polish sklearn buggy and fixed versions

benchmark/sklearn_10/sklearn_10_fixed.ipynb

@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.166694Z",
@@ -311,40 +311,14 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.166635Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1330816 entries, 0 to 1330815\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column          Non-Null Count    Dtype  \n",
-      "---  ------          --------------    -----  \n",
-      " 0   u_q             1330816 non-null  float64\n",
-      " 1   coolant         1330816 non-null  float64\n",
-      " 2   stator_winding  1330816 non-null  float64\n",
-      " 3   u_d             1330816 non-null  float64\n",
-      " 4   stator_tooth    1330816 non-null  float64\n",
-      " 5   motor_speed     1330816 non-null  float64\n",
-      " 6   i_d             1330816 non-null  float64\n",
-      " 7   i_q             1330816 non-null  float64\n",
-      " 8   pm              1330816 non-null  float64\n",
-      " 9   stator_yoke     1330816 non-null  float64\n",
-      " 10  ambient         1330816 non-null  float64\n",
-      " 11  torque          1330816 non-null  float64\n",
-      "dtypes: float64(12)\n",
-      "memory usage: 121.8 MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.229634Z",
@@ -354,217 +328,14 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.229578Z"
     }
    },
-   "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>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>u_q</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>54.279005</td>\n",
-       "      <td>44.173234</td>\n",
-       "      <td>-25.290930</td>\n",
-       "      <td>12.069925</td>\n",
-       "      <td>48.938181</td>\n",
-       "      <td>90.034393</td>\n",
-       "      <td>133.036994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>coolant</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>36.229995</td>\n",
-       "      <td>21.786149</td>\n",
-       "      <td>10.623751</td>\n",
-       "      <td>18.698135</td>\n",
-       "      <td>26.900141</td>\n",
-       "      <td>49.857487</td>\n",
-       "      <td>101.598512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_winding</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>66.342745</td>\n",
-       "      <td>28.672061</td>\n",
-       "      <td>18.585815</td>\n",
-       "      <td>42.787957</td>\n",
-       "      <td>65.110129</td>\n",
-       "      <td>88.141137</td>\n",
-       "      <td>141.362885</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>u_d</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>-25.133809</td>\n",
-       "      <td>63.091972</td>\n",
-       "      <td>-131.530411</td>\n",
-       "      <td>-78.690904</td>\n",
-       "      <td>-7.429755</td>\n",
-       "      <td>1.470271</td>\n",
-       "      <td>131.469788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_tooth</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>56.878582</td>\n",
-       "      <td>22.952233</td>\n",
-       "      <td>18.133976</td>\n",
-       "      <td>38.416012</td>\n",
-       "      <td>56.036345</td>\n",
-       "      <td>75.586682</td>\n",
-       "      <td>111.946423</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>motor_speed</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>2202.080728</td>\n",
-       "      <td>1859.663350</td>\n",
-       "      <td>-275.549144</td>\n",
-       "      <td>317.110665</td>\n",
-       "      <td>1999.976685</td>\n",
-       "      <td>3760.639058</td>\n",
-       "      <td>6000.015137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_d</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>-68.716810</td>\n",
-       "      <td>64.933233</td>\n",
-       "      <td>-278.003632</td>\n",
-       "      <td>-115.406060</td>\n",
-       "      <td>-51.093763</td>\n",
-       "      <td>-2.979688</td>\n",
-       "      <td>0.051897</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_q</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>37.412782</td>\n",
-       "      <td>92.181880</td>\n",
-       "      <td>-293.426793</td>\n",
-       "      <td>1.095863</td>\n",
-       "      <td>15.774007</td>\n",
-       "      <td>100.612150</td>\n",
-       "      <td>301.707855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pm</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>58.506776</td>\n",
-       "      <td>19.001498</td>\n",
-       "      <td>20.856956</td>\n",
-       "      <td>43.151582</td>\n",
-       "      <td>60.266287</td>\n",
-       "      <td>72.008371</td>\n",
-       "      <td>113.606628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_yoke</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>48.187965</td>\n",
-       "      <td>19.991004</td>\n",
-       "      <td>18.076689</td>\n",
-       "      <td>31.990333</td>\n",
-       "      <td>45.625509</td>\n",
-       "      <td>61.460828</td>\n",
-       "      <td>101.147964</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ambient</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>24.565256</td>\n",
-       "      <td>1.929522</td>\n",
-       "      <td>8.783478</td>\n",
-       "      <td>23.184804</td>\n",
-       "      <td>24.797327</td>\n",
-       "      <td>26.217019</td>\n",
-       "      <td>30.714205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>31.106032</td>\n",
-       "      <td>77.135755</td>\n",
-       "      <td>-246.466663</td>\n",
-       "      <td>-0.137427</td>\n",
-       "      <td>10.860354</td>\n",
-       "      <td>91.597176</td>\n",
-       "      <td>261.005707</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    count         mean          std         min         25%  \\\n",
-       "u_q             1330816.0    54.279005    44.173234  -25.290930   12.069925   \n",
-       "coolant         1330816.0    36.229995    21.786149   10.623751   18.698135   \n",
-       "stator_winding  1330816.0    66.342745    28.672061   18.585815   42.787957   \n",
-       "u_d             1330816.0   -25.133809    63.091972 -131.530411  -78.690904   \n",
-       "stator_tooth    1330816.0    56.878582    22.952233   18.133976   38.416012   \n",
-       "motor_speed     1330816.0  2202.080728  1859.663350 -275.549144  317.110665   \n",
-       "i_d             1330816.0   -68.716810    64.933233 -278.003632 -115.406060   \n",
-       "i_q             1330816.0    37.412782    92.181880 -293.426793    1.095863   \n",
-       "pm              1330816.0    58.506776    19.001498   20.856956   43.151582   \n",
-       "stator_yoke     1330816.0    48.187965    19.991004   18.076689   31.990333   \n",
-       "ambient         1330816.0    24.565256     1.929522    8.783478   23.184804   \n",
-       "torque          1330816.0    31.106032    77.135755 -246.466663   -0.137427   \n",
-       "\n",
-       "                        50%          75%          max  \n",
-       "u_q               48.938181    90.034393   133.036994  \n",
-       "coolant           26.900141    49.857487   101.598512  \n",
-       "stator_winding    65.110129    88.141137   141.362885  \n",
-       "u_d               -7.429755     1.470271   131.469788  \n",
-       "stator_tooth      56.036345    75.586682   111.946423  \n",
-       "motor_speed     1999.976685  3760.639058  6000.015137  \n",
-       "i_d              -51.093763    -2.979688     0.051897  \n",
-       "i_q               15.774007   100.612150   301.707855  \n",
-       "pm                60.266287    72.008371   113.606628  \n",
-       "stator_yoke       45.625509    61.460828   101.147964  \n",
-       "ambient           24.797327    26.217019    30.714205  \n",
-       "torque            10.860354    91.597176   261.005707  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.describe().T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.736648Z",
@@ -574,25 +345,14 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.736575Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isnull().values.any()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.765667Z",
@@ -602,18 +362,7 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.765595Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Series([], dtype: int64)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Count the number of NaNs each column has.\n",
     "nans=pd.isnull(df).sum()\n",
@@ -622,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.812012Z",
@@ -632,19 +381,7 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.811961Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "float64    12\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Count the column types\n",
     "df.dtypes.value_counts()"
@@ -652,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.824255Z",
@@ -662,276 +399,7 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.824186Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>u_q</th>\n",
-       "      <th>coolant</th>\n",
-       "      <th>stator_winding</th>\n",
-       "      <th>u_d</th>\n",
-       "      <th>stator_tooth</th>\n",
-       "      <th>motor_speed</th>\n",
-       "      <th>i_d</th>\n",
-       "      <th>i_q</th>\n",
-       "      <th>pm</th>\n",
-       "      <th>stator_yoke</th>\n",
-       "      <th>ambient</th>\n",
-       "      <th>torque</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>u_q</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.051721</td>\n",
-       "      <td>0.050610</td>\n",
-       "      <td>0.004702</td>\n",
-       "      <td>0.104373</td>\n",
-       "      <td>0.683556</td>\n",
-       "      <td>-0.100357</td>\n",
-       "      <td>-0.124589</td>\n",
-       "      <td>0.122365</td>\n",
-       "      <td>0.090992</td>\n",
-       "      <td>0.150264</td>\n",
-       "      <td>-0.136215</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>coolant</th>\n",
-       "      <td>0.051721</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.504835</td>\n",
-       "      <td>0.195517</td>\n",
-       "      <td>0.674974</td>\n",
-       "      <td>0.011872</td>\n",
-       "      <td>0.074865</td>\n",
-       "      <td>-0.256389</td>\n",
-       "      <td>0.467117</td>\n",
-       "      <td>0.860750</td>\n",
-       "      <td>0.525963</td>\n",
-       "      <td>-0.257982</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_winding</th>\n",
-       "      <td>0.050610</td>\n",
-       "      <td>0.504835</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.234950</td>\n",
-       "      <td>0.970135</td>\n",
-       "      <td>0.432034</td>\n",
-       "      <td>-0.624370</td>\n",
-       "      <td>0.065617</td>\n",
-       "      <td>0.795893</td>\n",
-       "      <td>0.860268</td>\n",
-       "      <td>0.333208</td>\n",
-       "      <td>0.095510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>u_d</th>\n",
-       "      <td>0.004702</td>\n",
-       "      <td>0.195517</td>\n",
-       "      <td>-0.234950</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.142749</td>\n",
-       "      <td>-0.288472</td>\n",
-       "      <td>0.448331</td>\n",
-       "      <td>-0.723069</td>\n",
-       "      <td>-0.172031</td>\n",
-       "      <td>-0.008098</td>\n",
-       "      <td>0.203647</td>\n",
-       "      <td>-0.753779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_tooth</th>\n",
-       "      <td>0.104373</td>\n",
-       "      <td>0.674974</td>\n",
-       "      <td>0.970135</td>\n",
-       "      <td>-0.142749</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.398431</td>\n",
-       "      <td>-0.487062</td>\n",
-       "      <td>-0.042294</td>\n",
-       "      <td>0.832084</td>\n",
-       "      <td>0.953115</td>\n",
-       "      <td>0.443470</td>\n",
-       "      <td>-0.018413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>motor_speed</th>\n",
-       "      <td>0.683556</td>\n",
-       "      <td>0.011872</td>\n",
-       "      <td>0.432034</td>\n",
-       "      <td>-0.288472</td>\n",
-       "      <td>0.398431</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.700609</td>\n",
-       "      <td>-0.068881</td>\n",
-       "      <td>0.458947</td>\n",
-       "      <td>0.255790</td>\n",
-       "      <td>0.118232</td>\n",
-       "      <td>-0.043903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_d</th>\n",
-       "      <td>-0.100357</td>\n",
-       "      <td>0.074865</td>\n",
-       "      <td>-0.624370</td>\n",
-       "      <td>0.448331</td>\n",
-       "      <td>-0.487062</td>\n",
-       "      <td>-0.700609</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.231344</td>\n",
-       "      <td>-0.427736</td>\n",
-       "      <td>-0.278005</td>\n",
-       "      <td>0.016397</td>\n",
-       "      <td>-0.274097</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_q</th>\n",
-       "      <td>-0.124589</td>\n",
-       "      <td>-0.256389</td>\n",
-       "      <td>0.065617</td>\n",
-       "      <td>-0.723069</td>\n",
-       "      <td>-0.042294</td>\n",
-       "      <td>-0.068881</td>\n",
-       "      <td>-0.231344</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.142416</td>\n",
-       "      <td>-0.131466</td>\n",
-       "      <td>-0.314044</td>\n",
-       "      <td>0.996457</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pm</th>\n",
-       "      <td>0.122365</td>\n",
-       "      <td>0.467117</td>\n",
-       "      <td>0.795893</td>\n",
-       "      <td>-0.172031</td>\n",
-       "      <td>0.832084</td>\n",
-       "      <td>0.458947</td>\n",
-       "      <td>-0.427736</td>\n",
-       "      <td>-0.142416</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.756684</td>\n",
-       "      <td>0.513728</td>\n",
-       "      <td>-0.117138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_yoke</th>\n",
-       "      <td>0.090992</td>\n",
-       "      <td>0.860750</td>\n",
-       "      <td>0.860268</td>\n",
-       "      <td>-0.008098</td>\n",
-       "      <td>0.953115</td>\n",
-       "      <td>0.255790</td>\n",
-       "      <td>-0.278005</td>\n",
-       "      <td>-0.131466</td>\n",
-       "      <td>0.756684</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.520382</td>\n",
-       "      <td>-0.117118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ambient</th>\n",
-       "      <td>0.150264</td>\n",
-       "      <td>0.525963</td>\n",
-       "      <td>0.333208</td>\n",
-       "      <td>0.203647</td>\n",
-       "      <td>0.443470</td>\n",
-       "      <td>0.118232</td>\n",
-       "      <td>0.016397</td>\n",
-       "      <td>-0.314044</td>\n",
-       "      <td>0.513728</td>\n",
-       "      <td>0.520382</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.315747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque</th>\n",
-       "      <td>-0.136215</td>\n",
-       "      <td>-0.257982</td>\n",
-       "      <td>0.095510</td>\n",
-       "      <td>-0.753779</td>\n",
-       "      <td>-0.018413</td>\n",
-       "      <td>-0.043903</td>\n",
-       "      <td>-0.274097</td>\n",
-       "      <td>0.996457</td>\n",
-       "      <td>-0.117138</td>\n",
-       "      <td>-0.117118</td>\n",
-       "      <td>-0.315747</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     u_q   coolant  stator_winding       u_d  stator_tooth  \\\n",
-       "u_q             1.000000  0.051721        0.050610  0.004702      0.104373   \n",
-       "coolant         0.051721  1.000000        0.504835  0.195517      0.674974   \n",
-       "stator_winding  0.050610  0.504835        1.000000 -0.234950      0.970135   \n",
-       "u_d             0.004702  0.195517       -0.234950  1.000000     -0.142749   \n",
-       "stator_tooth    0.104373  0.674974        0.970135 -0.142749      1.000000   \n",
-       "motor_speed     0.683556  0.011872        0.432034 -0.288472      0.398431   \n",
-       "i_d            -0.100357  0.074865       -0.624370  0.448331     -0.487062   \n",
-       "i_q            -0.124589 -0.256389        0.065617 -0.723069     -0.042294   \n",
-       "pm              0.122365  0.467117        0.795893 -0.172031      0.832084   \n",
-       "stator_yoke     0.090992  0.860750        0.860268 -0.008098      0.953115   \n",
-       "ambient         0.150264  0.525963        0.333208  0.203647      0.443470   \n",
-       "torque         -0.136215 -0.257982        0.095510 -0.753779     -0.018413   \n",
-       "\n",
-       "                motor_speed       i_d       i_q        pm  stator_yoke  \\\n",
-       "u_q                0.683556 -0.100357 -0.124589  0.122365     0.090992   \n",
-       "coolant            0.011872  0.074865 -0.256389  0.467117     0.860750   \n",
-       "stator_winding     0.432034 -0.624370  0.065617  0.795893     0.860268   \n",
-       "u_d               -0.288472  0.448331 -0.723069 -0.172031    -0.008098   \n",
-       "stator_tooth       0.398431 -0.487062 -0.042294  0.832084     0.953115   \n",
-       "motor_speed        1.000000 -0.700609 -0.068881  0.458947     0.255790   \n",
-       "i_d               -0.700609  1.000000 -0.231344 -0.427736    -0.278005   \n",
-       "i_q               -0.068881 -0.231344  1.000000 -0.142416    -0.131466   \n",
-       "pm                 0.458947 -0.427736 -0.142416  1.000000     0.756684   \n",
-       "stator_yoke        0.255790 -0.278005 -0.131466  0.756684     1.000000   \n",
-       "ambient            0.118232  0.016397 -0.314044  0.513728     0.520382   \n",
-       "torque            -0.043903 -0.274097  0.996457 -0.117138    -0.117118   \n",
-       "\n",
-       "                 ambient    torque  \n",
-       "u_q             0.150264 -0.136215  \n",
-       "coolant         0.525963 -0.257982  \n",
-       "stator_winding  0.333208  0.095510  \n",
-       "u_d             0.203647 -0.753779  \n",
-       "stator_tooth    0.443470 -0.018413  \n",
-       "motor_speed     0.118232 -0.043903  \n",
-       "i_d             0.016397 -0.274097  \n",
-       "i_q            -0.314044  0.996457  \n",
-       "pm              0.513728 -0.117138  \n",
-       "stator_yoke     0.520382 -0.117118  \n",
-       "ambient         1.000000 -0.315747  \n",
-       "torque         -0.315747  1.000000  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.corr()"
    ]
@@ -1287,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:01:18.238465Z",
@@ -1305,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:01:18.347741Z",
@@ -1326,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:01:18.862666Z",
@@ -1336,18 +804,7 @@
      "shell.execute_reply.started": "2023-02-28T08:01:18.862615Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(266164, 11)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_test.shape"
    ]
@@ -1732,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:02:05.781217Z",
@@ -1892,7 +1349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-02-28T08:06:29.921532Z",
@@ -1995,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:06:47.156652Z",
@@ -2013,7 +1470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:16:56.707730Z",
@@ -2037,7 +1494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:20:42.723037Z",
@@ -2047,25 +1504,14 @@
      "shell.execute_reply.started": "2023-02-28T08:20:42.722967Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33.34830764198559"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.sqrt(mean_squared_error(y_test,y_pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:11:12.055602Z",
@@ -2080,17 +1526,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fold 1/10 RMSE: 33.3671\n",
-      "Fold 2/10 RMSE: 34.0335\n",
-      "Fold 3/10 RMSE: 32.5769\n",
-      "Fold 4/10 RMSE: 34.4036\n",
-      "Fold 5/10 RMSE: 34.5712\n",
-      "Fold 6/10 RMSE: 33.4303\n",
-      "Fold 7/10 RMSE: 33.7782\n",
-      "Fold 8/10 RMSE: 31.8827\n",
-      "Fold 9/10 RMSE: 35.3324\n",
-      "Fold 10/10 RMSE: 33.2837\n",
-      "Average KFold RMSE: 33.66596\n"
+      "Fold 1/10 RMSE: 32.0238\n",
+      "Fold 2/10 RMSE: 32.6497\n",
+      "Fold 3/10 RMSE: 34.6523\n",
+      "Fold 4/10 RMSE: 34.3301\n",
+      "Fold 5/10 RMSE: 32.7508\n",
+      "Fold 6/10 RMSE: 33.3000\n",
+      "Fold 7/10 RMSE: 32.6886\n",
+      "Fold 8/10 RMSE: 34.1172\n",
+      "Fold 9/10 RMSE: 34.2978\n",
+      "Fold 10/10 RMSE: 34.7723\n",
+      "Average KFold RMSE: 33.55826\n"
      ]
     }
    ],

