examples/lgbm/1_lgbm.ipynb

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    "# LGBMClassifier → PMML\n",
    "\n",
    "### Exporter: LGBMClassifier \n",
    "\n",
    "### Data Set used: Iris, Auto-mpg\n",
    "\n",
    "\n",
    "### **STEPS**: \n",
    "- Build the Pipeline with model using LGBMClassifier\n",
    "- Build PMML using Nyoka exporter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LGBMClassifier Model building (using pipeline) for Iris data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-13T17:10:27.044227Z",
     "start_time": "2018-08-13T17:10:25.677727Z"
    }
   },
   "outputs": [
    {
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;lgbmc&#x27;, LGBMClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;lgbmc&#x27;, LGBMClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('lgbmc', LGBMClassifier())])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from lightgbm import LGBMRegressor,LGBMClassifier\n",
    "\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "irisd = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "irisd['Species'] = iris.target\n",
    "\n",
    "features = irisd.columns.drop('Species')\n",
    "target = 'Species'\n",
    "\n",
    "pipeline_obj = Pipeline([\n",
    "    ('lgbmc',LGBMClassifier())\n",
    "])\n",
    "\n",
    "pipeline_obj.fit(irisd[features],irisd[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "irisd.to_csv(\"iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nyoka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5.4.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyoka.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.3.5'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lightgbm.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the Pipeline object into PMML using the Nyoka package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-13T17:10:46.702727Z",
     "start_time": "2018-08-13T17:10:46.077227Z"
    }
   },
   "outputs": [],
   "source": [
    "from nyoka import lgb_to_pmml\n",
    "\n",
    "lgb_to_pmml(pipeline_obj,features,target,\"lgbmc_pmml.pmml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LGBMRegressor Model building (using pipeline) for Auto-mpg data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;lgbmr&#x27;, LGBMRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;lgbmr&#x27;, LGBMRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('lgbmr', LGBMRegressor())])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto = pd.read_csv('auto-mpg.csv')\n",
    "X = auto.drop(['mpg','car name'], axis=1)\n",
    "y = auto['mpg']\n",
    "\n",
    "feature_names = [name for name in auto.columns if name not in ('mpg','car name')]\n",
    "target_name='mpg'\n",
    "\n",
    "pipeline_obj = Pipeline([\n",
    "    ('lgbmr',LGBMRegressor())\n",
    "])\n",
    "\n",
    "pipeline_obj.fit(auto[feature_names],auto[target_name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the Pipeline object into PMML using the Nyoka package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_to_pmml(pipeline_obj,feature_names,target_name,\"lgbmr_pmml.pmml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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