From bd61ab0f9925590c505438ec7c745a92ab1294f3 Mon Sep 17 00:00:00 2001
From: John Mount <jmount@win-vector.com>
Date: Thu, 13 Jun 2024 08:12:39 -0700
Subject: [PATCH] tune pooling est and rebuild

---
 Examples/KDD2009Example/KDD2009Example.ipynb  | 512 +++++++++---------
 Examples/Pooling/PartialPoolingExample.ipynb  |  62 +--
 coverage.txt                                  |   6 +-
 docs/vtreat/partial_pooling_estimator.html    | 190 ++++---
 .../lib/vtreat/partial_pooling_estimator.py   |  22 +-
 pkg/dist/vtreat-1.3.1-py3-none-any.whl        | Bin 33420 -> 33469 bytes
 pkg/dist/vtreat-1.3.1.tar.gz                  | Bin 56195 -> 56698 bytes
 pkg/tests/test_partial_pooling.py             |   6 +-
 pkg/vtreat/partial_pooling_estimator.py       |  22 +-
 9 files changed, 427 insertions(+), 393 deletions(-)

diff --git a/Examples/KDD2009Example/KDD2009Example.ipynb b/Examples/KDD2009Example/KDD2009Example.ipynb
index 91e06db..5bf0c16 100644
--- a/Examples/KDD2009Example/KDD2009Example.ipynb
+++ b/Examples/KDD2009Example/KDD2009Example.ipynb
@@ -529,26 +529,26 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>orig_index</th>\n",
-       "      <th>Var161_is_bad</th>\n",
-       "      <th>Var38_is_bad</th>\n",
-       "      <th>Var23_is_bad</th>\n",
-       "      <th>Var200_is_bad</th>\n",
-       "      <th>Var162_is_bad</th>\n",
-       "      <th>Var26_is_bad</th>\n",
-       "      <th>Var2_is_bad</th>\n",
-       "      <th>Var225_is_bad</th>\n",
-       "      <th>Var18_is_bad</th>\n",
+       "      <th>Var174_is_bad</th>\n",
+       "      <th>Var85_is_bad</th>\n",
+       "      <th>Var40_is_bad</th>\n",
+       "      <th>Var37_is_bad</th>\n",
+       "      <th>Var120_is_bad</th>\n",
+       "      <th>Var218_is_bad</th>\n",
+       "      <th>Var36_is_bad</th>\n",
+       "      <th>Var7_is_bad</th>\n",
+       "      <th>Var75_is_bad</th>\n",
        "      <th>...</th>\n",
-       "      <th>Var206_lev_kxE9</th>\n",
-       "      <th>Var206_lev_y6dw</th>\n",
-       "      <th>Var194_logit_code</th>\n",
-       "      <th>Var194_prevalence_code</th>\n",
-       "      <th>Var194_lev__NA_</th>\n",
-       "      <th>Var194_lev_SEuy</th>\n",
-       "      <th>Var192_logit_code</th>\n",
-       "      <th>Var220_logit_code</th>\n",
-       "      <th>Var220_prevalence_code</th>\n",
-       "      <th>Var220_lev_4UxGlow</th>\n",
+       "      <th>Var207_prevalence_code</th>\n",
+       "      <th>Var207_lev_me75fM6ugJ</th>\n",
+       "      <th>Var207_lev_7M47J5GA0pTYIFxg5uy</th>\n",
+       "      <th>Var207_lev_DHn_WUyBhW_whjA88g9bvA64_</th>\n",
+       "      <th>Var221_logit_code</th>\n",
+       "      <th>Var221_prevalence_code</th>\n",
+       "      <th>Var221_lev_oslk</th>\n",
+       "      <th>Var221_lev_zCkv</th>\n",
+       "      <th>Var221_lev_d0EEeJi</th>\n",
+       "      <th>Var221_lev_QKW8DRm</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -560,20 +560,20 @@
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.701267</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.744933</td>\n",
+       "      <td>0.081400</td>\n",
+       "      <td>0.739733</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.176859</td>\n",
-       "      <td>0.039383</td>\n",
-       "      <td>0.002244</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -586,18 +586,18 @@
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.701267</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.744933</td>\n",
+       "      <td>0.083992</td>\n",
+       "      <td>0.739733</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.064982</td>\n",
-       "      <td>0.334880</td>\n",
-       "      <td>0.003778</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -606,22 +606,22 @@
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.069933</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.114034</td>\n",
-       "      <td>0.250733</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>-0.101773</td>\n",
-       "      <td>-0.315349</td>\n",
-       "      <td>0.001533</td>\n",
+       "      <td>-0.168048</td>\n",
+       "      <td>0.033156</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -630,22 +630,22 @@
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.701267</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.114034</td>\n",
-       "      <td>0.250733</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.083992</td>\n",
+       "      <td>0.739733</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.131015</td>\n",
-       "      <td>0.113057</td>\n",
-       "      <td>0.000644</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -656,71 +656,71 @@
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.069933</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.744933</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.239988</td>\n",
+       "      <td>0.124333</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.080591</td>\n",
-       "      <td>-0.556931</td>\n",
-       "      <td>0.000733</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 257 columns</p>\n",
+       "<p>5 rows × 255 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   orig_index  Var161_is_bad  Var38_is_bad  Var23_is_bad  Var200_is_bad  \\\n",
-       "0           0            1.0           0.0           1.0            1.0   \n",
-       "1           1            1.0           0.0           1.0            1.0   \n",
-       "2           2            1.0           0.0           1.0            0.0   \n",
-       "3           4            1.0           0.0           1.0            0.0   \n",
-       "4           5            1.0           0.0           1.0            1.0   \n",
-       "\n",
-       "   Var162_is_bad  Var26_is_bad  Var2_is_bad  Var225_is_bad  Var18_is_bad  ...  \\\n",
-       "0            1.0           1.0          1.0            1.0           1.0  ...   \n",
-       "1            1.0           1.0          1.0            1.0           1.0  ...   \n",
-       "2            1.0           1.0          1.0            0.0           1.0  ...   \n",
-       "3            1.0           1.0          1.0            0.0           1.0  ...   \n",
-       "4            1.0           1.0          1.0            1.0           1.0  ...   \n",
-       "\n",
-       "   Var206_lev_kxE9  Var206_lev_y6dw  Var194_logit_code  \\\n",
-       "0              0.0              0.0           0.000000   \n",
-       "1              0.0              0.0           0.000000   \n",
-       "2              0.0              0.0          -0.114034   \n",
-       "3              0.0              0.0          -0.114034   \n",
-       "4              0.0              0.0           0.000000   \n",
-       "\n",
-       "   Var194_prevalence_code  Var194_lev__NA_  Var194_lev_SEuy  \\\n",
-       "0                0.744933              1.0              0.0   \n",
-       "1                0.744933              1.0              0.0   \n",
-       "2                0.250733              0.0              1.0   \n",
-       "3                0.250733              0.0              1.0   \n",
-       "4                0.744933              1.0              0.0   \n",
-       "\n",
-       "   Var192_logit_code  Var220_logit_code  Var220_prevalence_code  \\\n",
-       "0          -0.176859           0.039383                0.002244   \n",
-       "1           0.064982           0.334880                0.003778   \n",
-       "2          -0.101773          -0.315349                0.001533   \n",
-       "3           0.131015           0.113057                0.000644   \n",
-       "4           0.080591          -0.556931                0.000733   \n",
-       "\n",
-       "   Var220_lev_4UxGlow  \n",
-       "0                 0.0  \n",
-       "1                 0.0  \n",
-       "2                 0.0  \n",
-       "3                 0.0  \n",
-       "4                 0.0  \n",
-       "\n",
-       "[5 rows x 257 columns]"
+       "   orig_index  Var174_is_bad  Var85_is_bad  Var40_is_bad  Var37_is_bad  \\\n",
+       "0           0            1.0           0.0           1.0           1.0   \n",
+       "1           1            1.0           0.0           1.0           1.0   \n",
+       "2           2            1.0           0.0           1.0           1.0   \n",
+       "3           4            1.0           0.0           1.0           1.0   \n",
+       "4           5            1.0           0.0           1.0           1.0   \n",
+       "\n",
+       "   Var120_is_bad  Var218_is_bad  Var36_is_bad  Var7_is_bad  Var75_is_bad  ...  \\\n",
+       "0            1.0            0.0           1.0          0.0           1.0  ...   \n",
+       "1            1.0            1.0           1.0          0.0           1.0  ...   \n",
+       "2            1.0            0.0           1.0          0.0           1.0  ...   \n",
+       "3            1.0            0.0           1.0          0.0           1.0  ...   \n",
+       "4            1.0            0.0           1.0          0.0           1.0  ...   \n",
+       "\n",
+       "   Var207_prevalence_code  Var207_lev_me75fM6ugJ  \\\n",
+       "0                0.701267                    1.0   \n",
+       "1                0.701267                    1.0   \n",
+       "2                0.069933                    0.0   \n",
+       "3                0.701267                    1.0   \n",
+       "4                0.069933                    0.0   \n",
+       "\n",
+       "   Var207_lev_7M47J5GA0pTYIFxg5uy  Var207_lev_DHn_WUyBhW_whjA88g9bvA64_  \\\n",
+       "0                             0.0                                   0.0   \n",
+       "1                             0.0                                   0.0   \n",
+       "2                             0.0                                   1.0   \n",
+       "3                             0.0                                   0.0   \n",
+       "4                             0.0                                   1.0   \n",
+       "\n",
+       "   Var221_logit_code  Var221_prevalence_code  Var221_lev_oslk  \\\n",
+       "0           0.081400                0.739733              1.0   \n",
+       "1           0.083992                0.739733              1.0   \n",
+       "2          -0.168048                0.033156              0.0   \n",
+       "3           0.083992                0.739733              1.0   \n",
+       "4          -0.239988                0.124333              0.0   \n",
+       "\n",
+       "   Var221_lev_zCkv  Var221_lev_d0EEeJi  Var221_lev_QKW8DRm  \n",
+       "0              0.0                 0.0                 0.0  \n",
+       "1              0.0                 0.0                 0.0  \n",
+       "2              0.0                 0.0                 0.0  \n",
+       "3              0.0                 0.0                 0.0  \n",
+       "4              1.0                 0.0                 0.0  \n",
+       "\n",
+       "[5 rows x 255 columns]"
       ]
      },
      "execution_count": 13,
@@ -740,7 +740,7 @@
     {
      "data": {
       "text/plain": [
-       "(45000, 257)"
+       "(45000, 255)"
       ]
      },
      "execution_count": 14,
@@ -801,8 +801,8 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Var161_is_bad</td>\n",
-       "      <td>Var161</td>\n",
+       "      <td>Var174_is_bad</td>\n",
+       "      <td>Var174</td>\n",
        "      <td>missing_indicator</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
@@ -815,8 +815,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>Var38_is_bad</td>\n",
-       "      <td>Var38</td>\n",
+       "      <td>Var85_is_bad</td>\n",
+       "      <td>Var85</td>\n",
        "      <td>missing_indicator</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
@@ -829,42 +829,42 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Var23_is_bad</td>\n",
-       "      <td>Var23</td>\n",
+       "      <td>Var186_is_bad</td>\n",
+       "      <td>Var186</td>\n",
        "      <td>missing_indicator</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.017267</td>\n",
-       "      <td>0.000641</td>\n",
-       "      <td>9.976726e-05</td>\n",
+       "      <td>0.004328</td>\n",
+       "      <td>0.000037</td>\n",
+       "      <td>3.496301e-01</td>\n",
        "      <td>193.0</td>\n",
        "      <td>0.001036</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>Var200_is_bad</td>\n",
-       "      <td>Var200</td>\n",
+       "      <td>Var40_is_bad</td>\n",
+       "      <td>Var40</td>\n",
        "      <td>missing_indicator</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.053420</td>\n",
-       "      <td>0.005481</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>0.016358</td>\n",
+       "      <td>0.000577</td>\n",
+       "      <td>2.223155e-04</td>\n",
        "      <td>193.0</td>\n",
        "      <td>0.001036</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>Var162_is_bad</td>\n",
-       "      <td>Var162</td>\n",
+       "      <td>Var37_is_bad</td>\n",
+       "      <td>Var37</td>\n",
        "      <td>missing_indicator</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.016358</td>\n",
-       "      <td>0.000577</td>\n",
-       "      <td>2.223155e-04</td>\n",
+       "      <td>0.020327</td>\n",
+       "      <td>0.000906</td>\n",
+       "      <td>3.760268e-06</td>\n",
        "      <td>193.0</td>\n",
        "      <td>0.001036</td>\n",
        "      <td>True</td>\n",
@@ -875,18 +875,18 @@
       ],
       "text/plain": [
        "        variable orig_variable          treatment  y_aware  has_range  \\\n",
-       "0  Var161_is_bad        Var161  missing_indicator    False       True   \n",
-       "1   Var38_is_bad         Var38  missing_indicator    False       True   \n",
-       "2   Var23_is_bad         Var23  missing_indicator    False       True   \n",
-       "3  Var200_is_bad        Var200  missing_indicator    False       True   \n",
-       "4  Var162_is_bad        Var162  missing_indicator    False       True   \n",
+       "0  Var174_is_bad        Var174  missing_indicator    False       True   \n",
+       "1   Var85_is_bad         Var85  missing_indicator    False       True   \n",
+       "2  Var186_is_bad        Var186  missing_indicator    False       True   \n",
+       "3   Var40_is_bad         Var40  missing_indicator    False       True   \n",
+       "4   Var37_is_bad         Var37  missing_indicator    False       True   \n",
        "\n",
        "   PearsonR        R2  significance  vcount  default_threshold  recommended  \n",
        "0  0.020327  0.000906  3.760268e-06   193.0           0.001036         True  \n",
        "1 -0.031791  0.002143  1.127987e-12   193.0           0.001036         True  \n",
-       "2  0.017267  0.000641  9.976726e-05   193.0           0.001036         True  \n",
-       "3  0.053420  0.005481  0.000000e+00   193.0           0.001036         True  \n",
-       "4  0.016358  0.000577  2.223155e-04   193.0           0.001036         True  "
+       "2  0.004328  0.000037  3.496301e-01   193.0           0.001036        False  \n",
+       "3  0.016358  0.000577  2.223155e-04   193.0           0.001036         True  \n",
+       "4  0.020327  0.000906  3.760268e-06   193.0           0.001036         True  "
       ]
      },
      "execution_count": 15,
@@ -914,7 +914,7 @@
        "treatment\n",
        "missing_indicator    124\n",
        "indicator_code        63\n",
-       "logit_code            29\n",
+       "logit_code            27\n",
        "prevalence_code       25\n",
        "clean_copy            15\n",
        "Name: count, dtype: int64"
@@ -938,7 +938,7 @@
     {
      "data": {
       "text/plain": [
-       "256"
+       "254"
       ]
      },
      "execution_count": 17,
@@ -975,17 +975,17 @@
      "data": {
       "text/plain": [
        "orig_index                               int64\n",
-       "Var161_is_bad                          float64\n",
-       "Var38_is_bad                           float64\n",
-       "Var23_is_bad                           float64\n",
-       "Var200_is_bad                          float64\n",
+       "Var174_is_bad                          float64\n",
+       "Var85_is_bad                           float64\n",
+       "Var40_is_bad                           float64\n",
+       "Var37_is_bad                           float64\n",
        "                                  ...         \n",
-       "Var194_lev_SEuy           Sparse[float64, 0.0]\n",
-       "Var192_logit_code                      float64\n",
-       "Var220_logit_code                      float64\n",
-       "Var220_prevalence_code                 float64\n",
-       "Var220_lev_4UxGlow        Sparse[float64, 0.0]\n",
-       "Length: 257, dtype: object"
+       "Var221_prevalence_code                 float64\n",
+       "Var221_lev_oslk           Sparse[float64, 0.0]\n",
+       "Var221_lev_zCkv           Sparse[float64, 0.0]\n",
+       "Var221_lev_d0EEeJi        Sparse[float64, 0.0]\n",
+       "Var221_lev_QKW8DRm        Sparse[float64, 0.0]\n",
+       "Length: 255, dtype: object"
       ]
      },
      "execution_count": 18,
@@ -1086,38 +1086,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.274741</td>\n",
-       "      <td>0.001472</td>\n",
-       "      <td>0.275500</td>\n",
-       "      <td>0.002319</td>\n",
+       "      <td>0.274729</td>\n",
+       "      <td>0.001423</td>\n",
+       "      <td>0.275473</td>\n",
+       "      <td>0.002355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.263237</td>\n",
-       "      <td>0.001417</td>\n",
-       "      <td>0.264131</td>\n",
-       "      <td>0.002324</td>\n",
+       "      <td>0.263206</td>\n",
+       "      <td>0.001370</td>\n",
+       "      <td>0.264090</td>\n",
+       "      <td>0.002361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.255042</td>\n",
-       "      <td>0.001582</td>\n",
-       "      <td>0.256283</td>\n",
-       "      <td>0.002369</td>\n",
+       "      <td>0.254969</td>\n",
+       "      <td>0.001520</td>\n",
+       "      <td>0.256277</td>\n",
+       "      <td>0.002293</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.249507</td>\n",
-       "      <td>0.001501</td>\n",
-       "      <td>0.251205</td>\n",
-       "      <td>0.002500</td>\n",
+       "      <td>0.249259</td>\n",
+       "      <td>0.001280</td>\n",
+       "      <td>0.250900</td>\n",
+       "      <td>0.002749</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.244884</td>\n",
-       "      <td>0.001640</td>\n",
-       "      <td>0.247587</td>\n",
-       "      <td>0.002634</td>\n",
+       "      <td>0.244634</td>\n",
+       "      <td>0.001380</td>\n",
+       "      <td>0.247347</td>\n",
+       "      <td>0.002802</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1125,11 +1125,11 @@
       ],
       "text/plain": [
        "   train-logloss-mean  train-logloss-std  test-logloss-mean  test-logloss-std\n",
-       "0            0.274741           0.001472           0.275500          0.002319\n",
-       "1            0.263237           0.001417           0.264131          0.002324\n",
-       "2            0.255042           0.001582           0.256283          0.002369\n",
-       "3            0.249507           0.001501           0.251205          0.002500\n",
-       "4            0.244884           0.001640           0.247587          0.002634"
+       "0            0.274729           0.001423           0.275473          0.002355\n",
+       "1            0.263206           0.001370           0.264090          0.002361\n",
+       "2            0.254969           0.001520           0.256277          0.002293\n",
+       "3            0.249259           0.001280           0.250900          0.002749\n",
+       "4            0.244634           0.001380           0.247347          0.002802"
       ]
      },
      "execution_count": 23,
@@ -1175,11 +1175,11 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>0.214965</td>\n",
-       "      <td>0.001973</td>\n",
-       "      <td>0.233625</td>\n",
-       "      <td>0.002935</td>\n",
+       "      <th>33</th>\n",
+       "      <td>0.21938</td>\n",
+       "      <td>0.001991</td>\n",
+       "      <td>0.233241</td>\n",
+       "      <td>0.002431</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1187,7 +1187,7 @@
       ],
       "text/plain": [
        "    train-logloss-mean  train-logloss-std  test-logloss-mean  test-logloss-std\n",
-       "47            0.214965           0.001973           0.233625          0.002935"
+       "33             0.21938           0.001991           0.233241          0.002431"
       ]
      },
      "execution_count": 24,
@@ -1209,7 +1209,7 @@
     {
      "data": {
       "text/plain": [
-       "47"
+       "33"
       ]
      },
      "execution_count": 25,
@@ -1643,7 +1643,7 @@
        "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
        "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=47, n_jobs=None,\n",
+       "              multi_strategy=None, n_estimators=33, n_jobs=None,\n",
        "              num_parallel_tree=None, random_state=None, ...)</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 \">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
        "              colsample_bylevel=None, colsample_bynode=None,\n",
        "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
@@ -1653,7 +1653,7 @@
        "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
        "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=47, n_jobs=None,\n",
+       "              multi_strategy=None, n_estimators=33, n_jobs=None,\n",
        "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
@@ -1666,7 +1666,7 @@
        "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
        "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=47, n_jobs=None,\n",
+       "              multi_strategy=None, n_estimators=33, n_jobs=None,\n",
        "              num_parallel_tree=None, random_state=None, ...)"
