc={"lines.linewidth": 1.5, 'legend.fontsize': 20})
sns.violinplot(x='Predicted Probability', y='Diagnosis', hue='Outcome',
data=data, split=True, inner="quart",
palette={'--': "y", '+': "b"}, orient='h', width=1.0,
scale='area',#count
order=['VaD','Tauopathy NOS','AG','DLB','LB','ILBD',
'PD','AD','Parkinsonism NOS','PSP'])
leg = plt.gca().get_legend()
ltext = leg.get_texts() # all the text.Text instance in the legend
plt.setp(ltext, fontsize=24) # the legend text fontsize
plt.xlim(0,1)
sns.despine(left=True)
def violin_roc(data):
plt.figure(figsize=(15, 15))
sns.set_context("notebook", font_scale=2.5,
rc={"lines.linewidth": 1.5, 'legend.fontsize': 20})
sns.violinplot(x='Predicted Probability', y='Diagnosis', hue='Outcome',
data=data, split=True, inner="quart",
palette={'--': "y", '+': "b"}, orient='h', width=1.0,
scale='area',#count
order=['VaD','Tauopathy NOS','AG','DLB','LB','ILBD',
'PD','AD','Parkinsonism NOS','PSP'])
leg = plt.gca().get_legend()
ltext = leg.get_texts() # all the text.Text instance in the legend
plt.setp(ltext, fontsize=24) # the legend text fontsize
plt.xlim(0,1)
sns.despine(left=True)
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
def correct_corrs(tests, kind=None): matrix = correct_matrix(tests, kind=kind) for test in tests: if (test.subject.label is None) or (test.subject.label == kind): correct[test.subject]= [int(test.response_set.responses[i].correct) \ for i in range(1,41)] corrs = np.corrcoef(matrix.transpose()) plt.figure() plt.pcolor(np.arange(0.5,41.5,1),np.arange(0.5,41.5,1),corrs,cmap='RdBu_r',vmin=-1,vmax=1) plt.colorbar() plt.xlim(0.5,40.5) plt.ylim(0.5,40.5) return corrs
