docs/WebService-MLP_Deploy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sqlalchemy as sa\n",
    "import pickle, json, requests, base64\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-Learn Model Deployment Use Case\n",
    "\n",
    "In this case, we will build a model (here, MLP model) on a IRIS dataset  (not very original !!!) and generate a SQL code for deployment using the web service. \n",
    "\n",
    "We then execute the SQL code on a local database (postgresql) and compare the SQL execution result with scikit-learn predict/predict_proba/.predict_log_proba result. \n",
    "\n",
    "Both results are stored in pandas dataframes. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a scikit-learn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data  \n",
    "Y = iris.target\n",
    "# print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "MLPClassifier(random_state=1960)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(random_state=1960)\n",
    "clf.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate SQL Code from the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def test_ws_sql_gen(pickle_data):\n",
    "    WS_URL=\"https://sklearn2sql.herokuapp.com/model\"\n",
    "    b64_data = base64.b64encode(pickle_data).decode('utf-8')\n",
    "    data={\"Name\":\"model1\", \"PickleData\":b64_data , \"SQLDialect\":\"postgresql\"}\n",
    "    r = requests.post(WS_URL, json=data)\n",
    "    #print(r.__dict__)\n",
    "    content = r.json()\n",
    "    # print(content)\n",
    "    lSQL = content[\"model\"][\"SQLGenrationResult\"][0][\"SQL\"]\n",
    "    return lSQL;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH \"IL\" AS \n",
      "(SELECT \"ADS\".\"KEY\" AS \"KEY\", CAST(\"ADS\".\"Feature_0\" AS FLOAT) AS \"Feature_0\", CAST(\"ADS\".\"Feature_1\" AS FLOAT) AS \"Feature_1\", CAST(\"ADS\".\"Feature_2\" AS FLOAT) AS \"Feature_2\", CAST(\"ADS\".\"Feature_3\" AS FLOAT) AS \"Feature_3\" \n",
      "FROM \"INPUT_DATA\" AS \"ADS\"), \n",
      "\"HL_BA_1\" AS \n",
      "(SELECT \"IL\".\"KEY\" AS \"KEY\", -0.0015728749851640802 * \"IL\".\"Feature_0\" + 0.007738492836496263 * \"IL\".\"Feature_1\" + -0.10834230254441445 * \"IL\".\"Feature_2\" + 0.016128851363088125 * \"IL\".\"Feature_3\" + -0.0839798413234357 AS \"NEUR_1_1\", 0.0031119285993132335 * \"IL\".\"Feature_0\" + -0.0239251031949356 * \"IL\".\"Feature_1\" + -0.02458011484953259 * \"IL\".\"Feature_2\" + -0.08175952564419868 * \"IL\".\"Feature_3\" + 0.02754451873665828 AS \"NEUR_1_2\", 0.24895621146508548 * \"IL\".\"Feature_0\" + -0.055288663322814596 * \"IL\".\"Feature_1\" + 0.21204946241500228 * \"IL\".\"Feature_2\" + 0.1369475017682187 * \"IL\".\"Feature_3\" + -0.13481963860377522 AS \"NEUR_1_3\", -0.04711121223553422 * \"IL\".\"Feature_0\" + -0.055848670768729534 * \"IL\".\"Feature_1\" + 0.025449702416132976 * \"IL\".\"Feature_2\" + -0.0003313898412632615 * \"IL\".\"Feature_3\" + -0.11835148571612969 AS \"NEUR_1_4\", -0.006925999220817759 * \"IL\".\"Feature_0\" + -0.04409454657426376 * \"IL\".\"Feature_1\" + -0.056004572666487985 * \"IL\".\"Feature_2\" + -0.03152806691770877 * \"IL\".\"Feature_3\" + 0.1388098994284134 AS \"NEUR_1_5\", -0.04865156798477877 * \"IL\".\"Feature_0\" + -0.10643246191956014 * \"IL\".\"Feature_1\" + -0.010296149552785164 * \"IL\".\"Feature_2\" + -0.044526968237887966 * \"IL\".\"Feature_3\" + -0.216774857325529 AS \"NEUR_1_6\", -0.07132617903042265 * \"IL\".\"Feature_0\" + 0.008590197847034386 * \"IL\".\"Feature_1\" + 0.004528500387734072 * \"IL\".\"Feature_2\" + -0.07285814247561907 * \"IL\".\"Feature_3\" + -0.008842309558458133 AS \"NEUR_1_7\", -0.07250147733895082 * \"IL\".\"Feature_0\" + -0.0972097204842449 * \"IL\".\"Feature_1\" + 0.11761168883990436 * \"IL\".\"Feature_2\" + -0.0862785011866662 * \"IL\".\"Feature_3\" + -0.054428535692556894 AS \"NEUR_1_8\", 0.07669141008252676 * \"IL\".\"Feature_0\" + -0.120499817105\n"
