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Mounir
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modif subfunctions + relabeling method + example udpate
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3 files changed

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‎adapt/parameter_based/decision_trees/example.py

Lines changed: 118 additions & 74 deletions
Original file line numberDiff line numberDiff line change
@@ -15,17 +15,27 @@
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import transfer_tree as TL
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methods = [
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'relab',
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'ser',
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'strut',
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'ser_nr',
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'ser_nr_lambda',
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'strut_nd',
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'strut_lambda',
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'strut_lambda_np'
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# 'strut_hi'
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]
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labels = [
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'SER',
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'STRUT',
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'SER$^{*}$',
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'relab',
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'$SER$',
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'$STRUT$',
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'$SER_{NP}$',
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'$SER_{NP}(\lambda)$',
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'$STRUT_{ND}$',
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'$STRUT(\lambda)$',
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'$STRUT_{NP}(\lambda)$'
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# 'STRUT$^{*}$',
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'STRUT$^{*}$',
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#'STRUT$^{*}$',
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]
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np.random.seed(0)
@@ -76,16 +86,50 @@
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#transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,Xt=Xt,yt=yt)
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for method in methods:
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Nkmin = sum(yt == 0 )
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root_source_values = clf_source.tree_.value[0].reshape(-1)
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props_s = root_source_values
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props_s = props_s / sum(props_s)
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props_t = np.zeros(props_s.size)
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for k in range(props_s.size):
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props_t[k] = np.sum(yt == k) / yt.size
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coeffs = np.divide(props_t, props_s)
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clf_transfer = copy.deepcopy(clf_source)
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if method == 'relab':
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transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="")
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transferred_dt.fit(Xt,yt)
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if method == 'ser':
81-
transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,Xt=Xt,yt=yt,algo="ser")
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transferred_dt._ser(Xt, yt, node=0, original_ser=True)
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transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="ser")
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transferred_dt.fit(Xt,yt)
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#transferred_dt._ser(Xt, yt, node=0, original_ser=True)
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#ser.SER(0, clf_transfer, Xt, yt, original_ser=True)
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if method == 'ser_nr':
85-
transferred_dt._ser(Xt, yt,node=0,original_ser=False,no_red_on_cl=True,cl_no_red=[0],ext_cond=True)
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transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="ser")
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transferred_dt._ser(Xt, yt,node=0,original_ser=False,no_red_on_cl=True,cl_no_red=[0])
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if method == 'ser_nr_lambda':
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transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="ser")
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transferred_dt._ser(Xt, yt,node=0,original_ser=False,no_red_on_cl=True,cl_no_red=[0],
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leaf_loss_quantify=True,leaf_loss_threshold=0.5,
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root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs)
86116
#ser.SER(0, clf_transfer, Xt, yt,original_ser=False,no_red_on_cl=True,cl_no_red=[0],ext_cond=True)
87117
if method == 'strut':
88-
transferred_dt._strut(Xt, yt,node=0)
118+
transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="strut")
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transferred_dt.fit(Xt,yt)
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#transferred_dt._strut(Xt, yt,node=0)
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if method == 'strut_nd':
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transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="strut")
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transferred_dt._strut(Xt, yt,node=0,use_divergence=False)
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if method == 'strut_lambda':
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transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="strut")
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transferred_dt._strut(Xt, yt,node=0,adapt_prop=True,root_source_values=root_source_values,
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Nkmin=Nkmin,coeffs=coeffs)
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if method == 'strut_lambda_np':
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transferred_dt = TL.TransferTreeClassifier(estimator=clf_transfer,algo="strut")
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transferred_dt._strut(Xt, yt,node=0,adapt_prop=False,no_prune_on_cl=True,cl_no_prune=[0],
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leaf_loss_quantify=False,leaf_loss_threshold=0.5,no_prune_with_translation=False,
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root_source_values=root_source_values,Nkmin=Nkmin,coeffs=coeffs)
89133
#if method == 'strut_hi':
90134
#transferred_dt._strut(Xt, yt,node=0,no_prune_on_cl=False,adapt_prop=True,coeffs=[0.2, 1])
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#strut.STRUT(clf_transfer, 0, Xt, yt, Xt, yt,pruning_updated_node=True,no_prune_on_cl=False,adapt_prop=True,simple_weights=False,coeffs=[0.2, 1])
@@ -96,69 +140,69 @@
96140
#clfs.append(clf_transfer)
97141
scores.append(score)
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99-
## Plot decision functions
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#
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## Data on which to plot source
