本文整理汇总了Python中sklearn.svm.OneClassSVM.predict_proba方法的典型用法代码示例。如果您正苦于以下问题：Python OneClassSVM.predict_proba方法的具体用法？Python OneClassSVM.predict_proba怎么用？Python OneClassSVM.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.svm.OneClassSVM的用法示例。
在下文中一共展示了OneClassSVM.predict_proba方法的2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: embed_dat_matrix_two_dimensions
# 需要导入模块: from sklearn.svm import OneClassSVM [as 别名]
# 或者: from sklearn.svm.OneClassSVM import predict_proba [as 别名]
def embed_dat_matrix_two_dimensions(low_dimension_data_matrix,
y=None,
labels=None,
density_colormap='Blues',
instance_colormap='YlOrRd'):
from sklearn.preprocessing import scale
low_dimension_data_matrix = scale(low_dimension_data_matrix)
# make mesh
x_min, x_max = low_dimension_data_matrix[:, 0].min(), low_dimension_data_matrix[:, 0].max()
y_min, y_max = low_dimension_data_matrix[:, 1].min(), low_dimension_data_matrix[:, 1].max()
step_num = 50
h = min((x_max - x_min) / step_num, (y_max - y_min) / step_num) # step size in the mesh
b = h * 10 # border size
x_min, x_max = low_dimension_data_matrix[:, 0].min() - b, low_dimension_data_matrix[:, 0].max() + b
y_min, y_max = low_dimension_data_matrix[:, 1].min() - b, low_dimension_data_matrix[:, 1].max() + b
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# induce a one class model to estimate densities
from sklearn.svm import OneClassSVM
gamma = max(x_max - x_min, y_max - y_min)
clf = OneClassSVM(gamma=gamma, nu=0.1)
clf.fit(low_dimension_data_matrix)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max] . [y_min, y_max].
if hasattr(clf, "decision_function"):
score_matrix = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
score_matrix = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
# Put the result into a color plot
levels = np.linspace(min(score_matrix), max(score_matrix), 40)
score_matrix = score_matrix.reshape(xx.shape)
if y is None:
y = 'white'
plt.contourf(xx, yy, score_matrix, cmap=plt.get_cmap(density_colormap), alpha=0.9, levels=levels)
plt.scatter(low_dimension_data_matrix[:, 0], low_dimension_data_matrix[:, 1],
alpha=.5,
s=70,
edgecolors='gray',
c=y,
cmap=plt.get_cmap(instance_colormap))
# labels
if labels is not None:
for id in range(low_dimension_data_matrix.shape[0]):
label = labels[id]
x = low_dimension_data_matrix[id, 0]
y = low_dimension_data_matrix[id, 1]
plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')示例2: transform_cat_with_tfidf
# 需要导入模块: from sklearn.svm import OneClassSVM [as 别名]
# 或者: from sklearn.svm.OneClassSVM import predict_proba [as 别名]
### Record bidder_ids for output submission
bidder_ids = test_data[:, 0]
### Convert features to float
# features = features.astype(np.float)
### Transform sparse matrix
# features = transform_cat_with_tfidf (features, tfidf)
### SelectKBest
if select_k == True:
features = selector.transform(features)
### Predict output
predict_prob = clf.predict_proba(features)
# open in byte mode for older Python2.7
# csv_out = csv.writer(open('D:/Kaggle/HumanVRobot/results/temp.csv', 'wb'))
### Dump to csv
# csv_out = csv.writer(open('D:/Kaggle/HumanVRobot/results/gbc_tune_59_to_40feat_ccv5.csv', 'w', newline = ''))
# csv_out = csv.writer(open('D:/Kaggle/HumanVRobot/results/rfc_tune_59_to_40feat_ccv5.csv', 'w', newline = ''))
# csv_out = csv.writer(open('D:/Kaggle/HumanVRobot/results/etc_gini_tune_59feat_ccv5.csv', 'w', newline = ''))
csv_out = csv.writer(open('D:/Kaggle/HumanVRobot/results/temp.csv', 'w', newline = ''))
# csv_out = csv.writer(open('D:/Kaggle/HumanVRobot/results/bagging_tune_59feat_cv5.csv', 'w', newline = ''))
data = []
data.append(['bidder_id', 'prediction'])
for idx in range(len(predict_prob)):
data.append([bidder_ids[idx], predict_prob[idx, 1]]) # We want to only dump class probabilities for bot