       ]
      },
@@ -1728,7 +1728,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1739,7 +1739,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7969458107340726"
+       "0.7829071226337962"
       ]
      },
      "execution_count": 29,
@@ -1776,7 +1776,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1787,7 +1787,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7375928920161583"
+       "0.737621859051672"
       ]
      },
      "execution_count": 30,
@@ -1921,11 +1921,11 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>0.064519</td>\n",
-       "      <td>0.000866</td>\n",
-       "      <td>0.080306</td>\n",
-       "      <td>0.000947</td>\n",
+       "      <th>98</th>\n",
+       "      <td>0.106513</td>\n",
+       "      <td>0.000865</td>\n",
+       "      <td>0.12832</td>\n",
+       "      <td>0.001558</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1933,7 +1933,7 @@
       ],
       "text/plain": [
        "    train-logloss-mean  train-logloss-std  test-logloss-mean  test-logloss-std\n",
-       "92            0.064519           0.000866           0.080306          0.000947"
+       "98            0.106513           0.000865            0.12832          0.001558"
       ]
      },
      "execution_count": 35,
@@ -1954,7 +1954,7 @@
     {
      "data": {
       "text/plain": [
-       "92"
+       "98"
       ]
      },
      "execution_count": 36,
@@ -2388,7 +2388,7 @@
        "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
        "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=92, n_jobs=None,\n",
+       "              multi_strategy=None, n_estimators=98, n_jobs=None,\n",
        "              num_parallel_tree=None, random_state=None, ...)</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 \">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
        "              colsample_bylevel=None, colsample_bynode=None,\n",
        "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
@@ -2398,7 +2398,7 @@
        "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
        "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=92, n_jobs=None,\n",
+       "              multi_strategy=None, n_estimators=98, n_jobs=None,\n",
        "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
@@ -2411,7 +2411,7 @@
        "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
        "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=92, n_jobs=None,\n",
+       "              multi_strategy=None, n_estimators=98, n_jobs=None,\n",
        "              num_parallel_tree=None, random_state=None, ...)"
       ]
      },
@@ -2460,7 +2460,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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+flJX7z927Bh8fX1lnj8kcspNAc59CuRnAo9+fff6Vm5A81GAIR+w9QZ4hpItPUIIUZKYuEyMmf0YIlHp/NTRdvhmrgCbdyn/vTgNxNevX+PBgwfi+cTERNy4cQO2traoU6cOwsPD8fTpU2zbtg1AaY/StWvXIiwsDJMmTcKFCxewefNm8UWfiYKS/gD2dJdv3fdmAB5dAEP6jwchRD1yXwsR9kWKRBiu/codr7MVu5G1vDgNxKtXr6JLly7i+bJzfWPHjkV0dDRSU1ORnJwsft7LywuHDh3CnDlzsG7dOri4uGD16tX48MMP1V671vtrHhC/7O3rmFgBLccCXnKGJiGEKJGlhSGOxdRH1+AEBPethbVfucPAQHWjAzRmHKK65OTkwNraGtnZ2fp5DvHSt8DZcNnPeXUB6vYCbLwAI8XunE4IIaqS/LQIbs7G4jDMefUS1i1ClP49rlXnEEk15WcCkbI7HQEAWo0BGg5WXz2EEFKFi9feoG0rM4mWYB1Xvlremy7urcsYA/b3f3sY9l5PYUgI0QgxcZkIGPgvQsOTIRKp/+AltRB11R/Tgb8jq36+xwrApp766iGEkLeo2Jv0p+0Z6OJnieED1Dv+mAJRF622BIpfSy+3dAF6RZYPjieEEA0ga2hFcL9aaq+DAlHXbPeTHYYDfintNUoIIRpEVhiqujdpVSgQdcnRECD1ouSyoPWApSs39RBCyFtoUhgCFIi6Y5mMX6A+PwHmjtLLCSGEY5oWhgD1MtUNssKw7XQKQ0KIRtp/JEvjwhCgQNRuohLZYdh/G+DVQ/31EEKIHNq3NkfDegIAmhOGAB0y1W4rZFxXdMA2wMRG7aUQQoi8nByMcWJnA2zemY7wGU4aEYYABaJ2EpXIDsPBe0rvQEEIIRqGMSZxuyYnB2MsnCl9U3cu0SFTbcNEssMw+CCFISFEI8XEZaL7iAd4kyfkupS3okDUJkcnAssNpZcP3qf+WgghRA5lvUn/PJuLPuMeoqBAxHVJVaJDptpiozvwOkV6+dADgJLvGk0IIcpQeWhF4/oC8Pma+31FLURtsLmBdBjaeZceJqUwJIRoIE0cZ/gu1ELUdLKGVfRaDVh5qr0UQgiRhzaGIUCBqNl+HSK9bOB2gG+h/loIIUQO2hqGAB0y1VwPfwPu75Fc1ucnCkNCiMbS5jAEqIWomdKuAPv7Si4buAPgm3NTDyGEvANjDDsOZGptGAIUiJrnwQHgwADJZQ17UxgSQjQaj8fDng11MWDiI9Stw9e6MAQoEDVP5TB0agG0msJJKYQQogiBwAAHNteFsTFP68IQoHOImuXMJ5LzrcYBnb7ipBRCCHmXfYdfISW1SGKZiYmBVoYhQIGoWS5HSM43HMRNHYQQ8g4xcZkYEpqIzkMSpEJRW1EgaoqTcyTnB27npg5CCHmHir1JHyYVIio2g+uSlIICUVNcWyk5T8MrCCEaSNbQik9nOnFblJJQIGqCxCOS8/22cFMHIYS8hbaPM3wXCkSuFb8B9vWSXGZam5taCCGkCroehgAFIvdWVzo0+sF33NRBCCFV0IcwBCgQuXX3Z8l5IwFg15ibWgghRIarf7/RizAEKBC5dXiM5PyHu7ipgxBCqtCmhRlmjLMHoNthCNCVarhz6RvJ+V5ruamDEELegsfjYeXnbujY1gKDetnobBgC1ELkBmPA2YWSy6zqcFMLIYRUkpVdIjHP4/EwuHctnQ5DgAKRG1ubS87338pNHYQQUklMXCbqdbiDy9ffcF2K2lEgqhsTARl3yudNLAFBLe7qIYSQ/5T1Js3MEiJw1AM8SirkuiS1okBUt/OLJef7/8JNHYQQUkHloRUj+teCpzuf26LUjAJR3S5WuHuFwArg6fYxeUKI5tOXcYbvQoGoTjnJkvPdlnFTByGE/IfCsBwFojodGSc5b+7ISRmEEAJQGFZGgahOT06WT7eZyF0dhBC9R2EojQJRXf6JlZyv15ebOgghBEBevojCsBK6Uo26/D5Mcp460xBCODRphB0A4MadPKz5ksIQoEBUj6yHkvN9fuSmDkIIqaAsFEkpOmSqDpvrS86b68bdpQkh2iMmLhOxBzO5LkOjUQtR1URCyflmQ7mpgxCit8o60JQJ7mfLXTEajFqIqra9veR801Hc1EEI0UsVe5OKRMDZK/p3jVJ5USCq2vOr5dM2dEcLQoj6yBpasWqJG7dFaTAKRFUqeCU534PueUgIUQ8aZ6g4CkRVWkfH6Qkh6kdhWD0UiKrCRJLzbu24qYMQolcoDKuPAlFVYioFoP8n3NRBCNEb6ZklmPpJMoVhNVEgqkrFzjR0mydCiBrY2RrhYFQ9mAp4FIbVQOMQVeHlLcn53lHc1EEI0Tud/Sxx7XBjeNc1oTBUELUQVWFbC8l5I/266zQhRH1u3ssDY0xiWaP6AgrDaqBAVLan5yTn233ETR2EEJ0XE5cJn57/4PPlqVKhSBRHgahsBwZKznt256YOQohOq9ib9IuVafj9zxyuS9J6FIjKlv+yfLottQ4JIcona2hFUFcrbovSARSIynTpW8l5j67c1EEI0Vk0zlB1KBCV6Wx4+bRpLcDAkLtaCCE6h8JQtSgQVaXHaq4rIIToEApD1aNAVJbUS5LzJtbc1EEI0Tm7f3tFYagGFIjKUvG+hwZ0vQNCiPL4NDWFs4MxAApDVaJvblXwC+O6AkKIDqnvJcBfuxpg655MLJnrTGGoIhSIylCcJznv1oGbOgghOoMxBl6FayDX9xLgy49dOKxI93F+yDQyMhJeXl4QCARo06YNzpw589b1Y2Ji0LJlS5iZmcHZ2Rnjx49HRkaGmqqtwqFR3L4/IUSnxMRlYvj0xygupqvPqBOngRgbG4vZs2dj4cKFuH79Ojp27IhevXohOTlZ5vpnz57FmDFjEBISgjt37mD37t24cuUKJk6cqObKK3kQVz5dl8YeEkKqr6w3aeyvrzDio0SUlFAoqgungbh8+XKEhIRg4sSJaNy4MVauXAl3d3esX79e5voXL16Ep6cnZs6cCS8vL3To0AFTpkzB1atXZa6vFsIiyXnfmdzUQQjRepWHVtjbGsGA8+N4+oOzXV1UVIT4+HgEBgZKLA8MDMT58+dlvsbf3x8pKSk4dOgQGGN4/vw59uzZg969e1f5PoWFhcjJyZF4KNWj3yTnefTbSwhRHI0z5B5n397p6ekQCoVwdHSUWO7o6Ii0tDSZr/H390dMTAyCg4PB5/Ph5OQEGxsbrFmzpsr3iYiIgLW1tfjh7u6u1M+BC0vKpx2aKHfbhBC9QGGoGThvzvAq3Um+cs+qiu7evYuZM2di0aJFiI+Px5EjR5CYmIjQ0NAqtx8eHo7s7Gzx48mTJ0qtHy9vlk83G6ncbRNCdB6FoebgbNiFnZ0dDA0NpVqDL168kGo1lomIiEBAQAA+/vhjAECLFi1gbm6Ojh074quvvoKzs7PUa0xMTGBiYqL8DwAAuSmS83bNVPM+hBCdRGGoWThrIfL5fLRp0wbHjx+XWH78+HH4+/vLfE1eXh4MKp1hNjQsvYA2JzfH3FopAKto2RJCSGVCIcPa6JcUhhqE04H5YWFhGD16NHx9feHn54cff/wRycnJ4kOg4eHhePr0KbZt2wYA6Nu3LyZNmoT169ejR48eSE1NxezZs9G2bVu4uHAwYLUwu3zaZ7z6358QorUMDXk4vK0eAkc+gG8LMwpDDcBpIAYHByMjIwNffPEFUlNT0axZMxw6dAgeHh4AgNTUVIkxiePGjUNubi7Wrl2LuXPnwsbGBl27dsV3332n/uKLXkvOew9Ufw2EEK1mY22EE7ENYGZqQGGoAXiMk2ON3MnJyYG1tTWys7NhZVWDO0zH9QMe/Vo6bWYL9I1WSn2EEN312x/ZCPA1Ry0bumpmTeS8egnrFiE1/x6vROGfSmFhIS5fvozHjx8jLy8P9vb28PHxgZeXl9KK0gplYQgAdt7c1UEI0QplHWhaNTXFH9sbUChqILl/IufPn8eaNWuwf/9+FBUVwcbGBqampsjMzERhYSHq1q2LyZMnIzQ0FJaWlqqsmXvCYsn59+ZwUwchRCtU7E167VY+ftqRjvlTnbgui1QiVy/T/v37Y/DgwXB1dcXRo0eRm5uLjIwMpKSkIC8vDwkJCfj000/x559/wtvbW6rnqM65+IXkvJEpN3UQQjSerKEV86bIHlpGuCVXCzEwMBC7d+8Gn8+X+XzdunVRt25djB07Fnfu3MGzZ8+UWqTGuRFZPu3YlLs6CCEajcYZahe5AnH69Olyb7Bp06Zo2lSHQ4KJgILM8vl287mrhRCisSgMtY/CA/PHjRuH06dPq6IW7ZCwT3LetBY3dRBCNBaFoXZSOBBzc3MRGBiIBg0a4JtvvsHTp09VUZfmerC/fNpExzsPEUIUdvpiLoWhllI4EPfu3YunT59ixowZ2L17Nzw9PdGrVy/s2bMHxcXF796Atnt1v3y61QTu6iCEaCR/XwsM6V165IjCULtU61qmtWvXxqxZs3D9+nVcvnwZ9evXx+jRo+Hi4oI5c+YgISFB2XVqjrQr5dMOLbirgxCikYyMePhltSeifvCgMNQyNbq4d2pqKo4dO4Zjx47B0NAQQUFBuHPnDpo0aYIVK1Yoq0bNUZwnOW9qx00dhBCNkpcvkpg3MuJhfHBtCkMto3AgFhcXY+/evejTpw88PDywe/duzJkzB6mpqdi6dSuOHTuGn3/+GV988cW7N6ZtHhyQnKe7WxCi92LiMtGo8x3cf1TAdSmkhhS+dpCzszNEIhGGDx+Oy5cvo1WrVlLr9OjRAzY2NkooT8Nc/aF8uo4fd3UQQjRCxd6knYckIP5QIzg7GnNdFqkmhQNxxYoVGDJkCAQCQZXr1KpVC4mJiTUqTCNlPSif9h7EXR2EEM5VHloxoIc1HO3p+qTaTOFDpidPnpTZm/TNmzeYMEHHe10W5ZRP16rPXR2EEE7ROEPdpHAgbt26Ffn5+VLL8/PzxTfy1UmFOZLzBobc1EEI4RSFoe6Su32fk5MDxhgYY8jNzZU4ZCoUCnHo0CE4ODiopEiNcHsz1xUQQjhGYajb5A5EGxsb8Hg88Hg8eHtL3/+Px+NhyZIlSi1Oo/wbWz7t9h53dRBCOEFhqPvkDsSTJ0+CMYauXbti7969sLW1FT/H5/Ph4eEBFxcXlRSpEVIvlU836M9dHYQQTiQmF1IY6ji5A7FTp04AgMTERNSpUwc8fRqDV1JpfJF9M27qIIRw5tNZzhAxIO1FMYWhjpIrEG/evIlmzZrBwMAA2dnZuHXrVpXrtmihg5czy7gjOc+r0QV+CCFaatFsZzDG9KtBoEfkCsRWrVohLS0NDg4OaNWqFXg8HhhjUuvxeDwIhUKlF8m59AqBaNeAuzoIIWqzPS4TjvZG+KCDlcRyCkPdJVcgJiYmwt7eXjytdy59XT5N4w8J0XllHWj4xjz8Fl1PKhSJbpIrED08PMTT9vb2MDMzU1lBGqniLZ8cWnJXByFE5Sr2Ji0oZPjtjxwKRD2h8MkwBwcHjBo1CkePHoVIJHr3C3SNa3uuKyCEqIisoRXLFrlyWxRRG4UDcdu2bSgsLMTAgQPh4uKCWbNm4cqVK+9+obbKz5Ccpw41hOgkGmdIFP52HzRoEHbv3o3nz58jIiIC9+7dg7+/P7y9vXXzlk8Pfy2fNqSr2BOiiygMCVCDGwRbWlpi/PjxOHbsGP7++2+Ym5vr5pVqnseXTzu14qwMQohqUBiSMtUOxIKCAuzatQsDBgxA69atkZGRgXnz5imzNs1wt8IFy13okm2E6JKnqUWYMC+JwpAAqEYgHjt2DGPHjoWjoyNCQ0Ph4OCAo0ePIjk5Gd99950qauRWxVs+Obfjrg5CiNK5OvMRs9oThoYUhqQaNwgeMGAAevfuja1bt6J3794wNtb182o8AP9dhMC0FqeVEEKUb3DvWrjkbgKfZqYUhnpO4UBMS0uDlZWejMlhIojDkBCiEx4kFqC+l0BiWZsWeja2msgk1yHTnJwcqfmqHjrl2cXyaUsn7uoghChFTFwmGnW5i/XbXnJdCtFAcrUQa9WqhdTUVDg4OIjvi1hZ2QVvdepaprejyqeZHl6EgBAdUrE36bSFT9DUW4D321tyXRbRIHIF4okTJ8T3Pzx58qRKC9IowsLy6Xo9uKuDEFIjsoZWdGhrwW1RROPIFYhl90IEAC8vL7i7u0u1EhljePLkiXKr41rFMYhuHbmrgxBSbTTOkMhL4WEXXl5eePlS+vh7ZmYmvLy8lFKUxsi8Vz5toicdiQjRIRSGRBEKB2JVN8d8/fo1BAKBjFdoKVGlc6HG1AuNEG1CYUgUJfewi7CwMAClN8f87LPPJG4BJRQKcenSJbRq1UrpBXImN5nrCggh1bTzAIUhUZzcgXj9+nUApS3EW7dugc/ni5/j8/lo2bKlbl26LflE+XQtj6rXI4RonAZeAlhZGiIrW0hhSOQmdyCW9S4dP348Vq1apfuD81MrjEG0rsNdHYQQhbVpYYY/tjdA7K+Z+DbclcKQyEXhK9Vs2bJFFXVonmfny6dd/bmrgxBSLW1amNEVaIhC5ArEQYMGITo6GlZWVhg0aNBb1923b59SCuNc5j/l07aNuKuDEPJOMXGZuHT9DVYtcZPZ6Y8QecgViNbW1uJfMmtra5UWpDEqXpnG1Ja7Ogghb1WxN6lQyLD2K+lx0oTIQ65ArHiYVG8OmVZEf1yEaKTKQysYK33QnyypDoXHIebn5yMvL088n5SUhJUrV+LYsWNKLYxTxXnvXocQwikaZ0iUTeFA7N+/P7ZtK72LfFZWFtq2bYtly5ahf//+WL9+vdIL5ETmv+XTZnbc1UEIkYnCkKiCwoF47do1dOxYel3PPXv2wMnJCUlJSdi2bRtWr16t9AI5kXKqfFpAFwAmRJNQGBJVUTgQ8/LyYGlZesuUY8eOYdCgQTAwMED79u2RlJSk9AI5kfWgfNrWm7s6CCESKAyJKikciPXr18f+/fvx5MkTHD16FIGBgQCAFy9e6M5g/YqHTO1bclcHIUSsqEiEr9ekURgSlVE4EBctWoR58+bB09MT7dq1g5+fH4DS1qKPj4/SC+SEuVP5tIUzd3UQQsT4fAP8sb0+GniZUBgSlVD4SjWDBw9Ghw4dkJqaipYty1tPH3zwAQYOHKjU4jiTUOHiAqa1uKuDECLBxYmPiwcawsbakMKQKJ3CgQgATk5OcHJykljWtm1bpRSkEUoqDLvg68hhYEK00PHTOejwngVMTcsPZtnWqtbXFiHvpPBv1ps3b/Dtt9/izz//xIsXLyASiSSef/TokdKK4wRjkvOGxtzUQYieK+tA0zXAEgc315MIRUJUQeFAnDhxIk6dOoXRo0fD2dlZ9y6RlJvCdQWE6L2KvUn/OJOLzbHpmDHOgeuyiI5TOBAPHz6M33//HQEBAaqoh3v56eXT1q7c1UGInpI1tGLaGHtuiyJ6QeFjELVq1YKtrQ5f7Lo4t3xaQB1qCFEnGmdIuKRwIH755ZdYtGiRxPVMdcrTs+XT5nSIhhB1oTAkXFP4kOmyZcvw8OFDODo6wtPTE8bGkp1Orl27prTiOJH3snyaR73ZCFEHCkOiCRT+xh8wYIAKytAgGXfKp53bcFcHIXri2KkcCkOiERQOxMWLF6uiDs2Rdrl82rIOd3UQoic6tLVAF39L/Hk2l8KQcKpaA3uysrKwadMmhIeHIzMzE0DpodKnT58qtThOGJmVT5vV5q4OQvSEmakBDkbVw5ov3SgMCacUbiHevHkT3bp1g7W1NR4/foxJkybB1tYWcXFx4ttAabU3qeXTRgLu6iBEhxUVicDnl/9/3MzUgMYZEs4p3EIMCwvDuHHjkJCQAIGgPDB69eqF06dPK7U4QojuiYnLhE+vf5D6vJjrUgiRoHAgXrlyBVOmTJFa7urqirS0NIULiIyMhJeXFwQCAdq0aYMzZ868df3CwkIsXLgQHh4eMDExQb169RAVFaXw+8pUUlg+bWxW9XqEkGop6016934BOg+9j8xXJVyXRIiYwodMBQIBcnJypJb/+++/sLdX7GoSsbGxmD17NiIjIxEQEICNGzeiV69euHv3LurUkd2hZejQoXj+/Dk2b96M+vXr48WLFygpUdIfVfbD8mkDQ+VskxACQHpoxQcBlrCxpr8zojkUbiH2798fX3zxBYqLSw938Hg8JCcnY8GCBfjwww8V2tby5csREhKCiRMnonHjxli5ciXc3d2xfv16mesfOXIEp06dwqFDh9CtWzd4enqibdu28Pf3V/RjyJZ+u3zaxFI52ySE0DhDohUUDsQffvgBL1++hIODA/Lz89GpUyfUr18flpaW+Prrr+XeTlFREeLj4xEYGCixPDAwEOfPn5f5moMHD8LX1xdLly6Fq6srvL29MW/ePOTn51f5PoWFhcjJyZF4VF1Uhcu2WbvL/VkIIVWjMCTaQuFDplZWVjh79ixOnDiBa9euQSQSoXXr1ujWrZtC20lPT4dQKISjo6PEckdHxyrPRT569Ahnz56FQCBAXFwc0tPTMW3aNGRmZlZ5HjEiIgJLliyRr6jnV8un7ZrK9xpCSJUoDIk2qfa1ybp27YquXbvWuIDKt49ijFV5SymRSAQej4eYmBhYW1sDKD3sOnjwYKxbtw6mpqZSrwkPD0dYWJh4PicnB+7uVbT+Xtwonxbo8AXMCVEDCkOibeQ+ZHrp0iUcPnxYYtm2bdvg5eUFBwcHTJ48GYWFhVW8WpqdnR0MDQ2lWoMvXryQajWWcXZ2hqurqzgMAaBx48ZgjCElRfZ9DE1MTGBlZSXxqNLrChcWsPaS+7MQQqRdvPaGwpBoFbkD8fPPP8fNmzfF87du3UJISAi6deuGBQsW4Ndff0VERITcb8zn89GmTRscP35cYvnx48er7CQTEBCAZ8+e4fXr1+Jl9+/fh4GBAdzc3OR+7yrlPimftnSp+fYI0WOrlrhh6mg7CkOiNeQOxBs3buCDDz4Qz+/cuRPt2rXDTz/9hLCwMKxevRq7du1S6M3DwsKwadMmREVF4d69e5gzZw6Sk5MRGhoKoPRw55gxY8TrjxgxArVr18b48eNx9+5dnD59Gh9//DEmTJgg83BpjdCwC0JqxMCAh7VfuVMYEq0h9znEV69eSRzKPHXqFHr27Cmef++99/DkyRNZL61ScHAwMjIy8MUXXyA1NRXNmjXDoUOH4OHhAQBITU1FcnKyeH0LCwscP34cH330EXx9fVG7dm0MHToUX331lULvK1Nhds23QYge23kgE951BWjdvPyiFhSERJvIHYiOjo5ITEyEu7s7ioqKcO3aNYnem7m5uVL3RpTHtGnTMG3aNJnPRUdHSy1r1KiR1GFWpcgpD14IrKtejxAipawDjbWlIf7Y0UAiFAnRFnIfMu3ZsycWLFiAM2fOIDw8HGZmZujYsaP4+Zs3b6JevXoqKVItKl7U21oJ5yMJ0RMVe5O+yhYi9tdXXJdESLXI3UL86quvMGjQIHTq1AkWFhbYunUr+Hy++PmoqCipQfZa5fWz8mlDussFIfKQNbQiYgF1SCPaSe5AtLe3x5kzZ5CdnQ0LCwsYGkp2Otm9ezcsLCyUXqDaCAvKp80VuyYrIfqIxhkSXaPwwPyKYwArsrXV8oHsSRXOS9rQGERC3obCkOgiuc4hhoaGyt2DNDY2FjExMTUqihNPz5VPm1ILkZCqUBgSXSVXC9He3h7NmjWDv78/+vXrB19fX7i4uEAgEODVq1e4e/cuzp49i507d8LV1RU//vijqutWvrzn5dM2Wtw5iBAVepBYgLFzKAyJbuIxxpg8K7548QKbN2/Gzp07cfv2bYnnLC0t0a1bN0yePFnjO9bk5OTA2toa2dnZkpdxW1bhD3roAaCK66kSou/WRb/EjM+eUBgSzuS8egnrFiHS3+M1JHcgVpSVlYWkpCTk5+fDzs4O9erVq/KC3JpGZiAyBiyvcPQ4+CA3xRGiJU5dyEXHdhYUhoQTqgrEat3twsbGBjY2NkorgnMlBe9ehxA99TS1CK7OfIllnfzoBtpE9yh8g2CdlP+yfNrKlbs6CNEwMXGZqNfxDvYeosH2RPdRIAKVBuXzq16PED1S1pu0sJBh2PREXLuVx3VJhKgUBSIAvLhWPm1Ch4IIqTy0YtJwO7RqquQ7yhCiYSgQASDjbvm0mR13dRCiAWicIdFX1QrEkpIS/PHHH9i4cSNyc3MBQOrGvVrl1f3yaYdWnJVBCNcoDIk+U7iXaVJSEnr27Ink5GQUFhaie/fusLS0xNKlS1FQUIANGzaook7VKsopn7Zy564OQjhEYUj0ncItxFmzZsHX1xevXr2SuEv9wIED8eeffyq1OLXJTiyfNtXya7ISUg3bKQwJUbyFePbsWZw7d07i1k8A4OHhgadPnyqtMLUqyCyfNqGbAxP9Y1/bCHxjHgoKGYUh0VsKB6JIJIJQKJRanpKSAktLLe2haeUJZD0oneZRPyOif7q/b4WDUfVw6EQOli1ypTAkeknhb//u3btj5cqV4nkej4fXr19j8eLFCAoKUmZt6lMWhnwtvp8jITXU/X0rrPjcjcKQ6C2FW4grVqxAly5d0KRJExQUFGDEiBFISEiAnZ0dduzYoYoaVavipVwNqHVI9ENMXCaSUorwyUdOXJdCiMZQOBBdXFxw48YN7Ny5E/Hx8RCJRAgJCcHIkSMlOtlojYIKl6QqyKl6PUJ0RMXepCIRw6eznLkuiRCNoHAgnj59Gv7+/hg/fjzGjx8vXl5SUoLTp0/j/fffV2qBKldS4XJUZrW5q4MQNag8tOLZ82IwxrTmbjWEqJLCxwi7dOmCzMxMqeXZ2dno0qWLUopSq4p3urB2464OQlSsqnGGFIaElFI4EKv632RGRgbMzc2VUpRaVbzThZEWHvIlRA406J6Qd5P7kOmgQYMAlPYqHTduHExMTMTPCYVC3Lx5E/7+/sqvUNWKKlxuLp9ucUN0D4UhIfKROxCtrUsHrDPGYGlpKdGBhs/no3379pg0aZLyK1S1NxVu/WRdh7s6CFEBCkNC5Cd3IG7ZsgUA4OnpiXnz5mnn4VFZsh6WTxvTIVOiO/LyRVgQ8ZTCkBA5KXwOcfHixboThgBgZFY+bWLDWRmEKJuZqQH+2NEATg5GFIaEyEHhYRcAsGfPHuzatQvJyckoKiqSeO7atWtVvEpDJf9RPk13uiA6pmE9AeJ/bwQnB2MKQ0LeQeEW4urVqzF+/Hg4ODjg+vXraNu2LWrXro1Hjx6hV69eqqhRtSre6YIu7E203JlLr1FSwiSWuTjxKQwJkYPCgRgZGYkff/wRa9euBZ/Px/z583H8+HHMnDkT2dnZqqhRtYQVWrgWLtzVQUgNxcRlovPQ+xgz+7FUKBJC3k3hQExOThYPrzA1NUVubi4AYPTo0dp5LdPXKeXTfC29WwfRexV7k+448Aq/7JO+eAYh5O0UDkQnJydkZGQAKL0H4sWLFwEAiYmJYEzL/1dKV+wgWkjW0Ioxg+lG14QoSuFA7Nq1K3799VcAQEhICObMmYPu3bsjODgYAwcOVHqBKqXtAU70Ho0zJER5FO5l+uOPP0L0319faGgobG1tcfbsWfTt2xehoaFKL1ClCiocVjKj/1ET7UJhSIhyKRyIBgYGMKhw38ChQ4di6NChAICnT5/C1dVVedWpWmFW+bSBMWdlEKIoCkNClE8pd8RNS0vDRx99hPr16ytjc+pTWKFXLN+CuzoIUcCvx7MoDAlRAbkDMSsrCyNHjoS9vT1cXFywevVqiEQiLFq0CHXr1sXFixcRFRWlylqVr+KdLkxrcVcHIQoI8LVAyyallxmkMCREeeQ+ZPrJJ5/g9OnTGDt2LI4cOYI5c+bgyJEjKCgowOHDh9GpUydV1qkaFe+FCPpCIdrBtpYR/tjeAJt2pmPeFEcKQ0KURO5A/P3337FlyxZ069YN06ZNQ/369eHt7Y2VK1eqsDwVExaWT1vSoHyiuYRCBkPD8uCzrWWE+VOdOKyIEN0j9yHTZ8+eoUmTJgCAunXrQiAQYOLEiSorTC1yn5RPm1hxVwchbxETl4n3B99Hdo6Q61II0WlyB6JIJIKxcXlPTENDQ+2/60XFFqKwqOr1COFIWW/S81ffIHBkAt7kUSgSoipyHzJljGHcuHEwMTEBABQUFCA0NFQqFPft26fcClXp5c3yaQtn7uogRIbKQyvaNDeDqUApHcMJITLIHYhjx46VmB81apTSi1E78wohaEzDLojmoHGGhKif3IG4ZcsWVdbBDWGFXqYCGnZBNAOFISHc0O/jL8/jy6cN+NzVQch/KAwJ4Y5+B2JhTvm0kQl3dRACCkNCuKbfgZjzuHyaDpkSDjHGcOBYFoUhIRxS+OLeOsXKE3j1b+k0tRAJh3g8HmJWe0EoTISjnRGFISEc0O9ALAtDYy0fT0l0grExDzvXecHQEBSGhHCgWodMf/75ZwQEBMDFxQVJSUkAgJUrV+LAgQNKLU7lDP/rSFNxgD4harLn91d4+Fjyd8/YmEdhSAhHFA7E9evXIywsDEFBQcjKyoJQWHrlDBsbG+26rilj5VenYXT1D6JeMXGZCJ6WiM5D70uFIiGEGwoH4po1a/DTTz9h4cKFMDQ0FC/39fXFrVu3lFqcSlW8VFstL+7qIHqnYm/SlNRiRO/O4LokQgiqEYiJiYnw8fGRWm5iYoI3b94opSi1KMkrn856UvV6hCiRrKEVS+bSZQMJ0QQKB6KXlxdu3Lghtfzw4cPiu2FoBWFx+XQtT87KIPqDxhkSotkU7mX68ccfY/r06SgoKABjDJcvX8aOHTsQERGBTZs2qaJG1ah4yNTYjLs6iF6gMCRE8ykciOPHj0dJSQnmz5+PvLw8jBgxAq6urli1ahWGDRumihpVo2LPUkO6bBtRHQpDQrRDtcYhTpo0CZMmTUJ6ejpEIhEcHByUXZfqFb8uny7SonOfRKv8fTePwpAQLaHwOcQlS5bg4cOHAAA7OzvtDENAcqiFgX5fwY6oTovGpvhkhhMACkNCNJ3CSbB37154e3ujffv2WLt2LV6+fKmKulSvpMIhUzM77uogOo3H4+GLec44GFWXwpAQDadwIN68eRM3b95E165dsXz5cri6uiIoKAjbt29HXl7euzegKd48K582MOauDqJzMl6VSMzzeDz07W5DYUiIhqvWscKmTZvim2++waNHj3Dy5El4eXlh9uzZcHJyUnZ9KsTKJ4teV70aIQqIicuEl/9tnLqQy3UphBAF1fjkmbm5OUxNTcHn81FcXPzuF2iKrIfl07b1uKuD6Iyy3qS5r0UIGvsQ/z4s4LokQogCqhWIiYmJ+Prrr9GkSRP4+vri2rVr+Pzzz5GWlqbwtiIjI+Hl5QWBQIA2bdrgzJkzcr3u3LlzMDIyQqtWrRR+TwCAQcWhFoZVrkaIPCoPrRg72BYNvOiWYoRoE4WHXfj5+eHy5cto3rw5xo8fLx6HWB2xsbGYPXs2IiMjERAQgI0bN6JXr164e/cu6tSpU+XrsrOzMWbMGHzwwQd4/vx5td5b4tJtZvbV2wYhoHGGhOgKhVuIXbp0wc2bN3Hjxg18/PHH1Q5DAFi+fDlCQkIwceJENG7cGCtXroS7uzvWr1//1tdNmTIFI0aMgJ+fX7XfGy+ulU/zLaq/HaLXKAwJ0R0KB+I333yDpk2b1viNi4qKEB8fj8DAQInlgYGBOH/+fJWv27JlCx4+fIjFixfL9T6FhYXIycmReAAA8l6Ur2RkqnD9hFAYEqJb5DpkGhYWhi+//BLm5uYICwt767rLly+X643T09MhFArh6OgosdzR0bHKc5EJCQlYsGABzpw5AyMj+Y72RkREYMmSJdJPmLsAuTdLpwW15NoWIWUoDAnRPXKlyvXr18U9SK9fv67UAng8yS8QxpjUMgAQCoUYMWIElixZAm9vb7m3Hx4eLhHiOTk5cHd3B0ryy1cyNle8cKLXKuYehSEhukGuQDx58qTM6Zqws7ODoaGhVGvwxYsXUq1GAMjNzcXVq1dx/fp1zJgxAwAgEonAGIORkRGOHTuGrl27Sr3OxMQEJiYyevulnAIE/00bCaSfJ+Qthg+wBQCcj3+DVUvcKAwJ0QEKn0OcMGECcnOlBx2/efMGEyZMkHs7fD4fbdq0wfHjxyWWHz9+HP7+/lLrW1lZ4datW7hx44b4ERoaioYNG+LGjRto166dYh+kYggaVOsa50TPDR9gizVfUsuQEF2hcCBu3boV+fn5Usvz8/Oxbds2hbYVFhaGTZs2ISoqCvfu3cOcOXOQnJyM0NBQAKWHO8eMGVNaqIEBmjVrJvFwcHCAQCBAs2bNYG6u4GHPkgqDpmUcoiWkopi4TGzemc51GYQQFZK7aZSTkwPGGBhjyM3NhUBQ3sISCoU4dOiQwne+CA4ORkZGBr744gukpqaiWbNmOHToEDw8PAAAqampSE5OVmibcjOpBeCVarZNdErlDjQhw+hi8IToIh5jjL17tdIWmqzOLuIN8XhYsmQJFi5cqLTiVCEnJwfW1tbI/gqwEgAwtwf6bOa6LKKhKofhtDF2WPd11ReNIISoXs6rl7BuEYLs7GxYWVkpbbtytxBPnjwJxhi6du2KvXv3wtbWVvwcn8+Hh4cHXFxclFaY2tDhUlIFWUMr1nzpzm1RhBCVkTsQO3XqBKD0OqZ16tR5a2tRq7x+8e51iN6hcYaE6B+5AvHmzZto1qwZDAwMkJ2djVu3blW5bosWLZRWnFrUpjtdEEkUhoToJ7kCsVWrVkhLS4ODgwNatWoFHo8HWaceeTwehEKh0otUKUMag0jKURgSor/kCsTExETY29uLp3UKjUEk/8nKLsFHnz2hMCRET8mVBmXDICpP64Scp1xXQDSEjbURDv9cH4EjEzBygC2FISF6ploD83///Xfx/Pz582FjYwN/f38kJSUptTi1sG/EdQVEg7TzMce1Q40pDAnRQ9W6/ZOpaentki5cuIC1a9di6dKlsLOzw5w5c5ReoMoJi7iugHDo6t9vpM6H1/M0oTAkRA8pHIhPnjxB/fr1AQD79+/H4MGDMXnyZERERODMmTNKL1DleIZcV0A4EhOXiXb9/kXYkqcyO4kRQvSLwoFoYWGBjIwMAMCxY8fQrVs3AIBAIJB5jVONZ27PdQWEAxV7k67c/AL7DmdxXRIhhGMKd7Hs3r07Jk6cCB8fH9y/fx+9e/cGANy5cweenp7Krk/1qJep3pE1tGJgTxtOayKEcE/hFuK6devg5+eHly9fYu/evahduzYAID4+HsOHD1d6gSpXUsh1BUSNaJwhIaQqCjePbGxssHbtWqnlS5YsUUpBaiewffc6RCdQGBJC3qZaxwuzsrKwefNm3Lt3DzweD40bN0ZISAisra2VXZ/qmVhyXQFRAwpDQsi7KHzI9OrVq6hXrx5WrFiBzMxMpKenY8WKFahXrx6uXbumihoJqZF9h19RGBJC3knhFuKcOXPQr18//PTTTzAyKn15SUkJJk6ciNmzZ+P06dNKL1KlDE25roCo2HstzeHpxsej5CIKQ0JIlRQOxKtXr0qEIQAYGRlh/vz58PX1VWpxamGivJtLEs3k7sLHX7u9ERWbgc9mOVEYEkJkUviQqZWVFZKTk6WWP3nyBJaWWng+zsiE6wqIClQeaO/uwsfiOc4UhoSQKikciMHBwQgJCUFsbCyePHmClJQU7Ny5ExMnTtTOYRc8Goeoa2LiMtFvwkMUFIi4LoUQokUUToMffvgBPB4PY8aMQUlJCQDA2NgYU6dOxbfffqv0AlWOb8F1BUSJKvYmHTT5EfZvqgs+X+H/9xFC9JDCgcjn87Fq1SpERETg4cOHYIyhfv36MDMzU0V9qmdgzHUFREkqD63wdOPDyIgOkRJC5CP3f53z8vIwffp0uLq6wsHBARMnToSzszNatGihvWEI0KXbdASNMySE1JTcgbh48WJER0ejd+/eGDZsGI4fP46pU6eqsjb1MKQWorajMCSEKIPczaN9+/Zh8+bNGDZsGABg1KhRCAgIgFAohKGhFt9CyZDPdQWkBigMCSHKIncL8cmTJ+jYsaN4vm3btjAyMsKzZ89UUpja0DlErUVhSAhRJrkDUSgUgs+XbE0ZGRmJe5pqJZ4hwKMvT20kEjFExWZQGBJClEbuQ6aMMYwbNw4mJuUD2QsKChAaGgpzc3Pxsn379im3QlViQq4rINVkYMDDwai6CBrzEE29BRSGhJAakzsQx44dK7Vs1KhRSi1G7ej8oVYzNzPEkZ/rw8SER2FICKkxuQNxy5YtqqyDGxSIWuXA0Sy0b20OR/vy876mpjTonhCiHPr9bcLT74+vTWLiMjFo8iN0HZaA5y+LuS6HEKKD9DsRqEONVqjYm/Tu/QJs2pHOdUmEEB2k34FIV6nReLKGVoTPcOK2KEKITtLvQMzL4LoC8hY0zpAQok76HYgWDlxXQKpAYUgIUbdqBeLPP/+MgIAAuLi4ICkpCQCwcuVKHDhwQKnFqRxdpUYjURgSQrigcCCuX78eYWFhCAoKQlZWFoTC0sHtNjY2WLlypbLrUy3qZapxLsS/pjAkhHBC4URYs2YNfvrpJyxcuFDiot6+vr64deuWUotTOZ4WX5RcR7XzMcf4obUBUBgSQtRL4W6WiYmJ8PHxkVpuYmKCN2/eKKUo9RFxXQCpxMCAhx+/q4Ou/pYY1r8WhSEhRG0UbiF6eXnhxo0bUssPHz6MJk2aKKMm9aFziBoh97XkNWUNDHgYMdCWwpAQolYKtxA//vhjTJ8+HQUFBWCM4fLly9ixYwciIiKwadMmVdSoOjQOkXMxcZmY+2UKjsXUR4vGZlyXQwjRYwonwvjx41FSUoL58+cjLy8PI0aMgKurK1atWiW+ebDWKCnkugK9VrE3adfgBFw73Bh1XOn6soQQblSriTRp0iRMmjQJ6enpEIlEcHDQ0vF8eXQJMK5UHloxtE8tuDnTIWxCCHdqdMzQzs5OWXVww7Y+1xXoJRpnSAjRRAoHopeXF3hvuSj2o0ePalSQWtHFvdWOwpAQoqkUDsTZs2dLzBcXF+P69es4cuQIPv74Y2XVpR7UqUatKAwJIZpM4USYNWuWzOXr1q3D1atXa1yQWlEgqg2FISFE0ynt2mW9evXC3r17lbU59SjJ47oCvfEyo4TCkBCi0ZTWRNqzZw9sbW2VtTn1KKJAVJfZE0t7It9/VEBhSAjRSAoHoo+Pj0SnGsYY0tLS8PLlS0RGRiq1OJWjXqZqNXuiAxhjb+2URQghXFE4EAcMGCAxb2BgAHt7e3Tu3BmNGjVSVl1qwrguQGfFxGXC0twA/QJtJJZTGBJCNJVCgVhSUgJPT0/06NEDTk5OqqpJfQpyuK5AJ5V1oDE05GHPBi+pUCSEEE2kUKcaIyMjTJ06FYWFOnLJM2sPrivQORV7kxYXM/x5LpfrkgghRC4K9zJt164drl+/ropa1I+GXSiVrKEVKxa7cVsUIYTISeFEmDZtGubOnYuUlBS0adMG5ubmEs+3aNFCacWpHJ3PUhoaZ0gI0XZyB+KECROwcuVKBAcHAwBmzpwpfo7H44l7DwqFwqo2QXQUhSEhRBfIHYhbt27Ft99+i8TERFXWo150yLTGKAwJIbpC7kRgrHSIgoeHDnVEoUCskecvizH5f8kUhoQQnaBQpxqdG0NWTFeqqQlHe2Ps2egFPp9HYUgI0XoKNZG8vb3fGYqZmZk1KkitTGtzXYHW69XFGld/a4SmDQUUhoQQraZQIC5ZsgTW1taqqkX96JCpwu4l5KNxA1OJZc0bm1axNiGEaA+FEmHYsGFwcHBQVS3qxzPkugKtUtaBJmKBC+ZP1YErFRFCSAVyn0PUufOHAGBAgSivir1J//fNMxw7RZe9I4ToFrkDsayXqU6hFqJcZA2t6NbRktuiCCFEyeQORJFIpJLDpZGRkfDy8oJAIECbNm1w5syZKtfdt28funfvDnt7e1hZWcHPzw9Hjx6t/pvzlHZ/ZJ1F4wwJIfqC00SIjY3F7NmzsXDhQly/fh0dO3ZEr169kJycLHP906dPo3v37jh06BDi4+PRpUsX9O3bt/rXVhWV1KB63UdhSAjRJzzG4bHQdu3aoXXr1li/fr14WePGjTFgwABERETItY2mTZsiODgYixYtkmv9nJwcWFtbI/srwCroG8C+WbVq13UUhoQQTZXz6iWsW4QgOzsbVlZWStsuZy3EoqIixMfHIzAwUGJ5YGAgzp8/L9c2RCIRcnNzYWtrW+U6hYWFyMnJkXiI0TlEmXb9+orCkBCidzgLxPT0dAiFQjg6Okosd3R0RFpamlzbWLZsGd68eYOhQ4dWuU5ERASsra3FD3d39/InqZepTE29BbCzLR2RQ2FICNEXnPcqqTyco+yuGe+yY8cOfP7554iNjX1rZ5/w8HBkZ2eLH0+ePKnw5sbVrluXNW1oihM7GyB8uiOFISFEb3B2qRY7OzsYGhpKtQZfvHgh1WqsLDY2FiEhIdi9eze6dev21nVNTExgYmIi+0lqIVapaUNTfLPAlesyCCFEbThrIfL5fLRp0wbHjx+XWH78+HH4+/tX+bodO3Zg3Lhx2L59O3r37l2zIigQAZR2oJkwNwlCoQ6ONSWEEDlxejHPsLAwjB49Gr6+vvDz88OPP/6I5ORkhIaGAig93Pn06VNs27YNQGkYjhkzBqtWrUL79u3FrUtTU9NqXmOVDgVW7E0qFDFE/eABQ0PaL4QQ/cNpIAYHByMjIwNffPEFUlNT0axZMxw6dEh8z8XU1FSJMYkbN25ESUkJpk+fjunTp4uXjx07FtHR0YoXYKjf5xArD60wNzWALl6hjxBC5MHpOEQuSIxDHLpFb28BReMMCSHaSufGIWoEPR2HSGFICCHS9DwQ9e/jUxgSQohs+pcIFelZIFIYEkJI1fQrESrTo0AsKWFY9uNzCkNCCKmC/iSCLHp0DtHIiIdjMQ3QvJGAwpAQQmTgdNgF5/SohQgAdrZGOLuvISzMDSgMCSGkEv1KhMoMdPv/A4dPZiP3tVBimZWlIYUhIYTIoN+BqMOj0GPiMtFn3EP0HP1AKhQJIYRI099A1PEwLOtNev7qG2zakc51SYQQovH0NxB19DqmsoZWzAqp+vZYhBBCSulvIOpgC5HGGRJCSPXpcSDq1kenMCSEkJrRrVRQhA61ECkMCSGk5vQ3EHXkHOKJc7kUhoQQogT6G4g6csi0w3vm6N219ObIFIaEEFJ9uj0y/a10IzT4fAPs2eiF6N2ZmDi8NoUhIYRUk240k6qjOI/rCqqtoEAkMc/nG2DySDsKQ0IIqQH9DUQtFROXiaYf3MXjJ4Vcl0IIITpFfwORb8l1BQor6036KLkInYcm4GVGMdclEUKIztDfQNSyc4iVh1YEdbFC7Vp6fAqYEEKUTI8DUXvQOENCCFE9/Q1ELckSCkNCCFEP/Q1Exriu4J0oDAkhRH30NxA1fGA+hSEhhKiXZqeCSml2sNz+N5/CkBBC1Ii6KWqob/7nApEIyH0tpDAkhBA10N9A1PB84fF4+DbcRTxNCCFEtfT4kKlm2bE/E+euvJZYxuPxKAwJIURN9DgQNSdoYuIyMWrWY/Qc/UAqFAkhhKiHHgeiZgy7qNib9PUbEfYdzuK6JEII0Ut6HIjctxBlDa34/lNXbosihBA9pceByG0LkcYZEkKIZtHfQOSwswqFISGEaB79DUSOUBgSQohm0t9xiBxISinEuDAKQ23FGFDCDCAU8aAJ56AJ0U0MhgYMRjyR2g/k6XEgqv8LzcPNBJu/98C4sCSEjqIw1CZFQgOkvrFCntAUFIaEqBqDmWE+nM1zwDcUqe1d9TgQuTFmcG008DJBOx9zCkMtIWJAYo4dDPkWcHGsBb6xEUUiISrCABQVl+Bl+isk5vDRwOYF1PVVSYGoYkkphfBwM5FY5tfGgqNqSHUUCQ0hghHcnexgZmry7hcQQmrEVMCHsZEhkpILUCQ0hMBIqJb31eNONar/L0dMXCbqd7yDbXsyVP5eRJVKf1cM6DJ6hKhN+d+b+v7u9DcQVbyPy3qTlpQA48KScCGeLslGCCGaTH8DUYUD8ysPrQgdZYd2PuYqez9CCCE1p8eBqJomIo0zJEQ3ZGRkwsGtAR4/Tua6FJ2zNvJH9Bs0nOsypFAgKhGFIdE04yZOA8+kFngmtWBkZoc69Zth6owwvHqVJbXu+QuXENRvCGo5ekJg5YTmrf2xbMVaCIXSHRpO/nUGQf2GoLZzXZjZuKBJy/aYO/9TPH36TA2fSj0ilq5A39494elZh+tSVObU6XNo074zBFZOqNuwFTb8GPXO1/x54hT8OwXCsrY7nD0a4X+fLEZJSYn4+c+//Fb8O1fxYV6r/DrNk0LG4kr8dZw9d0Eln6u69DgQlYvCkGiqnoEfIDXpHzy+/zc2rV+NXw8dxbSZcyXWiTvwGzp16wM3V1ecPPYr/rl5GbNmhOLr75Zh2KgQMFZ+imHjT1vQrdcAODk5Yu/Obbh74yI2rF2G7JwcLFu5Tm2fq6ioSGXbzs/Px+bonzFx/OgabUeVNdZUYmISgvoPRccAP1y/dAqf/C8MM8MWYG/cwSpfc/PWbQT1H4qegd1w/dIp7Px5Mw7+dgQLFi4RrzNvzgykJv0j8WjSuBGGfNhfvI6JiQlGBA/GmsifVPoZFUWBqAQ79lMYEs1lYmICJydHuLm5IrB7VwQPHohjf5wUP//mzRtMmjoL/fr0wo/rV6JVy+bw9KyDiRPGYOumSOzZdwC79sQBAFJSnmJm2ALMnD4FUT+uRedOHeDpWQfvdwzApg2rsWjh/CrryMrKxuSps+Ho7g2BlROa+fjht9+PAChtVbR6r6PE+itXr4endwvx/LiJ0zBg8EhELF0OF8/G8G7mi/BPl6B9x+5S79WiTQAWfxEhnt+yNQaNW7SDwMoJjZq3ReSGTW/dZ4eP/gEjIyP4tW8rXiYUChEy5SN4ebeEqbUzGjZ7D6vWbJB4nawaAeDp02cIHjkBtRw9Udu5Lvp/OELiUOyVq9fQvddA2LnUg7V9HXTq1hvXrv/91hprasNPUajj7oaVyyLQuHFDTJwwBhPGjsQPK9ZW+Zqdu/ahRfOmWLRwPurXr4tO7wcg4stFWLdhE3JzcwEAFhYWcHJyFD+ev3iBu/f+Qci4URLb6tenF/Yf/B35+fkq/ZyK0N9xiErsQl/HlQ8zUwO8fiOiMNQnsV2ANy/U/77mDkDwyXevJ8OjR49x5NifMDY2Fi879sdJZGRkYt6cGVLr9+3TC94N6mNH7F4EDxmE3XsPoKioCPPnzpS5fRsba5nLRSIRevUbgtzcXPyyZSPq1fXC3Xv/wNDQUKH6/zx5GlZWljh+aJ+41frt9yvx8GEi6tXzAgDcuXsPt27fxZ4dWwEAP23eisVffou1K5fCp2ULXP/7JiZNnQVzc3OMHS37PNbpM+fh27qV1Gdwc3XBru1bYFe7Ns5fvITJ0+bA2dkRQwcPrLLGvLw8dAnsh44d/HD6z99hZGiEr779AT37DsbN+LPg8/nIzX2NsaOHYfXybwEAy1atQ1D/oUi4cxWWlpYya4zZsQtTpoe9dX9tXLccI4cPlfnchUtXENiti8SyHoEfYHP0LyguLpb4HSlTWFgEgUByLK6pqQAFBQWIv/Y3OnfqIPWaTVE/w7tBfXTs4C+x3LdNKxQXF+PylWvo9H7AWz+HuuhvICpRwHsWOPJzfew7nIXvP3WlMNQXb14AbzT/nNlvh47CwtYNQqEQBQUFAIDlS78WP38/4QEAoHGjhjJf36hhA9xPeAgASHjwEFZWlnB2dlKohj/+/AuXr8Tj3t+X4O1dHwBQt66noh8F5uZm2LRhNfh8vnhZi+ZNsT12Dz775GMAQMyO3XjPt7X4fb6M+B7LvvsSgwb0BQB4eXng7r1/sXHTlioD8XFSMlxcnCWWGRsbY8micPG8l5cHzl+4jF179ksEYuUao6J/gYGBATZtWA3ef/8R3/LTOtg4eOKvU2cR2L0runZ5X+K9Nq5bgVqOXjh1+hz69O4ps8Z+fXqh3Xu+b91fjo72VT6XlvYCjoGSzzs62KOkpATp6Rkyf8Y9unfFyjXrsSN2D4YOHoi0tOf46tsfAACpaWlS6xcWFiJm524smDdb6jlzc3PY2FjjcVIyOoECkWPKDa2A9ywQ8B5dgUavmDtoxft26dQR69csQ15+HjZF/Yz7CQ/x0fTJUutVPE9YeXnZF3nFaUXc+PsW3NxcxCFVXc2bNpEIQwAYOWwIorbG4LNPPgZjDDti92L2R1MBAC9fpuPJk6cImTITk6bOFr+mpKQE1tZWVb5Pfn4BBCbSVyXa8GMUNm35GUnJT5CfX4CioiK0atn8rTXGX7uBBw8fwbK2u8R6BQUFePgoEQDw4sVLLFryDU78dQbPX7yAUChCXl4ekp+kVFmjpaVlla1HeVX+WZb9DlT1Mw7s3hXfR3yB0BlhGD0+FCYmJvjsk3k4e+6izNb+vv2/Ijf3NcaMGiZze6ampsjLy6vRZ1AmPQ7E6o9DjInLxO1/8/HN/1yq9eVAdEQ1D1uqm7m5GerXrwsAWL3iO3QJ7IslX32HLz9fCADwblAaUvf++Rf+fu2kXv/Pvwlo0riheN3s7BykpqYp1Eo0NTV96/MGBgZSgVxcXCzzs1Q2YthgLPh0Ca5d/xv5+fl4kvIUw4YOAlB6mBMAflq/Uqo19bbDtXZ2tniVlSWxbNeeOMz5eCGWffcl/Nq3haWFBb5fvhqXrsS/tUaRSIQ2rVshJvpHqfext68NoPTc48v0DKz84Rt4eLjDhG8Cv06BKCqS3gdlanrI1MnJAWlpkof8X7xMh5GREWrXtq1ym2Gzp2POrGlITU1DrVo2ePw4GeGffgEvTw+pdTdF/Yw+QT3g5OQoc1uZma9gb2/31s+gTnociNULsoq9SUUi4NtwCkWiXRYv/B969RuCqZMnwMXFGYHdusDWthaWrVwnFYgHfz2EhAcP8eXnnwAABg/qhwWfLsHSZaux4odvpLadlZUt8zxii+ZNkZLyDPfvP5DZSrS3q4205y8kWqA3bt6S6/O4ubni/Y7+iNmxG/n5+ejWtTMcHUtb0Y6ODnB1dcGjxKQqg0EWn5Yt8MuOXRLLzpy9AP/2bTEtdKJ42cNHj9+5rdY+LRG7Jw4ODnawspLdKj1z7iIiV3+PoF6BAIAnT1KQnv72Sz7W9JCpX7v38OvvRyWWHTt+Ar5tfGSeP6yIx+OJDynv2LUX7u6uaO3TUmKdxMQknDx1Bgf3bpe5jYcPE1FQUACfli1kPs8F6mWqgMpDK3Jfq+eCs4QoU+dOHdC0SSN8891yAKXncjauW4EDvx7C5KmzcfPWbTx+nIzNW37GuEnTMXhQf/E5Mnd3N6z4/musWrsBIVM+wqnT55CUlIxz5y9iyrTZ+PKb72W+Z6f3A/B+R398OGwMjv9xEomJSTh85DiOHP2jtKb3O+Dly3QsXbYKDx8mYt36n3D4v+fkMXLYEOzcvQ+79x3AqBFDJJ77/NP/IWLpCqxaswH37z/Ardt3sGVrDJa/ZYhIj8CuuHP3H4nxmvXr1cXVa9dx9NifuH//AT77/Gtcib/27tqGD4Fd7droP3gkzpw9j8TEJJw6fQ6zwhYgJeXpf9v2ws8xu3Dv3r+4dPkqRo6b/M5WtaWlJerXr/vWx9sOqYZOmoCk5CcI+3gh7t37F1HRv2Bz9C8SnaviDvyGRs3bSrzu+2Wrcev2Hdy5ew9ffvM9vv1+JVYv+06qxR219Rc4OzuhV0/pXsAAcObcBdT18hR3htIE+huICrbqqhpnSK1Doo3CZk3DT1Hb8OS/c1SDB/XHyWMH8SQlBe9/0BsNm7+H5asisfB/c7Hzl80Sv+fTQifi2O/78PRpKgYOHYVGLdphYugsWFlZyuypWmbvzm14z7c1ho+ZiCat2mP+J4vFg/4bN26IyNU/YN2GTWj5XkdcvnrtrduqbMiHA5CRkYm8vHwM6Ndb4rmJE8Zg04ZViP55O5q3CUCnbn0Q/fN2eHlJH+Ir07xZU/i28REPNwGA0MnjMWhAXwSPmoB2HbshIyMT06aEvLM2MzMznP7zd9Rxd8Og4DFo3LIdJkyegfz8AlhZlQZW1I9r8SorCz7tOmH0+FDMnD4FDg6qPZTo5eWBQwd24a/TZ9Gq7fv48pvvsXr5t/hwYD/xOtnZOfj3foLE6w4f+wMduwbB168rfj98DAf2xGBAf8l9LhKJEP3zdowbPbzKQ9M7YvdiUsgY5X+wGuCxqs6k66icnBxYW1sje6UjrD6Ub1AoDbrXbwUlRkjMdYCXhzsEAv67X0B0wqHDxzBvwSLcvn4eBgb623ZQhdt37uKDngNw//YVWFvLHqpTUFCExKQn8LJ8AYFRicRzOa9ewrpFCLKzs6s8DF0denwOUT4UhoTop6BegUh48AhPnz6Du7sb1+XolGfP0rBt8/oqw5ArehyI7w40CkNC9Nusj0K5LkEnBXbvynUJMtFxgCoUFIiw6IdnFIaEEKIn9DcQ35FrAoEB/tzZAB5ufApDQgjRA/p7yFSOrkSe7ia48ltD1K5lRGGo19h//+pV/zNCOFX+96a+vzs9biFKB9yJc7koKhJJLLOvbUxhqOeMDUQARMjL19xb+RCia0r/3kT//f2ph/62ECsp60DTu6s19mz0Ap+vv/9XIJIMDRhsjF/jxct0AICZKR88FdxgmhBS2jLMyy/Ci5fpsDF+DUMD9bUQ9TgQy7/QKvYm/fWPbETvzsTkkZpzfT3CPSfz18Ab4MWLEujzgRVC1EMEG+PXpX93aqTHgVhK1tCKicNrc1sU0Tg8HuBs8RoOojcoFhlA2XdLIYSUYTA2EKm1ZViG80CMjIzE999/j9TUVDRt2hQrV65Ex44dq1z/1KlTCAsLw507d+Di4oL58+cjNLR6Y4VonCFRlKEBg6EBXcOWEF3E6bGf2NhYzJ49GwsXLsT169fRsWNH9OrVC8nJyTLXT0xMRFBQEDp27Ijr16/jk08+wcyZM7F3717F3/uqF4UhIYQQMU6vZdquXTu0bt0a69evFy9r3LgxBgwYgIiICKn1//e//+HgwYO4d++eeFloaCj+/vtvXLhwQa73LLuWKQ//A0Pp1eQpDAkhRHuo6lqmnLUQi4qKEB8fj8DAQInlgYGBOH/+vMzXXLhwQWr9Hj164OrVqzJvJvo27L9zQBSGhBBCAA7PIaanp0MoFMLRUfJOyo6OjkhLS5P5mrS0NJnrl5SUID09Hc7OzlKvKSwsRGFhoXg+Ozu77BmEDDHFN7OK8DrjUc0+DCGEELXJyckFACj7ACfnnWoq30+w4h2z5V1f1vIyERERWLJkiYxnVmDzbmDzbsXqJYQQohkyMjKUescMzgLRzs4OhoaGUq3BFy9eSLUCyzg5Oclc38jICLVryx4qER4ejrCwMPF8VlYWPDw8kJycrHG3HtEkOTk5cHd3x5MnT5R6jF7X0H6SD+0n+dB+kk92djbq1KkDW1tbpW6Xs0Dk8/lo06YNjh8/joEDB4qXHz9+HP3795f5Gj8/P/z6668Sy44dOwZfX18YGxvLfI2JiQlMTEyklltbW9MvnBysrKxoP8mB9pN8aD/Jh/aTfJR942ZOh12EhYVh06ZNiIqKwr179