     ]
    }
   ],
   "source": [
    "pickle_data = pickle.dumps(clf)\n",
    "lSQL = test_ws_sql_gen(pickle_data)\n",
    "print(lSQL[0:2000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute the SQL Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the dataset in a database table\n",
    "\n",
    "#engine = sa.create_engine('sqlite://' , echo=False)\n",
    "engine = sa.create_engine(\"postgresql://db:db@localhost/db?port=5432\", echo=False)\n",
    "conn = engine.connect()\n",
    "\n",
    "lTable = pd.DataFrame(X);\n",
    "lTable.columns = ['Feature_0', 'Feature_1', 'Feature_2', 'Feature_3']\n",
    "lTable['KEY'] = range(lTable.shape[0])\n",
    "lTable.to_sql(\"INPUT_DATA\" , conn,   if_exists='replace', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_output = pd.read_sql(lSQL , conn);\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KEY</th>\n",
       "      <th>Score_0</th>\n",
       "      <th>Score_1</th>\n",
       "      <th>Score_2</th>\n",
       "      <th>Proba_0</th>\n",
       "      <th>Proba_1</th>\n",
       "      <th>Proba_2</th>\n",
       "      <th>LogProba_0</th>\n",
       "      <th>LogProba_1</th>\n",
       "      <th>LogProba_2</th>\n",
       "      <th>Decision</th>\n",
       "      <th>DecisionProba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>114</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000819</td>\n",
       "      <td>0.062837</td>\n",
       "      <td>0.936344</td>\n",
       "      <td>-7.107648</td>\n",
       "      <td>-2.767214</td>\n",
       "      <td>-0.065772</td>\n",
       "      <td>2</td>\n",
       "      <td>0.936344</td>\n",
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       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.035002</td>\n",
       "      <td>0.853701</td>\n",
       "      <td>0.111297</td>\n",
       "      <td>-3.352343</td>\n",
       "      <td>-0.158174</td>\n",
       "      <td>-2.195556</td>\n",
       "      <td>1</td>\n",
       "      <td>0.853701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.939008</td>\n",
       "      <td>0.060175</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>-0.062931</td>\n",
       "      <td>-2.810502</td>\n",
       "      <td>-7.110160</td>\n",
       "      <td>0</td>\n",
       "      <td>0.939008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.055523</td>\n",
       "      <td>0.730194</td>\n",
       "      <td>0.214283</td>\n",
       "      <td>-2.890950</td>\n",
       "      <td>-0.314445</td>\n",
       "      <td>-1.540459</td>\n",
       "      <td>1</td>\n",
       "      <td>0.730194</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.908572</td>\n",
       "      <td>0.090141</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>-0.095881</td>\n",
       "      <td>-2.406382</td>\n",
       "      <td>-6.655265</td>\n",
       "      <td>0</td>\n",
       "      <td>0.908572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.963711</td>\n",
       "      <td>0.035854</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>-0.036963</td>\n",
       "      <td>-3.328292</td>\n",
       "      <td>-7.741599</td>\n",
       "      <td>0</td>\n",
       "      <td>0.963711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.970557</td>\n",
       "      <td>0.029151</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>-0.029885</td>\n",
       "      <td>-3.535249</td>\n",
       "      <td>-8.139519</td>\n",
       "      <td>0</td>\n",
       "      <td>0.970557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>117</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.146018</td>\n",
       "      <td>0.853282</td>\n",
       "      <td>-7.264880</td>\n",
       "      <td>-1.924024</td>\n",
       "      <td>-0.158665</td>\n",
       "      <td>2</td>\n",