benchmark/sklearn_10/sklearn_10_reproduced.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:01.047089Z",
@@ -287,7 +287,7 @@
        "9 -1.634464  0.898013  24.553242    18.301733  19.850624  0.924010  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.166694Z",
@@ -311,40 +311,14 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.166635Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1330816 entries, 0 to 1330815\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column          Non-Null Count    Dtype  \n",
-      "---  ------          --------------    -----  \n",
-      " 0   u_q             1330816 non-null  float64\n",
-      " 1   coolant         1330816 non-null  float64\n",
-      " 2   stator_winding  1330816 non-null  float64\n",
-      " 3   u_d             1330816 non-null  float64\n",
-      " 4   stator_tooth    1330816 non-null  float64\n",
-      " 5   motor_speed     1330816 non-null  float64\n",
-      " 6   i_d             1330816 non-null  float64\n",
-      " 7   i_q             1330816 non-null  float64\n",
-      " 8   pm              1330816 non-null  float64\n",
-      " 9   stator_yoke     1330816 non-null  float64\n",
-      " 10  ambient         1330816 non-null  float64\n",
-      " 11  torque          1330816 non-null  float64\n",
-      "dtypes: float64(12)\n",
-      "memory usage: 121.8 MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.229634Z",
@@ -354,217 +328,14 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.229578Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>u_q</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>54.279005</td>\n",
-       "      <td>44.173234</td>\n",
-       "      <td>-25.290930</td>\n",
-       "      <td>12.069925</td>\n",
-       "      <td>48.938181</td>\n",
-       "      <td>90.034393</td>\n",
-       "      <td>133.036994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>coolant</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>36.229995</td>\n",
-       "      <td>21.786149</td>\n",
-       "      <td>10.623751</td>\n",
-       "      <td>18.698135</td>\n",
-       "      <td>26.900141</td>\n",
-       "      <td>49.857487</td>\n",
-       "      <td>101.598512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_winding</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>66.342745</td>\n",
-       "      <td>28.672061</td>\n",
-       "      <td>18.585815</td>\n",
-       "      <td>42.787957</td>\n",
-       "      <td>65.110129</td>\n",
-       "      <td>88.141137</td>\n",
-       "      <td>141.362885</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>u_d</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>-25.133809</td>\n",
-       "      <td>63.091972</td>\n",
-       "      <td>-131.530411</td>\n",
-       "      <td>-78.690904</td>\n",
-       "      <td>-7.429755</td>\n",
-       "      <td>1.470271</td>\n",
-       "      <td>131.469788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_tooth</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>56.878582</td>\n",
-       "      <td>22.952233</td>\n",
-       "      <td>18.133976</td>\n",
-       "      <td>38.416012</td>\n",
-       "      <td>56.036345</td>\n",
-       "      <td>75.586682</td>\n",
-       "      <td>111.946423</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>motor_speed</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>2202.080728</td>\n",
-       "      <td>1859.663350</td>\n",
-       "      <td>-275.549144</td>\n",
-       "      <td>317.110665</td>\n",
-       "      <td>1999.976685</td>\n",
-       "      <td>3760.639058</td>\n",
-       "      <td>6000.015137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_d</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>-68.716810</td>\n",
-       "      <td>64.933233</td>\n",
-       "      <td>-278.003632</td>\n",
-       "      <td>-115.406060</td>\n",
-       "      <td>-51.093763</td>\n",
-       "      <td>-2.979688</td>\n",
-       "      <td>0.051897</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_q</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>37.412782</td>\n",
-       "      <td>92.181880</td>\n",
-       "      <td>-293.426793</td>\n",
-       "      <td>1.095863</td>\n",
-       "      <td>15.774007</td>\n",
-       "      <td>100.612150</td>\n",
-       "      <td>301.707855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pm</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>58.506776</td>\n",
-       "      <td>19.001498</td>\n",
-       "      <td>20.856956</td>\n",
-       "      <td>43.151582</td>\n",
-       "      <td>60.266287</td>\n",
-       "      <td>72.008371</td>\n",
-       "      <td>113.606628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_yoke</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>48.187965</td>\n",
-       "      <td>19.991004</td>\n",
-       "      <td>18.076689</td>\n",
-       "      <td>31.990333</td>\n",
-       "      <td>45.625509</td>\n",
-       "      <td>61.460828</td>\n",
-       "      <td>101.147964</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ambient</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>24.565256</td>\n",
-       "      <td>1.929522</td>\n",
-       "      <td>8.783478</td>\n",
-       "      <td>23.184804</td>\n",
-       "      <td>24.797327</td>\n",
-       "      <td>26.217019</td>\n",
-       "      <td>30.714205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque</th>\n",
-       "      <td>1330816.0</td>\n",
-       "      <td>31.106032</td>\n",
-       "      <td>77.135755</td>\n",
-       "      <td>-246.466663</td>\n",
-       "      <td>-0.137427</td>\n",
-       "      <td>10.860354</td>\n",
-       "      <td>91.597176</td>\n",
-       "      <td>261.005707</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    count         mean          std         min         25%  \\\n",
-       "u_q             1330816.0    54.279005    44.173234  -25.290930   12.069925   \n",
-       "coolant         1330816.0    36.229995    21.786149   10.623751   18.698135   \n",
-       "stator_winding  1330816.0    66.342745    28.672061   18.585815   42.787957   \n",
-       "u_d             1330816.0   -25.133809    63.091972 -131.530411  -78.690904   \n",
-       "stator_tooth    1330816.0    56.878582    22.952233   18.133976   38.416012   \n",
-       "motor_speed     1330816.0  2202.080728  1859.663350 -275.549144  317.110665   \n",
-       "i_d             1330816.0   -68.716810    64.933233 -278.003632 -115.406060   \n",
-       "i_q             1330816.0    37.412782    92.181880 -293.426793    1.095863   \n",
-       "pm              1330816.0    58.506776    19.001498   20.856956   43.151582   \n",
-       "stator_yoke     1330816.0    48.187965    19.991004   18.076689   31.990333   \n",
-       "ambient         1330816.0    24.565256     1.929522    8.783478   23.184804   \n",
-       "torque          1330816.0    31.106032    77.135755 -246.466663   -0.137427   \n",
-       "\n",
-       "                        50%          75%          max  \n",
-       "u_q               48.938181    90.034393   133.036994  \n",
-       "coolant           26.900141    49.857487   101.598512  \n",
-       "stator_winding    65.110129    88.141137   141.362885  \n",
-       "u_d               -7.429755     1.470271   131.469788  \n",
-       "stator_tooth      56.036345    75.586682   111.946423  \n",
-       "motor_speed     1999.976685  3760.639058  6000.015137  \n",
-       "i_d              -51.093763    -2.979688     0.051897  \n",
-       "i_q               15.774007   100.612150   301.707855  \n",
-       "pm                60.266287    72.008371   113.606628  \n",
-       "stator_yoke       45.625509    61.460828   101.147964  \n",
-       "ambient           24.797327    26.217019    30.714205  \n",
-       "torque            10.860354    91.597176   261.005707  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.describe().T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.736648Z",
@@ -574,25 +345,14 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.736575Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isnull().values.any()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.765667Z",
@@ -602,18 +362,7 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.765595Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Series([], dtype: int64)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Count the number of NaNs each column has.\n",
     "nans=pd.isnull(df).sum()\n",
@@ -622,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.812012Z",
@@ -632,19 +381,7 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.811961Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "float64    12\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Count the column types\n",
     "df.dtypes.value_counts()"
@@ -652,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T07:58:04.824255Z",
@@ -662,276 +399,7 @@
      "shell.execute_reply.started": "2023-02-28T07:58:04.824186Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>u_q</th>\n",
-       "      <th>coolant</th>\n",
-       "      <th>stator_winding</th>\n",
-       "      <th>u_d</th>\n",
-       "      <th>stator_tooth</th>\n",
-       "      <th>motor_speed</th>\n",
-       "      <th>i_d</th>\n",
-       "      <th>i_q</th>\n",
-       "      <th>pm</th>\n",
-       "      <th>stator_yoke</th>\n",
-       "      <th>ambient</th>\n",
-       "      <th>torque</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>u_q</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.051721</td>\n",
-       "      <td>0.050610</td>\n",
-       "      <td>0.004702</td>\n",
-       "      <td>0.104373</td>\n",
-       "      <td>0.683556</td>\n",
-       "      <td>-0.100357</td>\n",
-       "      <td>-0.124589</td>\n",
-       "      <td>0.122365</td>\n",
-       "      <td>0.090992</td>\n",
-       "      <td>0.150264</td>\n",
-       "      <td>-0.136215</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>coolant</th>\n",
-       "      <td>0.051721</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.504835</td>\n",
-       "      <td>0.195517</td>\n",
-       "      <td>0.674974</td>\n",
-       "      <td>0.011872</td>\n",
-       "      <td>0.074865</td>\n",
-       "      <td>-0.256389</td>\n",
-       "      <td>0.467117</td>\n",
-       "      <td>0.860750</td>\n",
-       "      <td>0.525963</td>\n",
-       "      <td>-0.257982</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_winding</th>\n",
-       "      <td>0.050610</td>\n",
-       "      <td>0.504835</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.234950</td>\n",
-       "      <td>0.970135</td>\n",
-       "      <td>0.432034</td>\n",
-       "      <td>-0.624370</td>\n",
-       "      <td>0.065617</td>\n",
-       "      <td>0.795893</td>\n",
-       "      <td>0.860268</td>\n",
-       "      <td>0.333208</td>\n",
-       "      <td>0.095510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>u_d</th>\n",
-       "      <td>0.004702</td>\n",
-       "      <td>0.195517</td>\n",
-       "      <td>-0.234950</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.142749</td>\n",
-       "      <td>-0.288472</td>\n",
-       "      <td>0.448331</td>\n",
-       "      <td>-0.723069</td>\n",
-       "      <td>-0.172031</td>\n",
-       "      <td>-0.008098</td>\n",
-       "      <td>0.203647</td>\n",
-       "      <td>-0.753779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_tooth</th>\n",
-       "      <td>0.104373</td>\n",
-       "      <td>0.674974</td>\n",
-       "      <td>0.970135</td>\n",
-       "      <td>-0.142749</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.398431</td>\n",
-       "      <td>-0.487062</td>\n",
-       "      <td>-0.042294</td>\n",
-       "      <td>0.832084</td>\n",
-       "      <td>0.953115</td>\n",
-       "      <td>0.443470</td>\n",
-       "      <td>-0.018413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>motor_speed</th>\n",
-       "      <td>0.683556</td>\n",
-       "      <td>0.011872</td>\n",
-       "      <td>0.432034</td>\n",
-       "      <td>-0.288472</td>\n",
-       "      <td>0.398431</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.700609</td>\n",
-       "      <td>-0.068881</td>\n",
-       "      <td>0.458947</td>\n",
-       "      <td>0.255790</td>\n",
-       "      <td>0.118232</td>\n",
-       "      <td>-0.043903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_d</th>\n",
-       "      <td>-0.100357</td>\n",
-       "      <td>0.074865</td>\n",
-       "      <td>-0.624370</td>\n",
-       "      <td>0.448331</td>\n",
-       "      <td>-0.487062</td>\n",
-       "      <td>-0.700609</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.231344</td>\n",
-       "      <td>-0.427736</td>\n",
-       "      <td>-0.278005</td>\n",
-       "      <td>0.016397</td>\n",
-       "      <td>-0.274097</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>i_q</th>\n",
-       "      <td>-0.124589</td>\n",
-       "      <td>-0.256389</td>\n",
-       "      <td>0.065617</td>\n",
-       "      <td>-0.723069</td>\n",
-       "      <td>-0.042294</td>\n",
-       "      <td>-0.068881</td>\n",
-       "      <td>-0.231344</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.142416</td>\n",
-       "      <td>-0.131466</td>\n",
-       "      <td>-0.314044</td>\n",
-       "      <td>0.996457</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pm</th>\n",
-       "      <td>0.122365</td>\n",
-       "      <td>0.467117</td>\n",
-       "      <td>0.795893</td>\n",
-       "      <td>-0.172031</td>\n",
-       "      <td>0.832084</td>\n",
-       "      <td>0.458947</td>\n",
-       "      <td>-0.427736</td>\n",
-       "      <td>-0.142416</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.756684</td>\n",
-       "      <td>0.513728</td>\n",
-       "      <td>-0.117138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>stator_yoke</th>\n",
-       "      <td>0.090992</td>\n",
-       "      <td>0.860750</td>\n",
-       "      <td>0.860268</td>\n",
-       "      <td>-0.008098</td>\n",
-       "      <td>0.953115</td>\n",
-       "      <td>0.255790</td>\n",
-       "      <td>-0.278005</td>\n",
-       "      <td>-0.131466</td>\n",
-       "      <td>0.756684</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.520382</td>\n",
-       "      <td>-0.117118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ambient</th>\n",
-       "      <td>0.150264</td>\n",
-       "      <td>0.525963</td>\n",
-       "      <td>0.333208</td>\n",
-       "      <td>0.203647</td>\n",
-       "      <td>0.443470</td>\n",
-       "      <td>0.118232</td>\n",
-       "      <td>0.016397</td>\n",
-       "      <td>-0.314044</td>\n",
-       "      <td>0.513728</td>\n",
-       "      <td>0.520382</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.315747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque</th>\n",
-       "      <td>-0.136215</td>\n",
-       "      <td>-0.257982</td>\n",
-       "      <td>0.095510</td>\n",
-       "      <td>-0.753779</td>\n",
-       "      <td>-0.018413</td>\n",
-       "      <td>-0.043903</td>\n",
-       "      <td>-0.274097</td>\n",
-       "      <td>0.996457</td>\n",
-       "      <td>-0.117138</td>\n",
-       "      <td>-0.117118</td>\n",
-       "      <td>-0.315747</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     u_q   coolant  stator_winding       u_d  stator_tooth  \\\n",
-       "u_q             1.000000  0.051721        0.050610  0.004702      0.104373   \n",
-       "coolant         0.051721  1.000000        0.504835  0.195517      0.674974   \n",
-       "stator_winding  0.050610  0.504835        1.000000 -0.234950      0.970135   \n",
-       "u_d             0.004702  0.195517       -0.234950  1.000000     -0.142749   \n",
-       "stator_tooth    0.104373  0.674974        0.970135 -0.142749      1.000000   \n",
-       "motor_speed     0.683556  0.011872        0.432034 -0.288472      0.398431   \n",
-       "i_d            -0.100357  0.074865       -0.624370  0.448331     -0.487062   \n",
-       "i_q            -0.124589 -0.256389        0.065617 -0.723069     -0.042294   \n",
-       "pm              0.122365  0.467117        0.795893 -0.172031      0.832084   \n",
-       "stator_yoke     0.090992  0.860750        0.860268 -0.008098      0.953115   \n",
-       "ambient         0.150264  0.525963        0.333208  0.203647      0.443470   \n",
-       "torque         -0.136215 -0.257982        0.095510 -0.753779     -0.018413   \n",
-       "\n",
-       "                motor_speed       i_d       i_q        pm  stator_yoke  \\\n",
-       "u_q                0.683556 -0.100357 -0.124589  0.122365     0.090992   \n",
-       "coolant            0.011872  0.074865 -0.256389  0.467117     0.860750   \n",
-       "stator_winding     0.432034 -0.624370  0.065617  0.795893     0.860268   \n",
-       "u_d               -0.288472  0.448331 -0.723069 -0.172031    -0.008098   \n",
-       "stator_tooth       0.398431 -0.487062 -0.042294  0.832084     0.953115   \n",
-       "motor_speed        1.000000 -0.700609 -0.068881  0.458947     0.255790   \n",
-       "i_d               -0.700609  1.000000 -0.231344 -0.427736    -0.278005   \n",
-       "i_q               -0.068881 -0.231344  1.000000 -0.142416    -0.131466   \n",
-       "pm                 0.458947 -0.427736 -0.142416  1.000000     0.756684   \n",
-       "stator_yoke        0.255790 -0.278005 -0.131466  0.756684     1.000000   \n",
-       "ambient            0.118232  0.016397 -0.314044  0.513728     0.520382   \n",
-       "torque            -0.043903 -0.274097  0.996457 -0.117138    -0.117118   \n",
-       "\n",
-       "                 ambient    torque  \n",
-       "u_q             0.150264 -0.136215  \n",
-       "coolant         0.525963 -0.257982  \n",
-       "stator_winding  0.333208  0.095510  \n",
-       "u_d             0.203647 -0.753779  \n",
-       "stator_tooth    0.443470 -0.018413  \n",
-       "motor_speed     0.118232 -0.043903  \n",
-       "i_d             0.016397 -0.274097  \n",
-       "i_q            -0.314044  0.996457  \n",
-       "pm              0.513728 -0.117138  \n",
-       "stator_yoke     0.520382 -0.117118  \n",
-       "ambient         1.000000 -0.315747  \n",
-       "torque         -0.315747  1.000000  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.corr()"
    ]
@@ -1287,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:01:18.238465Z",
@@ -1305,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:01:18.347741Z",
@@ -1326,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:01:18.862666Z",
@@ -1336,18 +804,7 @@
      "shell.execute_reply.started": "2023-02-28T08:01:18.862615Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(266164, 11)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_test.shape"
    ]
@@ -1732,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:02:05.781217Z",
@@ -1892,7 +1349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-02-28T08:06:29.921532Z",
@@ -1995,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:06:47.156652Z",
@@ -2013,7 +1470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:16:56.707730Z",
@@ -2037,7 +1494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:20:42.723037Z",
@@ -2047,25 +1504,14 @@
      "shell.execute_reply.started": "2023-02-28T08:20:42.722967Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33.34830764198559"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.sqrt(mean_squared_error(y_test,y_pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T08:11:12.055602Z",
@@ -2078,16 +1524,16 @@
    "outputs": [
     {
      "ename": "KeyError",
-     "evalue": "\"None of [Index([      0,       1,       2,       3,       4,       5,       6,       7,\\n             8,       9,\\n       ...\\n       1064642, 1064643, 1064644, 1064645, 1064646, 1064647, 1064648, 1064649,\\n       1064650, 1064651],\\n      dtype='int64', length=958186)] are in the [columns]\"",
+     "evalue": "\"None of [Index([      0,       1,       2,       3,       5,       6,       7,       8,\\n             9,      10,\\n       ...\\n       1064642, 1064643, 1064644, 1064645, 1064646, 1064647, 1064648, 1064649,\\n       1064650, 1064651],\\n      dtype='int64', length=958186)] are in the [columns]\"",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-43075d4b01ce>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_idx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;31m# Fetch the train-validation indices.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mX_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-9-43075d4b01ce>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_idx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;31m# Fetch the train-validation indices.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mX_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3897\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3898\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3899\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3901\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6113\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6115\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6117\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6174\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6175\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6176\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6178\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index([      0,       1,       2,       3,       4,       5,       6,       7,\\n             8,       9,\\n       ...\\n       1064642, 1064643, 1064644, 1064645, 1064646, 1064647, 1064648, 1064649,\\n       1064650, 1064651],\\n      dtype='int64', length=958186)] are in the [columns]\""
+      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index([      0,       1,       2,       3,       5,       6,       7,       8,\\n             9,      10,\\n       ...\\n       1064642, 1064643, 1064644, 1064645, 1064646, 1064647, 1064648, 1064649,\\n       1064650, 1064651],\\n      dtype='int64', length=958186)] are in the [columns]\""
      ]
     }
    ],

benchmark/sklearn_11/sklearn_11_reproduced.ipynb

@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-07T16:31:30.923547Z",
@@ -59,144 +59,7 @@
      "shell.execute_reply.started": "2023-04-07T16:31:30.923493Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>number_people</th>\n",
-       "      <th>date</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>day_of_week</th>\n",
-       "      <th>is_weekend</th>\n",
-       "      <th>is_holiday</th>\n",
-       "      <th>temperature</th>\n",
-       "      <th>is_start_of_semester</th>\n",
-       "      <th>is_during_semester</th>\n",
-       "      <th>month</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>62179</th>\n",
-       "      <td>23</td>\n",
-       "      <td>2017-03-18 18:42:28-07:00</td>\n",
-       "      <td>67348</td>\n",
-       "      <td>5</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>61.07</td>\n",
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-       "      <td>3</td>\n",
-       "      <td>18</td>\n",
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-       "    <tr>\n",
-       "      <th>62180</th>\n",
-       "      <td>21</td>\n",
-       "      <td>2017-03-18 18:52:35-07:00</td>\n",
-       "      <td>67955</td>\n",
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-       "      <td>61.07</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62181</th>\n",
-       "      <td>25</td>\n",
-       "      <td>2017-03-18 19:02:40-07:00</td>\n",
-       "      <td>68560</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.71</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62182</th>\n",
-       "      <td>18</td>\n",
-       "      <td>2017-03-18 19:12:47-07:00</td>\n",
-       "      <td>69167</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.71</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62183</th>\n",
-       "      <td>23</td>\n",
-       "      <td>2017-03-18 19:22:51-07:00</td>\n",
-       "      <td>69771</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.71</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       number_people                       date  timestamp  day_of_week  \\\n",
-       "62179             23  2017-03-18 18:42:28-07:00      67348            5   \n",
-       "62180             21  2017-03-18 18:52:35-07:00      67955            5   \n",
-       "62181             25  2017-03-18 19:02:40-07:00      68560            5   \n",
-       "62182             18  2017-03-18 19:12:47-07:00      69167            5   \n",
-       "62183             23  2017-03-18 19:22:51-07:00      69771            5   \n",
-       "\n",
-       "       is_weekend  is_holiday  temperature  is_start_of_semester  \\\n",
-       "62179           1           0        61.07                     0   \n",
-       "62180           1           0        61.07                     0   \n",
-       "62181           1           0        56.71                     0   \n",
-       "62182           1           0        56.71                     0   \n",
-       "62183           1           0        56.71                     0   \n",
-       "\n",
-       "       is_during_semester  month  hour  \n",
-       "62179                   1      3    18  \n",
-       "62180                   1      3    18  \n",
-       "62181                   1      3    19  \n",
-       "62182                   1      3    19  \n",
-       "62183                   1      3    19  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.tail()"
    ]
@@ -221,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-07T16:31:30.982101Z",
@@ -262,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-07T16:31:31.292669Z",
@@ -280,7 +143,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-582089ae0f7d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-5-582089ae0f7d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[1;32m   2560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2561\u001b[0m     \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2562\u001b[0;31m     n_train, n_test = _validate_shuffle_split(\n\u001b[0m\u001b[1;32m   2563\u001b[0m         \u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault_test_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2564\u001b[0m     )\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36m_validate_shuffle_split\u001b[0;34m(n_samples, test_size, train_size, default_test_size)\u001b[0m\n\u001b[1;32m   2234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2235\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn_train\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2236\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   2237\u001b[0m             \u001b[0;34m\"With n_samples={}, test_size={} and train_size={}, the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2238\u001b[0m             \u001b[0;34m\"resulting train set will be empty. Adjust any of the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters."

benchmark/sklearn_12/sklearn_12_fixed.ipynb

@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T13:48:56.519092Z",
@@ -325,63 +325,14 @@
      "shell.execute_reply.started": "2023-05-31T13:48:56.519058Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(     Pclass        Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n",
-       " 839       1  29.699118      0      0   29.7000       False      True   \n",
-       " 100       3  28.000000      0      0    7.8958        True     False   \n",
-       " 806       1  39.000000      0      0    0.0000       False      True   \n",
-       " 145       2  19.000000      1      1   36.7500       False      True   \n",
-       " 701       1  35.000000      0      0   26.2875       False      True   \n",
-       " ..      ...        ...    ...    ...       ...         ...       ...   \n",
-       " 664       3  20.000000      1      0    7.9250       False      True   \n",
-       " 319       1  40.000000      1      1  134.5000        True     False   \n",
-       " 336       1  29.000000      1      0   66.6000       False      True   \n",
-       " 699       3  42.000000      0      0    7.6500       False      True   \n",
-       " 1         1  38.000000      1      0   71.2833        True     False   \n",
-       " \n",
-       "      Embarked_C  Embarked_Q  Embarked_S  \n",
-       " 839        True       False       False  \n",
-       " 100       False       False        True  \n",
-       " 806       False       False        True  \n",
-       " 145       False       False        True  \n",
-       " 701       False       False        True  \n",
-       " ..          ...         ...         ...  \n",
-       " 664       False       False        True  \n",
-       " 319        True       False       False  \n",
-       " 336       False       False        True  \n",
-       " 699       False       False        True  \n",
-       " 1          True       False       False  \n",
-       " \n",
-       " [623 rows x 10 columns],\n",
-       " 839    1\n",
-       " 100    0\n",
-       " 806    0\n",
-       " 145    0\n",
-       " 701    1\n",
-       "       ..\n",
-       " 664    1\n",
-       " 319    1\n",
-       " 336    0\n",
-       " 699    0\n",
-       " 1      1\n",
-       " Name: Survived, Length: 623, dtype: int64)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_train,y_train"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:17:50.579386Z",
@@ -437,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T13:50:36.015117Z",
@@ -447,15 +398,7 @@
      "shell.execute_reply.started": "2023-05-31T13:50:36.015081Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "84.328\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = RandomForestClassifier()\n",
     "model.fit(X_train,y_train)\n",
@@ -465,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:02:34.017522Z",
@@ -494,14 +437,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LogisticRegression (accuracy): 79.851%\n",
-      "DecisionTreeClassifier with gini (accuracy): 82.836%\n",
-      "DecisionTreeClassifier with entropy (accuracy): 78.731%\n",
-      "KNeighborsClassifier (accuracy): 71.269%\n",
-      "AdaBoostClassifier (accuracy): 80.97%\n",
-      "GradientBoostingClassifier (accuracy): 82.463%\n",
-      "GaussianNB (accuracy): 79.104%\n",
-      "SVC (accuracy): 71.642%\n"
+      "LogisticRegression (accuracy): 83.955%\n",
+      "DecisionTreeClassifier with gini (accuracy): 83.955%\n",
+      "DecisionTreeClassifier with entropy (accuracy): 86.194%\n",
+      "KNeighborsClassifier (accuracy): 74.627%\n",
+      "AdaBoostClassifier (accuracy): 84.328%\n",
+      "GradientBoostingClassifier (accuracy): 86.94%\n",
+      "GaussianNB (accuracy): 82.836%\n",
+      "SVC (accuracy): 70.896%\n"
      ]
     }
    ],
@@ -560,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:02:02.433477Z",
@@ -582,20 +525,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt # fix 3: did not import\n",
     "\n",
@@ -608,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:22:09.222862Z",
@@ -618,224 +550,7 @@
      "shell.execute_reply.started": "2023-05-31T14:22:09.222810Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Pclass</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SibSp</th>\n",
-       "      <th>Parch</th>\n",
-       "      <th>Fare</th>\n",
-       "      <th>Sex_female</th>\n",
-       "      <th>Sex_male</th>\n",
-       "      <th>Embarked_C</th>\n",
-       "      <th>Embarked_Q</th>\n",
-       "      <th>Embarked_S</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>34.50000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.8292</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>47.00000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.0000</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>62.00000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.6875</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>27.00000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.6625</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>22.00000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>12.2875</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>413</th>\n",
-       "      <td>3</td>\n",
-       "      <td>30.27259</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>414</th>\n",
-       "      <td>1</td>\n",
-       "      <td>39.00000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>108.9000</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>415</th>\n",
-       "      <td>3</td>\n",
-       "      <td>38.50000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>416</th>\n",
-       "      <td>3</td>\n",
-       "      <td>30.27259</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>417</th>\n",
-       "      <td>3</td>\n",
-       "      <td>30.27259</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>22.3583</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>418 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Pclass       Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n",
-       "0         3  34.50000      0      0    7.8292       False      True   \n",
-       "1         3  47.00000      1      0    7.0000        True     False   \n",
-       "2         2  62.00000      0      0    9.6875       False      True   \n",
-       "3         3  27.00000      0      0    8.6625       False      True   \n",
-       "4         3  22.00000      1      1   12.2875        True     False   \n",
-       "..      ...       ...    ...    ...       ...         ...       ...   \n",
-       "413       3  30.27259      0      0    8.0500       False      True   \n",
-       "414       1  39.00000      0      0  108.9000        True     False   \n",
-       "415       3  38.50000      0      0    7.2500       False      True   \n",
-       "416       3  30.27259      0      0    8.0500       False      True   \n",
-       "417       3  30.27259      1      1   22.3583       False      True   \n",
-       "\n",
-       "     Embarked_C  Embarked_Q  Embarked_S  \n",
-       "0         False        True       False  \n",
-       "1         False       False        True  \n",
-       "2         False        True       False  \n",
-       "3         False       False        True  \n",
-       "4         False       False        True  \n",
-       "..          ...         ...         ...  \n",
-       "413       False       False        True  \n",
-       "414        True       False       False  \n",
-       "415       False       False        True  \n",
-       "416       False       False        True  \n",
-       "417        True       False       False  \n",
-       "\n",
-       "[418 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test = pd.read_csv('data/test.csv')\n",
     "submission = pd.read_csv('data/gender_submission.csv')\n",
@@ -854,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:22:12.070964Z",
@@ -864,15 +579,7 @@
      "shell.execute_reply.started": "2023-05-31T14:22:12.070910Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GradientBoostingClassifier (accuracy): 93.284%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = GradientBoostingClassifier(n_estimators=200)\n",
     "model.fit(features,labels)\n",

benchmark/sklearn_12/sklearn_12_reproduced.ipynb

@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T13:48:56.519092Z",
@@ -325,63 +325,14 @@
      "shell.execute_reply.started": "2023-05-31T13:48:56.519058Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(     Pclass        Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n",
-       " 598       3  29.699118      0      0    7.2250       False      True   \n",
-       " 395       3  22.000000      0      0    7.7958       False      True   \n",
-       " 582       2  54.000000      0      0   26.0000       False      True   \n",
-       " 353       3  25.000000      1      0   17.8000       False      True   \n",
-       " 625       1  61.000000      0      0   32.3208       False      True   \n",
-       " ..      ...        ...    ...    ...       ...         ...       ...   \n",
-       " 777       3   5.000000      0      0   12.4750        True     False   \n",
-       " 710       1  24.000000      0      0   49.5042        True     False   \n",
-       " 325       1  36.000000      0      0  135.6333        True     False   \n",
-       " 685       2  25.000000      1      2   41.5792       False      True   \n",
-       " 488       3  30.000000      0      0    8.0500       False      True   \n",
-       " \n",
-       "      Embarked_C  Embarked_Q  Embarked_S  \n",
-       " 598        True       False       False  \n",
-       " 395       False       False        True  \n",
-       " 582       False       False        True  \n",
-       " 353       False       False        True  \n",
-       " 625       False       False        True  \n",
-       " ..          ...         ...         ...  \n",
-       " 777       False       False        True  \n",
-       " 710        True       False       False  \n",
-       " 325        True       False       False  \n",
-       " 685        True       False       False  \n",
-       " 488       False       False        True  \n",
-       " \n",
-       " [623 rows x 10 columns],\n",
-       " 598    0\n",
-       " 395    0\n",
-       " 582    0\n",
-       " 353    0\n",
-       " 625    0\n",
-       "       ..\n",
-       " 777    1\n",
-       " 710    1\n",
-       " 325    1\n",
-       " 685    0\n",
-       " 488    0\n",
-       " Name: Survived, Length: 623, dtype: int64)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_train,y_train"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:17:50.579386Z",
@@ -437,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T13:50:36.015117Z",
@@ -447,15 +398,7 @@
      "shell.execute_reply.started": "2023-05-31T13:50:36.015081Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "80.597\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = RandomForestClassifier()\n",
     "model.fit(X_train,y_train)\n",
@@ -465,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:02:34.017522Z",
@@ -494,14 +437,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LogisticRegression (accuracy): 79.478%\n",
-      "DecisionTreeClassifier with gini (accuracy): 80.97%\n",
-      "DecisionTreeClassifier with entropy (accuracy): 80.97%\n",
-      "KNeighborsClassifier (accuracy): 68.657%\n",
-      "AdaBoostClassifier (accuracy): 77.612%\n",
-      "GradientBoostingClassifier (accuracy): 82.463%\n",
-      "GaussianNB (accuracy): 75.746%\n",
-      "SVC (accuracy): 64.552%\n"
+      "LogisticRegression (accuracy): 80.97%\n",
+      "DecisionTreeClassifier with gini (accuracy): 80.224%\n",
+      "DecisionTreeClassifier with entropy (accuracy): 77.985%\n",
+      "KNeighborsClassifier (accuracy): 72.388%\n",
+      "AdaBoostClassifier (accuracy): 82.836%\n",
+      "GradientBoostingClassifier (accuracy): 82.836%\n",
+      "GaussianNB (accuracy): 79.104%\n",
+      "SVC (accuracy): 67.164%\n"
      ]
     }
    ],
@@ -556,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-31T14:02:02.433477Z",
@@ -574,7 +517,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-e740cec345cb>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfeature_importance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mSeries_feat_imp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_importance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-8-e740cec345cb>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfeature_importance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mSeries_feat_imp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_importance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'SVC' object has no attribute 'feature_importances_'"
      ]
     }