zBnzhwkJyeLxxWGh4djzJgx4vVDQ0ORlJSEsLAw3Lt3D1FRUdi8eTPmzZvH1UcghBCiIzg9hxgcHIyMjAx88cUXSE1NRbNmzXDo0CF4eHgAAFJTUyXGJHp5eeHQoUOYM2cO1q1bBxcXF6xevRoffvghVx+BEEKIjuC8U820adMwbdo0mc9FR0dLLevUqROuXbtW7fczMTHB4sWLZR5GJeVoP8mH9pN8aD/Jh/aTfFS1nzgdmE8IIYRoCrpsPyGEEAIKREIIIQQABSIhhBACgAKREEIIAaCjgRgZGQkvLy8IBAK0adMGZ86ceev6p06dQps2bSAQCFC3bl1s2LBBTZVyS5H9tG/fPnTv3h329vawsrKCn58fjh49qsZquaPo71OZc+fOwcjICK1atVJtgRpC0f1UWFiIhQsXwsPDAyYmJqhXrx6ioqLUVC13FN1PMTExaNmyJczMzODs7Izx48cjIyNDTdVy4/Tp0+jbty9cXFzA4/Gwf//+d75GKd/jTMfs3LmTGRsbs59++ondvXuXzZo1i5mbm7OkpCSZ6z969IiZmZmxWbNmsbt377KffvqJGRsbsz179qi5cvVSdD/NmjWLfffdd+zy5cvs/v37LDw8nBkbG7Nr166puXL1UnQ/lcnKymJ169ZlgYGBrGXLluoplkPV2U/9+vVj7dq1Y8ePH2eJiYns0qVL7Ny5c2qsWv0U3U9nzpxhBgYGbNWqVezRo0fszJkzrGnTpmzAgAFqrly9Dh06xBYuXMj27t3LALC4uLi3rq+s73GdC8S2bduy0NBQiWWNGjViCxYskLn+/PnzWaNGjSSWTZkyhbVv315lNWoCRfeTLE2aNGFLlixRdmkapbr7KTg4mH366ads8eLFehGIiu6nw4cPM2tra5aRkaGO8jSGovvp+++/Z3Xr1pVYtnr1aubm5qayGjWNPIGorO9xnTpkyvU9FrVFdfZTZSKRCLm5uUq/uK4mqe5+2rJlCx4+fIjFixerukSNUJ39dPDgQfj6+mLp0qVwdXWFt7c35s2bh/z8fHWUzInq7Cd/f3+kpKTg0KFDYIzh+fPn2LNnD3r37q2OkrWGsr7HOb9SjTKp6x6L2q46+6myZcuW4c2bNxg6dKgqStQI1dlPCQkJWLBgAc6cOQMjI53686pSdfbTo0ePcPbsWQgEAsTFxSE9PR3Tpk1DZmamzp5HrM5+8vf3R0xMDIKDg1FQUICSkhL069cPa9asUUfJWkNZ3+M61UIso+p7LOoKRfdTmR07duDzzz9HbGwsHBwcVFWexpB3PwmFQowYMQJLliyBt7e3usrTGIr8PolEIvB4PMTExKBt27YICgrC8uXLER0drdOtRECx/XT37l3MnDkTixYtQnx8PI4cOYLExETxDRBIOWV8j+vUf2HVdY9FbVed/VQmNjYWISEh2L17N7p166bKMjmn6H7Kzc3F1atXcf36dcyYMQNA6Rc/YwxGRkY4duwYunbtqpba1ak6v0/Ozs5wdXWVuCdp48aNwRhDSkoKGjRooNKauVCd/RQREYGAgAB8/PHHAIAWLVrA3NwcHTt2xFdffaWTR7CqQ1nf4zrVQqx4j8WKjh8/Dn9/f5mv8fPzk1r/XfdY1HbV2U9Aactw3Lhx2L59u16cw1B0P1lZWeHWrVu4ceOG+BEaGoqGDRvixo0baNeunbpKV6vq/D4FBATg2bNneP36tXjZ/fv3YWBgADc3N5XWy5Xq7Ke8vDype/4ZGhoCKG8BESV+jyvUBUcLlHVr3rx5M7t79y6bPXs2Mzc3Z48fP2aMMbZgwQI2evRo8fpl3XXnzJnD7t69yzZv3qxXwy7k3U/bt29nRkZGbN26dSw1NVX8yMrK4uojqIWi+6kyfellquh+ys3NZW5ubmzw4MHszp077NSpU6xBgwZs4sSJXH0EtVB0P23ZsoUZGRmxyMhI9vDhQ3b27Fnm6+vL2rZty9VHUIvc3Fx2/fp1dv36dQaALV++nF2/fl08PEVV3+M6F4iMMbZu3Trm4eHB+Hw+a926NTt16pT4ubFjx7JOnTpJrP/XX38xHx8fxufzmaenJ1u/fr2aK+aGIvupU6dODIDUY+zYseovXM0U/X2qSF8CkTHF99O9e/dYt27dmKmpKXNzc2NhYWEsLy9PzVWrn6L7afXq1axJkybM1NSUOTs7s5EjR7KUlBQ1V61eJ0+efOv3jaq+x+n2T4QQQgh07BwiIYQQUl0UiIQQQggoEAkhhBAAFIiEEEIIAApEQgghBAAFIiGEEAKAApEQQggBQIFIlCg6Oho2NjZcl1Ftnp6eWLly5VvX+fzzz9GqVSu11KNpTpw4gUaNGkEkEnFdilJUvhP7P//8g/bt20MgEKBVq1Z4/PgxeDwebty4Idf2xo0bhwEDBtSopsLCQtSpUwfx8fE12g6pphpfUoDolLFjx8q8QkRCQsI7X7tlyxZmbW2tstq2bNkiUZOTkxMbMmQIe/TokVK2/+LFC/bmzRvxPGTcmDQ3N5elp6cr5f2qUvlzOjg4sD59+rDbt28rvB1l/jzatGnDtm3bJp5/9uwZGz58OPP29mY8Ho/NmjVLru08f/6cTZ48mbm7uzM+n88cHR1ZYGAgO3/+vNJqlUdqaiorKCgQzw8dOpR17dqVPX78mKWnp7OSkhKWmprKiouL5dpeVlYWe/XqlXi+U6dOcu+TilatWsU++OADhV9Hao5aiERKz549kZqaKvHw8vLiuiwApRfQTk1NxbNnz7B9+3bcuHED/fr1g1AorPG27e3tYWZm9tZ1LCws1HIXlIqf8/fff8ebN2/Qu3dvFBUVqfy9ZTl//jwSEhIwZMgQ8bLCwkLY29tj4cKFaNmypdzb+vDDD/H3339j69atuH//Pg4ePIjOnTsjMzNTFaVXycnJCSYmJuL5hw8fokOHDvDw8EDt2rVhaGgIJycnue9raW1trZQjJCNHjsSZM2dw7969Gm+LKIjrRCaaZezYsax///4yn1u2bBlr1qwZMzMzY25ubmzq1KksNzdX/HzlFsmNGzdY586dmYWFBbO0tGStW7dmV65cET9/7tw51rFjRyYQCJibmxv76KOP2OvXr6usTVaL55dffmEA2D///MMYYywyMpLVrVuXGRsbM29vb4kWDWOl1xYta5k4Ozuzjz76SPych4cHW7FihXgaFVppHh4e4teXXZv0yJEjzMTERKJVwBhjH330EXv//feV+jkPHjzIALCbN2+Kl73t5yHrWpCLFy9mjDFWWFjIPv74Y+bi4sLMzMxY27Zt2cmTJ6usp+wzDR48uMrn5W0NvXr1igFgf/3111vXA8AiIyNZz549mUAgYJ6enmzXrl0S66SkpLChQ4cyGxsbZmtry/r168cSExMl1tm8eTNr0qQJ4/P5zMnJiU2fPl3iPcqOAMjaV4mJiQwAu379uvg1t2/fZkFBQczS0pJZWFiwDh06sAcPHjDGJP92ZB1pefToEatXrx77/vvvJWq8desW4/F44u0wxljnzp3ZZ5999s79SZSLWohEbgYGBli9ejVu376NrVu34sSJE5g/f36V648cORJubm64cuUK4uPjsWDBAvGtWG7duoUePXpg0KBBuHnzJmJjY3H27FnxfQTlZWpqCgAoLi5GXFwcZs2ahblz5+L27duYMmUKxo8fj5MnTwIA9uzZgxUrVmDjxo1ISEjA/v370bx5c5nbvXLlCgBgy5YtSE1NFc9X1K1bN9jY2GDv3r3iZUKhELt27cLIkSOV9jmzsrKwfft2AJC4lc3bfh7+/v5YuXKluKWZmpqKefPmAQDGjx+Pc+fOYefOnbh58yaGDBmCnj17IiEhocoaTp8+DV9fX7lrroqFhQUsLCywf/9+FBYWvnXdzz77TNyaHDVqFIYPHy5uNeXl5aFLly6wsLDA6dOncfbsWVhYWKBnz57iVvT69esxffp0TJ48Gbdu3cLBgwdRv359me+VmpqKpk2bYu7cuRL7qqKnT5/i/fffh0AgwIkTJxAfH48JEyagpKREat1Vq1bBz88PkyZNEu//OnXqYMKECdiyZYvEulFRUejYsSPq1asnXta2bVucOXPm7TuTKB/XiUw0y9ixY5mhoSEzNzcXP6pqGezatYvVrl1bPF+5ZWNpacmio6Nlvnb06NFs8uTJEsvOnDnDDAwMWH5+vszXVN7+kydPWPv27ZmbmxsrLCxk/v7+bNKkSRKvGTJkCAsKCmKMlbaovL29WVFRkcztV2whMib7HGLlu1fMnDmTde3aVTx/9OhRxufzWWZmZo0+JwBmbm7OzMzMxC2Mfv36yVy/zLt+Howx9uDBA8bj8djTp08lln/wwQcsPDy8ym1bW1tLtbYrUuR82Z49e1itWrWYQCBg/v7+LDw8nP39998S6wBgoaGhEsvatWvHpk6dyhgrbfk1bNiQiUQi8fOFhYXM1NSUHT16lDHGmIuLC1u4cGGVdVT++bZs2VLcimaMSbUQw8PDmZeXV5W/P5WPrsjaJ8+ePWOGhobs0qVLjDHGioqKmL29vdTfyapVq5inp2eVtRPVoBYikdKlSxeJm9yuXr0aAHDy5El0794drq6usLS0xJgxY5CRkYE3b97I3E5YWBgmTpyIbt264dtvv8XDhw/Fz8XHxyM6OlrcYrCwsECPHj0gEomQmJhYZW3Z2dmwsLCAubk53N3dUVRUhH379oHP5+PevXsICAiQWD8gIEDcqhgyZAjy8/NRt25dTJo0CXFxcTL/d6+IkSNH4q+//sKzZ88AADExMQgKCkKtWrVq9DktLS1x48YNxMfHY8OGDahXrx42bNggsY6iPw8AuHbtGhhj8Pb2lqjp1KlTEj+fyvLz8yEQCBTZNThz5ozEe8TExAAoPYf47NkzHDx4ED169MBff/2F1q1bIzo6WuL1fn5+UvNlP8v4+Hg8ePAAlpaW4u3b2tqioKAADx8+xIsXL/Ds2TN88MEHCtX8Njdu3EDHjh1rdONwZ2dn9O7dG1FRUQCA3377DQUFBRLnZoHSIx95eXk1qpcoTr6zxUSvmJubSx1aSkpKQlBQEEJDQ/Hll1/C1tYWZ8+eRUhICIqLi2Vu5/PPP8eIESPw+++/4/Dhw1i8eDF27tyJgQMHQiQSYcqUKZg5c6bU6+rUqVNlbZaWlrh27RoMDAzg6OgIc3Nzied5PJ7EPGNMvMzd3R3//vsvjh8/jj/++APTpk3D999/j1OnTlX7S65t27aoV68edu7cialTpyIuLk7ikFh1P6eBgYH4Z9CoUSOkpaUhODgYp0+fBlC9n0dZPYaGhoiPjxffeb2MhYVFla+zs7PDq1evqnxeFl9fX4khC46OjuJpgUCA7t27o3v37li0aBEmTpyIxYsXY9y4cW/dZtnPUiQSoU2bNuKQrcje3l7qLvPKUHZ4vqYmTpyI0aNHY8WKFdiyZQuCg4OlOnNlZmbC3t5eKe9H5EeBSORy9epVlJSUYNmyZeIvm127dr3zdd7e3vD29sacOXMwfPhwbNmyBQMHDkTr1q1x586dKs/pVKViUFTWuHFjnD17FmPGjBEvO3/+PBo3biyeNzU1Rb9+/dCvXz9Mnz4djRo1wq1bt9C6dWup7RkbG8vVe3XEiBGIiYmBm5sbDAwM0Lt3b/Fz1f2clc2ZMwfLly9HXFwcBg4cKNfPg8/nS9Xv4+MDoVCIFy9eoGPHjnK/v4+PD+7evatQzaampnJ/7iZNmkiMCQSAixcvSvwsL168CB8fHwCl+zU2NhYODg6wsrKSuU1PT0/8+eef6NKli0J1V6VFixbYunUriouL5foPlKz9DwBBQUEwNzfH+vXrcfjwYfF/ciq6ffu2+LMS9aFDpkQu9erVQ0lJCdasWYNHjx7h559/ljqEV1F+fj5mzJiBv/76C0lJSTh37hyuXLkiDqf//e9/uHDhAqZPn44bN24gISEBBw8exEcffVTtGj/++GNER0djw4YNSEhIwPLly7Fv3z5xB4no6Ghs3rwZt2/fFn8GU1NTeHh4yNxe2RdqWlraW1tHI0eOxLVr1/D1119j8ODBEocWlfU5raysxK0oxphcPw9PT0+8fv0af/75J9LT05GXlwdvb2+MHDkSY8aMwb59+5CYmIgrV67gu+++w6FDh6p8/x49euDs2bNSy8sOq79+/RovX77EjRs33hqcGRkZ6Nq1K3755RfcvHkTiYmJ2L17N5YuXYr+/ftLrLt7925ERUXh/v37WLx4MS5fvizujDRy5EjY2dmhf//+OHPmDBITE3Hq1CnMmjULKSkpAEqPUCxbtgyrV69GQkICrl27hjVr1si9zyubMWMGcnJyMGzYMFy9ehUJCQn4+eef8e+//8pc39PTE5cuXcLjx4+Rnp4uvqCBoaEhxo0bh/DwcNSvX1/q0DBQerg5MDCw2rWSauL4HCbRMG8bdrF8+XLm7OzMTE1NWY8ePdi2bdsYAPGwg4qdOAoLC9mwYcPEQxxcXFzYjBkzJDqSXL58mXXv3p1ZWFgwc3Nz1qJFC/b1119XWZs8A83fNuwiLi6OtWvXjllZWTFzc3PWvn179scff4ifr9yp5uDBg6x+/frMyMhI5rCLit577z0GgJ04cULqOWV9zqSkJGZkZMRiY2MZY+/+eTDGWGhoKKtdu7bEsIuioiK2aNEi5unpyYyNjZmTkxMbOHCgxJCOyjIzM5mpqal4eEsZyLiIQ9m+kqWgoIAtWLCAtW7dmllbWzMzMzPWsGFD9umnn7K8vDyJ7a5bt451796dmZiYMA8PD7Zjxw6JbaWmprIxY8YwOzs7ZmJiwurWrcsmTZrEsrOzxets2LCBNWzYkBkbG0sNs4GCnWoYY+zvv/9mgYGBzMzMjFlaWrKOHTuyhw8fMsak/3b+/fdf1r59e2ZqasoASAwJefjwIQPAli5dKrWPzp8/z2xsbCT2B1EPHmOMcRHEhBDtMn/+fGRnZ2Pjxo0qfy8ej4e4uLgaXwpNU507dw6dO3dGSkqKxLlVoLTzl4+PDz755BOOqtNfdMiUECKXhQsXwsPDQylXBdJXhYWFePDgAT777DMMHTpUKgwLCwvRsmVLzJkzh6MK9Ru1EAkhGkdXW4jR0dEICQlBq1atcPDgQbi6unJdEqmAApEQQggBHTIlhBBCAFAgEkIIIQAoEAkhhBAAFIiEEEIIAApEQgghBAAFIiGEEAKAApEQQggBQIFICCGEAKBAJIQQQgAA/wepOT805f1o4wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2471,7 +2471,7 @@
     {
      "data": {
       "text/plain": [
-       "0.9887056900142546"
+       "0.9683019317794956"
       ]
      },
      "execution_count": 40,
@@ -2501,7 +2501,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2512,7 +2512,7 @@
     {
      "data": {
       "text/plain": [
-       "0.5838221154830853"
+       "0.5902364119732755"
       ]
      },
      "execution_count": 41,
@@ -2592,51 +2592,51 @@
        "      <th>0</th>\n",
        "      <td>IndicateMissingTransform</td>\n",
        "      <td>missing_indicator</td>\n",
-       "      <td>Var161</td>\n",
+       "      <td>Var174</td>\n",
        "      <td>True</td>\n",
-       "      <td>Var161_is_bad</td>\n",
+       "      <td>Var174_is_bad</td>\n",
        "      <td>_NA_</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>IndicateMissingTransform</td>\n",
        "      <td>missing_indicator</td>\n",
-       "      <td>Var38</td>\n",
+       "      <td>Var85</td>\n",
        "      <td>True</td>\n",
-       "      <td>Var38_is_bad</td>\n",
+       "      <td>Var85_is_bad</td>\n",
        "      <td>_NA_</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>IndicateMissingTransform</td>\n",
        "      <td>missing_indicator</td>\n",
-       "      <td>Var23</td>\n",
+       "      <td>Var186</td>\n",
        "      <td>True</td>\n",
-       "      <td>Var23_is_bad</td>\n",
+       "      <td>Var186_is_bad</td>\n",
        "      <td>_NA_</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>IndicateMissingTransform</td>\n",
        "      <td>missing_indicator</td>\n",
-       "      <td>Var200</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Var200_is_bad</td>\n",
+       "      <td>Var40</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Var40_is_bad</td>\n",
        "      <td>_NA_</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>IndicateMissingTransform</td>\n",
        "      <td>missing_indicator</td>\n",
-       "      <td>Var162</td>\n",
+       "      <td>Var37</td>\n",
        "      <td>True</td>\n",
-       "      <td>Var162_is_bad</td>\n",
+       "      <td>Var37_is_bad</td>\n",
        "      <td>_NA_</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2650,53 +2650,53 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135839</th>\n",
-       "      <td>MappedCodeTransform</td>\n",
-       "      <td>prevalence_code</td>\n",
-       "      <td>Var220</td>\n",
+       "      <td>IndicatorCodeTransform</td>\n",
+       "      <td>indicator_code</td>\n",
+       "      <td>Var221</td>\n",
        "      <td>False</td>\n",
-       "      <td>Var220_prevalence_code</td>\n",
-       "      <td>zxm6hzI</td>\n",
-       "      <td>0.000333</td>\n",
+       "      <td>Var221_lev_oslk</td>\n",
+       "      <td>oslk</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135840</th>\n",
-       "      <td>MappedCodeTransform</td>\n",
-       "      <td>prevalence_code</td>\n",
-       "      <td>Var220</td>\n",
+       "      <td>IndicatorCodeTransform</td>\n",
+       "      <td>indicator_code</td>\n",
+       "      <td>Var221</td>\n",
        "      <td>False</td>\n",
-       "      <td>Var220_prevalence_code</td>\n",
-       "      <td>zxmVxx9</td>\n",
-       "      <td>0.000067</td>\n",
+       "      <td>Var221_lev_zCkv</td>\n",
+       "      <td>zCkv</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135841</th>\n",
-       "      <td>MappedCodeTransform</td>\n",
-       "      <td>prevalence_code</td>\n",
-       "      <td>Var220</td>\n",
+       "      <td>IndicatorCodeTransform</td>\n",
+       "      <td>indicator_code</td>\n",
+       "      <td>Var221</td>\n",
        "      <td>False</td>\n",
-       "      <td>Var220_prevalence_code</td>\n",
-       "      <td>zxmyH3X</td>\n",
-       "      <td>0.000044</td>\n",
+       "      <td>Var221_lev_d0EEeJi</td>\n",
+       "      <td>d0EEeJi</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135842</th>\n",
        "      <td>IndicatorCodeTransform</td>\n",
        "      <td>indicator_code</td>\n",
-       "      <td>Var220</td>\n",
+       "      <td>Var221</td>\n",
        "      <td>False</td>\n",
-       "      <td>Var220_lev_4UxGlow</td>\n",
-       "      <td>4UxGlow</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>Var221_lev_QKW8DRm</td>\n",
+       "      <td>QKW8DRm</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135843</th>\n",
        "      <td>IndicatorCodeTransform</td>\n",
        "      <td>indicator_code</td>\n",
-       "      <td>Var220</td>\n",
+       "      <td>Var221</td>\n",
        "      <td>False</td>\n",
-       "      <td>Var220_lev_UF16siJ</td>\n",
-       "      <td>UF16siJ</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>Var221_lev_Al6ZaUT</td>\n",
+       "      <td>Al6ZaUT</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2705,30 +2705,30 @@
       ],
       "text/plain": [
        "                 treatment_class          treatment orig_var  \\\n",
-       "0       IndicateMissingTransform  missing_indicator   Var161   \n",
-       "1       IndicateMissingTransform  missing_indicator    Var38   \n",
-       "2       IndicateMissingTransform  missing_indicator    Var23   \n",
-       "3       IndicateMissingTransform  missing_indicator   Var200   \n",
-       "4       IndicateMissingTransform  missing_indicator   Var162   \n",
+       "0       IndicateMissingTransform  missing_indicator   Var174   \n",
+       "1       IndicateMissingTransform  missing_indicator    Var85   \n",
+       "2       IndicateMissingTransform  missing_indicator   Var186   \n",
+       "3       IndicateMissingTransform  missing_indicator    Var40   \n",
+       "4       IndicateMissingTransform  missing_indicator    Var37   \n",
        "...                          ...                ...      ...   \n",
-       "135839       MappedCodeTransform    prevalence_code   Var220   \n",
-       "135840       MappedCodeTransform    prevalence_code   Var220   \n",
-       "135841       MappedCodeTransform    prevalence_code   Var220   \n",
-       "135842    IndicatorCodeTransform     indicator_code   Var220   \n",
-       "135843    IndicatorCodeTransform     indicator_code   Var220   \n",
-       "\n",
-       "        orig_was_numeric                variable    value  replacement  \n",
-       "0                   True           Var161_is_bad     _NA_     1.000000  \n",
-       "1                   True            Var38_is_bad     _NA_     1.000000  \n",
-       "2                   True            Var23_is_bad     _NA_     1.000000  \n",
-       "3                  False           Var200_is_bad     _NA_     1.000000  \n",
-       "4                   True           Var162_is_bad     _NA_     1.000000  \n",
-       "...                  ...                     ...      ...          ...  \n",
-       "135839             False  Var220_prevalence_code  zxm6hzI     0.000333  \n",