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#x_min, x_max = Xs[:, 0].min() - 1, Xs[:, 0].max() + 1
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#y_min, y_max = Xs[:, 1].min() - 1, Xs[:, 1].max() + 1
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#xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
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# np.arange(y_min, y_max, plot_step))
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## Plot source model
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#Z = clf_source.predict(np.c_[xx.ravel(), yy.ravel()])
108-
#Z = Z.reshape(xx.shape)
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#fig, ax = plt.subplots(nrows=1, ncols=len(methods) + 1, figsize=(13, 3))
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#ax[0].contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
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#ax[0].scatter(Xs[0, 0], Xs[0, 1],
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# marker='o',
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# edgecolor='black',
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# color='white',
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# label='source data',
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# )
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#ax[0].scatter(Xs[:ns_perclass, 0], Xs[:ns_perclass, 1],
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# marker='o',
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# edgecolor='black',
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# color='blue',
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# )
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#ax[0].scatter(Xs[ns_perclass:, 0], Xs[ns_perclass:, 1],
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# marker='o',
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# edgecolor='black',
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# color='red',
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# )
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#ax[0].set_title('Model: Source\nAcc on source data: {:.2f}\nAcc on target data: {:.2f}'.format(score_src_src, score_src_trgt),
128-
# fontsize=11)
129-
#ax[0].legend()
130-
#
131-
## Data on which to plot target
132-
#x_min, x_max = Xt[:, 0].min() - 1, Xt[:, 0].max() + 1
133-
#y_min, y_max = Xt[:, 1].min() - 1, Xt[:, 1].max() + 1
134-
#xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
135-
# np.arange(y_min, y_max, plot_step))
136-
## Plot transfer models
137-
#for i, (method, label, score) in enumerate(zip(methods, labels, scores)):
138-
# clf_transfer = clfs[i]
139-
# Z_transfer = clf_transfer.predict(np.c_[xx.ravel(), yy.ravel()])
140-
# Z_transfer = Z_transfer.reshape(xx.shape)
141-
# ax[i + 1].contourf(xx, yy, Z_transfer, cmap=plt.cm.coolwarm, alpha=0.8)
142-
# ax[i + 1].scatter(Xt[0, 0], Xt[0, 1],
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# marker='o',
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# edgecolor='black',
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# color='white',
146-
# label='target data',
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# )
148-
# ax[i + 1].scatter(Xt[:nt_0, 0], Xt[:nt_0, 1],
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# marker='o',
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# edgecolor='black',
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# color='blue',
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# )
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# ax[i + 1].scatter(Xt[nt_0:, 0], Xt[nt_0:, 1],
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# marker='o',
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# edgecolor='black',
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# color='red',
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# )
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# ax[i + 1].set_title('Model: {}\nAcc on target data: {:.2f}'.format(label, score),
159-
# fontsize=11)
160-
# ax[i + 1].legend()
161-
#
162-
## fig.savefig('../images/ser_strut.png')
163-
#plt.show()
164-
#
143+
# Plot decision functions
144+
145+
# Data on which to plot source
146+
x_min, x_max = Xs[:, 0].min() - 1, Xs[:, 0].max() + 1
147+
y_min, y_max = Xs[:, 1].min() - 1, Xs[:, 1].max() + 1
148+
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
149+
np.arange(y_min, y_max, plot_step))
150+
# Plot source model
151+
Z = clf_source.predict(np.c_[xx.ravel(), yy.ravel()])
152+
Z = Z.reshape(xx.shape)
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fig, ax = plt.subplots(nrows=1, ncols=len(methods) + 1, figsize=(30, 3))
154+
ax[0].contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
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ax[0].scatter(Xs[0, 0], Xs[0, 1],
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marker='o',
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edgecolor='black',
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color='white',
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label='source data',
160+
)
161+
ax[0].scatter(Xs[:ns_perclass, 0], Xs[:ns_perclass, 1],
162+
marker='o',
163+
edgecolor='black',
164+
color='blue',
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)
166+
ax[0].scatter(Xs[ns_perclass:, 0], Xs[ns_perclass:, 1],
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marker='o',
168+
edgecolor='black',
169+
color='red',
170+
)
171+
ax[0].set_title('Model: Source\nAcc on source data: {:.2f}\nAcc on target data: {:.2f}'.format(score_src_src, score_src_trgt),
172+
fontsize=11)
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ax[0].legend()
174+
175+
# Data on which to plot target
176+
x_min, x_max = Xt[:, 0].min() - 1, Xt[:, 0].max() + 1
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y_min, y_max = Xt[:, 1].min() - 1, Xt[:, 1].max() + 1
178+
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
179+
np.arange(y_min, y_max, plot_step))
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# Plot transfer models
181+
for i, (method, label, score) in enumerate(zip(methods, labels, scores)):
182+
clf_transfer = clfs[i]
183+
Z_transfer = clf_transfer.predict(np.c_[xx.ravel(), yy.ravel()])
184+
Z_transfer = Z_transfer.reshape(xx.shape)
185+
ax[i + 1].contourf(xx, yy, Z_transfer, cmap=plt.cm.coolwarm, alpha=0.8)
186+
ax[i + 1].scatter(Xt[0, 0], Xt[0, 1],
187+
marker='o',
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edgecolor='black',
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color='white',
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label='target data',
191+
)
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ax[i + 1].scatter(Xt[:nt_0, 0], Xt[:nt_0, 1],
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marker='o',
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edgecolor='black',
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color='blue',
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)
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ax[i + 1].scatter(Xt[nt_0:, 0], Xt[nt_0:, 1],
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marker='o',
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edgecolor='black',
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color='red',
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)
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ax[i + 1].set_title('Model: {}\nAcc on target data: {:.2f}'.format(label, score),
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fontsize=11)
204+
ax[i + 1].legend()
205+
206+
# fig.savefig('../images/ser_strut.png')
207+
plt.show()
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