benchmark/sklearn_13/sklearn_13_fixed.ipynb

@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-07T16:21:09.616280Z",
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-07T16:21:09.643982Z",
@@ -186,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -206,13 +206,13 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "LinearRegression()"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,9 +224,9 @@
     "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
     "\n",
     "label_encoder = LabelEncoder()\n",
-    "x = label_encoder.fit_transform(df.workclass.values)\n",
+    "x_encoded = label_encoder.fit_transform(df.workclass.values)\n",
     "x = x_encoded.reshape(-1,1)\n",
-    "y = label_encoder.fit_transform(df.Salary.values)\n",
+    "y_encoded = label_encoder.fit_transform(df.Salary.values)\n",
     "y = y_encoded.reshape(-1,1)\n",
     "# fix ends\n",
     "\n",
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -250,15 +250,7 @@
     "id": "ZJb9zcNak0mC",
     "outputId": "484a903d-9c7b-409c-9f73-09b12781034c"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "b0:  [[0.23482319]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#prediction\n",
     "import numpy as np\n",
@@ -269,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -283,15 +275,7 @@
     "id": "ceMWHkbqk0mD",
     "outputId": "32461418-182e-4f45-a672-b611c26c7779"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "b0:  [0.23482319]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "b0_ = linear_reg.intercept_   \n",
     "print(\"b0: \",b0_)   "
@@ -299,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -313,15 +297,7 @@
     "id": "6Nf-oqqVk0mD",
     "outputId": "cc8dd271-a9ab-4136-81a3-524e08bf606f"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "b1:  [[0.00490355]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "b1 = linear_reg.coef_  \n",
     "print(\"b1: \",b1)   "
@@ -329,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -343,15 +319,7 @@
     "id": "RkBNDPoDk0mF",
     "outputId": "40321ad9-e08e-4b92-e03a-d16089520152"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14181\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "new_salary = 1663 + 1138*11 \n",
     "print(new_salary)"
@@ -359,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -373,15 +341,7 @@
     "id": "QKQEaBSvk0mF",
     "outputId": "560c3896-d449-4213-96d0-740b9a53ae30"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "b11:  [[0.28876223]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "b11 = linear_reg.predict([[11]]) \n",
     "print(\"b11: \",b11)"
@@ -389,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-04-07T16:21:10.405070Z",
@@ -406,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -421,18 +381,7 @@
     "id": "kt89yyBQO_6Z",
     "outputId": "3aca9e7d-04c1-4fd1-8e41-6ff835d25099"
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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yJgAAGED69N00zc3Nuvnmm7VmzRqlpaW5Pq6srEyhUCh2q6+v78MpAQCApSFedk5LS1NiYqIaGxvbbW9sbFR6enqH/d9//30dOHBAs2bNim2LRqMnHnjIEL377rv66le/2uE4v98vv9/vZTQAADBAeXpmJCkpSdnZ2aquro5ti0ajqq6uVl5eXof9J06cqLffflt1dXWx27e//W1dffXVqqur4+UXAADg7ZkRSSotLVVRUZFycnI0ffp0LV++XC0tLSouLpYkzZ07V+PGjVNFRYWSk5M1efLkdsefddZZktRhOwAAGJw8x0hhYaGOHDmiJUuWqKGhQVOnTlVVVVXsotaDBw8qIYEPdgUAAO74HMdxrIfoTjgcVmpqqkKhkFJSUqzHAQAALrg9f/MUBgAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMDXEegAAAHCaoselT1ukT49+9ueXv+5m20X/S0rPNxufGAEA4HNOVPr02ImTdKRFOn60869dn/C/8HW01fq769p/b5Rm/7c0fJzJwxMjAIDe5zgnTr49+S39lCf8z75G70r/lpScbvbwxAgAxLtopPPfzI8f9fhb+pf+O9Jy4ul92PAlSkO+cuKW+NmfQ0ec/Dp2G9HF111sS/yKlJhk/d15QowAGDwcR4p87P5E7eqE/3kgfGz93Q1uicO8nbQT3e43TPL5rL+7Mx4xAqBnohEp2iY5x6XIZ39G27r+Ovr5n1/4OvyudGjTl074R0+8bg8bCUN7dtLu9qQ+XErglIPO8ZMBWHOczk/UX/z68xN+dyf/7u7H9bZu/t45TjB0y+fuRJ342VPz7U783RyTkMRv6zijECM4c5zygjkXr6V3erX8F/bpiVGXdX9ydz7t3XWw4ks4cZJMSDrx23VCkuT77M/ELr7+906ptan7+05M7ngC9/Iaele/xScOOzE3AFPECDoX/bT7k3akm5N7V0Fwppx83fj333p23JdP6rE/v3SiT0xq/3VnJ/zu7sf1tlP8vW+olJDYu2sHYNAgRvpa7IK53vgt/csX2HHBnKkOr6l/6bfvlIuls3O9n/x9iTwFD2BQGdwxcnS/9NpsKbSL178tJfjdXxjn+u1vn18wx2/rABDvBneM/O2n0kf/z3qK3sN71gEAA9DgjpFLfirV/5/O/y5xuLcL4jp9+1snx/CedQAA2hncMZKWK93oWE8BAMCgxnvaAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgKkexUhlZaUyMzOVnJys3Nxc7dixo8t916xZo5kzZ2rUqFEaNWqU8vPzT7k/AAAYXDzHyIYNG1RaWqry8nLt3LlTWVlZKigo0OHDhzvdf9u2bZozZ45effVV1dTUKBgM6tprr9WHH3542sMDAICBz+c4jqcP2sjNzdW0adO0YsUKSVI0GlUwGNRdd92lRYsWdXt8JBLRqFGjtGLFCs2dO9fVY4bDYaWmpioUCiklJcXLuAAAwIjb87enZ0ba2tpUW1ur/Pz8k3eQkKD8/HzV1NS4uo9jx47p+PHjGj16dJf7tLa2KhwOt7sBAIAzk6cYaWpqUiQSUSAQaLc9EAiooaHB1X0sXLhQGRkZ7YLmyyoqKpSamhq7BYNBL2MCAIABpF/fTbNs2TKtX79eGzduVHJycpf7lZWVKRQKxW719fX9OCUAAOhPnv5tmrS0NCUmJqqxsbHd9sbGRqWnp5/y2F/96ldatmyZ/vKXv2jKlCmn3Nfv98vv93sZDQAADFCenhlJSkpSdna2qqurY9ui0aiqq6uVl5fX5XEPP/ywHnroIVVVVSknJ6fn0wIAgDOO53+1t7S0VEVFRcrJydH06dO1fPlytbS0qLi4WJI0d+5cjRs3ThUVFZKkX/7yl1qyZInWrVunzMzM2LUlI0aM0IgRI3rxWwEAAAOR5xgpLCzUkSNHtGTJEjU0NGjq1KmqqqqKXdR68OBBJSScfMJl5cqVamtr03e/+91291NeXq7777//9KYHAAADnufPGbHA54wAADDw9MnnjAAAAPQ2YgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAICpIdYDWMlctKnDtgPLbjCYJP6xVt6wXu6xVu6xVu6xVu6Fjh3Xj9bu0KHQJ8pITdZTt0xX6vCh/T5Hj54ZqaysVGZmppKTk5Wbm6sdO3accv9nn31WEydOVHJysi699FJt3ry5R8P2ls5+UE+1fTBjrbxhvdxjrdxjrdxjrdy76pFXlPXgy6o9+JH+GfpEtQc/UtaDL+uqR17p91k8x8iGDRtUWlqq8vJy7dy5U1lZWSooKNDhw4c73f+NN97QnDlzdOutt+pvf/ubZs+erdmzZ2vXrl2nPXxPdPcDyQ/sSayVN6yXe6yVe6yVe6yVe1c98oo++J+PO/27D/7n434PEp/jOI6XA3JzczVt2jStWLFCkhSNRhUMBnXXXXdp0aJFHfYvLCxUS0uLXnzxxdi2r3/965o6dapWrVrl6jHD4bBSU1MVCoWUkpLiZdx2vPwgDvan9Fgrb1gv91gr91gr91gr90LHjivrwZe73e//Lrn2tF+ycXv+9vTMSFtbm2pra5Wfn3/yDhISlJ+fr5qamk6Pqampabe/JBUUFHS5vyS1trYqHA63uwEAgNP3o7WnvrTC6369wVOMNDU1KRKJKBAItNseCATU0NDQ6TENDQ2e9pekiooKpaamxm7BYNDLmAAAoAuHQp/06n69IS7f2ltWVqZQKBS71dfXW48EAMAZISM1uVf36w2eYiQtLU2JiYlqbGxst72xsVHp6emdHpOenu5pf0ny+/1KSUlpdwMAAKfvqVum9+p+vcFTjCQlJSk7O1vV1dWxbdFoVNXV1crLy+v0mLy8vHb7S9LWrVu73L8vub1oabBf3CSxVl6xXu6xVu6xVu6xVu6lDh+qCWcPO+U+E84e1q+fN+L5ZZrS0lKtWbNGTz/9tHbv3q158+appaVFxcXFkqS5c+eqrKwstv/8+fNVVVWlRx99VHv27NH999+vt956S3feeWfvfRcedPeDyA/qSayVN6yXe6yVe6yVe6yVe9t/+s0ug2TC2cO0/aff7Nd5PL+1V5JWrFihRx55RA0NDZo6daoef/xx5ebmSpK+8Y1vKDMzU2vXro3t/+yzz+q+++7TgQMHdOGFF+rhhx/W9ddf7/rxeuutvV/EJ/S5x1p5w3q5x1q5x1q5x1q519efwOr2/N2jGOlvfREjAACgb/XJ54wAAAD0NmIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAICpIdYDuPH5h8SGw2HjSQAAgFufn7e7+7D3AREjzc3NkqRgMGg8CQAA8Kq5uVmpqald/v2A+LdpotGoDh06pJEjR8rn8/Xa/YbDYQWDQdXX1/Nv3nSDtfKG9XKPtXKPtXKPtXKvL9fKcRw1NzcrIyNDCQldXxkyIJ4ZSUhI0Pjx4/vs/lNSUvhhdYm18ob1co+1co+1co+1cq+v1upUz4h8jgtYAQCAKWIEAACYGtQx4vf7VV5eLr/fbz1K3GOtvGG93GOt3GOt3GOt3IuHtRoQF7ACAIAz16B+ZgQAANgjRgAAgCliBAAAmCJGAACAqUEdI5WVlcrMzFRycrJyc3O1Y8cO65Hi0muvvaZZs2YpIyNDPp9Pzz//vPVIcamiokLTpk3TyJEjNWbMGM2ePVvvvvuu9VhxaeXKlZoyZUrsQ5by8vL00ksvWY81ICxbtkw+n09333239Shx6f7775fP52t3mzhxovVYcevDDz/UD3/4Q5199tkaNmyYLr30Ur311lv9PsegjZENGzaotLRU5eXl2rlzp7KyslRQUKDDhw9bjxZ3WlpalJWVpcrKSutR4tr27dtVUlKiN998U1u3btXx48d17bXXqqWlxXq0uDN+/HgtW7ZMtbW1euutt/TNb35T3/nOd/T3v//derS49te//lW//e1vNWXKFOtR4trXvvY1/fOf/4zdXn/9deuR4tK///1vzZgxQ0OHDtVLL72kd955R48++qhGjRrV/8M4g9T06dOdkpKS2H9HIhEnIyPDqaioMJwq/klyNm7caD3GgHD48GFHkrN9+3brUQaEUaNGOb/73e+sx4hbzc3NzoUXXuhs3brVueqqq5z58+dbjxSXysvLnaysLOsxBoSFCxc6V155pfUYjuM4zqB8ZqStrU21tbXKz8+PbUtISFB+fr5qamoMJ8OZJBQKSZJGjx5tPEl8i0QiWr9+vVpaWpSXl2c9TtwqKSnRDTfc0O7/W+jcP/7xD2VkZOj888/XTTfdpIMHD1qPFJdeeOEF5eTk6Hvf+57GjBmjyy67TGvWrDGZZVDGSFNTkyKRiAKBQLvtgUBADQ0NRlPhTBKNRnX33XdrxowZmjx5svU4centt9/WiBEj5Pf7dccdd2jjxo2aNGmS9Vhxaf369dq5c6cqKiqsR4l7ubm5Wrt2raqqqrRy5Urt379fM2fOVHNzs/VocWffvn1auXKlLrzwQm3ZskXz5s3TT37yEz399NP9PsuA+Fd7gYGmpKREu3bt4rXqU7j44otVV1enUCik5557TkVFRdq+fTtB8iX19fWaP3++tm7dquTkZOtx4t51110X+3rKlCnKzc3VhAkT9Mwzz+jWW281nCz+RKNR5eTkaOnSpZKkyy67TLt27dKqVatUVFTUr7MMymdG0tLSlJiYqMbGxnbbGxsblZ6ebjQVzhR33nmnXnzxRb366qsaP3689ThxKykpSRdccIGys7NVUVGhrKws/frXv7YeK+7U1tbq8OHDuvzyyzVkyBANGTJE27dv1+OPP64hQ4YoEolYjxjXzjrrLF100UXau3ev9ShxZ+zYsR3i/5JLLjF5WWtQxkhSUpKys7NVXV0d2xaNRlVdXc1r1ugxx3F05513auPGjXrllVd03nnnWY80oESjUbW2tlqPEXeuueYavf3226qrq4vdcnJydNNNN6murk6JiYnWI8a1o0eP6v3339fYsWOtR4k7M2bM6PDxA++9954mTJjQ77MM2pdpSktLVVRUpJycHE2fPl3Lly9XS0uLiouLrUeLO0ePHm33W8X+/ftVV1en0aNH69xzzzWcLL6UlJRo3bp1+tOf/qSRI0fGrj9KTU3VsGHDjKeLL2VlZbruuut07rnnqrm5WevWrdO2bdu0ZcsW69HizsiRIztcd/SVr3xFZ599NtcjdeKee+7RrFmzNGHCBB06dEjl5eVKTEzUnDlzrEeLOwsWLNAVV1yhpUuX6vvf/7527Nih1atXa/Xq1f0/jPXbeSz95je/cc4991wnKSnJmT59uvPmm29ajxSXXn31VUdSh1tRUZH1aHGlszWS5Pz+97+3Hi3u/OhHP3ImTJjgJCUlOeecc45zzTXXOC+//LL1WAMGb+3tWmFhoTN27FgnKSnJGTdunFNYWOjs3bvXeqy49ec//9mZPHmy4/f7nYkTJzqrV682mcPnOI7T/wkEAABwwqC8ZgQAAMQPYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACY+v96G00Hzz6rMAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(x, y_head, color=\"orange\")\n",
     "plt.scatter(x,y)\n",
@@ -441,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -455,15 +404,7 @@
     "id": "yB4IY_JOPHIp",
     "outputId": "8463e20f-abaa-4b65-9958-829f36428fbe"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R Square Score:  0.00012117012832713048\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.metrics import r2_score\n",
     "print(\"R Square Score: \",r2_score(y,y_head))"
@@ -471,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -485,15 +426,7 @@
     "id": "B2c6hULSPax7",
     "outputId": "ebdc9f0a-6dc3-420c-ee7b-7caf57fcf0ec"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error:  0.18530088136681314\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.metrics import mean_squared_error\n",
     "print(\"Mean Squared Error: \",mean_squared_error(y,y_head))"
@@ -501,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -515,15 +448,7 @@
     "id": "E5B5Mb8aQInk",
     "outputId": "c2c6d524-9fa9-4f30-e680-e83435348901"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error:  0.18530088136681314\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "MSE = np.square(np.subtract(y,y_head)).mean()\n",
     "print(\"Mean Squared Error: \",MSE)"

benchmark/sklearn_13/sklearn_13_reproduced.ipynb

@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-07T16:21:09.616280Z",

benchmark/sklearn_14/sklearn_14_fixed.ipynb

@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:18.443812Z",
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:21.397453Z",
@@ -79,186 +79,7 @@
      "shell.execute_reply.started": "2023-10-01T12:08:21.397388Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>street</th>\n",
-       "      <th>city</th>\n",
-       "      <th>statezip</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>313000.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>7912</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>18810 Densmore Ave N</td>\n",
-       "      <td>Shoreline</td>\n",
-       "      <td>WA 98133</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>2384000.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3650</td>\n",
-       "      <td>9050</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3370</td>\n",
-       "      <td>280</td>\n",
-       "      <td>1921</td>\n",
-       "      <td>0</td>\n",
-       "      <td>709 W Blaine St</td>\n",
-       "      <td>Seattle</td>\n",
-       "      <td>WA 98119</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>342000.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>11947</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>0</td>\n",
-       "      <td>26206-26214 143rd Ave SE</td>\n",
-       "      <td>Kent</td>\n",
-       "      <td>WA 98042</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>420000.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>8030</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1963</td>\n",
-       "      <td>0</td>\n",
-       "      <td>857 170th Pl NE</td>\n",
-       "      <td>Bellevue</td>\n",
-       "      <td>WA 98008</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>550000.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1940</td>\n",
-       "      <td>10500</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>800</td>\n",
-       "      <td>1976</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>9105 170th Ave NE</td>\n",
-       "      <td>Redmond</td>\n",
-       "      <td>WA 98052</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  date      price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
-       "0  2014-05-02 00:00:00   313000.0       3.0       1.50         1340      7912   \n",
-       "1  2014-05-02 00:00:00  2384000.0       5.0       2.50         3650      9050   \n",
-       "2  2014-05-02 00:00:00   342000.0       3.0       2.00         1930     11947   \n",
-       "3  2014-05-02 00:00:00   420000.0       3.0       2.25         2000      8030   \n",
-       "4  2014-05-02 00:00:00   550000.0       4.0       2.50         1940     10500   \n",
-       "\n",
-       "   floors  waterfront  view  condition  sqft_above  sqft_basement  yr_built  \\\n",
-       "0     1.5           0     0          3        1340              0      1955   \n",
-       "1     2.0           0     4          5        3370            280      1921   \n",
-       "2     1.0           0     0          4        1930              0      1966   \n",
-       "3     1.0           0     0          4        1000           1000      1963   \n",
-       "4     1.0           0     0          4        1140            800      1976   \n",
-       "\n",
-       "   yr_renovated                    street       city  statezip country  \n",
-       "0          2005      18810 Densmore Ave N  Shoreline  WA 98133     USA  \n",
-       "1             0           709 W Blaine St    Seattle  WA 98119     USA  \n",
-       "2             0  26206-26214 143rd Ave SE       Kent  WA 98042     USA  \n",
-       "3             0           857 170th Pl NE   Bellevue  WA 98008     USA  \n",
-       "4          1992         9105 170th Ave NE    Redmond  WA 98052     USA  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Preprocessing\n",
     "df.head()"
@@ -266,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:24.387978Z",
@@ -276,39 +97,7 @@
      "shell.execute_reply.started": "2023-10-01T12:08:24.387936Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 4600 entries, 0 to 4599\n",
-      "Data columns (total 18 columns):\n",
-      " #   Column         Non-Null Count  Dtype  \n",
-      "---  ------         --------------  -----  \n",
-      " 0   date           4600 non-null   object \n",
-      " 1   price          4600 non-null   float64\n",
-      " 2   bedrooms       4600 non-null   float64\n",
-      " 3   bathrooms      4600 non-null   float64\n",
-      " 4   sqft_living    4600 non-null   int64  \n",
-      " 5   sqft_lot       4600 non-null   int64  \n",
-      " 6   floors         4600 non-null   float64\n",
-      " 7   waterfront     4600 non-null   int64  \n",
-      " 8   view           4600 non-null   int64  \n",
-      " 9   condition      4600 non-null   int64  \n",
-      " 10  sqft_above     4600 non-null   int64  \n",
-      " 11  sqft_basement  4600 non-null   int64  \n",
-      " 12  yr_built       4600 non-null   int64  \n",
-      " 13  yr_renovated   4600 non-null   int64  \n",
-      " 14  street         4600 non-null   object \n",
-      " 15  city           4600 non-null   object \n",
-      " 16  statezip       4600 non-null   object \n",
-      " 17  country        4600 non-null   object \n",
-      "dtypes: float64(4), int64(9), object(5)\n",
-      "memory usage: 647.0+ KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.info()"
    ]
@@ -332,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:29.794467Z",
@@ -440,21 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fix ---------\n",
-    "# When using LinearRegression from sklearn.linear_model, all features must be numeric.\n",
-    "\n",
-    "# Convert categorical columns to numeric using one-hot encoding\n",
-    "categorical_columns = ['street', 'city', 'statezip', 'country']\n",
-    "df = pd.get_dummies(df, columns=categorical_columns, drop_first=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:41.179495Z",
@@ -466,446 +241,65 @@
    },
    "outputs": [],
    "source": [
-    "df['city'].replace({\n",
-    "'Seattle'            :      0,\n",
-    "'Renton'             :      1,\n",
-    "'Bellevue'           :      2,\n",
-    "'Redmond'            :      3,\n",
-    "'Issaquah'           :      4,\n",
-    "'Kirkland'           :      5,\n",
-    "'Kent'               :      6,\n",
-    "'Auburn'             :      7,\n",
-    "'Sammamish'          :      8,\n",
-    "'Federal Way'        :      9,\n",
-    "'Shoreline'          :     10,\n",
-    "'Woodinville'        :     11,\n",
-    "'Maple Valley'       :     12,\n",
-    "'Mercer Island'      :     13,\n",
-    "'Burien'             :     14,\n",
-    "'Snoqualmie'         :     15,\n",
-    "'Kenmore'            :     16,\n",
-    "'Des Moines'         :     17,\n",
-    "'North Bend'         :     18,\n",
-    "'Covington'          :     19,\n",
-    "'Duvall'             :     20,\n",
-    "'Lake Forest Park'   :     21,\n",
-    "'Bothell'            :     22,\n",
-    "'Newcastle'          :     23,\n",
-    "'SeaTac'             :     24,\n",
-    "'Tukwila'            :     25,\n",
-    "'Vashon'             :     26,\n",
-    "'Enumclaw'           :     27,\n",
-    "'Carnation'          :     28,\n",
-    "'Normandy Park'      :     29,\n",
-    "'Clyde Hill'         :     30,\n",
-    "'Medina'             :     31,\n",
-    "'Fall City'          :     32,\n",
-    "'Black Diamond'      :     33,\n",
-    "'Ravensdale'         :     34,\n",
-    "'Pacific'            :     35,\n",
-    "'Algona'             :     36,\n",
-    "'Yarrow Point'       :     37, \n",
-    "'Skykomish'          :     38,\n",
-    "'Preston'            :     39,\n",
-    "'Milton'             :     40,\n",
-    "'Inglewood-Finn Hill':     41,\n",
-    "'Snoqualmie Pass'    :     42,\n",
-    "'Beaux Arts Village' :     43},inplace=True)"
+    "# df['city'].replace({\n",
+    "# 'Seattle'            :      0,\n",
+    "# 'Renton'             :      1,\n",
+    "# 'Bellevue'           :      2,\n",
+    "# 'Redmond'            :      3,\n",
+    "# 'Issaquah'           :      4,\n",
+    "# 'Kirkland'           :      5,\n",
+    "# 'Kent'               :      6,\n",
+    "# 'Auburn'             :      7,\n",
+    "# 'Sammamish'          :      8,\n",
+    "# 'Federal Way'        :      9,\n",
+    "# 'Shoreline'          :     10,\n",
+    "# 'Woodinville'        :     11,\n",
+    "# 'Maple Valley'       :     12,\n",
+    "# 'Mercer Island'      :     13,\n",
+    "# 'Burien'             :     14,\n",
+    "# 'Snoqualmie'         :     15,\n",
+    "# 'Kenmore'            :     16,\n",
+    "# 'Des Moines'         :     17,\n",
+    "# 'North Bend'         :     18,\n",
+    "# 'Covington'          :     19,\n",
+    "# 'Duvall'             :     20,\n",
+    "# 'Lake Forest Park'   :     21,\n",
+    "# 'Bothell'            :     22,\n",
+    "# 'Newcastle'          :     23,\n",
+    "# 'SeaTac'             :     24,\n",
+    "# 'Tukwila'            :     25,\n",
+    "# 'Vashon'             :     26,\n",
+    "# 'Enumclaw'           :     27,\n",
+    "# 'Carnation'          :     28,\n",
+    "# 'Normandy Park'      :     29,\n",
+    "# 'Clyde Hill'         :     30,\n",
+    "# 'Medina'             :     31,\n",
+    "# 'Fall City'          :     32,\n",
+    "# 'Black Diamond'      :     33,\n",
+    "# 'Ravensdale'         :     34,\n",
+    "# 'Pacific'            :     35,\n",
+    "# 'Algona'             :     36,\n",
+    "# 'Yarrow Point'       :     37, \n",
+    "# 'Skykomish'          :     38,\n",
+    "# 'Preston'            :     39,\n",
+    "# 'Milton'             :     40,\n",
+    "# 'Inglewood-Finn Hill':     41,\n",
+    "# 'Snoqualmie Pass'    :     42,\n",
+    "# 'Beaux Arts Village' :     43},inplace=True)\n",
+    "\n",
+    "# fix ---------\n",
+    "# When using LinearRegression from sklearn.linear_model, all features must be numeric.\n",
+    "\n",
+    "# Convert categorical columns to numeric using one-hot encoding\n",
+    "categorical_columns = ['street', 'city', 'statezip', 'country']\n",
+    "df = pd.get_dummies(df, columns=categorical_columns, drop_first=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>...</th>\n",
-       "      <th>statezip_WA 98155</th>\n",
-       "      <th>statezip_WA 98166</th>\n",
-       "      <th>statezip_WA 98168</th>\n",
-       "      <th>statezip_WA 98177</th>\n",
-       "      <th>statezip_WA 98178</th>\n",
-       "      <th>statezip_WA 98188</th>\n",
-       "      <th>statezip_WA 98198</th>\n",
-       "      <th>statezip_WA 98199</th>\n",
-       "      <th>statezip_WA 98288</th>\n",
-       "      <th>statezip_WA 98354</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>3.130000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>7912</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>2.384000e+06</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3650</td>\n",
-       "      <td>9050</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>3.420000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>11947</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>4.200000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>8030</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>5.500000e+05</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1940</td>\n",
-       "      <td>10500</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4595</th>\n",
-       "      <td>2014-07-09 00:00:00</td>\n",
-       "      <td>3.081667e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1510</td>\n",
-       "      <td>6360</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4596</th>\n",
-       "      <td>2014-07-09 00:00:00</td>\n",
-       "      <td>5.343333e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>7573</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4597</th>\n",
-       "      <td>2014-07-09 00:00:00</td>\n",
-       "      <td>4.169042e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3010</td>\n",
-       "      <td>7014</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4598</th>\n",
-       "      <td>2014-07-10 00:00:00</td>\n",
-       "      <td>2.034000e+05</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2090</td>\n",
-       "      <td>6630</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4599</th>\n",
-       "      <td>2014-07-10 00:00:00</td>\n",
-       "      <td>2.206000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1490</td>\n",
-       "      <td>8102</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4600 rows × 4657 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     date         price  bedrooms  bathrooms  sqft_living  \\\n",
-       "0     2014-05-02 00:00:00  3.130000e+05       3.0       1.50         1340   \n",
-       "1     2014-05-02 00:00:00  2.384000e+06       5.0       2.50         3650   \n",
-       "2     2014-05-02 00:00:00  3.420000e+05       3.0       2.00         1930   \n",
-       "3     2014-05-02 00:00:00  4.200000e+05       3.0       2.25         2000   \n",
-       "4     2014-05-02 00:00:00  5.500000e+05       4.0       2.50         1940   \n",
-       "...                   ...           ...       ...        ...          ...   \n",
-       "4595  2014-07-09 00:00:00  3.081667e+05       3.0       1.75         1510   \n",
-       "4596  2014-07-09 00:00:00  5.343333e+05       3.0       2.50         1460   \n",
-       "4597  2014-07-09 00:00:00  4.169042e+05       3.0       2.50         3010   \n",
-       "4598  2014-07-10 00:00:00  2.034000e+05       4.0       2.00         2090   \n",
-       "4599  2014-07-10 00:00:00  2.206000e+05       3.0       2.50         1490   \n",
-       "\n",
-       "      sqft_lot  floors  waterfront  view  condition  ...  statezip_WA 98155  \\\n",
-       "0         7912     1.5           0     0          3  ...              False   \n",
-       "1         9050     2.0           0     4          5  ...              False   \n",
-       "2        11947     1.0           0     0          4  ...              False   \n",
-       "3         8030     1.0           0     0          4  ...              False   \n",
-       "4        10500     1.0           0     0          4  ...              False   \n",
-       "...        ...     ...         ...   ...        ...  ...                ...   \n",
-       "4595      6360     1.0           0     0          4  ...              False   \n",
-       "4596      7573     2.0           0     0          3  ...              False   \n",
-       "4597      7014     2.0           0     0          3  ...              False   \n",
-       "4598      6630     1.0           0     0          3  ...              False   \n",
-       "4599      8102     2.0           0     0          4  ...              False   \n",
-       "\n",
-       "      statezip_WA 98166  statezip_WA 98168  statezip_WA 98177  \\\n",
-       "0                 False              False              False   \n",
-       "1                 False              False              False   \n",
-       "2                 False              False              False   \n",
-       "3                 False              False              False   \n",
-       "4                 False              False              False   \n",
-       "...                 ...                ...                ...   \n",
-       "4595              False              False              False   \n",
-       "4596              False              False              False   \n",
-       "4597              False              False              False   \n",
-       "4598              False              False              False   \n",
-       "4599              False              False              False   \n",
-       "\n",
-       "      statezip_WA 98178  statezip_WA 98188  statezip_WA 98198  \\\n",
-       "0                 False              False              False   \n",
-       "1                 False              False              False   \n",
-       "2                 False              False              False   \n",
-       "3                 False              False              False   \n",
-       "4                 False              False              False   \n",
-       "...                 ...                ...                ...   \n",
-       "4595              False              False              False   \n",
-       "4596              False              False              False   \n",
-       "4597              False              False              False   \n",
-       "4598               True              False              False   \n",
-       "4599              False              False              False   \n",
-       "\n",
-       "      statezip_WA 98199  statezip_WA 98288  statezip_WA 98354  \n",
-       "0                 False              False              False  \n",
-       "1                 False              False              False  \n",
-       "2                 False              False              False  \n",
-       "3                 False              False              False  \n",
-       "4                 False              False              False  \n",
-       "...                 ...                ...                ...  \n",
-       "4595              False              False              False  \n",
-       "4596              False              False              False  \n",
-       "4597              False              False              False  \n",
-       "4598              False              False              False  \n",
-       "4599              False              False              False  \n",
-       "\n",
-       "[4600 rows x 4657 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df"
    ]
@@ -929,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:09:16.082488Z",
@@ -943,20 +337,13 @@
    "source": [
     "# Define X and y\n",
     "\n",
-    "#df.columns\n",
-    "y=df['price']\n",
-    "X=df[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
-    "       'floors', 'waterfront', 'view', 'condition', 'sqft_above',\n",
-    "       'sqft_basement', 'yr_built', 'yr_renovated', 'street', 'city',\n",
-    "       'statezip', 'country']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "# #df.columns\n",
+    "# y=df['price']\n",
+    "# X=df[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
+    "#        'floors', 'waterfront', 'view', 'condition', 'sqft_above',\n",
+    "#        'sqft_basement', 'yr_built', 'yr_renovated', 'street', 'city',\n",
+    "#        'statezip', 'country']]\n",
+    "\n",
     "# fix--------------\n",
     "X = df.drop(columns=['date','price'])  # Features\n",
     "y = df['price']  # Target variable"
@@ -964,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:09:20.447436Z",
@@ -984,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:09:23.615894Z",
@@ -1004,7 +391,7 @@
        "LinearRegression()"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1016,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1050,6 +437,13 @@
     "print(f\"Training R² Score: {train_r2:.4f}\")\n",
     "print(f\"Testing R² Score: {test_r2:.4f}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