-       "135840             False  Var220_prevalence_code  zxmVxx9     0.000067  \n",
-       "135841             False  Var220_prevalence_code  zxmyH3X     0.000044  \n",
-       "135842             False      Var220_lev_4UxGlow  4UxGlow     1.000000  \n",
-       "135843             False      Var220_lev_UF16siJ  UF16siJ     1.000000  \n",
+       "135839    IndicatorCodeTransform     indicator_code   Var221   \n",
+       "135840    IndicatorCodeTransform     indicator_code   Var221   \n",
+       "135841    IndicatorCodeTransform     indicator_code   Var221   \n",
+       "135842    IndicatorCodeTransform     indicator_code   Var221   \n",
+       "135843    IndicatorCodeTransform     indicator_code   Var221   \n",
+       "\n",
+       "        orig_was_numeric            variable    value  replacement  \n",
+       "0                   True       Var174_is_bad     _NA_          1.0  \n",
+       "1                   True        Var85_is_bad     _NA_          1.0  \n",
+       "2                   True       Var186_is_bad     _NA_          1.0  \n",
+       "3                   True        Var40_is_bad     _NA_          1.0  \n",
+       "4                   True        Var37_is_bad     _NA_          1.0  \n",
+       "...                  ...                 ...      ...          ...  \n",
+       "135839             False     Var221_lev_oslk     oslk          1.0  \n",
+       "135840             False     Var221_lev_zCkv     zCkv          1.0  \n",
+       "135841             False  Var221_lev_d0EEeJi  d0EEeJi          1.0  \n",
+       "135842             False  Var221_lev_QKW8DRm  QKW8DRm          1.0  \n",
+       "135843             False  Var221_lev_Al6ZaUT  Al6ZaUT          1.0  \n",
        "\n",
        "[135844 rows x 7 columns]"
       ]
diff --git a/Examples/Pooling/PartialPoolingExample.ipynb b/Examples/Pooling/PartialPoolingExample.ipynb
index f388e6e..3505594 100644
--- a/Examples/Pooling/PartialPoolingExample.ipynb
+++ b/Examples/Pooling/PartialPoolingExample.ipynb
@@ -598,7 +598,7 @@
        "      <td>2</td>\n",
        "      <td>0.519000</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>-0.013500</td>\n",
+       "      <td>-0.019948</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -608,7 +608,7 @@
        "      <td>4</td>\n",
        "      <td>0.333864</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>-0.198636</td>\n",
+       "      <td>-0.205084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -618,7 +618,7 @@
        "      <td>3</td>\n",
        "      <td>0.611252</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>0.078752</td>\n",
+       "      <td>0.072304</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -628,7 +628,7 @@
        "      <td>3</td>\n",
        "      <td>0.065500</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>-0.467000</td>\n",
+       "      <td>-0.473448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -636,9 +636,9 @@
        "      <td>1.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.786364</td>\n",
+       "      <td>0.532500</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>0.253864</td>\n",
+       "      <td>-0.006448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -656,9 +656,9 @@
        "      <td>0.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.243339</td>\n",
+       "      <td>0.532500</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>-0.289161</td>\n",
+       "      <td>-0.006448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>173</th>\n",
@@ -666,9 +666,9 @@
        "      <td>0.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.243339</td>\n",
+       "      <td>0.532500</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>-0.289161</td>\n",
+       "      <td>-0.006448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>174</th>\n",
@@ -676,9 +676,9 @@
        "      <td>1.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.786364</td>\n",
+       "      <td>0.532500</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>0.253864</td>\n",
+       "      <td>-0.006448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>175</th>\n",
@@ -688,7 +688,7 @@
        "      <td>8</td>\n",
        "      <td>0.608035</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>0.075535</td>\n",
+       "      <td>0.069088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>176</th>\n",
@@ -698,7 +698,7 @@
        "      <td>2</td>\n",
        "      <td>0.519000</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>-0.013500</td>\n",
+       "      <td>-0.019948</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -707,17 +707,17 @@
       ],
       "text/plain": [
        "    location_id      mean       var  size  estimate  grand_mean    impact\n",
-       "0         loc_0  0.500000  0.500000     2  0.519000      0.5325 -0.013500\n",
-       "1         loc_1  0.250000  0.250000     4  0.333864      0.5325 -0.198636\n",
-       "2        loc_10  0.666667  0.333333     3  0.611252      0.5325  0.078752\n",
-       "3       loc_101  0.000000  0.000000     3  0.065500      0.5325 -0.467000\n",
-       "4       loc_102  1.000000       NaN     1  0.786364      0.5325  0.253864\n",
+       "0         loc_0  0.500000  0.500000     2  0.519000      0.5325 -0.019948\n",
+       "1         loc_1  0.250000  0.250000     4  0.333864      0.5325 -0.205084\n",
+       "2        loc_10  0.666667  0.333333     3  0.611252      0.5325  0.072304\n",
+       "3       loc_101  0.000000  0.000000     3  0.065500      0.5325 -0.473448\n",
+       "4       loc_102  1.000000       NaN     1  0.532500      0.5325 -0.006448\n",
        "..          ...       ...       ...   ...       ...         ...       ...\n",
-       "172      loc_94  0.000000       NaN     1  0.243339      0.5325 -0.289161\n",
-       "173      loc_95  0.000000       NaN     1  0.243339      0.5325 -0.289161\n",
-       "174      loc_96  1.000000       NaN     1  0.786364      0.5325  0.253864\n",
-       "175      loc_97  0.625000  0.267857     8  0.608035      0.5325  0.075535\n",
-       "176      loc_98  0.500000  0.500000     2  0.519000      0.5325 -0.013500\n",
+       "172      loc_94  0.000000       NaN     1  0.532500      0.5325 -0.006448\n",
+       "173      loc_95  0.000000       NaN     1  0.532500      0.5325 -0.006448\n",
+       "174      loc_96  1.000000       NaN     1  0.532500      0.5325 -0.006448\n",
+       "175      loc_97  0.625000  0.267857     8  0.608035      0.5325  0.069088\n",
+       "176      loc_98  0.500000  0.500000     2  0.519000      0.5325 -0.019948\n",
        "\n",
        "[177 rows x 7 columns]"
       ]
@@ -777,7 +777,7 @@
        "      <td>5</td>\n",
        "      <td>0.975165</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>0.442665</td>\n",
+       "      <td>0.436218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65</th>\n",
@@ -787,7 +787,7 @@
        "      <td>5</td>\n",
        "      <td>0.028288</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>-0.504212</td>\n",
+       "      <td>-0.510660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>155</th>\n",
@@ -797,7 +797,7 @@
        "      <td>5</td>\n",
        "      <td>0.975165</td>\n",
        "      <td>0.5325</td>\n",
-       "      <td>0.442665</td>\n",
+       "      <td>0.436218</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -805,9 +805,9 @@
       ],
       "text/plain": [
        "    location_id  mean  var  size  estimate  grand_mean    impact\n",
-       "41       loc_14   1.0  0.0     5  0.975165      0.5325  0.442665\n",
-       "65      loc_165   0.0  0.0     5  0.028288      0.5325 -0.504212\n",
-       "155      loc_74   1.0  0.0     5  0.975165      0.5325  0.442665"
+       "41       loc_14   1.0  0.0     5  0.975165      0.5325  0.436218\n",
+       "65      loc_165   0.0  0.0     5  0.028288      0.5325 -0.510660\n",
+       "155      loc_74   1.0  0.0     5  0.975165      0.5325  0.436218"
       ]
      },
      "execution_count": 8,
@@ -829,7 +829,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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"
      },
      "metadata": {
       "image/png": {
diff --git a/coverage.txt b/coverage.txt
index f332f5a..0a42062 100644
--- a/coverage.txt
+++ b/coverage.txt
@@ -70,7 +70,7 @@ pkg/vtreat/__init__.py                        6      0   100%
 pkg/vtreat/cross_plan.py                     50      1    98%
 pkg/vtreat/da_adapter.py                    109      1    99%
 pkg/vtreat/effect_scaler.py                  59      4    93%
-pkg/vtreat/partial_pooling_estimator.py      32      0   100%
+pkg/vtreat/partial_pooling_estimator.py      35      0   100%
 pkg/vtreat/stats_utils.py                   132      0   100%
 pkg/vtreat/test_util.py                      84     18    79%
 pkg/vtreat/transform.py                      14      0   100%
@@ -79,6 +79,6 @@ pkg/vtreat/vtreat_api.py                    285     34    88%
 pkg/vtreat/vtreat_db_adapter.py               1      0   100%
 pkg/vtreat/vtreat_impl.py                   711     61    91%
 -------------------------------------------------------------
-TOTAL                                      1632    127    92%
+TOTAL                                      1635    127    92%
 
-================= 47 passed, 81 warnings in 111.33s (0:01:51) ==================
+================= 47 passed, 81 warnings in 104.41s (0:01:44) ==================
diff --git a/docs/vtreat/partial_pooling_estimator.html b/docs/vtreat/partial_pooling_estimator.html
index 7160af3..1c6aba9 100644
--- a/docs/vtreat/partial_pooling_estimator.html
+++ b/docs/vtreat/partial_pooling_estimator.html
@@ -83,51 +83,59 @@ <h1 class="modulename">
 </span><span id="L-24"><a href="#L-24"><span class="linenos">24</span></a>    <span class="n">means</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;location_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span><span id="L-25"><a href="#L-25"><span class="linenos">25</span></a>    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;estimate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;mean&#39;</span><span class="p">]</span>
 </span><span id="L-26"><a href="#L-26"><span class="linenos">26</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;grand_mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">observations</span><span class="p">[</span><span class="s2">&quot;observation&quot;</span><span class="p">])</span>
-</span><span id="L-27"><a href="#L-27"><span class="linenos">27</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">means</span><span class="p">[</span><span class="s2">&quot;grand_mean&quot;</span><span class="p">]</span>
-</span><span id="L-28"><a href="#L-28"><span class="linenos">28</span></a>    <span class="n">means</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;location_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</span><span id="L-29"><a href="#L-29"><span class="linenos">29</span></a>    <span class="k">return</span> <span class="n">means</span>
-</span><span id="L-30"><a href="#L-30"><span class="linenos">30</span></a>
-</span><span id="L-31"><a href="#L-31"><span class="linenos">31</span></a>
-</span><span id="L-32"><a href="#L-32"><span class="linenos">32</span></a><span class="c1"># define the pooled estimator</span>
-</span><span id="L-33"><a href="#L-33"><span class="linenos">33</span></a><span class="k">def</span> <span class="nf">pooled_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="L-34"><a href="#L-34"><span class="linenos">34</span></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-</span><span id="L-35"><a href="#L-35"><span class="linenos">35</span></a><span class="sd">    Get the pooled estimate of effect.</span>
-</span><span id="L-36"><a href="#L-36"><span class="linenos">36</span></a><span class="sd">    See: https://github.com/WinVector/Examples/blob/main/PartialPooling/PartialPooling.ipynb .</span>
-</span><span id="L-37"><a href="#L-37"><span class="linenos">37</span></a>
-</span><span id="L-38"><a href="#L-38"><span class="linenos">38</span></a><span class="sd">    :param observations: the observations data frame, with columns location_id and observation</span>
-</span><span id="L-39"><a href="#L-39"><span class="linenos">39</span></a><span class="sd">    :return: pooled estimates</span>
-</span><span id="L-40"><a href="#L-40"><span class="linenos">40</span></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="L-41"><a href="#L-41"><span class="linenos">41</span></a>    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">observations</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span>
-</span><span id="L-42"><a href="#L-42"><span class="linenos">42</span></a>    <span class="n">observations</span> <span class="o">=</span> <span class="n">observations</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s2">&quot;location_id&quot;</span><span class="p">,</span> <span class="s2">&quot;observation&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span>
-</span><span id="L-43"><a href="#L-43"><span class="linenos">43</span></a>        <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span>
-</span><span id="L-44"><a href="#L-44"><span class="linenos">44</span></a>    <span class="p">)</span>
-</span><span id="L-45"><a href="#L-45"><span class="linenos">45</span></a>    <span class="c1"># get the standard estimates</span>
-</span><span id="L-46"><a href="#L-46"><span class="linenos">46</span></a>    <span class="n">estimated_centers</span> <span class="o">=</span> <span class="n">standard_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="o">=</span><span class="n">observations</span><span class="p">)</span>
-</span><span id="L-47"><a href="#L-47"><span class="linenos">47</span></a>    <span class="k">if</span> <span class="n">estimated_centers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
-</span><span id="L-48"><a href="#L-48"><span class="linenos">48</span></a>        <span class="c1"># no pooling possible</span>
-</span><span id="L-49"><a href="#L-49"><span class="linenos">49</span></a>        <span class="k">return</span> <span class="n">estimated_centers</span>
