本文整理汇总了Python中sklearn.linear_model.BayesianRidge.predict_proba方法的典型用法代码示例。如果您正苦于以下问题：Python BayesianRidge.predict_proba方法的具体用法？Python BayesianRidge.predict_proba怎么用？Python BayesianRidge.predict_proba使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.linear_model.BayesianRidge的用法示例。
在下文中一共展示了BayesianRidge.predict_proba方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from sklearn.linear_model import BayesianRidge [as 别名]
# 或者: from sklearn.linear_model.BayesianRidge import predict_proba [as 别名]
#.........这里部分代码省略.........
test_a = np.array(data_hdf5_in['test_add'])
add_labels = np.array(data_hdf5_in['add_labels'])
if options.regex_add:
fi = filter_regex(options.regex_add, add_labels)
train_a, test_a, add_labels = train_a[:,fi], test_a[:,fi], add_labels[fi]
# append additional features
if options.add_only:
add_i = 0
train_x, test_x = train_a, test_a
else:
add_i = train_x.shape[1]
train_x = np.concatenate((train_x,train_a), axis=1)
test_x = np.concatenate((test_x,test_a), axis=1)
data_hdf5_in.close()
if options.target_hdf5:
target_hdf5_in.close()
# balance
if options.balance:
train_x, train_y = balance(train_x, train_y)
# sample
if options.sample is not None and options.sample < train_x.shape[0]:
sample_indexes = random.sample(range(train_x.shape[0]), options.sample)
train_x = train_x[sample_indexes]
train_y = train_y[sample_indexes]
#######################################################
# model
#######################################################
if options.regression:
# fit
model = BayesianRidge(fit_intercept=True)
model.fit(train_x, train_y)
# accuracy
acc_out = open('%s/r2.txt' % options.out_dir, 'w')
print >> acc_out, model.score(test_x, test_y)
acc_out.close()
test_preds = model.predict(test_x)
# plot a sample of predictions versus actual
plt.figure()
sns.jointplot(test_preds[:5000], test_y[:5000], joint_kws={'alpha':0.3})
plt.savefig('%s/scatter.pdf' % options.out_dir)
plt.close()
# plot the distribution of residuals
plt.figure()
sns.distplot(test_y-test_preds)
plt.savefig('%s/residuals.pdf' % options.out_dir)
plt.close()
else:
# fit
model = LogisticRegression(penalty='l2', C=1000)
model.fit(train_x, train_y)
# accuracy
test_preds = model.predict_proba(test_x)[:,1].flatten()
acc_out = open('%s/auc.txt' % options.out_dir, 'w')
print >> acc_out, roc_auc_score(test_y, test_preds)
acc_out.close()
# compute and print ROC curve
fpr, tpr, thresholds = roc_curve(test_y, test_preds)
roc_out = open('%s/roc.txt' % options.out_dir, 'w')
for i in range(len(fpr)):
print >> roc_out, '%f\t%f\t%f' % (fpr[i], tpr[i], thresholds[i])
roc_out.close()
# compute and print precision-recall curve
precision, recall, thresholds = precision_recall_curve(test_y, test_preds)
prc_out = open('%s/prc.txt' % options.out_dir, 'w')
for i in range(len(precision)):
print >> prc_out, '%f\t%f' % (precision[i], recall[i])
prc_out.close()
# save model
joblib.dump(model, '%s/model.pkl' % options.out_dir)
#######################################################
# analyze
#######################################################
# print coefficients table
coef_out = open('%s/add_coefs.txt' % options.out_dir, 'w')
for ai in range(len(add_labels)):
if options.regression:
coefi = model.coef_[add_i+ai]
else:
coefi = model.coef_[0,add_i+ai]
print >> coef_out, add_labels[ai], coefi
coef_out.close()