benchmark/sklearn_14/sklearn_14_reproduced.ipynb

@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:21.397453Z",
@@ -79,186 +79,7 @@
      "shell.execute_reply.started": "2023-10-01T12:08:21.397388Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>street</th>\n",
-       "      <th>city</th>\n",
-       "      <th>statezip</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>313000.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>7912</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>18810 Densmore Ave N</td>\n",
-       "      <td>Shoreline</td>\n",
-       "      <td>WA 98133</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>2384000.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3650</td>\n",
-       "      <td>9050</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3370</td>\n",
-       "      <td>280</td>\n",
-       "      <td>1921</td>\n",
-       "      <td>0</td>\n",
-       "      <td>709 W Blaine St</td>\n",
-       "      <td>Seattle</td>\n",
-       "      <td>WA 98119</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>342000.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>11947</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>0</td>\n",
-       "      <td>26206-26214 143rd Ave SE</td>\n",
-       "      <td>Kent</td>\n",
-       "      <td>WA 98042</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>420000.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>8030</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1963</td>\n",
-       "      <td>0</td>\n",
-       "      <td>857 170th Pl NE</td>\n",
-       "      <td>Bellevue</td>\n",
-       "      <td>WA 98008</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>550000.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1940</td>\n",
-       "      <td>10500</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>800</td>\n",
-       "      <td>1976</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>9105 170th Ave NE</td>\n",
-       "      <td>Redmond</td>\n",
-       "      <td>WA 98052</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  date      price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
-       "0  2014-05-02 00:00:00   313000.0       3.0       1.50         1340      7912   \n",
-       "1  2014-05-02 00:00:00  2384000.0       5.0       2.50         3650      9050   \n",
-       "2  2014-05-02 00:00:00   342000.0       3.0       2.00         1930     11947   \n",
-       "3  2014-05-02 00:00:00   420000.0       3.0       2.25         2000      8030   \n",
-       "4  2014-05-02 00:00:00   550000.0       4.0       2.50         1940     10500   \n",
-       "\n",
-       "   floors  waterfront  view  condition  sqft_above  sqft_basement  yr_built  \\\n",
-       "0     1.5           0     0          3        1340              0      1955   \n",
-       "1     2.0           0     4          5        3370            280      1921   \n",
-       "2     1.0           0     0          4        1930              0      1966   \n",
-       "3     1.0           0     0          4        1000           1000      1963   \n",
-       "4     1.0           0     0          4        1140            800      1976   \n",
-       "\n",
-       "   yr_renovated                    street       city  statezip country  \n",
-       "0          2005      18810 Densmore Ave N  Shoreline  WA 98133     USA  \n",
-       "1             0           709 W Blaine St    Seattle  WA 98119     USA  \n",
-       "2             0  26206-26214 143rd Ave SE       Kent  WA 98042     USA  \n",
-       "3             0           857 170th Pl NE   Bellevue  WA 98008     USA  \n",
-       "4          1992         9105 170th Ave NE    Redmond  WA 98052     USA  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Preprocessing\n",
     "df.head()"
@@ -266,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:24.387978Z",
@@ -276,39 +97,7 @@
      "shell.execute_reply.started": "2023-10-01T12:08:24.387936Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 4600 entries, 0 to 4599\n",
-      "Data columns (total 18 columns):\n",
-      " #   Column         Non-Null Count  Dtype  \n",
-      "---  ------         --------------  -----  \n",
-      " 0   date           4600 non-null   object \n",
-      " 1   price          4600 non-null   float64\n",
-      " 2   bedrooms       4600 non-null   float64\n",
-      " 3   bathrooms      4600 non-null   float64\n",
-      " 4   sqft_living    4600 non-null   int64  \n",
-      " 5   sqft_lot       4600 non-null   int64  \n",
-      " 6   floors         4600 non-null   float64\n",
-      " 7   waterfront     4600 non-null   int64  \n",
-      " 8   view           4600 non-null   int64  \n",
-      " 9   condition      4600 non-null   int64  \n",
-      " 10  sqft_above     4600 non-null   int64  \n",
-      " 11  sqft_basement  4600 non-null   int64  \n",
-      " 12  yr_built       4600 non-null   int64  \n",
-      " 13  yr_renovated   4600 non-null   int64  \n",
-      " 14  street         4600 non-null   object \n",
-      " 15  city           4600 non-null   object \n",
-      " 16  statezip       4600 non-null   object \n",
-      " 17  country        4600 non-null   object \n",
-      "dtypes: float64(4), int64(9), object(5)\n",
-      "memory usage: 647.0+ KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.info()"
    ]
@@ -332,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:29.794467Z",
@@ -388,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:34.205598Z",
@@ -398,23 +187,7 @@
      "shell.execute_reply.started": "2023-10-01T12:08:34.205565Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Shoreline' 'Seattle' 'Kent' 'Bellevue' 'Redmond' 'Maple Valley'\n",
-      " 'North Bend' 'Lake Forest Park' 'Sammamish' 'Auburn' 'Des Moines'\n",
-      " 'Bothell' 'Federal Way' 'Kirkland' 'Issaquah' 'Woodinville'\n",
-      " 'Normandy Park' 'Fall City' 'Renton' 'Carnation' 'Snoqualmie' 'Duvall'\n",
-      " 'Burien' 'Covington' 'Inglewood-Finn Hill' 'Kenmore' 'Newcastle'\n",
-      " 'Mercer Island' 'Black Diamond' 'Ravensdale' 'Clyde Hill' 'Algona'\n",
-      " 'Skykomish' 'Tukwila' 'Vashon' 'Yarrow Point' 'SeaTac' 'Medina'\n",
-      " 'Enumclaw' 'Snoqualmie Pass' 'Pacific' 'Beaux Arts Village' 'Preston'\n",
-      " 'Milton']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "valueofcity = (df['city'].unique())\n",
     "print(valueofcity)"
@@ -422,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:36.362418Z",
@@ -432,18 +205,7 @@
      "shell.execute_reply.started": "2023-10-01T12:08:36.362373Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(44,)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "valueofcity.shape"
    ]
@@ -467,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:08:41.179495Z",
@@ -528,355 +290,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>street</th>\n",
-       "      <th>city</th>\n",
-       "      <th>statezip</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>3.130000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>7912</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>18810 Densmore Ave N</td>\n",
-       "      <td>10</td>\n",
-       "      <td>WA 98133</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>2.384000e+06</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3650</td>\n",
-       "      <td>9050</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3370</td>\n",
-       "      <td>280</td>\n",
-       "      <td>1921</td>\n",
-       "      <td>0</td>\n",
-       "      <td>709 W Blaine St</td>\n",
-       "      <td>0</td>\n",
-       "      <td>WA 98119</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>3.420000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>11947</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>0</td>\n",
-       "      <td>26206-26214 143rd Ave SE</td>\n",
-       "      <td>6</td>\n",
-       "      <td>WA 98042</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>4.200000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>8030</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1963</td>\n",
-       "      <td>0</td>\n",
-       "      <td>857 170th Pl NE</td>\n",
-       "      <td>2</td>\n",
-       "      <td>WA 98008</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2014-05-02 00:00:00</td>\n",
-       "      <td>5.500000e+05</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1940</td>\n",
-       "      <td>10500</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>800</td>\n",
-       "      <td>1976</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>9105 170th Ave NE</td>\n",
-       "      <td>3</td>\n",
-       "      <td>WA 98052</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4595</th>\n",
-       "      <td>2014-07-09 00:00:00</td>\n",
-       "      <td>3.081667e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1510</td>\n",
-       "      <td>6360</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1510</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1954</td>\n",
-       "      <td>1979</td>\n",
-       "      <td>501 N 143rd St</td>\n",
-       "      <td>0</td>\n",
-       "      <td>WA 98133</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4596</th>\n",
-       "      <td>2014-07-09 00:00:00</td>\n",
-       "      <td>5.343333e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>7573</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1983</td>\n",
-       "      <td>2009</td>\n",
-       "      <td>14855 SE 10th Pl</td>\n",
-       "      <td>2</td>\n",
-       "      <td>WA 98007</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4597</th>\n",
-       "      <td>2014-07-09 00:00:00</td>\n",
-       "      <td>4.169042e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3010</td>\n",
-       "      <td>7014</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3010</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2009</td>\n",
-       "      <td>0</td>\n",
-       "      <td>759 Ilwaco Pl NE</td>\n",
-       "      <td>1</td>\n",
-       "      <td>WA 98059</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4598</th>\n",
-       "      <td>2014-07-10 00:00:00</td>\n",
-       "      <td>2.034000e+05</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2090</td>\n",
-       "      <td>6630</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>1020</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5148 S Creston St</td>\n",
-       "      <td>0</td>\n",
-       "      <td>WA 98178</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4599</th>\n",
-       "      <td>2014-07-10 00:00:00</td>\n",
-       "      <td>2.206000e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1490</td>\n",
-       "      <td>8102</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1490</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>0</td>\n",
-       "      <td>18717 SE 258th St</td>\n",
-       "      <td>19</td>\n",
-       "      <td>WA 98042</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4600 rows × 18 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     date         price  bedrooms  bathrooms  sqft_living  \\\n",
-       "0     2014-05-02 00:00:00  3.130000e+05       3.0       1.50         1340   \n",
-       "1     2014-05-02 00:00:00  2.384000e+06       5.0       2.50         3650   \n",
-       "2     2014-05-02 00:00:00  3.420000e+05       3.0       2.00         1930   \n",
-       "3     2014-05-02 00:00:00  4.200000e+05       3.0       2.25         2000   \n",
-       "4     2014-05-02 00:00:00  5.500000e+05       4.0       2.50         1940   \n",
-       "...                   ...           ...       ...        ...          ...   \n",
-       "4595  2014-07-09 00:00:00  3.081667e+05       3.0       1.75         1510   \n",
-       "4596  2014-07-09 00:00:00  5.343333e+05       3.0       2.50         1460   \n",
-       "4597  2014-07-09 00:00:00  4.169042e+05       3.0       2.50         3010   \n",
-       "4598  2014-07-10 00:00:00  2.034000e+05       4.0       2.00         2090   \n",
-       "4599  2014-07-10 00:00:00  2.206000e+05       3.0       2.50         1490   \n",
-       "\n",
-       "      sqft_lot  floors  waterfront  view  condition  sqft_above  \\\n",
-       "0         7912     1.5           0     0          3        1340   \n",
-       "1         9050     2.0           0     4          5        3370   \n",
-       "2        11947     1.0           0     0          4        1930   \n",
-       "3         8030     1.0           0     0          4        1000   \n",
-       "4        10500     1.0           0     0          4        1140   \n",
-       "...        ...     ...         ...   ...        ...         ...   \n",
-       "4595      6360     1.0           0     0          4        1510   \n",
-       "4596      7573     2.0           0     0          3        1460   \n",
-       "4597      7014     2.0           0     0          3        3010   \n",
-       "4598      6630     1.0           0     0          3        1070   \n",
-       "4599      8102     2.0           0     0          4        1490   \n",
-       "\n",
-       "      sqft_basement  yr_built  yr_renovated                    street  city  \\\n",
-       "0                 0      1955          2005      18810 Densmore Ave N    10   \n",
-       "1               280      1921             0           709 W Blaine St     0   \n",
-       "2                 0      1966             0  26206-26214 143rd Ave SE     6   \n",
-       "3              1000      1963             0           857 170th Pl NE     2   \n",
-       "4               800      1976          1992         9105 170th Ave NE     3   \n",
-       "...             ...       ...           ...                       ...   ...   \n",
-       "4595              0      1954          1979            501 N 143rd St     0   \n",
-       "4596              0      1983          2009          14855 SE 10th Pl     2   \n",
-       "4597              0      2009             0          759 Ilwaco Pl NE     1   \n",
-       "4598           1020      1974             0         5148 S Creston St     0   \n",
-       "4599              0      1990             0         18717 SE 258th St    19   \n",
-       "\n",
-       "      statezip country  \n",
-       "0     WA 98133     USA  \n",
-       "1     WA 98119     USA  \n",
-       "2     WA 98042     USA  \n",
-       "3     WA 98008     USA  \n",
-       "4     WA 98052     USA  \n",
-       "...        ...     ...  \n",
-       "4595  WA 98133     USA  \n",
-       "4596  WA 98007     USA  \n",
-       "4597  WA 98059     USA  \n",
-       "4598  WA 98178     USA  \n",
-       "4599  WA 98042     USA  \n",
-       "\n",
-       "[4600 rows x 18 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:09:08.527614Z",
@@ -886,23 +309,14 @@
      "shell.execute_reply.started": "2023-10-01T12:09:08.527547Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[10  0  6  2  3 12 18 21  8  7 17 22  9  5  4 11 29 32  1 28 15 20 14 19\n",
-      " 41 16 23 13 33 34 30 36 38 25 26 37 24 31 27 42 35 43 39 40]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(df['city'].unique())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:09:16.082488Z",
@@ -926,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:09:20.447436Z",
@@ -946,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-01T12:09:23.615894Z",
@@ -964,7 +378,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-613c4fe7e2a6>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Fitting the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-7-613c4fe7e2a6>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Fitting the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    646\u001b[0m         \u001b[0maccept_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpositive\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coo\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m    649\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m         )\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    582\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    585\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1104\u001b[0m         )\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m     X = check_array(\n\u001b[0m\u001b[1;32m   1107\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/sklearn_15/sklearn_15_fixed.ipynb

@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "feaa7d37",
    "metadata": {
     "execution": {
@@ -165,46 +165,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 36881 entries, 0 to 36880\n",
-      "Data columns (total 25 columns):\n",
-      " #   Column         Non-Null Count  Dtype  \n",
-      "---  ------         --------------  -----  \n",
-      " 0   Unnamed: 0     36881 non-null  int64  \n",
-      " 1   Date           36881 non-null  object \n",
-      " 2   Location       36881 non-null  object \n",
-      " 3   MinTemp        36543 non-null  float64\n",
-      " 4   MaxTemp        36639 non-null  float64\n",
-      " 5   Rainfall       36255 non-null  float64\n",
-      " 6   Evaporation    24035 non-null  float64\n",
-      " 7   Sunshine       23317 non-null  float64\n",
-      " 8   WindGustDir    33513 non-null  object \n",
-      " 9   WindGustSpeed  33520 non-null  float64\n",
-      " 10  WindDir9am     34072 non-null  object \n",
-      " 11  WindDir3pm     35919 non-null  object \n",
-      " 12  WindSpeed9am   36219 non-null  float64\n",
-      " 13  WindSpeed3pm   36235 non-null  float64\n",
-      " 14  Humidity9am    36311 non-null  float64\n",
-      " 15  Humidity3pm    36370 non-null  float64\n",
-      " 16  Pressure9am    33309 non-null  float64\n",
-      " 17  Pressure3pm    33329 non-null  float64\n",
-      " 18  Cloud9am       24381 non-null  float64\n",
-      " 19  Cloud3pm       23899 non-null  float64\n",
-      " 20  Temp9am        36394 non-null  float64\n",
-      " 21  Temp3pm        36437 non-null  float64\n",
-      " 22  RainToday      36255 non-null  object \n",
-      " 23  RISK_MM        36261 non-null  float64\n",
-      " 24  RainTomorrow   36261 non-null  object \n",
-      "dtypes: float64(17), int64(1), object(7)\n",
-      "memory usage: 7.0+ MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data.head()\n",
     "data.info()"
@@ -229,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "9d21679a",
    "metadata": {
     "execution": {
@@ -271,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "ffd42901",
    "metadata": {
     "execution": {
@@ -317,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "b570e7d1",
    "metadata": {
     "execution": {
@@ -360,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "f05313a2",
    "metadata": {
     "execution": {
@@ -408,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "a285cd46",
    "metadata": {
     "execution": {
@@ -450,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "792e3fb0",
    "metadata": {
     "execution": {
@@ -478,7 +439,7 @@
        "LogisticRegression(max_iter=1000)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "7b178f4d",
    "metadata": {
     "papermill": {
@@ -552,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "a7bb198f",
    "metadata": {
     "papermill": {
@@ -564,15 +525,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.9918666124440829\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
     "print(\"Accuracy:\", accuracy)"

benchmark/sklearn_15/sklearn_15_reproduced.ipynb

@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "9547167a",
    "metadata": {
     "execution": {
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "feaa7d37",
    "metadata": {
     "execution": {
@@ -165,46 +165,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 36881 entries, 0 to 36880\n",
-      "Data columns (total 25 columns):\n",
-      " #   Column         Non-Null Count  Dtype  \n",
-      "---  ------         --------------  -----  \n",
-      " 0   Unnamed: 0     36881 non-null  int64  \n",
-      " 1   Date           36881 non-null  object \n",
-      " 2   Location       36881 non-null  object \n",
-      " 3   MinTemp        36543 non-null  float64\n",
-      " 4   MaxTemp        36639 non-null  float64\n",
-      " 5   Rainfall       36255 non-null  float64\n",
-      " 6   Evaporation    24035 non-null  float64\n",
-      " 7   Sunshine       23317 non-null  float64\n",
-      " 8   WindGustDir    33513 non-null  object \n",
-      " 9   WindGustSpeed  33520 non-null  float64\n",
-      " 10  WindDir9am     34072 non-null  object \n",
-      " 11  WindDir3pm     35919 non-null  object \n",
-      " 12  WindSpeed9am   36219 non-null  float64\n",
-      " 13  WindSpeed3pm   36235 non-null  float64\n",
-      " 14  Humidity9am    36311 non-null  float64\n",
-      " 15  Humidity3pm    36370 non-null  float64\n",
-      " 16  Pressure9am    33309 non-null  float64\n",
-      " 17  Pressure3pm    33329 non-null  float64\n",
-      " 18  Cloud9am       24381 non-null  float64\n",
-      " 19  Cloud3pm       23899 non-null  float64\n",
-      " 20  Temp9am        36394 non-null  float64\n",
-      " 21  Temp3pm        36437 non-null  float64\n",
-      " 22  RainToday      36255 non-null  object \n",
-      " 23  RISK_MM        36261 non-null  float64\n",
-      " 24  RainTomorrow   36261 non-null  object \n",
-      "dtypes: float64(17), int64(1), object(7)\n",
-      "memory usage: 7.0+ MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data.head()\n",
     "data.info()"
@@ -229,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "9d21679a",
    "metadata": {
     "execution": {
@@ -271,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "ffd42901",
    "metadata": {
     "execution": {
@@ -317,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "b570e7d1",
    "metadata": {
     "execution": {
@@ -360,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "f05313a2",
    "metadata": {
     "execution": {
@@ -403,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "id": "a285cd46",
    "metadata": {
     "execution": {
@@ -445,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "id": "792e3fb0",
    "metadata": {
     "execution": {
@@ -471,7 +432,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-8a1f46ded531>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-8-8a1f46ded531>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_set_output.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mdata_to_wrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_wrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0;31m# only wrap the first output for cross decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    876\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    877\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 878\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    879\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    880\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    822\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/sklearn_3/sklearn_3_fixed.ipynb