-</span><span id="L-50"><a href="#L-50"><span class="linenos">50</span></a>    <span class="c1"># get counts per group</span>
-</span><span id="L-51"><a href="#L-51"><span class="linenos">51</span></a>    <span class="n">n_j</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;size&quot;</span><span class="p">]</span>
-</span><span id="L-52"><a href="#L-52"><span class="linenos">52</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;var&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-</span><span id="L-53"><a href="#L-53"><span class="linenos">53</span></a>    <span class="c1"># inflate a bit</span>
-</span><span id="L-54"><a href="#L-54"><span class="linenos">54</span></a>    <span class="n">per_location_observation_var</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">per_location_observation_var</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
-</span><span id="L-55"><a href="#L-55"><span class="linenos">55</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">per_location_observation_var</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">observations</span><span class="p">[</span><span class="s1">&#39;observation&#39;</span><span class="p">]))</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-</span><span id="L-56"><a href="#L-56"><span class="linenos">56</span></a>    <span class="c1"># get the observed variance between locations</span>
-</span><span id="L-57"><a href="#L-57"><span class="linenos">57</span></a>    <span class="n">between_location_var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">],</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-</span><span id="L-58"><a href="#L-58"><span class="linenos">58</span></a>    <span class="c1"># get v, the pooling coefficient</span>
-</span><span id="L-59"><a href="#L-59"><span class="linenos">59</span></a>    <span class="k">if</span> <span class="n">between_location_var</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-</span><span id="L-60"><a href="#L-60"><span class="linenos">60</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">0</span> <span class="o">*</span> <span class="n">per_location_observation_var</span>
-</span><span id="L-61"><a href="#L-61"><span class="linenos">61</span></a>    <span class="k">else</span><span class="p">:</span>
-</span><span id="L-62"><a href="#L-62"><span class="linenos">62</span></a>        <span class="c1"># as between_location_var &gt; 0 and per_location_observation_var &gt; 0 here</span>
-</span><span id="L-63"><a href="#L-63"><span class="linenos">63</span></a>        <span class="c1"># v will be in the range 0 to 1</span>
-</span><span id="L-64"><a href="#L-64"><span class="linenos">64</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">per_location_observation_var</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">between_location_var</span><span class="p">))</span>
-</span><span id="L-65"><a href="#L-65"><span class="linenos">65</span></a>    <span class="c1"># this quantity can be improved using knowledge of the variances</span>
-</span><span id="L-66"><a href="#L-66"><span class="linenos">66</span></a>    <span class="n">grand_mean</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;grand_mean&#39;</span><span class="p">]</span>
-</span><span id="L-67"><a href="#L-67"><span class="linenos">67</span></a>    <span class="c1"># build the pooled estimate</span>
-</span><span id="L-68"><a href="#L-68"><span class="linenos">68</span></a>    <span class="n">pooled_estimate</span> <span class="o">=</span> <span class="n">v</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v</span><span class="p">)</span> <span class="o">*</span> <span class="n">grand_mean</span>
-</span><span id="L-69"><a href="#L-69"><span class="linenos">69</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pooled_estimate</span>
-</span><span id="L-70"><a href="#L-70"><span class="linenos">70</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;impact&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pooled_estimate</span> <span class="o">-</span> <span class="n">grand_mean</span>
-</span><span id="L-71"><a href="#L-71"><span class="linenos">71</span></a>    <span class="k">return</span> <span class="n">estimated_centers</span>
+</span><span id="L-27"><a href="#L-27"><span class="linenos">27</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span>
+</span><span id="L-28"><a href="#L-28"><span class="linenos">28</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+</span><span id="L-29"><a href="#L-29"><span class="linenos">29</span></a>        <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> 
+</span><span id="L-30"><a href="#L-30"><span class="linenos">30</span></a>        <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">means</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;impact&#39;</span><span class="p">])</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">means</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]))</span>
+</span><span id="L-31"><a href="#L-31"><span class="linenos">31</span></a>    <span class="n">means</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;location_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</span><span id="L-32"><a href="#L-32"><span class="linenos">32</span></a>    <span class="k">return</span> <span class="n">means</span>
+</span><span id="L-33"><a href="#L-33"><span class="linenos">33</span></a>
+</span><span id="L-34"><a href="#L-34"><span class="linenos">34</span></a>
+</span><span id="L-35"><a href="#L-35"><span class="linenos">35</span></a><span class="c1"># define the pooled estimator</span>
+</span><span id="L-36"><a href="#L-36"><span class="linenos">36</span></a><span class="k">def</span> <span class="nf">pooled_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="L-37"><a href="#L-37"><span class="linenos">37</span></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+</span><span id="L-38"><a href="#L-38"><span class="linenos">38</span></a><span class="sd">    Get the pooled estimate of effect.</span>
+</span><span id="L-39"><a href="#L-39"><span class="linenos">39</span></a><span class="sd">    See: https://github.com/WinVector/Examples/blob/main/PartialPooling/PartialPooling.ipynb .</span>
+</span><span id="L-40"><a href="#L-40"><span class="linenos">40</span></a>
+</span><span id="L-41"><a href="#L-41"><span class="linenos">41</span></a><span class="sd">    :param observations: the observations data frame, with columns location_id and observation</span>
+</span><span id="L-42"><a href="#L-42"><span class="linenos">42</span></a><span class="sd">    :return: pooled estimates</span>
+</span><span id="L-43"><a href="#L-43"><span class="linenos">43</span></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="L-44"><a href="#L-44"><span class="linenos">44</span></a>    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">observations</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span>
+</span><span id="L-45"><a href="#L-45"><span class="linenos">45</span></a>    <span class="n">observations</span> <span class="o">=</span> <span class="n">observations</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s2">&quot;location_id&quot;</span><span class="p">,</span> <span class="s2">&quot;observation&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span>
+</span><span id="L-46"><a href="#L-46"><span class="linenos">46</span></a>        <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span>
+</span><span id="L-47"><a href="#L-47"><span class="linenos">47</span></a>    <span class="p">)</span>
+</span><span id="L-48"><a href="#L-48"><span class="linenos">48</span></a>    <span class="c1"># get the standard estimates</span>
+</span><span id="L-49"><a href="#L-49"><span class="linenos">49</span></a>    <span class="n">estimated_centers</span> <span class="o">=</span> <span class="n">standard_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="o">=</span><span class="n">observations</span><span class="p">)</span>
+</span><span id="L-50"><a href="#L-50"><span class="linenos">50</span></a>    <span class="k">if</span> <span class="n">estimated_centers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
+</span><span id="L-51"><a href="#L-51"><span class="linenos">51</span></a>        <span class="c1"># no pooling possible</span>
+</span><span id="L-52"><a href="#L-52"><span class="linenos">52</span></a>        <span class="k">return</span> <span class="n">estimated_centers</span>
+</span><span id="L-53"><a href="#L-53"><span class="linenos">53</span></a>    <span class="c1"># get counts per group</span>
+</span><span id="L-54"><a href="#L-54"><span class="linenos">54</span></a>    <span class="n">n_j</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;size&quot;</span><span class="p">]</span>
+</span><span id="L-55"><a href="#L-55"><span class="linenos">55</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;var&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+</span><span id="L-56"><a href="#L-56"><span class="linenos">56</span></a>    <span class="n">per_location_observation_var</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">per_location_observation_var</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+</span><span id="L-57"><a href="#L-57"><span class="linenos">57</span></a>    <span class="c1"># inflate per-loc a bit</span>
+</span><span id="L-58"><a href="#L-58"><span class="linenos">58</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="p">(</span>
+</span><span id="L-59"><a href="#L-59"><span class="linenos">59</span></a>        <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">per_location_observation_var</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">observations</span><span class="p">[</span><span class="s1">&#39;observation&#39;</span><span class="p">]))</span> 
+</span><span id="L-60"><a href="#L-60"><span class="linenos">60</span></a>        <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+</span><span id="L-61"><a href="#L-61"><span class="linenos">61</span></a>    <span class="c1"># get the observed variance between locations</span>
+</span><span id="L-62"><a href="#L-62"><span class="linenos">62</span></a>    <span class="n">between_location_var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">],</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</span><span id="L-63"><a href="#L-63"><span class="linenos">63</span></a>    <span class="c1"># get v, the pooling coefficient</span>
+</span><span id="L-64"><a href="#L-64"><span class="linenos">64</span></a>    <span class="k">if</span> <span class="n">between_location_var</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+</span><span id="L-65"><a href="#L-65"><span class="linenos">65</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">0</span> <span class="o">*</span> <span class="n">per_location_observation_var</span>
+</span><span id="L-66"><a href="#L-66"><span class="linenos">66</span></a>    <span class="k">else</span><span class="p">:</span>
+</span><span id="L-67"><a href="#L-67"><span class="linenos">67</span></a>        <span class="c1"># as between_location_var &gt; 0 and per_location_observation_var &gt; 0 here</span>
+</span><span id="L-68"><a href="#L-68"><span class="linenos">68</span></a>        <span class="c1"># v will be in the range 0 to 1</span>
+</span><span id="L-69"><a href="#L-69"><span class="linenos">69</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">per_location_observation_var</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">between_location_var</span><span class="p">))</span>
+</span><span id="L-70"><a href="#L-70"><span class="linenos">70</span></a>    <span class="n">v</span><span class="p">[</span><span class="n">n_j</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># no information in size one items</span>
+</span><span id="L-71"><a href="#L-71"><span class="linenos">71</span></a>    <span class="n">v</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">v</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+</span><span id="L-72"><a href="#L-72"><span class="linenos">72</span></a>    <span class="c1"># build the pooled estimate</span>
+</span><span id="L-73"><a href="#L-73"><span class="linenos">73</span></a>    <span class="n">pooled_estimate</span> <span class="o">=</span> <span class="n">v</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v</span><span class="p">)</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;grand_mean&quot;</span><span class="p">]</span>
+</span><span id="L-74"><a href="#L-74"><span class="linenos">74</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pooled_estimate</span>
+</span><span id="L-75"><a href="#L-75"><span class="linenos">75</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> 
+</span><span id="L-76"><a href="#L-76"><span class="linenos">76</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+</span><span id="L-77"><a href="#L-77"><span class="linenos">77</span></a>        <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> 
+</span><span id="L-78"><a href="#L-78"><span class="linenos">78</span></a>        <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;impact&#39;</span><span class="p">])</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]))</span>
+</span><span id="L-79"><a href="#L-79"><span class="linenos">79</span></a>    <span class="k">return</span> <span class="n">estimated_centers</span>
 </span></pre></div>
 
 
@@ -164,9 +172,12 @@ <h1 class="modulename">
 </span><span id="standard_effect_estimate-25"><a href="#standard_effect_estimate-25"><span class="linenos">25</span></a>    <span class="n">means</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;location_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span><span id="standard_effect_estimate-26"><a href="#standard_effect_estimate-26"><span class="linenos">26</span></a>    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;estimate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;mean&#39;</span><span class="p">]</span>
 </span><span id="standard_effect_estimate-27"><a href="#standard_effect_estimate-27"><span class="linenos">27</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;grand_mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">observations</span><span class="p">[</span><span class="s2">&quot;observation&quot;</span><span class="p">])</span>
