commit 81bed44ac61392f592c7bed27ae2cee54828cd2a
Author: aboelhamd <aboelhamd.abotreka@gmail.com>
Date:   Fri Jul 26 23:13:58 2019 +0200

    save the encoder too

diff --git a/all-datasets-models.ipynb b/all-datasets-models.ipynb
index 48d893e..24137bb 100644
--- a/all-datasets-models.ipynb
+++ b/all-datasets-models.ipynb
@@ -9,30 +9,6 @@
     "%matplotlib inline"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Classifier comparison\n",
-    "\n",
-    "\n",
-    "A comparison of a several classifiers in scikit-learn on synthetic datasets.\n",
-    "The point of this example is to illustrate the nature of decision boundaries\n",
-    "of different classifiers.\n",
-    "This should be taken with a grain of salt, as the intuition conveyed by\n",
-    "these examples does not necessarily carry over to real datasets.\n",
-    "\n",
-    "Particularly in high-dimensional spaces, data can more easily be separated\n",
-    "linearly and the simplicity of classifiers such as naive Bayes and linear SVMs\n",
-    "might lead to better generalization than is achieved by other classifiers.\n",
-    "\n",
-    "The plots show training points in solid colors and testing points\n",
-    "semi-transparent. The lower right shows the classification accuracy on the test\n",
-    "set.\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -15521,6 +15497,10 @@
     "    from sklearn.preprocessing import OrdinalEncoder\n",
     "    enc = OrdinalEncoder(dtype=np.int32)\n",
     "    features = enc.fit_transform(data.iloc[:,2:])\n",
+    "    \n",
+    "    # save the encoder\n",
+    "    modelname = 'models/'+'encoder'+'-'+file[:-4]\n",
+    "    joblib.dump(clf, modelname)\n",
     "#     display(enc.categories_)\n",
     "#     display(data.iloc[:,2:],features)\n",
     "    # target and weights\n",
@@ -15547,9 +15527,9 @@
     "        print(\" score =\", score)\n",
     "        \n",
     "        # save models\n",
-    "        name+'-'+file[:-4]+'.model'\n",
-    "        filename = 'models/'+name+'-'+file[:-4]+'.model'\n",
-    "        joblib.dump(clf, filename)\n",
+    "#         name+'-'+file[:-4]+'.model'\n",
+    "        modelname = 'models/'+name+'-'+file[:-4]\n",
+    "        joblib.dump(clf, modelname)\n",
     "    print(\"----------------------------------------------\\n\")\n"
    ]
   },