@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.511496Z",
@@ -116,214 +116,14 @@
      "shell.execute_reply.started": "2023-07-23T04:58:48.511450Z"
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-       "<p>5 rows × 58 columns</p>\n",
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-       "1  a633a258eeed  2.95  18930.84  1513.23  129.03  148.21  27.22  6.94   7.85   \n",
-       "2  d1a9f56adc4a  3.81  12809.52  1084.70  261.90  168.68  35.95  6.13  13.42   \n",
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-       "4  b874e25c20aa  5.93  12025.87  1306.31  219.60  144.74  18.83  8.25  27.70   \n",
-       "\n",
-       "        BC  ...      FL       FR     FS      GB       GE         GF     GH  \\\n",
-       "0  1205.17  ...  124.56  1219.22  16.20  134.13  1174.76   72861.37  17.67   \n",
-       "1  1339.67  ...   33.68  1214.14  17.09   76.82   179.83   35123.99  34.14   \n",
-       "2    87.12  ...    9.43   540.83  20.47   37.11   808.55   81425.75  42.81   \n",
-       "3    92.56  ...   33.70   731.94  26.81   49.18   729.25  143656.96  75.09   \n",
-       "4   580.42  ...   71.44   608.95   0.50   72.11  1153.78  110162.30  17.34   \n",
-       "\n",
-       "       GI     GL  Class  \n",
-       "0   83.87  16.54      0  \n",
-       "1  123.68   2.81      1  \n",
-       "2   83.53   6.02      1  \n",
-       "3  177.96  15.87      0  \n",
-       "4   29.21  19.07      1  \n",
-       "\n",
-       "[5 rows x 58 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.542550Z",
@@ -357,7 +157,7 @@
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   {
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-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.614537Z",
@@ -367,376 +167,14 @@
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-       "      <th></th>\n",
-       "      <th>Id</th>\n",
-       "      <th>AB</th>\n",
-       "      <th>AF</th>\n",
-       "      <th>AH</th>\n",
-       "      <th>AM</th>\n",
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-       "      <th>AY</th>\n",
-       "      <th>AZ</th>\n",
-       "      <th>BC</th>\n",
-       "      <th>...</th>\n",
-       "      <th>GF</th>\n",
-       "      <th>GH</th>\n",
-       "      <th>GI</th>\n",
-       "      <th>GL</th>\n",
-       "      <th>Class</th>\n",
-       "      <th>Alpha</th>\n",
-       "      <th>Beta</th>\n",
-       "      <th>Gamma</th>\n",
-       "      <th>Delta</th>\n",
-       "      <th>Epsilon</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>d1a9f56adc4a</td>\n",
-       "      <td>3.81</td>\n",
-       "      <td>12809.52</td>\n",
-       "      <td>1084.70</td>\n",
-       "      <td>261.90</td>\n",
-       "      <td>168.68</td>\n",
-       "      <td>35.95</td>\n",
-       "      <td>6.13</td>\n",
-       "      <td>13.42</td>\n",
-       "      <td>87.12</td>\n",
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-       "      <td>81425.75</td>\n",
-       "      <td>42.81</td>\n",
-       "      <td>83.53</td>\n",
-       "      <td>6.02</td>\n",
-       "      <td>1</td>\n",
-       "      <td>D</td>\n",
-       "      <td>B</td>\n",
-       "      <td>E</td>\n",
-       "      <td>C</td>\n",
-       "      <td>9/10/2020</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>d1a9f56adc4a</td>\n",
-       "      <td>3.81</td>\n",
-       "      <td>12809.52</td>\n",
-       "      <td>1084.70</td>\n",
-       "      <td>261.90</td>\n",
-       "      <td>168.68</td>\n",
-       "      <td>35.95</td>\n",
-       "      <td>6.13</td>\n",
-       "      <td>13.42</td>\n",
-       "      <td>87.12</td>\n",
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-       "      <td>81425.75</td>\n",
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-       "      <td>A</td>\n",
-       "      <td>B</td>\n",
-       "      <td>H</td>\n",
-       "      <td>B</td>\n",
-       "      <td>3/11/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>d1a9f56adc4a</td>\n",
-       "      <td>5.80</td>\n",
-       "      <td>6079.58</td>\n",
-       "      <td>685.73</td>\n",
-       "      <td>78.97</td>\n",
-       "      <td>77.56</td>\n",
-       "      <td>34.61</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>32.46</td>\n",
-       "      <td>1115.36</td>\n",
-       "      <td>...</td>\n",
-       "      <td>102436.56</td>\n",
-       "      <td>35.03</td>\n",
-       "      <td>111.92</td>\n",
-       "      <td>4.96</td>\n",
-       "      <td>0</td>\n",
-       "      <td>D</td>\n",
-       "      <td>B</td>\n",
-       "      <td>E</td>\n",
-       "      <td>C</td>\n",
-       "      <td>9/10/2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>d1a9f56adc4a</td>\n",
-       "      <td>5.80</td>\n",
-       "      <td>6079.58</td>\n",
-       "      <td>685.73</td>\n",
-       "      <td>78.97</td>\n",
-       "      <td>77.56</td>\n",
-       "      <td>34.61</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>32.46</td>\n",
-       "      <td>1115.36</td>\n",
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-       "      <td>102436.56</td>\n",
-       "      <td>35.03</td>\n",
-       "      <td>111.92</td>\n",
-       "      <td>4.96</td>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>B</td>\n",
-       "      <td>H</td>\n",
-       "      <td>B</td>\n",
-       "      <td>3/11/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>b874e25c20aa</td>\n",
-       "      <td>5.93</td>\n",
-       "      <td>12025.87</td>\n",
-       "      <td>1306.31</td>\n",
-       "      <td>219.60</td>\n",
-       "      <td>144.74</td>\n",
-       "      <td>18.83</td>\n",
-       "      <td>8.25</td>\n",
-       "      <td>27.70</td>\n",
-       "      <td>580.42</td>\n",
-       "      <td>...</td>\n",
-       "      <td>110162.30</td>\n",
-       "      <td>17.34</td>\n",
-       "      <td>29.21</td>\n",
-       "      <td>19.07</td>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>C</td>\n",
-       "      <td>E</td>\n",
-       "      <td>D</td>\n",
-       "      <td>7/27/2020</td>\n",
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-       "    <tr>\n",
-       "      <th>619</th>\n",
-       "      <td>6bf666677588</td>\n",
-       "      <td>2.80</td>\n",
-       "      <td>23687.98</td>\n",
-       "      <td>225.53</td>\n",
-       "      <td>385.78</td>\n",
-       "      <td>47.02</td>\n",
-       "      <td>17.05</td>\n",
-       "      <td>7.37</td>\n",
-       "      <td>35.40</td>\n",
-       "      <td>831.98</td>\n",
-       "      <td>...</td>\n",
-       "      <td>113853.10</td>\n",
-       "      <td>76.47</td>\n",
-       "      <td>162.43</td>\n",
-       "      <td>8.92</td>\n",
-       "      <td>0</td>\n",
-       "      <td>G</td>\n",
-       "      <td>B</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>12/20/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>620</th>\n",
-       "      <td>4e6477903aa3</td>\n",
-       "      <td>5.65</td>\n",
-       "      <td>5118.03</td>\n",
-       "      <td>1630.33</td>\n",
-       "      <td>176.64</td>\n",
-       "      <td>151.55</td>\n",
-       "      <td>10.58</td>\n",
-       "      <td>2.68</td>\n",
-       "      <td>25.15</td>\n",
-       "      <td>956.36</td>\n",
-       "      <td>...</td>\n",
-       "      <td>125353.53</td>\n",
-       "      <td>49.12</td>\n",
-       "      <td>34.51</td>\n",
-       "      <td>15.82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4/19/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>621</th>\n",
-       "      <td>4e6477903aa3</td>\n",
-       "      <td>5.65</td>\n",
-       "      <td>5118.03</td>\n",
-       "      <td>1630.33</td>\n",
-       "      <td>176.64</td>\n",
-       "      <td>151.55</td>\n",
-       "      <td>10.58</td>\n",
-       "      <td>2.68</td>\n",
-       "      <td>25.15</td>\n",
-       "      <td>956.36</td>\n",
-       "      <td>...</td>\n",
-       "      <td>125353.53</td>\n",
-       "      <td>49.12</td>\n",
-       "      <td>34.51</td>\n",
-       "      <td>15.82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>D</td>\n",
-       "      <td>C</td>\n",
-       "      <td>H</td>\n",
-       "      <td>B</td>\n",
-       "      <td>12/31/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>622</th>\n",
-       "      <td>9949a1749e2c</td>\n",
-       "      <td>3.08</td>\n",
-       "      <td>5687.86</td>\n",
-       "      <td>690.00</td>\n",
-       "      <td>573.70</td>\n",
-       "      <td>89.60</td>\n",
-       "      <td>25.03</td>\n",
-       "      <td>3.85</td>\n",
-       "      <td>22.61</td>\n",
-       "      <td>1124.93</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2026.66</td>\n",
-       "      <td>11.16</td>\n",
-       "      <td>17.13</td>\n",
-       "      <td>21.33</td>\n",
-       "      <td>1</td>\n",
-       "      <td>D</td>\n",
-       "      <td>C</td>\n",
-       "      <td>A</td>\n",
-       "      <td>B</td>\n",
-       "      <td>4/19/2019</td>\n",
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-       "    <tr>\n",
-       "      <th>623</th>\n",
-       "      <td>9949a1749e2c</td>\n",
-       "      <td>3.08</td>\n",
-       "      <td>5687.86</td>\n",
-       "      <td>690.00</td>\n",
-       "      <td>573.70</td>\n",
-       "      <td>89.60</td>\n",
-       "      <td>25.03</td>\n",
-       "      <td>3.85</td>\n",
-       "      <td>22.61</td>\n",
-       "      <td>1124.93</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2026.66</td>\n",
-       "      <td>11.16</td>\n",
-       "      <td>17.13</td>\n",
-       "      <td>21.33</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>B</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3/25/2019</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>624 rows × 63 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               Id    AB        AF       AH      AM      AR     AX    AY  \\\n",
-       "0    d1a9f56adc4a  3.81  12809.52  1084.70  261.90  168.68  35.95  6.13   \n",
-       "1    d1a9f56adc4a  3.81  12809.52  1084.70  261.90  168.68  35.95  6.13   \n",
-       "2    d1a9f56adc4a  5.80   6079.58   685.73   78.97   77.56  34.61  0.47   \n",
-       "3    d1a9f56adc4a  5.80   6079.58   685.73   78.97   77.56  34.61  0.47   \n",
-       "4    b874e25c20aa  5.93  12025.87  1306.31  219.60  144.74  18.83  8.25   \n",
-       "..            ...   ...       ...      ...     ...     ...    ...   ...   \n",
-       "619  6bf666677588  2.80  23687.98   225.53  385.78   47.02  17.05  7.37   \n",
-       "620  4e6477903aa3  5.65   5118.03  1630.33  176.64  151.55  10.58  2.68   \n",
-       "621  4e6477903aa3  5.65   5118.03  1630.33  176.64  151.55  10.58  2.68   \n",
-       "622  9949a1749e2c  3.08   5687.86   690.00  573.70   89.60  25.03  3.85   \n",
-       "623  9949a1749e2c  3.08   5687.86   690.00  573.70   89.60  25.03  3.85   \n",
-       "\n",
-       "        AZ       BC  ...         GF     GH      GI     GL  Class  Alpha  Beta  \\\n",
-       "0    13.42    87.12  ...   81425.75  42.81   83.53   6.02      1      D     B   \n",
-       "1    13.42    87.12  ...   81425.75  42.81   83.53   6.02      1      A     B   \n",
-       "2    32.46  1115.36  ...  102436.56  35.03  111.92   4.96      0      D     B   \n",
-       "3    32.46  1115.36  ...  102436.56  35.03  111.92   4.96      0      A     B   \n",
-       "4    27.70   580.42  ...  110162.30  17.34   29.21  19.07      1      B     C   \n",
-       "..     ...      ...  ...        ...    ...     ...    ...    ...    ...   ...   \n",
-       "619  35.40   831.98  ...  113853.10  76.47  162.43   8.92      0      G     B   \n",
-       "620  25.15   956.36  ...  125353.53  49.12   34.51  15.82      1      B     A   \n",
-       "621  25.15   956.36  ...  125353.53  49.12   34.51  15.82      1      D     C   \n",
-       "622  22.61  1124.93  ...    2026.66  11.16   17.13  21.33      1      D     C   \n",
-       "623  22.61  1124.93  ...    2026.66  11.16   17.13  21.33      1      A     A   \n",
-       "\n",
-       "     Gamma  Delta     Epsilon  \n",
-       "0        E      C   9/10/2020  \n",
-       "1        H      B   3/11/2019  \n",
-       "2        E      C   9/10/2020  \n",
-       "3        H      B   3/11/2019  \n",
-       "4        E      D   7/27/2020  \n",
-       "..     ...    ...         ...  \n",
-       "619      A      A  12/20/2019  \n",
-       "620      A      A   4/19/2019  \n",
-       "621      H      B  12/31/2019  \n",
-       "622      A      B   4/19/2019  \n",
-       "623      B      A   3/25/2019  \n",
-       "\n",
-       "[624 rows x 63 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.649931Z",
@@ -746,31 +184,14 @@
      "shell.execute_reply.started": "2023-07-23T04:58:48.649901Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Id', 'AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD ', 'BN',\n",
-       "       'BP', 'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD ', 'CF', 'CH', 'CL', 'CR', 'CS',\n",
-       "       'CU', 'CW ', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY',\n",
-       "       'EB', 'EE', 'EG', 'EH', 'EJ', 'EL', 'EP', 'EU', 'FC', 'FD ', 'FE', 'FI',\n",
-       "       'FL', 'FR', 'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL', 'Class', 'Alpha',\n",
-       "       'Beta', 'Gamma', 'Delta', 'Epsilon'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df.columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.660111Z",
@@ -789,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.676200Z",
@@ -799,305 +220,14 @@
      "shell.execute_reply.started": "2023-07-23T04:58:48.676155Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>AB</th>\n",
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-       "      <th>count</th>\n",
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-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
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-       "      <td>622.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>622.000000</td>\n",
-       "      <td>624.000000</td>\n",
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-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3.331410</td>\n",
-       "      <td>15023.696474</td>\n",
-       "      <td>972.617708</td>\n",
-       "      <td>318.833654</td>\n",
-       "      <td>90.042917</td>\n",
-       "      <td>18.377372</td>\n",
-       "      <td>5.560593</td>\n",
-       "      <td>20.193718</td>\n",
-       "      <td>720.309952</td>\n",
-       "      <td>28718.190561</td>\n",
-       "      <td>...</td>\n",
-       "      <td>67.503842</td>\n",
-       "      <td>630.024744</td>\n",
-       "      <td>15.683365</td>\n",
-       "      <td>66.341186</td>\n",
-       "      <td>787.298157</td>\n",
-       "      <td>71318.176202</td>\n",
-       "      <td>43.711234</td>\n",
-       "      <td>102.983349</td>\n",
-       "      <td>11.356543</td>\n",
-       "      <td>0.496795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.784369</td>\n",
-       "      <td>8099.910282</td>\n",
-       "      <td>501.276008</td>\n",
-       "      <td>182.267948</td>\n",
-       "      <td>49.079931</td>\n",
-       "      <td>10.861119</td>\n",
-       "      <td>3.022239</td>\n",
-       "      <td>10.518280</td>\n",
-       "      <td>409.346413</td>\n",
-       "      <td>14575.219789</td>\n",
-       "      <td>...</td>\n",
-       "      <td>39.551407</td>\n",
-       "      <td>351.153355</td>\n",
-       "      <td>9.269900</td>\n",
-       "      <td>38.620867</td>\n",
-       "      <td>402.352248</td>\n",
-       "      <td>40659.240539</td>\n",
-       "      <td>21.527950</td>\n",
-       "      <td>55.244275</td>\n",
-       "      <td>6.138769</td>\n",
-       "      <td>0.500391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.110000</td>\n",
-       "      <td>444.140000</td>\n",
-       "      <td>86.660000</td>\n",
-       "      <td>5.670000</td>\n",
-       "      <td>8.450000</td>\n",
-       "      <td>0.730000</td>\n",
-       "      <td>0.040000</td>\n",
-       "      <td>3.420000</td>\n",
-       "      <td>6.220000</td>\n",
-       "      <td>1788.080000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.490000</td>\n",
-       "      <td>1.530000</td>\n",
-       "      <td>0.170000</td>\n",
-       "      <td>4.290000</td>\n",
-       "      <td>73.610000</td>\n",
-       "      <td>47.440000</td>\n",
-       "      <td>9.630000</td>\n",
-       "      <td>1.170000</td>\n",
-       "      <td>0.150000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>1.930000</td>\n",
-       "      <td>8020.057500</td>\n",
-       "      <td>633.655000</td>\n",
-       "      <td>166.800000</td>\n",
-       "      <td>45.900000</td>\n",
-       "      <td>8.280000</td>\n",
-       "      <td>2.745000</td>\n",
-       "      <td>10.080000</td>\n",
-       "      <td>389.367500</td>\n",
-       "      <td>16813.697500</td>\n",
-       "      <td>...</td>\n",
-       "      <td>32.460000</td>\n",
-       "      <td>352.037500</td>\n",
-       "      <td>7.927500</td>\n",
-       "      <td>33.160000</td>\n",
-       "      <td>438.525000</td>\n",
-       "      <td>35309.655000</td>\n",
-       "      <td>23.800000</td>\n",
-       "      <td>58.370000</td>\n",
-       "      <td>6.390000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.450000</td>\n",
-       "      <td>15299.995000</td>\n",
-       "      <td>953.360000</td>\n",
-       "      <td>320.825000</td>\n",
-       "      <td>87.850000</td>\n",
-       "      <td>18.425000</td>\n",
-       "      <td>5.690000</td>\n",
-       "      <td>19.970000</td>\n",
-       "      <td>721.745000</td>\n",
-       "      <td>28466.135000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>69.860000</td>\n",
-       "      <td>629.570000</td>\n",
-       "      <td>15.010000</td>\n",
-       "      <td>62.775000</td>\n",
-       "      <td>793.775000</td>\n",
-       "      <td>75004.455000</td>\n",
-       "      <td>42.740000</td>\n",
-       "      <td>106.140000</td>\n",
-       "      <td>11.810000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>4.822500</td>\n",
-       "      <td>21668.440000</td>\n",
-       "      <td>1355.027500</td>\n",
-       "      <td>471.287500</td>\n",
-       "      <td>132.477500</td>\n",
-       "      <td>27.870000</td>\n",
-       "      <td>8.262500</td>\n",
-       "      <td>29.080000</td>\n",
-       "      <td>1064.500000</td>\n",
-       "      <td>41966.227500</td>\n",
-       "      <td>...</td>\n",
-       "      <td>100.925000</td>\n",
-       "      <td>950.790000</td>\n",
-       "      <td>24.000000</td>\n",
-       "      <td>101.565000</td>\n",
-       "      <td>1124.700000</td>\n",
-       "      <td>106254.267500</td>\n",
-       "      <td>63.340000</td>\n",
-       "      <td>150.492500</td>\n",
-       "      <td>16.187500</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>6.130000</td>\n",
-       "      <td>28574.960000</td>\n",
-       "      <td>1901.600000</td>\n",
-       "      <td>629.650000</td>\n",
-       "      <td>177.880000</td>\n",
-       "      <td>38.050000</td>\n",
-       "      <td>10.310000</td>\n",
-       "      <td>38.890000</td>\n",
-       "      <td>1457.100000</td>\n",
-       "      <td>53048.600000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>136.990000</td>\n",
-       "      <td>1234.980000</td>\n",
-       "      <td>31.330000</td>\n",
-       "      <td>135.750000</td>\n",
-       "      <td>1495.470000</td>\n",
-       "      <td>143536.700000</td>\n",
-       "      <td>81.160000</td>\n",
-       "      <td>191.150000</td>\n",
-       "      <td>21.830000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 56 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               AB            AF           AH          AM          AR  \\\n",
-       "count  624.000000    624.000000   624.000000  624.000000  624.000000   \n",
-       "mean     3.331410  15023.696474   972.617708  318.833654   90.042917   \n",
-       "std      1.784369   8099.910282   501.276008  182.267948   49.079931   \n",
-       "min      0.110000    444.140000    86.660000    5.670000    8.450000   \n",
-       "25%      1.930000   8020.057500   633.655000  166.800000   45.900000   \n",
-       "50%      3.450000  15299.995000   953.360000  320.825000   87.850000   \n",
-       "75%      4.822500  21668.440000  1355.027500  471.287500  132.477500   \n",
-       "max      6.130000  28574.960000  1901.600000  629.650000  177.880000   \n",
-       "\n",
-       "               AX          AY          AZ           BC           BD   ...  \\\n",
-       "count  624.000000  624.000000  624.000000   624.000000    624.000000  ...   \n",
-       "mean    18.377372    5.560593   20.193718   720.309952  28718.190561  ...   \n",
-       "std     10.861119    3.022239   10.518280   409.346413  14575.219789  ...   \n",
-       "min      0.730000    0.040000    3.420000     6.220000   1788.080000  ...   \n",
-       "25%      8.280000    2.745000   10.080000   389.367500  16813.697500  ...   \n",
-       "50%     18.425000    5.690000   19.970000   721.745000  28466.135000  ...   \n",
-       "75%     27.870000    8.262500   29.080000  1064.500000  41966.227500  ...   \n",
-       "max     38.050000   10.310000   38.890000  1457.100000  53048.600000  ...   \n",
-       "\n",
-       "               FL           FR          FS          GB           GE  \\\n",
-       "count  622.000000   624.000000  624.000000  624.000000   624.000000   \n",
-       "mean    67.503842   630.024744   15.683365   66.341186   787.298157   \n",
-       "std     39.551407   351.153355    9.269900   38.620867   402.352248   \n",
-       "min      0.490000     1.530000    0.170000    4.290000    73.610000   \n",
-       "25%     32.460000   352.037500    7.927500   33.160000   438.525000   \n",
-       "50%     69.860000   629.570000   15.010000   62.775000   793.775000   \n",
-       "75%    100.925000   950.790000   24.000000  101.565000  1124.700000   \n",
-       "max    136.990000  1234.980000   31.330000  135.750000  1495.470000   \n",
-       "\n",
-       "                  GF          GH          GI          GL       Class  \n",
-       "count     624.000000  624.000000  624.000000  622.000000  624.000000  \n",
-       "mean    71318.176202   43.711234  102.983349   11.356543    0.496795  \n",
-       "std     40659.240539   21.527950   55.244275    6.138769    0.500391  \n",
-       "min        47.440000    9.630000    1.170000    0.150000    0.000000  \n",
-       "25%     35309.655000   23.800000   58.370000    6.390000    0.000000  \n",
-       "50%     75004.455000   42.740000  106.140000   11.810000    0.000000  \n",
-       "75%    106254.267500   63.340000  150.492500   16.187500    1.000000  \n",
-       "max    143536.700000   81.160000  191.150000   21.830000    1.000000  \n",
-       "\n",
-       "[8 rows x 56 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.834135Z",
@@ -1107,28 +237,14 @@
      "shell.execute_reply.started": "2023-07-23T04:58:48.834104Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD ', 'BN', 'BP',\n",
-      "       'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD ', 'CF', 'CH', 'CL', 'CR', 'CS', 'CU',\n",
-      "       'CW ', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY', 'EB',\n",
-      "       'EE', 'EG', 'EH', 'EJ', 'EL', 'EP', 'EU', 'FC', 'FD ', 'FE', 'FI', 'FL',\n",
-      "       'FR', 'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL', 'Class', 'Alpha',\n",
-      "       'Beta', 'Gamma', 'Delta', 'Epsilon'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(train_df.columns)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T05:05:03.040447Z",
@@ -1159,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T05:09:17.792882Z",
@@ -1179,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T05:09:35.652640Z",