       "      <td>0.853282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.004844</td>\n",
       "      <td>0.302749</td>\n",
       "      <td>0.692408</td>\n",
       "      <td>-5.330100</td>\n",
       "      <td>-1.194852</td>\n",
       "      <td>-0.367580</td>\n",
       "      <td>2</td>\n",
       "      <td>0.692408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>105</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.115297</td>\n",
       "      <td>0.884395</td>\n",
       "      <td>-8.083683</td>\n",
       "      <td>-2.160248</td>\n",
       "      <td>-0.122852</td>\n",
       "      <td>2</td>\n",
       "      <td>0.884395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.960722</td>\n",
       "      <td>0.038849</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>-0.040070</td>\n",
       "      <td>-3.248061</td>\n",
       "      <td>-7.755058</td>\n",
       "      <td>0</td>\n",
       "      <td>0.960722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.096355</td>\n",
       "      <td>0.785138</td>\n",
       "      <td>0.118506</td>\n",
       "      <td>-2.339714</td>\n",
       "      <td>-0.241895</td>\n",
       "      <td>-2.132787</td>\n",
       "      <td>1</td>\n",
       "      <td>0.785138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     KEY Score_0 Score_1 Score_2   Proba_0   Proba_1   Proba_2  LogProba_0  \\\n",
       "114  114    None    None    None  0.000819  0.062837  0.936344   -7.107648   \n",
       "74    74    None    None    None  0.035002  0.853701  0.111297   -3.352343   \n",
       "9      9    None    None    None  0.939008  0.060175  0.000817   -0.062931   \n",
       "88    88    None    None    None  0.055523  0.730194  0.214283   -2.890950   \n",
       "25    25    None    None    None  0.908572  0.090141  0.001287   -0.095881   \n",
       "5      5    None    None    None  0.963711  0.035854  0.000434   -0.036963   \n",
       "48    48    None    None    None  0.970557  0.029151  0.000292   -0.029885   \n",
       "117  117    None    None    None  0.000700  0.146018  0.853282   -7.264880   \n",
       "83    83    None    None    None  0.004844  0.302749  0.692408   -5.330100   \n",
       "105  105    None    None    None  0.000309  0.115297  0.884395   -8.083683   \n",
       "27    27    None    None    None  0.960722  0.038849  0.000429   -0.040070   \n",
       "64    64    None    None    None  0.096355  0.785138  0.118506   -2.339714   \n",
       "\n",
       "     LogProba_1  LogProba_2  Decision  DecisionProba  \n",
       "114   -2.767214   -0.065772         2       0.936344  \n",
       "74    -0.158174   -2.195556         1       0.853701  \n",
       "9     -2.810502   -7.110160         0       0.939008  \n",
       "88    -0.314445   -1.540459         1       0.730194  \n",
       "25    -2.406382   -6.655265         0       0.908572  \n",
       "5     -3.328292   -7.741599         0       0.963711  \n",
       "48    -3.535249   -8.139519         0       0.970557  \n",
       "117   -1.924024   -0.158665         2       0.853282  \n",
       "83    -1.194852   -0.367580         2       0.692408  \n",
       "105   -2.160248   -0.122852         2       0.884395  \n",
       "27    -3.248061   -7.755058         0       0.960722  \n",
       "64    -0.241895   -2.132787         1       0.785138  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_output.sample(12, random_state=1960)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    53\n",
       "0    50\n",
       "1    47\n",
       "Name: Decision, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_output.Decision.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KEY</th>\n",
       "      <th>Score_0</th>\n",
       "      <th>Score_1</th>\n",
       "      <th>Score_2</th>\n",
       "      <th>Proba_0</th>\n",
       "      <th>Proba_1</th>\n",
       "      <th>Proba_2</th>\n",
