Modified example jupyter notebooks.

SoftwareAG · Oct 17, 2018 · 5812c15 · 5812c15
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diff --git a/examples/lgbm/1_lgbm.ipynb b/examples/lgbm/1_lgbm.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>LGBMClassifier → PMML (using Nyoka) </font>\n",
+    "# LGBMClassifier → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: LGBMClassifier </font>\n",
-    "### <font color=purple> Data Set used: Iris, Auto-mpg </font>\n",
+    "### Exporter: LGBMClassifier \n",
     "\n",
+    "### Data Set used: Iris, Auto-mpg\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with model using LGBMClassifier\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### LGBMClassifier Model building (using pipeline) for Iris data set"
    ]
   },
@@ -78,7 +75,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -102,7 +98,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### LGBMRegressor Model building (using pipeline) for Auto-mpg data set"
    ]
   },
@@ -147,7 +142,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -159,13 +153,6 @@
    "source": [
     "lgb_to_pmml(pipeline_obj,feature_names,target_name,\"lgbmr_pmml.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/examples/lgbm/2_lgbm_With_Scaling.ipynb b/examples/lgbm/2_lgbm_With_Scaling.ipynb
@@ -4,16 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<font size = 30>LGBM → PMML (using Nyoka) </font>\n",
+    "# LGBM → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "<font color=purple> Exporter: LGBM Classifier </font>\n",
-    "<font color=purple> Data Set used: Iris </font>\n",
+    "### Exporter: LGBM Classifier\n",
+    "### Data Set used: Iris \n",
     "\n",
     "\n",
-    "<font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with preprocessing \n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +19,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Iris data set"
    ]
   },
@@ -79,7 +75,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -97,13 +92,6 @@
     "from nyoka import lgb_to_pmml\n",
     "lgb_to_pmml(pipeline_obj,features,target,\"lgbmc_pmml_preprocess.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/examples/lgbm/3_lgbm_With_PreProcess .ipynb b/examples/lgbm/3_lgbm_With_PreProcess .ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<font size = 30>LGBM → PMML (using Nyoka) </font>\n",
+    "# LGBM → PMML \n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "<font color=purple> Exporter: LGBMRegressor </font>\n",
-    "<font color=purple> Data Set used: Auto </font>\n",
+    "### Exporter: LGBMRegressor\n",
     "\n",
+    "### Data Set used: Auto\n",
     "\n",
-    "<font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with preprocessing \n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Auto data set"
    ]
   },
@@ -84,7 +81,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -102,13 +98,6 @@
     "from nyoka import lgb_to_pmml\n",
     "lgb_to_pmml(pipeline_obj,feature_names,target_name,\"lgbmr_pmml_preprocess.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/examples/skl/1_SVM.ipynb b/examples/skl/1_SVM.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Support Vector Classifier </font>\n",
-    "### <font color=purple> Data Set used: Iris </font>\n",
+    "### Exporter: Support Vector Classifier\n",
     "\n",
+    "### Data Set used: Iris \n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with model using sklearn SVC\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Model building (using pipeline) for Iris data set"
    ]
   },
@@ -76,7 +73,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },
@@ -125,15 +121,6 @@
     "\n",
     "skl_to_pmml(pipeline_obj,features,target,\"svc_pmml.pmml\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -152,7 +139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

diff --git a/examples/skl/3_RF_With_pre-processing.ipynb b/examples/skl/3_RF_With_pre-processing.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Random Forest </font>\n",
-    "### <font color=purple> Data Set used: Iris </font>\n",
     "\n",
+    "### Exporter: Random Forest\n",
+    "### Data Set used: Iris\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**:\n",
     "- Build the Pipeline with preprocessing (using DataFrameMapper)\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Iris data set"
    ]
   },
@@ -81,7 +78,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },

diff --git a/examples/skl/4_GB_With_pre-processing.ipynb b/examples/skl/4_GB_With_pre-processing.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Gradient Boosting </font>\n",
-    "### <font color=purple> Data Set used: Titanic </font>\n",
     "\n",
+    "### Exporter: Gradient Boosting\n",
+    "### Data Set used: Titanic\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**: \n",
     "- Build the Pipeline with preprocessing (using DataFrameMapper)\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Pre-processing, Model building (using pipeline) for Titanic data set"
    ]
   },
@@ -104,7 +101,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },

diff --git a/examples/skl/5_Decision_Tree_With_Tf-Idf.ipynb b/examples/skl/5_Decision_Tree_With_Tf-Idf.ipynb
@@ -4,16 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "# <font size = 30>scikit-learn → PMML (using Nyoka) </font>\n",
+    "# scikit-learn → PMML\n",
     "\n",
-    "<div class=\"alert alert-block alert-success\">\n",
-    "### <font color=purple> Exporter: Decision Tree Regressor</font>\n",
-    "### <font color=purple> Data Set used: auto_mpg </font>\n",
     "\n",
+    "### Exporter: Decision Tree Regressor\n",
+    "### Data Set used: auto_mpg\n",
     "\n",
-    "### <font color=purple>**STEPS**: </font>\n",
-    "<font color=brown>\n",
+    "\n",
+    "### **STEPS**:\n",
     "- Build the Pipeline with model and pre-processing (tf-idf vectorizer) using sklearn Decision Tree Regressor\n",
     "- Build PMML using Nyoka exporter"
    ]
@@ -22,7 +20,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Model building (using pipeline) for Iris data set"
    ]
   },
@@ -81,7 +78,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
     "### Export the Pipeline object into PMML using the Nyoka package"
    ]
   },