def reduce(self, result):
if self.select_regexp:
inputs = [key3 for key3 in result
if re.search(self.select_regexp, str(key3))]
else:
inputs = result.keys()
if len(inputs) != 2:
raise KeyError("Need to find exactly two results to compute a score."
" Found %i: %s" % (len(inputs), inputs))
key_true = [k for k in inputs if k.find(conf.TRUE) != -1][0]
key_pred = [k for k in inputs if k.find(conf.PREDICTION) != -1][0]
y_true = result[key_true]
y_pred = result[key_pred]
try: # If list of arrays (CV, LOO, etc.) concatenate them
y_true = np.concatenate(y_true)
y_pred = np.concatenate(y_pred)
except ValueError:
pass
out = Result(key=result["key"])
p, r, f1, s = precision_recall_fscore_support(y_true, y_pred, average=None)
key, _ = key_pop(key_pred, -1)
out[key_push(key, conf.SCORE_PRECISION)] = p
out[key_push(key, conf.SCORE_RECALL)] = r
out[key_push(key, conf.SCORE_RECALL_MEAN)] = r.mean()
out[key_push(key, conf.SCORE_F1)] = f1
out[key_push(key, conf.SCORE_ACCURACY)] = accuracy_score(y_true, y_pred)
if self.keep:
out.update(result)
return out
def reduce(self, result):
if self.select_regexp:
inputs = [key3 for key3 in result
if re.search(self.select_regexp, str(key3))]
else:
inputs = result.keys()
if len(inputs) != 2:
raise KeyError("Need to find exactly two results to compute a "
"score. Found %i: %s" % (len(inputs), inputs))
key_true = [k for k in inputs if k.find(conf.TRUE) != -1][0]
key_pred = [k for k in inputs if k.find(conf.PREDICTION) != -1][0]
y_true = result[key_true]
y_pred = result[key_pred]
try: # If list of arrays (CV, LOO, etc.) concatenate them
y_true = np.concatenate(y_true)
y_pred = np.concatenate(y_pred)
except ValueError:
pass
out = Result(key=result["key"])
p, r, f1, s = precision_recall_fscore_support(y_true,
y_pred,
average=None)
# Compute p-value of recall for each class
def recall_test(recall, n_trials, apriori_p):
n_success = recall * n_trials
pval = binom_test(n_success, n=n_trials, p=apriori_p)
if recall > apriori_p:
return (pval / 2)
else:
return 1 - (pval / 2)
n_classes = len(s) # Number of classes
n_obs = len(y_true)
prior_p = s.astype(np.float)/s.sum() # A priori probability of each class
r_pvalues = np.zeros_like(r)
for class_index in range(n_classes):
n_trials = s[class_index]
#print "Class {class_index}: {n_success} success on {n_trials} trials".format(n_success=n_success, n_trials=n_trials, class_index=class_index)
r_pvalues[class_index] = recall_test(r[class_index],
n_trials,
prior_p[class_index])
# Compute p-value of mean recall
mean_r = r.mean()
mean_r_pvalue = binom_test(int(mean_r * n_obs), n=n_obs, p=.5)
key, _ = key_pop(key_pred, -1)
out[key_push(key, conf.SCORE_PRECISION)] = p
out[key_push(key, conf.SCORE_RECALL)] = r
out[key_push(key, conf.SCORE_RECALL_PVALUES)] = r_pvalues
out[key_push(key, conf.SCORE_RECALL_MEAN)] = mean_r
out[key_push(key, conf.SCORE_RECALL_MEAN_PVALUE)] = mean_r_pvalue
out[key_push(key, conf.SCORE_F1)] = f1
out[key_push(key, conf.SCORE_ACCURACY)] = accuracy_score(y_true,
y_pred)
if self.keep:
out.update(result)
return out
def reduce(self, result):
if self.select_regexp:
select_keys = [key for key in result
if re.search(self.select_regexp, str(key))]
#if re.search(self.select_regexp) != -1]
else:
select_keys = result.keys()
out = Result(key=result.key())
for key in select_keys:
out[key] = result[key][0]
randm_res = np.vstack(result[key][1:])
count = np.sum(randm_res > result[key][0], axis=0).astype("float")
pval = count / (randm_res.shape[0])
out[key_push(key, "pval")] = pval
if self.keep:
out.update(result)
return out
