def validate(self, plot=False, cutoff=0.0, validation_set = None, fname=None):
'''
predict titers of the validation set (separate set of test_titers aside previously)
and compare against known values. If requested by plot=True,
a figure comparing predicted and measured titers is produced
'''
from scipy.stats import linregress, pearsonr
if validation_set is None:
validation_set=self.test_titers
self.validation = {}
for key, val in validation_set.iteritems():
pred_titer = self.predict_titer(key[0], key[1], cutoff=cutoff)
self.validation[key] = (val, pred_titer)
a = np.array(self.validation.values())
print ("number of prediction-measurement pairs",a.shape)
self.abs_error = np.mean(np.abs(a[:,0]-a[:,1]))
self.rms_error = np.sqrt(np.mean((a[:,0]-a[:,1])**2))
self.slope, self.intercept, tmpa, tmpb, tmpc = linregress(a[:,0], a[:,1])
print ("error (abs/rms): ",self.abs_error, self.rms_error)
print ("slope, intercept:", self.slope, self.intercept)
self.r2 = pearsonr(a[:,0], a[:,1])[0]**2
print ("pearson correlation:", self.r2)
if plot:
import matplotlib.pyplot as plt
import seaborn as sns
fs=16
sns.set_style('darkgrid')
plt.figure()
ax = plt.subplot(111)
plt.plot([-1,6], [-1,6], 'k')
plt.scatter(a[:,0], a[:,1])
plt.ylabel(r"predicted $\log_2$ distance", fontsize = fs)
plt.xlabel(r"measured $\log_2$ distance" , fontsize = fs)
ax.tick_params(axis='both', labelsize=fs)
plt.text(-2.5,6,'regularization:\nprediction error:\nR^2:', fontsize = fs-2)
plt.text(1.2,6, str(self.lam_drop)+'/'+str(self.lam_pot)+'/'+str(self.lam_avi)+' (HI/pot/avi)'
+'\n'+str(round(self.abs_error, 2))+'/'+str(round(self.rms_error, 2))+' (abs/rms)'
+ '\n' + str(self.r2), fontsize = fs-2)
plt.tight_layout()
if fname:
plt.savefig(fname)
评论列表
文章目录