-</span><span id="standard_effect_estimate-28"><a href="#standard_effect_estimate-28"><span class="linenos">28</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">means</span><span class="p">[</span><span class="s2">&quot;grand_mean&quot;</span><span class="p">]</span>
-</span><span id="standard_effect_estimate-29"><a href="#standard_effect_estimate-29"><span class="linenos">29</span></a>    <span class="n">means</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;location_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</span><span id="standard_effect_estimate-30"><a href="#standard_effect_estimate-30"><span class="linenos">30</span></a>    <span class="k">return</span> <span class="n">means</span>
+</span><span id="standard_effect_estimate-28"><a href="#standard_effect_estimate-28"><span class="linenos">28</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span>
+</span><span id="standard_effect_estimate-29"><a href="#standard_effect_estimate-29"><span class="linenos">29</span></a>    <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+</span><span id="standard_effect_estimate-30"><a href="#standard_effect_estimate-30"><span class="linenos">30</span></a>        <span class="n">means</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> 
+</span><span id="standard_effect_estimate-31"><a href="#standard_effect_estimate-31"><span class="linenos">31</span></a>        <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">means</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;impact&#39;</span><span class="p">])</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">means</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]))</span>
+</span><span id="standard_effect_estimate-32"><a href="#standard_effect_estimate-32"><span class="linenos">32</span></a>    <span class="n">means</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;location_id&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</span><span id="standard_effect_estimate-33"><a href="#standard_effect_estimate-33"><span class="linenos">33</span></a>    <span class="k">return</span> <span class="n">means</span>
 </span></pre></div>
 
 
@@ -199,45 +210,50 @@ <h6 id="returns">Returns</h6>
 
     </div>
     <a class="headerlink" href="#pooled_effect_estimate"></a>
-            <div class="pdoc-code codehilite"><pre><span></span><span id="pooled_effect_estimate-34"><a href="#pooled_effect_estimate-34"><span class="linenos">34</span></a><span class="k">def</span> <span class="nf">pooled_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="pooled_effect_estimate-35"><a href="#pooled_effect_estimate-35"><span class="linenos">35</span></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-</span><span id="pooled_effect_estimate-36"><a href="#pooled_effect_estimate-36"><span class="linenos">36</span></a><span class="sd">    Get the pooled estimate of effect.</span>
-</span><span id="pooled_effect_estimate-37"><a href="#pooled_effect_estimate-37"><span class="linenos">37</span></a><span class="sd">    See: https://github.com/WinVector/Examples/blob/main/PartialPooling/PartialPooling.ipynb .</span>
-</span><span id="pooled_effect_estimate-38"><a href="#pooled_effect_estimate-38"><span class="linenos">38</span></a>
-</span><span id="pooled_effect_estimate-39"><a href="#pooled_effect_estimate-39"><span class="linenos">39</span></a><span class="sd">    :param observations: the observations data frame, with columns location_id and observation</span>
-</span><span id="pooled_effect_estimate-40"><a href="#pooled_effect_estimate-40"><span class="linenos">40</span></a><span class="sd">    :return: pooled estimates</span>
-</span><span id="pooled_effect_estimate-41"><a href="#pooled_effect_estimate-41"><span class="linenos">41</span></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="pooled_effect_estimate-42"><a href="#pooled_effect_estimate-42"><span class="linenos">42</span></a>    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">observations</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span>
-</span><span id="pooled_effect_estimate-43"><a href="#pooled_effect_estimate-43"><span class="linenos">43</span></a>    <span class="n">observations</span> <span class="o">=</span> <span class="n">observations</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s2">&quot;location_id&quot;</span><span class="p">,</span> <span class="s2">&quot;observation&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span>
-</span><span id="pooled_effect_estimate-44"><a href="#pooled_effect_estimate-44"><span class="linenos">44</span></a>        <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span>
-</span><span id="pooled_effect_estimate-45"><a href="#pooled_effect_estimate-45"><span class="linenos">45</span></a>    <span class="p">)</span>
-</span><span id="pooled_effect_estimate-46"><a href="#pooled_effect_estimate-46"><span class="linenos">46</span></a>    <span class="c1"># get the standard estimates</span>
-</span><span id="pooled_effect_estimate-47"><a href="#pooled_effect_estimate-47"><span class="linenos">47</span></a>    <span class="n">estimated_centers</span> <span class="o">=</span> <span class="n">standard_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="o">=</span><span class="n">observations</span><span class="p">)</span>
-</span><span id="pooled_effect_estimate-48"><a href="#pooled_effect_estimate-48"><span class="linenos">48</span></a>    <span class="k">if</span> <span class="n">estimated_centers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
-</span><span id="pooled_effect_estimate-49"><a href="#pooled_effect_estimate-49"><span class="linenos">49</span></a>        <span class="c1"># no pooling possible</span>
-</span><span id="pooled_effect_estimate-50"><a href="#pooled_effect_estimate-50"><span class="linenos">50</span></a>        <span class="k">return</span> <span class="n">estimated_centers</span>
-</span><span id="pooled_effect_estimate-51"><a href="#pooled_effect_estimate-51"><span class="linenos">51</span></a>    <span class="c1"># get counts per group</span>
-</span><span id="pooled_effect_estimate-52"><a href="#pooled_effect_estimate-52"><span class="linenos">52</span></a>    <span class="n">n_j</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;size&quot;</span><span class="p">]</span>
-</span><span id="pooled_effect_estimate-53"><a href="#pooled_effect_estimate-53"><span class="linenos">53</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;var&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-</span><span id="pooled_effect_estimate-54"><a href="#pooled_effect_estimate-54"><span class="linenos">54</span></a>    <span class="c1"># inflate a bit</span>
-</span><span id="pooled_effect_estimate-55"><a href="#pooled_effect_estimate-55"><span class="linenos">55</span></a>    <span class="n">per_location_observation_var</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">per_location_observation_var</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
-</span><span id="pooled_effect_estimate-56"><a href="#pooled_effect_estimate-56"><span class="linenos">56</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">per_location_observation_var</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">observations</span><span class="p">[</span><span class="s1">&#39;observation&#39;</span><span class="p">]))</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-</span><span id="pooled_effect_estimate-57"><a href="#pooled_effect_estimate-57"><span class="linenos">57</span></a>    <span class="c1"># get the observed variance between locations</span>
-</span><span id="pooled_effect_estimate-58"><a href="#pooled_effect_estimate-58"><span class="linenos">58</span></a>    <span class="n">between_location_var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">],</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-</span><span id="pooled_effect_estimate-59"><a href="#pooled_effect_estimate-59"><span class="linenos">59</span></a>    <span class="c1"># get v, the pooling coefficient</span>
-</span><span id="pooled_effect_estimate-60"><a href="#pooled_effect_estimate-60"><span class="linenos">60</span></a>    <span class="k">if</span> <span class="n">between_location_var</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-</span><span id="pooled_effect_estimate-61"><a href="#pooled_effect_estimate-61"><span class="linenos">61</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">0</span> <span class="o">*</span> <span class="n">per_location_observation_var</span>
-</span><span id="pooled_effect_estimate-62"><a href="#pooled_effect_estimate-62"><span class="linenos">62</span></a>    <span class="k">else</span><span class="p">:</span>
-</span><span id="pooled_effect_estimate-63"><a href="#pooled_effect_estimate-63"><span class="linenos">63</span></a>        <span class="c1"># as between_location_var &gt; 0 and per_location_observation_var &gt; 0 here</span>
-</span><span id="pooled_effect_estimate-64"><a href="#pooled_effect_estimate-64"><span class="linenos">64</span></a>        <span class="c1"># v will be in the range 0 to 1</span>
-</span><span id="pooled_effect_estimate-65"><a href="#pooled_effect_estimate-65"><span class="linenos">65</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">per_location_observation_var</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">between_location_var</span><span class="p">))</span>
-</span><span id="pooled_effect_estimate-66"><a href="#pooled_effect_estimate-66"><span class="linenos">66</span></a>    <span class="c1"># this quantity can be improved using knowledge of the variances</span>
-</span><span id="pooled_effect_estimate-67"><a href="#pooled_effect_estimate-67"><span class="linenos">67</span></a>    <span class="n">grand_mean</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;grand_mean&#39;</span><span class="p">]</span>
-</span><span id="pooled_effect_estimate-68"><a href="#pooled_effect_estimate-68"><span class="linenos">68</span></a>    <span class="c1"># build the pooled estimate</span>
-</span><span id="pooled_effect_estimate-69"><a href="#pooled_effect_estimate-69"><span class="linenos">69</span></a>    <span class="n">pooled_estimate</span> <span class="o">=</span> <span class="n">v</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v</span><span class="p">)</span> <span class="o">*</span> <span class="n">grand_mean</span>
-</span><span id="pooled_effect_estimate-70"><a href="#pooled_effect_estimate-70"><span class="linenos">70</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pooled_estimate</span>
-</span><span id="pooled_effect_estimate-71"><a href="#pooled_effect_estimate-71"><span class="linenos">71</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;impact&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pooled_estimate</span> <span class="o">-</span> <span class="n">grand_mean</span>
-</span><span id="pooled_effect_estimate-72"><a href="#pooled_effect_estimate-72"><span class="linenos">72</span></a>    <span class="k">return</span> <span class="n">estimated_centers</span>
+            <div class="pdoc-code codehilite"><pre><span></span><span id="pooled_effect_estimate-37"><a href="#pooled_effect_estimate-37"><span class="linenos">37</span></a><span class="k">def</span> <span class="nf">pooled_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="pooled_effect_estimate-38"><a href="#pooled_effect_estimate-38"><span class="linenos">38</span></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+</span><span id="pooled_effect_estimate-39"><a href="#pooled_effect_estimate-39"><span class="linenos">39</span></a><span class="sd">    Get the pooled estimate of effect.</span>
+</span><span id="pooled_effect_estimate-40"><a href="#pooled_effect_estimate-40"><span class="linenos">40</span></a><span class="sd">    See: https://github.com/WinVector/Examples/blob/main/PartialPooling/PartialPooling.ipynb .</span>
+</span><span id="pooled_effect_estimate-41"><a href="#pooled_effect_estimate-41"><span class="linenos">41</span></a>
+</span><span id="pooled_effect_estimate-42"><a href="#pooled_effect_estimate-42"><span class="linenos">42</span></a><span class="sd">    :param observations: the observations data frame, with columns location_id and observation</span>
+</span><span id="pooled_effect_estimate-43"><a href="#pooled_effect_estimate-43"><span class="linenos">43</span></a><span class="sd">    :return: pooled estimates</span>
+</span><span id="pooled_effect_estimate-44"><a href="#pooled_effect_estimate-44"><span class="linenos">44</span></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="pooled_effect_estimate-45"><a href="#pooled_effect_estimate-45"><span class="linenos">45</span></a>    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">observations</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span>
+</span><span id="pooled_effect_estimate-46"><a href="#pooled_effect_estimate-46"><span class="linenos">46</span></a>    <span class="n">observations</span> <span class="o">=</span> <span class="n">observations</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s2">&quot;location_id&quot;</span><span class="p">,</span> <span class="s2">&quot;observation&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span>
+</span><span id="pooled_effect_estimate-47"><a href="#pooled_effect_estimate-47"><span class="linenos">47</span></a>        <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span>
+</span><span id="pooled_effect_estimate-48"><a href="#pooled_effect_estimate-48"><span class="linenos">48</span></a>    <span class="p">)</span>
+</span><span id="pooled_effect_estimate-49"><a href="#pooled_effect_estimate-49"><span class="linenos">49</span></a>    <span class="c1"># get the standard estimates</span>
+</span><span id="pooled_effect_estimate-50"><a href="#pooled_effect_estimate-50"><span class="linenos">50</span></a>    <span class="n">estimated_centers</span> <span class="o">=</span> <span class="n">standard_effect_estimate</span><span class="p">(</span><span class="n">observations</span><span class="o">=</span><span class="n">observations</span><span class="p">)</span>
