[3]:

np.random.seed(0)Xs=np.random.multivariate_normal(np.array([0,0]),np.array([[0.001,0],[0,1]]),100)Xt=np.random.multivariate_normal(np.array([0,0]),np.array([[0.1,0.2],[0.2,0.5]]),100)ys=np.zeros(100)yt=np.zeros(100)ys[Xs[:,1]>0]=1yt[(Xt[:,1]-0.5*Xt[:,0])>0]=1x_grid,y_grid=np.meshgrid(np.linspace(-0.65,0.75,100),np.linspace(-2.1,2.1,100))X_grid=np.stack([x_grid.ravel(),y_grid.ravel()],-1)cm=matplotlib.colors.ListedColormap(['w','r','w'])plt.figure(figsize=(8,6))plt.plot(Xs[:,0],Xs[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt[:,0],Xt[:,1],'o',markeredgecolor="k",label="target")plt.plot([-0.65,0.75],[0,0],c="black",ls="--",label="source class separation")plt.plot([-0.65,0.75],[-0.65*0.5,0.75*0.5],c="black",ls=":",label="target class separation")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("X0",fontsize=16),plt.ylabel("X1",fontsize=16)plt.show()

[4]:

model=LogisticRegression(C=1000)model.fit(Xs,ys);scores_s=model.score(Xs,ys)scores_t=model.score(Xt,yt)yp_grid=model.predict(X_grid).reshape(100,100)plt.figure(figsize=(8,6))plt.plot(Xs[:,0],Xs[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt[:,0],Xt[:,1],'o',markeredgecolor="k",label="target")plt.contourf(x_grid,y_grid,yp_grid,cmap=cm)plt.plot([0],[0],c="red",label="predicted class separation")plt.plot([-0.65,0.75],[-0.65*0.5,0.75*0.5],c="black",ls=":",label="target class separation")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("X0",fontsize=16),plt.ylabel("X1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()

[11]:

model=CORAL(LogisticRegression(C=1000),lambda_=1e-4)model.fit(Xs,ys,Xt);scores_s=model.score_estimator(Xs,ys)scores_t=model.score_estimator(Xt,yt)Xs_enc=model.transform(Xs,domain="src")yp_grid=model.predict(X_grid).reshape(100,100)plt.figure(figsize=(8,6))plt.plot(Xs[:,0],Xs[:,1],'o',color="w",markeredgecolor="k",alpha=0.5,label="source")plt.plot(Xs_enc[:,0],Xs_enc[:,1],'o',markeredgecolor="k",label="source modified")plt.plot(Xt[:,0],Xt[:,1],'o',markeredgecolor="k",label="target")plt.contourf(x_grid,y_grid,yp_grid,cmap=cm)plt.plot([0],[0],c="red",label="predicted class separation")plt.plot([-0.65,0.75],[-0.65*0.5,0.75*0.5],c="black",ls=":",label="target class separation")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("X0",fontsize=16),plt.ylabel("X1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()

Fit transform...Previous covariance difference: 0.258273New covariance difference: 0.003795Fit Estimator...

[12]:

encoder=tf.keras.Sequential()encoder.add(tf.keras.layers.Dense(2,activation="sigmoid"))task=tf.keras.Sequential()task.add(tf.keras.layers.Dense(1,activation="sigmoid"))discriminator=tf.keras.Sequential()discriminator.add(tf.keras.layers.Dense(1,activation="sigmoid"))

[14]:

model=DANN(encoder,task,discriminator,lambda_=0.1,random_state=0)model.fit(Xs,ys,Xt,epochs=500,verbose=0);scores_s=(np.abs(model.predict(Xs).ravel()-ys)<0.5).mean()scores_t=(np.abs(model.predict(Xt).ravel()-yt)<0.5).mean()yp_grid=model.predict(X_grid).reshape(100,100)>0.5plt.figure(figsize=(8,6))plt.plot(Xs[:,0],Xs[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt[:,0],Xt[:,1],'o',markeredgecolor="k",label="target")plt.contourf(x_grid,y_grid,yp_grid,cmap=cm)plt.plot([0],[0],c="red",label="predicted class separation")plt.plot([-0.65,0.75],[-0.65*0.5,0.75*0.5],c="black",ls=":",label="target class separation")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("X0",fontsize=16),plt.ylabel("X1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()

[16]:

Xs_enc=model.transform(Xs)Xt_enc=model.transform(Xt)plt.figure(figsize=(8,6))plt.plot(Xs_enc[:,0],Xs_enc[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt_enc[:,0],Xt_enc[:,1],'o',markeredgecolor="k",label="target")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("U0",fontsize=16),plt.ylabel("U1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()

[17]:

model=ADDA(encoder,task,discriminator,random_state=0)model.fit(Xs,ys,Xt,epochs=500,verbose=0);scores_s=(np.abs(model.predict(Xs).ravel()-ys)<0.5).mean()scores_t=(np.abs(model.predict(Xt).ravel()-yt)<0.5).mean()yp_grid=model.predict(X_grid).reshape(100,100)>0.5plt.figure(figsize=(8,6))plt.plot(Xs[:,0],Xs[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt[:,0],Xt[:,1],'o',markeredgecolor="k",label="target")plt.contourf(x_grid,y_grid,yp_grid,cmap=cm)plt.plot([0],[0],c="red",label="predicted class separation")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("X0",fontsize=16),plt.ylabel("X1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()

[18]:

Xs_enc=model.transform(Xs)Xt_enc=model.transform(Xt)plt.figure(figsize=(8,6))plt.plot(Xs_enc[:,0],Xs_enc[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt_enc[:,0],Xt_enc[:,1],'o',markeredgecolor="k",label="target")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("U0",fontsize=16),plt.ylabel("U1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()

[10]:

model=mSDA(encoder=encoder,estimator=LogisticRegression(C=1000.),noise_lvl=0.1,random_state=0)model.fit(Xs,ys,Xt,epochs=500,verbose=0);scores_s=(np.abs(model.predict(Xs).ravel()-ys)<0.5).mean()scores_t=(np.abs(model.predict(Xt).ravel()-yt)<0.5).mean()yp_grid=model.predict(X_grid).reshape(100,100)>0.5plt.figure(figsize=(8,6))plt.plot(Xs[:,0],Xs[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt[:,0],Xt[:,1],'o',markeredgecolor="k",label="target")plt.contourf(x_grid,y_grid,yp_grid,cmap=cm)plt.plot([0],[0],c="red",label="predicted class separation")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("X0",fontsize=16),plt.ylabel("X1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()

Fit autoencoder...Fit estimator...

[11]:

Xs_enc=model.predict_features(Xs)Xt_enc=model.predict_features(Xt)plt.figure(figsize=(8,6))plt.plot(Xs_enc[:,0],Xs_enc[:,1],'o',markeredgecolor="k",label="source")plt.plot(Xt_enc[:,0],Xt_enc[:,1],'o',markeredgecolor="k",label="target")plt.legend(fontsize=16,bbox_to_anchor=(1.05,1),loc='upper left')plt.xlabel("U0",fontsize=16),plt.ylabel("U1",fontsize=16)plt.title("Src Acc =%.2f -- Tgt Acc =%.2f"%(scores_s,scores_t),fontsize=18)plt.show()