一、Combination of over- and under-sampling
主要是解决SMOTE算法中生成噪声样本,解决方法为cleaning the space resulting from over-sampling。
主要思路是先使用SMOTE进行上采样,再通过Tomek’s link或者edited nearest-neighbours方法去获得一个
cleaner space.对应的函数为:SMOTETomek和SMOTEENN.
from imblearn.combine import SMOTEENN
smote_enn = SMOTEENN(random_state=0)
X_resampled, y_resampled = smote_enn.fit_resample(X, y)
from imblearn.combine import SMOTETomek
smote_tomek = SMOTETomek(random_state=0)
X_resampled, y_resampled = smote_tomek.fit_resample(X, y)
二、Ensemble of samplers
2.1 Bagging classifier
Bagging:有放回的取出样本产生样本的不同子集,再在每个子集上建立分类器(要给定分类器类型)。
在scikit-learn中,有类BaggingClassifier,但对于不平衡数据,不能保证每个子集的数据是平衡的,因此分类结果会偏向多数类。
在imblearn中,类BalaceBaggingClassifier使得在训练每个分类器之前,在每个子集上进行重采样，其参数与sklearn中的BaggingClassifier相同,除了增加了两个参数:sampling_strategy和replacement来控制随机下采样的方式。
from imblearn.ensemble import BalancedBaggingClassifier
from sklearn.metrics import balanced_accuracy_score
bbc = BalancedBaggingClassifier(base_estimator=DecisionTreeClassifier(),
sampling_strategy='auto',
replacement=False,
random_state=0)
bbc.fit(X_train, y_train)
y_pred =bbc.predict(X_test)
balanced_accuracy_score(y_test, y_pred)#计算平衡精度
2.2 Forest of randomized trees (随机森林)
在构建每棵树时使用平衡的bootstrap数据子集。
from imblearn.ensemble import BalancedRandomForestClassifier
brf = BalancedRandomForestClassifier(n_estimators=100,random_state=0)
brf.fit(X_train, y_train)
2.3 Boosting
在数据集子集上训练n个弱分类器,对这n个弱分类器进行加权融合，产生最后结果的分类器.
2.3.1 RUSBoostClassifier
在执行boosting迭代之前执行一个随机下采样。
from imblearn.ensemble import RUSBoostClassifier
rusboost = RUSBoostClassifier(random_state=0)
rusboost.fit(X_train, y_train)
2.3.2 EasyEnsembleClassifier,即采用Adaboost
计算弱分类器的错误率,对错误分类的样本分配更大的权值,正确分类的样本赋予更小权值。只要分类精度大于0.5即可做最终分类器中一员,弱分类器精度越高,权重越大。
from imblearn.ensemble import EasyEnsembleClassifier
eec = EasyEnsembleClassifier(random_state=0)
eec.fit(X_train, y_train)
三、Miscellaneous samplers
3.1 Custom sampler (自定义采样器):FunctionSampler
from imblearn import FunctionSampler
def fuc(X, y):
return X[:10], y[:10]
sampler = FunctionSampler(func=func)
X_res, y_res = sampler.fit_resample(X, y)
3.2 Custom generators (为TensorFlow和Keras生成平衡的mini-batches)
3.2.1 Tensorflow generator: imblearn.tensorflow.balanced_batch_generator
import numpy as np
X = X.astype(np.float32)
from imblearn.under_sampling import RandomUnderSampler
from imblearn.tensorflow import balanced_batch_generator
training_generator, steps_per_epoch = balanced_batch_generator(
X, y, sample_weight=None, sampler=RandomUnderSampler(),
batch_size=10, random_state=42)
#training_generator和 steps_per_epoch的使用方法：
learning_rate, epochs = 0.01, 10
input_size, output_size = X.shape[1], 3
import tensorflow as tf
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def accuracy(y_true, y_pred):
return np.mean(np.argmax(y_pred, axis=1) == y_true)
# input and output
data = tf.placeholder("float32", shape=[None, input_size])
targets = tf.placeholder("int32", shape=[None])
# build the model and weights
W = init_weights([input_size, output_size])
b = init_weights([output_size])
out_act = tf.nn.sigmoid(tf.matmul(data, W) + b)
# build the loss, predict, and train operator
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=out_act, labels=targets)
loss = tf.reduce_sum(cross_entropy)
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)
predict = tf.nn.softmax(out_act)
# Initialization of all variables in the graph
init = tf.global_variables_initializer()
with tf.Session() as sess:
print('Starting training')
sess.run(init)
for e in range(epochs):
for i in range(steps_per_epoch): ##主要是这里
X_batch, y_batch = next(training_generator) ##主要是这里
sess.run([train_op, loss], feed_dict={data: X_batch, targets: y_batch})
# For each epoch, run accuracy on train and test
feed_dict = dict()
predicts_train = sess.run(predict, feed_dict={data: X})
print("epoch: {} train accuracy: {:.3f}"
.format(e, accuracy(y, predicts_train)))
3.2 Keras generator
##定义一个逻辑回归模型
import keras
y = keras.utils.to_categorical(y, 3)
model = keras.Sequential()
model.add(keras.layers.Dense(y.shape[1], input_dim=X.shape[1],
activation='softmax'))
model.compile(optimizer='sgd', loss='categorical_crossentropy',
metrics=['accuracy'])
##keras.balanced_batch_generator生成平衡的min-batch
from imblearn.keras import balanced_batch_generator
training_generator, steps_per_epoch = balanced_batch_generator(
X, y, sampler=RandomUnderSampler(), batch_size=10, random_state=42)
##或者使用keras.BalancedBatchGenerator
from imblearn.keras import BalancedBatchGenerator
training_generator = BalancedBatchGenerator(
X, y, sampler=RandomUnderSampler(), batch_size=10, random_state=42)
callback_history = model.fit_generator(generator=training_generator,
epochs=10, verbose=0)
四.Metrics(度量)
目前,sklearn对于不平衡数据的度量只有sklearn.metrics.balanced_accuracy_score.
imblearn.metrics提供了两个其它评价分类器质量的度量
4.1 Sensitivity and specificity metrics
- Sensitivity：true positive rate即recall。
-
Specificity：true negative rate。
因此增加了三个度量 -
sensitivity_specificity_support:输出sensitivity和pecificity和support sensitivity_scorespecificity_score
4.2 Additional metrics specific to imbalanced datasets
专门为不平衡数据增加的度量
-
geometric_mean_score:计算几何平均数(G-mean,各类sensitivity乘积的开方),具体描述如下:
The The geometric mean (G-mean) is the root of the product of class-wise sensitivity. This measure tries to maximize the accuracy on each of the classes while keeping these accuracies balanced. For binary classification G-mean is the squared root of the product of the sensitivity and specificity. For multi-class problems it is a higher root of the product of sensitivity for each class.
-
make_index_balanced_accuracy: 根据balanced accuracy平衡任何scoring function
