Remove the usage of the deprecated parameter.

dmlc · Sep 27, 2024 · a7f502e · a7f502e
1 parent adbefc6
commit a7f502e
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Showing 6 changed files with 7 additions and 7 deletions.
diff --git a/examples/distributed/graphsage/node_classification_unsupervised.py b/examples/distributed/graphsage/node_classification_unsupervised.py
@@ -175,7 +175,7 @@ def compute_acc(emb, labels, train_nids, val_nids, test_nids):
     labels = labels.cpu().numpy()
 
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], labels[train_nids])
 
     pred = lr.predict(emb)

diff --git a/examples/pytorch/graphsage/advanced/model.py b/examples/pytorch/graphsage/advanced/model.py
@@ -104,7 +104,7 @@ def compute_acc_unsupervised(emb, labels, train_nids, val_nids, test_nids):
 
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
 
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], train_labels)
 
     pred = lr.predict(emb)

diff --git a/examples/pytorch/graphsage/dist/train_dist_unsupervised.py b/examples/pytorch/graphsage/dist/train_dist_unsupervised.py
@@ -175,7 +175,7 @@ def compute_acc(emb, labels, train_nids, val_nids, test_nids):
     labels = labels.cpu().numpy()
 
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], labels[train_nids])
 
     pred = lr.predict(emb)

diff --git a/examples/pytorch/metapath2vec/test.py b/examples/pytorch/metapath2vec/test.py
@@ -95,11 +95,11 @@
         file.close()
         print("beging predicting")
         clf_venue = LogisticRegression(
-            random_state=0, solver="lbfgs", multi_class="multinomial"
+            random_state=0, solver="lbfgs"
         ).fit(venue_training, venue_label)
         y_pred_venue = clf_venue.predict(venue_testing)
         clf_author = LogisticRegression(
-            random_state=0, solver="lbfgs", multi_class="multinomial"
+            random_state=0, solver="lbfgs"
         ).fit(author_training, author_label)
         y_pred_author = clf_author.predict(author_testing)
         macro_average_venue += f1_score(

diff --git a/examples/pytorch/multigpu/multi_gpu_link_prediction.py b/examples/pytorch/multigpu/multi_gpu_link_prediction.py
@@ -142,7 +142,7 @@ def compute_acc_unsupervised(emb, labels, train_nids, val_nids, test_nids):
     test_nids = test_nids.cpu().numpy()
     test_labels = labels[test_nids]
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], train_labels)
     pred = lr.predict(emb)
     f1_micro_eval = skm.f1_score(val_labels, pred[val_nids], average="micro")

diff --git a/examples/pytorch/node2vec/model.py b/examples/pytorch/node2vec/model.py
@@ -191,7 +191,7 @@ def evaluate(self, x_train, y_train, x_val, y_val):
         x_train, y_train = x_train.cpu().numpy(), y_train.cpu().numpy()
         x_val, y_val = x_val.cpu().numpy(), y_val.cpu().numpy()
         lr = LogisticRegression(
-            solver="lbfgs", multi_class="auto", max_iter=150
+            solver="lbfgs", max_iter=150
         ).fit(x_train, y_train)
 
         return lr.score(x_val, y_val)