       "      <th>LogProba_0</th>\n",
       "      <th>LogProba_1</th>\n",
       "      <th>LogProba_2</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000819</td>\n",
       "      <td>0.062837</td>\n",
       "      <td>0.936344</td>\n",
       "      <td>-7.107648</td>\n",
       "      <td>-2.767214</td>\n",
       "      <td>-0.065772</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035002</td>\n",
       "      <td>0.853701</td>\n",
       "      <td>0.111297</td>\n",
       "      <td>-3.352343</td>\n",
       "      <td>-0.158174</td>\n",
       "      <td>-2.195556</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.939008</td>\n",
       "      <td>0.060175</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>-0.062931</td>\n",
       "      <td>-2.810502</td>\n",
       "      <td>-7.110160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.055523</td>\n",
       "      <td>0.730194</td>\n",
       "      <td>0.214283</td>\n",
       "      <td>-2.890950</td>\n",
       "      <td>-0.314445</td>\n",
       "      <td>-1.540459</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.908572</td>\n",
       "      <td>0.090141</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>-0.095881</td>\n",
       "      <td>-2.406382</td>\n",
       "      <td>-6.655265</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.963711</td>\n",
       "      <td>0.035854</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>-0.036963</td>\n",
       "      <td>-3.328292</td>\n",
       "      <td>-7.741599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.970557</td>\n",
       "      <td>0.029151</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>-0.029885</td>\n",
       "      <td>-3.535249</td>\n",
       "      <td>-8.139519</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.146018</td>\n",
       "      <td>0.853282</td>\n",
       "      <td>-7.264880</td>\n",
       "      <td>-1.924024</td>\n",
       "      <td>-0.158665</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004844</td>\n",
       "      <td>0.302749</td>\n",
       "      <td>0.692408</td>\n",
       "      <td>-5.330100</td>\n",
       "      <td>-1.194852</td>\n",
       "      <td>-0.367580</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.115297</td>\n",
       "      <td>0.884395</td>\n",
       "      <td>-8.083683</td>\n",
       "      <td>-2.160248</td>\n",
       "      <td>-0.122852</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.960722</td>\n",
       "      <td>0.038849</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>-0.040070</td>\n",
       "      <td>-3.248061</td>\n",
       "      <td>-7.755058</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096355</td>\n",
       "      <td>0.785138</td>\n",
       "      <td>0.118506</td>\n",
       "      <td>-2.339714</td>\n",
       "      <td>-0.241895</td>\n",
       "      <td>-2.132787</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     KEY Score_0 Score_1 Score_2   Proba_0   Proba_1   Proba_2  LogProba_0  \\\n",
       "114  114     NaN     NaN     NaN  0.000819  0.062837  0.936344   -7.107648   \n",
       "74    74     NaN     NaN     NaN  0.035002  0.853701  0.111297   -3.352343   \n",
       "9      9     NaN     NaN     NaN  0.939008  0.060175  0.000817   -0.062931   \n",
       "88    88     NaN     NaN     NaN  0.055523  0.730194  0.214283   -2.890950   \n",
       "25    25     NaN     NaN     NaN  0.908572  0.090141  0.001287   -0.095881   \n",
       "5      5     NaN     NaN     NaN  0.963711  0.035854  0.000434   -0.036963   \n",
       "48    48     NaN     NaN     NaN  0.970557  0.029151  0.000292   -0.029885   \n",
       "117  117     NaN     NaN     NaN  0.000700  0.146018  0.853282   -7.264880   \n",
       "83    83     NaN     NaN     NaN  0.004844  0.302749  0.692408   -5.330100   \n",
