updated notebooks

claesenm · Jul 9, 2015 · 061d509 · 061d509
1 parent 9bb7ed6
commit 061d509
Show file tree

Hide file tree

Showing 2 changed files with 20 additions and 28 deletions.
diff --git a/docs/notebooks/notebooks/sklearn-svc.rst b/docs/notebooks/notebooks/sklearn-svc.rst
@@ -1,4 +1,7 @@
 
+Tuning a scikit-learn SVM classifier
+====================================
+
 In this example we will use Optunity to optimize hyperparameters for a
 support vector machine classifier (SVC) in scikit-learn. We will learn a
 model to distinguish digits 8 and 9 in the MNIST data set in two
@@ -55,12 +58,12 @@ First, lets see the performance of an SVC with default hyperparameters.
 
 .. parsed-literal::
 
-    0.6817249486770811
+    0.7838539081885857
 
 
 
 Tune SVC with RBF kernel 
-=========================
+-------------------------
 
 In order to use Optunity to optimize hyperparameters, we start by
 defining the objective function. We will use 5-fold cross-validated area
@@ -110,12 +113,12 @@ Now we can use Optunity to find the hyperparameters that maximize AUROC.
 
 .. parsed-literal::
 
-    Optimal parameters: {'C': 2.7282633076523837, 'gamma': 0.0003793484996538702}
-    AUROC of tuned SVM with RBF kernel: 0.981
+    Optimal parameters: {'C': 5.145039160286679, 'gamma': 0.0011649329771152538}
+    AUROC of tuned SVM with RBF kernel: 0.985
 
 
 Tune SVC without deciding the kernel in advance 
-================================================
+------------------------------------------------
 
 In the previous part we choose to use an RBF kernel. Even though the RBF
 kernel is known to work well for a large variety of problems (and
@@ -169,17 +172,8 @@ hyperparameters!
     print("Optimal parameters" + str(optimal_svm_pars))
     print("AUROC of tuned SVM: %1.3f" % info.optimum)
 
-::
-
-
-    ---------------------------------------------------------------------------
-    AttributeError                            Traceback (most recent call last)
-
-    <ipython-input-8-510629d48255> in <module>()
-    ----> 1 optimal_svm_pars, info, _ = optunity.maximize_structured(svm_tuned_auroc, space, num_evals=150)
-          2 print("Optimal parameters" + str(optimal_svm_pars))
-          3 print("AUROC of tuned SVM: %1.3f" % info.optimum)
-
+.. parsed-literal::
 
-    AttributeError: 'module' object has no attribute 'maximize_structured'
+    Optimal parameters{'kernel': 'rbf', 'C': 7.919921875, 'coef0': None, 'gamma': 0.00107421875, 'degree': None}
+    AUROC of tuned SVM: 0.986
 
diff --git a/notebooks/sklearn-svc.ipynb b/notebooks/sklearn-svc.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a1f6146842bf7ec231af71435516d6b7921e86d3dd5698060e0cd6fbde5bf65e"
+  "signature": "sha256:b612a358c9e392e8474fa3ac13e0e29107507c7fd42cccd42be68cf3f94feb02"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -104,7 +104,7 @@
        "output_type": "pyout",
        "prompt_number": 3,
        "text": [
-        "0.6817249486770811"
+        "0.7838539081885857"
        ]
       }
      ],
@@ -187,8 +187,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Optimal parameters: {'C': 2.7282633076523837, 'gamma': 0.0003793484996538702}\n",
-        "AUROC of tuned SVM with RBF kernel: 0.981\n"
+        "Optimal parameters: {'C': 5.145039160286679, 'gamma': 0.0011649329771152538}\n",
+        "AUROC of tuned SVM with RBF kernel: 0.985\n"
        ]
       }
      ],
@@ -281,13 +281,11 @@
      "metadata": {},
      "outputs": [
       {
-       "ename": "AttributeError",
-       "evalue": "'module' object has no attribute 'maximize_structured'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-8-510629d48255>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moptimal_svm_pars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptunity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximize_structured\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msvm_tuned_auroc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_evals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Optimal parameters\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimal_svm_pars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"AUROC of tuned SVM: %1.3f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'maximize_structured'"
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Optimal parameters{'kernel': 'rbf', 'C': 7.919921875, 'coef0': None, 'gamma': 0.00107421875, 'degree': None}\n",
+        "AUROC of tuned SVM: 0.986\n"
        ]
       }
      ],