DOC Increase execution speed of plot_sgd_comparison (#21610)

Sven Eschlbeck · thomasjpfan · glemaitre · commit 2017d993c49c · 2021-12-25T12:26:49.000+01:00
Co-authored-by: Thomas J. Fan &lt;thomasjpfan@gmail.com&gt;
diff --git a/examples/linear_model/plot_sgd_comparison.py b/examples/linear_model/plot_sgd_comparison.py
@@ -2,10 +2,8 @@
 ==================================
 Comparing various online solvers
 ==================================
-
 An example showing how different online solvers perform
 on the hand-written digits dataset.
-
 """
 
 # Author: Rob Zinkov <rob at zinkov dot com>
@@ -21,22 +19,28 @@
 from sklearn.linear_model import LogisticRegression
 
 heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
-rounds = 20
+# Number of rounds to fit and evaluate an estimator.
+rounds = 10
 X, y = datasets.load_digits(return_X_y=True)
 
 classifiers = [
-    ("SGD", SGDClassifier(max_iter=100)),
-    ("ASGD", SGDClassifier(average=True)),
-    ("Perceptron", Perceptron()),
+    ("SGD", SGDClassifier(max_iter=110)),
+    ("ASGD", SGDClassifier(max_iter=110, average=True)),
+    ("Perceptron", Perceptron(max_iter=110)),
     (
         "Passive-Aggressive I",
-        PassiveAggressiveClassifier(loss="hinge", C=1.0, tol=1e-4),
+        PassiveAggressiveClassifier(max_iter=110, loss="hinge", C=1.0, tol=1e-4),
     ),
     (
         "Passive-Aggressive II",
-        PassiveAggressiveClassifier(loss="squared_hinge", C=1.0, tol=1e-4),
+        PassiveAggressiveClassifier(
+            max_iter=110, loss="squared_hinge", C=1.0, tol=1e-4
+        ),
+    ),
+    (
+        "SAG",
+        LogisticRegression(max_iter=110, solver="sag", tol=1e-1, C=1.0e4 / X.shape[0]),
     ),
-    ("SAG", LogisticRegression(solver="sag", tol=1e-1, C=1.0e4 / X.shape[0])),
 ]
 
 xx = 1.0 - np.array(heldout)
