带拉普拉斯正则化的OT用于领域适应

本例介绍了在二维环境中的领域适应和带有拉普拉斯正则化的OTDA方法。

# Authors: Ievgen Redko <ievgen.redko@univ-st-etienne.fr>

# License: MIT License

import matplotlib.pylab as pl
import ot

生成数据

n_source_samples = 150
n_target_samples = 150

Xs, ys = ot.datasets.make_data_classif("3gauss", n_source_samples)
Xt, yt = ot.datasets.make_data_classif("3gauss2", n_target_samples)

实例化不同的传输算法并进行拟合

# EMD Transport
ot_emd = ot.da.EMDTransport()
ot_emd.fit(Xs=Xs, Xt=Xt)

# Sinkhorn Transport
ot_sinkhorn = ot.da.SinkhornTransport(reg_e=0.01)
ot_sinkhorn.fit(Xs=Xs, Xt=Xt)

# EMD Transport with Laplacian regularization
ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)
ot_emd_laplace.fit(Xs=Xs, Xt=Xt)

# transport source samples onto target samples
transp_Xs_emd = ot_emd.transform(Xs=Xs)
transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)

/home/circleci/project/ot/bregman/_sinkhorn.py:903: UserWarning: Sinkhorn did not converge. You might want to increase the number of iterations `numItermax` or the regularization parameter `reg`.
  warnings.warn(
/home/circleci/project/ot/backend.py:1165: RuntimeWarning: overflow encountered in exp
  return np.exp(a)

图 1 : 绘制源样本和目标样本

pl.figure(1, figsize=(10, 5))
pl.subplot(1, 2, 1)
pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker="+", label="Source samples")
pl.xticks([])
pl.yticks([])
pl.legend(loc=0)
pl.title("Source  samples")

pl.subplot(1, 2, 2)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker="o", label="Target samples")
pl.xticks([])
pl.yticks([])
pl.legend(loc=0)
pl.title("Target samples")
pl.tight_layout()

图 2 : 绘制最优耦合和传输样本

param_img = {"interpolation": "nearest"}

pl.figure(2, figsize=(15, 8))
pl.subplot(2, 3, 1)
pl.imshow(ot_emd.coupling_, **param_img)
pl.xticks([])
pl.yticks([])
pl.title("Optimal coupling\nEMDTransport")

pl.figure(2, figsize=(15, 8))
pl.subplot(2, 3, 2)
pl.imshow(ot_sinkhorn.coupling_, **param_img)
pl.xticks([])
pl.yticks([])
pl.title("Optimal coupling\nSinkhornTransport")

pl.subplot(2, 3, 3)
pl.imshow(ot_emd_laplace.coupling_, **param_img)
pl.xticks([])
pl.yticks([])
pl.title("Optimal coupling\nEMDLaplaceTransport")

pl.subplot(2, 3, 4)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker="o", label="Target samples", alpha=0.3)
pl.scatter(
    transp_Xs_emd[:, 0],
    transp_Xs_emd[:, 1],
    c=ys,
    marker="+",
    label="Transp samples",
    s=30,
)
pl.xticks([])
pl.yticks([])
pl.title("Transported samples\nEmdTransport")
pl.legend(loc="lower left")

pl.subplot(2, 3, 5)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker="o", label="Target samples", alpha=0.3)
pl.scatter(
    transp_Xs_sinkhorn[:, 0],
    transp_Xs_sinkhorn[:, 1],
    c=ys,
    marker="+",
    label="Transp samples",
    s=30,
)
pl.xticks([])
pl.yticks([])
pl.title("Transported samples\nSinkhornTransport")

pl.subplot(2, 3, 6)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker="o", label="Target samples", alpha=0.3)
pl.scatter(
    transp_Xs_emd_laplace[:, 0],
    transp_Xs_emd_laplace[:, 1],
    c=ys,
    marker="+",
    label="Transp samples",
    s=30,
)
pl.xticks([])
pl.yticks([])
pl.title("Transported samples\nEMDLaplaceTransport")
pl.tight_layout()

pl.show()