+</span><span id="pooled_effect_estimate-51"><a href="#pooled_effect_estimate-51"><span class="linenos">51</span></a>    <span class="k">if</span> <span class="n">estimated_centers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
+</span><span id="pooled_effect_estimate-52"><a href="#pooled_effect_estimate-52"><span class="linenos">52</span></a>        <span class="c1"># no pooling possible</span>
+</span><span id="pooled_effect_estimate-53"><a href="#pooled_effect_estimate-53"><span class="linenos">53</span></a>        <span class="k">return</span> <span class="n">estimated_centers</span>
+</span><span id="pooled_effect_estimate-54"><a href="#pooled_effect_estimate-54"><span class="linenos">54</span></a>    <span class="c1"># get counts per group</span>
+</span><span id="pooled_effect_estimate-55"><a href="#pooled_effect_estimate-55"><span class="linenos">55</span></a>    <span class="n">n_j</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;size&quot;</span><span class="p">]</span>
+</span><span id="pooled_effect_estimate-56"><a href="#pooled_effect_estimate-56"><span class="linenos">56</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;var&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+</span><span id="pooled_effect_estimate-57"><a href="#pooled_effect_estimate-57"><span class="linenos">57</span></a>    <span class="n">per_location_observation_var</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">per_location_observation_var</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+</span><span id="pooled_effect_estimate-58"><a href="#pooled_effect_estimate-58"><span class="linenos">58</span></a>    <span class="c1"># inflate per-loc a bit</span>
+</span><span id="pooled_effect_estimate-59"><a href="#pooled_effect_estimate-59"><span class="linenos">59</span></a>    <span class="n">per_location_observation_var</span> <span class="o">=</span> <span class="p">(</span>
+</span><span id="pooled_effect_estimate-60"><a href="#pooled_effect_estimate-60"><span class="linenos">60</span></a>        <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">per_location_observation_var</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">observations</span><span class="p">[</span><span class="s1">&#39;observation&#39;</span><span class="p">]))</span> 
+</span><span id="pooled_effect_estimate-61"><a href="#pooled_effect_estimate-61"><span class="linenos">61</span></a>        <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+</span><span id="pooled_effect_estimate-62"><a href="#pooled_effect_estimate-62"><span class="linenos">62</span></a>    <span class="c1"># get the observed variance between locations</span>
+</span><span id="pooled_effect_estimate-63"><a href="#pooled_effect_estimate-63"><span class="linenos">63</span></a>    <span class="n">between_location_var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">],</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</span><span id="pooled_effect_estimate-64"><a href="#pooled_effect_estimate-64"><span class="linenos">64</span></a>    <span class="c1"># get v, the pooling coefficient</span>
+</span><span id="pooled_effect_estimate-65"><a href="#pooled_effect_estimate-65"><span class="linenos">65</span></a>    <span class="k">if</span> <span class="n">between_location_var</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+</span><span id="pooled_effect_estimate-66"><a href="#pooled_effect_estimate-66"><span class="linenos">66</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">0</span> <span class="o">*</span> <span class="n">per_location_observation_var</span>
+</span><span id="pooled_effect_estimate-67"><a href="#pooled_effect_estimate-67"><span class="linenos">67</span></a>    <span class="k">else</span><span class="p">:</span>
+</span><span id="pooled_effect_estimate-68"><a href="#pooled_effect_estimate-68"><span class="linenos">68</span></a>        <span class="c1"># as between_location_var &gt; 0 and per_location_observation_var &gt; 0 here</span>
+</span><span id="pooled_effect_estimate-69"><a href="#pooled_effect_estimate-69"><span class="linenos">69</span></a>        <span class="c1"># v will be in the range 0 to 1</span>
+</span><span id="pooled_effect_estimate-70"><a href="#pooled_effect_estimate-70"><span class="linenos">70</span></a>        <span class="n">v</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">per_location_observation_var</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_j</span> <span class="o">*</span> <span class="n">between_location_var</span><span class="p">))</span>
+</span><span id="pooled_effect_estimate-71"><a href="#pooled_effect_estimate-71"><span class="linenos">71</span></a>    <span class="n">v</span><span class="p">[</span><span class="n">n_j</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># no information in size one items</span>
+</span><span id="pooled_effect_estimate-72"><a href="#pooled_effect_estimate-72"><span class="linenos">72</span></a>    <span class="n">v</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">v</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+</span><span id="pooled_effect_estimate-73"><a href="#pooled_effect_estimate-73"><span class="linenos">73</span></a>    <span class="c1"># build the pooled estimate</span>
+</span><span id="pooled_effect_estimate-74"><a href="#pooled_effect_estimate-74"><span class="linenos">74</span></a>    <span class="n">pooled_estimate</span> <span class="o">=</span> <span class="n">v</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v</span><span class="p">)</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;grand_mean&quot;</span><span class="p">]</span>
+</span><span id="pooled_effect_estimate-75"><a href="#pooled_effect_estimate-75"><span class="linenos">75</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pooled_estimate</span>
+</span><span id="pooled_effect_estimate-76"><a href="#pooled_effect_estimate-76"><span class="linenos">76</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">]</span> 
+</span><span id="pooled_effect_estimate-77"><a href="#pooled_effect_estimate-77"><span class="linenos">77</span></a>    <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+</span><span id="pooled_effect_estimate-78"><a href="#pooled_effect_estimate-78"><span class="linenos">78</span></a>        <span class="n">estimated_centers</span><span class="p">[</span><span class="s2">&quot;impact&quot;</span><span class="p">]</span> 
+</span><span id="pooled_effect_estimate-79"><a href="#pooled_effect_estimate-79"><span class="linenos">79</span></a>        <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;impact&#39;</span><span class="p">])</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">estimated_centers</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]))</span>
+</span><span id="pooled_effect_estimate-80"><a href="#pooled_effect_estimate-80"><span class="linenos">80</span></a>    <span class="k">return</span> <span class="n">estimated_centers</span>
 </span></pre></div>
 
 
diff --git a/pkg/build/lib/vtreat/partial_pooling_estimator.py b/pkg/build/lib/vtreat/partial_pooling_estimator.py
index 84dc51d..34a14c4 100644
--- a/pkg/build/lib/vtreat/partial_pooling_estimator.py
+++ b/pkg/build/lib/vtreat/partial_pooling_estimator.py
@@ -24,7 +24,10 @@ def standard_effect_estimate(observations: pd.DataFrame) -> pd.DataFrame:
     means.sort_values(["location_id"], inplace=True, ignore_index=True)
     means['estimate'] = means['mean']
     means["grand_mean"] = np.mean(observations["observation"])
-    means["impact"] = means["estimate"] - means["grand_mean"]
+    means["impact"] = means["estimate"]
+    means["impact"] = (
+        means["impact"] 
+        - np.sum(means['size'] * means['impact']) / np.sum(means['size']))
     means.sort_values(["location_id"], inplace=True, ignore_index=True)
     return means
 
@@ -50,9 +53,11 @@ def pooled_effect_estimate(observations: pd.DataFrame) -> pd.DataFrame:
     # get counts per group
     n_j = estimated_centers["size"]
     per_location_observation_var = estimated_centers['var'].copy()
-    # inflate a bit
     per_location_observation_var[pd.isnull(per_location_observation_var)] = 0
-    per_location_observation_var = (n_j * per_location_observation_var + np.var(observations['observation'])) / (n_j + 1)
+    # inflate per-loc a bit
+    per_location_observation_var = (
+        (n_j * per_location_observation_var + np.var(observations['observation'])) 
+        / (n_j + 1))
     # get the observed variance between locations
     between_location_var = np.var(estimated_centers["estimate"], ddof=1)
     # get v, the pooling coefficient
@@ -62,10 +67,13 @@ def pooled_effect_estimate(observations: pd.DataFrame) -> pd.DataFrame:
         # as between_location_var > 0 and per_location_observation_var > 0 here
         # v will be in the range 0 to 1
         v = 1 / (1 + per_location_observation_var / (n_j * between_location_var))
-    # this quantity can be improved using knowledge of the variances
-    grand_mean = estimated_centers['grand_mean']
+    v[n_j <= 1] = 0  # no information in size one items
+    v[pd.isnull(v)] = 0
     # build the pooled estimate
-    pooled_estimate = v * estimated_centers["estimate"] + (1 - v) * grand_mean
+    pooled_estimate = v * estimated_centers["estimate"] + (1 - v) * estimated_centers["grand_mean"]
     estimated_centers["estimate"] = pooled_estimate
-    estimated_centers['impact'] = pooled_estimate - grand_mean
+    estimated_centers["impact"] = estimated_centers["estimate"] 
+    estimated_centers["impact"] = (
+        estimated_centers["impact"] 
+        - np.sum(estimated_centers['size'] * estimated_centers['impact']) / np.sum(estimated_centers['size']))
     return estimated_centers
diff --git a/pkg/dist/vtreat-1.3.1-py3-none-any.whl b/pkg/dist/vtreat-1.3.1-py3-none-any.whl
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diff --git a/pkg/dist/vtreat-1.3.1.tar.gz b/pkg/dist/vtreat-1.3.1.tar.gz
index 9f71d4207778667fb6cbf39ab1e152d2563d8c67..86ec0f71ea24ee54722fd85bde1f2e00c1e59b18 100644
GIT binary patch
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diff --git a/pkg/tests/test_partial_pooling.py b/pkg/tests/test_partial_pooling.py
index 3def058..a0bafd9 100644
--- a/pkg/tests/test_partial_pooling.py
+++ b/pkg/tests/test_partial_pooling.py
@@ -10,6 +10,7 @@ def test_standard_effect_estimate():
         'observation': [  1,   2,   3,   4,   5,   6],
     })
     r = standard_effect_estimate(d)
+    assert np.abs(np.sum(r['size'] * r['impact'])) < 1e-5
     expect = pd.DataFrame({
         'location_id': ['a', 'b', 'c'],
         'mean': [2.0, 4.5, 6.0],
@@ -28,14 +29,15 @@ def test_pooled_effect_estimate():
         'observation': [  1,   2,   3,   4,   5,   6],
     })
     r = pooled_effect_estimate(d)
+    assert np.abs(np.sum(r['size'] * r['impact'])) < 1e-5
     expect = pd.DataFrame({
         'location_id': ['a', 'b', 'c'],
         'mean': [2.0, 4.5, 6.0],
         'var': [1.0, 0.5, np.nan],
         'size': [3, 2, 1],
-        'estimate': [2.161608, 4.362170, 5.342105],
+        'estimate': [2.161608, 4.362170, 3.500000],
         'grand_mean': [3.5, 3.5, 3.5],
-        'impact': [-1.338392, 0.862170, 1.842105],
+        'impact': [-0.956586, 1.243976, 0.381806],
     })
     assert r.shape == expect.shape
     assert np.all(r.columns == expect.columns)
diff --git a/pkg/vtreat/partial_pooling_estimator.py b/pkg/vtreat/partial_pooling_estimator.py
index 84dc51d..34a14c4 100644
--- a/pkg/vtreat/partial_pooling_estimator.py
+++ b/pkg/vtreat/partial_pooling_estimator.py
@@ -24,7 +24,10 @@ def standard_effect_estimate(observations: pd.DataFrame) -> pd.DataFrame:
     means.sort_values(["location_id"], inplace=True, ignore_index=True)
     means['estimate'] = means['mean']
     means["grand_mean"] = np.mean(observations["observation"])
-    means["impact"] = means["estimate"] - means["grand_mean"]
+    means["impact"] = means["estimate"]
+    means["impact"] = (
+        means["impact"] 
+        - np.sum(means['size'] * means['impact']) / np.sum(means['size']))
     means.sort_values(["location_id"], inplace=True, ignore_index=True)
     return means
 
@@ -50,9 +53,11 @@ def pooled_effect_estimate(observations: pd.DataFrame) -> pd.DataFrame:
     # get counts per group
     n_j = estimated_centers["size"]
     per_location_observation_var = estimated_centers['var'].copy()
-    # inflate a bit
     per_location_observation_var[pd.isnull(per_location_observation_var)] = 0
-    per_location_observation_var = (n_j * per_location_observation_var + np.var(observations['observation'])) / (n_j + 1)
+    # inflate per-loc a bit
+    per_location_observation_var = (
+        (n_j * per_location_observation_var + np.var(observations['observation'])) 
+        / (n_j + 1))
     # get the observed variance between locations
     between_location_var = np.var(estimated_centers["estimate"], ddof=1)
     # get v, the pooling coefficient
@@ -62,10 +67,13 @@ def pooled_effect_estimate(observations: pd.DataFrame) -> pd.DataFrame:
         # as between_location_var > 0 and per_location_observation_var > 0 here
         # v will be in the range 0 to 1
         v = 1 / (1 + per_location_observation_var / (n_j * between_location_var))
-    # this quantity can be improved using knowledge of the variances
-    grand_mean = estimated_centers['grand_mean']
+    v[n_j <= 1] = 0  # no information in size one items
+    v[pd.isnull(v)] = 0
     # build the pooled estimate
-    pooled_estimate = v * estimated_centers["estimate"] + (1 - v) * grand_mean
+    pooled_estimate = v * estimated_centers["estimate"] + (1 - v) * estimated_centers["grand_mean"]
     estimated_centers["estimate"] = pooled_estimate
-    estimated_centers['impact'] = pooled_estimate - grand_mean
+    estimated_centers["impact"] = estimated_centers["estimate"] 
+    estimated_centers["impact"] = (
+        estimated_centers["impact"] 
+        - np.sum(estimated_centers['size'] * estimated_centers['impact']) / np.sum(estimated_centers['size']))
     return estimated_centers