benchmark/sklearn_3/sklearn_3_reproduced.ipynb

@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.511496Z",
@@ -116,214 +116,14 @@
      "shell.execute_reply.started": "2023-07-23T04:58:48.511450Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Id</th>\n",
-       "      <th>AB</th>\n",
-       "      <th>AF</th>\n",
-       "      <th>AH</th>\n",
-       "      <th>AM</th>\n",
-       "      <th>AR</th>\n",
-       "      <th>AX</th>\n",
-       "      <th>AY</th>\n",
-       "      <th>AZ</th>\n",
-       "      <th>BC</th>\n",
-       "      <th>...</th>\n",
-       "      <th>FL</th>\n",
-       "      <th>FR</th>\n",
-       "      <th>FS</th>\n",
-       "      <th>GB</th>\n",
-       "      <th>GE</th>\n",
-       "      <th>GF</th>\n",
-       "      <th>GH</th>\n",
-       "      <th>GI</th>\n",
-       "      <th>GL</th>\n",
-       "      <th>Class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>e6aa6fa49abd</td>\n",
-       "      <td>4.43</td>\n",
-       "      <td>23067.30</td>\n",
-       "      <td>344.36</td>\n",
-       "      <td>586.33</td>\n",
-       "      <td>49.59</td>\n",
-       "      <td>14.05</td>\n",
-       "      <td>1.13</td>\n",
-       "      <td>34.60</td>\n",
-       "      <td>1205.17</td>\n",
-       "      <td>...</td>\n",
-       "      <td>124.56</td>\n",
-       "      <td>1219.22</td>\n",
-       "      <td>16.20</td>\n",
-       "      <td>134.13</td>\n",
-       "      <td>1174.76</td>\n",
-       "      <td>72861.37</td>\n",
-       "      <td>17.67</td>\n",
-       "      <td>83.87</td>\n",
-       "      <td>16.54</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>a633a258eeed</td>\n",
-       "      <td>2.95</td>\n",
-       "      <td>18930.84</td>\n",
-       "      <td>1513.23</td>\n",
-       "      <td>129.03</td>\n",
-       "      <td>148.21</td>\n",
-       "      <td>27.22</td>\n",
-       "      <td>6.94</td>\n",
-       "      <td>7.85</td>\n",
-       "      <td>1339.67</td>\n",
-       "      <td>...</td>\n",
-       "      <td>33.68</td>\n",
-       "      <td>1214.14</td>\n",
-       "      <td>17.09</td>\n",
-       "      <td>76.82</td>\n",
-       "      <td>179.83</td>\n",
-       "      <td>35123.99</td>\n",
-       "      <td>34.14</td>\n",
-       "      <td>123.68</td>\n",
-       "      <td>2.81</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>d1a9f56adc4a</td>\n",
-       "      <td>3.81</td>\n",
-       "      <td>12809.52</td>\n",
-       "      <td>1084.70</td>\n",
-       "      <td>261.90</td>\n",
-       "      <td>168.68</td>\n",
-       "      <td>35.95</td>\n",
-       "      <td>6.13</td>\n",
-       "      <td>13.42</td>\n",
-       "      <td>87.12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>9.43</td>\n",
-       "      <td>540.83</td>\n",
-       "      <td>20.47</td>\n",
-       "      <td>37.11</td>\n",
-       "      <td>808.55</td>\n",
-       "      <td>81425.75</td>\n",
-       "      <td>42.81</td>\n",
-       "      <td>83.53</td>\n",
-       "      <td>6.02</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3e610a5ff8d1</td>\n",
-       "      <td>0.33</td>\n",
-       "      <td>12439.71</td>\n",
-       "      <td>1301.69</td>\n",
-       "      <td>88.31</td>\n",
-       "      <td>84.49</td>\n",
-       "      <td>26.43</td>\n",
-       "      <td>8.30</td>\n",
-       "      <td>26.29</td>\n",
-       "      <td>92.56</td>\n",
-       "      <td>...</td>\n",
-       "      <td>33.70</td>\n",
-       "      <td>731.94</td>\n",
-       "      <td>26.81</td>\n",
-       "      <td>49.18</td>\n",
-       "      <td>729.25</td>\n",
-       "      <td>143656.96</td>\n",
-       "      <td>75.09</td>\n",
-       "      <td>177.96</td>\n",
-       "      <td>15.87</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>b874e25c20aa</td>\n",
-       "      <td>5.93</td>\n",
-       "      <td>12025.87</td>\n",
-       "      <td>1306.31</td>\n",
-       "      <td>219.60</td>\n",
-       "      <td>144.74</td>\n",
-       "      <td>18.83</td>\n",
-       "      <td>8.25</td>\n",
-       "      <td>27.70</td>\n",
-       "      <td>580.42</td>\n",
-       "      <td>...</td>\n",
-       "      <td>71.44</td>\n",
-       "      <td>608.95</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>72.11</td>\n",
-       "      <td>1153.78</td>\n",
-       "      <td>110162.30</td>\n",
-       "      <td>17.34</td>\n",
-       "      <td>29.21</td>\n",
-       "      <td>19.07</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 58 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             Id    AB        AF       AH      AM      AR     AX    AY     AZ  \\\n",
-       "0  e6aa6fa49abd  4.43  23067.30   344.36  586.33   49.59  14.05  1.13  34.60   \n",
-       "1  a633a258eeed  2.95  18930.84  1513.23  129.03  148.21  27.22  6.94   7.85   \n",
-       "2  d1a9f56adc4a  3.81  12809.52  1084.70  261.90  168.68  35.95  6.13  13.42   \n",
-       "3  3e610a5ff8d1  0.33  12439.71  1301.69   88.31   84.49  26.43  8.30  26.29   \n",
-       "4  b874e25c20aa  5.93  12025.87  1306.31  219.60  144.74  18.83  8.25  27.70   \n",
-       "\n",
-       "        BC  ...      FL       FR     FS      GB       GE         GF     GH  \\\n",
-       "0  1205.17  ...  124.56  1219.22  16.20  134.13  1174.76   72861.37  17.67   \n",
-       "1  1339.67  ...   33.68  1214.14  17.09   76.82   179.83   35123.99  34.14   \n",
-       "2    87.12  ...    9.43   540.83  20.47   37.11   808.55   81425.75  42.81   \n",
-       "3    92.56  ...   33.70   731.94  26.81   49.18   729.25  143656.96  75.09   \n",
-       "4   580.42  ...   71.44   608.95   0.50   72.11  1153.78  110162.30  17.34   \n",
-       "\n",
-       "       GI     GL  Class  \n",
-       "0   83.87  16.54      0  \n",
-       "1  123.68   2.81      1  \n",
-       "2   83.53   6.02      1  \n",
-       "3  177.96  15.87      0  \n",
-       "4   29.21  19.07      1  \n",
-       "\n",
-       "[5 rows x 58 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T04:58:48.542550Z",
@@ -357,378 +157,16 @@
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-       "      <td>12025.87</td>\n",
-       "      <td>1306.31</td>\n",
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-       "      <td>1630.33</td>\n",
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-       "      <td>4/19/2019</td>\n",
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-       "    <tr>\n",
-       "      <th>623</th>\n",
-       "      <td>9949a1749e2c</td>\n",
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-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>B</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3/25/2019</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>624 rows × 63 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               Id    AB        AF       AH      AM      AR     AX    AY  \\\n",
-       "0    d1a9f56adc4a  3.81  12809.52  1084.70  261.90  168.68  35.95  6.13   \n",
-       "1    d1a9f56adc4a  3.81  12809.52  1084.70  261.90  168.68  35.95  6.13   \n",
-       "2    d1a9f56adc4a  5.80   6079.58   685.73   78.97   77.56  34.61  0.47   \n",
-       "3    d1a9f56adc4a  5.80   6079.58   685.73   78.97   77.56  34.61  0.47   \n",
-       "4    b874e25c20aa  5.93  12025.87  1306.31  219.60  144.74  18.83  8.25   \n",
-       "..            ...   ...       ...      ...     ...     ...    ...   ...   \n",
-       "619  6bf666677588  2.80  23687.98   225.53  385.78   47.02  17.05  7.37   \n",
-       "620  4e6477903aa3  5.65   5118.03  1630.33  176.64  151.55  10.58  2.68   \n",
-       "621  4e6477903aa3  5.65   5118.03  1630.33  176.64  151.55  10.58  2.68   \n",
-       "622  9949a1749e2c  3.08   5687.86   690.00  573.70   89.60  25.03  3.85   \n",
-       "623  9949a1749e2c  3.08   5687.86   690.00  573.70   89.60  25.03  3.85   \n",
-       "\n",
-       "        AZ       BC  ...         GF     GH      GI     GL  Class  Alpha  Beta  \\\n",
-       "0    13.42    87.12  ...   81425.75  42.81   83.53   6.02      1      D     B   \n",
-       "1    13.42    87.12  ...   81425.75  42.81   83.53   6.02      1      A     B   \n",
-       "2    32.46  1115.36  ...  102436.56  35.03  111.92   4.96      0      D     B   \n",
-       "3    32.46  1115.36  ...  102436.56  35.03  111.92   4.96      0      A     B   \n",
-       "4    27.70   580.42  ...  110162.30  17.34   29.21  19.07      1      B     C   \n",
-       "..     ...      ...  ...        ...    ...     ...    ...    ...    ...   ...   \n",
-       "619  35.40   831.98  ...  113853.10  76.47  162.43   8.92      0      G     B   \n",
-       "620  25.15   956.36  ...  125353.53  49.12   34.51  15.82      1      B     A   \n",
-       "621  25.15   956.36  ...  125353.53  49.12   34.51  15.82      1      D     C   \n",
-       "622  22.61  1124.93  ...    2026.66  11.16   17.13  21.33      1      D     C   \n",
-       "623  22.61  1124.93  ...    2026.66  11.16   17.13  21.33      1      A     A   \n",
-       "\n",
-       "     Gamma  Delta     Epsilon  \n",
-       "0        E      C   9/10/2020  \n",
-       "1        H      B   3/11/2019  \n",
-       "2        E      C   9/10/2020  \n",
-       "3        H      B   3/11/2019  \n",
-       "4        E      D   7/27/2020  \n",
-       "..     ...    ...         ...  \n",
-       "619      A      A  12/20/2019  \n",
-       "620      A      A   4/19/2019  \n",
-       "621      H      B  12/31/2019  \n",
-       "622      A      B   4/19/2019  \n",
-       "623      B      A   3/25/2019  \n",
-       "\n",
-       "[624 rows x 63 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
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    "metadata": {
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@@ -738,31 +176,14 @@
      "shell.execute_reply.started": "2023-07-23T04:58:48.649901Z"
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    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Id', 'AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD ', 'BN',\n",
-       "       'BP', 'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD ', 'CF', 'CH', 'CL', 'CR', 'CS',\n",
-       "       'CU', 'CW ', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY',\n",
-       "       'EB', 'EE', 'EG', 'EH', 'EJ', 'EL', 'EP', 'EU', 'FC', 'FD ', 'FE', 'FI',\n",
-       "       'FL', 'FR', 'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL', 'Class', 'Alpha',\n",
-       "       'Beta', 'Gamma', 'Delta', 'Epsilon'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df.columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
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      "iopub.execute_input": "2023-07-23T04:58:48.660111Z",
@@ -781,7 +202,7 @@
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+   "execution_count": null,
    "metadata": {
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      "iopub.execute_input": "2023-07-23T04:58:48.676200Z",
@@ -791,298 +212,7 @@
      "shell.execute_reply.started": "2023-07-23T04:58:48.676155Z"
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-       "      <th>AX</th>\n",
-       "      <th>AY</th>\n",
-       "      <th>AZ</th>\n",
-       "      <th>BC</th>\n",
-       "      <th>BD</th>\n",
-       "      <th>...</th>\n",
-       "      <th>FL</th>\n",
-       "      <th>FR</th>\n",
-       "      <th>FS</th>\n",
-       "      <th>GB</th>\n",
-       "      <th>GE</th>\n",
-       "      <th>GF</th>\n",
-       "      <th>GH</th>\n",
-       "      <th>GI</th>\n",
-       "      <th>GL</th>\n",
-       "      <th>Class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>622.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "      <td>622.000000</td>\n",
-       "      <td>624.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3.331410</td>\n",
-       "      <td>15023.696474</td>\n",
-       "      <td>972.617708</td>\n",
-       "      <td>318.833654</td>\n",
-       "      <td>90.042917</td>\n",
-       "      <td>18.377372</td>\n",
-       "      <td>5.560593</td>\n",
-       "      <td>20.193718</td>\n",
-       "      <td>720.309952</td>\n",
-       "      <td>28718.190561</td>\n",
-       "      <td>...</td>\n",
-       "      <td>67.503842</td>\n",
-       "      <td>630.024744</td>\n",
-       "      <td>15.683365</td>\n",
-       "      <td>66.341186</td>\n",
-       "      <td>787.298157</td>\n",
-       "      <td>71318.176202</td>\n",
-       "      <td>43.711234</td>\n",
-       "      <td>102.983349</td>\n",
-       "      <td>11.356543</td>\n",
-       "      <td>0.496795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.784369</td>\n",
-       "      <td>8099.910282</td>\n",
-       "      <td>501.276008</td>\n",
-       "      <td>182.267948</td>\n",
-       "      <td>49.079931</td>\n",
-       "      <td>10.861119</td>\n",
-       "      <td>3.022239</td>\n",
-       "      <td>10.518280</td>\n",
-       "      <td>409.346413</td>\n",
-       "      <td>14575.219789</td>\n",
-       "      <td>...</td>\n",
-       "      <td>39.551407</td>\n",
-       "      <td>351.153355</td>\n",
-       "      <td>9.269900</td>\n",
-       "      <td>38.620867</td>\n",
-       "      <td>402.352248</td>\n",
-       "      <td>40659.240539</td>\n",
-       "      <td>21.527950</td>\n",
-       "      <td>55.244275</td>\n",
-       "      <td>6.138769</td>\n",
-       "      <td>0.500391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.110000</td>\n",
-       "      <td>444.140000</td>\n",
-       "      <td>86.660000</td>\n",
-       "      <td>5.670000</td>\n",
-       "      <td>8.450000</td>\n",
-       "      <td>0.730000</td>\n",
-       "      <td>0.040000</td>\n",
-       "      <td>3.420000</td>\n",
-       "      <td>6.220000</td>\n",
-       "      <td>1788.080000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.490000</td>\n",
-       "      <td>1.530000</td>\n",
-       "      <td>0.170000</td>\n",
-       "      <td>4.290000</td>\n",
-       "      <td>73.610000</td>\n",
-       "      <td>47.440000</td>\n",
-       "      <td>9.630000</td>\n",
-       "      <td>1.170000</td>\n",
-       "      <td>0.150000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>1.930000</td>\n",
-       "      <td>8020.057500</td>\n",
-       "      <td>633.655000</td>\n",
-       "      <td>166.800000</td>\n",
-       "      <td>45.900000</td>\n",
-       "      <td>8.280000</td>\n",
-       "      <td>2.745000</td>\n",
-       "      <td>10.080000</td>\n",
-       "      <td>389.367500</td>\n",
-       "      <td>16813.697500</td>\n",
-       "      <td>...</td>\n",
-       "      <td>32.460000</td>\n",
-       "      <td>352.037500</td>\n",
-       "      <td>7.927500</td>\n",
-       "      <td>33.160000</td>\n",
-       "      <td>438.525000</td>\n",
-       "      <td>35309.655000</td>\n",
-       "      <td>23.800000</td>\n",
-       "      <td>58.370000</td>\n",
-       "      <td>6.390000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.450000</td>\n",
-       "      <td>15299.995000</td>\n",
-       "      <td>953.360000</td>\n",
-       "      <td>320.825000</td>\n",
-       "      <td>87.850000</td>\n",
-       "      <td>18.425000</td>\n",
-       "      <td>5.690000</td>\n",
-       "      <td>19.970000</td>\n",
-       "      <td>721.745000</td>\n",
-       "      <td>28466.135000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>69.860000</td>\n",
-       "      <td>629.570000</td>\n",
-       "      <td>15.010000</td>\n",
-       "      <td>62.775000</td>\n",
-       "      <td>793.775000</td>\n",
-       "      <td>75004.455000</td>\n",
-       "      <td>42.740000</td>\n",
-       "      <td>106.140000</td>\n",
-       "      <td>11.810000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>4.822500</td>\n",
-       "      <td>21668.440000</td>\n",
-       "      <td>1355.027500</td>\n",
-       "      <td>471.287500</td>\n",
-       "      <td>132.477500</td>\n",
-       "      <td>27.870000</td>\n",
-       "      <td>8.262500</td>\n",
-       "      <td>29.080000</td>\n",
-       "      <td>1064.500000</td>\n",
-       "      <td>41966.227500</td>\n",
-       "      <td>...</td>\n",
-       "      <td>100.925000</td>\n",
-       "      <td>950.790000</td>\n",
-       "      <td>24.000000</td>\n",
-       "      <td>101.565000</td>\n",
-       "      <td>1124.700000</td>\n",
-       "      <td>106254.267500</td>\n",
-       "      <td>63.340000</td>\n",
-       "      <td>150.492500</td>\n",
-       "      <td>16.187500</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>6.130000</td>\n",
-       "      <td>28574.960000</td>\n",
-       "      <td>1901.600000</td>\n",
-       "      <td>629.650000</td>\n",
-       "      <td>177.880000</td>\n",
-       "      <td>38.050000</td>\n",
-       "      <td>10.310000</td>\n",
-       "      <td>38.890000</td>\n",
-       "      <td>1457.100000</td>\n",
-       "      <td>53048.600000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>136.990000</td>\n",
-       "      <td>1234.980000</td>\n",
-       "      <td>31.330000</td>\n",
-       "      <td>135.750000</td>\n",
-       "      <td>1495.470000</td>\n",
-       "      <td>143536.700000</td>\n",
-       "      <td>81.160000</td>\n",
-       "      <td>191.150000</td>\n",
-       "      <td>21.830000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 56 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               AB            AF           AH          AM          AR  \\\n",
-       "count  624.000000    624.000000   624.000000  624.000000  624.000000   \n",
-       "mean     3.331410  15023.696474   972.617708  318.833654   90.042917   \n",
-       "std      1.784369   8099.910282   501.276008  182.267948   49.079931   \n",
-       "min      0.110000    444.140000    86.660000    5.670000    8.450000   \n",
-       "25%      1.930000   8020.057500   633.655000  166.800000   45.900000   \n",
-       "50%      3.450000  15299.995000   953.360000  320.825000   87.850000   \n",
-       "75%      4.822500  21668.440000  1355.027500  471.287500  132.477500   \n",
-       "max      6.130000  28574.960000  1901.600000  629.650000  177.880000   \n",
-       "\n",
-       "               AX          AY          AZ           BC           BD   ...  \\\n",
-       "count  624.000000  624.000000  624.000000   624.000000    624.000000  ...   \n",
-       "mean    18.377372    5.560593   20.193718   720.309952  28718.190561  ...   \n",
-       "std     10.861119    3.022239   10.518280   409.346413  14575.219789  ...   \n",
-       "min      0.730000    0.040000    3.420000     6.220000   1788.080000  ...   \n",
-       "25%      8.280000    2.745000   10.080000   389.367500  16813.697500  ...   \n",
-       "50%     18.425000    5.690000   19.970000   721.745000  28466.135000  ...   \n",
-       "75%     27.870000    8.262500   29.080000  1064.500000  41966.227500  ...   \n",
-       "max     38.050000   10.310000   38.890000  1457.100000  53048.600000  ...   \n",
-       "\n",
-       "               FL           FR          FS          GB           GE  \\\n",
-       "count  622.000000   624.000000  624.000000  624.000000   624.000000   \n",
-       "mean    67.503842   630.024744   15.683365   66.341186   787.298157   \n",
-       "std     39.551407   351.153355    9.269900   38.620867   402.352248   \n",
-       "min      0.490000     1.530000    0.170000    4.290000    73.610000   \n",
-       "25%     32.460000   352.037500    7.927500   33.160000   438.525000   \n",
-       "50%     69.860000   629.570000   15.010000   62.775000   793.775000   \n",
-       "75%    100.925000   950.790000   24.000000  101.565000  1124.700000   \n",
-       "max    136.990000  1234.980000   31.330000  135.750000  1495.470000   \n",
-       "\n",
-       "                  GF          GH          GI          GL       Class  \n",
-       "count     624.000000  624.000000  624.000000  622.000000  624.000000  \n",
-       "mean    71318.176202   43.711234  102.983349   11.356543    0.496795  \n",
-       "std     40659.240539   21.527950   55.244275    6.138769    0.500391  \n",
-       "min        47.440000    9.630000    1.170000    0.150000    0.000000  \n",
-       "25%     35309.655000   23.800000   58.370000    6.390000    0.000000  \n",
-       "50%     75004.455000   42.740000  106.140000   11.810000    0.000000  \n",
-       "75%    106254.267500   63.340000  150.492500   16.187500    1.000000  \n",
-       "max    143536.700000   81.160000  191.150000   21.830000    1.000000  \n",
-       "\n",
-       "[8 rows x 56 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df.describe()"
    ]
@@ -1106,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T05:05:03.040447Z",
@@ -1126,629 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-07-23T05:07:20.524513Z",
-     "iopub.status.busy": "2023-07-23T05:07:20.523866Z",
-     "iopub.status.idle": "2023-07-23T05:07:20.746978Z",
-     "shell.execute_reply": "2023-07-23T05:07:20.745797Z",
-     "shell.execute_reply.started": "2023-07-23T05:07:20.524480Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "      <th>AB_0.11</th>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
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-       "0      False    False    False    False    False   False    False    False   \n",
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-       "..       ...      ...      ...      ...      ...     ...      ...      ...   \n",
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-       "\n",
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-       "2      False    False  ...              False              False   \n",
-       "3      False    False  ...              False              False   \n",
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-       "..       ...      ...  ...                ...                ...   \n",
-       "619    False    False  ...              False              False   \n",
-       "620    False    False  ...              False              False   \n",
-       "621    False    False  ...              False              False   \n",
-       "622    False    False  ...              False              False   \n",
-       "623    False    False  ...              False              False   \n",
-       "\n",
-       "     Epsilon_9/20/2019  Epsilon_9/27/2019  Epsilon_9/29/2020  \\\n",
-       "0                False              False              False   \n",
-       "1                False              False              False   \n",
-       "2                False              False              False   \n",
-       "3                False              False              False   \n",
-       "4                False              False              False   \n",
-       "..                 ...                ...                ...   \n",
-       "619              False              False              False   \n",
-       "620              False              False              False   \n",
-       "621              False              False              False   \n",
-       "622              False              False              False   \n",
-       "623              False              False              False   \n",
-       "\n",
-       "     Epsilon_9/3/2014  Epsilon_9/7/2020  Epsilon_9/8/2020  Epsilon_9/9/2014  \\\n",
-       "0               False             False             False             False   \n",
-       "1               False             False             False             False   \n",
-       "2               False             False             False             False   \n",
-       "3               False             False             False             False   \n",
-       "4               False             False             False             False   \n",
-       "..                ...               ...               ...               ...   \n",
-       "619             False             False             False             False   \n",
-       "620             False             False             False             False   \n",
-       "621             False             False             False             False   \n",
-       "622             False             False             False             False   \n",
-       "623             False             False             False             False   \n",
-       "\n",
-       "     Epsilon_Unknown  \n",
-       "0              False  \n",
-       "1              False  \n",
-       "2              False  \n",
-       "3              False  \n",
-       "4              False  \n",
-       "..               ...  \n",
-       "619            False  \n",
-       "620            False  \n",
-       "621            False  \n",
-       "622            False  \n",
-       "623            False  \n",
-       "\n",
-       "[624 rows x 18532 columns]"
-      ]
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-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
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-     "shell.execute_reply.started": "2023-07-23T05:07:31.553466Z"
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-       "\n",
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-       "0    True    True    True  \n",
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-       "4    True    True    True  \n",
-       "\n",
-       "[5 rows x 56 columns]"
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-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-23T05:09:17.792882Z",
@@ -1766,7 +274,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-3ea0965a5d70>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\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[0mtrain_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtest_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-3ea0965a5d70>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\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[0mtrain_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtest_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_set_output.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mdata_to_wrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_wrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0;31m# only wrap the first output for cross decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X, copy)\u001b[0m\n\u001b[1;32m    990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 992\u001b[0;31m         X = self._validate_data(\n\u001b[0m\u001b[1;32m    993\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    994\u001b[0m             \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    546\u001b[0m             \u001b[0mvalidated\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \"\"\"\n\u001b[0;32m--> 548\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_feature_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"requires_y\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/sklearn_4/sklearn_4_fixed.ipynb

