file.close()
# weather_targs = []
# weather_preds = []
# for t in targs:
# weather_targs.append(np.argmax(t[:4]))
# for p in preds:
# weather_preds.append(np.argmax(p[:4]))
# print weather_preds[:10]
# print weather_targs[:10]
# print sklearn.metrics.confusion_matrix(weather_targs,weather_preds)
print 'Calculating F2 scores'
threshold = 0.53
qpreds = preds > threshold
print app.f2_score(targs[:, :17], qpreds[:, :17])
print app.f2_score(targs[:, :17], qpreds[:, :17], average=None)
# print 'Calculating F2 scores (argmax for weather class)'
# w_pred = preds[:,:4]
# cw_pred = np.argmax(w_pred,axis=1)
# qw_pred = np.zeros((preds.shape[0],4))
# qw_pred[np.arange(preds.shape[0]),cw_pred] = 1
# qpreds[:,:4] = qw_pred
print app.f2_score(targs[:, :17], qpreds[:, :17])
print app.f2_score(targs[:, :17], qpreds[:, :17], average=None)
print 'Calculating F2 scores only for weather labels'
print app.f2_score(targs[:, :4], qpreds[:, :4])
print app.f2_score(targs[:, :4], qpreds[:, :4], average=None)
print 'loglosses'
print app.logloss(preds.flatten(), targs.flatten())
def test_score(gts, preds, bias=app.get_biases()[label_id], epsilon=1.e-11):
predictions = np.array(preds) / 0.5 * bias
preds_cutoff = [1 if p > 0.5 else 0 for p in predictions]
return app.f2_score(gts, preds_cutoff, average=None)
def score(gts, preds):
preds_cutoff = [1 if p > 0.5 else 0 for p in preds]
return app.f2_score(gts, preds_cutoff, average=None)
def test_score(gts, preds, bias=app.get_biases()[label_id], epsilon=1.e-11):
predictions = expit(logit(preds) - logit(0.5) + logit(bias))
preds_cutoff = [1 if p > 0.5 else 0 for p in predictions]
return app.f2_score(gts, preds_cutoff, average=None)
def score(gts, preds):
return app.f2_score(gts, preds)
def score(gts, preds):
preds_cutoff = np.digitize(preds,bins=[-0.01,0.5,1.01])
preds_cutoff = preds_cutoff-1
return app.f2_score(gts, preds_cutoff)