       "105  105     NaN     NaN     NaN  0.000309  0.115297  0.884395   -8.083683   \n",
       "27    27     NaN     NaN     NaN  0.960722  0.038849  0.000429   -0.040070   \n",
       "64    64     NaN     NaN     NaN  0.096355  0.785138  0.118506   -2.339714   \n",
       "\n",
       "     LogProba_1  LogProba_2  Decision  \n",
       "114   -2.767214   -0.065772         2  \n",
       "74    -0.158174   -2.195556         1  \n",
       "9     -2.810502   -7.110160         0  \n",
       "88    -0.314445   -1.540459         1  \n",
       "25    -2.406382   -6.655265         0  \n",
       "5     -3.328292   -7.741599         0  \n",
       "48    -3.535249   -8.139519         0  \n",
       "117   -1.924024   -0.158665         2  \n",
       "83    -1.194852   -0.367580         2  \n",
       "105   -2.160248   -0.122852         2  \n",
       "27    -3.248061   -7.755058         0  \n",
       "64    -0.241895   -2.132787         1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skl_outputs = pd.DataFrame()\n",
    "skl_output_key = pd.DataFrame(list(range(X.shape[0])), columns=['KEY']);\n",
    "skl_output_score = pd.DataFrame(columns=['Score_0', 'Score_1', 'Score_2']);\n",
    "skl_output_proba = pd.DataFrame(clf.predict_proba(X), columns=['Proba_0', 'Proba_1', 'Proba_2'])\n",
    "skl_output_log_proba = pd.DataFrame(clf.predict_log_proba(X), columns=['LogProba_0', 'LogProba_1', 'LogProba_2'])\n",
    "skl_output_decision = pd.DataFrame(clf.predict(X), columns=['Decision'])\n",
    "skl_output = pd.concat([skl_output_key, skl_output_score, skl_output_proba, skl_output_log_proba, skl_output_decision] , axis=1)\n",
    "skl_output.sample(12, random_state=1960)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the SQL and Scikit-learn Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_skl_join = skl_output.join(sql_output , how='left', on='KEY', lsuffix='_skl', rsuffix='_sql')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KEY_skl</th>\n",
       "      <th>Score_0_skl</th>\n",
       "      <th>Score_1_skl</th>\n",
       "      <th>Score_2_skl</th>\n",
       "      <th>Proba_0_skl</th>\n",
       "      <th>Proba_1_skl</th>\n",
       "      <th>Proba_2_skl</th>\n",
       "      <th>LogProba_0_skl</th>\n",
       "      <th>LogProba_1_skl</th>\n",
       "      <th>LogProba_2_skl</th>\n",
       "      <th>...</th>\n",
       "      <th>Score_1_sql</th>\n",
       "      <th>Score_2_sql</th>\n",
       "      <th>Proba_0_sql</th>\n",
       "      <th>Proba_1_sql</th>\n",
       "      <th>Proba_2_sql</th>\n",
       "      <th>LogProba_0_sql</th>\n",
       "      <th>LogProba_1_sql</th>\n",
       "      <th>LogProba_2_sql</th>\n",
       "      <th>Decision_sql</th>\n",
       "      <th>DecisionProba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000819</td>\n",
       "      <td>0.062837</td>\n",
       "      <td>0.936344</td>\n",
       "      <td>-7.107648</td>\n",
       "      <td>-2.767214</td>\n",
       "      <td>-0.065772</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.062837</td>\n",
       "      <td>0.936344</td>\n",
       "      <td>-7.107648</td>\n",
       "      <td>-2.767214</td>\n",
       "      <td>-0.065772</td>\n",
       "      <td>2</td>\n",
       "      <td>0.936344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035002</td>\n",
       "      <td>0.853701</td>\n",
       "      <td>0.111297</td>\n",
       "      <td>-3.352343</td>\n",
       "      <td>-0.158174</td>\n",
       "      <td>-2.195556</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.035002</td>\n",
       "      <td>0.853701</td>\n",
       "      <td>0.111297</td>\n",
       "      <td>-3.352343</td>\n",
       "      <td>-0.158174</td>\n",
       "      <td>-2.195556</td>\n",
       "      <td>1</td>\n",
       "      <td>0.853701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.939008</td>\n",