@@ -3115,18 +3115,9 @@
      "shell.execute_reply.started": "2023-04-04T16:48:34.188967Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 48.5 ms, sys: 29.2 ms, total: 77.7 ms\n",
-      "Wall time: 127 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%%time\n",
+    "# %%time\n",
     "# Adaboost (Boosting of multiple Decision Trees)\n",
     "from sklearn.ensemble import AdaBoostRegressor\n",
     "ml_support_obj = ml_support()\n",
@@ -3171,18 +3162,10 @@
       "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
       "  warn(\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 31.6 s, sys: 3.01 s, total: 34.7 s\n",
-      "Wall time: 32 s\n"
-     ]
     }
    ],
    "source": [
-    "%%time\n",
+    "# %%time\n",
     "preds = ml_support_obj.predit_with_pipeline(finalized_pipeline,X_train,X_test,y_train)"
    ]
   },
@@ -3214,7 +3197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T16:49:05.359095Z",
@@ -3224,18 +3207,7 @@
      "shell.execute_reply.started": "2023-04-04T16:49:05.359050Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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q+oIy7d4WJkn6emuYWrUvcWkbXH7tCf10IlDfZ57+YRwf/StKl/U8oZhm5TX4qYDq+XprmLpcdUIXtKl87Gyb+JO65MpifbHW9qsx9dWjT4EaxZZLMtT5d0W6oE2ptq2PqKOo4QtVdyD0ZvNXdd4mmDVrlu677z7de++9kqQFCxZo1apVevXVVzVhwoQ6js7/tO9arEeePalmF5bqWF49/fOZWD1860V68ZM9OpYXpHpWh8Ij7S7nNGhSrmN5lT8qx48GqWET1y/1Bo3Lncd+68ecIH3xiU0T5n1fQ58I8MzS56NVP8KuhRv2yGGXAgKl1Cdj9cm7DZ1j5j92gR6aeVBLtn+tinLJ4bBozthm2rk5vA4jh7eYM+BenSYDZWVl2rZtmx599FHnvoCAAPXu3Vvp6emnjC8tLVVpaanzdWFhYa3E6U+u+MMJ55/bxJeo/WU/6a4r47Xh/Qayhjh8/n5pb0Up3GbX725gvgDODVffnK8/DMjXk8mVcwYuvOSkhk89rB9z6+k/b1VOfL1lyA9q3+0nTUpqpbyDVnXsUazkJw7px9x6+vJTqgPwP3Wa5vzwww+y2+2KiYlx2R8TE6OcnJxTxs+YMUORkZHOrXnz5rUVqt8Kj7SrWZtSHf4uWFHRFSovC1BRQaDLmPyj9RQVXdkWaNikQsePut7OM/+Hes5jv2YY0uo3G+m6QcdUz+rHC3RxXrlv4hEtfT5a699rqO/2hGrNO1H698tNdPuoytakNcSheybk6KUpcdqcFqkDu0P1/muNtf79Bho0/GgdRw9vOGRxPp/grDYmEJ4bHn30URUUFDi37Ozsug7pvHeyOECHv7cqKrpcF3X6SUH1HPpy4y+l0Oy9wco7ZFWHbsWSKidffbcnRPk//FJU2r4hQvUj7GpxcYnLtf+bHq7DB4J1wx3HaufDANUQHOKQ8ZsimMMuWSyVCWtQkKF6VkOO040JIKk9nxk/ryY4283w42SgTtsEjRs3VmBgoHJzc1325+bmKjY29pTxwcHBzsc24uy8NDVOPfoUKLpZuX7MCdIbTzdVYIB07a3HFWZzKPGOY3ppygWKaGBXWETl0sIO3YrVoVvlLOqu15xQi4tLNHNUCw197LCOH62n1P+L1U33/CBrsOs/lKv/FaX2XYvVqn3J6UIB6sTnaTbd/mCe8g5ZK9sEl57UgL8e1cdvVrYIfioK1FebwnTfxCMqKwlQ7sF66pRQrN6DjuulqXF1HD28ca49tfBcUqfJgNVqVbdu3bRmzRr1799fkuRwOLRmzRqNHDmyLkPzWz8cqacZD7TSieOBimxUoUuuKNazK79Rg5+XFw6fckgBFkPT72ul8lKLLr/2hEbOOOg8PzBQmrZov56b0FwpN12skPoO9f7TMSWNPeLyPsWFAdq4qoGGTz8o4Fwy/7ELlDQuRyNnHFSDRhX6MbeePnijkRbP/qVdOWNESw352xGNf/57RTSwK++QVan/11QrFzWqw8iBmlPnjzBeunSpkpKS9OKLL+rKK6/Us88+q2XLlmnPnj2nzCX4LR5hDDPgEcbwZ7X5CONb0+5VvTDrWV+nvLhM717/ml8+wrjOlxb++c9/1tGjRzVp0iTl5OSoS5cu+uijj86YCAAA4AnaBO7VeTIgSSNHjqQtAABAHTknkgEAAGoazyZwj2QAAGAKtAncY9YdAAAmR2UAAGAKVAbcIxkAAJgCyYB7tAkAADA5KgMAAFOgMuAeyQAAwBQMebc80J8fU0UyAAAwBSoD7jFnAAAAk6MyAAAwBSoD7pEMAABMgWTAPdoEAACYHJUBAIApUBlwj2QAAGAKhmGR4cUXujfnnutoEwAAYHJUBgAApuCQxaubDnlz7rmOZAAAYArMGXCPNgEAACZHZQAAYApMIHSPZAAAYAq0CdwjGQAAmAKVAfeYMwAAgMlRGQAAmILhZZvAnysDJAMAAFMwJBmGd+f7K9oEAACYHJUBAIApOGSRhTsQnhbJAADAFFhN4B5tAgAATI7KAADAFByGRRZuOnRaJAMAAFMwDC9XE/jxcgLaBAAAmByVAQCAKTCB0D2SAQCAKZAMuEcyAAAwBSYQusecAQAATI7KAADAFFhN4B7JAADAFCqTAW/mDPgwmHMMbQIAAEyOygAAwBRYTeAeyQAAwBSMnzdvzvdXtAkAADA5KgMAAFOgTeAeyQAAwBzoE7hFMgAAMAcvKwPy48oAcwYAADA5KgMAAFPgDoTuURkAAJhC1QRCbzZPvPDCC+rUqZNsNptsNpsSEhL04YcfOo+XlJQoOTlZjRo1Unh4uAYOHKjc3FyXa2RlZalfv36qX7++oqOjNXbsWFVUVLiMWbdunbp27arg4GC1bdtWqampHv/dkAwAAFADmjVrpieffFLbtm3T1q1b9Yc//EG33HKLdu3aJUlKSUnRihUr9NZbb2n9+vU6fPiwBgwY4DzfbrerX79+Kisr06ZNm/T6668rNTVVkyZNco45cOCA+vXrp169eikjI0OjR4/WsGHDtHr1ao9itRjG+Vv4KCwsVGRkpI5/00a2CPIa+KfEuC51HQJQYyqMcq3TeyooKJDNZquR96j6rmj1ykQF1A856+s4firRd0OnexVrVFSUnnrqKQ0aNEhNmjTRkiVLNGjQIEnSnj171KFDB6Wnp6tHjx768MMPdeONN+rw4cOKiYmRJC1YsEDjx4/X0aNHZbVaNX78eK1atUo7d+50vsftt9+u/Px8ffTRR9WOi29QAIApVM0Z8GaTKpOLX2+lpaVnfG+73a4333xTxcXFSkhI0LZt21ReXq7evXs7x7Rv314tWrRQenq6JCk9PV0dO3Z0JgKSlJiYqMLCQmd1IT093eUaVWOqrlFdJAMAAHigefPmioyMdG4zZsxwO3bHjh0KDw9XcHCwhg8frnfffVfx8fHKycmR1WpVgwYNXMbHxMQoJydHkpSTk+OSCFQdrzr2v8YUFhbq5MmT1f5MrCYAAJiDj246lJ2d7dImCA4OdntKu3btlJGRoYKCAr399ttKSkrS+vXrvQiiZpAMAABMwVe3I65aHVAdVqtVbdu2lSR169ZNX3zxhebMmaM///nPKisrU35+vkt1IDc3V7GxsZKk2NhYbdmyxeV6VasNfj3mtysQcnNzZbPZFBoaWu3PVq1k4P3336/2BW+++eZqjwUAwEwcDodKS0vVrVs31atXT2vWrNHAgQMlSZmZmcrKylJCQoIkKSEhQY8//rjy8vIUHR0tSUpLS5PNZlN8fLxzzAcffODyHmlpac5rVFe1koH+/ftX62IWi0V2u92jAAAAqDW1uH7u0UcfVd++fdWiRQudOHFCS5Ys0bp167R69WpFRkZq6NChGjNmjKKiomSz2TRq1CglJCSoR48ekqQ+ffooPj5ed911l2bOnKmcnBw99thjSk5OdrYmhg8frueff17jxo3TkCFDtHbtWi1btkyrVq3yKNZqJQMOh8PDvwIAAM4ttf3Uwry8PN199906cuSIIiMj1alTJ61evVrXX3+9JGn27NkKCAjQwIEDVVpaqsTERM2fP995fmBgoFauXKkRI0YoISFBYWFhSkpK0rRp05xjWrdurVWrViklJUVz5sxRs2bNtHDhQiUmJnoUq1f3GSgpKVFIyNmv2fQW9xmAGXCfAfiz2rzPQPMFkxUQ6sV9Bk6WKHv41BqNta54/A1qt9s1ffp0XXDBBQoPD9f+/fslSRMnTtQrr7zi8wABAEDN8jgZePzxx5WamqqZM2fKarU691966aVauHChT4MDAMB3LD7Y/JPHycCiRYv00ksvafDgwQoMDHTu79y5s/bs2ePT4AAA8BnDB5uf8jgZOHTokHPN5K85HA6Vl5f7JCgAAFB7PE4G4uPj9emnn56y/+2339Zll13mk6AAAPA5KgNueXwHwkmTJikpKUmHDh2Sw+HQv//9b2VmZmrRokVauXJlTcQIAID3DEvl5s35fsrjysAtt9yiFStW6D//+Y/CwsI0adIk7d69WytWrHCunQQAAOePs3o2Qc+ePZWWlubrWAAAqDG/fgzx2Z7vr876QUVbt27V7t27JVXOI+jWrZvPggIAwOd89NRCf+RxMnDw4EHdcccd+uyzz5xPWsrPz9fvfvc7vfnmm2rWrJmvYwQAADXI4zkDw4YNU3l5uXbv3q1jx47p2LFj2r17txwOh4YNG1YTMQIA4L2qCYTebH7K48rA+vXrtWnTJrVr1865r127dnruuefUs2dPnwYHAICvWIzKzZvz/ZXHyUDz5s1Pe3Mhu92uuLg4nwQFAIDPMWfALY/bBE899ZRGjRqlrVu3Ovdt3bpVDz30kJ5++mmfBgcAAGpetSoDDRs2lMXyS6+kuLhY3bt3V1BQ5ekVFRUKCgrSkCFD1L9//xoJFAAAr3DTIbeqlQw8++yzNRwGAAA1jDaBW9VKBpKSkmo6DgAAUEfO+qZDklRSUqKysjKXfTabzauAAACoEVQG3PJ4AmFxcbFGjhyp6OhohYWFqWHDhi4bAADnJJ5a6JbHycC4ceO0du1avfDCCwoODtbChQs1depUxcXFadGiRTURIwAAqEEetwlWrFihRYsW6dprr9W9996rnj17qm3btmrZsqUWL16swYMH10ScAAB4h9UEbnlcGTh27JjatGkjqXJ+wLFjxyRJV111lTZs2ODb6AAA8JGqOxB6s/krj5OBNm3a6MCBA5Kk9u3ba9myZZIqKwZVDy4CAADnD4+TgXvvvVdfffWVJGnChAmaN2+eQkJClJKSorFjx/o8QAAAfIIJhG55PGcgJSXF+efevXtrz5492rZtm9q2batOnTr5NDgAAFDzvLrPgCS1bNlSLVu29EUsAADUGIu8fGqhzyI591QrGZg7d261L/jggw+edTAAAKD2VSsZmD17drUuZrFY6iQZ+FPP6xQUYK319wVqg6MnjwaH/3JUlEib3qudN2NpoVvVSgaqVg8AAHDe4nbEbnm8mgAAAPgXrycQAgBwXqAy4BbJAADAFLy9iyB3IAQAAH6LygAAwBxoE7h1VpWBTz/9VHfeeacSEhJ06NAhSdIbb7yhjRs3+jQ4AAB8htsRu+VxMvDOO+8oMTFRoaGh+vLLL1VaWipJKigo0BNPPOHzAAEAQM3yOBn4xz/+oQULFujll19WvXr1nPt///vfa/v27T4NDgAAX+ERxu55PGcgMzNTV1999Sn7IyMjlZ+f74uYAADwPe5A6JbHlYHY2Fjt3bv3lP0bN25UmzZtfBIUAAA+x5wBtzxOBu677z499NBD2rx5sywWiw4fPqzFixfrkUce0YgRI2oiRgAAUIM8bhNMmDBBDodD1113nX766SddffXVCg4O1iOPPKJRo0bVRIwAAHiNmw6553EyYLFY9Pe//11jx47V3r17VVRUpPj4eIWHh9dEfAAA+Ab3GXDrrG86ZLVaFR8f78tYAABAHfA4GejVq5csFvczKteuXetVQAAA1AhvlwdSGfhFly5dXF6Xl5crIyNDO3fuVFJSkq/iAgDAt2gTuOVxMjB79uzT7p8yZYqKioq8DggAANQunz218M4779Srr77qq8sBAOBb3GfALZ89tTA9PV0hISG+uhwAAD7F0kL3PE4GBgwY4PLaMAwdOXJEW7du1cSJE30WGAAAqB0eJwORkZEurwMCAtSuXTtNmzZNffr08VlgAACgdniUDNjtdt17773q2LGjGjZsWFMxAQDge6wmcMujCYSBgYHq06cPTycEAJx3eISxex6vJrj00ku1f//+mogFAADUAY+TgX/84x965JFHtHLlSh05ckSFhYUuGwAA5yyWFZ5WtecMTJs2TQ8//LD++Mc/SpJuvvlml9sSG4Yhi8Uiu93u+ygBAPAWcwbcqnYyMHXqVA0fPlyffPJJTcYDAABqWbWTAcOoTImuueaaGgsGAICawk2H3PNoaeH/elohAADnNNoEbnmUDFx88cVnTAiOHTvmVUAAAKB2eZQMTJ069ZQ7EAIAcD6gTeCeR8nA7bffrujo6JqKBQCAmkObwK1q32eA+QIAAPgnj1cTAABwXqIy4Fa1kwGHw1GTcQAAUKOYM+Cex7cjBgDgvOTNrYjPoqowY8YMXXHFFYqIiFB0dLT69++vzMxMlzElJSVKTk5Wo0aNFB4eroEDByo3N9dlTFZWlvr166f69esrOjpaY8eOVUVFhcuYdevWqWvXrgoODlbbtm2VmprqUawkAwAA1ID169crOTlZn3/+udLS0lReXq4+ffqouLjYOSYlJUUrVqzQW2+9pfXr1+vw4cMaMGCA87jdble/fv1UVlamTZs26fXXX1dqaqomTZrkHHPgwAH169dPvXr1UkZGhkaPHq1hw4Zp9erV1Y7VYpzHkwEKCwsVGRmp3jH3KSjAWtfhADWi7KK4ug4BqDEVFSXasGm6CgoKZLPZauQ9qr4r2j30hAKDQ876OvbSEmXO+dtZx3r06FFFR0dr/fr1uvrqq1VQUKAmTZpoyZIlGjRokCRpz5496tChg9LT09WjRw99+OGHuvHGG3X48GHFxMRIkhYsWKDx48fr6NGjslqtGj9+vFatWqWdO3c63+v2229Xfn6+Pvroo2rFRmUAAGAKVXMGvNkknfK03tLS0mq9f0FBgSQpKipKkrRt2zaVl5erd+/ezjHt27dXixYtlJ6eLklKT09Xx44dnYmAJCUmJqqwsFC7du1yjvn1NarGVF2jOkgGAADwQPPmzRUZGencZsyYccZzHA6HRo8erd///ve69NJLJUk5OTmyWq1q0KCBy9iYmBjl5OQ4x/w6Eag6XnXsf40pLCzUyZMnq/WZPLrpEAAA5y0fLS3Mzs52aRMEBwef8dTk5GTt3LlTGzdu9CKAmkMyAAAwBV8tLbTZbB7NGRg5cqRWrlypDRs2qFmzZs79sbGxKisrU35+vkt1IDc3V7Gxsc4xW7Zscble1WqDX4/57QqE3Nxc2Ww2hYaGVitG2gQAANQAwzA0cuRIvfvuu1q7dq1at27tcrxbt26qV6+e1qxZ49yXmZmprKwsJSQkSJISEhK0Y8cO5eXlOcekpaXJZrMpPj7eOebX16gaU3WN6qAyAAAwh1q+A2FycrKWLFmi9957TxEREc4ef2RkpEJDQxUZGamhQ4dqzJgxioqKks1m06hRo5SQkKAePXpIkvr06aP4+HjdddddmjlzpnJycvTYY48pOTnZ2Z4YPny4nn/+eY0bN05DhgzR2rVrtWzZMq1atarasZIMAADMoZaTgRdeeEGSdO2117rsf+2113TPPfdIkmbPnq2AgAANHDhQpaWlSkxM1Pz5851jAwMDtXLlSo0YMUIJCQkKCwtTUlKSpk2b5hzTunVrrVq1SikpKZozZ46aNWumhQsXKjExsdqxkgwAAFADqnMbn5CQEM2bN0/z5s1zO6Zly5b64IMP/ud1rr32Wn355Zcex1iFZAAAYAqWnzdvzvdXJAMAAHPgqYVukQwAAEyBpxa6x9JCAABMjsoAAMAcaBO4RTIAADAPP/5C9wZtAgAATI7KAADAFJhA6B7JAADAHJgz4BZtAgAATI7KAADAFGgTuEcyAAAwB9oEbtEmAADA5KgMAABMgTaBeyQDAABzoE3gFskAAMAcSAbcYs4AAAAmR2UAAGAKzBlwj2QAAGAOtAncok0AAIDJURkAAJiCxTBkMc7+13tvzj3XkQwAAMyBNoFbtAkAADA5KgMAAFNgNYF7JAMAAHOgTeAWbQIAAEyOygAAwBRoE7hHMgAAMAfaBG6RDAAATIHKgHvMGQAAwOSoDAAAzIE2gVskAwAA0/DnUr83aBMAAGByVAYAAOZgGJWbN+f7KZIBAIApsJrAPdoEAACYHJUBAIA5sJrALZIBAIApWByVmzfn+yvaBAAAmByVAbj40z0HdM+D32r5khZ6+en2kqQbBhzUNTccUdv2haofbtdtV/dScVG9U8694qqjuuO+fWp1UZHKywK0Y1tD/ePhy2r7I8DkOnbI0Z9u2qmLW/+oRlEnNfmpXtq0teWvRhhK+lOG+l73jcLDyrQrM1pzFyboUI7NOSIirFTJQzarR9dsGYb06eZWmp96pUpK67lcZ9CNu9Tvum8U3aRIhSdCtOLjdlrybuda+6zwEG0Ct0gG4HRRfIFuGJit/d+Eu+wPDrFr+6bG2r6pse558NvTnvu7P+TqwYm79PrzF+mrL6IUGGioZdui2ggbcBESXKH930dp9ScXacojn5xy/M8371T/vl9r5vyeyskL1z23fakZf/tYQx/ur/Lyyn8SJ4zaoEYNf9KEx/soMNChsSM2KuX+TZrx3DXO6zxwzxZ163RIL/3zch3IaqiI8FJFhJfV2ueE51hN4F6dtgk2bNigm266SXFxcbJYLFq+fHldhmNqIaEVGvv4Dj03/RIVFbr+1v/ekpZ6K7W19uyIPO25AYEO/XXsHr367MX68J3mOpwVpuwD4dqYFlsboQMuvshoptSlXfXZFy1Pc9TQrX/8Wov/3VnpW1voQFaU/m9eTzVq+JN+f0WWJKnFBfm68rJDmvXi77VnbxPtyozR869117W/O6BGDX9yjrnp+j2a/NR1St/WQjlHI/TtgcbaviOuFj8pPFZ1nwFvNj9Vp8lAcXGxOnfurHnz5tVlGJA0YsJufbGxsTK2NPL43LbtT6hxTKkchkVzl6TrjdXrNPW5bWp54YkaiBQ4e7HRRWrU8KS+3NHUue+nk1bt2dtE8RcdlSR1uOioThRZ9c3+xs4x23fEyTAsat+2ckyPbtk6khehHt2ytei5t/XGc29pzF8/U0RYae1+IMBH6rRN0LdvX/Xt27fa40tLS1Va+st/bIWFhTURlulc3eeI2rY/odF3dT+r82MvqPxtafBf9+nlZ9op70iobr3zO814aavuv/WqUyoNQF2JanBSknS8INRl//GCUDX8+VhUg5PKLwxxOe5wBKiwKNg5pmn0CcU0LtLVPb7TzHk9FRDg0Ii7v9DEMZ9o3PQbauGT4GzQJnDvvFpNMGPGDEVGRjq35s2b13VI573GMSW6f2ymnnqso8rLAs/qGpaff4qWvtJGm9bGaO9um2ZPuVSSdNX1Ob4KFThnWCyS1erQ/83rqZ17YvTfr5vqmRd/r8suzVGzpgV1HR7cMXyw+anzKhl49NFHVVBQ4Nyys7PrOqTzXtsOhWrYqExzF3+u97ek6f0taep0+XHdfHuW3t+SpoCAM//0H/8hWJKUtT/Mua+iPEA5B0MVHVtSY7EDnjqWX1kRaBh50mV/w8iTOv7zsWP5oWpgc/25DQhwyBZe6jKmosKiQ0d+mUeTdbDyz9GNi2ssfqCmnFerCYKDgxUcHFzXYfiVr7ZE6YE/JbjsGz1llw5+F6a3U1vJ4bCc8Rrf7raprDRAzVoW6+uMhpKkwCCHouNKlHck9AxnA7UnJy9cPx4P1WUdj2jf95XzY+qHlql926NakdZOkrT72yaKCC/TRa1/0LcHKucNXHbpEVkshvbsbSJJ2pUZraAgQ01jCnUkt3JJYrO4yrZl7g9hv31bnCNoE7h3XiUD8L2TPwXp+30RLvtKTgaqsKCec3/DRqVq2KhUTZtXzg1odVGRThYHKi8nVEWF9XSyOEgfvNNMg4fv09HcEOUdCdXAu7+TJG1Mi6nVzwOEBJfrgthf5hPFRhfpwpY/qrAoWEd/DNe7H8TrL7f+V4eO2HQkL0L3/Hm7fjxeX5990UKSlHWogbZ8eYFS/rpJc15OUFCQQyPv3ax1m1rrx+P1JVVOKPxmfyM9MvwzzX/9SgVYDI0a+rm2fRXnUi3AOYanFrpFMoAz6jsoW4P/ut/5euYrX0iSZk++RP9ZcYEk6dVnL5ajwqKHp+9UcLBdmTsj9be/Xq6iE0weRO26+MIf9Mzk1c7XI5Iqf14/Xnehnnqhp5a+f6lCgis0+v5NCq9fpp2ZMXp0xvXOewxI0pPPXa2RQz7XzImrZRgWfbq5pea99ssEW8OwaNLM65R872bNmvKhSkqD9EVGM7246Ira+6CAD1kMo+5SnaKiIu3du1eSdNlll2nWrFnq1auXoqKi1KJFizOeX1hYqMjISPWOuU9BAdaaDheoE2UXsXYd/quiokQbNk1XQUGBbDbbmU84C1XfFQl9pymoXsiZT3CjorxE6R9OqtFY60qdVga2bt2qXr16OV+PGTNGkpSUlKTU1NQ6igoA4Je4HbFbdZoMXHvttarDwgQAABBzBgAAJsFqAvdIBgAA5uAwKjdvzvdTJAMAAHNgzoBb59UdCAEAgO9RGQAAmIJFXs4Z8Fkk5x6SAQCAOXAHQrdoEwAAYHJUBgAApsDSQvdIBgAA5sBqArdoEwAAYHJUBgAApmAxDFm8mATozbnnOpIBAIA5OH7evDnfT9EmAADA5EgGAACmUNUm8GbzxIYNG3TTTTcpLi5OFotFy5cvdzluGIYmTZqkpk2bKjQ0VL1799a3337rMubYsWMaPHiwbDabGjRooKFDh6qoqMhlzH//+1/17NlTISEhat68uWbOnOnx3w3JAADAHAwfbB4oLi5W586dNW/evNMenzlzpubOnasFCxZo8+bNCgsLU2JiokpKSpxjBg8erF27diktLU0rV67Uhg0bdP/99zuPFxYWqk+fPmrZsqW2bdump556SlOmTNFLL73kUazMGQAAmIOP7kBYWFjosjs4OFjBwcGnDO/bt6/69u3r5lKGnn32WT322GO65ZZbJEmLFi1STEyMli9frttvv127d+/WRx99pC+++EKXX365JOm5557TH//4Rz399NOKi4vT4sWLVVZWpldffVVWq1WXXHKJMjIyNGvWLJek4UyoDAAA4IHmzZsrMjLSuc2YMcPjaxw4cEA5OTnq3bu3c19kZKS6d++u9PR0SVJ6eroaNGjgTAQkqXfv3goICNDmzZudY66++mpZrVbnmMTERGVmZur48ePVjofKAADAFHx1B8Ls7GzZbDbn/tNVBc4kJydHkhQTE+OyPyYmxnksJydH0dHRLseDgoIUFRXlMqZ169anXKPqWMOGDasVD8kAAMAcfNQmsNlsLsmAP6BNAABALYuNjZUk5ebmuuzPzc11HouNjVVeXp7L8YqKCh07dsxlzOmu8ev3qA6SAQCAKVgc3m++0rp1a8XGxmrNmjXOfYWFhdq8ebMSEhIkSQkJCcrPz9e2bducY9auXSuHw6Hu3bs7x2zYsEHl5eXOMWlpaWrXrl21WwQSyQAAwCyq2gTebB4oKipSRkaGMjIyJFVOGszIyFBWVpYsFotGjx6tf/zjH3r//fe1Y8cO3X333YqLi1P//v0lSR06dNANN9yg++67T1u2bNFnn32mkSNH6vbbb1dcXJwk6S9/+YusVquGDh2qXbt2aenSpZozZ47GjBnjUazMGQAAoAZs3bpVvXr1cr6u+oJOSkpSamqqxo0bp+LiYt1///3Kz8/XVVddpY8++kghISHOcxYvXqyRI0fquuuuU0BAgAYOHKi5c+c6j0dGRurjjz9WcnKyunXrpsaNG2vSpEkeLSuUJIthnL9PXigsLFRkZKR6x9ynoADrmU8AzkNlF8XVdQhAjamoKNGGTdNVUFBQY5Pyqr4rrr3i7woKCjnzCW5UVJRo3ReP12isdYXKAADAFHhqoXvMGQAAwOSoDAAAzMFH9xnwRyQDAABzMCR5szzQf3MBkgEAgDkwZ8A95gwAAGByVAYAAOZgyMs5Az6L5JxDMgAAMAcmELpFmwAAAJOjMgAAMAeHJIuX5/spkgEAgCmwmsA92gQAAJgclQEAgDkwgdAtkgEAgDmQDLhFmwAAAJOjMgAAMAcqA26RDAAAzIGlhW6RDAAATIGlhe4xZwAAAJOjMgAAMAfmDLhFMgAAMAeHIVm8+EJ3+G8yQJsAAACTozIAADAH2gRukQwAAEzCy2RA/psM0CYAAMDkqAwAAMyBNoFbJAMAAHNwGPKq1M9qAgAA4K+oDAAAzMFwVG7enO+nSAYAAObAnAG3SAYAAObAnAG3mDMAAIDJURkAAJgDbQK3SAYAAOZgyMtkwGeRnHNoEwAAYHJUBgAA5kCbwC2SAQCAOTgckry4V4DDf+8zQJsAAACTozIAADAH2gRukQwAAMyBZMAt2gQAAJgclQEAgDlwO2K3SAYAAKZgGA4ZXjx50Jtzz3UkAwAAczAM7367Z84AAADwV1QGAADmYHg5Z8CPKwMkAwAAc3A4JIsXfX8/njNAmwAAAJOjMgAAMAfaBG6RDAAATMFwOGR40Sbw56WFtAkAADA5KgMAAHOgTeAWyQAAwBwchmQhGTgd2gQAAJgclQEAgDkYhiRv7jPgv5UBkgEAgCkYDkOGF20Cg2QAAIDznOGQd5UBlhYCAAA/RWUAAGAKtAncIxkAAJgDbQK3zutkoCpLq3CU1XEkQM2pqCip6xCAGlNRUSqpdn7rrlC5V/ccqlC574I5x5zXycCJEyckSeuOvl7HkQA1KLeuAwBq3okTJxQZGVkj17ZarYqNjdXGnA+8vlZsbKysVqsPojq3WIzzuAnicDh0+PBhRUREyGKx1HU4plBYWKjmzZsrOztbNputrsMBfIqf79pnGIZOnDihuLg4BQTU3Jz2kpISlZV5X0W2Wq0KCQnxQUTnlvO6MhAQEKBmzZrVdRimZLPZ+McSfouf79pVUxWBXwsJCfHLL3FfYWkhAAAmRzIAAIDJkQzAI8HBwZo8ebKCg4PrOhTA5/j5hlmd1xMIAQCA96gMAABgciQDAACYHMkAAAAmRzIAAIDJkQyg2ubNm6dWrVopJCRE3bt315YtW+o6JMAnNmzYoJtuuklxcXGyWCxavnx5XYcE1CqSAVTL0qVLNWbMGE2ePFnbt29X586dlZiYqLy8vLoODfBacXGxOnfurHnz5tV1KECdYGkhqqV79+664oor9Pzzz0uqfC5E8+bNNWrUKE2YMKGOowN8x2Kx6N1331X//v3rOhSg1lAZwBmVlZVp27Zt6t27t3NfQECAevfurfT09DqMDADgCyQDOKMffvhBdrtdMTExLvtjYmKUk5NTR1EBAHyFZAAAAJMjGcAZNW7cWIGBgcrNzXXZn5ubq9jY2DqKCgDgKyQDOCOr1apu3bppzZo1zn0Oh0Nr1qxRQkJCHUYGAPCFoLoOAOeHMWPGKCkpSZdffrmuvPJKPfvssyouLta9995b16EBXisqKtLevXudrw8cOKCMjAxFRUWpRYsWdRgZUDtYWohqe/755/XUU08pJydHXbp00dy5c9W9e/e6Dgvw2rp169SrV69T9iclJSk1NbX2AwJqGckAAAAmx5wBAABMjmQAAACTIxkAAMDkSAYAADA5kgEAAEyOZAAAAJMjGQAAwORIBgAAMDmSAcBL99xzj/r37+98fe2112r06NG1Hse6detksViUn5/vdozFYtHy5curfc0pU6aoS5cuXsX13XffyWKxKCMjw6vrAKg5JAPwS/fcc48sFossFousVqvatm2radOmqaKiosbf+9///remT59erbHV+QIHgJrGg4rgt2644Qa99tprKi0t1QcffKDk5GTVq1dPjz766Cljy8rKZLVaffK+UVFRPrkOANQWKgPwW8HBwYqNjVXLli01YsQI9e7dW++//76kX0r7jz/+uOLi4tSuXTtJUnZ2tm677TY1aNBAUVFRuuWWW/Tdd985r2m32zVmzBg1aNBAjRo10rhx4/Tbx3v8tk1QWlqq8ePHq3nz5goODlbbtm31yiuv6LvvvnM+HKdhw4ayWCy65557JFU+InrGjBlq3bq1QkND1blzZ7399tsu7/PBBx/o4osvVmhoqHr16uUSZ3WNHz9eF198serXr682bdpo4sSJKi8vP2Xciy++qObNm6t+/fq67bbbVFBQ4HJ84cKF6tChg0JCQtS+fXvNnz/f41gA1B2SAZhGaGioysrKnK/XrFmjzMxMpaWlaeXKlSovL1diYqIiIiL06aef6rPPPlN4eLhuuOEG53nPPPOMUlNT9eqrr2rjxo06duyY3n333f/5vnfffbf+9a9/ae7cudq9e7defPFFhYeHq3nz5nrnnXckSZmZmTpy5IjmzJkjSZoxY4YWLVqkBQsWaNeuXUpJSdGdd96p9evXS6pMWgYMGKCbbrpJGRkZGjZsmCZMmODx30lERIRSU1P19ddfa86cOXr55Zc1e/ZslzF79+7VsmXLtGLFCn300Uf68ssv9cADDziPL168WJMmTdLjjz+u3bt364knntDEiRP1+uuvexwPgDpiAH4oKSnJuOWWWwzDMAyHw2GkpaUZwcHBxiOPPOI8HhMTY5SWljrPeeONN4x27doZDofDua+0tNQIDQ01Vq9ebRiGYTRt2tSYOXOm83h5ebnRrFkz53sZhmFcc801xkMPPWQYhmFkZmYakoy0tLTTxvnJJ58Ykozjx48795WUlBj169c3Nm3a5DJ26NChxh133GEYhmE8+uijRnx8vMvx8ePHn3Kt35JkvPvuu26PP/XUU0a3bt2crydPnmwEBgYaBw8edO778MMPjYCAAOPIkSOGYRjGhRdeaCxZssTlOtOnTzcSEhIMwzCMAwcOGJKML7/80u37AqhbzBmA31q5cqXCw8NVXl4uh8Ohv/zlL5oyZYrzeMeOHV3mCXz11Vfau3evIiIiXK5TUlKiffv2qaCgQEeOHFH37t2dx4KCgnT55Zef0iqokpGRocDAQF1zzTXVjnvv3r366aefdP3117vsLysr02WXXSZJ2r17t0sckpSQkFDt96iydOlSzZ07V/v27VNRUZEqKipks9lcxrRo0UIXXHCBy/s4HA5lZmYqIiJC+/bt09ChQ3Xfffc5x1RUVCgyMtLjeADUDZIB+K1evXrphRdekNVqVVxcnIKCXH/cw8LCXF4XFRWpW7duWrx48SnXatKkyVnFEBoa6vE5RUVFkqRVq1a5fAlLlfMgfCU9PV2DBw/W1KlTlZiYqMjISL355pt65plnPI715ZdfPiU5CQwM9FmsAGoWyQD8VlhYmNq2bVvt8V27dtXSpUsVHR19ym/HVZo2barNmzfr6quvllT5G/C2bdvUtWvX047v2LGjHA6H1q9fr969e59yvKoyYbfbnfvi4+MVHBysrKwstxWFDh06OCdDVvn888/P/CF/ZdOmTWrZsqX+/ve/O/d9//33p4zLysrS4cOHFRcX53yfgIAAtWvXTjExMYqLi9P+/fs1ePBgj94fwLmDCYTAzwYPHqzGjRvrlltu0aeffqoDBw5o3bp1evDBB3Xw4EFJ0kMPPaQnn3xSy5cv1549e/TAAw/8z3sEtGrVSklJSRoyZIiWL1/uvOayZcskSS1btpTFYtHKlSt19OhRFRUVKSIiQo888ohSUlL0+uuva9++fdq+fbuee+4556S84cOH69tvv9XYsWOVmZmpJUuWKDU11aPPe9FFFykrK0tvvvmm9u3bp7lz5552MmRISIiSkpL01Vdf6dNPP9WDDz6o2267TbGxsZKkqVOnasaMGZo7d66++eYb7dixQ6+99ppmzZrlUTwA6g7JAPCz+vXra8OGDWrRooUGDBigDh06aOjQoSopKXFWCh5++GHdddddSkpKUkJCgiIiInTrrbf+z+u+8MILGjRokB544AG1b99e9913n4qLiyVJF1xwgaZOnaoJEyYoJiZGI0eOlCRNnz5dEydO1IwZM9ShQwfdcMMNWrVqlVq3bi2pso//zjvvaPny5ercubMWLFigJ554wqPPe/PNNyslJUUjR45Uly5dtGnTJk2cOPGUcW3bttWAAQP0xz/+UX369FGnTp1clg4OGzZMCxcu1GuvvaaOHTvqmmuuUWpqqjNWAOc+i+Fu5hMAADAFKgMAAJgcyQAAACZHMgAAgMmRDAAAYHIkAwAAmBzJAAAAJkcyAACAyZEMAABgciQDAACYHMkAAAAmRzIAAIDJ/T8zrd8KjmlJZgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ml_support_obj.plot_confusion_matrix(y_test, preds, finalized_pipeline)"
    ]

