Temporarily allows to set algorithm switching through an environment …

…variable
scikit-learn · Nov 6, 2014 · bf98916 · bf98916
1 parent c31d565
commit bf98916
Show file tree

Hide file tree

Showing 3 changed files with 2,927 additions and 2,647 deletions.
diff --git a/benchmarks/bench_20newsgroups.py b/benchmarks/bench_20newsgroups.py
@@ -15,12 +15,12 @@
 
 ESTIMATORS = {
     "dummy": DummyClassifier(),
-    "random_forest": RandomForestClassifier(n_estimators=200,
-                                            max_features="log2",
+    "random_forest": RandomForestClassifier(n_estimators=100,
+                                            max_features="sqrt",
                                             # min_samples_split=10
                                             ),
-    "extra_trees": ExtraTreesClassifier(n_estimators=200,
-                                        max_features="log2",
+    "extra_trees": ExtraTreesClassifier(n_estimators=100,
+                                        max_features="sqrt",
                                         # min_samples_split=10
                                         ),
 }
@@ -44,21 +44,21 @@
     y_train = data_train.target
     y_test = data_test.target
 
-    print("20 newsgroups")
-    print("=============")
-    print("X_train.shape = {0}".format(X_train.shape))
-    print("X_train.format = {0}".format(X_train.format))
-    print("X_train.dtype = {0}".format(X_train.dtype))
-    print("X_train density = {0}"
-          "".format(X_train.nnz / np.product(X_train.shape)))
-    print("y_train {0}".format(y_train.shape))
-    print("X_test {0}".format(X_test.shape))
-    print("X_test.format = {0}".format(X_test.format))
-    print("X_test.dtype = {0}".format(X_test.dtype))
-    print("y_test {0}".format(y_test.shape))
-    print()
-    print("Classifier Training")
-    print("===================")
+    # print("20 newsgroups")
+    # print("=============")
+    # print("X_train.shape = {0}".format(X_train.shape))
+    # print("X_train.format = {0}".format(X_train.format))
+    # print("X_train.dtype = {0}".format(X_train.dtype))
+    # print("X_train density = {0}"
+    #       "".format(X_train.nnz / np.product(X_train.shape)))
+    # print("y_train {0}".format(y_train.shape))
+    # print("X_test {0}".format(X_test.shape))
+    # print("X_test.format = {0}".format(X_test.format))
+    # print("X_test.dtype = {0}".format(X_test.dtype))
+    # print("y_test {0}".format(y_test.shape))
+    # print()
+    # print("Classifier Training")
+    # print("===================")
     accuracy, train_time, test_time = {}, {}, {}
     for name in sorted(args["estimators"]):
         clf = ESTIMATORS[name]
@@ -67,15 +67,15 @@
         except (TypeError, ValueError):
             pass
 
-        print("Training %s ... " % name, end="")
+        # print("Training %s ... " % name, end="")
         t0 = time()
         clf.fit(X_train, y_train)
         train_time[name] = time() - t0
         t0 = time()
         y_pred = clf.predict(X_test)
         test_time[name] = time() - t0
         accuracy[name] = accuracy_score(y_test, y_pred)
-        print("done")
+        # print("done")
 
     print()
     print("Classification performance:")