       "      <td>0.060175</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>-0.062931</td>\n",
       "      <td>-2.810502</td>\n",
       "      <td>-7.110160</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.939008</td>\n",
       "      <td>0.060175</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>-0.062931</td>\n",
       "      <td>-2.810502</td>\n",
       "      <td>-7.110160</td>\n",
       "      <td>0</td>\n",
       "      <td>0.939008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.055523</td>\n",
       "      <td>0.730194</td>\n",
       "      <td>0.214283</td>\n",
       "      <td>-2.890950</td>\n",
       "      <td>-0.314445</td>\n",
       "      <td>-1.540459</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.055523</td>\n",
       "      <td>0.730194</td>\n",
       "      <td>0.214283</td>\n",
       "      <td>-2.890950</td>\n",
       "      <td>-0.314445</td>\n",
       "      <td>-1.540459</td>\n",
       "      <td>1</td>\n",
       "      <td>0.730194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.908572</td>\n",
       "      <td>0.090141</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>-0.095881</td>\n",
       "      <td>-2.406382</td>\n",
       "      <td>-6.655265</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.908572</td>\n",
       "      <td>0.090141</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>-0.095881</td>\n",
       "      <td>-2.406382</td>\n",
       "      <td>-6.655265</td>\n",
       "      <td>0</td>\n",
       "      <td>0.908572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.963711</td>\n",
       "      <td>0.035854</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>-0.036963</td>\n",
       "      <td>-3.328292</td>\n",
       "      <td>-7.741599</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.963711</td>\n",
       "      <td>0.035854</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>-0.036963</td>\n",
       "      <td>-3.328292</td>\n",
       "      <td>-7.741599</td>\n",
       "      <td>0</td>\n",
       "      <td>0.963711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.970557</td>\n",
       "      <td>0.029151</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>-0.029885</td>\n",
       "      <td>-3.535249</td>\n",
       "      <td>-8.139519</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.970557</td>\n",
       "      <td>0.029151</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>-0.029885</td>\n",
       "      <td>-3.535249</td>\n",
       "      <td>-8.139519</td>\n",
       "      <td>0</td>\n",
       "      <td>0.970557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.146018</td>\n",
       "      <td>0.853282</td>\n",
       "      <td>-7.264880</td>\n",
       "      <td>-1.924024</td>\n",
       "      <td>-0.158665</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.146018</td>\n",
       "      <td>0.853282</td>\n",
       "      <td>-7.264880</td>\n",
       "      <td>-1.924024</td>\n",
       "      <td>-0.158665</td>\n",
       "      <td>2</td>\n",
       "      <td>0.853282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004844</td>\n",
       "      <td>0.302749</td>\n",
       "      <td>0.692408</td>\n",
       "      <td>-5.330100</td>\n",
       "      <td>-1.194852</td>\n",
       "      <td>-0.367580</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.004844</td>\n",
       "      <td>0.302749</td>\n",
       "      <td>0.692408</td>\n",
       "      <td>-5.330100</td>\n",
       "      <td>-1.194852</td>\n",
       "      <td>-0.367580</td>\n",
       "      <td>2</td>\n",
       "      <td>0.692408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.115297</td>\n",
       "      <td>0.884395</td>\n",
       "      <td>-8.083683</td>\n",
       "      <td>-2.160248</td>\n",
       "      <td>-0.122852</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.115297</td>\n",
