jda算法的python代码实现
时间: 2023-05-17 16:01:01 浏览: 205
JDA算法(Joint Distribution Adaptation)是一种域适应方法,它通过对源域数据和目标域数据分别建模,利用最大化它们之间的相似性来实现跨域知识转移。本文将介绍如何使用Python实现JDA算法。
首先,需要导入以下库:numpy,scipy,sklearn,和Cython。其中Cython是Python语言的扩展,主要用于编写C语言的扩展模块。
初始化函数中,我们需要指定两个域的标签、源域特征和目标域特征。在建模之前,需要计算出两个域的协方差矩阵。
然后,我们需要用高斯核函数来计算源域和目标域的核矩阵。接着,通过解决广义特征值问题来获取最大化领域间距离的变换矩阵,该矩阵可以将源域和目标域的特征转换成低维表示。
最后,在训练完变换矩阵后,我们可以将它应用于测试数据,以获得更好的分类效果。
下面是JDA算法的Python代码实现:
```
import numpy as np
from scipy import linalg
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array, check_random_state
from scipy.spatial.distance import cdist
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
try:
from .jda_cython import inner_jda
except ImportError:
print('Cython not found. To compile cython .pyx file you need '
'to run command "python setup.py build_ext --inplace" in'
'"jda_cython" folder')
from .jda_python import inner_jda
class JDA(BaseEstimator, TransformerMixin):
def __init__(self, dim=30, n_iter=10, gamma=1.0, kernel='rbf', random_state=None):
self.dim = dim
self.n_iter = n_iter
self.gamma = gamma
self.kernel = kernel
self.random_state = random_state
def fit(self, X, y, Xt=None, yt=None):
'''
Parameters
----------
X : array-like, shape (n_samples, n_features)
Source data
y : array-like, shape (n_samples, )
Source labels
Xt : array-like, shape (n_target_samples, n_features), optional
Target data
yt : array-like, shape (n_target_samples,), optional
Target labels
Returns
-------
self : object
Returns self.
'''
if Xt is None:
# use the source data as target data as well
Xt = X
yt = y
random_state = check_random_state(self.random_state)
# compute the covariance matrices of the source and target domains
Cs = np.cov(X.T)
Ct = np.cov(Xt.T)
# compute the kernel matrices of the source and target domains
Ks = rbf_kernel(X, gamma=self.gamma)
Kt = rbf_kernel(Xt, X, gamma=self.gamma)
self.scaler_ = PCA(n_components=self.dim).fit(
np.vstack((X, Xt)))
Xs_pca = self.scaler_.transform(X)
Xt_pca = self.scaler_.transform(Xt)
X_pca = np.vstack((Xs_pca, Xt_pca))
V_src = np.eye(Xs_pca.shape[1])
V_trg = np.eye(Xt_pca.shape[1])
for i in range(self.n_iter):
W = JDA._calculate_projection(
X_pca, np.array(source_labels+target_labels), V_src, V_trg, Ks, Kt)
Xs_pca = Xs_pca.dot(W)
Xt_pca = Xt_pca.dot(W)
self.W_ = W
self.Xs_pca_ = Xs_pca
self.Xt_pca_ = Xt_pca
self.clf_ = LogisticRegression(random_state=random_state,
solver='lbfgs',
max_iter=1000,
)
self.clf_.fit(Xs_pca, y)
return self
def transform(self, X):
"""Transforms data X using the fitted models
Parameters
----------
X : array-like, shape (n_samples, n_features)
Data to transform
Returns
-------
Xt_new : array, shape (n_samples, n_components)
Transformed data
"""
return self.scaler_.transform(X).dot(self.W_)
def fit_transform(self, X, y, Xt=None, yt=None):
"""Fit and transform data X using the fitted models
Parameters
----------
X : array-like, shape (n_samples, n_features)
Data to transform
y : array-like, shape (n_samples, )
Labels
Xt : array-like, shape (n_target_samples, n_features), optional
Target data
yt : array-like, shape (n_target_samples,), optional
Target labels
Returns
-------
Xt_new : array, shape (n_target_samples, n_components)
Transformed data
"""
self.fit(X, y, Xt, yt)
return self.transform(Xt)
@staticmethod
def _calculate_projection(X, Y, V_src, V_trg, Ks, Kt):
n = X.shape[0]
ns = Ks.shape[0]
nt = Kt.shape[0]
eps = 1e-4
H_s = np.eye(ns) - 1.0 / ns * np.ones((ns, ns))
H_t = np.eye(nt) - 1.0 / nt * np.ones((nt, nt))
A = np.vstack((np.hstack((Ks + eps * np.eye(ns), np.zeros((ns, nt)))),
np.hstack((np.zeros((nt, ns)), Kt + eps * np.eye(nt)))))
B = np.vstack((H_s, H_t))
# solve the generalized eigenvalue problem Ax = lambda Bx
lambda_, p = linalg.eig(A, B)
# sort eigenvalues in ascending order
idx = np.argsort(-lambda_.real)
lambda_ = lambda_[idx]
p = p[:, idx]
t = Y
c1 = 1.0 / ns * sum(p[:ns, :].T.dot(t == 1))
c2 = 1.0 / nt * sum(p[ns:, :].T.dot(t == -1))
MMD = sum(sum(p[:ns, :].T.dot(Ks).dot(p[:ns, :])) / ns ** 2
+ sum(p[ns:, :].T.dot(Kt).dot(p[ns:, :])) / nt ** 2
- 2 * sum(p[:ns, :].T.dot(Kt).dot(p[ns:, :])) / (ns * nt))
# calculate the optimal projection matrix
V = p[:ns, :].dot(np.diag(1.0 / lambda_[:ns])).dot(
p[:ns, :].T).dot(H_s - H_t).dot(p[ns:, :]).dot(
np.diag(1.0 / lambda_[ns:])).dot(p[ns:, :].T)
# calculate the transformation matrix
W = X.T.dot(V).dot(X)
return W
if __name__ == "__main__":
np.random.seed(1234)
# generate example data
n = 100
d = 100
X = np.random.randn(n, d)
y = np.concatenate((np.ones(n // 2, dtype=np.int), -np.ones(n // 2, dtype=np.int)))
Xs = X[:n // 2, :]
ys = y[:n // 2]
Xt = X[n // 2:, :]
yt = y[n // 2:]
# train and evaluate model
model = JDA(n_iter=10)
Xt_new = model.fit_transform(Xs, ys, Xt, yt)
clf = LogisticRegression(random_state=1234)
clf.fit(model.transform(Xs), ys)
print('Accuracy on source domain: {:.2f}%'.format(clf.score(model.transform(Xs), ys) * 100))
print('Accuracy on target domain: {:.2f}%'.format(clf.score(Xt_new, yt) * 100))
```
以上就是JDA算法的Python代码实现。我们可以使用上述代码来实现域适应问题中的知识转移。