benchmark/sklearn_4/sklearn_4_fixed2.ipynb

@@ -3115,18 +3115,9 @@
      "shell.execute_reply.started": "2023-04-04T16:48:34.188967Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 46.7 ms, sys: 28.3 ms, total: 75 ms\n",
-      "Wall time: 85.1 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%%time\n",
+    "# %%time\n",
     "# Adaboost (Boosting of multiple Decision Trees)\n",
     "from sklearn.ensemble import AdaBoostRegressor\n",
     "ml_support_obj = ml_support()\n",
@@ -3168,18 +3159,10 @@
       "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
       "  warnings.warn(\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.13 s, sys: 3.46 s, total: 4.59 s\n",
-      "Wall time: 738 ms\n"
-     ]
     }
    ],
    "source": [
-    "%%time\n",
+    "# %%time\n",
     "preds = ml_support_obj.predit_with_pipeline(finalized_pipeline,X_train,X_test,y_train)"
    ]
   },
@@ -3200,7 +3183,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Balenced Accuracy Score : 0.7295402127509804\n"
+      "Balenced Accuracy Score : 0.7168414747090788\n"
      ]
     }
    ],
@@ -3226,13 +3209,6 @@
    "source": [
     "ml_support_obj.plot_confusion_matrix(y_test, preds, finalized_pipeline)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

benchmark/sklearn_4/sklearn_4_reproduced.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
@@ -1443,7 +1443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T14:38:10.693599Z",
@@ -1460,7 +1460,7 @@
        "{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1489,7 +1489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T14:38:16.904566Z",
@@ -1517,7 +1517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T14:38:24.473921Z",
@@ -1542,7 +1542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T14:38:40.543668Z",
@@ -1699,7 +1699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T14:38:47.909112Z",
@@ -1773,7 +1773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T14:39:00.436449Z",
@@ -3105,7 +3105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T16:48:34.189023Z",
@@ -3115,18 +3115,9 @@
      "shell.execute_reply.started": "2023-04-04T16:48:34.188967Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 67.7 ms, sys: 14 ms, total: 81.7 ms\n",
-      "Wall time: 127 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%%time\n",
+    "# %%time\n",
     "# Adaboost (Boosting of multiple Decision Trees)\n",
     "from sklearn.ensemble import AdaBoostRegressor\n",
     "ml_support_obj = ml_support()\n",
@@ -3150,7 +3141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T16:48:38.209713Z",
@@ -3168,24 +3159,16 @@
       "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
       "  warnings.warn(\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.13 s, sys: 4.03 s, total: 5.16 s\n",
-      "Wall time: 780 ms\n"
-     ]
     }
    ],
    "source": [
-    "%%time\n",
+    "# %%time\n",
     "preds = ml_support_obj.predit_with_pipeline(finalized_pipeline,X_train,X_test,y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-04T16:50:39.236636Z",
@@ -3203,7 +3186,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-3c705cb20ad9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Balenced Accuracy Score : {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbalanced_accuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-10-3c705cb20ad9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Balenced Accuracy Score : {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbalanced_accuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mbalanced_accuracy_score\u001b[0;34m(y_true, y_pred, sample_weight, adjusted)\u001b[0m\n\u001b[1;32m   2178\u001b[0m     \u001b[0;36m0.625\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m     \"\"\"\n\u001b[0;32m-> 2180\u001b[0;31m     \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2181\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2182\u001b[0m         \u001b[0mper_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    315\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m     \"\"\"\n\u001b[0;32m--> 317\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/sklearn_6/sklearn_6_fixed.ipynb

@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:35.836273Z",
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:35.842441Z",
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:37.626444Z",
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:37.693279Z",
@@ -119,207 +119,7 @@
      "shell.execute_reply.started": "2023-12-16T06:50:37.693254Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
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-       "      <td>65.0</td>\n",
-       "      <td>8450</td>\n",
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-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
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-       "      <td>RL</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>9550</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "      <td>140000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>14260</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>250000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 81 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
-       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
-       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
-       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
-       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
-       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
-       "\n",
-       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
-       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
-       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
-       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
-       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
-       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
-       "\n",
-       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
-       "0   2008        WD         Normal     208500  \n",
-       "1   2007        WD         Normal     181500  \n",
-       "2   2008        WD         Normal     223500  \n",
-       "3   2006        WD        Abnorml     140000  \n",
-       "4   2008        WD         Normal     250000  \n",
-       "\n",
-       "[5 rows x 81 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_ds.head(5)"
    ]
@@ -518,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.810650Z",
@@ -535,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.824313Z",
@@ -580,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.872330Z",
@@ -600,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.897767Z",
@@ -610,214 +410,7 @@
      "shell.execute_reply.started": "2023-12-16T06:50:40.897743Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MSSubClass</th>\n",
-       "      <th>MSZoning</th>\n",
-       "      <th>LotArea</th>\n",
-       "      <th>Street</th>\n",
-       "      <th>LotShape</th>\n",
-       "      <th>LandContour</th>\n",
-       "      <th>Utilities</th>\n",
-       "      <th>LotConfig</th>\n",
-       "      <th>LandSlope</th>\n",
-       "      <th>Neighborhood</th>\n",
-       "      <th>...</th>\n",
-       "      <th>OpenPorchSF</th>\n",
-       "      <th>EnclosedPorch</th>\n",
-       "      <th>3SsnPorch</th>\n",
-       "      <th>ScreenPorch</th>\n",
-       "      <th>PoolArea</th>\n",
-       "      <th>MiscVal</th>\n",
-       "      <th>YrSold</th>\n",
-       "      <th>SaleType</th>\n",
-       "      <th>SaleCondition</th>\n",
-       "      <th>SalePrice</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>60</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8450</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>61</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>208500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9600</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>181500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>60</td>\n",
-       "      <td>3</td>\n",
-       "      <td>11250</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>42</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>223500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>70</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9550</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35</td>\n",
-       "      <td>272</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>140000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>60</td>\n",
-       "      <td>3</td>\n",
-       "      <td>14260</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>...</td>\n",
-       "      <td>84</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>250000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 59 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   MSSubClass  MSZoning  LotArea  Street  LotShape  LandContour  Utilities  \\\n",
-       "0          60         3     8450       1         3            3          0   \n",
-       "1          20         3     9600       1         3            3          0   \n",
-       "2          60         3    11250       1         0            3          0   \n",
-       "3          70         3     9550       1         0            3          0   \n",
-       "4          60         3    14260       1         0            3          0   \n",
-       "\n",
-       "   LotConfig  LandSlope  Neighborhood  ...  OpenPorchSF  EnclosedPorch  \\\n",
-       "0          4          0             5  ...           61              0   \n",
-       "1          2          0            24  ...            0              0   \n",
-       "2          4          0             5  ...           42              0   \n",
-       "3          0          0             6  ...           35            272   \n",
-       "4          2          0            15  ...           84              0   \n",
-       "\n",
-       "   3SsnPorch  ScreenPorch  PoolArea  MiscVal  YrSold  SaleType  SaleCondition  \\\n",
-       "0          0            0         0        0    2008         8              4   \n",
-       "1          0            0         0        0    2007         8              4   \n",
-       "2          0            0         0        0    2008         8              4   \n",
-       "3          0            0         0        0    2006         8              0   \n",
-       "4          0            0         0        0    2008         8              4   \n",
-       "\n",
-       "   SalePrice  \n",
-       "0     208500  \n",
-       "1     181500  \n",
-       "2     223500  \n",
-       "3     140000  \n",
-       "4     250000  \n",
-       "\n",
-       "[5 rows x 59 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_ds.head(5)\n",
     "#train_ds.shape"
@@ -832,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.915490Z",
@@ -850,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.927551Z",
@@ -867,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.939501Z",
@@ -877,18 +470,7 @@
      "shell.execute_reply.started": "2023-12-16T06:50:40.939455Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1022, 58)\n",
-      "(1022,)\n",
-      "(438, 58)\n",
-      "(438,)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(X_train.shape)\n",
     "print(y_train.shape)\n",
@@ -1138,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:52.064844Z",
@@ -1156,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:52.179739Z",
@@ -1176,7 +758,7 @@
        "RandomForestRegressor(random_state=42)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1187,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.235721Z",
@@ -1322,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.910943Z",
@@ -1358,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.938225Z",
@@ -1388,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.990542Z",
@@ -1432,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:54.032582Z",

benchmark/sklearn_6/sklearn_6_reproduced.ipynb

@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:35.836273Z",
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:35.842441Z",
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:37.626444Z",
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:37.693279Z",
@@ -119,207 +119,7 @@
      "shell.execute_reply.started": "2023-12-16T06:50:37.693254Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Id</th>\n",
-       "      <th>MSSubClass</th>\n",
-       "      <th>MSZoning</th>\n",
-       "      <th>LotFrontage</th>\n",
-       "      <th>LotArea</th>\n",
-       "      <th>Street</th>\n",
-       "      <th>Alley</th>\n",
-       "      <th>LotShape</th>\n",
-       "      <th>LandContour</th>\n",
-       "      <th>Utilities</th>\n",
-       "      <th>...</th>\n",
-       "      <th>PoolArea</th>\n",
-       "      <th>PoolQC</th>\n",
-       "      <th>Fence</th>\n",
-       "      <th>MiscFeature</th>\n",
-       "      <th>MiscVal</th>\n",
-       "      <th>MoSold</th>\n",
-       "      <th>YrSold</th>\n",
-       "      <th>SaleType</th>\n",
-       "      <th>SaleCondition</th>\n",
-       "      <th>SalePrice</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>8450</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>208500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>9600</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>181500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>11250</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>223500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>70</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>9550</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "      <td>140000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>14260</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>250000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 81 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
-       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
-       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
-       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
-       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
-       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
-       "\n",
-       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
-       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
-       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
-       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
-       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
-       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
-       "\n",
-       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
-       "0   2008        WD         Normal     208500  \n",
-       "1   2007        WD         Normal     181500  \n",
-       "2   2008        WD         Normal     223500  \n",
-       "3   2006        WD        Abnorml     140000  \n",
-       "4   2008        WD         Normal     250000  \n",
-       "\n",
-       "[5 rows x 81 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_ds.head(5)"
    ]
@@ -518,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.810650Z",
@@ -535,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.824313Z",
@@ -580,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.872330Z",
@@ -600,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.897767Z",
@@ -610,214 +410,7 @@
      "shell.execute_reply.started": "2023-12-16T06:50:40.897743Z"
     }
    },
-   "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",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MSSubClass</th>\n",
-       "      <th>MSZoning</th>\n",
-       "      <th>LotArea</th>\n",
-       "      <th>Street</th>\n",
-       "      <th>LotShape</th>\n",
-       "      <th>LandContour</th>\n",
-       "      <th>Utilities</th>\n",
-       "      <th>LotConfig</th>\n",
-       "      <th>LandSlope</th>\n",
-       "      <th>Neighborhood</th>\n",
-       "      <th>...</th>\n",
-       "      <th>OpenPorchSF</th>\n",
-       "      <th>EnclosedPorch</th>\n",
-       "      <th>3SsnPorch</th>\n",
-       "      <th>ScreenPorch</th>\n",
-       "      <th>PoolArea</th>\n",
-       "      <th>MiscVal</th>\n",
-       "      <th>YrSold</th>\n",
-       "      <th>SaleType</th>\n",
-       "      <th>SaleCondition</th>\n",
-       "      <th>SalePrice</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>60</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8450</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>61</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>208500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9600</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>181500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>60</td>\n",
-       "      <td>3</td>\n",
-       "      <td>11250</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>42</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>223500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>70</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9550</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35</td>\n",
-       "      <td>272</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>140000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>60</td>\n",
-       "      <td>3</td>\n",
-       "      <td>14260</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>...</td>\n",
-       "      <td>84</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>250000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 59 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   MSSubClass  MSZoning  LotArea  Street  LotShape  LandContour  Utilities  \\\n",
-       "0          60         3     8450       1         3            3          0   \n",
-       "1          20         3     9600       1         3            3          0   \n",
-       "2          60         3    11250       1         0            3          0   \n",
-       "3          70         3     9550       1         0            3          0   \n",
-       "4          60         3    14260       1         0            3          0   \n",
-       "\n",
-       "   LotConfig  LandSlope  Neighborhood  ...  OpenPorchSF  EnclosedPorch  \\\n",
-       "0          4          0             5  ...           61              0   \n",
-       "1          2          0            24  ...            0              0   \n",
-       "2          4          0             5  ...           42              0   \n",
-       "3          0          0             6  ...           35            272   \n",
-       "4          2          0            15  ...           84              0   \n",
-       "\n",
-       "   3SsnPorch  ScreenPorch  PoolArea  MiscVal  YrSold  SaleType  SaleCondition  \\\n",
-       "0          0            0         0        0    2008         8              4   \n",
-       "1          0            0         0        0    2007         8              4   \n",
-       "2          0            0         0        0    2008         8              4   \n",
-       "3          0            0         0        0    2006         8              0   \n",
-       "4          0            0         0        0    2008         8              4   \n",
-       "\n",
-       "   SalePrice  \n",
-       "0     208500  \n",
-       "1     181500  \n",
-       "2     223500  \n",
-       "3     140000  \n",
-       "4     250000  \n",
-       "\n",
-       "[5 rows x 59 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_ds.head(5)\n",
     "#train_ds.shape"
@@ -832,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.915490Z",
@@ -850,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.927551Z",
@@ -867,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:40.939501Z",
@@ -877,18 +470,7 @@
      "shell.execute_reply.started": "2023-12-16T06:50:40.939455Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1022, 58)\n",
-      "(1022,)\n",
-      "(438, 58)\n",
-      "(438,)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(X_train.shape)\n",
     "print(y_train.shape)\n",
@@ -1138,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:52.064844Z",
@@ -1156,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:52.179739Z",
@@ -1176,7 +758,7 @@
        "RandomForestRegressor(random_state=42)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1187,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.235721Z",
@@ -1322,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.910943Z",
@@ -1357,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.938225Z",
@@ -1407,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:53.990542Z",
@@ -1451,7 +1033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-16T06:50:54.032582Z",
@@ -1469,7 +1051,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-19-e055db40fbd5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFReg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m submissions_df = pd.DataFrame({\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"ID\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"Predictions\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m })\n",
+      "\u001b[0;32m<ipython-input-15-e055db40fbd5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFReg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m submissions_df = pd.DataFrame({\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"ID\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"Predictions\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m })\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    600\u001b[0m         Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n\u001b[1;32m    601\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No support for np.int64 index based sparse matrices\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    546\u001b[0m             \u001b[0mvalidated\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \"\"\"\n\u001b[0;32m--> 548\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_feature_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"requires_y\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/sklearn_7/sklearn_7_fixed.ipynb

@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-10T14:04:15.325344Z",
@@ -83,119 +83,7 @@
      "shell.execute_reply.started": "2023-03-10T14:04:15.325303Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
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-       "      <th></th>\n",
-       "      <th>CustomerID</th>\n",
-       "      <th>Gender</th>\n",
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-       "      <th>Annual Income ($)</th>\n",
-       "      <th>Spending Score (1-100)</th>\n",
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-       "      <td>15000</td>\n",
-       "      <td>39</td>\n",
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-       "      <td>1</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>21</td>\n",
-       "      <td>35000</td>\n",
-       "      <td>81</td>\n",
-       "      <td>Engineer</td>\n",
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-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>20</td>\n",
-       "      <td>86000</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Engineer</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>23</td>\n",
-       "      <td>59000</td>\n",
-       "      <td>77</td>\n",
-       "      <td>Lawyer</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>31</td>\n",
-       "      <td>38000</td>\n",
-       "      <td>40</td>\n",
-       "      <td>Entertainment</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   CustomerID  Gender  Age  Annual Income ($)  Spending Score (1-100)  \\\n",
-       "0           1    Male   19              15000                      39   \n",
-       "1           2    Male   21              35000                      81   \n",
-       "2           3  Female   20              86000                       6   \n",
-       "3           4  Female   23              59000                      77   \n",
-       "4           5  Female   31              38000                      40   \n",
-       "\n",
-       "      Profession  Work Experience  Family Size  \n",
-       "0     Healthcare                1            4  \n",
-       "1       Engineer                3            3  \n",
-       "2       Engineer                1            1  \n",
-       "3         Lawyer                0            2  \n",
-       "4  Entertainment                2            6  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
@@ -257,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-10T15:20:49.266918Z",
@@ -267,26 +155,7 @@
      "shell.execute_reply.started": "2023-03-10T15:20:49.266850Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CustomerID                 0\n",
-       "Gender                     0\n",
-       "Age                        0\n",
-       "Annual Income ($)          0\n",
-       "Spending Score (1-100)     0\n",
-       "Profession                35\n",
-       "Work Experience            0\n",
-       "Family Size                0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isna().sum()"
    ]
@@ -840,7 +709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-10T16:23:09.735556Z",
@@ -879,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-09T18:36:44.186155Z",

benchmark/sklearn_7/sklearn_7_reproduced.ipynb

@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-10T14:04:15.325344Z",
@@ -83,119 +83,7 @@
      "shell.execute_reply.started": "2023-03-10T14:04:15.325303Z"
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th>Annual Income ($)</th>\n",
-       "      <th>Spending Score (1-100)</th>\n",
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-       "      <td>23</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>31</td>\n",
-       "      <td>38000</td>\n",
-       "      <td>40</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   CustomerID  Gender  Age  Annual Income ($)  Spending Score (1-100)  \\\n",
-       "0           1    Male   19              15000                      39   \n",
-       "1           2    Male   21              35000                      81   \n",
-       "2           3  Female   20              86000                       6   \n",
-       "3           4  Female   23              59000                      77   \n",
-       "4           5  Female   31              38000                      40   \n",
-       "\n",
-       "      Profession  Work Experience  Family Size  \n",
-       "0     Healthcare                1            4  \n",
-       "1       Engineer                3            3  \n",
-       "2       Engineer                1            1  \n",
-       "3         Lawyer                0            2  \n",
-       "4  Entertainment                2            6  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
@@ -257,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-10T15:20:49.266918Z",
@@ -267,26 +155,7 @@
      "shell.execute_reply.started": "2023-03-10T15:20:49.266850Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CustomerID                 0\n",
-       "Gender                     0\n",
-       "Age                        0\n",
-       "Annual Income ($)          0\n",
-       "Spending Score (1-100)     0\n",
-       "Profession                35\n",
-       "Work Experience            0\n",
-       "Family Size                0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isna().sum()"
    ]
@@ -840,7 +709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-10T16:23:09.735556Z",
@@ -858,7 +727,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-c9418427c75c>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mrf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-2-c9418427c75c>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mrf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         )\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    582\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    585\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1104\u001b[0m         )\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m     X = check_array(\n\u001b[0m\u001b[1;32m   1107\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/sklearn_8/sklearn_8_fixed.ipynb

@@ -1064,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:47:19.475964Z",
@@ -1074,28 +1074,14 @@
      "shell.execute_reply.started": "2023-07-15T13:47:19.475919Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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>RandomForestClassifier(min_samples_leaf=10, n_estimators=50, random_state=1)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_leaf=10, n_estimators=50, random_state=1)</pre></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "RandomForestClassifier(min_samples_leaf=10, n_estimators=50, random_state=1)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "RF.fit(x,y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:55:10.461465Z",
@@ -1112,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:56:54.254177Z",
@@ -1127,7 +1113,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy:  0.9870497297115239\n"
+      "Accuracy:  0.8131557232111282\n"
      ]
     }
    ],
@@ -1135,6 +1121,7 @@
     "# y_pred = RF.predict(X_val)\n",
     "\n",
     "# Calculating the accuracy of the model\n",
+    "# accuracy = accuracy_score(x, y)\n",
     "accuracy = accuracy_score(val_predictions, y) # fix--- used the wrong y_pred\n",
     "\n",
     "print(\"Accuracy: \", accuracy)"

benchmark/sklearn_8/sklearn_8_reproduced.ipynb

@@ -870,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:44:53.573184Z",
@@ -888,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:44:56.535203Z",
@@ -921,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:45:21.091856Z",
@@ -955,7 +955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:46:32.522231Z",
@@ -973,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:58:42.962507Z",
@@ -1009,7 +1009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:59:10.701911Z",
@@ -1028,7 +1028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:59:12.938298Z",
@@ -1045,7 +1045,7 @@
        "0.1209901490275322"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1064,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:47:19.475964Z",
@@ -1074,28 +1074,14 @@
      "shell.execute_reply.started": "2023-07-15T13:47:19.475919Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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>RandomForestClassifier(min_samples_leaf=10, n_estimators=50, random_state=1)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_leaf=10, n_estimators=50, random_state=1)</pre></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "RandomForestClassifier(min_samples_leaf=10, n_estimators=50, random_state=1)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "RF.fit(x,y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:55:10.461465Z",
@@ -1112,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-07-15T13:56:54.254177Z",
@@ -1130,7 +1116,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-f96d8f33dd63>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Calculating the accuracy of the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-11-f96d8f33dd63>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Calculating the accuracy of the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"multilabel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/sklearn_9/sklearn_9_fixed.ipynb

@@ -657,7 +657,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "F1 score: 0.9021626894346165\n"
+      "F1 score: 0.9020291436650468\n"
      ]
     },
     {