       "      <td>0.884395</td>\n",
       "      <td>-8.083683</td>\n",
       "      <td>-2.160248</td>\n",
       "      <td>-0.122852</td>\n",
       "      <td>2</td>\n",
       "      <td>0.884395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.960722</td>\n",
       "      <td>0.038849</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>-0.040070</td>\n",
       "      <td>-3.248061</td>\n",
       "      <td>-7.755058</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.960722</td>\n",
       "      <td>0.038849</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>-0.040070</td>\n",
       "      <td>-3.248061</td>\n",
       "      <td>-7.755058</td>\n",
       "      <td>0</td>\n",
       "      <td>0.960722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096355</td>\n",
       "      <td>0.785138</td>\n",
       "      <td>0.118506</td>\n",
       "      <td>-2.339714</td>\n",
       "      <td>-0.241895</td>\n",
       "      <td>-2.132787</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.096355</td>\n",
       "      <td>0.785138</td>\n",
       "      <td>0.118506</td>\n",
       "      <td>-2.339714</td>\n",
       "      <td>-0.241895</td>\n",
       "      <td>-2.132787</td>\n",
       "      <td>1</td>\n",
       "      <td>0.785138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     KEY_skl Score_0_skl Score_1_skl Score_2_skl  Proba_0_skl  Proba_1_skl  \\\n",
       "114      114         NaN         NaN         NaN     0.000819     0.062837   \n",
       "74        74         NaN         NaN         NaN     0.035002     0.853701   \n",
       "9          9         NaN         NaN         NaN     0.939008     0.060175   \n",
       "88        88         NaN         NaN         NaN     0.055523     0.730194   \n",
       "25        25         NaN         NaN         NaN     0.908572     0.090141   \n",
       "5          5         NaN         NaN         NaN     0.963711     0.035854   \n",
       "48        48         NaN         NaN         NaN     0.970557     0.029151   \n",
       "117      117         NaN         NaN         NaN     0.000700     0.146018   \n",
       "83        83         NaN         NaN         NaN     0.004844     0.302749   \n",
       "105      105         NaN         NaN         NaN     0.000309     0.115297   \n",
       "27        27         NaN         NaN         NaN     0.960722     0.038849   \n",
       "64        64         NaN         NaN         NaN     0.096355     0.785138   \n",
       "\n",
       "     Proba_2_skl  LogProba_0_skl  LogProba_1_skl  LogProba_2_skl  ...  \\\n",
       "114     0.936344       -7.107648       -2.767214       -0.065772  ...   \n",
       "74      0.111297       -3.352343       -0.158174       -2.195556  ...   \n",
       "9       0.000817       -0.062931       -2.810502       -7.110160  ...   \n",
       "88      0.214283       -2.890950       -0.314445       -1.540459  ...   \n",
       "25      0.001287       -0.095881       -2.406382       -6.655265  ...   \n",
       "5       0.000434       -0.036963       -3.328292       -7.741599  ...   \n",
       "48      0.000292       -0.029885       -3.535249       -8.139519  ...   \n",
       "117     0.853282       -7.264880       -1.924024       -0.158665  ...   \n",
       "83      0.692408       -5.330100       -1.194852       -0.367580  ...   \n",
       "105     0.884395       -8.083683       -2.160248       -0.122852  ...   \n",
       "27      0.000429       -0.040070       -3.248061       -7.755058  ...   \n",
       "64      0.118506       -2.339714       -0.241895       -2.132787  ...   \n",
       "\n",
       "     Score_1_sql  Score_2_sql Proba_0_sql Proba_1_sql Proba_2_sql  \\\n",
       "114         None         None    0.000819    0.062837    0.936344   \n",
       "74          None         None    0.035002    0.853701    0.111297   \n",
