ENH Improve script output display

MRXLT · Jan 12, 2015 · b0dba06 · b0dba06
1 parent fa0ceee
commit b0dba06
Showing 1 changed file with 15 additions and 18 deletions.
diff --git a/benchmarks/bench_mnist.py b/benchmarks/bench_mnist.py
@@ -8,18 +8,15 @@
 10 classes -  digits from 0 to 9 from their raw images. By contrast to the
 covertype dataset, the feature space is homogenous.
 
+Example of output :
+
     [..]
     Classification performance:
     ===========================
-    Classifier       train-time test-time error-rate
-    --------------------------------------------
-    Nystroem-SVM     118.4512s   0.8624s     0.0231
-    ExtraTrees        47.5039s   0.5075s     0.0288
-    RandomForest      46.2317s   0.4342s     0.0304
-    SampledRBF-SVM   137.0131s   0.8688s     0.0488
-    CART              21.0593s   0.0134s     0.1214
-
-
+    Classifier               train-time   test-time   error-rate
+    ------------------------------------------------------------
+    Nystroem-SVM                115.31s       1.23s       0.0227
+    ExtraTrees                   55.90s       1.27s       0.0288
 
 """
 from __future__ import division, print_function
@@ -83,9 +80,9 @@ def load_data(dtype=np.float32, order='F'):
     'ExtraTrees': ExtraTreesClassifier(n_estimators=100),
     'RandomForest': RandomForestClassifier(n_estimators=100),
     'Nystroem-SVM':
-    make_pipeline(Nystroem(gamma=0.031, n_components=1000), LinearSVC(C=100)),
+    make_pipeline(Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
     'SampledRBF-SVM':
-    make_pipeline(RBFSampler(gamma=0.031, n_components=1000), LinearSVC(C=100))
+    make_pipeline(RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100))
 }
 
 
@@ -152,12 +149,12 @@ def load_data(dtype=np.float32, order='F'):
     print()
     print("Classification performance:")
     print("===========================")
-    print("%s %s %s %s"
-          % ("Classifier".ljust(16), "train-time", "test-time", "error-rate"))
-    print("-" * 44)
+    print("{0: <24} {1: >10} {2: >11} {3: >12}"
+          "".format("Classifier  ", "train-time", "test-time", "error-rate"))
+    print("-" * 60)
     for name in sorted(args["classifiers"], key=error.get):
-        print("%s %s %s %s" % (name.ljust(16),
-                               ("%.4fs" % train_time[name]).center(10),
-                               ("%.4fs" % test_time[name]).center(10),
-                               ("%.4f" % error[name]).center(10)))
+
+        print("{0: <23} {1: >10.2f}s {2: >10.2f}s {3: >12.4f}"
+              "".format(name, train_time[name], test_time[name], error[name]))
+
     print()