       "9           None         None    0.939008    0.060175    0.000817   \n",
       "88          None         None    0.055523    0.730194    0.214283   \n",
       "25          None         None    0.908572    0.090141    0.001287   \n",
       "5           None         None    0.963711    0.035854    0.000434   \n",
       "48          None         None    0.970557    0.029151    0.000292   \n",
       "117         None         None    0.000700    0.146018    0.853282   \n",
       "83          None         None    0.004844    0.302749    0.692408   \n",
       "105         None         None    0.000309    0.115297    0.884395   \n",
       "27          None         None    0.960722    0.038849    0.000429   \n",
       "64          None         None    0.096355    0.785138    0.118506   \n",
       "\n",
       "     LogProba_0_sql  LogProba_1_sql  LogProba_2_sql  Decision_sql  \\\n",
       "114       -7.107648       -2.767214       -0.065772             2   \n",
       "74        -3.352343       -0.158174       -2.195556             1   \n",
       "9         -0.062931       -2.810502       -7.110160             0   \n",
       "88        -2.890950       -0.314445       -1.540459             1   \n",
       "25        -0.095881       -2.406382       -6.655265             0   \n",
       "5         -0.036963       -3.328292       -7.741599             0   \n",
       "48        -0.029885       -3.535249       -8.139519             0   \n",
       "117       -7.264880       -1.924024       -0.158665             2   \n",
       "83        -5.330100       -1.194852       -0.367580             2   \n",
       "105       -8.083683       -2.160248       -0.122852             2   \n",
       "27        -0.040070       -3.248061       -7.755058             0   \n",
       "64        -2.339714       -0.241895       -2.132787             1   \n",
       "\n",
       "     DecisionProba  \n",
       "114       0.936344  \n",
       "74        0.853701  \n",
       "9         0.939008  \n",
       "88        0.730194  \n",
       "25        0.908572  \n",
       "5         0.963711  \n",
       "48        0.970557  \n",
       "117       0.853282  \n",
       "83        0.692408  \n",
       "105       0.884395  \n",
       "27        0.960722  \n",
       "64        0.785138  \n",
       "\n",
       "[12 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_skl_join.sample(12, random_state=1960)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KEY_skl</th>\n",
       "      <th>Score_0_skl</th>\n",
       "      <th>Score_1_skl</th>\n",
       "      <th>Score_2_skl</th>\n",
       "      <th>Proba_0_skl</th>\n",
       "      <th>Proba_1_skl</th>\n",
       "      <th>Proba_2_skl</th>\n",
       "      <th>LogProba_0_skl</th>\n",
       "      <th>LogProba_1_skl</th>\n",
       "      <th>LogProba_2_skl</th>\n",
       "      <th>...</th>\n",
       "      <th>Score_1_sql</th>\n",
       "      <th>Score_2_sql</th>\n",
       "      <th>Proba_0_sql</th>\n",
       "      <th>Proba_1_sql</th>\n",
       "      <th>Proba_2_sql</th>\n",
       "      <th>LogProba_0_sql</th>\n",
       "      <th>LogProba_1_sql</th>\n",
       "      <th>LogProba_2_sql</th>\n",
       "      <th>Decision_sql</th>\n",
       "      <th>DecisionProba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [KEY_skl, Score_0_skl, Score_1_skl, Score_2_skl, Proba_0_skl, Proba_1_skl, Proba_2_skl, LogProba_0_skl, LogProba_1_skl, LogProba_2_skl, Decision_skl, KEY_sql, Score_0_sql, Score_1_sql, Score_2_sql, Proba_0_sql, Proba_1_sql, Proba_2_sql, LogProba_0_sql, LogProba_1_sql, LogProba_2_sql, Decision_sql, DecisionProba]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = (sql_skl_join.Decision_sql != sql_skl_join.Decision_skl)\n",
    "sql_skl_join[condition]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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